Challenges from Probabilistic Learning for Models of Brain and Behavior

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Probabilistic learning is a research program that aims to understand how animals and humans learn and adapt their behavior in situations where the pairing between cues and outcomes is not always completely reliable. This chapter provides an overview of the challenges of probabilistic learning for models of the brain and behavior. We discuss the historical background of probabilistic learning, its theoretical foundations, and its applications in various fields such as psychology, neuroscience, and artificial intelligence. We also review some key findings from experimental studies on probabilistic learning, including the role of feedback, attention, memory, and decision-making processes. Finally, we highlight some of the current debates and future directions in this field.

Original languageEnglish
Title of host publicationSTEAM-H
Subtitle of host publicationScience, Technology, Engineering, Agriculture, Mathematics and Health
PublisherSpringer Nature
Pages73-84
Number of pages12
DOIs
StatePublished - 2023
Externally publishedYes

Publication series

NameSTEAM-H: Science, Technology, Engineering, Agriculture, Mathematics and Health
VolumePart F1986
ISSN (Print)2520-193X
ISSN (Electronic)2520-1948

Keywords

  • Category learning
  • Cognitive models
  • Decision-making
  • Feedback
  • Probabilistic learning

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