Rumination Derails Reinforcement Learning With Possible Implications for Ineffective Behavior. (July 2022)

• Record Type: Journal Article
• Authors: Hitchcock, Peter; Forman, Evan; Rothstein, Nina; Zhang, Fengqing; Kounios, John; Niv, Yael; Sims, Chris
• Is Part Of: Clinical psychological science. Volume 10:Number 4(2022)
• Page Start: 714
• Page End: 733
• Publication Date: 2022-07
• Subjects: rumination -- reinforcement learning -- adaptive behavior -- attention -- computational modeling -- computational psychiatry; Clinical psychology -- Periodicals; 616.89
• DOI: 10.1177/21677026211051324
• Languages: English
• ISSNs: 2167-7026

Abstract

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.