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A Hierarchical Reinforcement Learning Model Explains Individual Differences in Attentional Set Shifting

View ORCID ProfileAnahita Talwar, View ORCID ProfileQuentin Huys, View ORCID ProfileFrancesca Cormack, View ORCID ProfileJonathan P Roiser
doi: https://doi.org/10.1101/2021.10.05.463165
Anahita Talwar
1Neuroscience and Mental Health Group, UCL Institute of Cognitive Neuroscience, 17-19 Queen Square, London, WC1N 3AZ
3Cambridge Cognition Ltd, Tunbridge Court, Bottisham, Cambridge, CB25 9TU
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  • For correspondence: anahita.talwar.15@ucl.ac.uk
Quentin Huys
2Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Russell Square House, London, WC1B 5EH
4Division of Psychiatry, University College London, Maple House, 149 Tottenham Court Rd, London W1T 7BN
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Francesca Cormack
3Cambridge Cognition Ltd, Tunbridge Court, Bottisham, Cambridge, CB25 9TU
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Jonathan P Roiser
1Neuroscience and Mental Health Group, UCL Institute of Cognitive Neuroscience, 17-19 Queen Square, London, WC1N 3AZ
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Abstract

Attentional set shifting refers to the ease with which the focus of attention is directed and switched. Cognitive tasks such as CANTAB IED reveal great variation in set shifting ability in the general population, with notable impairments in those with psychiatric diagnoses. The attentional and learning processes underlying this cognitive ability, and how they lead to the observed variation remain unknown. To directly test this, we used a modelling approach on two independent large-scale online general-population samples performing CANTAB IED and psychiatric symptom assessment. We found a hierarchical model that learnt both feature values and dimension attention best explained the data, and that compulsive symptoms were associated with slower learning and higher attentional bias to the first relevant stimulus dimension. This data showcase a new methodology to analyse data from the CANTAB IED task, and suggest a possible mechanistic explanation for the variation in set shifting performance, and its relationship to compulsive symptoms.

Competing Interest Statement

The authors thank the MRC and Cambridge Cognition Ltd. who funded this work jointly through an MRC iCASE studentship grant MR/R015759/1. Cambridge Cognition employ FC and completed the data collection in this research.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 05, 2021.
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A Hierarchical Reinforcement Learning Model Explains Individual Differences in Attentional Set Shifting
Anahita Talwar, Quentin Huys, Francesca Cormack, Jonathan P Roiser
bioRxiv 2021.10.05.463165; doi: https://doi.org/10.1101/2021.10.05.463165
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A Hierarchical Reinforcement Learning Model Explains Individual Differences in Attentional Set Shifting
Anahita Talwar, Quentin Huys, Francesca Cormack, Jonathan P Roiser
bioRxiv 2021.10.05.463165; doi: https://doi.org/10.1101/2021.10.05.463165

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