Using appropriate stimuli to evoke emotions is especially important for researching emotion. Psychologists have provided several standardized affective stimulus databases-such as the International Affective Picture System (IAPS) and the Nencki Affective Picture System (NAPS) as visual stimulus databases, as well as the International Affective Digitized Sounds (IADS) and the Montreal Affective Voices as auditory stimulus databases for emotional experiments. However, considering the limitations of the existing auditory stimulus database studies, research using auditory stimuli is relatively limited compared with the studies using visual stimuli. First, the number of sample sounds is limited, making it difficult to equate across emotional conditions and semantic categories. Second, some artificially created materials (music or human voice) may fail to accurately drive the intended emotional processes. Our principal aim was to expand existing auditory affective sample database to sufficiently cover natural sounds. We asked 207 participants to rate 935 sounds (including the sounds from the IADS-2) using the Self-Assessment Manikin (SAM) and three basic-emotion rating scales. The results showed that emotions in sounds can be distinguished on the affective rating scales, and the stability of the evaluations of sounds revealed that we have successfully provided a larger corpus of natural, emotionally evocative auditory stimuli, covering a wide range of semantic categories. Our expanded, standardized sound sample database may promote a wide range of research in auditory systems and the possible interactions with other sensory modalities, encouraging direct reliable comparisons of outcomes from different researchers in the field of psychology.

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.