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  Interactions between Model-free and Model-based Reinforcement Learning

Dayan, P. (2011). Interactions between Model-free and Model-based Reinforcement Learning. Talk presented at 21st Annual Conference of the Japanese Neural Network Society (JNNS 2011). Okinawa, Japan. 2011-12-15 - 2011-12-17.

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Dayan, P1, Author              
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1External Organizations, ou_persistent22              

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 Abstract: Substantial recent work has explored multiple mechanisms of decision-making in humans and other animals. Functionally and anatomically distinct modules have been identified, and their individual properties have been examined using intricate behavioural and neural tools. I will discuss the background of these studies, and show fMRI results that suggest closer and more complex interactions between the mechanisms than originally conceived. In some circumstances, model-free methods seize control after much less experience than would seem normative; in others, temporal difference prediction errors, which are epiphenomenal for the model-based system, are nevertheless present and apparently effective. Finally, I will show that model-free and model-based methods on occasion both cower in the face of Pavlovian influences, and will try and reconcile this as a form of robust control.

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 Dates: 2011-12
 Publication Status: Published online
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Title: 21st Annual Conference of the Japanese Neural Network Society (JNNS 2011)
Place of Event: Okinawa, Japan
Start-/End Date: 2011-12-15 - 2011-12-17
Invited: Yes

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