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.

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Choosing an option increases a person’s preference for that option. This phenomenon, called choice-based learning (CBL), has been investigated separately in the contexts of internally guided decision-making (IDM, e.g., preference judgment), for which no objectively correct answer exists, and externally guided decision making (EDM, e.g., perceptual decision making), for which one objectively correct answer exists. For the present study, we compared decision making of these two types to examine differences of underlying neural processes of CBL. As IDM and EDM tasks, occupation preference judgment and salary judgment were used, respectively. To compare CBL for the two types of decision making, we developed a novel measurement of CBL: decision consistency. When CBL occurs, decision consistency is higher in the last-half trials than in first-half trials. Electroencephalography (EEG) data have demonstrated that the change of decision consistency is positively correlated with the fronto-central beta–gamma power after response in the first-half trials for IDM, but not for EDM. Those results demonstrate for the first time the difference of CBL between IDM and EDM. The fronto-central beta–gamma power is expected to reflect a key process of CBL, specifically for IDM.

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Prediction is essential for the efficiency of many cognitive processes; however, this process is not always perfect. Predictive coding theory suggests that the brain generates and updates a prediction to respond to an upcoming event. Although an electrophysiological index of prediction, the stimulus preceding negativity (SPN), has been reported, it remains unknown whether the SPN reflects the prediction accuracy, or whether it is associated with the prediction error, which corresponds to a mismatch between a prediction and an actual input. Thus, the present study aimed to investigate this question using electroencephalography (EEG). Participants were asked to predict the original pictures from pictures that had undergone different levels of pixelation. The SPN amplitude was affected by the level of pixelation and correlated with the subjective evaluation of the prediction accuracy. Furthermore, late positive components (LPC) were negatively correlated with SPN. These results suggest that the amplitude of SPN reflects the prediction accuracy; more accurate prediction increases the SPN and reduces the prediction error, resulting in reduced LPC amplitudes.