scholarly journals Test-retest reliability of canonical reinforcement learning models

2019 ◽  
Author(s):  
Laura Weidinger ◽  
Andrea Gradassi ◽  
Lucas Molleman ◽  
Wouter van den Bos
Keyword(s):  
Reinforcement Learning ◽  
Learning Models ◽  
Retest Reliability ◽  
Reinforcement Learning Models ◽  
Test Retest Reliability

Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reinforcement Learning ◽  
Iowa Gambling Task ◽  
Bayes Factors ◽  
Gambling Task ◽  
Learning Models ◽  
Reinforcement Learning Models

scholarly journals Parallel model-based and model-free reinforcement learning for card sorting performance

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexander Steinke ◽  
Florian Lange ◽  
Bruno Kopp
Keyword(s):  
Reinforcement Learning ◽  
Cognitive Flexibility ◽  
Theoretical Perspective ◽  
Behavioral Flexibility ◽  
Wisconsin Card Sorting Test ◽  
Card Sorting ◽  
Learning Models ◽  
Model Based ◽  
Model Free ◽  
Reinforcement Learning Models

Abstract The Wisconsin Card Sorting Test (WCST) is considered a gold standard for the assessment of cognitive flexibility. On the WCST, repeating a sorting category following negative feedback is typically treated as indicating reduced cognitive flexibility. Therefore such responses are referred to as ‘perseveration’ errors. Recent research suggests that the propensity for perseveration errors is modulated by response demands: They occur less frequently when their commitment repeats the previously executed response. Here, we propose parallel reinforcement-learning models of card sorting performance, which assume that card sorting performance can be conceptualized as resulting from model-free reinforcement learning at the level of responses that occurs in parallel with model-based reinforcement learning at the categorical level. We compared parallel reinforcement-learning models with purely model-based reinforcement learning, and with the state-of-the-art attentional-updating model. We analyzed data from 375 participants who completed a computerized WCST. Parallel reinforcement-learning models showed best predictive accuracies for the majority of participants. Only parallel reinforcement-learning models accounted for the modulation of perseveration propensity by response demands. In conclusion, parallel reinforcement-learning models provide a new theoretical perspective on card sorting and it offers a suitable framework for discerning individual differences in latent processes that subserve behavioral flexibility.

Sign in / Sign up

Export Citation Format

Share Document