Brain-inspired bodily self-perception model for robot rubber hand illusion

The core of bodily self-consciousness involves perceiving ownership of one's body. A central question is how body illusions like the rubber hand illusion (RHI) occur. Existing theoretical models still lack satisfying computational explanations from connectionist perspectives, especially for how the brain encodes body perception and generates illusions from neuronal interactions. Moreover, the integration of disability experiments is also neglected. Here, we integrate biological findings of bodily self-consciousness to propose a brain-inspired bodily self-perception model by which perceptions of bodily self are autonomously constructed without any supervision signals. We successfully validated the model with six RHI experiments and a disability experiment on an iCub humanoid robot and simulated environments. The results show that our model can not only well-replicate the behavioral and neural data of monkeys in biological experiments but also reasonably explain the causes and results of RHI at the neuronal level, thus contributing to the revelation of mechanisms underlying RHI.

Audio-Tactile and Peripersonal Space Processing Around the Trunk in Human Parietal and Temporal Cortex: An Intracranial EEG Study

Interactions with the environment happen within one’s peripersonal space (PPS)—the space surrounding the body. Studies in monkeys and humans have highlighted a multisensory distributed cortical network representing the PPS. However, knowledge about the temporal dynamics of PPS processing around the trunk is lacking. Here, we recorded intracranial electroencephalography (iEEG) in humans while administering tactile stimulation (T), approaching auditory stimuli (A), and the 2 combined (AT). To map PPS, tactile stimulation was delivered when the sound was far, intermediate, or close to the body. The 19% of the electrodes showed AT multisensory integration. Among those, 30% showed a PPS effect, a modulation of the response as a function of the distance between the sound and body. AT multisensory integration and PPS effects had similar spatiotemporal characteristics, with an early response (~50 ms) in the insular cortex, and later responses (~200 ms) in precentral and postcentral gyri. Superior temporal cortex showed a different response pattern with AT multisensory integration at ~100 ms without a PPS effect. These results, represent the first iEEG delineation of PPS processing in humans and show that PPS and multisensory integration happen at similar neural sites and time periods, suggesting that PPS representation is based on a spatial modulation of multisensory integration.

An Application of Deep Learning to Tactile Data for Object Recognition under Visual Guidance

Drawing inspiration from haptic exploration of objects by humans, the current work proposes a novel framework for robotic tactile object recognition, where visual information in the form of a set of visually interesting points is employed to guide the process of tactile data acquisition. Neuroscience research confirms the integration of cutaneous data as a response to surface changes sensed by humans with data from joints, muscles, and bones (kinesthetic cues) for object recognition. On the other hand, psychological studies demonstrate that humans tend to follow object contours to perceive their global shape, which leads to object recognition. In compliance with these findings, a series of contours are determined around a set of 24 virtual objects from which bimodal tactile data (kinesthetic and cutaneous) are obtained sequentially and by adaptively changing the size of the sensor surface according to the object geometry for each object. A virtual Force Sensing Resistor array (FSR) is employed to capture cutaneous cues. Two different methods for sequential data classification are then implemented using Convolutional Neural Networks (CNN) and conventional classifiers, including support vector machines and k-nearest neighbors. In the case of conventional classifiers, we exploit contourlet transformation to extract features from tactile images. In the case of CNN, two networks are trained for cutaneous and kinesthetic data and a novel hybrid decision-making strategy is proposed for object recognition. The proposed framework is tested both for contours determined blindly (randomly determined contours of objects) and contours determined using a model of visual attention. Trained classifiers are tested on 4560 new sequential tactile data and the CNN trained over tactile data from object contours selected by the model of visual attention yields an accuracy of 98.97% which is the highest accuracy among other implemented approaches.

Over my fake body: body ownership illusions for studying the multisensory basis of own-body perception

Which is my body and how do I distinguish it from the bodies of others, or from objects in the surrounding environment? The perception of our own body and more particularly our sense of body ownership is taken for granted. Nevertheless, experimental findings from body ownership illusions (BOIs), show that under specific multisensory conditions, we can experience artificial body parts or fake bodies as our own body parts or body, respectively. The aim of the present paper is to discuss how and why BOIs are induced. We review several experimental findings concerning the spatial, temporal, and semantic principles of crossmodal stimuli that have been applied to induce BOIs. On the basis of these principles, we discuss theoretical approaches concerning the underlying mechanism of BOIs. We propose a conceptualization based on Bayesian causal inference for addressing how our nervous system could infer whether an object belongs to our own body, using multisensory, sensorimotor, and semantic information, and we discuss how this can account for several experimental findings. Finally, we point to neural network models as an implementational framework within which the computational problem behind BOIs could be addressed in the future.

A neural network model can explain ventriloquism aftereffect and its generalization across sound frequencies

Exposure to synchronous but spatially disparate auditory and visual stimuli produces a perceptual shift of sound location towards the visual stimulus (ventriloquism effect). After adaptation to a ventriloquism situation, enduring sound shift is observed in the absence of the visual stimulus (ventriloquism aftereffect). Experimental studies report opposing results as to aftereffect generalization across sound frequencies varying from aftereffect being confined to the frequency used during adaptation to aftereffect generalizing across some octaves. Here, we present an extension of a model of visual-auditory interaction we previously developed. The new model is able to simulate the ventriloquism effect and, via Hebbian learning rules, the ventriloquism aftereffect and can be used to investigate aftereffect generalization across frequencies. The model includes auditory neurons coding both for the spatial and spectral features of the auditory stimuli and mimicking properties of biological auditory neurons. The model suggests that different extent of aftereffect generalization across frequencies can be obtained by changing the intensity of the auditory stimulus that induces different amounts of activation in the auditory layer. The model provides a coherent theoretical framework to explain the apparently contradictory results found in the literature. Model mechanisms and hypotheses are discussed in relation to neurophysiological and psychophysical data.

Dynamic characteristics of multisensory facilitation and inhibition

Multimodal integration, which mainly refers to multisensory facilitation and multisensory inhibition, is the process of merging multisensory information in the human brain. However, the neural mechanisms underlying the dynamic characteristics of multimodal integration are not fully understood. The objective of this study is to investigate the basic mechanisms of multimodal integration by assessing the intermodal influences of vision, audition, and somatosensory sensations (the influence of multisensory background events to the target event). We used a timed target detection task, and measured both behavioral and electroencephalographic responses to visual target events (green solid circle), auditory target events (2 kHz pure tone) and somatosensory target events (1.5 ± 0.1 mA square wave pulse) from 20 normal participants. There were significant differences in both behavior performance and ERP components when comparing the unimodal target stimuli with multimodal (bimodal and trimodal) target stimuli for all target groups. Significant correlation among reaction time and P3 latency was observed across all target conditions. The perceptual processing of auditory target events (A) was inhibited by the background events, while the perceptual processing of somatosensory target events (S) was facilitated by the background events. In contrast, the perceptual processing of visual target events (V) remained impervious to multisensory background events.