Beyond AI Project“AI x Tojisha-Kenkyu: Computational Neuroscience for
Systematic Understanding of Cognitive Individuality”
Project Leader
Yukie Nagai (Project Professor, IRCN, The University of Tokyo)

This project has integrated two approaches to explore cognitive individuality: AI research, whichinvestigates individuality from a constructivist approach, and Tojisha-kenkyu, which analyticallyexamines it from a first-person perspective.
Tojisha-kenkyu has involved a new hypothesis-testing type of research based on the predictive
coding theory. We conducted a thematic analysis of narrative data obtained from Tojisha-kenkyu
meetings and reviewed existing studies on autism spectrum disorder to formulate a hypothesis anddevelop new questionnaires for later studies. To expand the community and further refine thehypothesis, we established the Tojisha-kenkyu Episode Bank (https://token.episodebank.com) andthe Introductory Lecture on Tojisha-kenkyu (https://www.utokyo-ext.co.jp/ids/top). The former
enables users to provide and search their own episodes, whereas the latter enables them to learn
how to conduct first-person studies. These initiatives have already been implemented in manycompanies and shown to improve psychological safety and inclusion in the workplace.
In AI research, we have developed neural network models that imitate the functions of the humanbrain and demonstrated that diverse cognitive individualities can be explained by modifications ofspecific parameters in the predictive coding processing. Our findings suggest that an imbalance inthe precision of predictive and sensory signals can lead to learning difficulties or overlearning for aparticular experience, resulting in unstructured or biased internal models. This result implies that
hypersensitivity and hyposensitivity, which have bipolar characteristics, are inextricably linked due
to modulations of a specific function in the brain. We have also designed and conducted drawing

and composition tasks with children and adults to test the hypothesis obtained in the model study.
Our results demonstrated that parameter fluctuations as described above cause the individuality
appearing in these tasks. Our further experiments showed that variations in the predictive encoding
processing can explain cognitive individuality in various cognitive behaviors such as language ability,speech, and tactile and visual perception.
The above findings from Tojisha-kenkyu and AI research have enabled us to develop prototype
systems that manifest cognitive individuality. Users can visualize their cognitive individuality byusing smartphone applications, virtual reality, and robots. We believe that these systems willcontribute to the improvement of inclusion and the realization of a neurodiverse society, in thesame way as Tojisha-kenkyu at companies.
Selected Publications
1. Philippsen and Y. Nagai, “A predictive coding account for cognition in human children andchimpanzees: A case study of drawing,” IEEE Transactions on Cognitive and DevelopmentalSystems, 14(4):1306-1319, 2022.
2. Philippsen, S. Tsuji, and Y. Nagai, “Quantifying developmental and individual differences inspontaneous drawing completion among children,” Frontiers in Psychology, 13:783446, 2022.
3. M. Tsfasman, A. Philippsen, C. Mazzola, S. Thill, A. Sciutti, and Y. Nagai, “The world seems
different in a social context: A neural network analysis of human experimental data,” PLoSONE, 17(8):e0273643, 2022.
4. Philippsen, S. Tsuji, and Y. Nagai, “Simulating Developmental and Individual Differences ofDrawing Behavior in Children Using a Predictive Coding Model,” Frontiers in Neurorobotics,
16:856184, 2022.
5. J.-J. Hsieh, Y. Nagai, S. Kumagaya, S. Ayaya, and M. Asada, “Atypical Auditory Perception
Caused by Environmental Stimuli in Autism Spectrum Disorder: A Systematic Approach to theEvaluation of Self-Reports,” Frontiers in Psychiatry, 13:888627, 2022.
6. K. Asada, S. Itakura, M. Okanda, Y. Moriguchi, K. Yokawa, S. Kumagaya, K. Konishi, and Y.
Konishi, “Understanding of the Gricean maxims in children with autism spectrum disorder:
Implications for pragmatic language development,” Journal of Neurolinguistics, 63:101085,2022.
7. K. Kasai, S. Kumagaya, Y. Takahashi, Y. Sawai, A. Uno, Y. Kumakura, M. Yamagishi, A.
Kanehara, K. Morita, M. Tada, Y. Satomura, N. Okada, S. Koike, and S. Yagishita, ““WorldInformed” Neuroscience for Diversity and Inclusion: An Organizational Change in CognitiveSciences,” Clinical EEG and Neuroscience, 2022.
8. M. Wada, Y. Umesawa, M. Sano, S. Tajima, S. Kumagaya, and M. Miyazaki, “Weakened
Bayesian Calibration for Tactile Temporal Order Judgment in Individuals with Higher AutisticTraits,” Journal of Autism and Developmental Disorders, 2022.
9. M. Tsujita, M. Ban, and S. Kumagaya, “The Japanese Multidimensional Attitudes Scale Toward
Persons With Autism Spectrum Disorders,” Japanese Psychological Research, 63(3):129-139,
2021.
Homepages• https://beyondai.jp/contents/projects/nagai/?lang=en• https://developmental-robotics.jp/en/past_projects/beyond-ai/