Xiang, T;
Ray, D;
Lohrenz, T;
Dayan, P;
Montague, PR;
(2012)
Computational phenotyping of two-person interactions reveals differential neural response to depth-of-thought.
PLoS Comput Biol
, 8
(12)
, Article e1002841. 10.1371/journal.pcbi.1002841.
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Abstract
Reciprocating exchange with other humans requires individuals to infer the intentions of their partners. Despite the importance of this ability in healthy cognition and its impact in disease, the dimensions employed and computations involved in such inferences are not clear. We used a computational theory-of-mind model to classify styles of interaction in 195 pairs of subjects playing a multi-round economic exchange game. This classification produces an estimate of a subject's depth-of-thought in the game (low, medium, high), a parameter that governs the richness of the models they build of their partner. Subjects in each category showed distinct neural correlates of learning signals associated with different depths-of-thought. The model also detected differences in depth-of-thought between two groups of healthy subjects: one playing patients with psychiatric disease and the other playing healthy controls. The neural response categories identified by this computational characterization of theory-of-mind may yield objective biomarkers useful in the identification and characterization of pathologies that perturb the capacity to model and interact with other humans.
| Type: | Article |
|---|---|
| Title: | Computational phenotyping of two-person interactions reveals differential neural response to depth-of-thought. |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pcbi.1002841 |
| Publisher version: | http://dx.doi.org/10.1371/journal.pcbi.1002841 |
| Language: | English |
| Additional information: | © 2012 Xiang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by a Wellcome Trust Principal Research Fellowship (PRM), The Kane Family Foundation (PRM), NIDA grant R01DA11723 (PRM), NIMH grant R01MH085496 (PRM), NIA grant RC4AG039067 (PRM), and The Gatsby Charitable Foundation (DR, PD). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. |
| Keywords: | Analysis of Variance, Biosensing Techniques, Central Nervous System, Computational Biology, Humans, Interpersonal Relations, Reference Values |
| UCL classification: | UCL 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 Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1382897 |
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