Biology subjects

Katahira, K.

Publications and source records attributed to Katahira, K..

7 recordsLinked to original sources

bioRxiv2026

Monoaminergic variation, cortical structure, and disaster trauma interact to shape emotional vulnerability

Emotional vulnerability arises from the interplay of genetic variation, cortical network capacity, and subjective processing of adverse experiences, yet these components are rarely examined together in population-scale datasets. Monoaminergic signaling offers an opportunity for understanding such interactions because variation in related genes shapes sensitivity to environmental inputs. Among them, the vesicular monoamine transporter 1 (VMAT1) Thr136Ile variant is notable for its functional effects and its long-term maintenance at intermediate frequencies, a pattern consistent with context-dependent selection. These properties make it an informative marker for investigating how genetic sensitivity interacts with neurobiological and experiential factors to shape affective functioning. Using data from up to 9,625 participants in the Tohoku Medical Megabank Project, a cohort established after the 2011 Great East Japan Earthquake, we investigated how monoaminergic variation, cortical morphology, and traumatic memory shape affective functioning. We found associations between the Thr136Ile variant and negative affectivity and depressive symptoms, consistent with prior reports of heightened emotional reactivity associated with the 136Thr allele. However, magnitude of these effects was substantially amplified in specific experiential and neurobiological contexts. Subjective trauma sensitivity, capturing the discrepancy between earthquake disruption and current traumatic memory, displayed a genotype-dependent association with depressive symptoms, and this relationship was strongest when parietal or insular cortical surface area was smaller. Together, these results identify a multilevel pathway through which monoaminergic variation contributes to emotional vulnerability by interacting with both cortical network capacity and trauma processing. The context-dependent influence of monoaminergic variation may further contribute to maintenance of affective diversity in human populations.

neuroscience↗

bioRxiv2025

Recurrent neural network dynamics may not purely reflect cognitive strategies: A commentary on Ji-An et al. (Nature, 2025)

One of the fundamental goals of behavioural science is to explain seemingly complex human and animal behaviour in terms of simple mechanistic principles. Ji-An, Benna, & Mattar1 (henceforth, JBM) recently proposed a novel analytic and interpretative framework toward this goal. By employing tiny recurrent neural networks (RNNs) with a small number of degrees of freedom, they developed a method for extracting the dynamics of learning and decision-making. They demonstrated that tiny RNNs achieve superior predictive performance compared to classical cognitive models such as reinforcement learning (RL) and Bayesian inference models, and that dynamical systems analyses of the RNNs can reveal cognitive dynamics that differ from those assumed in the cognitive models. This framework is highly promising; however, we suggest allocating room for discussion as to whether the dynamics of RNNs purely reflect the cognitive processes at work in the individuals brain. In particular, RNNs may adapt to within-individual state changes (e.g., transitions between engaged and disengaged states) in ways not captured by cognitive models, thereby improving model fit without necessarily representing cognitive strategies per se. This raises the possibility that the dynamics of RNNs may include processes that deviate from, rather than directly reflect, individual cognitive strategies.

animal behavior and cognition↗

bioRxiv2024

The preference for surprise in reinforcement learning underlies the differences in developmental changes in risk preference between autistic and neurotypical youth

Risk preference changes nonlinearly across development. Although extensive developmental research on the neurotypical population has shown that risk preference is highest during adolescence, developmental changes in risk preference in autistic people, who tend to prefer predictable behaviors, have not been investigated. Here, we aimed to investigate these changes and underlying computational mechanisms. Using a game-like risk-sensitive reinforcement learning task, we found a significant difference in nonlinear developmental changes in risk preference between the autistic and neurotypical groups (N = 75; age range, 6-30 years). The computational modeling approach with reinforcement learning models revealed that individual preferences for surprise modulated such preferences. These findings indicate that for neurotypical people, adolescence is a developmental period involving risk preference, possibly due to lower surprise aversion. Conversely, for autistic people, who show opposite developmental trajectories of risk preference, adolescence could be a developmental period involving risk avoidance because of low surprise preference.

neuroscience↗

bioRxiv2023

Externally provided rewards increase internal preference, but not as much as preferred ones without extrinsic rewards

It is well known that preferences are formed through choices, known as choice-induced preference change (CIPC). However, whether value learned through externally provided rewards influences the preferences formed through CIPC remains unclear. To address this issue, we used tasks for decision-making guided by reward provided by the external environment (externally guided decision-making; EDM) and for decision-making guided by ones internal preference (internally guided decision-making; IDM). In the IDM task, we presented stimuli with learned value in the EDM and novel stimuli to examine whether the value in the EDM affects preferences. Stimuli reinforced by rewards given in the EDM were reflected in the IDMs initial preference and further increased through CIPC in the IDM. However, such stimuli were not as strongly preferred as the most preferred novel stimulus in the IDM, indicating the superiority of intrinsically learned values (SIV). The underlying process of this phenomenon is discussed in terms of the fundamental self-hypothesis. Author SummaryWe make decisions based on internal value criteria, which are individual preferences, or based on external value criteria, which are the values learned from the external environment. Although it is known that values are learned in both types of decisions, is there a difference in the nature of these values? Our study uses simulation and fits human behavioral data to address this question. The results showed that stimuli that were learned to be highly valued because of external feedback became preferred in subsequent preference judgments. However, it is interesting to note that such stimuli were not chosen as much as stimuli that were preferred without influence from the external environment. This finding suggests that values formed through ones own criteria have characteristics distinct from those formed through external environmental influence. Our findings promote an integrated understanding of the decision-making process.

animal behavior and cognition↗

bioRxiv2022

Cardiac cycle affects the asymmetric value updating in instrumental reward learning

This study aimed to investigate whether instrumental reward learning is affected by the cardiac cycle. To this end, we examined the effects of the cardiac cycle (systole or diastole) on the computational processes underlying the participants choices in the instrumental learning task. In the instrumental learning task, participants were required to select one of two discriminative stimuli (neutral visual stimuli) and immediately receive reward/punishment feedback depending on the probability assigned to the chosen stimuli. To manipulate the cardiac cycle, the presentation of discriminative stimuli was timed to coincide with either cardiac systole or diastole. We fitted the participants choices in the task with reinforcement learning (RL) models and estimated parameters involving instrumental learning (i.e., learning rate and inverse temperature) separately in the systole and diastole trials. Model-based analysis revealed that the learning rate for positive prediction errors was higher than that for negative prediction errors in the systole trials; however, learning rates did not differ between positive and negative prediction errors in the diastole trials. These results demonstrate that the natural fluctuation of cardiac afferent signals can affect asymmetric value updating in instrumental reward learning.

neuroscience↗

bioRxiv2020

Computational Psychiatry Research Map (CPSYMAP): a New Database for Visualizing Research Papers

The field of computational psychiatry is growing in prominence along with the recent advances in computational neuroscience, machine learning, and the cumulative scientific evidence of psychiatric disorders. Computational approaches provide an understanding of disorders using psychological and neuroscience terms and help to determine treatment protocols based on high-dimensional data. However, the multidisciplinary nature of this field seems to limit the development of computational psychiatry studies. Computational psychiatry combines knowledge from neuroscience, psychiatry, and computation; thus, there is an emerging need for a platform to integrate and coordinate these perspectives. In this study, we developed a new database for visualizing research papers as a two-dimensional "map" called the Computational Psychiatry Research Map (CPSYMAP). This map shows the distribution of papers along neuroscientific, psychiatric, and computational dimensions to enable anyone to find niche research and deepen their understanding of the field.

neuroscience↗

bioRxiv2019

Exploration-related strategy mediates negative coupling between decision-making performance and psychiatric symptoms

Various psychiatric symptoms are often accompanied by impairments in decision-making. Given the high comorbidity of psychiatric disorders, symptoms that specifically couple with the impairment remain unidentified. The computations involved in decision-making that mediate the coupling are also elusive. Here, we conducted an online experiment with healthy individuals (n=939), participating in a decision-making task and completing questionnaires about psychiatric symptoms. The questionnaire data identified two dimensions underlying various symptoms: the first is mainly associated with obsessive-compulsive traits whereas the second is associated with depression and anxiety. Furthermore, by model-based analysis, we found only the first dimension was negatively correlated with the overall performance of the decision-making task, and the negative correlation was mediated by choice stochasticity (random exploration) and propensity to choose options previously unchosen. Given that the preference for previously unchosen options can reflect uncertainty-driven exploration, our findings highlight a key role of exploration-related strategies in psychiatric deficits of decision-making.

neuroscience↗