Computational EEG modelling of decision making under ambiguity reveals spatio-temporal dynamics of outcome evaluation

• Published in: Behavioural brain research 2017-03, Vol.321, p.28-35
• Main Authors: Jollans, Lee, Whelan, Robert, Venables, Louise, Turnbull, Oliver H., Cella, Matteo, Dymond, Simon
• Format: Article
• Language: English
• Subjects: Adult; Analysis of Variance; Brain - physiology; Computational models; Computer Simulation; Decision making; Decision Making - physiology; EEG; Electroencephalography; Evoked Potentials; Female; Gambling - physiopathology; Humans; Iowa gambling task; Linear Models; Male; Models, Neurological; Neuropsychological Tests; Probability; Time Factors; Young Adult
• ISSN: 0166-4328; 1872-7549
• DOI: 10.1016/j.bbr.2016.12.033

•Complex human cognition is reflected in dynamic spatio-temporal activity.•We combined event-related potentials with computational modelling.•A general linear model created a three-dimensional map of neural dynamics. Complex human cognition, such as decision-making under ambiguity, is reflected in dynamic spatio-temporal activity in the brain. Here, we combined event-related potentials with computational modelling of the time course of decision-making and outcome evaluation during the Iowa Gambling Task. Measures of choice probability generated using the Prospect Valence Learning Delta (PVL-Delta) model, in addition to objective trial outcomes (outcome magnitude and valence), were applied as regressors in a general linear model of the EEG signal. The resulting three-dimensional spatio-temporal characterization of task-related neural dynamics demonstrated that outcome valence, outcome magnitude, and PVL-Delta choice probability were expressed in distinctly separate event related potentials. Our findings showed that the P3 component was associated with an experience-based measure of outcome expectancy.