Computational protocol: Threat of Shock and Aversive Inhibition: Induced Anxiety Modulates Pavlovian-Instrumental Interactions

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Protocol publication

[…] Sixty-two healthy participants (39 females; age range = 18–57; Mage = 27.16, SD = 7.83) were recruited from the University College London (UCL) Institute of Cognitive Neuroscience Subject Database. Sample size was determined by an a priori power analysis in G*Power (). The power analysis was based on the main finding from the reinforced go/no-go task showing that participants are significantly slower to respond in the punished conditions relative to the rewarded conditions, with a Cohen’s dz (within-subjects) effect size of 0.487 (). Detecting an effect size of this magnitude using a paired t test requires 57 participants at the 0.05 alpha level (two-tailed) with 95% power. The present study recruited 62 participants to allow for a small number of unusable data sets.Due to a recording fault during the sustained attention to response task (SART), one female participant was excluded, resulting in 61 participants in the SART. Participants reported no history of psychiatric, neurological or substance use disorders and no pacemaker implantation. Participants provided written informed consent and were reimbursed £7.50/hr for participation. To incentivize performance, participants were also informed that they could receive additional financial compensation based on task performance. The study obtained ethical approval from the UCL Research Ethics Committee (Project ID Number: 1764/001) and was conducted in accordance with the Declaration of Helsinki. Data and materials for the tasks are freely available for download ( and [...] All data were analyzed in SPSS version 22 (IBM Corp, Armonk, NY) and inspected for deviations from normality assumptions prior to analysis (of which none were found). For all analyses, p < .05 was considered statistically significant. For all paired t test analyses, Cohen’s dz effect size (within-subjects) was calculated (). The index of variation in figures was calculated according to the formula () by as standard error of the mean (SEM) is not appropriate error information for within-subjects designs. SEMwithin=SQRT(MSE/n),1 where MSE represents the mean squared error of the relevant main effect from the repeated-measures analysis and n represents the number of participants. The SEMwithin captures the within-subjects variance only (changes in scores from safe to threat conditions within each participant) by removing between-subjects variance (differences between participants) and is therefore an appropriate method to illustrate graphically the differences in means in within-subjects designs.Frequentist statistics were supplemented with Bayesian statistics to quantify the confidence in the main null effects. Bayesian analyses were performed in JASP Version using the default prior (; ; ). Bayesian statistics were used to obtain Bayes factors (BF10) for the model of interest, relative to the null model (main effect of participants). To facilitate interpretation of the magnitude difference between models (BF10 of model of interest divided by the BF10 of the comparison model), a model 1–3 times better than the comparison model was considered “anecdotal,” 3–10 was “substantial,” 10–30 was “strong,” 30–100 was “very strong,” and >100 was “decisive” (). […]

Pipeline specifications

Software tools G*Power, JASP
Application Miscellaneous