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Glitz, Leonie, Juechems, Keno, Summerfield, Christopher and Garrett, Neil 
ORCID: https://orcid.org/0000-0003-1440-472X
(2022)
Model sharing in the human medial temporal lobe.
The Journal of Neuroscience, 42 (27).
pp. 5410-5426.
ISSN 0270-6474
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Abstract
Effective planning involves knowing where different actions take us. However, natural environments are rich and complex, leading to an exponential increase in memory demand as a plan grows in depth. One potential solution is to filter out features of the environment irrelevant to the task at hand. This enables a shared model of transition dynamics to be used for planning over a range of different input features. Here, we asked human participants (13 male, 16 female) to perform a sequential decision-making task, designed so that knowledge should be integrated independently of the input features (visual cues) present in one case but not in another. Participants efficiently switched between using a low-dimensional (cue independent) and a high-dimensional (cue specific) representation of state transitions. fMRI data identified the medial temporal lobe as a locus for learning state transitions. Within this region, multivariate patterns of BOLD responses were less correlated between trials with differing input features but similar state associations in the high dimensional than in the low dimensional case, suggesting that these patterns switched between separable (specific to input features) and shared (invariant to input features) transition models. Finally, we show that transition models are updated more strongly following the receipt of positive compared with negative outcomes, a finding that challenges conventional theories of planning. Together, these findings propose a computational and neural account of how information relevant for planning can be shared and segmented in response to the vast array of contextual features we encounter in our world.
| Item Type: | Article |
|---|---|
| Additional Information: | Funding Information: This research was funded in part by the Wellcome Trust (Sir Henry Wellcome Postdoctoral Fellowship to N.G., Grant 209108/Z/17/Z), a European Research Council grant (ERC Consolidator Award: 725937) to C.S., support from the Human Brain Project (Special Grant Agreement No: 945539) to C.S. and a Waverley Scholarship to L.G. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Rights retention statement: N.G. has applied a CC BY public copyright license to any author accepted manuscript version arising from this submission. |
| Uncontrolled Keywords: | rsa,model based,planning,reinforcement learning,medicine(all) ,/dk/atira/pure/subjectarea/asjc/2700 |
| Faculty \ School: | Faculty of Social Sciences > School of Psychology |
| UEA Research Groups: | Faculty of Social Sciences > Research Groups > Cognition, Action and Perception Faculty of Social Sciences > Research Groups > Social Cognition Research Group |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 15 Jun 2022 11:31 |
| Last Modified: | 11 Nov 2024 00:58 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/85635 |
| DOI: | 10.1523/JNEUROSCI.1978-21.2022 |
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