Rumination Derails Reinforcement Learning With Possible Implications for Ineffective Behavior

Peter Hitchcock, Evan Forman, Nina Rothstein, Fengqing Zhang, John Kounios, Yael Niv, Chris Sims

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


How does rumination affect reinforcement learning—the ubiquitous process by which people adjust behavior after error to behave more effectively in the future? In a within-subjects design (N = 49), we tested whether experimentally manipulated rumination disrupts reinforcement learning in a multidimensional learning task previously shown to rely on selective attention. Rumination impaired performance, yet unexpectedly, this impairment could not be attributed to decreased attentional breadth (quantified using a decay parameter in a computational model). Instead, trait rumination (between subjects) was associated with higher decay rates (implying narrower attention) but not with impaired performance. Our task-performance results accord with the possibility that state rumination promotes stress-generating behavior in part by disrupting reinforcement learning. The trait-rumination finding accords with the predictions of a prominent model of trait rumination (the attentional-scope model). More work is needed to understand the specific mechanisms by which state rumination disrupts reinforcement learning.

Original languageEnglish (US)
Pages (from-to)714-733
Number of pages20
JournalClinical Psychological Science
Issue number4
StatePublished - Jul 2022

All Science Journal Classification (ASJC) codes

  • Clinical Psychology


  • adaptive behavior
  • attention
  • computational modeling
  • computational psychiatry
  • reinforcement learning
  • rumination


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