Curated Replications

Curated List of Large-Scale Replication Efforts

Searchable table of N=1,058 replications of 168 effects from the cognitive and social psychology published literature.

Examples: "RPP" for Reproducibility Project: Psychology replications; "ML1" or "ML3" for Many Labs 1 or 3 replications; "RRR" for Registered Replication Reports; "SP: Spec" for Social Psychology's Special Issue replications. For topical searches, try "priming", "anchoring", "gambler's fallacy", "love", "moral" (for morality), or "power posing."

Icon legend: = data; = study materials; = pre-registered protocol; = link to a replication's associated evidence collection. To sort replications, click on column headers. Reveal overflow text (...) by hovering over cell. For details about replication outcome values hover over cell and see about section. For additional replication study characteristics (& to see hidden imprecise large-scale-effort replications, see our public gSheet (see also our GitHub repo ).

target.effect	orig.study.number	o.N	orig.effect.size	r.N	rep.effect.size	rep.study.number	rep.outcome	IVs	DVs	rep.type	rep.effort.type	rep.differences	rep.active.evidence	other.outcomes
rebel vs obedient liking effect	Monin et al. (2008) Study 4	67	r = .35 ± .22	75	r = .16 ± .22	Holubar & Frank (2015)	signal - consistent			very close	RPP #1				Decreased liking for rebels compared to obedient confederates (in non-self-affirmed control condition) eliminated in self-affirmed condition
implicit-explicit correspondence effect	Payne et al. (2008) Study 4	70	r = .35 ± .22	180	r = .15 ± .14	Vianello (2015)	signal - inconsistent, smaller			very close	RPP #2				Positive relation between indirect ratings (AMP) and direct ratings (of Black vs. White faces) stronger in low (compared to high) pressure to respond in socially desirable manner conditions.
young acquiescent variability effect	Soto, John et al. (2008) Study 1	230047	r = .02 ± 0	455326	r = .02 ± 0	Soderberg (2015)	signal - consistent			very close	RPP #3				Younger respondents show greater variability (variance) in acquiescent response tendencies than older respondents.
anxious attachment mortality effect	Cox, Arndt et al. (2008) Study 6	100	r = .23 ± .19	200	r = -.05 ± .14	Wissink, Zeelenberg et al. (2015)	no signal - inconsistent			very close	RPP #4				Increased relative preference for parent among mortality-salience induced Ps scoring low on avoidance but high on anxiety compared to control conditions (dental pain)
action priming boosts cognitive performance	Albarracin et al. (2008) Study 5	36	r = .38 ± .30	88	r = -.03 ± .21	Kim, Frank et al. (2015)	no signal - inconsistent			very close	RPP #5				Ps primed with action words correctly solved higher number of SAT-type verbal and math problems than Ps primed with inaction words.
action priming boosts # of thoughts effect	Albarracin et al. (2008) Study 7	98	r = .21 ± .19	109	r = .16 ± .19	Voracek & Sonnleitner (2015)	no signal - consistent			very close	RPP #6	German instead of English language			Action-word primed Ps completing an inactive task generated a greater number of thoughts compared to inaction-primed Ps completing an active task, with no difference in thoughts in control conditions.
affective incoherence effect	Centerbar, Schnall et al. (2008) Study 5	133	r = .21 ± .16	113	r = .09 ± .18	Humphries, Brown et al. (2015)	no signal - consistent (imprecise)			very close	RPP #7				Affective coherence increased cognitive complexity in participant-generated narratives compared to affective incoherence.
intergroup bias regulation effect	Amodio, Devine et al. (2008) Study 2	48	r = .38 ± .25	75	r = .08 ± .23	Johnson, Graham et al. (2015)	no signal - inconsistent			very close	RPP #8				Low-prejudice Ps whose non-prejudiced responses were motivated by internal (but not external) factors exhibited better control on a stereotype-inhibition task than did Ps motivated by a combination of internal and external factors.
action-oriented mindset dissonance effect	Harmon-Jones et al. (2008) Study 2	57	r = .23 ± .26	71	r = .07 ± .24	Gable & Mechin (2015)	no signal - consistent			very close	RPP #9				Action-oriented mindset caused attitude changes from pre- to post-decision to both chosen and rejected alternatives, whereas positive-nonaction and neutral mindsets caused attitude changes solely to rejected alternative.
anger communication bargaining effect	van Dijk, van Kleef et al. (2008) Study 3	103	r = .38 ± .17	83	r = -.04 ± .22	Voracek & Slowik (2015)	no signal - inconsistent (imprecise)			very close	RPP #10				Ps made lower bargaining offers to angry (compared to happy) recipients than happy, but only when consequences of rejection were low.
walking on eggshells effect	Lemay & Clark (2008a) Study 5	186	r = .17 ± .14	280	r = .04 ± .12	Baranski (2015)	no signal - inconsistent			very close	RPP #11				Ps believed confederate's expressions were happier than their private feelings, but only in vulnerable conditions.
head liberates heart effect	Lemay & Clark (2008b) Study 5	153	R² = .68	216	b = .72	Marigold, Forest et al. (2015)	signal - consistent			very close	RPP #12				Ps own responsiveness was a significant predictor of their perception of their partners' responsiveness.
poignancy effect	Ersner-Hershfield et al. (2008) Study 2	110	r = .22 ± .18	222	r = .00 ± .14	Talhelm, Eggleston, & Lee (2015a)	no signal - inconsistent			very close	RPP #13				Ps (college seniors) reminded they were experiencing their last day as students at graduation reported fewer mixed emotions than Ps not reminded.
modulation of 1/f noise racial bias emission effect	Correll (2008) Study 2	71	r = .27 ± .22	148	r = .07 ± .16	LeBel (2015) Study 1	no signal - inconsistent	avoid/use race information vs. control instructions	1/f noise (weapon identification task)	very close	RPP #14	Canadian rather than US undergraduates; keyboard used instead of response box;	Racial bias (in terms of RT) higher in use and avoid race (compared to control) conditions (d = .34 ± .35); 1/f noise observed within each of the 3 conditions (p<.0001)		Ps in use-race or avoid-race instruction conditions exhibited less 1/f noise (as reflected in PSD slopes) on WIT than Ps in a control condition.
aggression priming perceptual effect	Forster, Liberman et al. (2008) Study 1	82	r = .43 ± .18	71	r = .11 ± .24	Reinhard (2015)	no signal - inconsistent (imprecise)			very close	RPP #15				Aggression priming increased perceptions of aggression (assimilation effect) among Ps whose processing style induced to be global (or control) whereas it decreased perceptions of aggression (contrast effect) among Ps whose processing style induced to be local.
not so innocent effect	Exline, Baumeister et al. (2008) Study 7	45	r = .43 ± .25	135	r = .01 ± .17	Lin & Frank (2015)	no signal - inconsistent			very close	RPP #16				Subtly asking Ps questions about their empathic understanding and capability of exhibiting similar transgressions reduced male (but not female) Ps' feelings of vengefulness.
tempting fate effect	Risen & Gilovich (2008) Study 6	122	r = .19 ± .17	226	r = .00 ± .13	Mathur & Frank (2015)	no signal - inconsistent			very close	RPP #17				Tempting fate effect (tempting fate increases perceived likelihood of negative outcomes) amplified under cognitive load compared to a control condition.
cross-cultural nonverbal pride expression effect	Tracy & Robins (2008) Study 4	211	Propor = .78	333	Propor = .77	Sullivan (2015)	signal - consistent			very close	RPP #18				Ps recognized conveyed emotions as pride at an above chance level, no matter what culture target represented.
Wason task performance and cognitive ability correlation	Stanovich & West (2008) Study 8	375	r = .22 ± .10	177	r = .07 ± .15	Baranski (2015)	no signal - consistent (imprecise)			very close	RPP #19				Ps correctly answering Wason's four-card selection task exhibited higher cognitive ability (SAT scores) than Ps answering card task incorrectly.
closed open mind effect	Blankenship & Wegener (2008) Study 5a	261	r = .21 ± .12	251	r = .04 ± .12	Lemm (2015)	no signal - inconsistent			very close	RPP #20				Strong arguments more persuasive than weak arguments among Ps forced to focus on important values in the messages, but no argument strength effect among Ps forced to focus on unimportant values.
descriptive deviant popularity effect	Morrison & Miller (2008) Study 3	37	r = .32 ± .30	120	r = .20 ± .18	Motyl (2015)	signal - consistent			very close	RPP #21				Bumper stickers of descriptive deviants rated as more popular than bumper stickers of prescriptive deviants.
(un)accomplished goal action effect	Koo & Fishbach (2008) Study 4	246	η² = .041	768703	OR = .159	Kidwell & Dodson (2015)	no signal			very close	RPP #22				Increased donations to HIV/AIDS initiative when emphasizing "to-date" information among Ps who hadn't yet donated, but emphasizing "to-go" information among Ps who had.
implemental mind-set attitude strength effect	Henderson, de Liver et al. (2008) Study 5	46	r = .26 ± .28	70	r = .16 ± .24	Lane & Gazarian (2015)	no signal - consistent			very close	RPP #23				Ps in one-sided focus condition exhibited less ambivalence than participants in two-sided focus condition.
need for acceptance reconcialiation effect	Shnabel & Nadler (2008) Study 4	94	r = .27 ± .19	141	r = -.10 ± .16	E. Gilbert (2015)	no signal - inconsistent			very close	RPP #24				Ps in perpetrator condition reported higher need for social acceptance and reconciliation after moral acceptance was restored, whereas Ps in victim condition reported higher need for power and reconciliation after sense of power was restored.
face of success effect	Rule & Ambady (2008) Study 1	50	r = .30 ± .26	50	r = .27 ± .27	Talhelm, Eggleston, & Lee (2015b)	signal - consistent (imprecise)			very close	RPP #25				Composite ratings of perceived power and leadership positively correlated with company profits, even after controlling for CEO age, CEO attractiveness, and affect CEOs displayed in photo.
selective exposure information quantity effect	Fischer, Schulz-Hardt et al. (2008) Study 4	52	r = .50 ± .21	150	r = .22 ± .16	Ratliff (2015)	signal - inconsistent, smaller			very close	RPP #26				Decision-makers prefer inconsistent information when choosing between two pieces of information, but prefer consistent information when choosing between more pieces of information.
ego depletion confirmatory bias effect	Fischer, Greitemeyer et al. (2008) Study 2	85	r = .21 ± .20	140	r = .12 ± .16	Galliani (2015)	no signal - consistent			very close	RPP #27				Ps whose self-regulation resources depleted exhibited increased biases in confirmatory information processing compared to non-depleted and ego-threatened condition Ps
stereotype threat interaction distance effect	Goff, Steele et al. (2008) Study 4	55	r = .40 ± .23	51	r = .01 ± .28	Kelso, Gampa, Wright et al. (2015)	no signal - inconsistent (imprecise)			very close	RPP #28				When learning goals absent, Ps sat closer to each other when they discussed love and relationships compared to when they discussed racial profiling.
generalized earning prospect predicts romantic interest effect	Eastwick & Finkel (2008) Study 1	163	r = .14 ± .16	304	r = .03 ± .11	Selterman, Chagnon et al. (2015)	no signal - consistent			very close	RPP #29				Perceived earning prospects significantly positively predicted romantic interest in speed-dating situation for both men and women (absence of evidence for interaction effect).
approach priming partner criticism effect	Murray, Derrick et al. (2008) Study 8	91	r = .32 ± .18	76	r = -.14 ± .22	Sinclair, Goldberg et al. (2015)	no signal - inconsistent (imprecise)			very close	RPP #30				When primed with approach goals, low self-esteem Ps in partner criticism condition believed their partner regarded them significantly more negatively than did controls.
math self-handicapping effect	McCrea (2008) Study 5	28	r = .34 ± .35	61	r = .29 ± .24	Chartier (2015)	signal - consistent			very close	RPP #31				Self-handicap condition Ps performed worse on mathematical questions task than control condition Ps.
social identity contingencies effect	Purdie-Vaughns et al. (2008) Study 2	90	r = .38 ± .18	1490	r = -.04 ± .05	Schmidt (2015)	no signal - inconsistent			very close	RPP #32				Among Black, but not White professionals, high (compared to low) fairness cue lowered expectations of threatening identity contingencies and increased trust.
fluency priming on psychological distance effect	Alter & Oppenheimer (2008) Study 2	236	r = .13 ± .12	1146	r = .02 ± .06	Foster (2015)	no signal - inconsistent			very close	RPP #33				Conceptually-fluent primed Ps reported increased preference for concrete descriptions of the prime later, compared to conceptually-disfluent primed Ps.
goal priming boosts learning effect	Eitam, Hassin et al. (2008) Study 2	86	r = .22 ± .20	158	r = .10 ± .16	Prenoveau & Kirkhart (2015)	no signal - consistent			very close	RPP #34				Goal primed Ps learned more on an incidental-learning task (serial reaction time task; SRT) than control-group Ps.
sex difference in perceived sexual interest effect	Farris, Treat et al. (2008) Study 1	280	r = .55 ± .08	144	r = .09 ± .16	Attwood, Easey, & Munafo (2015)	no signal - inconsistent (imprecise)			very close	RPP #35				Male Ps exhibited less sensitivity in distinguishing friendliness versus sexual interest compared to women (supporting a male perceptual insensitivity account).
prescribed optimism effect	Armor, Massey et al. (2008) Study 1	126	r = .68 ± .10	177	r = .76 ± .06	Lassetter, Brandt et al. (2015)	signal - inconsistent, larger			very close	RPP #36				Among accurate, optimistic, or pessimistic predictions, Ps most likely to recommend optimistic predictions.
age-related episodic memory effect	Addis, Wong et al. (2008) Study 1	32	r = .57 ± .26	32	r = .65 ± .22	Vasquez (2015)	signal - consistent			very close	RPP #37				Older adults produced fewer internal details, but more external details compared to young adults.
ego depletion boosts attraction effect	Masicampo & Baumeister (2008) Study 1	115	r = .21 ± .18	166	r = -.05 ± .16	Osborne, Vuu, & Henninger (2015)	no signal - inconsistent			very close	RPP #38				Ps in depletion/placebo group exhibited larger attraction effect compared to Ps in other three conditions (depletion with sugar, no depletion with sugar, no depletion with placebo).
aversive errors effect	Hajcak & Foti (2008) Study 1	31	r = -.38 ± .32	43	r = -.25 ± .29	Lewis & Pitts (2015)	no signal - consistent			very close	RPP #39				Startle response magnitude following an error negatively correlated to Error-Related Negativity (ERN) amplitude.
sunny side of fairness effect	Tabibnia, Satpute et al. (2008) Study 2	12	r = .85 ± .24	24	r = .83 ± .15	Beer, Rigney, & Flagan (2015)	signal - consistent			very close	RPP #40				Accepting unfair offers increased activation in right ventrolateral prefrontal cortex compared to baseline
loving woman who justify inequality effect	Lau, Kay & Spencer (2008) Study 1	36	r = .38 ± .30	70	r = -.03 ± .24	Stieger (2015)	no signal - inconsistent			very close	RPP #41				Male Ps whose faith in economic system was threatened reported greater romantic interest in women who embody (versus did not embody) benevolent sexist ideals
threat of appearing prejudiced attentional bias effect	Richeson & Trawalter (2008) Study 1	28	r = .37 ± .34	66	r = .02 ± .24	Lai (2015)	no signal - inconsistent			very close	RPP #42				Ps with high "external motivation" (EM) to be non-prejudiced exhibited attentional biases for neutral, but not happy Black faces.
offensive behavior looking effect	Crosby, Monin et al. (2008) Study 1	25	r = .25 ± .40	30	r = .18 ± .36	Skorinko & Jonas (2015)	no signal - consistent			very close	RPP #43				When White man made potentially offensive comment in presence of Black man, Ps who could hear interaction looked at Black man for a longer compared to when Ps couldnt hear interaction.
having what you want happiness effect	Larsen & McKibban (2008) Study 2	119	r = .21 ± .18	238	r = .50 ± .09	Seibel, Vermue et al. (2015)	signal - inconsistent, larger			very close	RPP #44				People who had what they wanted were happier than others, even after controlling for the degree to which they wanted what they had.
implicit attitude generalization occurs immediately effect	Ranganath & Nosek (2008) Study 1	684	r = .00 ± .08	3597	r = .11 ± .04	Cohn (2015)	signal - inconsistent, larger			very close	RPP #45				Information about original individuals automatically influenced Ps' implicit responses toward new individuals from same social category.
anti-free-will boosts cheating effect	Vohs & Schooler (2008) Study 1	30	r = .50 ± .30	58	r = .10 ± .26	Giner-Sorolla, Embley et al. (2015)	no signal - inconsistent			very close	RPP #46				Ps who read anti-free-will essay cheated more often that Ps who read a control essay.
distance priming boosts psychological closeness effect	Williams & Bargh (2008) Study 4	84	r = .23 ± .20	125	r = .04 ± .18	Joy-Gaba, Clay, & Cleary (2015)	no signal - inconsistent			very close	RPP #47				Distance-primed Ps reported higher levels of closeness toward one's family members and hometown compared to control condition Ps.
hedonic anger regulation effect	Tamir, Mitchell et al. (2008) Study 1	82	r = .59 ± .14	88	r = .61 ± .13	Masicampo (2015)	signal - consistent			very close	RPP #48				When Ps anticipated playing confrontational games, Ps preferred anger-inducing activities more so than exciting and neutral activities.
social disconnection boosts supernatural agent beliefs effect	Epley, Akalis et al. (2008) Study 3	57	r = .17 ± .26	78	r = .06 ± .23	Sandstrom & Dunn (2015)	no signal - consistent			very close	RPP #49				socially disconnected condition Ps reported stronger beliefs in supernatural agents and events (and attributed more social traits to pets) compared to control condition Ps.
intergroup prisoners dilemma-maximizing difference effect	Halevy, Bornstein et al. (2008) Study 1	80	r = .77 ± .09	40	r = .65 ± .20	Thomae, Woo, & Immelman (2015)	signal - consistent (imprecise)			very close	RPP #50				Ps in an intergroup prisoners dilemma-maximizing difference (IPD-MD) game contributed fewer tokens than Ps in an intergroup prisoners dilemma (IPD) game.
ovulation boosts attraction to single men effect	Bressan & Stranieri (2008) Study 2	208	r = .19 ± .14	263	r = -.03 ± .12	Frazier & Hasselman (2015) Study 1	no signal - inconsistent			very close	RPP #51				Attached women are more attracted to single men when they're ovulating (compared to not ovulating), but are more attracted to attached men when they're not ovulating (compared to ovulating).
ovulation boosts attraction to single men effect	Bressan & Stranieri (2008) Study 2	208	r = .19 ± .14	318	r = .02 ± .11	Frazier & Hasselman (2015) Study 2	no signal - inconsistent			very close	RPP #52				Attached women are more attracted to single men when they're ovulating (compared to not ovulating), but are more attracted to attached men when they're not ovulating (compared to ovulating).
cleanliness priming	Schnall, Benton & Harvey (2008) Study 2	43	r = -.40 ± .26	126	r = .00 ± .18	Cheung et al. (2015) Study 2	no signal - inconsistent	hand washing vs. control condition	moral wrongness (6 moral dilemmas)	very close	RPP #53	US rather than UK undergraduates	α = .62 on private body consciousness (PBC) subscale		After experiencing disgust, Ps who washed their hands judged questionable behaviors as less morally wrong than Ps who did not wash their hands.
conscientiousness validity correlation	Heine, Buchtel et al. (2008) Study 1	70	r = -.43 ± .20	16	r = -.11 ± .53	Lazarevic & Knezevic (2015)	no signal - consistent (imprecise)			very close	RPP #54				Perceptions of National Characters (PNC) measure negatively correlated with behavioural and demographic measures of conscientiousness (validity criteria).
social dominance verticality embodiment effect	Moeller, Robinson et al. (2008) Study 2	53	r = -.31 ± .25	72	r = .03 ± .24	Levitan & Fernandez-Castilla (2015)	no signal - inconsistent			very close	RPP #55				Ps scoring high in dominance exhibited faster RTs to vertical compare to horizontal stimuli, unlike Ps scoring low in dominance
conflict-triggered goal shielding effect	Goschke & Dreisbach (2008) Study 1	40	r = .37 ± .28	95	r = .41 ± .17	Costantini & Perugini (2015)	signal - consistent			very close	RPP #56				Interaction between Perspective Memory (PM) cue dimension and compatibility on mean proportion of errors (missed PM cues).
relative frequency value heuristic effect	Dai, Wertenbroch et al. (2008) Study 1	56	r = .28 ± .24	51	r = -.18 ± .28	Fuchs, Estel, & Goellner (2015)	no signal - inconsistent (imprecise)			very close	RPP #57				Ps underestimated number of pictures belonging to category for which they expected to be paid compared to control pictures in non-endowed category.
tracing attention effect	Roelofs (2008) Study 3	24	r = .59 ± .29	29	r = .15 ± .38	van Rijn (2015)	no signal - inconsistent (imprecise)			very close	RPP #58				Effect of relation between target and distractor was present for picture naming, but absent for word reading.
repetition blindness for nonwords effect	Morris & Still (2008) Study 6	24	r = .61 ± .28	24	r = .23 ± .40	Goodbourn (2015)	no signal - consistent (imprecise)			very close	RPP #59				Repetition blindness (RB) effect observed for nonwords.
working memory task switching costs effect	Liefooghe, Barouillet et al. (2008) Study 4	25	r = .42 ± .35	32	r = -.22 ± .35	Plessow, Moschl, & Pavel (2015)	no signal - inconsistent			very close	RPP #60				Lower recall for low-switch lists with degraded stimuli compared to low-switch lists with normal (non-degraded) stimuli.
retrieval-induced forgetting effect	Storm, Bjork & Bjork (2008) Study 1	240	r = .23 ± .12	270	r = -.01 ± .12	Callahan (2015)	no signal - inconsistent			very close	RPP #61				Larger relearning effect for non-practiced items from practiced categories than non-practiced items from non-practiced categories.
intermixed-blocked effect revisited	Mitchell, Nash & Hall (2008) Study 2	32	r = .46 ± .30	48	r = .13 ± .28	Lakens (2015)	no signal - inconsistent			very close	RPP #62				Intermixed–blocked effect: Intermixed pre-exposure to stimuli lead to better performance (perceptual learning) compared to non-intermixed pre-exposure.
independent memory sources for priming & recognition effect	Berry, Shanks & Henson (2008) Study 1	24	r = .59 ± .29	32	r = .40 ± .31	Meixner & Bruning (2015)	signal - consistent (imprecise)			very close	RPP #63				Faster RTs to misses compared to correct rejections (suggesting memory sources for priming and recognition are independent).
enhanced memory for word order effect	Beaman, Neath et al. (2008) Study 2	100	r = .72 ± .10	15	r = .13 ± .55	Kleinberg & Kunkels (2015)	no signal - inconsistent (imprecise)			very close	RPP #64				Enhanced memory for order of words when word lists contained only short words compared to when word lists only contained long words.
retrieval-provoked illusory source recollection effect	Dodson, Darragh et al. (2008) Study 3	48	r = .56 ± .20	33	r = -.11 ± .36	Calhoun-Sauls (2015)	no signal - inconsistent (imprecise)			very close	RPP #65				Higher number of incorrect judgments observed for schema-consistent attributions in neutral proportion condition and schema-inconsistent attributions in conflicting proportion condition.
comparative distance effect revisited	Ganor-Stern & Tzelgov (2008) Study 2	32	r = .70 ± .20	32	r = .78 ± .16	Shaki (2015)	signal - consistent			very close	RPP #66				Comparative distance effect observed when task required numerical matching.
semantic neighborhood density effect	Mirman & Magnuson (2008) Study 2	22	r = .67 ± .26	30	r = .47 ± .30	Bosco & Field (2015)	signal - consistent (imprecise)			very close	RPP #67				Words with many near neighbors (e.g., chicken) associated with increased level of processing (i.e., categorized more slowly) compared to words with few near neighbors (e.g., hyena)
between-sequence phonological similarity effect	Marsh, Vachon, & Jones (2008) Study 1	48	r = .18 ± .29	118	r = .08 ± .18	Bell (2015)	no signal - consistent			very close	RPP #68				Pronounced disruptive effect of between-sequence phonological similarity on free recall of lists of end rhyming words.
bidirectional association in multiplication memory	Campbell & Robert (2008) Study 3	36	r = .52 ± .25	36	r = .52 ± .25	Ricker (2015)	signal - consistent			very close	RPP #69				RTs faster when participants were given the same mathematical operation to practice and to test, relative to operational change or a new problem.
contingency Stroop effect	Schmidt & Besner (2008) Study 2	95	r = .20 ± .20	242	r = .25 ± .12	Cloud (2015)	signal - consistent			very close	RPP #70				Higher number of errors on low contingency trials compared to medium contingency trials.
ultimate sampling dilemma effect	Fiedler (2008) Study 2	39	r = .30 ± .30	47	r = .43 ± .24	Glockner & Jekel (2015)	signal - consistent			very close	RPP #71				Tendency to underestimate (overestimate) frequency of positive (negative) observations increased from provider with smallest to provider with highest overall frequency.
how to say no effect	Oberauer (2008) Study 3	32	r = .56 ± .26	21	r = .40 ± .40	Bosch, Neijenhuijs et al. (2015)	signal - consistent (imprecise)			very close	RPP #72				Higher new probe accuracy for short compared to long lists.
intentional forgetting effect revisited	Sahakyan, Delaney et al. (2008) Study 3	96	r = .22 ± .19	108	r = .02 ± .19	Fiedler & May (2015)	no signal - inconsistent			very close	RPP #73				Directed forgetting effect observed for spaced but not grouped (massed) items.
bilingualism executive control effect	Colzato, Bajo et al. (2008) Study 3	18	r = .22 ± .48	32	r = .06 ± .36	Kappes (2015)	no signal - consistent			very close	RPP #74				Bilingual Ps less successful than monolinguals at reporting T2 target when it lagged a successfully identified T1 target by 2 items (i.e., at lag 3).
addition facts priming effect	Bassok, Pedigo et al. (2008) Study 1	154	r = .36 ± .14	49	r = .28 ± .26	Hung, Lin, & Tsang (2015)	signal - consistent (imprecise)			very close	RPP #75				RT difference between sum and neutral targets larger when cue digits primed with categorical (compared to unrelated or functionally related) primes.
Hebb repetition effect revisited	Couture, Lafond, & Tremblay (2008) Study 1	24	r = .35 ± .38	63	r = .27 ± .24	Roebke & Penna (2015)	signal - consistent			very close	RPP #76				Likelihood of producing a given response increased as a function of the number of times same response had previously been recalled, even for errors.
reading aloud contextual effect	Reynolds & Besner (2008) Study 5	16	r = .16 ± .52	20	r = .14 ± .46	Lai & Simpson (2015)	no signal - consistent			very close	RPP #77				No delay in response when switching between pronouncing regular words and nonwords.
stimulus quality word frequency effect	Yap, Balota et al. (2008) Study 4	32	r = .38 ± .32	71	r = .38 ± .20	Chartier (2015)	signal - consistent			very close	RPP #78				Larger frequency effect for degraded compared to non-degraded words (interaction between stimulus quality and word frequency).
semantic interference in naming task revisited	Janssen, Schirm et al. (2008a) Study 1	32	r = .34 ± .32	91	r = .10 ± .21	Galak (2015)	no signal - inconsistent			very close	RPP #79				RTs slower for semantically-related compared to semantically unrelated word-picture pairs in a delayed picture naming task.
multidimensional visual statistical learning effect	Turk-Brown, Isola et al. (2008) Study 4b	8	r = .74 ± .54	15	r = .70 ± .32	Ostkamp & Jakel (2015)	signal - consistent			very close	RPP #80				Above-chance recall rate on real-triplet test trials compared to familiarization phase (can visual statistical learning be feature-based over colors of objects?).
contingency information in causal judgments effect	White (2008) Study 3	37	r = .62 ± .21	38	r = .48 ± .26	Muller & Renkewitz (2015)	signal - consistent (imprecise)			very close	RPP #81				A+ instances raised judgment of A and lowered judgment of B as causes for outcome.
short-term memory temporal gap effect	Farrell (2008) Study 2	40	r = .52 ± .24	40	r = .32 ± .30	Olsson & Saxe (2015)	signal - consistent (imprecise)			very close	RPP #82				Ps instructed to group items induced temporal gap between groups, both when trying to recall order of items and when trying to recall timing of items.
dependency learning effect	Pacton & Perruchet (2008) Study 4b	24	r = .71 ± .22	24	r = .68 ± .25	Jahn (2015)	signal - consistent (imprecise)			very close	RPP #83				Number of correct responses differed only as a function of the type of dependency.
visual working memory orienting attention effect	Makovski, Sussman et al. (2008) Study 3	12	r = .55 ± .49	18	r = .35 ± .45	Moore (2015)	no signal - consistent			very close	RPP #84				For both shape and color conditions, better recall for retro(active) compared to simultaneous cue at all set sizes (except set size = 1).
learning on risky decision making effect	Pleskac (2008) Study 1	68	r = .37 ± .21	153	r = .10 ± .16	Forsell, Dreber et al. (2015)	no signal - inconsistent			very close	RPP #85				Comparison of correlations between ART and drug use in sunny versus cloudy conditions.
directional language feature binding effect	Dessalegn & Landau (2008) Study 4	36	r = .38 ± .30	47	r = -.22 ± .28	Fitneva (2015)	no signal - inconsistent			very close	RPP #86				Directional language (e.g., "to the left") helps children bind color and shape information to a greater extent than relational language alone (e.g., "next to").
anchor precision anchoring effect	Janiszewski & Uy (2008) Study 4	59	r = .33 ± .24	120	r = .23 ± .17	Chandler (2015)	signal - consistent			very close	RPP #87				More precise (e.g., $9.99) compared to less price (e.g., $10) anchors decreased (anchoring effect) adjustment.
sample entropy decision making effect	McKinstry, Dale, & Spivey (2008) Study 1	141	r = -.70 ± .09	51	r = -.75 ± .13	Saxe, Velez, & Feather (2015)	signal - consistent (imprecise)			very close	RPP #88				Sample entropy was higher for middle-truth-value questions than for low- and high-truth-value questions.
perspective taking in word learning effect	Nurmsoo & Bloom (2008) Study 1	32	r = .50 ± .28	8	r = -.45 ± .72	Brown, Kleinberg et al. (2015)	no signal - inconsistent (imprecise)			very close	RPP #89				Older children selected correct object more frequently than would be expected by chance on "where" trials.
crowd within effect	Vul & Pashler (2008) Study 1	173	r = .29 ± .14	140	r = .32 ± .15	Steegen, Vanpaemel et al. (2015)	signal - consistent (imprecise)			very close	RPP #90				Averaging two guesses within one person provides a more accurate answer than either guess alone.
temporal selection in attentional blink effect	Vul, Nieuwenstein et al. (2008) Study 1	12	r = .13 ± .62	12	r = .12 ± .62	Barnett-Cowan (2015)	no signal - consistent			very close	RPP #91				Selection is (a) suppressed (efficacy), (b) delayed (latency), and (c) diffused (precision) in time during the attentional blink.
representation of ensemble visual features effect	Alvarez & Oliva (2008) Study 3	8	r = -.72 ± .55	17	r = -.92 ± .09	Schlegelmilch et al. (2015)	signal - inconsistent, larger			very close	RPP #92				Fewer localization errors for targets compared to distractors in the centroid condition.
motion affereffect effect	Winawer, Huk, & Boroditsky (2008) Study 3	32	r = -.69 ± .20	26	r = -.53 ± .30	Levitan, Errington & Gampa (2015)	signal - consistent (imprecise)			very close	RPP #93				Motion aftereffects were present following the presentation of a still photograph depicting motion
survival words memory boost effect	Nairne, Pandeirada et al. (2008) Study 2	24	r = .45 ± .35	38	r = .42 ± .28	Muller & Renkewitz (2015)	signal - consistent			very close	RPP #94				Higher recall for words rated for survival relevance compared to control words.
auditory change detection effect	Demany, Trost et al. (2008) Study 5	4	r = .50 ± 1.00	5	r = .51 ± .94	Snyder & Irsik (2015)	no signal - consistent			very close	RPP #95				Auditory memory makes change detection easier in audition than in vision.
word-order constraint effect	Janssen, Alario et al. (2008b) Study 2	30	r = .65 ± .23	24	r = .50 ± .34	Melinger (2015)	signal - consistent (imprecise)			very close	RPP #96				Color and object naming times in congruent condition faster than in incongruent condition in color and object naming tasks, respectively.
object viewpoint sensitivity effect	Forti & Humphreys (2008) Study 1	14	r = .72 ± .33	19	r = .21 ± .46	Dorrough & Fiedler (2015)	no signal - inconsistent (imprecise)			very close	RPP #97				Significant interaction between viewpoint (prototypical or non-prototypical) and visual field (upper visual field or lower visual field) on the probability of the first fixation being on the target.
extremal edge effect	Palmer & Ghose (2008) Study 2	8	r = .86 ± .38	8	r = .12 ± .78	Saxe, Velez, & Johnston (2015)	no signal - consistent (imprecise)			very close	RPP #98				Surfaces with a single EE (EE1) along the shared contour were perceived as closer than surfaces with two EE's orthogonal to the shared contour (Convex2)
snake in the grass detection effect	Lobue & DeLoache (2008) Study 3	48	r = .48 ± .23	48	r = .18 ± .29	Cramblet, Alvarez et al. (2015)	no signal - inconsistent (imprecise)			very close	RPP #99				Faster identification RTs for fear-relevant compared to fear-irrelevant stimuli, in both children and adults.
head up foot down effect	Estes, Verges, & Barsalou (2008) Study 1	18	r = .60 ± .35	22	r = .25 ± .42	Renkewitz & Muller (2015)	no signal - consistent (imprecise)			very close	RPP #100				Attentional effect of object words interfered with the visual perception of a target in the typical location of the object.
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	87	d = -.26 ± .42	Hunt & Krueger (2014,TAMUC)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #1
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	102	d = -.24 ± .39	Cheong (2014,Help)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #2
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	162	d = -.21 ± .40	Devos (2014,SDSU)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #3
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	96	d = -.20 ± .40	Swol (2014,Wisc)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #4
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	87	d = -.19 ± .42	John & Skorinko (2014,WPI)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #5
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	277	d = -.16 ± .39	Kappes (2014,LSE)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #6
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	187	d = -.16 ± .24	Davis & Hicks (2014,TAMU) Study 1	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #7
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	127	d = -.15 ± .35	Klein et al. (2014,UFL)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #8
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	84	d = -.14 ± .43	Vranka (2014,Charles)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #9
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	112	d = -.14 ± .37	Packard (2014,Laurier)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #10
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	113	d = -.13 ± .36	Cemalcilar (2014,KU)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #11
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	169	d = -.12 ± .30	Bocian & Frankowska (2014,SWPSON) Study 2	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #12
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	146	d = -.07 ± .33	Huntsinger & Mallett (2014,Luc)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #13
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	1000	d = -.06 ± .12	Schmidt & Nosek (2014, MTURK)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #14
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	108	d = -.05 ± .40	Hovermale & Joy-Gaba (2014,VCU)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #15
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	144	d = -.04 ± .34	Vianello & Galliani (2014,UNIPD)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #16
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	1329	d = -.03 ± .11	Schmidt & Nosek (2014,PI)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #17
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	84	d = .00 ± .43	Bernstein (2014,abington)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #18
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	90	d = .01 ± .40	Vaughn (2014,Ithaca)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #19
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	96	d = .01 ± .40	Rutchick (2014,CSUN)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #20
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	95	d = .01 ± .42	Adams & Nelson (2014,PSU)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #21
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	103	d = .03 ± .36	Brumbaugh & Storbeck (2014,QCCUNY) Study 1	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #22
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	123	d = .03 ± .39	Levitan (2014,Oxy)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #23
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	107	d = .10 ± .38	Smith (2014,OSU)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #24
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	174	d = .11 ± .30	Kurtz (2014,JMU)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #25
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	86	d = .13 ± .43	Brumbaugh & Storbeck (2014,QCCUNY2) Study 2	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #26
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	103	d = .18 ± .26	Wichman (2014,WKU)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #27
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	225	d = .18 ± .36	Davis & Hicks (2014,TAMUON) Study 2	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #28
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	120	d = .18 ± .40	Pilati (2014,brasilia)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #29
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	85	d = .19 ± .43	Furrow & Thompson (2014,MSVU)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #30
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	80	d = .21 ± .44	Brandt et al. (2014,Tilburg)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #31
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	79	d = .21 ± .45	Bocian & Frankowska (2014,SWPS) Study 1	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #32
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	95	d = .23 ± .40	Nier (2014,Conncoll)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #33
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	90	d = .24 ± .41	Woodzicka (2014,WL)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #34
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	81	d = .30 ± .44	Schmidt & Nosek (2014,UVA)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #35
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	98	d = .40 ± .40	Morris (2014,McDaniel)	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	ML1 #36
money priming (instruction background)	Caruso et al. (2013) Study 1	30	d = .77 ± .74	136	d = -.07 ± .34	Rohrer et al. (2015) Study 1	no signal - inconsistent	exposure to money vs. control (instruction background)	system justification scale	very close	individual effort
money priming (scrambled sentence)	Caruso et al. (2013) Study 2	168	d = .43 ± .30	420	d = .06 ± .19	Rohrer et al. (2015) Study 2	no signal - inconsistent	exposure to money vs. control (scrambled sentence)	belief in a just world scale	very close	individual effort
money priming (scrambled sentence)	Caruso et al. (2013) Study 2	168	d = .43 ± .30	115	d = -.09 ± .39	Schuler & Wänke (in press) Study 2	no signal - inconsistent (imprecise)	exposure to money vs. control (scrambled sentence)	belief in a just world scale	very close	individual effort
money priming (scrambled sentence)	Caruso et al. (2013) Study 3	80	d = .49 ± .44	156	d = -.06 ± .31	Rohrer et al. (2015) Study 3	no signal - inconsistent	exposure to money vs. control (scrambled sentence)	social dominance orientation scale	very close	individual effort
money priming (instruction background)	Caruso et al. (2013) Study 4	48	d = .69 ± .58	116	d = .13 ± .37	Rohrer et al. (2015) Study 4	no signal - inconsistent	exposure to money vs. control (instruction background)	Fair Market Ideology scale	very close	individual effort
money priming (scrambled sentence)	Vohs et al. (2006) Study 3	39	d = .65 ± .65	40	d = .07 ± .62	Grenier et al. (2012)	no signal - consistent	exposure to money vs. control (scrambled sentence)	helping others (coding sheets for RA)	close	individual effort
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	83	d = .07 ± .44	Bernstein (2014,abington)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #37
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	120	d = .00 ± .36	Pilati (2014,brasilia)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	close	ML1 #38	non-US sample
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	84	d = -.03 ± .43	Vranka (2014,Charles)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	close	ML1 #39	non-US sample
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	94	d = -.45 ± .42	Nier (2014,Conncoll)	signal - inconsistent, opposite	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #40
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	94	d = -.07 ± .41	Rutchick (2014,CSUN)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #41
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	102	d = -.11 ± .40	Cheong (2014,Help)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	close	ML1 #42	non-US sample
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	89	d = -.17 ± .42	Vaughn (2014,Ithaca)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #43
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	172	d = .04 ± .30	Kurtz (2014,JMU)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #44
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	110	d = .14 ± .38	Cemalcilar (2014,KU)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	close	ML1 #45	non-US sample
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	111	d = .04 ± .38	Packard (2014,Laurier)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	close	ML1 #46	non-US sample
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	275	d = .04 ± .24	Kappes (2014,LSE)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	close	ML1 #47	non-US sample
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	145	d = .06 ± .32	Huntsinger & Mallett (2014,Luc)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #48
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	98	d = .09 ± .40	Morris (2014,McDaniel)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #49
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	85	d = .09 ± .43	Furrow & Thompson (2014,MSVU)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	close	ML1 #50	non-US sample
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	982	d = .09 ± .12	Schmidt & Nosek (2014, MTURK)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #51
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	97	d = .27 ± .40	Smith (2014,OSU)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #52
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	121	d = -.01 ± .36	Levitan (2014,Oxy)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #53
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	1320	d = .03 ± .11	Schmidt & Nosek (2014,PI)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #54
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	91	d = .02 ± .42	Adams & Nelson (2014,PSU)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #55
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	99	d = -.22 ± .40	Brumbaugh & Storbeck (2014,QCCUNY) Study 1	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #56
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	85	d = .02 ± .43	Brumbaugh & Storbeck (2014,QCCUNY2) Study 2	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #57
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	160	d = -.11 ± .32	Devos (2014,SDSU)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #58
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	78	d = .19 ± .45	Bocian & Frankowska (2014,SWPS) Study 1	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	close	ML1 #59	non-US sample
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	169	d = -.22 ± .30	Bocian & Frankowska (2014,SWPSON) Study 2	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	close	ML1 #60	non-US sample
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	182	d = -.27 ± .30	Davis & Hicks (2014,TAMU) Study 1	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #61
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	86	d = .27 ± .43	Hunt & Krueger (2014,TAMUC)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #62
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	218	d = -.03 ± .27	Davis & Hicks (2014,TAMUON) Study 2	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #63
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	79	d = .21 ± .44	Brandt et al. (2014,Tilburg)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	close	ML1 #64	non-US sample
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	126	d = -.11 ± .36	Klein et al. (2014,UFL)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #65
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	142	d = .49 ± .34	Vianello & Galliani (2014,UNIPD)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	close	ML1 #66	non-US sample
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	80	d = -.15 ± .44	Schmidt & Nosek (2014,UVA)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #67
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	105	d = -.07 ± .39	Hovermale & Joy-Gaba (2014,VCU)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #68
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	95	d = -.03 ± .41	Swol (2014,Wisc)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #69
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	99	d = .11 ± .40	Wichman (2014,WKU)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #70
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	89	d = -.09 ± .42	Woodzicka (2014,WL)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #71
flag priming	Carter, Ferguson, & Hassin (2011) Study 2	66	d = .50 ± .50	86	d = .08 ± .43	John & Skorinko (2014,WPI)	no signal - inconsistent	exposure to US flag vs. control (photos)	political views (8-issues)	very close	ML1 #72
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	84	d = .11 ± .44	Bernstein (2014,abington)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #73
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	120	d = .08 ± .36	Pilati (2014,brasilia)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #74
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	84	d = -.01 ± .43	Vranka (2014,Charles)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #75
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	95	d = .21 ± .41	Nier (2014,Conncoll)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #76
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	96	d = .03 ± .40	Rutchick (2014,CSUN)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #77
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	102	d = -.22 ± .39	Cheong (2014,Help)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #78
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	90	d = .68 ± .43	Vaughn (2014,Ithaca)	signal - consistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #79
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	174	d = .18 ± .30	Kurtz (2014,JMU)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #80
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	113	d = -.16 ± .38	Cemalcilar (2014,KU)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #81
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	112	d = .02 ± .38	Packard (2014,Laurier)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #82
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	277	d = .18 ± .24	Kappes (2014,LSE)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #83
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	146	d = .15 ± .33	Huntsinger & Mallett (2014,Luc)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #84
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	98	d = .19 ± .40	Morris (2014,McDaniel)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #85
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	85	d = .33 ± .44	Furrow & Thompson (2014,MSVU)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #86
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	1000	d = .26 ± .12	Schmidt & Nosek (2014, MTURK)	signal - inconsistent, smaller	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #87
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	107	d = -.11 ± .38	Smith (2014,OSU)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #88
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	122	d = .29 ± .36	Levitan (2014,Oxy)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #89
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	1328	d = .15 ± .11	Schmidt & Nosek (2014,PI)	signal - inconsistent, smaller	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #90
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	95	d = .29 ± .41	Adams & Nelson (2014,PSU)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #91
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	101	d = -.29 ± .40	Brumbaugh & Storbeck (2014,QCCUNY) Study 1	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #92
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	85	d = .32 ± .44	Brumbaugh & Storbeck (2014,QCCUNY2) Study 2	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #93
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	162	d = .30 ± .31	Devos (2014,SDSU)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #94
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	79	d = .35 ± .46	Bocian & Frankowska (2014,SWPS) Study 1	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #95
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	168	d = .09 ± .30	Bocian & Frankowska (2014,SWPSON) Study 2	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #96
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	187	d = -.09 ± .29	Davis & Hicks (2014,TAMU) Study 1	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #97
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	87	d = -.11 ± .43	Hunt & Krueger (2014,TAMUC)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #98
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	225	d = .31 ± .26	Davis & Hicks (2014,TAMUON) Study 2	signal - inconsistent, smaller	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #99
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	80	d = -.52 ± .45	Brandt et al. (2014,Tilburg)	signal - inconsistent, opposite	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #100
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	127	d = .12 ± .35	Klein et al. (2014,UFL)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #101
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	143	d = .17 ± .33	Vianello & Galliani (2014,UNIPD)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #102
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	81	d = .04 ± .44	Schmidt & Nosek (2014,UVA)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #103
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	107	d = .00 ± .38	Hovermale & Joy-Gaba (2014,VCU)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #104
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	96	d = .08 ± .40	Swol (2014,Wisc)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #105
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	103	d = -.21 ± .40	Wichman (2014,WKU)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #106
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	90	d = .03 ± .42	Woodzicka (2014,WL)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #107
imagined contact effect	Husnu & Crisp (2010) Study 1	33	d = .86 ± .74	87	d = .14 ± .43	John & Skorinko (2014,WPI)	no signal - inconsistent	imagined contact (Muslim) vs. control	willingness to interact with Muslim (4-items)	close	ML1 #108
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	120	d = .24 ± .36	Pilati (2014,brasilia)	no signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #110
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	102	d = -.06 ± .39	Cheong (2014,Help)	no signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #114
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	174	d = .57 ± .30	Kurtz (2014,JMU)	signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #116
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	113	d = .49 ± .38	Cemalcilar (2014,KU)	signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #117
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	112	d = .45 ± .38	Packard (2014,Laurier)	signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #118
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	276	d = .41 ± .24	Kappes (2014,LSE)	signal - consistent	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #119
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	146	d = .27 ± .33	Huntsinger & Mallett (2014,Luc)	no signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #120
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	999	d = .31 ± .12	Schmidt & Nosek (2014, MTURK)	signal - consistent	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #123
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	107	d = .45 ± .39	Smith (2014,OSU)	signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #124
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	122	d = .68 ± .37	Levitan (2014,Oxy)	signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #125
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	1323	d = .18 ± .10	Schmidt & Nosek (2014,PI)	signal - consistent	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #126
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	102	d = -.08 ± .39	Brumbaugh & Storbeck (2014,QCCUNY) Study 1	no signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #128
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	162	d = .28 ± .32	Devos (2014,SDSU)	no signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #130
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	168	d = .12 ± .30	Bocian & Frankowska (2014,SWPSON) Study 2	no signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #132
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	187	d = .35 ± .29	Davis & Hicks (2014,TAMU) Study 1	signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #133
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	225	d = .32 ± .26	Davis & Hicks (2014,TAMUON) Study 2	signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #135
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	127	d = .15 ± .35	Klein et al. (2014,UFL)	no signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #137
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	144	d = .46 ± .33	Vianello & Galliani (2014,UNIPD)	signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #138
sunk cost effect (paid vs. free football ticket)	Oppenheimer et al. (2009 ) Study 1	115	d = .23 ± .25	108	d = .33 ± .38	Hovermale & Joy-Gaba (2014,VCU)	no signal - consistent (imprecise)	sunk cost vs. control (paid vs. free football ticket)	likelihood of attending rainy football game	very close	ML1 #140
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	120	d = .33 ± .36	Pilati (2014,brasilia)	no signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #146
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	95	d = .11 ± .40	Nier (2014,Conncoll)	no signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #148
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	96	d = .12 ± .40	Rutchick (2014,CSUN)	no signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #149
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	102	d = .14 ± .40	Cheong (2014,Help)	no signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #150
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	174	d = .42 ± .30	Kurtz (2014,JMU)	signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #152
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	113	d = -.07 ± .38	Cemalcilar (2014,KU)	no signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #153
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	112	d = .30 ± .38	Packard (2014,Laurier)	no signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #154
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	277	d = .20 ± .24	Kappes (2014,LSE)	no signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #155
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	146	d = .31 ± .33	Huntsinger & Mallett (2014,Luc)	no signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #156
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	1000	d = .46 ± .12	Schmidt & Nosek (2014, MTURK)	signal - inconsistent, larger	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #159
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	107	d = .66 ± .40	Smith (2014,OSU)	signal - inconsistent, larger	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #160
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	123	d = .14 ± .36	Levitan (2014,Oxy)	no signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #161
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	1329	d = .22 ± .11	Schmidt & Nosek (2014,PI)	signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #162
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	103	d = .29 ± .39	Brumbaugh & Storbeck (2014,QCCUNY) Study 1	no signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #164
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	162	d = .45 ± .32	Devos (2014,SDSU)	signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #166
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	169	d = .27 ± .30	Bocian & Frankowska (2014,SWPSON) Study 2	no signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #168
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	187	d = .58 ± .29	Davis & Hicks (2014,TAMU) Study 1	signal - inconsistent, larger	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #169
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	225	d = .30 ± .26	Davis & Hicks (2014,TAMUON) Study 2	signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #171
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	127	d = .25 ± .36	Klein et al. (2014,UFL)	no signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #173
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	144	d = .01 ± .33	Vianello & Galliani (2014,UNIPD)	no signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #174
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	108	d = .56 ± .39	Hovermale & Joy-Gaba (2014,VCU)	signal - inconsistent, larger	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #176
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	96	d = .07 ± .40	Swol (2014,Wisc)	no signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #177
reciprocity norm effect	Hyman & Sheatsley (1950)		d = .16	103	d = .50 ± .40	Wichman (2014,WKU)	signal - consistent	question order (allow US/N. Korean reporter into N. Korea/US first vs. vica versa)	allow US/N. Korean reporter into N. Korea/US (2 binary choices)	close	ML1 #178
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	119	d = .18 ± .36	Pilati (2014,brasilia)	no signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #182
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	94	d = .14 ± .41	Nier (2014,Conncoll)	no signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #184
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	96	d = .79 ± .42	Rutchick (2014,CSUN)	signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #185
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	102	d = .19 ± .39	Cheong (2014,Help)	no signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #186
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	90	d = .53 ± .42	Vaughn (2014,Ithaca)	signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #187
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	174	d = .68 ± .30	Kurtz (2014,JMU)	signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #188
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	113	d = -.09 ± .37	Cemalcilar (2014,KU)	no signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #189
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	112	d = .29 ± .38	Packard (2014,Laurier)	no signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #190
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	277	d = .38 ± .24	Kappes (2014,LSE)	signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #191
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	146	d = .41 ± .33	Huntsinger & Mallett (2014,Luc)	signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #192
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	98	d = .09 ± .40	Morris (2014,McDaniel)	no signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #193
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	999	d = .44 ± .12	Schmidt & Nosek (2014, MTURK)	signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #195
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	107	d = .27 ± .38	Smith (2014,OSU)	no signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #196
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	122	d = .17 ± .36	Levitan (2014,Oxy)	no signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #197
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	1322	d = .27 ± .10	Schmidt & Nosek (2014,PI)	signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #198
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	95	d = .43 ± .41	Adams & Nelson (2014,PSU)	signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #199
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	101	d = .94 ± .42	Brumbaugh & Storbeck (2014,QCCUNY) Study 1	signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #200
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	162	d = .41 ± .32	Devos (2014,SDSU)	signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #202
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	167	d = .31 ± .31	Bocian & Frankowska (2014,SWPSON) Study 2	signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #204
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	186	d = .34 ± .30	Davis & Hicks (2014,TAMU) Study 1	signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #205
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	224	d = .38 ± .27	Davis & Hicks (2014,TAMUON) Study 2	signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #207
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	127	d = .50 ± .36	Klein et al. (2014,UFL)	signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #209
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	144	d = -.26 ± .33	Vianello & Galliani (2014,UNIPD)	no signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #210
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	108	d = .31 ± .38	Hovermale & Joy-Gaba (2014,VCU)	no signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #212
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	96	d = .22 ± .41	Swol (2014,Wisc)	no signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #213
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	103	d = .65 ± .40	Wichman (2014,WKU)	signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #214
quote attribution (on attitudes) effect	Lorge & Curtiss (1936 )		d = NR	90	d = -.36 ± .42	Woodzicka (2014,WL)	no signal	liked (G. Washington) vs. disliked (O. Bin Laden) individual	agreement with political quote	close	ML1 #215
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	120	d = .96 ± .38	Pilati (2014,brasilia)	signal - consistent (imprecise)	low vs. high category scales	self-reported TV watching	very close	ML1 #218
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	95	d = .44 ± .41	Nier (2014,Conncoll)	signal - consistent (imprecise)	low vs. high category scales	self-reported TV watching	very close	ML1 #220
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	102	d = .45 ± .40	Cheong (2014,Help)	signal - consistent (imprecise)	low vs. high category scales	self-reported TV watching	very close	ML1 #222
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	174	d = .44 ± .30	Kurtz (2014,JMU)	signal - consistent	low vs. high category scales	self-reported TV watching	very close	ML1 #224
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	113	d = -.16 ± .38	Cemalcilar (2014,KU)	no signal - inconsistent (imprecise)	low vs. high category scales	self-reported TV watching	very close	ML1 #225
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	112	d = .60 ± .38	Packard (2014,Laurier)	signal - consistent (imprecise)	low vs. high category scales	self-reported TV watching	very close	ML1 #226
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	277	d = .52 ± .24	Kappes (2014,LSE)	signal - consistent	low vs. high category scales	self-reported TV watching	very close	ML1 #227
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	146	d = .55 ± .33	Huntsinger & Mallett (2014,Luc)	signal - consistent	low vs. high category scales	self-reported TV watching	very close	ML1 #228
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	98	d = .63 ± .41	Morris (2014,McDaniel)	signal - consistent (imprecise)	low vs. high category scales	self-reported TV watching	very close	ML1 #229
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	1000	d = .51 ± .13	Schmidt & Nosek (2014, MTURK)	signal - consistent	low vs. high category scales	self-reported TV watching	very close	ML1 #231
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	107	d = .43 ± .39	Smith (2014,OSU)	signal - consistent (imprecise)	low vs. high category scales	self-reported TV watching	very close	ML1 #232
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	123	d = .42 ± .36	Levitan (2014,Oxy)	signal - consistent (imprecise)	low vs. high category scales	self-reported TV watching	very close	ML1 #233
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	1329	d = .48 ± .11	Schmidt & Nosek (2014,PI)	signal - consistent	low vs. high category scales	self-reported TV watching	very close	ML1 #234
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	95	d = .42 ± .41	Adams & Nelson (2014,PSU)	signal - consistent (imprecise)	low vs. high category scales	self-reported TV watching	very close	ML1 #235
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	103	d = .61 ± .40	Brumbaugh & Storbeck (2014,QCCUNY) Study 1	signal - consistent (imprecise)	low vs. high category scales	self-reported TV watching	very close	ML1 #236
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	162	d = .68 ± .32	Devos (2014,SDSU)	signal - consistent	low vs. high category scales	self-reported TV watching	very close	ML1 #238
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	169	d = .31 ± .30	Bocian & Frankowska (2014,SWPSON) Study 2	signal - consistent	low vs. high category scales	self-reported TV watching	very close	ML1 #240
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	187	d = .35 ± .30	Davis & Hicks (2014,TAMU) Study 1	signal - consistent	low vs. high category scales	self-reported TV watching	very close	ML1 #241
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	225	d = .37 ± .26	Davis & Hicks (2014,TAMUON) Study 2	signal - consistent	low vs. high category scales	self-reported TV watching	very close	ML1 #243
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	127	d = .67 ± .36	Klein et al. (2014,UFL)	signal - consistent (imprecise)	low vs. high category scales	self-reported TV watching	very close	ML1 #245
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	144	d = .72 ± .34	Vianello & Galliani (2014,UNIPD)	signal - consistent	low vs. high category scales	self-reported TV watching	very close	ML1 #246
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	108	d = .56 ± .39	Hovermale & Joy-Gaba (2014,VCU)	signal - consistent (imprecise)	low vs. high category scales	self-reported TV watching	very close	ML1 #248
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	96	d = .41 ± .41	Swol (2014,Wisc)	signal - consistent (imprecise)	low vs. high category scales	self-reported TV watching	very close	ML1 #249
scale options anchoring effect	Schwarz, Strack et al. (1985) Study 1	132	d = .50	103	d = .74 ± .40	Wichman (2014,WKU)	signal - consistent (imprecise)	low vs. high category scales	self-reported TV watching	very close	ML1 #250
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	113	d = .36 ± .38	Pilati (2014,brasilia)	no signal - inconsistent	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #254
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	97	d = .17 ± .40	Cheong (2014,Help)	no signal - inconsistent	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #258
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	167	d = .64 ± .31	Kurtz (2014,JMU)	signal - inconsistent, smaller	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #260
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	105	d = .84 ± .40	Cemalcilar (2014,KU)	signal - consistent	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #261
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	108	d = .97 ± .40	Packard (2014,Laurier)	signal - consistent	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #262
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	267	d = .74 ± .25	Kappes (2014,LSE)	signal - inconsistent, smaller	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #263
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	141	d = .59 ± .34	Huntsinger & Mallett (2014,Luc)	signal - inconsistent, smaller	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #264
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	91	d = .53 ± .42	Morris (2014,McDaniel)	signal - inconsistent, smaller	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #265
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	894	d = .55 ± .14	Schmidt & Nosek (2014, MTURK)	signal - inconsistent, smaller	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #267
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	90	d = .58 ± .42	Smith (2014,OSU)	signal - inconsistent, smaller	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #268
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	117	d = .55 ± .37	Levitan (2014,Oxy)	signal - inconsistent, smaller	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #269
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	1236	d = .60 ± .12	Schmidt & Nosek (2014,PI)	signal - inconsistent, smaller	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #270
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	93	d = .25 ± .42	Brumbaugh & Storbeck (2014,QCCUNY) Study 1	no signal - inconsistent	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #272
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	155	d = .39 ± .32	Devos (2014,SDSU)	signal - inconsistent, smaller	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #274
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	165	d = .55 ± .32	Bocian & Frankowska (2014,SWPSON) Study 2	signal - inconsistent, smaller	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #276
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	179	d = .37 ± .30	Davis & Hicks (2014,TAMU) Study 1	signal - inconsistent, smaller	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #277
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	208	d = .10 ± .28	Davis & Hicks (2014,TAMUON) Study 2	no signal - inconsistent	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #279
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	125	d = .61 ± .36	Klein et al. (2014,UFL)	signal - inconsistent, smaller	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #281
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	134	d = .72 ± .35	Vianello & Galliani (2014,UNIPD)	signal - consistent	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #282
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	101	d = .65 ± .40	Hovermale & Joy-Gaba (2014,VCU)	signal - consistent	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #284
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	91	d = .64 ± .43	Swol (2014,Wisc)	signal - consistent	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #285
sex difference in implicit math attitudes	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = 1.01	95	d = .30 ± .41	Wichman (2014,WKU)	no signal - inconsistent	sex (male vs. female)	implicit math attitudes (math vs. art IAT)	very close	ML1 #286
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	120	d = .53 ± .37	Pilati (2014,brasilia)	signal - inconsistent, smaller (imprecise)	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #290
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	174	d = .74 ± .31	Kurtz (2014,JMU)	signal - inconsistent, smaller (imprecise)	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #296
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	113	d = .56 ± .38	Cemalcilar (2014,KU)	signal - inconsistent, smaller (imprecise)	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #297
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	112	d = .82 ± .39	Packard (2014,Laurier)	signal - consistent (imprecise)	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #298
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	277	d = .73 ± .24	Kappes (2014,LSE)	signal - inconsistent, smaller	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #299
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	146	d = .72 ± .34	Huntsinger & Mallett (2014,Luc)	signal - inconsistent, smaller (imprecise)	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #300
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	1000	d = .62 ± .13	Schmidt & Nosek (2014, MTURK)	signal - inconsistent, smaller	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #303
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	107	d = .67 ± .39	Smith (2014,OSU)	signal - inconsistent, smaller (imprecise)	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #304
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	123	d = .26 ± .36	Levitan (2014,Oxy)	no signal - inconsistent (imprecise)	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #305
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	1329	d = .57 ± .11	Schmidt & Nosek (2014,PI)	signal - inconsistent, smaller	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #306
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	162	d = .42 ± .32	Devos (2014,SDSU)	signal - inconsistent, smaller (imprecise)	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #310
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	169	d = .52 ± .31	Bocian & Frankowska (2014,SWPSON) Study 2	signal - inconsistent, smaller (imprecise)	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #312
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	187	d = .55 ± .30	Davis & Hicks (2014,TAMU) Study 1	signal - inconsistent, smaller (imprecise)	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #313
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	225	d = .49 ± .26	Davis & Hicks (2014,TAMUON) Study 2	signal - inconsistent, smaller	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #315
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	127	d = 1.17 ± .38	Klein et al. (2014,UFL)	signal - consistent (imprecise)	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #317
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	144	d = .86 ± .34	Vianello & Galliani (2014,UNIPD)	signal - consistent (imprecise)	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #318
gain versus loss framing (unusual disease)	Tversky & Kahneman (1981) Study 1	307	d = 1.20 ± .26	108	d = .32 ± .38	Hovermale & Joy-Gaba (2014,VCU)	no signal - inconsistent (imprecise)	gain (saved) vs. loss (will die) framing	% Ps choosing risky choice (binary choice)	close	ML1 #320
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	80	d = .60 ± .46	Bernstein (2014,abington)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #325
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	114	d = .36 ± .38	Pilati (2014,brasilia)	no signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #326
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	72	d = .87 ± .49	Vranka (2014,Charles)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #327
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	83	d = .62 ± .45	Nier (2014,Conncoll)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #328
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	92	d = .51 ± .42	Rutchick (2014,CSUN)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #329
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	96	d = .52 ± .41	Cheong (2014,Help)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #330
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	85	d = .77 ± .44	Vaughn (2014,Ithaca)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #331
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	164	d = .72 ± .32	Kurtz (2014,JMU)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #332
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	104	d = .53 ± .40	Cemalcilar (2014,KU)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #333
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	106	d = .97 ± .40	Packard (2014,Laurier)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #334
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	255	d = .65 ± .26	Kappes (2014,LSE)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #335
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	142	d = .53 ± .34	Huntsinger & Mallett (2014,Luc)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #336
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	93	d = .50 ± .42	Morris (2014,McDaniel)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #337
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	73	d = .34 ± .47	Furrow & Thompson (2014,MSVU)	no signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #338
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	981	d = .62 ± .13	Schmidt & Nosek (2014, MTURK)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #339
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	105	d = .11 ± .39	Smith (2014,OSU)	no signal - inconsistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #340
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	102	d = 1.16 ± .43	Levitan (2014,Oxy)	signal - inconsistent, larger	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #341
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	1204	d = .72 ± .12	Schmidt & Nosek (2014,PI)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #342
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	93	d = .58 ± .42	Adams & Nelson (2014,PSU)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #343
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	93	d = .42 ± .42	Brumbaugh & Storbeck (2014,QCCUNY) Study 1	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #344
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	80	d = .34 ± .45	Brumbaugh & Storbeck (2014,QCCUNY2) Study 2	no signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #345
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	159	d = .61 ± .32	Devos (2014,SDSU)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #346
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	78	d = .12 ± .45	Bocian & Frankowska (2014,SWPS) Study 1	no signal - inconsistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #347
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	154	d = .59 ± .32	Bocian & Frankowska (2014,SWPSON) Study 2	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #348
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	178	d = .74 ± .31	Davis & Hicks (2014,TAMU) Study 1	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #349
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	81	d = .74 ± .45	Hunt & Krueger (2014,TAMUC)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #350
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	214	d = .59 ± .28	Davis & Hicks (2014,TAMUON) Study 2	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #351
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	74	d = .68 ± .48	Brandt et al. (2014,Tilburg)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #352
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	117	d = .38 ± .37	Klein et al. (2014,UFL)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #353
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	126	d = .76 ± .37	Vianello & Galliani (2014,UNIPD)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #354
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	77	d = 1.12 ± .49	Schmidt & Nosek (2014,UVA)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #355
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	106	d = .72 ± .40	Hovermale & Joy-Gaba (2014,VCU)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #356
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	94	d = .79 ± .42	Swol (2014,Wisc)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #357
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	97	d = .43 ± .41	Wichman (2014,WKU)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #358
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	86	d = .07 ± .43	Woodzicka (2014,WL)	no signal - inconsistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #359
retroactive gambler's fallacy effect	Oppenheimer & Monin (2009) Study 2a	59	d = .69 ± .54	84	d = .98 ± .46	John & Skorinko (2014,WPI)	signal - consistent	three 6s vs. two 6s & one 3 (rolling dice)	# of (prior) times dice rolled (open-ended)	very close	ML1 #360
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	117	d = 1.00 ± .41	Pilati (2014,brasilia)	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #362
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	95	d = .42 ± .42	Rutchick (2014,CSUN)	signal - inconsistent, smaller	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #365
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	101	d = .63 ± .42	Cheong (2014,Help)	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #366
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	172	d = 1.14 ± .35	Kurtz (2014,JMU)	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #368
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	112	d = .65 ± .40	Cemalcilar (2014,KU)	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #369
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	112	d = .80 ± .40	Packard (2014,Laurier)	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #370
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	277	d = .84 ± .26	Kappes (2014,LSE)	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #371
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	146	d = .99 ± .37	Huntsinger & Mallett (2014,Luc)	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #372
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	95	d = .80 ± .44	Morris (2014,McDaniel)	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #373
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	973	d = .67 ± .13	Schmidt & Nosek (2014, MTURK)	signal - inconsistent, smaller	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #375
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	106	d = .53 ± .40	Smith (2014,OSU)	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #376
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	120	d = .82 ± .39	Levitan (2014,Oxy)	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #377
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	1308	d = .71 ± .12	Schmidt & Nosek (2014,PI)	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #378
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	103	d = .85 ± .43	Brumbaugh & Storbeck (2014,QCCUNY) Study 1	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #380
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	162	d = .74 ± .33	Devos (2014,SDSU)	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #382
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	167	d = .86 ± .33	Bocian & Frankowska (2014,SWPSON) Study 2	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #384
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	187	d = .70 ± .30	Davis & Hicks (2014,TAMU) Study 1	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #385
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	222	d = .94 ± .30	Davis & Hicks (2014,TAMUON) Study 2	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #387
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	127	d = 1.47 ± .43	Klein et al. (2014,UFL)	signal - inconsistent, larger	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #389
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	140	d = 1.13 ± .38	Vianello & Galliani (2014,UNIPD)	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #390
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	108	d = .98 ± .42	Hovermale & Joy-Gaba (2014,VCU)	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #392
implicit-explicit math attitude correlation	Nosek, Banaji, & Greenwald (2002) Study 2	91	d = .93 ± .44	102	d = .85 ± .43	Wichman (2014,WKU)	signal - consistent	implicit math attitudes (math vs. art IAT)	explicit math attitudes (composite)	close	ML1 #394
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	154	d = 2.14 ± .40	Kurtz (2014,JMU)	signal - inconsistent, larger (imprecise)	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #404			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	98	d = 1.58 ± .46	Packard (2014,Laurier)	signal - inconsistent, larger (imprecise)	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #406			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	223	d = 1.90 ± .32	Kappes (2014,LSE)	signal - inconsistent, larger	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #407			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	122	d = 1.70 ± .42	Huntsinger & Mallett (2014,Luc)	signal - inconsistent, larger (imprecise)	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #408			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	84	d = 1.79 ± .51	Morris (2014,McDaniel)	signal - inconsistent, larger (imprecise)	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #409			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	872	d = 1.68 ± .16	Schmidt & Nosek (2014, MTURK)	signal - inconsistent, larger	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #411			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	109	d = 1.48 ± .42	Levitan (2014,Oxy)	signal - inconsistent, larger (imprecise)	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #413			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	1113	d = 1.35 ± .13	Schmidt & Nosek (2014,PI)	signal - inconsistent, larger	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #414			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	150	d = 2.05 ± .40	Devos (2014,SDSU)	signal - inconsistent, larger (imprecise)	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #418			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	126	d = 1.81 ± .42	Bocian & Frankowska (2014,SWPSON) Study 2	signal - inconsistent, larger (imprecise)	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #420			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	161	d = 2.14 ± .39	Davis & Hicks (2014,TAMU) Study 1	signal - inconsistent, larger (imprecise)	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #421			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	198	d = 1.61 ± .32	Davis & Hicks (2014,TAMUON) Study 2	signal - inconsistent, larger	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #423			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	118	d = 2.59 ± .50	Klein et al. (2014,UFL)	signal - inconsistent, larger (imprecise)	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #425			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	77	d = 1.38 ± .50	Vianello & Galliani (2014,UNIPD)	signal - consistent (imprecise)	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #426			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	100	d = 2.02 ± .48	Hovermale & Joy-Gaba (2014,VCU)	signal - inconsistent, larger (imprecise)	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #428			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	88	d = 1.96 ± .52	Swol (2014,Wisc)	signal - inconsistent, larger (imprecise)	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #429			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
anchoring effect (population of Chicago)	Jacowitz & Kahneman (1995)	156	d = .93 ± .33	73	d = 1.35 ± .52	Woodzicka (2014,WL)	signal - consistent (imprecise)	high vs. low anchor (population of Chicago)	population estimate	very close	ML1 #431			3 other anchoring DVs (babies, height of Everest, distance to NYC; similar results observed)
question phrasing effect	Rugg (1941)		d = .65	96	d = 1.54 ± .46	Rutchick (2014,CSUN)	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #437
question phrasing effect	Rugg (1941)		d = .65	174	d = 2.12 ± .38	Kurtz (2014,JMU)	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #440
question phrasing effect	Rugg (1941)		d = .65	113	d = .07 ± .38	Cemalcilar (2014,KU)	no signal - inconsistent	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #441
question phrasing effect	Rugg (1941)		d = .65	112	d = .94 ± .40	Packard (2014,Laurier)	signal - consistent	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #442
question phrasing effect	Rugg (1941)		d = .65	277	d = 1.69 ± .28	Kappes (2014,LSE)	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #443
question phrasing effect	Rugg (1941)		d = .65	146	d = 2.18 ± .42	Huntsinger & Mallett (2014,Luc)	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #444
question phrasing effect	Rugg (1941)		d = .65	98	d = 2.21 ± .51	Morris (2014,McDaniel)	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #445
question phrasing effect	Rugg (1941)		d = .65	1000	d = 2.44 ± .17	Schmidt & Nosek (2014, MTURK)	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #447
question phrasing effect	Rugg (1941)		d = .65	123	d = 4.27 ± .64	Levitan (2014,Oxy)	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #449
question phrasing effect	Rugg (1941)		d = .65	1329	d = 2.70 ± .15	Schmidt & Nosek (2014,PI)	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #450
question phrasing effect	Rugg (1941)		d = .65	162	d = 2.43 ± .41	Devos (2014,SDSU)	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #454
question phrasing effect	Rugg (1941)		d = .65	169	d = 1.74 ± .36	Bocian & Frankowska (2014,SWPSON) Study 2	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #456
question phrasing effect	Rugg (1941)		d = .65	187	d = 1.48 ± .33	Davis & Hicks (2014,TAMU) Study 1	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #457
question phrasing effect	Rugg (1941)		d = .65	225	d = 1.71 ± .31	Davis & Hicks (2014,TAMUON) Study 2	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #459
question phrasing effect	Rugg (1941)		d = .65	127	d = 2.29 ± .45	Klein et al. (2014,UFL)	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #461
question phrasing effect	Rugg (1941)		d = .65	144	d = .78 ± .34	Vianello & Galliani (2014,UNIPD)	signal - consistent	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #462
question phrasing effect	Rugg (1941)		d = .65	108	d = 1.58 ± .43	Hovermale & Joy-Gaba (2014,VCU)	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #464
question phrasing effect	Rugg (1941)		d = .65	96	d = 1.80 ± .48	Swol (2014,Wisc)	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #465
question phrasing effect	Rugg (1941)		d = .65	90	d = 2.35 ± .54	Woodzicka (2014,WL)	signal - inconsistent, larger	allowed vs. forbidden question framing	question endorsement (yes vs. no)	close	ML1 #467
primacy-of-warmth effect	Asch (1946) Study 1	834	ES = NR	1023	ES = ?	Nauts, Langner et al. (2014)		warm vs. cold (warmth words) available in trait list	warmth vs. competence related personality judgements	close*	SP: Special Issue #1	mTurk (with no age restrictions) rather than student participants
deviation-rejection effect (sociometric test DV)	Schachter (1951)		d = 1.84	80	d = .39	Wesselmann, Williams et al. (2014)	signal - inconsistent, smaller	deviate vs. slider vs. mode (confederate behavior); time (5, 10, 15, 35, 40, 45 minutes from the beginning)	sociometric test	close	SP: Special Issue #2	"Schachter (1951) reported only basic instructions given to each confederate. We developed confederate scripts based upon these instructions. Representative arguments were taken from Schachter’s reenactment in the documentary The Social Animal (Mayer & Norris, 1970) and other relevant topics (e.g., news or media events, book, or film examples)."		2 other DVs (communication, committee nomination; pattern of results inconsistent with original)
Romeo & Juliet Effect	Driscoll et al. (1972)		r = .34 ± .32	396	r = -.05 ± .10	Sinclair, Hood, & Wright (2014)	no signal - inconsistent	increases in parental vs. friend interference	love (4 items); commitment to marrige (1 item)	close*	SP: Special Issue #3	mTurk (with no age restrictions) rather than student participants	Parental interference negatively associated with partner trust, r = -.18, p < .001	2 other DVs (commitment, trust; also inconsistent with originally observed pattern of results)	Increases in parental interference positively associated with increases in love over time
single-exposure musical conditioning effect	Gorn (1982) Study 1	195	OR = 7.69 [4.26,13.86]	143	OR = 1.25 [.69,2.29]	Vermeulen, Batenburg et al. (2014) Study 1	no signal - inconsistent	liked vs. disliked music (playing while pen advertised)	pen choice (advertised vs. non-advertised; binary choice)	very close	SP: Special Issue #4
single-exposure musical conditioning effect	Gorn (1982) Study 1	195	OR = 7.69 [4.26,13.86]	160	OR = 1.41 [.8,2.5]	Vermeulen, Batenburg et al. (2014) Study 2	no signal - inconsistent	liked vs. disliked music (playing while pen advertised)	pen choice (advertised vs. non-advertised; binary choice)	very close	SP: Special Issue #5
single-exposure musical conditioning effect	Gorn (1982) Study 1	195	OR = 7.69 [4.26,13.86]	72	OR = 3.77 [1.51,9.4]	Vermeulen, Batenburg et al. (2014) Study 3	signal - consistent	liked vs. disliked music (playing while pen advertised)	pen choice (advertised vs. non-advertised; binary choice)	close	SP: Special Issue #6	updated (modern) music rather than original music
automatic stereotype activation effect (response priming)	Banaji & Hardin (1996) Study 1	73	d = .45 ± .23	294	d = .51 ± .11	Muller & Rothermund (2014)	signal - consistent	male vs. female related primes (words); matching vs. not-matching target word	gender categorization speed (male vs. female, binary)	close	SP: Special Issue #7	excluded control or nonword primes; a new set of prime and target stimuli		additional semantic priming outcome (to rule out response priming alternative explanation); original pattern of results not replicated
stereotype susceptibility effect	Shih, Pittinsky, & Ambady (1999) Study 1	46	d = .73 ± .76	158	d = .27 ± .38	Gibson, Losee, & Vitiello (2014)	no signal - inconsistent	identity salience (Asian vs. control vs. female; answered questions about identity)	math performance (12-questions; accuracy DV)	very close	SP: Special Issue #8
stereotype susceptibility effect	Shih, Pittinsky, & Ambady (1999) Study 1	46	d = .73 ± .76	139	d = .17 ± .42	Moon & Roeder (2014)	no signal - inconsistent	identity salience (Asian vs. control vs. female; answered questions about identity)	math performance (12-questions; accuracy DV)	very close	SP: Special Issue #9
infidelity distress sex difference effect	Buss et al. (1999) Study 2 (Young)	234	d = 1.30 ± .28	87	d = 1.11 ± .50	IJzerman et al. (2014) Study 1 (Young)	signal - consistent (imprecise)	sex (male vs. female)	distress to sexual vs. emotional infidelity (8 dilemmas; binary choice)	close	SP: Special Issue #10	different number of items (6 vs. 8)			Men are more distressed by sexual (cf. emotional) infidelity whereas women are more distressed by emotional (cf. sexual) infidelity
infidelity distress sex difference effect	Buss et al. (1999) Study 2 (Young)	234	d = 1.30 ± .28	199	d = .30 ± .28	IJzerman et al. (2014) Study 2 (Young)	signal - inconsistent, smaller	sex (male vs. female)	distress to sexual vs. emotional infidelity (8 dilemmas; binary choice)	close	SP: Special Issue #11				Men are more distressed by sexual (cf. emotional) infidelity whereas women are more distressed by emotional (cf. sexual) infidelity
infidelity distress sex difference effect	Buss et al. (1999) Study 2 (Young)	234	d = 1.30 ± .28	1221	d = .50 ± .12	IJzerman et al. (2014) Study 4 (Young)	signal - inconsistent, smaller	sex (male vs. female)	distress to sexual vs. emotional infidelity (8 dilemmas; binary choice)	close	SP: Special Issue #12	mTurk sample; young (18–30 year olds) subset of Study 4			Men are more distressed by sexual (cf. emotional) infidelity whereas women are more distressed by emotional (cf. sexual) infidelity
infidelity distress sex difference effect	Shackelford et al. (2004) (Old)	202	d = .57 ± .28	143	d = -.09 ± .34	IJzerman et al. (2014) Study 3 (Old)	no signal - inconsistent (imprecise)	sex (male vs. female)	distress to sexual vs. emotional infidelity (8 dilemmas; binary choice)	close	SP: Special Issue #13				Men are more distressed by sexual (cf. emotional) infidelity whereas women are more distressed by emotional (cf. sexual) infidelity
infidelity distress sex difference effect	Shackelford et al. (2004) (Old)	202	d = .57 ± .28	245	d = .05 ± .28	IJzerman et al. (2014) Study 4 (Old)	no signal - inconsistent	sex (male vs. female)	distress to sexual vs. emotional infidelity (8 dilemmas; binary choice)	close	SP: Special Issue #14	mTurk sample; old (50–70 year olds) subset of Study 4			Men are more distressed by sexual (cf. emotional) infidelity whereas women are more distressed by emotional (cf. sexual) infidelity
cleanliness priming effect (scrambled sentence)	Schnall et al. (2008a) Study 1	40	d = -.59 ± .55	208	d = -.01 ± .27	Johnson et al. (2014a) Study 1	no signal - inconsistent	exposure to cleanliness-related vs. control words (scrambled sentence task)	moral wrongness (6 moral dilemmas)	close	SP: Special Issue #15	7 vs. 9 point scale; US rather than UK undergraduates
cleanliness priming effect (scrambled sentence)	Schnall et al. (2008a) Study 1	40	d = -.59 ± .55	736	d = .05 ± .14	Johnson et al. (2014b,online)	no signal - inconsistent	exposure to cleanliness-related vs. control words (scrambled sentence task)	moral wrongness (6 moral dilemmas)	close	SP: Special Issue #16	7 vs. 9 point scale; online instead of lab setting; US rather than UK undergraduates
cleanliness priming effect (hand washing)	Schnall et al. (2008a) Study 2	43	d = -.84 ± .62	126	d = .02 ± .35	Johnson et al. (2014a) Study 2	no signal - inconsistent	hand washing vs. control condition	moral wrongness (6 moral dilemmas)	close	SP: Special Issue #17	7 vs. 9 point scale; US rather than UK undergraduates	α = .62 on private body consciousness (PBC) subscale
cleanliness priming effect (scrambled sentence)	Schnall et al. (2008a) Study 1	40	d = -.59 ± .55	60	d = -.44 ± .50	Besman et al. (2013)	no signal - consistent	exposure to cleanliness-related vs. control words (scrambled sentence task)	moral wrongness (6 moral dilemmas)	close	individual effort	7 vs. 9 point scale; different words used in scrambled sentence task; US rather than UK undergraduates
cleanliness priming effect (scrambled sentence)	Schnall et al. (2008a) Study 1	40	d = -.59 ± .55	60	d = -.47 ± .51	Arbesfeld et al. (2014)	no signal - consistent	exposure to cleanliness-related vs. control words (scrambled sentence task)	moral wrongness (6 moral dilemmas)	close	individual effort	7 vs. 9 point scale; different words used in scrambled sentence task; US rather than UK undergraduates
cleanliness priming effect (scrambled sentence)	Schnall et al. (2008a) Study 1	40	d = -.59 ± .55	90	d = -.10 ± .41	Lee et al. (2013)	no signal - inconsistent	exposure to cleanliness-related vs. control words (scrambled sentence task)	moral wrongness (6 moral dilemmas)	close	individual effort	7 vs. 9 point scale; study administered on computer instead of paper-and-pencil; US rather than UK undergraduates
cleanliness priming effect (scrambled sentence)	Schnall et al. (2008a) Study 1	40	d = -.59 ± .55	189	d = -.20 ± .29	Huang (2014,online) Study 1	no signal - inconsistent	exposure to cleanliness-related vs. control words (scrambled sentence task)	moral wrongness (6 moral dilemmas)	close	individual effort	7 vs. 9 point scale; study administered on computer instead of paper-and-pencil; US rather than UK undergraduates
physical warmth boosts pro-sociality	Williams & Bargh (2008a) Study 2	53	OR = 3.52 [1.06,11.73]	282	OR = .61 [0.38, 0.98]	Lynott et al. (2014,Kenyon) Study 1	signal - inconsistent, opposite	evaluation of hot vs. cold instant therapeutic packs	reward choice (prosocial vs. selfish; binary)	close	SP: Special Issue #18	different prosocial and selfish rewards	hot pack rated as warmer than the cold pack, d = 2.50
physical warmth boosts pro-sociality	Williams & Bargh (2008a) Study 2	53	OR = 3.52 [1.06,11.73]	294	OR = .92 [0.56, 1.53]	Lynott et al. (2014,MSU) Study 2	no signal - inconsistent	evaluation of hot vs. cold instant therapeutic packs	reward choice (prosocial vs. selfish; binary)	close	SP: Special Issue #19	different prosocial and selfish rewards	hot pack rated as warmer than the cold pack, d = 2.50
physical warmth boosts pro-sociality	Williams & Bargh (2008a) Study 2	53	OR = 3.52 [1.06,11.73]	237	OR = .77 [0.47, 1.26]	Lynott et al. (2014,UK) Study 3	no signal - inconsistent	evaluation of hot vs. cold instant therapeutic packs	reward choice (prosocial vs. selfish; binary)	close	SP: Special Issue #20	different prosocial and selfish rewards	hot pack rated as warmer than the cold pack, d = 2.50
psychological distance increases wrongness of immoral acts	Eyal, Liberman, & Trope (2008) Study 2	58	d = .66 ± .27	116	d = -.06 ± .37	Zezelj & Jokic (2015) Study 1	no signal - inconsistent (imprecise)	temporally near vs. distant moral wrongnesses (imagining 3 descriptions of acts)	wrongness (1 item)	very close	SP: Special Issue #21	sample drawn from different culture
psychological distance increases wrongness of immoral acts	Eyal, Liberman, & Trope (2008) Study 3	40	d = .71 ± .17	105	d = .61 ± .14	Zezelj & Jokic (2015) Study 2	signal - consistent	socially near vs. distant moral wrongnesses (imagining 3 descriptions of acts)	wrongness (1 item)	very close	SP: Special Issue #22	sample drawn from different culture
psychological distance increases wrongness of immoral acts	Eyal, Liberman, & Trope (2008) Study 4	47	d = .80 ± .26	84	d = -.26 ± .26	Zezelj & Jokic (2015) Study 3	signal - inconsistent, opposite	temporally near vs. distant virtuous acts (imagining 3 descriptions of acts)	virtuousness (1 item)	very close	SP: Special Issue #23	sample drawn from different culture
psychological distance increases wrongness of immoral acts	Gong & Medin (2012) Study 1	34	d = -1.07	48	d = -.68 ± .36	Zezelj & Jokic (2015) Study 4	signal - inconsistent, smaller	low vs. high construal primes	acceptability (1 item)	very close	SP: Special Issue #24	sample drawn from different culture
moral licensing effect (donation amount)	Sachdeva, Iliev, & Medin (2009) Study 1	46	d = .62 ± .73	71	d = -.03 ± .48	Blanken et al. (2014) Study 1	no signal - inconsistent	writing stories with positive vs. neutral vs. negative trait words	donation amount	very close	SP: Special Issue #25			*additional negative trait (moral cleansing) condition; similar pattern of results observed
moral licensing effect (cooperative behavior)	Sachdeva, Iliev, & Medin (2009) Study 3	46	d = .59 ± .71	98	d = -.31 ± .39	Blanken et al. (2014) Study 2	no signal - inconsistent	writing stories with positive vs. neutral vs. negative trait words	co-operation (self-report)	very close	SP: Special Issue #26	study ran in a laboratory at a Dutch instead of a USA university		*additional negative trait (moral cleansing) condition; similar pattern of results observed
moral licensing effect (donation amount)	Sachdeva, Iliev, & Medin (2009) Study 1	46	d = .62 ± .73	632	d = .05 ± .20	Blanken et al. (2014) Study 3 (online)	no signal - inconsistent	writing stories with positive vs. neutral vs. negative trait words	donation amount	close*	SP: Special Issue #27	mTurk general sample; donating as part of potential winnings instead of money out-of-pocket		*additional negative trait (moral cleansing) condition; similar pattern of results observed
superstition boosts performance effect	Damisch et al. (2010) Study 1	28	d = .80 ± .75	116	d = .05 ± .40	Calin-Jageman & Caldwell (2014) Study 1	no signal - inconsistent	lucky vs. neutral ball (labeled by the experimenter)	golf task (10 putts)	close	SP: Special Issue #28	used a putting distance of 150 cm rather than 100 cm
superstition boosts performance effect	Damisch et al. (2010) Study 1	28	d = .80 ± .75	108	d = .04 ± .42	Calin-Jageman & Caldwell (2014) Study 2	no signal - inconsistent	lucky vs. neutral ball (randomly chosen by the participants and labeled by the experimenter)	golf task (10 putts)	close	SP: Special Issue #29
unethicality darkens light perception (perceived brightness)	Banerjee et al. (2012) Study 1	40	d = .65	475	d = .12 ± .18	Brandt, IJzerman, & Blanken (2014,online) Study 1a	no signal - inconsistent	recalling unethical vs. ethical behaviors	perceived brightness	very close*	SP: Special Issue #30	mTurk general sample
unethicality darkens light perception (lamp preference)	Banerjee et al. (2012) Study 2	74	d = 1.23	482	d = -.03 ± .18	Brandt, IJzerman, & Blanken (2014,online) Study 2a	no signal - inconsistent	recalling unethical vs. ethical behaviors	estimated watts; lamp preference; candle preference; flashlight preference	very close*	SP: Special Issue #31	mTurk general sample		3 other DVs (estimated watts, candle preference, flashlight preference; same pattern of results observed)
unethicality darkens light perception (perceived brightness)	Banerjee et al. (2012) Study 1	40	d = .65	100	d = -.11 ± .40	Brandt, IJzerman, & Blanken (2014) Study 1b	no signal - inconsistent	recalling unethical vs. ethical behaviors	perceived brightness	very close	SP: Special Issue #32	Dutch population
unethicality darkens light perception (lamp preference)	Banerjee et al. (2012) Study 2	74	d = 1.23	121	d = -.11 ± .36	Brandt, IJzerman, & Blanken (2014) Study 2b	no signal - inconsistent	recalling unethical vs. ethical behaviors	estimated watts; lamp preference; candle preference; flashlight preference	very close	SP: Special Issue #33	Dutch population		3 other DVs (estimated watts, candle preference, flashlight preference; same pattern of results observed)
Stroop effect	Stroop (1935) Study 2	100	dz = 2.04	119	dz = .90 ± .40	Hermann et al. (2016, Bradley)	signal - inconsistent, smaller	congruent vs. incongruent (color name-font word)	reaction time (RT)	close	ML3 #2
Stroop effect	Stroop (1935) Study 2	100	dz = 2.04	318	dz = .70 ± .24	Johnson et al. (2016, MichiganSU)	signal - inconsistent, smaller	congruent vs. incongruent (color name-font word)	reaction time (RT)	close	ML3 #6
Stroop effect	Stroop (1935) Study 2	100	dz = 2.04	124	dz = .99 ± .40	Allen (2016, MontanaSU)	signal - inconsistent, smaller	congruent vs. incongruent (color name-font word)	reaction time (RT)	close	ML3 #7
Stroop effect	Stroop (1935) Study 2	100	dz = 2.04	131	dz = .89 ± .38	Banks et al. (2016, NovaSU)	signal - inconsistent, smaller	congruent vs. incongruent (color name-font word)	reaction time (RT)	close	ML3 #8
Stroop effect	Stroop (1935) Study 2	100	dz = 2.04	158	dz = 1.08 ± .36	Wirth (2016, OSUNewark)	signal - inconsistent, smaller	congruent vs. incongruent (color name-font word)	reaction time (RT)	close	ML3 #9
Stroop effect	Stroop (1935) Study 2	100	dz = 2.04	117	dz = .82 ± .40	Bernstein (2016, PSAbington)	signal - inconsistent, smaller	congruent vs. incongruent (color name-font word)	reaction time (RT)	close	ML3 #11
Stroop effect	Stroop (1935) Study 2	100	dz = 2.04	142	dz = .87 ± .36	Devos et al. (2016, SDSU)	signal - inconsistent, smaller	congruent vs. incongruent (color name-font word)	reaction time (RT)	close	ML3 #12
Stroop effect	Stroop (1935) Study 2	100	dz = 2.04	178	dz = .88 ± .32	Davis & Hicks (2016, TexasA&M)	signal - inconsistent, smaller	congruent vs. incongruent (color name-font word)	reaction time (RT)	close	ML3 #13
Stroop effect	Stroop (1935) Study 2	100	dz = 2.04	119	dz = .83 ± .40	German et al. (2016, UCDavis)	signal - inconsistent, smaller	congruent vs. incongruent (color name-font word)	reaction time (RT)	close	ML3 #14
Stroop effect	Stroop (1935) Study 2	100	dz = 2.04	242	dz = 1.09 ± .29	Baranski et al. (2016, UCRiverside)	signal - inconsistent, smaller	congruent vs. incongruent (color name-font word)	reaction time (RT)	close	ML3 #15
Stroop effect	Stroop (1935) Study 2	100	dz = 2.04	137	dz = .75 ± .37	Brown et al. (2016, UofF)	signal - inconsistent, smaller	congruent vs. incongruent (color name-font word)	reaction time (RT)	close	ML3 #16
Stroop effect	Stroop (1935) Study 2	100	dz = 2.04	194	dz = .87 ± .31	Ebersole et al. (2016, UofV) Study 1	signal - inconsistent, smaller	congruent vs. incongruent (color name-font word)	reaction time (RT)	close	ML3 #19
Stroop effect	Stroop (1935) Study 2	100	dz = 2.04	620	d = .81 ± .17	Ebersole et al. (2016, mTurk) Study 2	signal - inconsistent, smaller	congruent vs. incongruent (color name-font word)	reaction time (RT)	close	ML3 #21
metaphoric restructuring effect	Boroditsky (2000) Study 1	56	d = .63 ± .56	58	d = .07 ± .53	Hermann et al. (2016, Bradley)	no signal - inconsistent	ego-priming vs. control vs. object-priming	ambiguous temporal statement task	very close	ML3 #23
metaphoric restructuring effect	Boroditsky (2000) Study 1	56	d = .63 ± .56	159	d = .09 ± .31	Johnson et al. (2016, MichiganSU)	no signal - inconsistent	ego-priming vs. control vs. object-priming	ambiguous temporal statement task	very close	ML3 #27
metaphoric restructuring effect	Boroditsky (2000) Study 1	56	d = .63 ± .56	64	d = .65 ± .53	Allen (2016, MontanaSU)	signal - consistent	ego-priming vs. control vs. object-priming	ambiguous temporal statement task	very close	ML3 #28
metaphoric restructuring effect	Boroditsky (2000) Study 1	56	d = .63 ± .56	68	d = .68 ± .52	Banks et al. (2016, NovaSU)	signal - consistent	ego-priming vs. control vs. object-priming	ambiguous temporal statement task	very close	ML3 #29
metaphoric restructuring effect	Boroditsky (2000) Study 1	56	d = .63 ± .56	73	d = .03 ± .47	Wirth (2016, OSUNewark)	no signal - inconsistent	ego-priming vs. control vs. object-priming	ambiguous temporal statement task	very close	ML3 #30
metaphoric restructuring effect	Boroditsky (2000) Study 1	56	d = .63 ± .56	65	d = .54 ± .52	Devos et al. (2016, SDSU)	no signal - inconsistent	ego-priming vs. control vs. object-priming	ambiguous temporal statement task	very close	ML3 #33
metaphoric restructuring effect	Boroditsky (2000) Study 1	56	d = .63 ± .56	109	d = .16 ± .38	Davis & Hicks (2016, TexasA&M)	no signal - inconsistent	ego-priming vs. control vs. object-priming	ambiguous temporal statement task	very close	ML3 #34
metaphoric restructuring effect	Boroditsky (2000) Study 1	56	d = .63 ± .56	63	d = .42 ± .52	German et al. (2016, UCDavis)	no signal - inconsistent	ego-priming vs. control vs. object-priming	ambiguous temporal statement task	very close	ML3 #35
metaphoric restructuring effect	Boroditsky (2000) Study 1	56	d = .63 ± .56	110	d = .11 ± .38	Baranski et al. (2016, UCRiverside)	no signal - inconsistent	ego-priming vs. control vs. object-priming	ambiguous temporal statement task	very close	ML3 #36
metaphoric restructuring effect	Boroditsky (2000) Study 1	56	d = .63 ± .56	72	d = .22 ± .48	Brown et al. (2016, UofF)	no signal - inconsistent	ego-priming vs. control vs. object-priming	ambiguous temporal statement task	very close	ML3 #37
metaphoric restructuring effect	Boroditsky (2000) Study 1	56	d = .63 ± .56	104	d = .04 ± .39	Ebersole et al. (2016, UofV) Study 1	no signal - inconsistent	ego-priming vs. control vs. object-priming	ambiguous temporal statement task	very close	ML3 #40
availability heuristic (word frequency)	Tversky & Kahneman (1973) Study 3	152	d = .82 ± .35	112	d = .00 ± .38	Hermann et al. (2016, Bradley)	no signal - inconsistent (imprecise)	letters in first vs. third position	word frequency	very close	ML3 #43
availability heuristic (word frequency)	Tversky & Kahneman (1973) Study 3	152	d = .82 ± .35	303	d = -.01 ± .23	Johnson et al. (2016, MichiganSU)	no signal - inconsistent	letters in first vs. third position	word frequency	very close	ML3 #47
availability heuristic (word frequency)	Tversky & Kahneman (1973) Study 3	152	d = .82 ± .35	116	d = .07 ± .37	Allen (2016, MontanaSU)	no signal - inconsistent (imprecise)	letters in first vs. third position	word frequency	very close	ML3 #48
availability heuristic (word frequency)	Tversky & Kahneman (1973) Study 3	152	d = .82 ± .35	122	d = -.03 ± .36	Banks et al. (2016, NovaSU)	no signal - inconsistent (imprecise)	letters in first vs. third position	word frequency	very close	ML3 #49
availability heuristic (word frequency)	Tversky & Kahneman (1973) Study 3	152	d = .82 ± .35	148	d = -.05 ± .33	Wirth (2016, OSUNewark)	no signal - inconsistent (imprecise)	letters in first vs. third position	word frequency	very close	ML3 #50
availability heuristic (word frequency)	Tversky & Kahneman (1973) Study 3	152	d = .82 ± .35	133	d = .01 ± .34	Devos et al. (2016, SDSU)	no signal - inconsistent	letters in first vs. third position	word frequency	very close	ML3 #53
availability heuristic (word frequency)	Tversky & Kahneman (1973) Study 3	152	d = .82 ± .35	171	d = -.03 ± .30	Davis & Hicks (2016, TexasA&M)	no signal - inconsistent	letters in first vs. third position	word frequency	very close	ML3 #54
availability heuristic (word frequency)	Tversky & Kahneman (1973) Study 3	152	d = .82 ± .35	114	d = .39 ± .38	German et al. (2016, UCDavis)	signal - inconsistent, smaller (imprecise)	letters in first vs. third position	word frequency	very close	ML3 #55
availability heuristic (word frequency)	Tversky & Kahneman (1973) Study 3	152	d = .82 ± .35	217	d = .21 ± .27	Baranski et al. (2016, UCRiverside)	no signal - inconsistent	letters in first vs. third position	word frequency	very close	ML3 #56
availability heuristic (word frequency)	Tversky & Kahneman (1973) Study 3	152	d = .82 ± .35	129	d = -.08 ± .35	Brown et al. (2016, UofF)	no signal - inconsistent	letters in first vs. third position	word frequency	very close	ML3 #57
availability heuristic (word frequency)	Tversky & Kahneman (1973) Study 3	152	d = .82 ± .35	190	d = .15 ± .29	Ebersole et al. (2016, UofV) Study 1	no signal - inconsistent	letters in first vs. third position	word frequency	very close	ML3 #60
availability heuristic (word frequency)	Tversky & Kahneman (1973) Study 3	152	d = .82 ± .35	591	d = .18 ± .16	Ebersole et al. (2016, mTurk) Study 2	signal - inconsistent, smaller	letters in first vs. third position	word frequency	very close	ML3 #62
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	84	d = -.01 ± .45	Bonfiglio et al. (2016, Ashland)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #63
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	112	d = -.41 ± .38	Hermann et al. (2016, Bradley)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #64
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	88	d = .23 ± .43	Vaughn (2016, Ithaca)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #66
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	89	d = .19 ± .42	Belanger et al. (2016, Miami)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #67
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	240	d = -.12 ± .26	Johnson et al. (2016, MichiganSU)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #68
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	118	d = -.12 ± .37	Allen (2016, MontanaSU)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #69
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	126	d = -.24 ± .36	Banks et al. (2016, NovaSU)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #70
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	125	d = -.30 ± .36	Wirth (2016, OSUNewark)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #71
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	96	d = .16 ± .41	Grahe et al. (2016, PacificLU)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #72
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	100	d = .03 ± .40	Bernstein (2016, PSAbington)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #73
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	133	d = .01 ± .34	Devos et al. (2016, SDSU)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #74
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	170	d = -.07 ± .30	Davis & Hicks (2016, TexasA&M)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #75
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	109	d = -.18 ± .38	German et al. (2016, UCDavis)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #76
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	232	d = -.02 ± .26	Baranski et al. (2016, UCRiverside)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #77
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	129	d = -.21 ± .35	Brown et al. (2016, UofF)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #78
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	82	d = .39 ± .44	Fletcher et al. (2016, UofSM)	no signal - consistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #79
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	64	d = .11 ± .51	Re et al. (2016, UofT)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #80
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	177	d = -.03 ± .30	Ebersole et al. (2016, UofV) Study 1	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #81
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	91	d = -.20 ± .42	Cairo et al. (2016, VirginiaCU)	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #82
power decreases perspective taking effect	Galinsky et al. (2006) Study 2a	42	d = .77 ± .65	591	d = .06 ± .16	Ebersole et al. (2016, mTurk) Study 2	no signal - inconsistent	high vs. low power (situation recall)	perspective-taking (perceived sarcasm)	very close	ML3 #83
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	72	d = .12 ± .47	Bonfiglio et al. (2016, Ashland)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #84
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	109	d = .20 ± .38	Hermann et al. (2016, Bradley)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #85
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	64	d = .06 ± .50	Vaughn (2016, Ithaca)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #87
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	82	d = .12 ± .44	Belanger et al. (2016, Miami)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #88
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	274	d = .02 ± .24	Johnson et al. (2016, MichiganSU)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #89
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	109	d = -.11 ± .38	Allen (2016, MontanaSU)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #90
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	85	d = .42 ± .44	Banks et al. (2016, NovaSU)	no signal - consistent	heavy vs. light clipboard	perceived importance	very close	ML3 #91
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	127	d = -.25 ± .36	Wirth (2016, OSUNewark)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #92
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	103	d = -.02 ± .40	Grahe et al. (2016, PacificLU)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #93
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	118	d = .19 ± .36	Bernstein (2016, PSAbington)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #94
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	131	d = -.11 ± .34	Devos et al. (2016, SDSU)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #95
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	153	d = .14 ± .32	Davis & Hicks (2016, TexasA&M)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #96
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	105	d = .02 ± .38	German et al. (2016, UCDavis)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #97
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	212	d = -.03 ± .27	Baranski et al. (2016, UCRiverside)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #98
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	121	d = .10 ± .36	Brown et al. (2016, UofF)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #99
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	79	d = .05 ± .45	Fletcher et al. (2016, UofSM)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #100
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	73	d = -.34 ± .47	Re et al. (2016, UofT)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #101
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	171	d = .09 ± .30	Ebersole et al. (2016, UofV) Study 1	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #102
weight-importance effect	Jostmann, Lakens et al. (2009) Study 2	51	d = .59 ± .58	89	d = .12 ± .42	Cairo et al. (2016, VirginiaCU)	no signal - inconsistent	heavy vs. light clipboard	perceived importance	very close	ML3 #103
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	79	d = .27 ± .45	Bonfiglio et al. (2016, Ashland)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #104
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	116	d = .11 ± .38	Hermann et al. (2016, Bradley)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #105
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	83	d = -.26 ± .44	Vaughn (2016, Ithaca)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #107
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	94	d = .09 ± .42	Belanger et al. (2016, Miami)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #108
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	298	d = -.07 ± .22	Johnson et al. (2016, MichiganSU)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #109
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	122	d = -.05 ± .36	Allen (2016, MontanaSU)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #110
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	129	d = .09 ± .36	Banks et al. (2016, NovaSU)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #111
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	153	d = .17 ± .32	Wirth (2016, OSUNewark)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #112
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	93	d = .17 ± .41	Grahe et al. (2016, PacificLU)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #113
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	112	d = .08 ± .38	Bernstein (2016, PSAbington)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #114
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	134	d = .06 ± .34	Devos et al. (2016, SDSU)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #115
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	172	d = -.05 ± .30	Davis & Hicks (2016, TexasA&M)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #116
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	115	d = -.37 ± .37	German et al. (2016, UCDavis)	signal - inconsistent, opposite	communal vs. agentic person description	ambient room temperature	very close	ML3 #117
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	237	d = .14 ± .26	Baranski et al. (2016, UCRiverside)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #118
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	132	d = -.01 ± .35	Brown et al. (2016, UofF)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #119
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	87	d = -.23 ± .43	Fletcher et al. (2016, UofSM)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #120
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	189	d = -.08 ± .29	Ebersole et al. (2016, UofV) Study 1	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #122
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	92	d = .30 ± .42	Cairo et al. (2016, VirginiaCU)	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #123
warmer-heart-warmer-room effect	Szymkow, Chandler, IJzerman et al. (2013) Study1	80	d = .86 ± .46	575	d = -.10 ± .16	Ebersole et al. (2016, mTurk) Study 2	no signal - inconsistent	communal vs. agentic person description	ambient room temperature	very close	ML3 #124
NFC amplifies strong argument persuasion effect	Cacioppo et al. (1983) Study 1	114	d = .91 ± .40	120	d = .12 ± .36	Fletcher et al. (2016, UofSM)	no signal - inconsistent	strong vs. weak arguments; need for cognition (NFC)	persuasion (argument quality & persuasiveness)	very close	ML3 #126				Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC)
NFC amplifies strong argument persuasion effect	Cacioppo et al. (1983) Study 1	114	d = .91 ± .40	318	d = .16 ± .22	German et al. (2016, UCDavis)	no signal - inconsistent	strong vs. weak arguments; need for cognition (NFC)	persuasion (argument quality & persuasiveness)	very close	ML3 #130				Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC)
NFC amplifies strong argument persuasion effect	Cacioppo et al. (1983) Study 1	114	d = .91 ± .40	128	d = .19 ± .36	Re et al. (2016, UofT)	no signal - inconsistent	strong vs. weak arguments; need for cognition (NFC)	persuasion (argument quality & persuasiveness)	very close	ML3 #131				Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC)
NFC amplifies strong argument persuasion effect	Cacioppo et al. (1983) Study 1	114	d = .91 ± .40	134	d = .07 ± .34	Capaldi et al. (2016, Carleton)	no signal - inconsistent	strong vs. weak arguments; need for cognition (NFC)	persuasion (argument quality & persuasiveness)	very close	ML3 #132				Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC)
NFC amplifies strong argument persuasion effect	Cacioppo et al. (1983) Study 1	114	d = .91 ± .40	158	d = .00 ± .32	Davis & Hicks (2016, TexasA&M)	no signal - inconsistent	strong vs. weak arguments; need for cognition (NFC)	persuasion (argument quality & persuasiveness)	very close	ML3 #133				Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC)
NFC amplifies strong argument persuasion effect	Cacioppo et al. (1983) Study 1	114	d = .91 ± .40	103	d = .09 ± .39	Brown et al. (2016, UofF)	no signal - inconsistent	strong vs. weak arguments; need for cognition (NFC)	persuasion (argument quality & persuasiveness)	very close	ML3 #134				Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC)
NFC amplifies strong argument persuasion effect	Cacioppo et al. (1983) Study 1	114	d = .91 ± .40	120	d = .10 ± .36	Grahe et al. (2016, PacificLU)	no signal - inconsistent	strong vs. weak arguments; need for cognition (NFC)	persuasion (argument quality & persuasiveness)	very close	ML3 #135				Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC)
NFC amplifies strong argument persuasion effect	Cacioppo et al. (1983) Study 1	114	d = .91 ± .40	143	d = -.02 ± .33	Bernstein (2016, PSAbington)	no signal - inconsistent	strong vs. weak arguments; need for cognition (NFC)	persuasion (argument quality & persuasiveness)	very close	ML3 #136				Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC)
NFC amplifies strong argument persuasion effect	Cacioppo et al. (1983) Study 1	114	d = .91 ± .40	179	d = -.09 ± .30	Hermann et al. (2016, Bradley)	no signal - inconsistent	strong vs. weak arguments; need for cognition (NFC)	persuasion (argument quality & persuasiveness)	very close	ML3 #137				Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC)
NFC amplifies strong argument persuasion effect	Cacioppo et al. (1983) Study 1	114	d = .91 ± .40	120	d = .07 ± .36	Baranski et al. (2016, UCRiverside)	no signal - inconsistent	strong vs. weak arguments; need for cognition (NFC)	persuasion (argument quality & persuasiveness)	very close	ML3 #138				Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC)
NFC amplifies strong argument persuasion effect	Cacioppo et al. (1983) Study 1	114	d = .91 ± .40	245	d = -.15 ± .26	Ebersole et al. (2016, UofV) Study 1	no signal - inconsistent	strong vs. weak arguments; need for cognition (NFC)	persuasion (argument quality & persuasiveness)	very close	ML3 #139				Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC)
NFC amplifies strong argument persuasion effect	Cacioppo et al. (1983) Study 1	114	d = .91 ± .40	137	d = -.08 ± .34	Johnson et al. (2016, MichiganSU)	no signal - inconsistent	strong vs. weak arguments; need for cognition (NFC)	persuasion (argument quality & persuasiveness)	very close	ML3 #140				Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC)
NFC amplifies strong argument persuasion effect	Cacioppo et al. (1983) Study 1	114	d = .91 ± .40	195	d = -.14 ± .28	Belanger et al. (2016, Miami)	no signal - inconsistent	strong vs. weak arguments; need for cognition (NFC)	persuasion (argument quality & persuasiveness)	very close	ML3 #143				Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC)
self-esteem and event valence on psychological distance	Ross & Wilson (2002) Study 2	357	d = .21 ± .21	307	d = .13 ± .22	Johnson et al. (2016, MichiganSU)	no signal - consistent (imprecise)	event valence (positive vs. negative); self-esteem	subjective temporal distance	very close	ML3 #150
self-esteem and event valence on psychological distance	Ross & Wilson (2002) Study 2	357	d = .21 ± .21	121	d = -.13 ± .36	Allen (2016, MontanaSU)	no signal - consistent (imprecise)	event valence (positive vs. negative); self-esteem	subjective temporal distance	very close	ML3 #151
self-esteem and event valence on psychological distance	Ross & Wilson (2002) Study 2	357	d = .21 ± .21	130	d = -.11 ± .35	Banks et al. (2016, NovaSU)	no signal - consistent (imprecise)	event valence (positive vs. negative); self-esteem	subjective temporal distance	very close	ML3 #152
self-esteem and event valence on psychological distance	Ross & Wilson (2002) Study 2	357	d = .21 ± .21	149	d = -.07 ± .32	Wirth (2016, OSUNewark)	no signal - consistent (imprecise)	event valence (positive vs. negative); self-esteem	subjective temporal distance	very close	ML3 #153
self-esteem and event valence on psychological distance	Ross & Wilson (2002) Study 2	357	d = .21 ± .21	137	d = -.12 ± .34	Devos et al. (2016, SDSU)	no signal - consistent (imprecise)	event valence (positive vs. negative); self-esteem	subjective temporal distance	very close	ML3 #156
self-esteem and event valence on psychological distance	Ross & Wilson (2002) Study 2	357	d = .21 ± .21	177	d = .05 ± .30	Davis & Hicks (2016, TexasA&M)	no signal - consistent (imprecise)	event valence (positive vs. negative); self-esteem	subjective temporal distance	very close	ML3 #157
self-esteem and event valence on psychological distance	Ross & Wilson (2002) Study 2	357	d = .21 ± .21	229	d = .38 ± .26	Baranski et al. (2016, UCRiverside)	signal - consistent (imprecise)	event valence (positive vs. negative); self-esteem	subjective temporal distance	very close	ML3 #159
self-esteem and event valence on psychological distance	Ross & Wilson (2002) Study 2	357	d = .21 ± .21	134	d = -.16 ± .34	Brown et al. (2016, UofF)	no signal - inconsistent (imprecise)	event valence (positive vs. negative); self-esteem	subjective temporal distance	very close	ML3 #160
self-esteem and event valence on psychological distance	Ross & Wilson (2002) Study 2	357	d = .21 ± .21	178	d = .00 ± .30	Ebersole et al. (2016, UofV) Study 1	no signal - consistent (imprecise)	event valence (positive vs. negative); self-esteem	subjective temporal distance	very close	ML3 #163
self-esteem and event valence on psychological distance	Ross & Wilson (2002) Study 2	357	d = .21 ± .21	574	d = .07 ± .16	Ebersole et al. (2016, mTurk) Study 2	no signal - consistent	event valence (positive vs. negative); self-esteem	subjective temporal distance	very close	ML3 #165
moral licensing effect	Monin & Miller (2001) Study 1	198	d = .43 ± .29	120	d = -.08 ± .36	Hermann et al. (2016, Bradley)	no signal - inconsistent (imprecise)	moral credentials (disagreeing with sexist statements) vs. control	prejudice (favoring man for job)	very close	ML3 #167
moral licensing effect	Monin & Miller (2001) Study 1	198	d = .43 ± .29	316	d = .14 ± .22	Johnson et al. (2016, MichiganSU)	no signal - inconsistent	moral credentials (disagreeing with sexist statements) vs. control	prejudice (favoring man for job)	very close	ML3 #171
moral licensing effect	Monin & Miller (2001) Study 1	198	d = .43 ± .29	124	d = -.22 ± .36	Allen (2016, MontanaSU)	no signal - inconsistent (imprecise)	moral credentials (disagreeing with sexist statements) vs. control	prejudice (favoring man for job)	very close	ML3 #172
moral licensing effect	Monin & Miller (2001) Study 1	198	d = .43 ± .29	130	d = -.09 ± .34	Banks et al. (2016, NovaSU)	no signal - inconsistent (imprecise)	moral credentials (disagreeing with sexist statements) vs. control	prejudice (favoring man for job)	very close	ML3 #173
moral licensing effect	Monin & Miller (2001) Study 1	198	d = .43 ± .29	155	d = -.07 ± .32	Wirth (2016, OSUNewark)	no signal - inconsistent (imprecise)	moral credentials (disagreeing with sexist statements) vs. control	prejudice (favoring man for job)	very close	ML3 #174
moral licensing effect	Monin & Miller (2001) Study 1	198	d = .43 ± .29	119	d = .27 ± .36	Bernstein (2016, PSAbington)	no signal - consistent (imprecise)	moral credentials (disagreeing with sexist statements) vs. control	prejudice (favoring man for job)	very close	ML3 #176
moral licensing effect	Monin & Miller (2001) Study 1	198	d = .43 ± .29	138	d = -.14 ± .34	Devos et al. (2016, SDSU)	no signal - inconsistent (imprecise)	moral credentials (disagreeing with sexist statements) vs. control	prejudice (favoring man for job)	very close	ML3 #177
moral licensing effect	Monin & Miller (2001) Study 1	198	d = .43 ± .29	178	d = .04 ± .30	Davis & Hicks (2016, TexasA&M)	no signal - inconsistent (imprecise)	moral credentials (disagreeing with sexist statements) vs. control	prejudice (favoring man for job)	very close	ML3 #178
moral licensing effect	Monin & Miller (2001) Study 1	198	d = .43 ± .29	243	d = .00 ± .25	Baranski et al. (2016, UCRiverside)	no signal - inconsistent	moral credentials (disagreeing with sexist statements) vs. control	prejudice (favoring man for job)	very close	ML3 #180
moral licensing effect	Monin & Miller (2001) Study 1	198	d = .43 ± .29	136	d = .02 ± .34	Brown et al. (2016, UofF)	no signal - inconsistent (imprecise)	moral credentials (disagreeing with sexist statements) vs. control	prejudice (favoring man for job)	very close	ML3 #181
moral licensing effect	Monin & Miller (2001) Study 1	198	d = .43 ± .29	192	d = .00 ± .29	Ebersole et al. (2016, UofV) Study 1	no signal - inconsistent	moral credentials (disagreeing with sexist statements) vs. control	prejudice (favoring man for job)	very close	ML3 #184
moral licensing effect	Monin & Miller (2001) Study 1	198	d = .43 ± .29	639	d = -.06 ± .16	Ebersole et al. (2016, mTurk) Study 2	no signal - inconsistent	moral credentials (disagreeing with sexist statements) vs. control	prejudice (favoring man for job)	very close	ML3 #186
verbal overshadowing effect (description immediately after video)	Schooler & Eng...-Schooler (1990) Study 4	75	IRD = -25% ± 20%	150	IRD = -15% ± 16%	Dellapaolera & Bornstein (2014) Study 1	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR1 #5
verbal overshadowing effect (description immediately after video)	Schooler & Eng...-Schooler (1990) Study 4	75	IRD = -25% ± 20%	150	IRD = -13% ± 16%	Carlson et al. (2014) Study 1	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR1 #8
verbal overshadowing effect (description immediately after video)	Schooler & Eng...-Schooler (1990) Study 4	75	IRD = -25% ± 20%	220	IRD = -12% ± 13%	Mammarella et al. (2014) Study 1	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR1 #9
verbal overshadowing effect (description immediately after video)	Schooler & Eng...-Schooler (1990) Study 4	75	IRD = -25% ± 20%	150	IRD = -7% ± 16%	Eggleston et al. (2014) Study 1	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR1 #13
verbal overshadowing effect (description immediately after video)	Schooler & Eng...-Schooler (1990) Study 4	75	IRD = -25% ± 20%	131	IRD = -5% ± 17%	Kehn et al. (2014) Study 1	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR1 #15
verbal overshadowing effect (description immediately after video)	Schooler & Eng...-Schooler (1990) Study 4	75	IRD = -25% ± 20%	135	IRD = -2% ± 17%	Was et al. (2014) Study 1	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR1 #18
verbal overshadowing effect (description immediately after video)	Schooler & Eng...-Schooler (1990) Study 4	75	IRD = -25% ± 20%	185	IRD = 0% ± 15%	Michael et al. (2014) Study 1	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR1 #22
verbal overshadowing effect (description immediately after video)	Schooler & Eng...-Schooler (1990) Study 4	75	IRD = -25% ± 20%	120	IRD = 2% ± 17%	Delvenne et al. (2014) Study 1	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR1 #23
verbal overshadowing effect (description immediately after video)	Schooler & Eng...-Schooler (1990) Study 4	75	IRD = -25% ± 20%	110	IRD = 4% ± 17%	Ulatowska & Cislak (2014) Study 1	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR1 #25
verbal overshadowing effect (description immediately after video)	Schooler & Eng...-Schooler (1990) Study 4	75	IRD = -25% ± 20%	390	IRD = 6% ± 10%	Michael et al. (2014, mTURK) Study 1	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR1 #27
verbal overshadowing effect (description immediately after video)	Schooler & Eng...-Schooler (1990) Study 4	75	IRD = -25% ± 20%	140	IRD = 10% ± 16%	Brandimonte (2014) Study 1	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR1 #30
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	95	IRD = -29% ± 20%	Poirer et al. (2014) Study 2	signal - consistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #1
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	98	IRD = -26% ± 19%	Delvenne et al. (2014) Study 2	signal - consistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #2
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	111	IRD = -24% ± 18%	Susa et al. (2014) Study 2	signal - consistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #4
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	160	IRD = -24% ± 15%	Carlson et al. (2014) Study 2	signal - consistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #5
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	124	IRD = -22% ± 20%	Echterhoff & Kopietz (2014) Study 2	signal - consistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #7
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	104	IRD = -22% ± 19%	Mammarella et al. (2014) Study 2	signal - consistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #8
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	164	IRD = -21% ± 16%	Dellapaolera & Bornstein (2014) Study 2	signal - consistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #9
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	109	IRD = -20% ± 19%	Mitchell & Petro (2014) Study 2	signal - consistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #10
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	106	IRD = -17% ± 19%	Ulatowska & Cislak (2014) Study 2	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #11
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	121	IRD = -17% ± 17%	Wade et al. (2014) Study 2	signal - consistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #12
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	156	IRD = -16% ± 18%	Birch (2014) Study 2	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #13
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	89	IRD = -15% ± 21%	McCoy & Rancourt (2014) Study 2	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #14
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	137	IRD = -12% ± 19%	Alogna et al. (2014) Study 2	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #16
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	615	IRD = -11% ± 10%	Michael et al. (2014, mTURK) Study 2	signal - consistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #17
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	102	IRD = -9% ± 22%	Thompson (2014) Study 2	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #19
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	110	IRD = -3% ± 18%	Rubinova et al. (2014) Study 2	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #20
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	100	IRD = -2% ± 20%	Brandimonte (2014) Study 2	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #21
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	93	IRD = -2% ± 20%	Eggleston et al. (2014) Study 2	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #22
verbal overshadowing effect	Schooler & Eng...-Schooler (1990) Study 1	88	IRD = -22% ± 22%	113	IRD = -1% ± 20%	Kehn et al. (2014) Study 2	no signal - inconsistent	verbal description vs. control task	identification of perpetrator in lineup	very close	RRR2 #23
grammar on intentionality effect (criminal intentionality DV)	Hart & Albarracín (2011) Study 3	48	MD = 1.20 ± .88	75	MD = -.98 ± .74	Berger (2016)	signal - inconsistent, opposite	imperfective (was doing) vs. perfective (did) behavior descriptions	perceived criminal intentionality	very close	RRR3 #1			2 other DVs (detailed processing [imagery], intention attribution; same pattern of results observed)	Imperfective (was doing) behavior descriptions increase perceived criminal intentionality compared to perfective (did)
grammar on intentionality effect (criminal intentionality DV)	Hart & Albarracín (2011) Study 3	48	MD = 1.20 ± .88	86	MD = -.95 ± .63	Knepp (2016)	signal - inconsistent, opposite	imperfective (was doing) vs. perfective (did) behavior descriptions	perceived criminal intentionality	very close	RRR3 #2			2 other DVs (detailed processing [imagery], intention attribution; same pattern of results observed)	Imperfective (was doing) behavior descriptions increase perceived criminal intentionality compared to perfective (did)
grammar on intentionality effect (criminal intentionality DV)	Hart & Albarracín (2011) Study 3	48	MD = 1.20 ± .88	83	MD = -.41 ± .64	Michael (2016)	no signal - inconsistent	imperfective (was doing) vs. perfective (did) behavior descriptions	perceived criminal intentionality	very close	RRR3 #3			2 other DVs (detailed processing [imagery], intention attribution; same pattern of results observed)	Imperfective (was doing) behavior descriptions increase perceived criminal intentionality compared to perfective (did)
grammar on intentionality effect (criminal intentionality DV)	Hart & Albarracín (2011) Study 3	48	MD = 1.20 ± .88	100	MD = -.38 ± .68	Prenoveau & Carlucci (2016)	no signal - inconsistent	imperfective (was doing) vs. perfective (did) behavior descriptions	perceived criminal intentionality	very close	RRR3 #4			2 other DVs (detailed processing [imagery], intention attribution; same pattern of results observed)	Imperfective (was doing) behavior descriptions increase perceived criminal intentionality compared to perfective (did)
grammar on intentionality effect (criminal intentionality DV)	Hart & Albarracín (2011) Study 3	48	MD = 1.20 ± .88	66	MD = -.38 ± .59	Birt & Aucoin (2016)	no signal - inconsistent	imperfective (was doing) vs. perfective (did) behavior descriptions	perceived criminal intentionality	very close	RRR3 #5			2 other DVs (detailed processing [imagery], intention attribution; same pattern of results observed)	Imperfective (was doing) behavior descriptions increase perceived criminal intentionality compared to perfective (did)
grammar on intentionality effect (criminal intentionality DV)	Hart & Albarracín (2011) Study 3	48	MD = 1.20 ± .88	67	MD = -.35 ± .72	Arnal (2016)	no signal - inconsistent	imperfective (was doing) vs. perfective (did) behavior descriptions	perceived criminal intentionality	very close	RRR3 #6			2 other DVs (detailed processing [imagery], intention attribution; same pattern of results observed)	Imperfective (was doing) behavior descriptions increase perceived criminal intentionality compared to perfective (did)
grammar on intentionality effect (criminal intentionality DV)	Hart & Albarracín (2011) Study 3	48	MD = 1.20 ± .88	260	MD = -.33 ± .29	Eerland, Zwaan et al. (2016, online)	signal - inconsistent, opposite	imperfective (was doing) vs. perfective (did) behavior descriptions	perceived criminal intentionality	very close	RRR3 #7			2 other DVs (detailed processing [imagery], intention attribution; same pattern of results observed)	Imperfective (was doing) behavior descriptions increase perceived criminal intentionality compared to perfective (did)
grammar on intentionality effect (criminal intentionality DV)	Hart & Albarracín (2011) Study 3	48	MD = 1.20 ± .88	120	MD = -.14 ± .60	Kurby & Kibbe (2016)	no signal - inconsistent	imperfective (was doing) vs. perfective (did) behavior descriptions	perceived criminal intentionality	very close	RRR3 #8			2 other DVs (detailed processing [imagery], intention attribution; same pattern of results observed)	Imperfective (was doing) behavior descriptions increase perceived criminal intentionality compared to perfective (did)
grammar on intentionality effect (criminal intentionality DV)	Hart & Albarracín (2011) Study 3	48	MD = 1.20 ± .88	85	MD = -.01 ± .42	Ferretti (2016)	no signal - inconsistent	imperfective (was doing) vs. perfective (did) behavior descriptions	perceived criminal intentionality	very close	RRR3 #9			2 other DVs (detailed processing [imagery], intention attribution; same pattern of results observed)	Imperfective (was doing) behavior descriptions increase perceived criminal intentionality compared to perfective (did)
grammar on intentionality effect (criminal intentionality DV)	Hart & Albarracín (2011) Study 3	48	MD = 1.20 ± .88	100	MD = .16 ± .65	Eerland, Zwaan et al. (2016)	no signal - inconsistent	imperfective (was doing) vs. perfective (did) behavior descriptions	perceived criminal intentionality	very close	RRR3 #10			2 other DVs (detailed processing [imagery], intention attribution; same pattern of results observed)	Imperfective (was doing) behavior descriptions increase perceived criminal intentionality compared to perfective (did)
grammar on intentionality effect (criminal intentionality DV)	Hart & Albarracín (2011) Study 3	48	MD = 1.20 ± .88	79	MD = .32 ± .66	Poirier, Capezza, & Crocker (2016)	no signal - inconsistent	imperfective (was doing) vs. perfective (did) behavior descriptions	perceived criminal intentionality	very close	RRR3 #11			2 other DVs (detailed processing [imagery], intention attribution; same pattern of results observed)	Imperfective (was doing) behavior descriptions increase perceived criminal intentionality compared to perfective (did)
grammar on intentionality effect (criminal intentionality DV)	Hart & Albarracín (2011) Study 3	48	MD = 1.20 ± .88	66	MD = .65 ± .92	Melcher (2016)	no signal - inconsistent (imprecise)	imperfective (was doing) vs. perfective (did) behavior descriptions	perceived criminal intentionality	very close	RRR3 #12			2 other DVs (detailed processing [imagery], intention attribution; same pattern of results observed)	Imperfective (was doing) behavior descriptions increase perceived criminal intentionality compared to perfective (did)
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	68	d = -.50 ± .48	Ringos & Carlucci (2016)	signal - consistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #1
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	87	d = -.46 ± .43	Wolff, Muzzi & Brand (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #2	German language
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	75	d = -.44 ± .56	Calvillo & Mills (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #3
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	73	d = -.40 ± .46	Crowell, Finley et al. (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #4
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	79	d = -.36 ± .44	Lynch, vanDellen et al. (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #5
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	59	d = -.31 ± .52	Birt & Muise (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #6
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	156	d = -.22 ± .31	Yusainy, Wimbarti et al. (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #7	Indonesian language
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	99	d = -.20 ± .40	Lau & Brewer (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #8
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	103	d = -.09 ± .39	Ullrich, Primoceri et al. (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #9	German language
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	90	d = -.04 ± .42	Elson (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #10	German language
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	181	d = -.04 ± .29	Cheung, Kroese et al. (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #11	Dutch language
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	101	d = .00 ± .39	Hagger et al. (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #12
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	79	d = .00 ± .44	Schlinkert, Schrama et al. (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #13	Dutch language
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	75	d = .04 ± .45	Philipp & Cannon (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #14
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	126	d = .09 ± .36	Carruth & Miyake (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #15
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	102	d = .11 ± .39	Brandt (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #16	Dutch language
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	93	d = .12 ± .41	Stamos, Bruyneel et al. (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #17	Dutch language
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	103	d = .18 ± .39	Rentzsch, Nalis et al. (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #18	German language
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	50	d = .18 ± .56	Francis & Inzlicht (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #19
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	106	d = .23 ± .38	Lange, Heise et al. (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #20	German language
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	89	d = .27 ± .42	Evans, Fay, & Mosser (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #21
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	82	d = .40 ± .43	Tinghög & Koppel (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #22
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	69	d = .41 ± .50	Otgaar, Martijn et al. (2016)	no signal - inconsistent	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #23	Dutch language
ego depletion effect	Sripada, Kessler, & Jonides (2014)	47	d = -.69 ± .59	78	d = .51 ± .46	Muller, Zerhouni et al. (2016)	signal - inconsistent, opposite	effortful letter crossing vs. control	multi-source interference task (MSIT; RTV)	very close	RRR4 #24	French language
ego depletion effect	Gaillot, Baumeister et al. (2007) Study 7	61	d = -1.19 ± .52	119	d = .22 ± .34	Cesario & Corker (2010)	no signal - inconsistent	sugar vs. splenda; video attention task vs. control	Stroop performance	very close	individual effort	No manipulation check	Positive correlation between baseline & post-manipulation error rates, r = .36, p < .001
ego depletion effect	Wang & Dvorak (2010)	61	d = -.99 ± .52	70	d = .13 ± .48	Lange & Eggert (2014) Study 1	no signal - inconsistent	sugar vs. splenda; future-discounting t1 vs. t2	future-discounting task	close	individual effort	Different choices in future-discounting task	Test-retest reliability of r = .80 across t1 and t2 scores
ego depletion effect	Muraven, Tice et al. (1998) Study 2	34	d = -.75 ± .71	51	d = -.01 ± .55	Murtagh & Todd (2004) Study 2	no signal - inconsistent	thought suppression vs. control	anagram performance	close	individual effort	Very difficult solvable anagrams used rather than "unsolvable"
ego depletion effect	Schmeichel, Vohs et al. (2003) Study 1	24	d = -1.58 ± .98	128	d = -.35 ± .52	Pond et al. (2011) Study 3	no signal - inconsistent	video attention task vs. control	GRE standardized test	close	individual effort	10 verbal GRE items used (instead of 13 analytic GRE items)
ego depletion effect	Schmeichel (2007) Study 1	79	d = -.37 ± .44	38	d = -1.31 ± .71	Healy et al. (2011) Study 1	signal - consistent (imprecise)	video attention task vs. control	working memory (OSPAN)	close	individual effort	% of target words recalled (rather than total)
ego depletion effect	Schmeichel (2007) Study 1	79	d = -.37 ± .44	138	d = .05 ± .45	Carter & McCullough (2013)	no signal - inconsistent (imprecise)	video attention task vs. control	working memory (OSPAN)	close	individual effort	Effortful essay task vs. control in between IV and DV (perfectly confounded w/ IV)
ego depletion effect	Schmeichel (2007) Study 1	79	d = -.37 ± .44	200	d = .21 ± .28	Lurquin et al. (2016)	no signal - inconsistent	video attention task vs. control	working memory (OSPAN)	close	individual effort	40 target words in OSPAN (rather than 48)	Main effect of OSPAN set sizes on performance, F(1, 199) = 4439.81, p < .001
ego depletion effect	Inzlicht & Gutsell (2007)	33	d = -1.06 ± .71	31	d = -.93 ± .73	Wang, Yang, & Wang (2014)	signal - consistent (imprecise)	emotion suppression (video) vs. control	EEG ERN during stroop task	close	individual effort
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	160	MD = -.42 ± .38	Bredow & Luna (2016)	signal - consistent	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #1	DV assessed via computer rather than paper-and-pencil	α = .90 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	201	MD = -.26 ± .39	Carson, Corretti, Kane & Ackerman (2016)	no signal - inconsistent	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #2	DV assessed via computer rather than paper-and-pencil	α = .92 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	120	MD = -.25 ± .46	Yong & Li (2016)	no signal - inconsistent (imprecise)	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #3	DV assessed via computer rather than paper-and-pencil	α = .91 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	111	MD = -.23 ± .38	Goldberg, Sinclair, Ritchey et al. (2016)	no signal - inconsistent	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #4	DV assessed via computer rather than paper-and-pencil	α = .91 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	160	MD = -.22 ± .41	Cheung, Campbell & LeBel (2016)	no signal - inconsistent	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #5	DV assessed via computer rather than paper-and-pencil	α = .91 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	124	MD = -.21 ± .58	Fuglestad, Leone & Kim (2016)	no signal - inconsistent (imprecise)	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #6	DV assessed via computer rather than paper-and-pencil	α = .92 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	162	MD = -.12 ± .43	Vranka, Bahnik, & Houdek (2016)	no signal - inconsistent	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #7	DV assessed via computer rather than paper-and-pencil	α = .93 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	102	MD = -.11 ± .42	Collins, Bowen, Winczewski et al. (2016)	no signal - inconsistent	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #8	DV assessed via computer rather than paper-and-pencil	α = .94 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	105	MD = -.04 ± .49	DiDonato & Golom (2016)	no signal - inconsistent (imprecise)	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #9	DV assessed via computer rather than paper-and-pencil	α = .91 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	187	MD = -.03 ± .45	Sucharyna & Morry (2016)	no signal - inconsistent	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #10	DV assessed via computer rather than paper-and-pencil	α = .90 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	100	MD = .03 ± .39	Carcedo & Fernandez-Rouco (2016)	no signal - inconsistent	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #11	DV assessed via computer rather than paper-and-pencil	α = .86 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	142	MD = .12 ± .43	Aykutoglu, Gündogdu-Aktürk et al. (2016)	no signal - inconsistent	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #12	DV assessed via computer rather than paper-and-pencil	α = .93 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	209	MD = .16 ± .35	Hoplock & Stinson (2016)	no signal - inconsistent	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #13	DV assessed via computer rather than paper-and-pencil	α = .88 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	173	MD = .16 ± .38	Cobb, Pink, Millman & Logan (2016)	no signal - inconsistent	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #14	DV assessed via computer rather than paper-and-pencil	α = .90 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	127	MD = .23 ± .43	Caprariello (2016)	no signal - inconsistent	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #15	DV assessed via computer rather than paper-and-pencil	α = .90 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
commitment priming boosts forgiveness effect (exit forgiveness DV)	Finkel et al. (2002) Study 1	89	MD = -.65 ± .45	101	MD = .29 ± .46	Tidwell & Kraus (2016)	no signal - inconsistent (imprecise)	high vs. low commitment priming (dependence vs. independence from partner)	forgiveness to hypothetical betrayals	very close	RRR5 #16	DV assessed via computer rather than paper-and-pencil	α = .90 on subjective commitment measure	3 other DVs (neglect, voice, loyalty forgiveness; same pattern of results observed)	High commitment priming (writing about interdependence to partner) increases forgiveness on hypothetical betrayals compared to low commitment priming (writing about independence)
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	87	MD = -.58 ± .83	Özdogru (2016)	no signal - inconsistent (imprecise)	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #1	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	110	MD = -.24 ± .52	Oosterwijk et al. (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #2	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	110	MD = -.20 ± .54	Wayand (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #3	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	100	MD = -.19 ± .54	Koch (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #4	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	120	MD = -.13 ± .63	Pacheco-Unguetti et al. (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #5	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	117	MD = -.11 ± .58	Capaldi et al. (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #6	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	94	MD = -.05 ± .60	Chasten et al. (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #7	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	115	MD = -.02 ± .50	Benning et al. (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #8	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	112	MD = .02 ± .54	Talarico & DeCicco (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #9	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	101	MD = .02 ± .67	Korb et al. (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #10	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	101	MD = .12 ± .55	Bulnes et al. (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #11	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	139	MD = .14 ± .53	Albohn et al. (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #12	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	130	MD = .15 ± .42	Wagenmakers et al. (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #13	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	125	MD = .16 ± .59	Allard & Zetzer (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #14	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	99	MD = .20 ± .55	Holmes et al. (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #15	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	108	MD = .35 ± .53	Zeelenberg et al. (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #16	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
facial feedback hypothesis	Strack et al. (1988) Study 1	60	MD = .82 ± .77	126	MD = .37 ± .55	Lynott et al. (2016)	no signal - inconsistent	hold pen with teeth vs. lips	amusement of cartoons (4 cartoons)	close	RRR6 #17	New set of Far Side cartoons normed to be moderately funny; instructions via video to minimize experimenter effects; sessions filmed to allow for manipulation check; minor wording change for funniness rating scale
intuitive-cooperation effect	Rand, Greene, & Nowak (2012) Study 7	211	MD = 8.58% ± 10.42%	203	MD = 5.49% ± 6.56%	Aczel et al. (2017)	no signal - consistent	time pressure (within 10 seconds) vs. forced delay (wait at least 10 seconds)	cooperation (money given to common project)	very close	RRR7 #1	slightly different money amounts for samples outside US and Canada	decision time manipulation check; DV comprehension questions (2-items)
intuitive-cooperation effect	Rand, Greene, & Nowak (2012) Study 7	211	MD = 8.58% ± 10.42%	221	MD = -.37% ± 9.65%	Bègue et al. (2017)	no signal - consistent	time pressure (within 10 seconds) vs. forced delay (wait at least 10 seconds)	cooperation (money given to common project)	very close	RRR7 #2	slightly different money amounts for samples outside US and Canada	decision time manipulation check; DV comprehension questions (2-items)
intuitive-cooperation effect	Rand, Greene, & Nowak (2012) Study 7	211	MD = 8.58% ± 10.42%	163	MD = 1.24% ± 9.63%	Ferreira-Santos et al. (2017)	no signal - consistent	time pressure (within 10 seconds) vs. forced delay (wait at least 10 seconds)	cooperation (money given to common project)	very close	RRR7 #6	slightly different money amounts for samples outside US and Canada	decision time manipulation check; DV comprehension questions (2-items)
intuitive-cooperation effect	Rand, Greene, & Nowak (2012) Study 7	211	MD = 8.58% ± 10.42%	166	MD = -2.02% ± 12.30%	Hauser et al. (2017)	no signal - consistent (imprecise)	time pressure (within 10 seconds) vs. forced delay (wait at least 10 seconds)	cooperation (money given to common project)	very close	RRR7 #8	slightly different money amounts for samples outside US and Canada	decision time manipulation check; DV comprehension questions (2-items)
intuitive-cooperation effect	Rand, Greene, & Nowak (2012) Study 7	211	MD = 8.58% ± 10.42%	182	MD = -2.47% ± 11.55%	Hernan et al. (2017)	no signal - consistent (imprecise)	time pressure (within 10 seconds) vs. forced delay (wait at least 10 seconds)	cooperation (money given to common project)	very close	RRR7 #9	slightly different money amounts for samples outside US and Canada	decision time manipulation check; DV comprehension questions (2-items)
intuitive-cooperation effect	Rand, Greene, & Nowak (2012) Study 7	211	MD = 8.58% ± 10.42%	188	MD = -5.10% ± 10.19%	Mischkowski et al. (2017)	no signal - inconsistent (imprecise)	time pressure (within 10 seconds) vs. forced delay (wait at least 10 seconds)	cooperation (money given to common project)	very close	RRR7 #11	slightly different money amounts for samples outside US and Canada	decision time manipulation check; DV comprehension questions (2-items)
intuitive-cooperation effect	Rand, Greene, & Nowak (2012) Study 7	211	MD = 8.58% ± 10.42%	202	MD = 2.53% ± 9.62%	Novakova et al. (2017)	no signal - consistent	time pressure (within 10 seconds) vs. forced delay (wait at least 10 seconds)	cooperation (money given to common project)	very close	RRR7 #13	slightly different money amounts for samples outside US and Canada	decision time manipulation check; DV comprehension questions (2-items)
intuitive-cooperation effect	Rand, Greene, & Nowak (2012) Study 7	211	MD = 8.58% ± 10.42%	216	MD = -7.56% ± 10.09%	Piovesan et al. (2017)	no signal - inconsistent	time pressure (within 10 seconds) vs. forced delay (wait at least 10 seconds)	cooperation (money given to common project)	very close	RRR7 #15	slightly different money amounts for samples outside US and Canada	decision time manipulation check; DV comprehension questions (2-items)
intuitive-cooperation effect	Rand, Greene, & Nowak (2012) Study 7	211	MD = 8.58% ± 10.42%	198	MD = -.25% ± 10.44%	Salomon et al. (2017)	no signal - consistent (imprecise)	time pressure (within 10 seconds) vs. forced delay (wait at least 10 seconds)	cooperation (money given to common project)	very close	RRR7 #16	slightly different money amounts for samples outside US and Canada	decision time manipulation check; DV comprehension questions (2-items)
intuitive-cooperation effect	Rand, Greene, & Nowak (2012) Study 7	211	MD = 8.58% ± 10.42%	177	MD = 7.51% ± 10.51%	Srinivasan et al. (2017)	no signal - consistent (imprecise)	time pressure (within 10 seconds) vs. forced delay (wait at least 10 seconds)	cooperation (money given to common project)	very close	RRR7 #17	slightly different money amounts for samples outside US and Canada	decision time manipulation check; DV comprehension questions (2-items)
intuitive-cooperation effect	Rand, Greene, & Nowak (2012) Study 7	211	MD = 8.58% ± 10.42%	164	MD = -9.36% ± 10.51%	Tinghög et al. (2017)	no signal - inconsistent (imprecise)	time pressure (within 10 seconds) vs. forced delay (wait at least 10 seconds)	cooperation (money given to common project)	very close	RRR7 #18	slightly different money amounts for samples outside US and Canada	decision time manipulation check; DV comprehension questions (2-items)
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	110	PD = 4.24% ± 4.36%	Schulte-Mecklenbeck et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #1	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	130	PD = 4.08% ± 4.29%	Baskin et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #2	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	136	PD = 4.01% ± 4.29%	Braithwaite et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #3	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	277	PD = 2.89% ± 3.28%	Vazire, Finnigan et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #4	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	122	PD = 1.66% ± 4.71%	Newell et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #5	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	200	PD = 1.58% ± 4.90%	O’Donnell et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #6	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	208	PD = 1.41% ± 3.42%	Tamayo et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #7	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	157	PD = 1.38% ± 4.45%	Karpinski et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #8	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	242	PD = .88% ± 2.93%	Klein et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #9	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	431	PD = .17% ± 2.61%	Keller et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #10	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	135	PD = .11% ± 5.01%	Shanks et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #11	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	136	PD = -.12% ± 3.98%	Bialobrzeska et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #12	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	139	PD = -.20% ± 4.07%	Koppel et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #13	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	140	PD = -.43% ± 4.75%	Philipp et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #14	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	199	PD = -.48% ± 3.14%	Ropovik et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #15	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	544	PD = -.58% ± 2.01%	Steele et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #16	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	169	PD = -.63% ± 4.05%	Susa et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #17	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	144	PD = -.84% ± 4.79%	Steffens et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #18	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	260	PD = -1.35% ± 2.95%	Aczel et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #19	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	152	PD = -1.87% ± 4.44%	Saunders et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #20	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	107	PD = -2.16% ± 4.95%	McLatchie et al. (2017)	no signal - inconsistent	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #21	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	205	PD = -3.99% ± 3.43%	Aveyard et al. (2017)	signal - inconsistent, opposite	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #22	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
professor priming	Dijksterhuis & van Knippenberg (1998) Study 4	43	PD = 13.20%	150	PD = -4.99% ± 4.68%	Boot et al. (2017)	signal - inconsistent, opposite	professor vs. hooligan priming (writing task)	Trivial pursuit performance (% of correct answers; out of 30 items)	close	RRR8 #23	new normed set of Trivial pursuit items (medium difficulty; 30 instead of 20 items); different cover story
playboy effect	Kenrick et al. (1989) Study 2	65	Δd = -.53 ± .64	223	Δd = .29 ± .46	Balzarini et al. (2017) Study 1	no signal - inconsistent	Playboy centerfolds vs. control; Participant sex	Love for partner (Rubin Love-scale)	close	individual effort	Updated pictures of abstract art & male/female nudes; Two attention check questions	Nudes rated as more pleasant than abstract art	1 other DV (partner attraction; same pattern of results observed)	Opposite-sex nudes exposure decreases love among men, but not women
playboy effect	Kenrick et al. (1989) Study 2	65	Δd = -.53 ± .64	263	Δd = .30 ± .42	Balzarini et al. (2017) Study 2	no signal - inconsistent	Playboy centerfolds vs. control; Participant sex	Love for partner (Rubin Love-scale)	close	individual effort	Updated pictures of abstract art & male/female nudes; Two attention check questions	Nudes rated as more pleasant than abstract art	1 other DV (partner attraction; same pattern of results observed)	Opposite-sex nudes exposure decreases love among men, but not women
playboy effect	Kenrick et al. (1989) Study 2	65	Δd = -.53 ± .64	225	Δd = -.38 ± .46	Balzarini et al. (2017) Study 3	no signal - inconsistent	Playboy centerfolds vs. control; Participant sex	Love for partner (Rubin Love-scale)	close	individual effort	Updated pictures of abstract art & male/female nudes; Two attention check questions	Nudes rated as more pleasant than abstract art	1 other DV (partner attraction; same pattern of results observed)	Opposite-sex nudes exposure decreases love among men, but not women
positive mood boosts helping effect	Isen & Levin (1972) Study 2	41	PD = 84% ± 18%	50	PD = 3% ± 30%	Blevins & Murphy (1974)	no signal - inconsistent	positive mood (finding dime) vs. control (not finding dime)	helping (picking up dropped papers)	close	individual effort				Positive mood increases percentage of Ps who help
positive mood boosts helping effect	Levin & Isen (1975) Study 1	24	PD = 60% ± 30%	106	PD = 25% ± 29%	Weyant & Clark (1977) Study 1	no signal - inconsistent	positive mood (finding dime) vs. control (not finding dime)	helping (mailing 'forgotten' letter)	close	individual effort				Positive mood increases percentage of Ps who help
positive mood boosts helping effect	Levin & Isen (1975) Study 1	24	PD = 60% ± 30%	32	PD = -7% ± 16%	Weyant & Clark (1977) Study 2	no signal - inconsistent	positive mood (finding dime) vs. control (not finding dime)	helping (mailing 'forgotten' letter)	close	individual effort				Positive mood increases percentage of Ps who help
reading fiction boosts empathy effect	Kidd & Castano (2013) Study 1	86	d = .56 ± .43	300	d = -.08 ± .23	Panero et al. (2016) Study 1	no signal - inconsistent	literary fiction vs. nonfiction	empathy (Reading the mind in the eyes test [RMET])	very close	individual effort
reading fiction boosts empathy effect	Kidd & Castano (2013) Study 3	69	d = .36 ± .21	300	d = .14 ± .23	Dijkstra, Zwaan et al. (2015)	no signal - inconsistent	literary fiction vs. nonfiction	empathy (Reading the mind in the eyes test [RMET])	very close	individual effort
reading fiction boosts empathy effect	Kidd & Castano (2013) Study 3	69	d = .36 ± .21	303	d = .04 ± .22	Panero et al. (2016) Study 2	no signal - inconsistent	literary fiction vs. popular fiction	empathy (Reading the mind in the eyes test [RMET])	very close	individual effort
reading fiction boosts empathy effect	Kidd & Castano (2013) Study 5	237	d = .25 ± .25	369	d = .10 ± .20	Panero et al. (2016) Study 3	no signal - inconsistent	literary fiction vs. popular fiction	empathy (Reading the mind in the eyes test [RMET])	very close	individual effort
pre-cognition (erotic premonition)	Bem (2011) Study 1	100	d = .25	200	d = -.05	Wagenmakers et al. (2012)	no signal - inconsistent	n/a	future location of erotic photo	close	individual effort	different erotic photos
pre-cognition (retroactive memory recall)	Bem (2011) Study 8	100	DR% = 2.3% ± 2.3%	112	DR% = -1.2% ± 1.6%	Galak et al. (2012) Study 1	no signal - inconsistent	rehearsed vs. non-rehearsed words (after memory test)	free recall memory test	close	individual effort
pre-cognition (retroactive memory recall)	Bem (2011) Study 8	100	DR% = 2.3% ± 2.3%	158	DR% = 0% ± 1.3%	Galak et al. (2012) Study 2	no signal - inconsistent	rehearsed vs. non-rehearsed words (after memory test)	free recall memory test	close	individual effort
pre-cognition (retroactive memory recall)	Bem (2011) Study 8	100	DR% = 2.3% ± 2.3%	124	DR% = 1.2% ± 1.4%	Galak et al. (2012) Study 3	no signal - inconsistent	rehearsed vs. non-rehearsed words (after memory test)	free recall memory test	exact	individual effort
pre-cognition (retroactive memory recall)	Bem (2011) Study 8	100	DR% = 2.3% ± 2.3%	2469	DR% = -.1% ± .1%	Galak et al. (2012) Study 7	signal - inconsistent, opposite	rehearsed vs. non-rehearsed words (after memory test)	free recall memory test	close	individual effort
pre-cognition (retroactive memory recall)	Bem (2011) Study 9	50	DR% = 4.2% ± 3.0%	109	DR% = 1.6% ± 1.1%	Galak et al. (2012) Study 4	signal - inconsistent, smaller	rehearsed vs. non-rehearsed words (after memory test)	free recall memory test	exact	individual effort				different rehearsal task (visualize, click, & type) than Bem Study 8
pre-cognition (retroactive memory recall)	Bem (2011) Study 9	50	DR% = 4.2% ± 3.0%	211	DR% = -.5% ± 1.5%	Galak et al. (2012) Study 5	no signal - inconsistent	rehearsed vs. non-rehearsed words (after memory test)	free recall memory test	exact	individual effort				different rehearsal task (visualize, click, & type) than Bem Study 8
pre-cognition (retroactive memory recall)	Bem (2011) Study 9	50	DR% = 4.2% ± 3.0%	106	DR% = -.3% ± 1.5%	Galak et al. (2012) Study 6	no signal - inconsistent	rehearsed vs. non-rehearsed words (after memory test)	free recall memory test	exact	individual effort				different rehearsal task (visualize, click, & type) than Bem Study 8
pre-cognition (retroactive memory recall)	Bem (2011) Study 9	50	DR% = 4.2% ± 3.0%	50	DR% = .2% ± 3.5%	Ritchie et al. (2012) Study 1	no signal - inconsistent (imprecise)	rehearsed vs. non-rehearsed words (after memory test)	free recall memory test	exact	individual effort				different rehearsal task (visualize, click, & type) than Bem Study 8
pre-cognition (retroactive memory recall)	Bem (2011) Study 9	50	DR% = 4.2% ± 3.0%	50	DR% = -2.7% ± 3.5%	Ritchie et al. (2012) Study 2	no signal - inconsistent (imprecise)	rehearsed vs. non-rehearsed words (after memory test)	free recall memory test	exact	individual effort				different rehearsal task (visualize, click, & type) than Bem Study 8
pre-cognition (retroactive memory recall)	Bem (2011) Study 9	50	DR% = 4.2% ± 3.0%	50	DR% = -.6% ± 3.5%	Ritchie et al. (2012) Study 3	no signal - inconsistent (imprecise)	rehearsed vs. non-rehearsed words (after memory test)	free recall memory test	exact	individual effort				different rehearsal task (visualize, click, & type) than Bem Study 8
pre-cognition (retroactive memory recall)	Bem (2011) Study 9	50	DR% = 4.2% ± 3.0%	50	DR% = -1.6% ± 3.4%	Robinson (2011)	no signal - inconsistent (imprecise)	rehearsed vs. non-rehearsed words (after memory test)	free recall memory test	exact	individual effort				different rehearsal task (visualize, click, & type) than Bem Study 8
power posing effect (risk-taking DV)	Carney, Cuddy & Yap (2010)	42	OR = 3.13 [.94,10.4]	200	OR = .72 [.43,1.21]	Ranehill et al. (2015)	no signal - inconsistent	pose (expansive vs contractive)	risky gambling decision (yes vs. no)	close	individual effort	3-minute poses instead of 1-minute; 5 additional gain domain gambling decisions		2 other DVS (testosterone level, cortisol level; same pattern of results observed)
power posing effect (risk-taking DV)	Carney, Cuddy & Yap (2010)	42	OR = 3.13 [.94,10.4]	305	OR = .67 [.38,1.18]	Garrison et al. (2016)	no signal - inconsistent	pose (expansive vs contractive)	risky gambling decision (yes vs. no)	close	individual effort	added ultimatum game dominance DV; also manipulated eye gaze (orthogonal to pose IV)
power posing effect (risk-taking DV)	Carney, Cuddy & Yap (2010)	42	OR = 3.13 [.94,10.4]	94	OR = 1.38 [.65,2.92]	Bailey, LaFrance et al. (2017)	no signal - inconsistent	pose (expansive vs contractive)	risky gambling decision (yes vs. no)	close	CRSP special issue	2 tokens for snacks instead of $2
power posing effect (risk-taking DV)	Carney, Cuddy & Yap (2010)	42	OR = 3.13 [.94,10.4]	108	OR = .53 [.17,1.59]	Ronay, Tybur et al. (2017)	no signal - inconsistent	pose (expansive vs contractive)	risky gambling decision (yes vs. no)	very close	CRSP special issue	Dutch undergraduates		2 other DVS (testosterone level, cortisol level; same pattern of results observed)
power posing effect (risk-taking DV)	Carney, Cuddy & Yap (2010)	42	OR = 3.13 [.94,10.4]	200	OR = 1.14 [.68,1.89]	Bombari, Schmid et al. (2017)	no signal - inconsistent	pose (expansive vs contractive)	risky gambling decision (yes vs. no)	close	CRSP special issue	Swiss undergrad or Master's students
power posing effect (risk-taking DV)	Carney, Cuddy & Yap (2010)	42	OR = 3.13 [.94,10.4]	276	OR = 1.17 [.91,1.51]	Keller, Johnson et al. (2017)	no signal - inconsistent	pose (expansive vs contractive)	risky gambling decision (yes vs. no)	close	CRSP special issue	2 tickets to win $50 gift card instead of $2		impromptu job interview performance (same pattern of results observed)
elderly priming	Bargh et al. (1996) Study 2a	30	d = 1.02 ± .76	31	d = .56 ± .72	Hull et al. (2002) Study 1a	no signal - consistent	exposure to elderly cues vs. control (scrambled sentence)	walking speed	close	individual effort
elderly priming	Bargh et al. (1996) Study 2a	30	d = 1.02 ± .76	40	d = .53 ± .63	Hull et al. (2002) Study 1b	no signal - consistent	exposure to elderly cues vs. control (scrambled sentence)	walking speed	close	individual effort
elderly priming	Bargh et al. (1996) Study 2a	30	d = 1.02 ± .76	45	d = .22 ± .58	Cesario et al. (2007) Study 2	no signal - inconsistent	exposure to elderly cues vs. control (scrambled sentence)	walking speed	very close	individual effort
elderly priming	Bargh et al. (1996) Study 2a	30	d = 1.02 ± .76	66	d = -.22 ± .48	Pashler et al. (2008)	no signal - inconsistent	exposure to elderly cues vs. control (scrambled sentence)	walking speed	very close	individual effort
elderly priming	Bargh et al. (1996) Study 2a	30	d = 1.02 ± .76	120	d = -.07 ± .36	Doyen et al. (2012) Study 1	no signal - inconsistent	exposure to elderly cues vs. control (scrambled sentence)	walking speed	close	individual effort
embodiment of secrets effect	Slepian et al. (2012) Study 1	40	d = .78 ± .62	314	d = .19 ± .22	Perfecto et al. (2012)	no signal - inconsistent	big vs. small secret (write about)	hill steepness estimate (open-ended)	very close	individual effort
embodiment of secrets effect	Slepian et al. (2012) Study 1	40	d = .78 ± .62	240	d = .18 ± .25	LeBel & Wilbur (2014) Study 1	no signal - inconsistent	big vs. small secret (write about)	hill steepness estimate (open-ended)	very close	individual effort
embodiment of secrets effect	Slepian et al. (2012) Study 1	40	d = .78 ± .62	90	d = -.32 ± .41	LeBel & Wilbur (2014) Study 2	no signal - inconsistent	big vs. small secret (write about)	hill steepness estimate (open-ended)	very close	individual effort
embodiment of secrets effect	Slepian et al. (2012) Study 1	40	d = .78 ± .62	100	d = .08 ± .39	Pecher et al. (2015) Study 1	no signal - inconsistent	big vs. small secret (write about)	hill steepness estimate (open-ended)	very close	individual effort
embodiment of secrets effect	Slepian et al. (2012) Study 1	40	d = .78 ± .62	100	d = .21 ± .39	Pecher et al. (2015) Study 2	no signal - inconsistent	big vs. small secret (write about)	hill steepness estimate (open-ended)	very close	individual effort
embodiment of secrets effect	Slepian et al. (2012) Study 2	36	d = .81 ± .73	60	d = .31 ± .52	Cobb et al. (2014)	no signal - inconsistent	meaningful vs. trivial secret (recall task)	beanbag toss distance	close	individual effort
embodiment of secrets effect	Slepian et al. (2012) Study 2	36	d = .81 ± .73	118	d = .21 ± .36	Pecher et al. (2015) Study 3	no signal - inconsistent	meaningful vs. trivial secret (recall task)	beanbag toss distance	very close	individual effort
Macbeth effect (transcribe text)	Zhong & Liljenquist (2006) Study 2	27	r = .45 ± .31	36	r = .04 ± .33	Gamez et al. (2011) Study 2	no signal - inconsistent (imprecise)	moral purity threat (transcribe unethical vs. ethical act)	need to cleanse oneself (cleaning products desirability)	very close	individual effort
Macbeth effect (transcribe text)	Zhong & Liljenquist (2006) Study 2	27	r = .45 ± .31	335	r = -.04 ± .11	Siev (2012) Study 1	no signal - inconsistent	moral purity threat (transcribe unethical vs. ethical act)	need to cleanse oneself (cleaning products desirability)	very close	individual effort
Macbeth effect (transcribe text)	Zhong & Liljenquist (2006) Study 2	27	r = .45 ± .31	148	r = -.09 ± .16	Siev (2012) Study 2	no signal - inconsistent	moral purity threat (transcribe unethical vs. ethical act)	need to cleanse oneself (cleaning products desirability)	very close	individual effort
Macbeth effect (transcribe text)	Zhong & Liljenquist (2006) Study 2	27	r = .45 ± .31	153	r = .00 ± .16	Earp et al. (2014) Study 1	no signal - inconsistent	moral purity threat (transcribe unethical vs. ethical act)	need to cleanse oneself (cleaning products desirability)	very close	individual effort
Macbeth effect (transcribe text)	Zhong & Liljenquist (2006) Study 2	27	r = .45 ± .31	156	r = -.07 ± .16	Earp et al. (2014) Study 2	no signal - inconsistent	moral purity threat (transcribe unethical vs. ethical act)	need to cleanse oneself (cleaning products desirability)	very close	individual effort
Macbeth effect (transcribe text)	Zhong & Liljenquist (2006) Study 2	27	r = .45 ± .31	286	r = -.11 ± .11	Earp et al. (2014) Study 3	signal - inconsistent, opposite	moral purity threat (transcribe unethical vs. ethical act)	need to cleanse oneself (cleaning products desirability)	very close	individual effort
Macbeth effect (recall [un]ethical act)	Zhong & Liljenquist (2006) Study 3	32	r = .38 ± .30	210	r = .03 ± .14	Fayard et al. (2009) Study 1	no signal - inconsistent	moral purity threat (recall unethical vs. ethical deed)	product choice (cleaning-related vs. unrelated)	very close	individual effort
Macbeth effect (recall [un]ethical act)	Zhong & Liljenquist (2006) Study 3	32	r = .38 ± .30	45	r = .15 ± .29	Gamez et al. (2011) Study 3	no signal - inconsistent	moral purity threat (recall unethical vs. ethical deed)	product choice (cleaning-related vs. unrelated)	very close	individual effort
Macbeth effect (physical cleansing)	Zhong & Liljenquist (2006) Study 4	45	r = .33 ± .26	115	r = .01 ± .18	Fayard et al. (2009) Study 2	no signal - inconsistent	physical cleansing (antiseptic wipe vs. control)	volunteerism (help student RA)	close	individual effort	minutes volunteering instead of binary choice to help
Macbeth effect (physical cleansing)	Zhong & Liljenquist (2006) Study 4	45	r = .33 ± .26	28	r = .19 ± .36	Gamez et al. (2011) Study 4	no signal - consistent (imprecise)	physical cleansing (antiseptic wipe vs. control)	volunteerism (help student RA)	very close	individual effort
Macbeth effect (physical cleansing)	Zhong & Liljenquist (2006) Study 4	45	r = .33 ± .26	29	r = .39 ± .31	Reuven et al. (2013)	signal - consistent (imprecise)	physical cleansing (antiseptic wipe vs. control)	volunteerism (help student RA)	close	individual effort	3-choice instead of binary choice DV
warmth embodiment (hot shower loneliness effect)	Bargh & Shalev (2012) Study 1a	51	r = .57 ± .19	197	r = -.13 ± .14	Donnellan et al. (2015a) Study 9	no signal - inconsistent	UCLA Loneliness scale (10-item v1)	showering/bathing behaviors (3-items)	very close	individual effort
warmth embodiment (hot shower loneliness effect)	Bargh & Shalev (2012) Study 1a	51	r = .57 ± .19	228	r = -.10 ± .13	Donnellan et al. (2015a) Study 4	no signal - inconsistent	UCLA Loneliness scale (20-item v3)	showering/bathing behaviors (3-items)	close	individual effort	online (SurveyMonkey); slightly different scale response options; 20-item (instead of 10-item) UCLA Loneliness Scale
warmth embodiment (hot shower loneliness effect)	Bargh & Shalev (2012) Study 1a	51	r = .57 ± .19	235	r = -.06 ± .13	Donnellan et al. (2015a) Study 1	no signal - inconsistent	UCLA Loneliness scale (20-item v3)	showering/bathing behaviors (3-items)	close	individual effort	slightly different scale response options; 20-item (instead of 10-item) UCLA Loneliness Scale
warmth embodiment (hot shower loneliness effect)	Bargh & Shalev (2012) Study 1a	51	r = .57 ± .19	291	r = -.04 ± .11	Donnellan et al.(2015b)	no signal - inconsistent	UCLA Loneliness scale (10-item v1)	showering/bathing behaviors (3-items)	very close	individual effort
warmth embodiment (hot shower loneliness effect)	Bargh & Shalev (2012) Study 1a	51	r = .57 ± .19	365	r = -.03 ± .10	Ferrell et al. (2013)	no signal - inconsistent	Revised UCLA Loneliness scale (3-item; Hughes et al., 2004)	showering/bathing behaviors (3-items)	close	individual effort	online (UT Austin)
warmth embodiment (hot shower loneliness effect)	Bargh & Shalev (2012) Study 1a	51	r = .57 ± .19	356	r = -.02 ± .10	McDonald & Donnellan (2015)	no signal - inconsistent	Revised UCLA Loneliness scale (3-item; Hughes et al., 2004)	showering/bathing behaviors (3-items)	close	individual effort	online Israeli sample
warmth embodiment (hot shower loneliness effect)	Bargh & Shalev (2012) Study 1a	51	r = .57 ± .19	480	r = -.01 ± .09	Donnellan et al. (2015a) Study 2	no signal - inconsistent	UCLA Loneliness scale (20-item v3)	showering/bathing behaviors (3-items)	close	individual effort	online (mTurk); slightly different scale response options; 20-item (instead of 10-item) UCLA Loneliness Scale
warmth embodiment (hot shower loneliness effect)	Bargh & Shalev (2012) Study 1a	51	r = .57 ± .19	365	r = .02 ± .10	Donnellan et al. (2015a) Study 8	no signal - inconsistent	UCLA Loneliness scale (10-item v1)	showering/bathing behaviors (3-items)	very close	individual effort
warmth embodiment (hot shower loneliness effect)	Bargh & Shalev (2012) Study 1a	51	r = .57 ± .19	311	r = .02 ± .11	Donnellan et al. (2015a) Study 7	no signal - inconsistent	UCLA Loneliness scale (10-item v1)	showering/bathing behaviors (3-items)	very close	individual effort
warmth embodiment (hot shower loneliness effect)	Bargh & Shalev (2012) Study 1a	51	r = .57 ± .19	531	r = .04 ± .08	Donnellan & Lucas (2014)	no signal - inconsistent	UCLA Loneliness scale (10-item v1)	showering/bathing behaviors (3-items)	very close	individual effort
warmth embodiment (hot shower loneliness effect)	Bargh & Shalev (2012) Study 1a	51	r = .57 ± .19	553	r = .06 ± .08	Donnellan et al. (2015a) Study 6	no signal - inconsistent	UCLA Loneliness scale (10-item v1)	showering/bathing behaviors (3-items)	very close	individual effort	online (mTurk)
warmth embodiment (hot shower loneliness effect)	Bargh & Shalev (2012) Study 1a	51	r = .57 ± .19	494	r = .10 ± .09	Donnellan et al. (2015a) Study 5	signal - inconsistent, smaller	UCLA Loneliness scale (10-item v1)	showering/bathing behaviors (3-items)	very close	individual effort	online (mTurk)
warmth embodiment (hot shower loneliness effect)	Bargh & Shalev (2012) Study 1a	51	r = .57 ± .19	210	r = .13 ± .13	Donnellan et al. (2015a) Study 3	signal - inconsistent, smaller	UCLA Loneliness scale (20-item v3)	showering/bathing behaviors (3-items)	close	individual effort	online (SurveyMonkey); slightly different scale response options; 20-item (instead of 10-item) UCLA Loneliness Scale
warmth embodiment (cold packs boost loneliness effect)	Bargh & Shalev (2012) Study 2	75	r = .29 ± .21	260	r = .01 ± .11	Wortman et al. (2014)	no signal - inconsistent	holding cold vs. warm pack	UCLA Loneliness scale (10-item v1)	very close	individual effort		Cold packs rated as colder than warm packs (d = +3.07 ± .92)
anxious attachment warm food effect	Vess (2012) Study 1	56	d = .60 ± .55	219	d = .03 ± .27	LeBel & Campbell (2013) Study 1	no signal - inconsistent	anxious attachment; recall breakup vs. control	warm food desirability ratings (3 items)	very close	individual effort	slightly different instructions & scale anchors for attachment measure	Known sex differences in food preferences (women liked vegetables, fruits, candy, and wine more than men whereas men liked meats, chili pepper, and beer more than women; Logan & Smith, 1986)		individuals with high (but not low) levels of anxious attachment reported heightened preferences for warm foods when attachment concerns were activated (i.e., by reflecting on a romantic breakup).
anxious attachment warm food effect	Vess (2012) Study 1	56	d = .60 ± .55	233	d = .05 ± .26	LeBel & Campbell (2013) Study 2	no signal - inconsistent	anxious attachment; recall breakup vs. control	warm food desirability ratings (3 items)	exact	individual effort		Known sex differences in food preferences (women liked vegetables, fruits, candy, and wine more than men whereas men liked meats, chili pepper, and beer more than women; Logan & Smith, 1986)		individuals with high (but not low) levels of anxious attachment reported heightened preferences for warm foods when attachment concerns were activated (i.e., by reflecting on a romantic breakup).
modulation of 1/f noise racial bias emission effect	Correll (2008) Study 2	71	d = .59 ± .51	148	d = .16 ± .34	Madurski & LeBel (2015) Study 1	no signal - inconsistent	avoid/use race information vs. control instructions	1/f noise (weapon identification task)	very close	individual effort	Canadian rather than US undergraduates; keyboard used instead of response box;	Racial bias (in terms of RT) higher in use and avoid race (compared to control) conditions (d = .34 ± .35); 1/f noise observed within each of the 3 conditions (p<.0001)		Use/avoid race conditions increases effort (via 1/f noise) on racial bias task (weapon identification task)
modulation of 1/f noise racial bias emission effect	Correll (2008) Study 2	71	d = .59 ± .51	148	d = -.09 ± .34	Madurski & LeBel (2015) Study 2	no signal - inconsistent	avoid/use race information vs. control instructions	1/f noise (weapon identification task)	very close	individual effort	Canadian rather than US undergraduates; keyboard used instead of response box;	Racial bias (in terms of RT) higher in use and avoid race (compared to control) conditions (d = .44 ± .35); 1/f noise observed within each of the 3 conditions (p<.0001)		Use/avoid race conditions increases effort (via 1/f noise) on racial bias task (weapon identification task)
high-approach positive affect constricts attention effect	Gable & Harmon-Jones (2008) Study 2	32	η² = .53 [.26,.67]	79	η² = .24 [.09,.39]	Domachowska et al. (2016) Study 1	signal - inconsistent, smaller	Affect (High-approach positive affect vs. neutral)	Attentional breadth (Navon, 1977 letter task RTs)	very close	individual effort	German language; Mood (PANAS) assessed at study outset	Neutral mood global bias effect observed, p < .01; Successful valence manipulation check, dz = 1.14
high-approach positive affect constricts attention effect	Gable & Harmon-Jones (2008) Study 2	32	η² = .53 [.26,.67]	80	η² = .08 [.00,.21]	Domachowska et al. (2016) Study 2	signal - inconsistent, smaller	Affect (High-approach positive affect vs. neutral)	Attentional breadth (Navon, 1977 letter task RTs)	close	individual effort	German-normed stimuli; Low-approach positive affect within-subject condition also included	Neutral mood global bias effect observed, p < .05; Successful valence manipulation check, dz = 2.51
honesty priming	Rasinski et al. (2005)	62	d = 1.21 ± .55	149	d = .18 ± .33	Pashler, Rohrer, & Harris (2012) Study 1	no signal - inconsistent	honesty vs. neutral words exposure (synonym judgment task)	candor (alcohol drinking questions)	very close	individual effort
honesty priming	Rasinski et al. (2005)	62	d = 1.21 ± .55	152	d = -.13 ± .33	Pashler, Rohrer, & Harris (2012) Study 2	no signal - inconsistent	honesty vs. neutral words exposure (synonym judgment task)	candor (alcohol drinking questions)	very close	individual effort	UCSD online sample
honesty priming	Rasinski et al. (2005)	62	d = 1.21 ± .55	151	d = -.14 ± .32	Pashler, Rohrer, & Harris (2012,online) Study 3	no signal - inconsistent	honesty vs. neutral words exposure (synonym judgment task)	candor (alcohol drinking questions)	close	individual effort	US mTurk sample; BIDR also measured
honesty priming	Rasinski et al. (2005)	62	d = 1.21 ± .55	153	d = .04 ± .32	Pashler, Rohrer, & Harris (2012,online) Study 4	no signal - inconsistent	honesty vs. neutral words exposure (synonym judgment task)	candor (alcohol drinking questions)	close	individual effort	Indian mTurk sample; BIDR also measured
achievement priming	Bargh et al. (2001) Study 1	78	d = .70 ± .46	106	d = -.24 ± .40	Harris, Rohrer, & Pashler (2013) Study 1	no signal - inconsistent	high-achievement vs. neutral words exposure (word search puzzle)	word search performance (3 IV-unrelated puzzles)	very close	individual effort
achievement priming (5-minute delay before DV)	Bargh et al. (2001) Study 3	72	d = NR	66	d = -.03 ± .49	Harris, Rohrer, & Pashler (2013) Study 2	no signal	high-achievement vs. neutral words exposure (word search puzzle)	word search performance (3 IV-unrelated puzzles)	close	individual effort	for each DV puzzle, 5 of 10 words were different (not described in original paper)
time is money reminder reduces pleasure (enjoyment DV)	Devoe & House (2012) Study 3	145	d = .75 ± .52	266	d = -.25 ± .27	Connors, Campbell et al. (2016) Study 1	no signal - inconsistent	time/money mindset vs. control; compensation vs. no compensation	enjoyment (3-items)	exact	individual effort			1 other DV (impatience; same pattern of results observed)
time is money reminder reduces pleasure (enjoyment DV)	Devoe & House (2012) Study 3	145	d = .75 ± .52	254	d = .21 ± .29	Connors, Campbell et al. (2016) Study 2	no signal - inconsistent	time/money mindset vs. control; compensation vs. no compensation	enjoyment (3-items)	exact	individual effort			1 other DV (impatience; same pattern of results observed)
orientation simulation effect	Stanfield & Zwaan (2001)	40	MD = 44 ± 35	164	MD = 33 ± 28	Zwaan & Pecher (2012) Study 1	signal - consistent	match vs. mismatch (between object orientation and implied object orientation in preceding sentence)	reaction time (RT; sentence-picture verification paradigm)	very close	individual effort	online (mTurk) instead of lab setting	word frequency effect (d = .63; lexical decision task)
orientation simulation effect	Stanfield & Zwaan (2001)	40	MD = 44 ± 35	172	MD = 38 ± 29	Zwaan & Pecher (2012) Study 2	signal - consistent	match vs. mismatch (between object orientation and implied object orientation in preceding sentence)	reaction time (RT; sentence-picture verification paradigm)	very close	individual effort	online (mTurk) instead of lab setting	word frequency effect (d = .51; lexical decision task)
shape simulation effect	Zwaan, Stanfield, & Yaxley (2002) Study 1	42	MD = 64 ± 36	176	MD = 57 ± 30	Zwaan & Pecher (2012) Study 3	signal - consistent	match vs. mismatch (between object shape and implied object shape in preceding sentence)	reaction time (RT; sentence-picture verification paradigm)	very close	individual effort	online (mTurk) instead of lab setting	word frequency effect (d = .46; lexical decision task)
shape simulation effect	Zwaan, Stanfield, & Yaxley (2002) Study 1	42	MD = 64 ± 36	176	MD = 70 ± 35	Zwaan & Pecher (2012) Study 4	signal - consistent	match vs. mismatch (between object shape and implied object shape in preceding sentence)	reaction time (RT; sentence-picture verification paradigm)	very close	individual effort	online (mTurk) instead of lab setting	word frequency effect (d = .32; lexical decision task)
color simulation effect	Connell (2005)	60	MD = -154 ± 102	152	MD = 157 ± 78	Zwaan & Pecher (2012) Study 5	signal - inconsistent, opposite	match vs. mismatch (between object color and implied object color in preceding sentence)	reaction time (RT; sentence-picture verification paradigm)	very close	individual effort	online (mTurk) instead of lab setting	word frequency effect (d = .61; lexical decision task)
color simulation effect	Connell (2005)	60	MD = -154 ± 102	152	MD = 85 ± 73	Zwaan & Pecher (2012) Study 6	signal - inconsistent, opposite	match vs. mismatch (between object color and implied object color in preceding sentence)	reaction time (RT; sentence-picture verification paradigm)	very close	individual effort	online (mTurk) instead of lab setting	word frequency effect (d = .47; lexical decision task)