The effects of induced positive and negative affect on Pavlovian-instrumental interactions

Isla Weber, Sam Zorowitz, Yael Niv, Daniel Bennett

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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 behavioural approach and negative affect with avoidance. Based on these theories, we predicted that individuals’ positive and negative affect levels should particularly influence their behaviour when innate Pavlovian approach/avoidance tendencies conflict with learned instrumental behaviours. 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 behaviour or Pavlovian-instrumental interactions. However, we did find evidence that the effects of induced affect on behaviour were moderated by individual differences in self-reported behavioural 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 behaviour.

Original languageEnglish (US)
Pages (from-to)1343-1360
Number of pages18
JournalCognition and Emotion
Issue number7
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)


  • Pavlovian learning
  • Reinforcement learning
  • computational modelling
  • experimental affect induction
  • instrumental learning


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