Planning Complexity Registers as a Cost in Metacontrol.
J Cogn Neurosci
; 30(10): 1391-1404, 2018 10.
Article
en En
| MEDLINE
| ID: mdl-29668390
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.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Desempeño Psicomotor
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Aprendizaje Seriado
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Toma de Decisiones
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Anticipación Psicológica
Tipo de estudio:
Health_economic_evaluation
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Prognostic_studies
Límite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
J Cogn Neurosci
Asunto de la revista:
NEUROLOGIA
Año:
2018
Tipo del documento:
Article