Belief updating is indexed by single-trial P3 amplitude: a neurocognitive modelling approach to EEG
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1
The University of Melbourne, Melbourne School of Psychological Sciences, Australia
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2
The University of Melbourne, Department of Finance, Australia
Background
Belief updating is the process whereby agents revise cognitive representations of their environment based on new information. The optimal use of information in such circumstances is prescribed by Bayesian principles. Recent theoretical approaches propose that belief updating, when quantified by Bayesian models of cognition, may be directly related to the amplitude of the P3 component of the event-related potential (ERP) in EEG (Kopp, 2008). An appropriate paradigm has not previously been developed to test this theorem.
Method
64-channel EEG was recorded while 18 participants completed a model-based reinforcement learning task (Doya et al., 2002). In this task, participants learned a mapping between the contrast of a visual stimulus and reward. Participants updated their beliefs regarding this mapping on each trial. We formulated a Bayesian computational model to describe the structure of beliefs, and quantified belief updating as the reduction in entropy of beliefs after feedback. We used robust regression of single-trial EEG to test the relationship between belief update magnitude and P3 amplitude at midline electrodes.
Results
Regression of single-trial EEG data demonstrated a significant positive relationship (p < .01) between belief update magnitude and P3 amplitude at a cluster of frontal midline electrodes.
Conclusions
Our results provide evidence for a close link in individual trials between belief updating, quantified by a Bayesian computational model, and the amplitude of the P3 component of the ERP. The frontal topography of the effect implicates the P3a, a subcomponent of the P3 associated with dopamine release and anterior cingulate cortex activation.
References
Doya, K., et al., (2002). Multiple model-based reinforcement learning. Neural Computation, 14, 1347-1369.
Kopp, B. (2008). The P300 component of the event-related potential and Bayes' theorem. In M. Sun (Ed.), Cognitive Sciences at the Leading Edge, 87-96. New York: Nova Science Publishers.
Keywords:
EEG,
reinforcement learning,
Bayesian,
Computational modelling,
Belief updating,
P3
Conference:
XII International Conference on Cognitive Neuroscience (ICON-XII), Brisbane, Queensland, Australia, 27 Jul - 31 Jul, 2014.
Presentation Type:
Poster
Topic:
Cognition and Executive Processes
Citation:
Bennett
D,
Bode
S and
Murawski
C
(2015). Belief updating is indexed by single-trial P3 amplitude: a neurocognitive modelling approach to EEG.
Conference Abstract:
XII International Conference on Cognitive Neuroscience (ICON-XII).
doi: 10.3389/conf.fnhum.2015.217.00025
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Received:
19 Feb 2015;
Published Online:
24 Apr 2015.
*
Correspondence:
Mr. Daniel Bennett, The University of Melbourne, Melbourne School of Psychological Sciences, Melbourne, Australia, danielbrianbennett@gmail.com