Abstract
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.
Highlights
Guided decision-making (EDM) and computational modelingThe value learning process used by humans and animals has been investigated using a decision-making task with a correct answer specified by the external environment
In Simulation 1, we confirmed that the parameters of the computational model during the generation of artificial data could be properly calculated by applying the same computational model to the artificial data
When each model was used to generate artificial data and the respective model showed better fit to the data it generated than the other models, that model can be regarded as able to recover the true model from the data
Summary
Tβ-CBL model: In this model, β increases with increasing experiences (i.e., the number of times the items are presented). F-CBL model: In this model, the value V of the items i that did not present in each trial t was updated as follows: Viðtþ1Þ 1⁄4 ð1 À aFÞ ViðtÞ if i was not presented ð10Þ where αF is the forgetting factor which modulates the degree of attenuation of the value. The forgetting factor was included by following the previous EDM study [33]. In this model, the forgetting factor was applied to unchosen items, unlike the present study. The simulations for parameter and model recovery were conducted in the same way as Study 1. Parameter recovery in Simulation 3 was conducted for the Tβ-CBL and F-CBL models
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