Robot in the mirror: toward an embodied computational model of mirror self-recognition

Self-recognition or self-awareness is a capacity attributed typically only to humans and few other species. The definitions of these concepts vary and little is known about the mechanisms behind them. However, there is a Turing test-like benchmark: the mirror self-recognition, which consists in covertly putting a mark on the face of the tested subject, placing her in front of a mirror, and observing the reactions. In this work, first, we provide a mechanistic decomposition, or process model, of what components are required to pass this test. Based on these, we provide suggestions for empirical research. In particular, in our view, the way the infants or animals reach for the mark should be studied in detail. Second, we develop a model to enable the humanoid robot Nao to pass the test. The core of our technical contribution is learning the appearance representation and visual novelty detection by means of learning the generative model of the face with deep auto-encoders and exploiting the prediction error. The mark is identified as a salient region on the face and reaching action is triggered, relying on a previously learned mapping to arm joint angles. The architecture is tested on two robots with a completely different face.Comment: To appear in KI - K\"unstliche Intelligenz - German Journal of Artificial Intelligence - Springe

Developmental Learning for Social Robots in Real-World Interactions

International audienceThis paper reports preliminary research work on applying developmental learning to social robotics for making human-robot interactions more instinctive and more natural. Developmental learning is an unsupervised learning strategy relying on the fact that the learning agent is intrinsically motivated, and is able to incrementally build its own representation of the world through its experiences of interaction with it. Our claim is that using developmental learning in social robots could dramatically change the way we envision human-robot interaction, notably by giving the robot an active role in the interaction building process, and even more importantly, in the way it autonomously learns suitable behaviors over time. Developmental learning appears to be an appropriate approach to develop a form of "interactional intelligence" for social robots. In this work, our goal was to set up a common framework for implementing, experimenting and evaluating developmental learning algorithms with various social robots

Brain-Inspired Coding of Robot Body Schema Through Visuo-Motor Integration of Touched Events

International audienceRepresenting objects in space is difficult because sensorimotor events are anchored in different reference frames, which can be either eye-, armor or target-centered. In the brain, Gain-Field (GF) neurons in the parietal cortex are involved in computing the necessary spatial transformations for aligning the tactile, visual and proprioceptive signals. In reaching tasks, these GF neurons exploit a mechanism based on multiplicative interaction for binding simultaneously touched events from the hand with visual and proprioception information.By doing so, they can infer new reference frames to represent dynamically the location of the body parts in the visual space (i.e., the body schema) and nearby targets (i.e., its peripersonal space). In this line, we propose a neural model based on GF neurons for integrating tactile events with arm postures and visual locations for constructing hand-and target-centered receptive fields in the visual space. In robotic experiments using an artificial skin, we show how our neural architecture reproduces the behaviors of parietal neurons (1) for encoding dynamically the body schema of our robotic arm without any visual tags on it and (2) for estimating the relative orientation and distance of targets to it. We demonstrate how tactile information facilitates the integration of visual and proprioceptive signals in order to construct the body space

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Temporal structure of neural oscillations underlying sensorimotor coordination: a theoretical approach with evolutionary robotics

The temporal structure of neural oscillations has become a widespread hypothetical \mechanism" to explain how neurodynamics give rise to neural functions. Despite the great number of empirical experiments in neuroscience and mathematical and computa- tional modelling investigating the temporal structure of the oscillations, there are still few systematic studies proposing dynamical explanations of how it operates within closed sensorimotor loops of agents performing minimally cognitive behaviours. In this thesis we explore this problem by developing and analysing theoretical models of evolutionary robotics controlled by oscillatory networks. The results obtained suggest that: i) the in- formational content in an oscillatory network about the sensorimotor dynamics is equally distributed throughout the entire range of phase relations; neither synchronous nor desyn- chronous oscillations carries a privileged status in terms of informational content in relation to an agent's sensorimotor activity; ii) although the phase relations of oscillations with a narrow frequency difference carry a relatively higher causal relevance than the rest of the phase relations to sensorimotor coordinations, overall there is no privileged functional causal contribution to either synchronous or desynchronous oscillations; and iii) oscilla- tory regimes underlying functional behaviours (e.g. phototaxis, categorical perception) are generated and sustained by the agent's sensorimotor loop dynamics, they depend not only on the dynamic structure of a sensory input but also on the coordinated coupling of the agent's motor-sensory dynamics. This thesis also contributes to the Coordination Dynam- ics framework (Kelso, 1995) by analysing the dynamics of the HKB (Haken-Kelso-Bunz) equation within a closed sensorimotor loop and by discussing the theoretical implications of such an analysis. Besides, it contributes to the ongoing philosophical debate about whether actions are either causally relevant or a constituent of cognitive functionalities by bringing this debate to the context of oscillatory neurodynamics and by illustrating the constitutive notion of actions to cognition

The neuropsychological measure (EEG) of flow under conditions of peak performance

Flow is a mental state characterised by a feeling of energised focus, complete involvement and success when fully immersed in an activity. The dimensions of and the conditions required for flow to occur have been explored in a broad spectrum of situational contexts. The close relationship between flow and peak performance sparked an interest in ways to induce flow. However, any process of flow induction requires a measure to trace the degree to which flow is in fact occurring. Self-reports of the flow experience are subjective and provide ad hoc information. Psycho-physiological measures, such as EEG, can provide objective and continuous indications of the degree to which flow is occurring. Unfortunately few studies have explored the relationships between psycho-physiological measures and flow. The present study was an attempt to determine the EEG correlates of flow under conditions of peak performance. Twenty participants were asked to perform a continuous visuomotor task 10 times. Time taken per task was used as an indicator of task performance. EEG recordings were done concurrently. Participants completed an Abbreviated Flow Questionnaire (AFQ) after each task and a Game Flow Inventory (GFI) after having finished all 10 tasks. On completion, performance times and associated flow scores were standardised where after the sample was segmented into a high flow - peak performance and a low flow - low performance level. Multi-variate analysis of variance (MANOVA) was conducted on the performance, flow and EEG data to establish that a significant difference existed between the two levels. In addition, a one-way analysis of variance between high and low flow data was conducted for all variables and main effects were established. Inter-correlations of all EEG data at both levels were then conducted across four brain sites (F3, C3, P3, O1). In high flow only, results indicated increased lobeta power in the sensorimotor cortex together with a unique EEG pattern showing beta band synchronisation between the prefrontal and sensori-motor areas and de-synchronisation between all other areas, while all other frequencies (delta, theta, alpha, lobeta, hibeta, and gamma) remained synchronised across all scalp locations. These findings supported a theoretical neuropsychological model of flow.PsychologyD. Com. (Consulting Psychology

Associative learning alone is insufficient for the evolution and maintenance of the human mirror neuron system

AbstractCook et al. argue that mirror neurons originate from associative learning processes, without evolutionary influence from social-cognitive mechanisms. We disagree with this claim and present arguments based upon cross-species comparisons, EEG findings, and developmental neuroscience that the evolution of mirror neurons is most likely driven simultaneously and interactively by evolutionarily adaptive psychological mechanisms and lower-level biological mechanisms that support them.</jats:p

Brain Computations and Connectivity [2nd edition]

This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics