• Wed, Jun 12th 2019 at 15:00 - 16:00 UTC (Other timezones)
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One of the key goals of computational psychiatry is to reveal how individuals differ from one another in the algorithms they implement to solve fundamental decision-making problems. Delineating algorithmic individual differences has the potential to explain why some people are more likely than others to develop a given psychiatric disorder, and which patients are more likely to respond to a given treatment. Towards this goal, most research has so far primarily relied on computational modeling of the choices that people make in a range of appropriately designed scenarios. However, while such choices have been immensely useful in revealing the trial-to-trial evolution of learning and decision-making processes, they carry little information about the sub-second timescale over which each individual decision evolves. In this talk, I will show how this gap can be bridged by complementing choice data with the decoding of magnetoencephalographic (MEG) signals. I will use MEG decoding to examine how representations of all aspects of a given decision problem – stimuli, choices and rewards – evolve from moment to moment as the subject reaches their choice. This method will be used to gain a better understanding of how people differ from one another in how they cope with two different scenarios, one that contrasts parallel and serial processing, and one that contrasts model-free and model-based strategies.

Eran Eldar

Senior Lecturer (assistant professor equivalent)
Department of Psychology
Department of Cognitive Sciences
Hebrew University of Jerusalem

Honorary Research Associate
Max Planck UCL Centre for Computational Psychiatry and Ageing Research
Wellcome Centre for Human Neuroimaging
University College London

Eran Eldar – Representational dynamics of distinct decision strategies