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BackgroundAberrant sense of agency (SoA, a feeling of control over one’s own actions and their subsequent events) has been considered as key to understanding the pathology of schizophrenia. Behavioral studies demonstrated that both excessive and diminished SoAs were observed in schizophrenia. Several neurophysiological studies have suggested that such aberrant SoA may be due to temporal delays (TDs) in sensory-motor prediction signals. In the current study, we examined this hypothesis via a computational modeling approach using a recurrent neural network (RNN) model.MethodsThe sensory-motor prediction process in the behavioral task of SoA was modeled using an RNN model that can learn to reproduce the SoA task performance of human participants. The RNN model receives inputs of the states of visual, auditory, and proprioceptive senses, and generates forward predictions for those at the next time step and SoA judgement. In the simulation, the RNN models were trained using the actual behavioral data of 17 healthy controls completing an SoA task; then, our hypothesis was tested by adding TDs in the signals between context units of the RNN.ResultsThe RNN model successfully reproduced the behavioral features of the healthy controls. Moreover, bidirectional (i.e., excessive and diminished) schizophrenia-pattern SoA was reproduced with the TDs in context units. Three control experiments (random noise addition, TDs in outputs, and TDs in inputs) with quantitative analysis demonstrated no schizophrenia-pattern aberrant SoA. ConclusionsThe results theoretically support the proposed hypothesis that the aberrant SoA in schizophrenia may be due to TDs in sensory-motor prediction signals.

Recognizing human cognitive performance is important for preserving working efficiency and preventing human error. This paper presents a method for estimating cognitive performance by leveraging multiple information available in a smartphone. The method employs the Go-NoGo task to measure cognitive performance, and fuses contextual and behavioral features to identify the level of performance. It was confirmed that the proposed method could recognize whether cognitive performance was high or low with an average accuracy of 71%, even when only referring to inertial sensor logs. Combining sensing modalities improved the accuracy up to 74%.

There have been so many and various researches of schizophrenia since the early days of modern psychiatry, but the pathology remains unexplained, and the treatmens not established. One of the reasons is the complexity of a brain which makes it difficult to form a bridge between biological findings and symptoms. Computational psychiatry has been recently expected to overcome this difficulty. In the field, a brain is seen as an information-processing system, and the dynamics of a brain are expressed using mathematical models. In addition, mental disorders are expressed as changes including parameters in the models. Here, we introduce some studies conducted in Japan.

In the free energy principle (FEP) proposed by Friston, it is supposed that agents seek to minimize the “surprise” – the negative log (marginal) likelihood of observations (i.e., sensory stimuli) – given the agents’ current belief. This is achieved by minimizing the free energy, which provides an upper bound on the surprise. The FEP has been applied to action selection in a framework called “active inference,” where agents are supposed to select an action so that they minimize the “expected free energy” (EFE). While EFE can be decomposed into interpretable components such as epistemic value and extrinsic value, it is difficult to understand intuitively how EFE itself is directly related to “surprise” and what psychological construct is related to EFE itself (as a single quantity). To facilitate the discussion and interpretation of psychological processes underlying active inference, we introduce a computational component termed the “retrospective surprise,” which is the surprise of an observation after updating the belief given the observation itself. The predicted retrospective surprise (PRS) is mathematically derived from a special case of EFE and provides a lower bound on EFE. We illustrate the properties of EFE and PRS using examples of inference for a binary hidden cause given a binary observation. We discuss how information-seeking behavior is accounted for by EFE and PRS. Our results highlight the role of prior distribution of future observation in EFE. By setting this prior distribution to be fixed irrespective of which action is selected, the epistemic value can exert an influence on EFE, leading to information-seeking behavior., ファイル公開：2022-06-01

Although mental illness is one of the most serious social problems, stress does not necessarily have a negative effect, on the contrary, an optimal amount of stress can result in individuals being able to perform better in tasks. While there have been several studies on estimating stress level from smartphone usage logs and several effective features were revealed as a result, there are few studies on estimating cognitive performance. Thus, in this paper, we explore several factors affecting the estimation of cognitive performance from smartphone logs. To conduct the analysis, we collected smartphone usage logs and Go/No-Go task data for 6 weeks from 39 participants in the wild. We found that the time to find intended app is related to cognitive performance. This result suggests that measuring the time to find intended app can be the effective feature of estimating cognitive performance from smartphone logs.

Recently, inferring the state of people’s mental health via passive mobile sensing has attracted significant attention. Previous studies have used the self-assessed stress levels as the ground truth; however, these are subjective measures. In this study, we use a physiologically-assessed stress metric to minimize the effect of participant subjectivity and further estimate it using behavioral features based on the smartphone usage logs. We initially requested the study participants (39 participants) to attach heart rate sensors for 8 hours per day and simultaneously collected continuous heart rate data and smartphone logs for 42 days. Further, we divided the participants into four types via clustering using the behavioral features derived from their smartphone sensor logs and trained each model via supervised learning using the heart rate data as the ground truth. Our results exhibit that the proposed method is more accurate (71%) as compared to the baseline method (54%). This demonstrates that physiologically-assessed stress levels can be estimated based on the implicit features that are gathered from the smartphone logs.

Self-disturbance, a core feature of schizophrenia, recently has been explained from the standpoint of an abnormal sense of agency (SoA). Previous studies showed that aberrant SoA in schizophrenia arise from imprecise predictions about the sensory consequences of actions. However, the nature of the malfunctioning predictions remains unclear. We examined the temporally "delayed" nature of inadequate predictions. We studied 30 patients with schizophrenia and 30 healthy controls. Our original SoA task evaluates explicit experience of the temporal causal relationship between an intentional action and an effect on a computer screen under the presence of temporal biases. We introduced an adaptation with a "trial-by-trial" method that prolonged or shortened the temporal biases. We hypothesized that delayed prediction signals in schizophrenia could lead to a match in timing between predictions and actual outcomes, resulting in self-agency. The adjustment courses to changing temporal biases were evaluated. Patients with schizophrenia continued to feel self-agency even when the adjusted temporal bias was longer than 1000ms. This result indicated that patient's prediction would be delayed in each trial. Our study empirically showed behavioral evidence for "delayed" prediction signals in a SoA paradigm for the first time.

Mental health has received increasing attention in recent years, and a correlation between social interaction during eating and mental health noted. The purpose of this research is to determine if wearable devices can be used to recognize whether a person is eating alone or has company. Using a watch-type device and smartphone, the data, while eating in daily life, was collected. Using this data, we were able to recognize whether a person is eating alone or has company with an accuracy of 96.3%.

This study predicts the change of stress levels using real-world and online behavioral features extracted from smartphone log information. Previous studies of stress detection using smartphone data focused on a single feature and did not consider all features simultaneously. We propose a method to extract a co-occurring combination of a user's real-world and online behavioral features by converting raw sensor data into categorical features. We conducted an experiment in which the State Trait Anxiety Inventory (STAI) was used to assess the anxiety-related stress levels of 20 healthy participants. The participants installed a log-collecting application on their smartphones and answered the STAI questions once a day for one month. The proposed method showed an F-score of 74.2%, which is 4.0% higher than the F-score of previous studies (70.2%) that used single non-combined features. The results demonstrate that anxiety-related stress levels can be predicted using combined features extracted from smartphone log data.

The Sense of Agency (SoA) is the subjective sense such that I am the causal factor of the own experience [1].

Working memory (WM) impairment has received attention as a behavioral characteristic of schizophrenia. Neurobiological studies have led to the hypothesis that a deficit in dopamine transmission through D1 receptors in the prefrontal cortex (PFC) is associated with WM impairment in schizophrenia. However, empirical approaches that aim to clarify the nature of the impairment and its underlying mechanism are difficult to enact, especially in unmedicated patients. By contrast, computational approaches using biologically plausible models have formed a powerful theoretical framework for the study of WM impairment in schizophrenia. This article attempts to directly connect neurobiological findings to the neuropsychological behaviors present in patients with schizophrenia. Using a biologically plausible prefrontal cortical circuit model, we simulated sustained activity during a simultaneous, multi-target WM task. We subsequently analyzed how dopaminergic modulation via D1 receptor activation alters the capacity and precision of WM and investigated the underlying mechanism. Hypodopaminergic modulation resulted in imprecision and a reduced capacity in WM primarily due to decreased N-methyl-d-aspartate (NMDA) conductance. Increasing NMDA conductance ameliorated both impairments. These results account for the mechanism that underlies WM impairments in schizophrenia and provide a theoretical basis for combination therapy with antipsychotic drugs and drugs that enhance NMDA receptor function, which is expected to be effective for the treatment of WM impairments in these patients.

Self-disturbances in schizophrenia have been regarded as a fundamental vulnerability marker for this disease, and have begun to be studied from the standpoint of an abnormal “sense of agency (SoA)” in cognitive neuroscience. To clarify the nature of aberrant SoA in schizophrenia, it needs to be investigated in various clinical subtypes and stages. The residual type of chronic schizophrenia with predominant negative symptoms (NS) has never been investigated for SoA. Accordingly, we investigated SoA by an original agency attribution task in NS-predominant schizophrenia, and evaluated the dynamic interplay between the predictive and postdictive components of SoA in the optimal cue integration framework. We studied 20 patients with NS-predominant schizophrenia, and compared with 30 patients with paranoid-type schizophrenia and 35 normal volunteers. NS-predominant schizophrenia showed markedly diminished SoA compared to normal controls and paranoid-type schizophrenia, indicating a completely opposite direction in agency attribution compared with excessive SoA demonstrated in paranoid-type schizophrenia. Reduced SoA was detected in experimental studies of schizophrenia for the first time. According to the optimal cue integration framework, these results indicate that there was no increase in compensatory contributions of the postdictive processes despite the existence of inadequate predictions, contrary to the exaggerated postdictive component in paranoid-type schizophrenia.

Schizophrenia is defined by operative diagnostic criteria in DSM-IV with some typical symptoms as hallucinations and duration of the disease. Huber focused on the subjective experience of patients and coined the term "basic symptoms" and created BSABS. Our study investigated the reliability and the diagnostic validity of the 5 clusters of BSABS for DSM-IV-based diagnosis of schizophrenia with a cohort of 105 patients. Good inter-rater reliability was obtained except for one item D.10. As evaluated by Spearman's rank correlation coefficients, among the 5 clusters excluding Cluster 2, internal consistency was good. This suggests that, although each cluster is heterogeneous, cluster symptoms are the expression of physiological and biological disturbances of schizophrenia. Receiver Operating Characteristic Curve analysis was also used to show the ability of each cluster to discriminate schizophrenia. Results showed that the area representing the powers in discriminate schizophrenia of Cluster 4 "Adynamia", which is considered related to the dynamic aspect of thinking, was highest, at 0.739. Cluster 1 "Information processing disturbances" which has a predictive ability for schizophrenia showed 0.714 and Cluster 3 "Impaired tolerance to normal stress" showed 0.711. Our findings suggest that, although these clusters symptoms differ from DSM-IV criteria, they are related to fundamental process of schizophrenia. Use of some of these three clusters with other neurophysiological markers could allow clinical evaluation of schizophrenia from a new perspective.