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Most experimental studies of depressive symptom effects on decision-making have examined situations in which a single correct answer exists based on external circumstances (externally guided decision-making, e.g., gambling task). In addition to such decision-making, for decision-making of other types, no correct answer exists based on external circumstances (internally guided decision-making, e.g., preference judgment). For internally guided decision-making, a phenomenon is known by which preference for the chosen item increases and preference for the rejected item is decreased after choosing between two equally preferred items which is designated as choice-induced preference change. Recent reports suggest that this phenomenon is explainable by reinforcement learning theory just as it is with externally guided decision-making. Although many earlier studies have revealed the effects of depression in externally guided decision-making, the relation between depressive symptoms and choice-induced preference change remains unclear. This study investigated the relation between depressive symptoms and choice-induced preference change using the blind choice paradigm. Results show that depressive symptoms are correlated with change in preference of rejected items (Spearman’s r = .28, p = .04): depressed individuals tend to show less decreased preference of rejected items. These results indicate that individual differences of depressive symptoms affect choice-induced preference change. We discuss the mechanisms underlying the relation between depression and choice-induced preference change.

The value of an item is learned through the decision-making sequence. The learning process has been investigated separately in the contexts of internally guided decision-making (IDM, e.g., preference judgment) and externally guided decision-making (EDM, e.g., gambling task). Regarding EDM, learning processes of item values have been explained by reinforcement learning theory. The amplitude of feedback-related negativity (FRN) is known to reflect prediction error, which modulates the degree of value updating. Recently, as with the EDM, the reinforcement learning-like mechanism is thought to explain value updating in IDM (choice-induced preference change: CIPC). This study used the blind choice paradigm to investigate whether the FRN is associated with CIPC, or not. In this paradigm, participants blindly choose the more preferred one form the two equally preferred items, and then feedback indicating the chosen item. Results showed that the FRN-like component was observed but not related to CIPC. These results suggest that the FRN-like component does not reflect the degree of value updating but reflects a participant s estimation about how much their preference is reflected in the feedback.

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