Computational modeling of choice-induced preference change: A Reinforcement-Learning-based approach

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.

Computational modeling of Choice-Induced Preference Change: A Reinforcement-Learning-based approach

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.

Exploratory Hand Movements Enhance the Liking Effect in Haptics

When people choose between two items, they usually look at them alternately before deciding. The frequency and duration of contact are usually determined unconsciously. However, in a previous study, looking at one item for longer than the other increased participants’ preference for the former, but only when they had to move their eyes to look at each item. This result implies that eye movements not only gather information but are also closely related to decision-making. By analogy, this study examines the relation between hand movements and haptic preference. When participants touched two handkerchiefs in a pre-determined order before choosing the one they preferred, the likelihood of choosing the more frequently touched handkerchief was greater than chance. Bias in the choice was greater with increased difference in the frequency of touching between the two handkerchiefs. It was also greater when participants moved their arm to touch the handkerchiefs, compared with when a machine carried the handkerchiefs to their hand. These results indicate that both the reaching movement for touching and the frequency of touching affect the preference judgment using haptics.