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Learning the volatility of a dynamic environment

Banff International Research Station for Mathematical Innovation and Discovery

Humans and other animals make perceptual decisions based on noisy sensory input. Recent studies focus on ecologically realistic situations in which the correct choice or the informative features of the stimulus change dynamically. Importantly, optimal evidence accumulation in changing environments requires discounting prior evidence at a rate determined by environmental volatility. To explain these observations, we extend previous accumulator models of decision making to the case where the correct choice changes at an unknown rate. An ideal observer can optimally infer these transition rates and accumulate evidence to make the best decision. We also discuss a neural implementation for this inference process whereby Hebbian plasticity shapes connectivity between populations representing each choice. Coauthors: Adrian Radillo, Alan Veliz-Cuba, Kresimir Josic

Mathematics; Biology and other natural sciences; Probability theory and stochastic processes; Mathematical biology

video/mp4

Author affiliation: University of Houston

2016-06-09

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58274

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/