Retrospective Model-Based Inference Guides Model-Free Credit Assignment

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Imperial College London

Data Science Institute

William Penney Lab

London, SW7 2AF

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Talk Abstract

To adapt to their environments, organisms need to learn which actions are rewarding in different states of the world. Extensive research in Reinforcement Learning (RL) has shown that organisms cope efficiently with this credit-assignment problem, even when their actions are executed under challenging conditions of state uncertainty. However, little is known about such credit-assignment even for the common case where this uncertainty is subsequently resolved. Such is the case, for example, when you ask for a salary-raise without knowing whether your employer is in a good or a bad mood and a few days later you learn that she was in a bad mood when she approved your request. Does this knowledge modulate your credit assignment? Here, I will examine this question from the perspective of the interaction between habitual (model-free; MF) and goal-directed (model-based; MB) systems. Whereas previous research in RL has mostly focused on an MB prospective-planning function, I will present a novel theory of MB retrospective-inference and an experimental test of this theory based on a novel bandit task. According to our theory, an MB system resolves the uncertainty that prevailed when actions were taken and hence guides MF credit-assignment. In support of our theory, we found that when subject’s momentary uncertainty about which bandit had generated an outcome was resolved by subsequent information, they assigned most of the credit to the bandit they inferred to have been responsible. I will discuss how these findings enrich our knowledge on the variety of MB functions and the scope of MB-MF interactions.


Speaker Biography

Dr Rani Moran earned his BSc (1995-1998) and MSc (1998-2001) in Mathematics from the Hebrew University in Jerusalem. After completing his MSc, he worked in industry, including areas such as communication systems, algorithmic financial trading, and solar energy. During these years, Rani patented several inventions (e.g., efficient solar reflectors) and developed a keen interest in Psychology. He earned a BA in Psychology from the Open University of Israel in 2009 and later decided (much to the chagrin of his friends, colleagues and accountant!) to embark on a PhD in Cognitive Psychology (2011-2015) at Tel-Aviv University. In his PhD research, Rani used his expertise in mathematics and statistical inference to push forward our understanding of control processes in human decision making and episodic memory. After finishing his PhD, Rani joined the Max Planck UCL Centre for Computational Psychiatry and Ageing Research in UCL as a Postdoctoral Research Associate where he studies the interaction between model-free and model-based interaction systems.


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Imperial College London

Data Science Institute

William Penney Lab

London, SW7 2AF

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