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Planning Complexity Registers as a Cost in Metacontrol.

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Title: Planning Complexity Registers as a Cost in Metacontrol.
Authors: Kool, Wouter, Gershman, Samuel J., Cushman, Fiery A.
Source: Journal of Cognitive Neuroscience. 2018, Vol. 30 Issue 10, p1391-1404. 14p. 1 Diagram, 1 Chart, 4 Graphs.
Subjects: Decision making, Algorithms, Learning, Reinforcement (Psychology), Conjoint analysis
Abstract: Decision-making algorithms face a basic tradeoff between accuracy and effort (i.e., computational demands). It is widely agreed that humans can choose between multiple decision-making processes that embody different solutions to this tradeoff: Some are computationally cheap but inaccurate, whereas others are computationally expensive but accurate. Recent progress in understanding this tradeoff has been catalyzed by formalizing it in terms of model-free (i.e., habitual) versus model-based (i.e., planning) approaches to reinforcement learning. Intuitively, if two tasks offer the same rewards for accuracy but one of them is much more demanding, we might expect people to rely on habit more in the difficult task: Devoting significant computation to achieve slight marginal accuracy gains would not be “worth it.” We test and verify this prediction in a sequential reinforcement learning task. Because our paradigm is amenable to formal analysis, it contributes to the development of a computational model of how people balance the costs and benefits of different decision-making processes in a task-specific manner; in other words, how we decide when hard thinking is worth it. [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: Planning Complexity Registers as a Cost in Metacontrol.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Cognitive+Neuroscience%22">Journal of Cognitive Neuroscience</searchLink>. 2018, Vol. 30 Issue 10, p1391-1404. 14p. 1 Diagram, 1 Chart, 4 Graphs.
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  Data: Decision-making algorithms face a basic tradeoff between accuracy and effort (i.e., computational demands). It is widely agreed that humans can choose between multiple decision-making processes that embody different solutions to this tradeoff: Some are computationally cheap but inaccurate, whereas others are computationally expensive but accurate. Recent progress in understanding this tradeoff has been catalyzed by formalizing it in terms of model-free (i.e., habitual) versus model-based (i.e., planning) approaches to reinforcement learning. Intuitively, if two tasks offer the same rewards for accuracy but one of them is much more demanding, we might expect people to rely on habit more in the difficult task: Devoting significant computation to achieve slight marginal accuracy gains would not be “worth it.” We test and verify this prediction in a sequential reinforcement learning task. Because our paradigm is amenable to formal analysis, it contributes to the development of a computational model of how people balance the costs and benefits of different decision-making processes in a task-specific manner; in other words, how we decide when hard thinking is worth it. [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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        Value: 10.1162/jocn_a_01263
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        Text: English
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      – SubjectFull: Decision making
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Learning
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      – TitleFull: Planning Complexity Registers as a Cost in Metacontrol.
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              Text: 2018
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