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Don't Think, Just Feel the Music: Individuals with Strong Pavlovian-to-Instrumental Transfer Effects Rely Less on Model-based Reinforcement Learning.

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Title: Don't Think, Just Feel the Music: Individuals with Strong Pavlovian-to-Instrumental Transfer Effects Rely Less on Model-based Reinforcement Learning.
Authors: Sebold, Miriam, Schad, Daniel J., Nebe, Stephan, Garbusow, Maria, Jünger, Elisabeth, Kroemer, Nils B., Kathmann, Norbert, Zimmermann, Ulrich S., Smolka, Michael N., Rapp, Michael A., Heinz, Andreas, Huys, Quentin J. M.
Source: Journal of Cognitive Neuroscience. 2016, Vol. 28 Issue 7, p985-995. 11p. 1 Diagram, 2 Charts, 3 Graphs.
Subjects: Music psychology, Classical conditioning, Reinforcement learning, Instrumental music, Experience, Psychology
Abstract: Behavioral choice can be characterized along two axes. One axis distinguishes reflexive, model-free systems that slowly accumulate values through experience and a model-based system that uses knowledge to reason prospectively. The second axis distinguishes Pavlovian valuation of stimuli from instrumental valuation of actions or stimulus-action pairs. This results in four values and many possible interactions between them, with important consequences for accounts of individual variation. We here explored whether individual variation along one axis was related to individual variation along the other. Specifically, we asked whether individuals' balance between model-based and model-free learning was related to their tendency to show Pavlovian interferences with instrumental decisions. In two independent samples with a total of 243 participants, Pavlovian- instrumental transfer effects were negatively correlated with the strength of model-based reasoning in a two-step task. This suggests a potential common underlying substrate predisposing individuals to both have strong Pavlovian interference and be less model-based and provides a framework within which to interpret the observation of both effects in addiction. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Cognitive Neuroscience is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Don't Think, Just Feel the Music: Individuals with Strong Pavlovian-to-Instrumental Transfer Effects Rely Less on Model-based Reinforcement Learning.
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  Data: <searchLink fieldCode="AR" term="%22Sebold%2C+Miriam%22">Sebold, Miriam</searchLink><br /><searchLink fieldCode="AR" term="%22Schad%2C+Daniel+J%2E%22">Schad, Daniel J.</searchLink><br /><searchLink fieldCode="AR" term="%22Nebe%2C+Stephan%22">Nebe, Stephan</searchLink><br /><searchLink fieldCode="AR" term="%22Garbusow%2C+Maria%22">Garbusow, Maria</searchLink><br /><searchLink fieldCode="AR" term="%22Jünger%2C+Elisabeth%22">Jünger, Elisabeth</searchLink><br /><searchLink fieldCode="AR" term="%22Kroemer%2C+Nils+B%2E%22">Kroemer, Nils B.</searchLink><br /><searchLink fieldCode="AR" term="%22Kathmann%2C+Norbert%22">Kathmann, Norbert</searchLink><br /><searchLink fieldCode="AR" term="%22Zimmermann%2C+Ulrich+S%2E%22">Zimmermann, Ulrich S.</searchLink><br /><searchLink fieldCode="AR" term="%22Smolka%2C+Michael+N%2E%22">Smolka, Michael N.</searchLink><br /><searchLink fieldCode="AR" term="%22Rapp%2C+Michael+A%2E%22">Rapp, Michael A.</searchLink><br /><searchLink fieldCode="AR" term="%22Heinz%2C+Andreas%22">Heinz, Andreas</searchLink><br /><searchLink fieldCode="AR" term="%22Huys%2C+Quentin+J%2E+M%2E%22">Huys, Quentin J. M.</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Cognitive+Neuroscience%22">Journal of Cognitive Neuroscience</searchLink>. 2016, Vol. 28 Issue 7, p985-995. 11p. 1 Diagram, 2 Charts, 3 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Music+psychology%22">Music psychology</searchLink><br /><searchLink fieldCode="DE" term="%22Classical+conditioning%22">Classical conditioning</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Instrumental+music%22">Instrumental music</searchLink><br /><searchLink fieldCode="DE" term="%22Experience%22">Experience</searchLink><br /><searchLink fieldCode="DE" term="%22Psychology%22">Psychology</searchLink>
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  Data: Behavioral choice can be characterized along two axes. One axis distinguishes reflexive, model-free systems that slowly accumulate values through experience and a model-based system that uses knowledge to reason prospectively. The second axis distinguishes Pavlovian valuation of stimuli from instrumental valuation of actions or stimulus-action pairs. This results in four values and many possible interactions between them, with important consequences for accounts of individual variation. We here explored whether individual variation along one axis was related to individual variation along the other. Specifically, we asked whether individuals' balance between model-based and model-free learning was related to their tendency to show Pavlovian interferences with instrumental decisions. In two independent samples with a total of 243 participants, Pavlovian- instrumental transfer effects were negatively correlated with the strength of model-based reasoning in a two-step task. This suggests a potential common underlying substrate predisposing individuals to both have strong Pavlovian interference and be less model-based and provides a framework within which to interpret the observation of both effects in addiction. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Cognitive Neuroscience is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 2016
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