A Neurorobotics Simulation of Autistic Behavior Induced by Unusual Sensory Precision

Recently, applying computational models developed in cognitive science to psychiatric disorders has been recognized as an essential approach for understanding cognitive mechanisms underlying psychiatric symptoms. Autism spectrum disorder is a neurodevelopmental disorder that is hypothesized to affect information processes in the brain involving the estimation of sensory precision (uncertainty), but the mechanism by which observed symptoms are generated from such abnormalities has not been thoroughly investigated. Using a humanoid robot controlled by a neural network using a precision-weighted prediction error minimization mechanism, it is suggested that both increased and decreased sensory precision could induce the behavioral rigidity characterized by resistance to change that is characteristic of autistic behavior. Specifically, decreased sensory precision caused any error signals to be disregarded, leading to invariability of the robot’s intention, while increased sensory precision caused an excessive response to error signals, leading to fluctuations and subsequent fixation of intention. The results may provide a system-level explanation of mechanisms underlying different types of behavioral rigidity in autism spectrum and other psychiatric disorders. In addition, our findings suggest that symptoms caused by decreased and increased sensory precision could be distinguishable by examining the internal experience of patients and neural activity coding prediction error signals in the biological brain

Emergence of sensory attenuation based upon the free-energy principle

The brain attenuates its responses to self-produced exteroceptions (e.g., we cannot tickle ourselves). Is this phenomenon, known as sensory attenuation, enabled innately, or acquired through learning? Here, our simulation study using a multimodal hierarchical recurrent neural network model, based on variational free-energy minimization, shows that a mechanism for sensory attenuation can develop through learning of two distinct types of sensorimotor experience, involving self-produced or externally produced exteroceptions. For each sensorimotor context, a particular free-energy state emerged through interaction between top-down prediction with precision and bottom-up sensory prediction error from each sensory area. The executive area in the network served as an information hub. Consequently, shifts between the two sensorimotor contexts triggered transitions from one free-energy state to another in the network via executive control, which caused shifts between attenuating and amplifying prediction-error-induced responses in the sensory areas. This study situates emergence of sensory attenuation (or self-other distinction) in development of distinct free-energy states in the dynamic hierarchical neural system

Patterns of Cooperative Rhythm Production Between One Leader and Two Followers Through Auditory and Visual Information

In a musical ensemble performance such as an orchestra, people synchronize tempo and timing with others using visual and auditory information. The synchronization/rhythmic pattern creation problems have long been studied in cognitive psychology[1], and more recently, also in complexity science. However, cooperative rhythm production (i.e., multiple people produce rhythm together), especially among three people (and more), has thus far remain insufficiently studied. Three-persons synchronization is obviously not just a sum of two-persons ones. We conducted alternate tapping experiments, in which each group of three participants was instructed to tap a pressure sensor alternately to keep a constant rhythm. A group consisted of one leader and two followers, and the leader was asked to maintain the pre-indicated tempo, whereas the two followers simultaneously synchronized with the leader. A Leader's tap was presented to the followers as a visual or auditory stimulus, and a followers' tap was presented to the leader and the other follower as an auditory stimulus. Our specific foci were on (i) how important the other follower's information for a follower is when syncing to a leader, and (ii) which kind of stimuli, visual or auditory, may be better for leaders to maintain the tempo. Through the experiments, we found that the groups that managed to maintain tempo had mainly two particular patterns of interdependency between the leader and the follower (see Figure). Here, we consider that A was dependent on B when A was influenced by the previous taps of B and performed the next tapping to correct the timing shift with B. Pattern (a), in which two followers are strongly dependent on a leader (= what was supposed to happen), was observed in both cases of when the leader's taps were presented as visual stimuli and when presented as audio stimuli. Interestingly, Pattern (b) (two followers are strongly dependent on each other) was also observed when visual stimuli were used. That is, even though two followers were not syncing so well with the leader, they could still manage to maintain the rhythm by syncing "locally" between the followers. This result suggests that, in an orchestra, it can be useful to match the timing first on a part-by-part basis, and then to match the overall timing by looking at the conductor

Homogeneous intrinsic neuronal excitability induces overfitting to sensory noise: A robot model of neurodevelopmental disorder

Neurodevelopmental disorders, including autism spectrum disorder, have been intensively investigated at the neural, cognitive, and behavioral levels, but the accumulated knowledge remains fragmented. Here, we propose a mechanistic explanation that unifies the disparate observations via a hierarchical predictive coding and developmental learning framework, which is demonstrated in experiments using a neural network-controlled robot. The results show that, through the developmental learning process, homogeneous intrinsic neuronal excitability at the neural level induced via self-organization changes at the information-processing level, such as hyper sensory precision and overfitting to sensory noise. These changes led to inflexibility, reduced generalization, and motor clumsiness at the behavioral level. These findings might bridge various levels of understandings in autism spectrum and other neurodevelopmental disorders and provide insights into the disease processes underlying observed behaviors and brain activities in individual patients. This study shows the potential of neurorobotics frameworks for modeling how psychiatric disorders arise from dynamic interactions among the brain, body, and uncertain environments

Paradoxical sensory reactivity induced by functional disconnection in a robot model of neurodevelopmental disorder

Hyper- and hyporeactivity to sensory stimuli is a diagnostic feature of autism spectrum disorder and has been reported in many neurodevelopmental disorders. However, the computational mechanisms underlying such paradoxical responses remain unclear. Here, using a robot controlled by a hierarchical recurrent neural network model with predictive processing and a learning mechanism, we simulated how functional disconnection alters the learning process and affects subsequent behavioral reactivity to environmental change. The results show that, through the learning process, functional disconnection between distinct network levels simultaneously lowered the precision of sensory information and higher-level prediction. These changes caused the robot to exhibit sensory-dominated and sensory-ignoring behaviors ascribed to sensory hyperreactivity and hyporeactivity, respectively. Furthermore, local functional disconnection at the sensory processing level similarly induced hyporeactivity due to low sensory precision. These findings suggest a computational explanation for co-existing sensory hyper- and hyporeactivity and insights at various levels of understanding in neurodevelopmental disorders

Extracting multi-modal dynamics of objects using RNNPB

Abstract- Dynamic features play an important role in recognizing objects that have similar static features in colors and or shapes. This paper focuses on active sensing that exploits dynamic feature of an object. An extended version of the robot, Robovie-IIs, moves an object by its arm to obtain its dynamic features. Its issue is how to extract symbols from various kinds of temporal states of the object. We use the recurrent neural network with parametric bias (RNNPB) that generates selforganized nodes in the parametric bias space. The RNNPB with 42 neurons was trained with the data of sounds, trajectories, and tactile sensors generated while the robot was moving/hitting an object with its own arm. The clusters of 20 kinds of objects were successfully self-organized. The experiments with unknown (not trained) objects demonstrated that our method configured them in the PB space appropriately, which proves its generalization capability

Deposition of long-range transported particulate matter on the needle surfaces of Japanese cypress (Chamaecyparis obtusa) grown in Nagasaki located in the western region of Japan

To characterize the deposition of long-range transported particulate matter (PM) on the foliar surface of Japanese forest trees, we periodically collected the PM deposited on the needle surface of mature Japanese cypress (Chamaecyparis obtusa) grown in the mountainous area of Nagasaki located in the western region of Japan from 24 April to 30 October, 2017. Metal element compositions and concentration ratios along with the ratios of Pb isotopes in the PM were analyzed. The total amount of metal elements (Na, Mg, Al, K, Ca, V, Cr, Mn, Fe, Ni, Cu, Zn, As and Pb) on the needle surfaces was relatively low during summer and autumn but was high during the spring when there was high atmospheric concentration of PM with diameter less than 2.5 μm due to influence of outflow from Asian continent. The seasonal variations in the amounts of less-abundant metal elements (Al, V, Ni, Cu, Zn, As and Pb) exhibited similar trends. The Pb amount varied with Zn amount at a constant ratio of 0.4, which was close to the Pb /Zn ratio of PM in China. Most of the Pb isotope ratios ( 207 Pb/ 206 Pb and 208 Pb / 206 Pb) in the PM were close to those observed in the Chinese coal. Therefore, Pb on the needle could have originated from the coal combustion in China. The enrichment factor (EF) of Pb ranged from 650 to 2270, and was significantly correlated with the 207 Pb / 206 Pb ratios, suggesting that components having EFs of greater than 650 could have originated from anthropogenic source. The amount of Pb significantly correlated with that of Ni, Cu, Zn, and As, which showed EFs more than 650. These results indicate that the long-range transported PM, including Pb, Ni, Cu, Zn, and As, originated from the anthropogenic sources in China, and deposited on the needle of C. obtusa grown in Nagasaki