The value of an item is learned through a series of decision-making. The learning process has been investigated using decision-making tasks with a correct answer specified by the external environment (externally guided decision-making, EDM). In this case, 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 the RL in EDM, value is learned through internally guided decision-making (IDM), in which no correct answer defined by external circumstances is available and one has to decide based on one’s own internal criteria, such as preferences. In IDM, it has been believed that the value of chosen item is increased and that of rejected item is decreased, called choice-induced preference change (CIPC). An RL-based model called the choice-based learning (CBL) model has been proposed to describe CIPC, in which the values of chosen and rejected items are updated as if the own choice is regarded as a correct answer. However, the validity of the CBL model has not been confirmed by fitting the model to IDM behavioral data, since the differences of initial preferences among the items make it difficult to estimate the model parameters. The aim of the present study is to examine the CBL model in IDM. To avoid the problem of different initial preferences, we used a preference judgment task with novel contour shapes. Through simulations and a behavioral experiment with model-based analysis, we showed for the first time that the CBL model fits the IDM behavioral data well. Although frequently in preference change only the value of the chosen or rejected item has been reported using subjective preference ratings, we confirmed that both values were changed by applying the computational model to the IDM.