Emergence of sensory attenuation based upon the free-energy principle

Migraine as an allostatic reset triggered by unresolved interoceptive prediction errors

Until now, a satisfying account of the cause and purpose of migraine has remained elusive. We explain migraine within the frameworks of allostasis (the situationally-flexible, forward-looking equivalent of homeostasis) and active inference (interacting with the environment via internally-generated predictions). Due to its multimodality, and long timescales between cause and effect, allostasis is inherently prone to catastrophic error, which might be impossible to correct once fully manifest, an early indicator which is elevated prediction error (discrepancy between prediction and sensory input) associated with internal sensations (interoception). Errors can usually be resolved in a targeted manner by action (correcting the physiological state) or perception (updating predictions in light of sensory input); persistent errors are amplified broadly and multimodally, to prioritise their resolution (the migraine premonitory phase); finally, if still unresolved, progressive amplification renders further changes to internal or external sensory inputs intolerably intense, enforcing physiological stability, and facilitating accurate allostatic prediction updating. As such, migraine is an effective 'failsafe' for allostasis, however it has potential to become excessively triggered, therefore maladaptive.

Elucidating multifinal and equifinal pathways to developmental disorders by constructing real-world neurorobotic models

Vigorous research has been conducted to accumulate biological and theoretical knowledge about neurodevelopmental disorders, including molecular, neural, computational, and behavioral characteristics; however, these findings remain fragmentary and do not elucidate integrated mechanisms. An obstacle is the heterogeneity of developmental pathways causing clinical phenotypes. Additionally, in symptom formations, the primary causes and consequences of developmental learning processes are often indistinguishable. Herein, we review developmental neurorobotic experiments tackling problems related to the dynamic and complex properties of neurodevelopmental disorders. Specifically, we focus on neurorobotic models under predictive processing lens for the study of developmental disorders. By constructing neurorobotic models with predictive processing mechanisms of learning, perception, and action, we can simulate formations of integrated causal relationships among neurodynamical, computational, and behavioral characteristics in the robot agents while considering developmental learning processes. This framework has the potential to bind neurobiological hypotheses (excitation-inhibition imbalance and functional disconnection), computational accounts (unusual encoding of uncertainty), and clinical symptoms. Developmental neurorobotic approaches may serve as a complementary research framework for integrating fragmented knowledge and overcoming the heterogeneity of neurodevelopmental disorders.

Action does not enhance but attenuates predicted touch

Dominant motor control theories propose that the brain predicts and attenuates the somatosensory consequences of actions, referred to as somatosensory attenuation. Support comes from psychophysical and neuroimaging studies showing that touch applied on a passive hand elicits attenuated perceptual and neural responses if it is actively generated by one's other hand, compared to an identical touch from an external origin. However, recent experimental findings have challenged this view by providing psychophysical evidence that the perceived intensity of touch on the passive hand is enhanced if the active hand does not receive touch simultaneously with the passive hand (somatosensory enhancement) and by further attributing attenuation to the double tactile stimulation of the hands upon contact. Here, we directly contrasted the hypotheses of the attenuation and enhancement models regarding how action influences somatosensory perception by manipulating whether the active hand contacts the passive hand. We further assessed somatosensory perception in the absence of any predictive cues in a condition that turned out to be essential for interpreting the experimental findings. In three pre-registered experiments, we demonstrate that action does not enhance the predicted touch (Experiment 1), that the previously reported 'enhancement' effects are driven by the reference condition used (Experiment 2), and that self-generated touch is robustly attenuated regardless of whether the two hands make contact (Experiment 3). Our results provide conclusive evidence that action does not enhance but attenuates predicted touch and prompt a reappraisal of recent experimental findings upon which theoretical frameworks proposing a perceptual enhancement by action prediction are based.

Aberrant sense of agency induced by delayed prediction signals in schizophrenia: a computational modeling study

Aberrant sense of agency (SoA, a feeling of control over one's own actions and their subsequent events) has been considered key to understanding the pathology of schizophrenia. Behavioral studies have demonstrated that a bidirectional (i.e., excessive and diminished) SoA is observed in schizophrenia. Several neurophysiological and theoretical studies have suggested that aberrancy may be due to temporal delays (TDs) in sensory-motor prediction signals. Here, we examined this hypothesis via computational modeling using a recurrent neural network (RNN) expressing the sensory-motor prediction process. The proposed model successfully reproduced the behavioral features of SoA in healthy controls. In addition, simulation of delayed prediction signals reproduced the bidirectional schizophrenia-pattern SoA, whereas three control experiments (random noise addition, TDs in outputs, and TDs in inputs) demonstrated no schizophrenia-pattern SoA. These results support the TD hypothesis and provide a mechanistic understanding of the pathology underlying aberrant SoA in schizophrenia.

Understanding brain functional architecture through robotics

Robotics is increasingly seen as a useful test bed for computational models of the brain functional architecture underlying animal behavior. We provide an overview of past and current work, focusing on probabilistic and dynamical models, including approaches premised on the free energy principle, situating this endeavor in relation to evidence that the brain constitutes a layered control system. We argue that future neurorobotic models should integrate multiple neurobiological constraints and be hybrid in nature.

Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy

Introduction

Investigating the pathological mechanisms of developmental disorders is a challenge because the symptoms are a result of complex and dynamic factors such as neural networks, cognitive behavior, environment, and developmental learning. Recently, computational methods have started to provide a unified framework for understanding developmental disorders, enabling us to describe the interactions among those multiple factors underlying symptoms. However, this approach is still limited because most studies to date have focused on cross-sectional task performance and lacked the perspectives of developmental learning. Here, we proposed a new research method for understanding the mechanisms of the acquisition and its failures in hierarchical Bayesian representations using a state-of-the-art computational model, referred to as in silico neurodevelopment framework for atypical representation learning.

Methods

Simple simulation experiments were conducted using the proposed framework to examine whether manipulating the neural stochasticity and noise levels in external environments during the learning process can lead to the altered acquisition of hierarchical Bayesian representation and reduced flexibility.

Results

Networks with normal neural stochasticity acquired hierarchical representations that reflected the underlying probabilistic structures in the environment, including higher-order representation, and exhibited good behavioral and cognitive flexibility. When the neural stochasticity was high during learning, top-down generation using higher-order representation became atypical, although the flexibility did not differ from that of the normal stochasticity settings. However, when the neural stochasticity was low in the learning process, the networks demonstrated reduced flexibility and altered hierarchical representation. Notably, this altered acquisition of higher-order representation and flexibility was ameliorated by increasing the level of noises in external stimuli.

Discussion

These results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment.

Neural processing of self-touch and other-touch in anorexia nervosa and autism spectrum condition

INTRODUCTION

The tactile sense plays a crucial role in the development and maintenance of a functional bodily self. The ability to differentiate between self- and nonself-generated touch contributes to the perception of the bodies' boundaries and more generally to self-other-distinction, both of which are thought be altered in anorexia nervosa (AN) and autism spectrum condition (AS). While it has been suggested that AN and AS are characterized by overlapping symptomatology, they might differ regarding body perception and self-other-distinction.

METHODS

Participants with a diagnosis of AN (n = 25), AS (n = 29), and a comparison group without diagnoses (n = 57) performed a self-other-touch task during functional brain imaging. In the experimental conditions, they stroked their own arm or were stroked on the arm by an experimenter.

RESULTS

As shown previously, the CG group showed lower activation or deactivation in response to self-touch compared to social touch from someone else. A main group effect was found in areas including somatosensory cortex, frontal and temporal gyri, insula, and subcortical regions. This was driven by increased activations in participants with AN, while participants in the AS group showed mostly comparable activations to the comparison group.

CONCLUSIONS

AN diagnosis was associated with an increased neural activity in response to both self-touch and social touch. Failure to attenuate self-touch might relate to altered predictions regarding the own body and reduced perception of bodily boundaries. Participants with an AS diagnosis were mostly comparable to the comparison group, potentially indicating unaltered tactile self-other-distinction.