Computational modeling of choice-induced preference change: A Reinforcement-Learning-based approach
| Autor: | Takashi Nakao, Kentaro Katahira, Jianhong Zhu, Makoto Hirakawa, Junya Hashimoto |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
Computer science Social Sciences computer.software_genre Choice Behavior Task (project management) Symmetry Cognition Learning and Memory Mathematical and Statistical Techniques Reinforcement learning Psychology Multidisciplinary Simulation and Modeling Statistics Preference Autocorrelation Physical Sciences Engineering and Technology Medicine Female Reinforcement Psychology Research Article Adult Preference change Adolescent Process (engineering) Cognitive Neuroscience Science Decision Making Geometry Models Psychological Machine learning Research and Analysis Methods Human Learning Young Adult Reaction Time Learning Humans Computer Simulation Statistical Methods Behavior business.industry Cognitive Psychology Biology and Life Sciences Correction Signal Processing Cognitive Science Artificial intelligence business Value (mathematics) computer Mathematics Neuroscience |
| Zdroj: | PLoS ONE PLoS ONE, Vol 16, Iss 1, p e0244434 (2021) |
| ISSN: | 1932-6203 |
| Popis: | The value learning process has been investigated using decision-making tasks with a correct answer specified by the external environment (externally guided decision-making, EDM). In EDM, people are required to adjust their choices based on feedback, and the learning process is generally explained by the reinforcement learning (RL) model. In addition to EDM, value is learned through internally guided decision-making (IDM), in which no correct answer defined by external circumstances is available, such as preference judgment. In IDM, it has been believed that the value of the chosen item is increased and that of the rejected item is decreased (choice-induced preference change; CIPC). An RL-based model called the choice-based learning (CBL) model had been proposed to describe CIPC, in which the values of chosen and/or rejected items are updated as if own choice were the correct answer. However, the validity of the CBL model has not been confirmed by fitting the model to IDM behavioral data. The present study aims to examine the CBL model in IDM. We conducted simulations, a preference judgment task for novel contour shapes, and applied computational model analyses to the behavioral data. The results showed that the CBL model with both the chosen and rejected value’s updated were a good fit for the IDM behavioral data compared to the other candidate models. Although previous studies using subjective preference ratings had repeatedly reported changes only in one of the values of either the chosen or rejected items, we demonstrated for the first time both items’ value changes were based solely on IDM choice behavioral data with computational model analyses. |
| Databáze: | OpenAIRE |
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