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  Predicting decisions in human social interactions using real-time fMRI and pattern classification

Hollmann, M., Rieger, J. W., Baecke, S., Lützkendorf, R., Müller, C., Adolf, D., et al. (2011). Predicting decisions in human social interactions using real-time fMRI and pattern classification. PLoS One, 6(10): e25304. doi:10.1371/journal.pone.0025304.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0012-2B02-2 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-C3F4-0
Genre: Journal Article

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Hollmann_PredictingDecisions.pdf (Publisher version), 527KB
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2011
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© 2011 Hollmann 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.

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 Creators:
Hollmann, Maurice1, Author              
Rieger, Jochem W.2, Author
Baecke, Sebastian3, Author
Lützkendorf, Ralf3, Author
Müller, Charles3, Author
Adolf, Daniela3, Author
Bernarding, Johannes3, Author
Villoslada, Pablo4, Editor
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Department of Neurology, Medical Faculty, University Magdeburg, Magdeburg, Germany, ou_persistent22              
3Medical Faculty, Institute for Biometry and Medical Computer Science, University Magdeburg, Magdeburg, Germany, ou_persistent22              
4Institute Biomedical Research August Pi Sunyer (IDIBAPS)-Hospital Clinic of Barcelona, Spain, ou_persistent22              

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 Abstract: Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI) and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives.

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Language(s): eng - English
 Dates: 2011-08-312011-10-07
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
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 Identifiers: PMC: 3189203
PMID: 22003388
Other: publisher-idPONE-D-11-06363
DOI: 10.1371/journal.pone.0025304
URI: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3189203&tool=pmcentrez&rendertype=abstract
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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Sciene
Pages: - Volume / Issue: 6 (10) Sequence Number: e25304 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: /journals/resource/1000000000277850