Optimal models of decision-making in dynamic environments. Journal Article uri icon

Overview

abstract

  • Nature is in constant flux, so animals must account for changes in their environment when making decisions. How animals learn the timescale of such changes and adapt their decision strategies accordingly is not well understood. Recent psychophysical experiments have shown humans and other animals can achieve near-optimal performance at two alternative forced choice (2AFC) tasks in dynamically changing environments. Characterization of performance requires the derivation and analysis of computational models of optimal decision-making policies on such tasks. We review recent theoretical work in this area, and discuss how models compare with subjects' behavior in tasks where the correct choice or evidence quality changes in dynamic, but predictable, ways.

publication date

  • July 18, 2019

Full Author List

  • Kilpatrick ZP; Holmes WR; Eissa TL; Josić K

Other Profiles

Additional Document Info

start page

  • 54

end page

  • 60

volume

  • 58