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CCN Seminar Series 2019-20

CCN seminar intra-brunel photo

It is our pleasure to invite you to our first research talk in this year’s CCN Seminar Series.

Speaker: Dr Miriam Klein-Flügge, University of Oxford, http://users.ox.ac.uk/~xpsy0747/

Title: Multiple neural mechanisms of decision making and knowledge acquisition

Place: LECT 110, THE LECTURE CENTRE, Brunel University London

Date: Thu, 17 October 2019 (next week) Time: 3 – 5pm, incl drinks and nibbles after the talk

Please forward this email to anyone potentially interested. For more details, please see below.

Multiple neural mechanisms of decision making and knowledge acquisition

Dr Miriam C Klein-Flugge, Department of Experimental Psychology and Wellcome Centre for Integrative Neuroimaging, University of Oxford

In this talk, I will present some of our recent work on the neural mechanisms underlying decision making and learning. Optimal decision-making relies on an integration of costs and benefits. The first part of the talk will focus on decisions requiring a trade-off between physical effort and reward. In this work, healthy volunteers made decisions between choice options that varied in their associated monetary reward and grip force. We developed a behavioural model to describe individual effort discounting preferences and which captures how much effort an individual is willing to invest for a given incentive. We then used functional magnetic resonance imaging (fMRI) to identify regions that signal the trade-off between the two choice options. We found that a region in midcingulate cortex fulfils the criteria for implementing this trade-off. By contrast, separate signals in supplementary motor area and ventromedial prefrontal cortex correlated with participants’ tendency to avoid effort and seek reward, respectively, suggesting that distinct frontal circuits drive behaviour towards reward-maximization and effort-minimization.

The second part of the talk will focus on multiple ways in which we learn about the world, which ultimately informs our decision-making. Humans and animals can learn from reward, but they also learn by observing statistical relationships in the world. While much is known about the neural encoding of updating signals during learning, there is relatively little knowledge on where and how such learnt representations are stored. I will present work exploring the neural representations or ‘associative structures’ created by multiple different learning mechanisms using human fMRI. We find that knowledge encoded via model-free RL is dissociable, neurally, from the encoding of statistically learnt relationships.

  Samantha Thomasson