Ahilan, S;
Solomon, RB;
Breton, Y-A;
Conover, K;
Niyogi, RK;
Shizgal, P;
Dayan, P;
(2019)
Learning to use past evidence in a sophisticated world model.
PLOS Computational Biology
, 15
(6)
, Article e1007093. 10.1371/journal.pcbi.1007093.
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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.
| Type: | Article |
|---|---|
| Title: | Learning to use past evidence in a sophisticated world model |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pcbi.1007093 |
| Publisher version: | https://doi.org/10.1371/journal.pcbi.1007093 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10082065 |
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