Across species, animals have an intrinsic drive to approach appetitive stimuli and to withdraw from aversive stimuli. In affective science, influential theories of emotion link positive affect with strengthened behavioral approach and negative affect with avoidance. Based on these theories, we predicted that individuals’ positive and negative affect levels should particularly influence their behavior when innate Pavlovian approach/avoidance tendencies conflict with learned instrumental behaviors. Here, across two experiments—exploratory Experiment 1 (N = 91) and a preregistered confirmatory Experiment 2 (N = 335)—we assessed how induced positive and negative affect influenced Pavlovian-instrumental interactions in a reward/punishment Go/No-Go task. Contrary to our hypotheses, we found no evidence for a main effect of positive/negative affect on either approach/avoidance behavior or Pavlovian-instrumental interactions. However, we did find evidence that the effects of induced affect on behavior were moderated by individual differences in self-reported behavioral inhibition and gender. Exploratory computational modelling analyses explained these demographic moderating effects as arising from positive correlations between demographic factors and individual differences in the strength of Pavlovian-instrumental interactions. These findings serve to sharpen our understanding of the effects of positive and negative affect on instrumental behavior.
Positive and negative affective states are respectively associated with optimistic and pessimistic expectations regarding future reward. One mechanism that might underlie these affect-related expectation biases is attention to positive- versus negative-valence stimulus features (e.g., attending to the positive reviews of a restaurant versus its expensive price). Here we tested the effects of experimentally induced positive and negative affect on feature-based attention in 120 participants completing a compound-generalization task with eye-tracking. We found that participants' reward expectations for novel compound stimuli were modulated by the affect induction in an affect-congruent way: positive affect increased reward expectations for compounds, whereas negative affect decreased reward expectations. Computational modelling and eye-tracking analyses each revealed that these effects were driven by affect-congruent changes in participants' allocation of attention to high- versus low-value features of compound stimuli. These results provide mechanistic insight into a process by which affect produces biases in generalized reward expectations.
A common research design in the field of computational psychiatry involves leveraging the power of online participant recruitment to assess correlations between behavior in cognitive tasks and the self-reported severity of psychiatric symptoms in large, diverse samples. Although large online samples have many advantages for psychiatric research, some potential pitfalls of this research design are not widely understood. Here we detail circumstances in which entirely spurious correlations may arise between task behavior and symptom severity as a result of inadequate screening of careless or low-effort responding on psychiatric symptom surveys. Specifically, since many psychiatric symptom surveys have asymmetric ground-truth score distributions in the general population, participants who respond carelessly on these surveys will show apparently elevated symptom levels. If these participants are similarly careless in their task performance, and are not excluded from analysis, this may result in a spurious association between greater symptom scores and worse behavioral task performance. Here, we demonstrate exactly this pattern of results in N = 386 participants recruited online to complete a self-report symptom battery and a short reversal-learning choice task. We show that many behavior-symptom correlations are entirely abolished when participants flagged for careless responding on surveys are excluded from analysis. We also show that exclusion based on task performance alone is not sufficient to prevent these spurious correlations. Of note, we demonstrate that false-positive rates for these spurious correlations increase with sample size, contrary to common assumptions. We offer guidance on how researchers using this general experimental design can guard against this issue in future research; in particular, we recommend the adoption of screening methods for self-report measures that are currently uncommon in this field.
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