From recurrent choice to skill learning: A reinforcement-learning model

Wai Tat Fu, John R. Anderson

Research output: Contribution to journalArticlepeer-review


The authors propose a reinforcement-learning mechanism as a model for recurrent choice and extend it to account for skill learning. The model was inspired by recent research in neurophysiological studies of the basal ganglia and provides an integrated explanation of recurrent choice behavior and skill learning. The behavior includes effects of differential probabilities, magnitudes, variabilities, and delay of reinforcement. The model can also produce the violation of independence, preference reversals, and the goal gradient of reinforcement in maze learning. An experiment was conducted to study learning of action sequences in a multistep task. The fit of the model to the data demonstrated its ability to account for complex skill learning. The advantages of incorporating the mechanism into a larger cognitive architecture are discussed.

Original languageEnglish (US)
Pages (from-to)184-206
Number of pages23
JournalJournal of Experimental Psychology: General
Issue number2
StatePublished - May 2006


  • Cognitive architecture
  • Credit assignment
  • Recurrent choice
  • Reinforcement learning
  • Skill learning

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • General Psychology
  • Developmental Neuroscience


Dive into the research topics of 'From recurrent choice to skill learning: A reinforcement-learning model'. Together they form a unique fingerprint.

Cite this