The successor representation and temporal context.

Abstract:

:The successor representation was introduced into reinforcement learning by Dayan ( 1993 ) as a means of facilitating generalization between states with similar successors. Although reinforcement learning in general has been used extensively as a model of psychological and neural processes, the psychological validity of the successor representation has yet to be explored. An interesting possibility is that the successor representation can be used not only for reinforcement learning but for episodic learning as well. Our main contribution is to show that a variant of the temporal context model (TCM; Howard & Kahana, 2002 ), an influential model of episodic memory, can be understood as directly estimating the successor representation using the temporal difference learning algorithm (Sutton & Barto, 1998 ). This insight leads to a generalization of TCM and new experimental predictions. In addition to casting a new normative light on TCM, this equivalence suggests a previously unexplored point of contact between different learning systems.

journal_name

Neural Comput

journal_title

Neural computation

authors

Gershman SJ,Moore CD,Todd MT,Norman KA,Sederberg PB

doi

10.1162/NECO_a_00282

subject

Has Abstract

pub_date

2012-06-01 00:00:00

pages

1553-68

issue

6

eissn

0899-7667

issn

1530-888X

journal_volume

24

pub_type

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