Yasushi Naruse

Background: Sense of agency (SoA) is the subjective experience of controlling one’s actions and their effects in the world. SoA is important for behavioral change and goal achievement, and its measurement via digital devices has gained attention in health promotion and therapeutic interventions. Objective: This study describes longitudinal changes and individual variations in general explicit sense of agency (GenExp-SoA) and local implicit sense of agency (LocalImp-SoA), measured in daily life using a mobile app, and examines their associations with demographic and personality traits. Methods: One hundred adults in Japan participated in a 3-month, prospective observational survey, completing daily mobile app-based assessments of GenExp-SoA (using the Sense of Agency Scale) and LocalImp-SoA (using effect binding with the Libet clock method). Personality traits were measured via the Big Five questionnaire. Linear mixed-effects models were used to evaluate longitudinal changes and interactions with personal characteristics for both types of SoA and their relationship. We also examined state persistence and cross-lagged effects using continuous-time structural equation modeling. A total of 13,391 paired observations were analyzed. Results: GenExp-SoA decreased over time (β=−0.008, P <.001). The decrease was notable for women (β=−0.019, P <.001) and participants with extraversion scores higher than the median (β=−0.019, P <.001). An increase was observed in those aged 50‐59 years (β=0.023, P <.001). LocalImp-SoA also decreased (β=−0.003, P =.02), but its association with personal characteristics was limited. No significant association was found between the 2 forms of SoA. The variance patterns differed between measures, with a high intraclass correlation coefficient of 0.77 for GenExp-SoA and a very low intraclass correlation coefficient of 0.02 for LocalImp-SoA, indicating predominant within-individual variability for the latter. Continuous-time structural equation modeling showed stronger self-persistence for GenExp-SoA, minimal persistence for LocalImp-SoA, and negligible cross-lagged effects. Findings were robust in bootstrap and sensitivity analyses. Conclusions: The 2 forms of SoA measured in daily life settings displayed distinct temporal patterns, as well as different associations with individual characteristics. These findings may inform the design and evaluation of epidemiological studies and digital health interventions targeting the SoA, including measurement strategies. The results support lower-frequency, between-individual assessments for explicit agency and high-frequency, within-individual designs for implicit agency.

User-adaptive systems increasingly rely on latent psychological constructs to personalize interaction and support user well-being. Among these states is the sense of agency (SoA), which is the subjective experience of being in control of one’s actions and their effects. While it has received growing attention, it remains underexplored as a user-modelable construct. This work investigates how SoA relates to affective states and autobiographical memory during music listening, and whether these latent constructs can be inferred from observable music signals. We conduct a set of empirical analyses examining correlations, mediation, and moderation pathways between SoA, affect, and memory-related processes. We further evaluate the feasibility of predicting these psychological constructs from music features using supervised learning models. Our results show that SoA is systematically embedded within affective and autobiographical memory contexts, exhibiting structured statistical relationships and feasibility-level predictability from music features. Rather than proposing a deployed adaptive system, this work positions SoA as a latent user mental state that can be empirically characterized and modeled, providing a foundation for future personalized and agency-aware interactive systems.

Gamification often employs competition-based mechanics to enhance motivation; however, their effectiveness varies depending on an individual’s competitive orientation. This study proposes a method to assess competitive orientation using EEG data in a gamified learning environment. Twenty-eight participants completed a gamified mental arithmetic task under two conditions: competing against an opponent (competitive condition) and attempting to surpass their best score (self-referential condition). Event-related potentials (ERPs) in response to outcome feedback—P300, feedback-related negativity, and reward positivity (RewP)—were analyzed. Results showed that RewP amplitude in the competitive condition was significantly correlated with competitive orientation scores. P300 amplitude was associated with the scores regardless of condition. Furthermore, a support vector regression model trained on ERP and behavioral features from the competitive condition predicted competitive orientation scores ( r = 0.468). These findings suggest that ERP responses provide objective information for estimating competitive orientation, highlighting a neurophysiological approach to personalized gamification design.

When we listen to music, we often feel a pleasurable urge to move to music, known as groove. While previous studies have identified musical features that contribute to the groove experience, such as syncopation and tempo, they also report individual differences in which kinds of music people experience groove. Therefore, recommending groove-eliciting music requires accounting for individual differences. In this study, we aimed to develop a groove brain-music interface (G-BMI) that generates personalized playlists to maximize each individual’s groove experience, using a neurofeedback system based on in-ear EEG. Twenty-four participants listened to three high-groove and three low-groove musical excerpts and rated their “urge to move.” Using these ratings and the recorded EEG, we trained two LASSO models to build the G-BMI. Model 1 predicted urge to move from acoustic features extracted with VGGish, a pretrained neural network. Model 2 classified EEG data as recorded during listening to high-groove or low-groove music. Using Model 1, we ranked 7,225 candidate songs by predicted groove and assembled one groove-augmenting and one groove-diminishing playlist. Using Models 1 and 2, we created two additional playlists that updated Model 1 and the ranking in real-time based on in-ear EEG. Participants then listened to all four playlists and rated them on items including “urge to move.” The groove-augmenting playlist that incorporated EEG achieved the highest “urge to move” ratings. These findings suggest that a personalized neurofeedback system employing EEG can help maximize individual groove experience.

Group identity induces social cognitive biases, and membership duration may amplify these effects. This study aimed to examine such bias by analysing similarities in neural processing among individuals in competitive scenarios. The fans of two Japanese baseball teams, the Hanshin Tigers and Orix Buffaloes, watched baseball matches between the teams, and EEG synchronisation was analysed for in-group (same team) and out-group (different team) pairs, considering fan history as a factor representing membership duration. The results revealed that in-group pairs showed stronger centroparietal alpha-phase synchronisation than out-group pairs, suggesting that top-down spatial attention modulated early visual processing in a similar way among in-group members. Furthermore, in-group pairs with longer fan histories exhibited higher parietal alpha power synchronisation, probably reflecting shared engagement and emotional responses, whereas this effect was absent in out-group pairs. Interestingly, longer fan histories were associated with reduced parietal delta and theta phase synchronisations, possibly due to diverse late-stage attentional processes among experienced fans. Additionally, frontal alpha-phase synchronisation increased with fan history, indicating enhanced auditory attention in long-term fans. These findings highlight how group identity and membership duration shape neural processing, and EEG synchronisation analysis provides a robust method for examining biases.

Music chills are pleasurable experiences while listening to music, often accompanied by physical responses, such as goosebumps ^1,2 . Enjoying music that induces chills is central to music appreciation, and engages the reward system in the brain ^3–5 . However, the specific songs that trigger chills vary with individual preferences ⁶ , and the neural substrates associated with musical rewards differ among individuals ^7–9 , making it challenging to establish a standard method for enhancing music chills. In this study, we developed the Chill Brain-Music Interface (C-BMI), a closed-loop neurofeedback system that uses in-ear electroencephalogram (EEG) for song selection. The C-BMI generates personalized playlists aimed at evoking chills by integrating individual song preferences and neural activity related to music reward processing. Twenty-four participants listened to both self-selected and other-selected songs, reporting higher pleasure levels and experiencing more chills in their self-selected songs. We constructed two LASSO regression models to support the C-BMI. Model 1 predicted pleasure based on the acoustic features of the self-selected songs. Model 2 classified the EEG responses when participants listened to self-selected versus other-selected songs. Model 1 was applied to over 7,000 candidate songs, predicting pleasure scores. We used these predicted scores and acoustic similarity to the self-selected songs to rank songs that were likely to induce pleasure. Using this ranking, four tailored playlists were generated. Two playlists were designed to augment pleasure by selecting top-ranked songs, one of which incorporated real-time pleasure estimates from Model 2 to continuously update Model 1 and refine song rankings. Additionally, two playlists aimed to diminish pleasure, with one updated using Model 2. We found that the pleasure-augmenting playlist with EEG-based updates elicited more chills and higher pleasure levels than pleasure-diminishing playlists. Our results indicate that C-BMI using in-ear EEG data can enhance music-induced chills.

Music-evoked nostalgia has the potential to assist in recalling autobiographical memories and enhancing well-being. However, nostalgic music preferences vary from person to person, presenting challenges for applying nostalgia-based music interventions in clinical settings, such as a non-pharmacological approach. To address these individual differences, we developed the Nostalgia Brain-Music Interface (N-BMI), a neurofeedback system that recommends nostalgic songs tailored to each individual. This system is based on prediction models of nostalgic feelings, developed by integrating subjective nostalgia ratings, acoustic features and in-ear electroencephalographic (EEG) data during song listening. To test the effects of N-BMI on nostalgic feelings, well-being, and memory recall, seventeen elderly and seventeen young participants took part in the study. The N-BMI was personalized for each individual, and songs were recommended under two conditions: the "nostalgia condition", where songs were selected to enhance nostalgic feelings, and the "control condition", to reduce nostalgic feelings. We found nostalgic feelings, well-being, and memory vividness were significantly higher after listening to the recommended songs in the nostalgia condition compared to the control condition in both groups. This indicates that the N-BMI enhanced nostalgic feelings, well-being, and memory recall across both groups. The N-BMI paves the way for innovative therapeutic interventions, including non-pharmacological approaches.

Background: The second demonstration experiment of supporting elderly people going out with the Choisoko system was conducted. The first study showed that for women, friends, shopping, convenience, and events are factors that have the potential to be effective motivational factors for encouraging these women to go out. On the other hand, these factors did not lead to any behavioral change in men. Since there are approximately 15 million men over the age of 65 in Japan, behavioral changes in the entire elderly population will not occur without guidance for elderly men to go out. Methods: Sixteen elderly men and forty-seven elderly women participated. Interestingly, men are far more passionate about games than women. Therefore, we hypothesized that a preference for games could be a hint as to how we might encourage older men to go out. Then, a second demonstration experiment was conducted, and we analyzed the relationship between six game preferences and the frequency of going out. Results: Among gaming preferences, men with gaming preferences such as Philanthropists, Achievers, and Free Spirits showed a tendency to go out. Conclusions: These stimuli may have the potential to be factors that may encourage elderly men to go out.