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Learning to use past evidence in a sophisticated world model.

Author(s): Ahilan S, Solomon RB, Breton YA, Conover K, Niyogi RK, Shizgal P, Dayan P

PLoS Comput Biol. 2019 Jun 24;15(6):e1007093 Authors: Ahilan S, Solomon RB, Breton YA, Conover K, Niyogi RK, Shizgal P, Dayan P

Article GUID: 31233559
Some work and some play: microscopic and macroscopic approaches to labor and leisure.

Author(s): Niyogi RK, Shizgal P, Dayan P

PLoS Comput Biol. 2014 Dec;10(12):e1003894 Authors: Niyogi RK, Shizgal P, Dayan P

Article GUID: 25474151
Title:Learning to use past evidence in a sophisticated world model.
Authors:Ahilan S,  Solomon RB,  Breton YA,  Conover K,  Niyogi RK,  Shizgal P,  Dayan P
Link:https://www.ncbi.nlm.nih.gov/pubmed/31233559?dopt=Abstract
DOI:10.1371/journal.pcbi.1007093
Category:PLoS Comput Biol
PMID:31233559
Dept Affiliation: CSBN
1 Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom.
2 Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, Canada.
3 Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom.
4 Max Planck Institute for Biological Cybernetics, Tübingen, Germany.

Description:

Learning to use past evidence in a sophisticated world model.

PLoS Comput Biol. 2019 Jun 24;15(6):e1007093

Authors: Ahilan S, Solomon RB, Breton YA, Conover K, Niyogi RK, Shizgal P, Dayan P

Abstract

Humans and other animals are able to discover underlying statistical structure in their environments and exploit it to achieve efficient and effective performance. However, such structure is often difficult to learn and use because it is obscure, involving long-range temporal dependencies. Here, we analysed behavioural data from an extended experiment with rats, showing that the subjects learned the underlying statistical structure, albeit suffering at times from immediate inferential imperfections as to their current state within it. We accounted for their behaviour using a Hidden Markov Model, in which recent observations are integrated with evidence from the past. We found that over the course of training, subjects came to track their progress through the task more accurately, a change that our model largely attributed to improved integration of past evidence. This learning reflected the structure of the task, decreasing reliance on recent observations, which were potentially misleading.

PMID: 31233559 [PubMed - as supplied by publisher]