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Disentangling the roles of approach, activation and valence in instrumental and pavlovian responding.


ABSTRACT: Hard-wired, Pavlovian, responses elicited by predictions of rewards and punishments exert significant benevolent and malevolent influences over instrumentally-appropriate actions. These influences come in two main groups, defined along anatomical, pharmacological, behavioural and functional lines. Investigations of the influences have so far concentrated on the groups as a whole; here we take the critical step of looking inside each group, using a detailed reinforcement learning model to distinguish effects to do with value, specific actions, and general activation or inhibition. We show a high degree of sophistication in Pavlovian influences, with appetitive Pavlovian stimuli specifically promoting approach and inhibiting withdrawal, and aversive Pavlovian stimuli promoting withdrawal and inhibiting approach. These influences account for differences in the instrumental performance of approach and withdrawal behaviours. Finally, although losses are as informative as gains, we find that subjects neglect losses in their instrumental learning. Our findings argue for a view of the Pavlovian system as a constraint or prior, facilitating learning by alleviating computational costs that come with increased flexibility.

SUBMITTER: Huys QJ 

PROVIDER: S-EPMC3080848 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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Disentangling the roles of approach, activation and valence in instrumental and pavlovian responding.

Huys Quentin J M QJ   Cools Roshan R   Gölzer Martin M   Friedel Eva E   Heinz Andreas A   Dolan Raymond J RJ   Dayan Peter P  

PLoS computational biology 20110421 4


Hard-wired, Pavlovian, responses elicited by predictions of rewards and punishments exert significant benevolent and malevolent influences over instrumentally-appropriate actions. These influences come in two main groups, defined along anatomical, pharmacological, behavioural and functional lines. Investigations of the influences have so far concentrated on the groups as a whole; here we take the critical step of looking inside each group, using a detailed reinforcement learning model to disting  ...[more]

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