上野 泰治

Generalized morality refers to the use of prior knowledge of others' morality to infer unknown aspects of morality, which is a crucial heuristic for obtaining cooperative partners in a large society. This research investigated how information is used for generalized morality. It was predicted that group-level generalized morality, or moral stereotyping, would come first when the information is limited, thereby people rely on a surface cue. As information increases, they come to use individual-level generalized morality, which is shown by Jackson et al. Following Jackson et al., the current research employed an agent-based modeling (Study 1) and human study (Study 2). Study 1 found that participants used moral stereotyping first in their interactions when the visual cue-morality association was salient. Study 2 also showed the same trend. This research worked as a replication of the previous study and as an important extension to understanding how moral cognition develops.

Early infant crying provides critical insights into neurodevelopment, with atypical acoustic features linked to conditions such as preterm birth. However, previous studies have focused on limited and specific acoustic features, hindering a more comprehensive understanding of crying. To address this, we employed a convolutional neural network to assess whether whole Mel-spectrograms of infant crying capture gestational age (GA) variations (79 preterm infants; 52 term neonates). Our convolutional neural network models showed high accuracy in classifying gestational groups (92.4%) and in estimating the relative and continuous differences in GA (r = 0.73; p < 0.0001), outperforming previous studies. Grad-CAM and spectrogram manipulations further revealed that GA variations in infant crying were prominently reflected in temporal structures, particularly at the onset and offset regions of vocalizations. These findings suggest that decoding spectrotemporal features in infant crying through deep learning may offer valuable insights into atypical neurodevelopment in preterm infants, with potential to enhance early detection and intervention strategies in clinical practice.

Abstract The COVID pandemic has been an unforeseen situation in which uncertainty, social distance, loss of stability, and significant changes have proven to have detrimental effects on people's well‐being and on mental health. The aim of the present study is to determine changes in subjective time speed, duration, and time distance, and to consider the factors that may have contributed to this subjective distortion. A questionnaire was designed to explore time perception along with autobiographical recollection, mental and physical activity, and mood before, during, and after the pandemic. Analysis revealed that the pandemic period differed from before and after on every scale; subjects reported relatively lower values on autobiographical memory for the pandemic period; felt this time period to be further away, slower, and longer; were less active; and had a more negative mood. A structural equation model revealed that mood was the main predictor of subjective time distortion.