How active perception and attractor dynamics shape perceptual categorization: A computational model

We propose a computational model of perceptual categorization that fuses elements of grounded and sensorimotor theories of cognition with dynamic models of decision-making. We assume that category information consists in anticipated patterns of agent–environment interactions that can be elicited through overt or covert (simulated) eye movements, object manipulation, etc. This information is firstly encoded when category information is acquired, and then re-enacted during perceptual categorization. The perceptual categorization consists in a dynamic competition between attractors that encode the sensorimotor patterns typical of each category; action prediction success counts as ‘‘evidence’’ for a given category and contributes to falling into the corresponding attractor. The evidence accumulation process is guided by an active perception loop, and the active exploration of objects (e.g., visual exploration) aims at eliciting expected sensorimotor patterns that count as evidence for the object category. We present a computational model incorporating these elements and describing action prediction, active perception, and attractor dynamics as key elements of perceptual categorizations. We test the model in three simulated perceptual categorization tasks, and we discuss its relevance for grounded and sensorimotor theories of cognition.Peer reviewe

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

Mind out of matter: topics in the physical foundations of consciousness and cognition

This dissertation begins with an exploration of a brand of dual aspect monism and some problems deriving from the distinction between a first person and third person point of view. I continue with an outline of one way in which the conscious experience of the subject might arise from organisational properties of a material substrate. With this picture to hand, I first examine theoretical features at the level of brain organisation which may be required to support conscious experience and then discuss what bearing some actual attributes of biological brains might have on such experience. I conclude the first half of the dissertation with comments on information processing and with artificial neural networks meant to display simple varieties of the organisational features initially described abstractly.While the first half begins with a view of conscious experience and infers downwards in the organisational hierarchy to explore neural features suggested by the view, attention in the second half shifts towards analysing low level dynamical features of material substrates and inferring upwards to possible effects on experience. There is particular emphasis on clarifying the role of chaotic dynamics, and I discuss relationships between levels of description of a cognitive system and comment on issues of complexity, computability, and predictability before returning to the topic of representation which earlier played a central part in isolating features of brain organisation which may underlie conscious experience.Some themes run throughout the dissertation, including an emphasis on understanding experience from both the first person and the third person points of view and on analysing the latter at different levels of description. Other themes include a sustained effort to integrate the picture offered here with existing empirical data and to situate current problems in the philosophy of mind within the new framework, as well as an appeal to tools from mathematics, computer science, and cognitive science to complement the more standard philosophical repertoire

On the mechanical contribution of head stabilization to passive dynamics of anthropometric walkers

During the steady gait, humans stabilize their head around the vertical orientation. While there are sensori-cognitive explanations for this phenomenon, its mechanical e fect on the body dynamics remains un-explored. In this study, we take profit from the similarities that human steady gait share with the locomotion of passive dynamics robots. We introduce a simplified anthropometric D model to reproduce a broad walking dynamics. In a previous study, we showed heuristically that the presence of a stabilized head-neck system significantly influences the dynamics of walking. This paper gives new insights that lead to understanding this mechanical e fect. In particular, we introduce an original cart upper-body model that allows to better understand the mechanical interest of head stabilization when walking, and we study how this e fect is sensitive to the choice of control parameters

Using Echo State Networks for Robot Navigation Behavior Acquisition

International audienceRobot Behavior Learning by Demonstration deals with the ability for a robot to learn a behavior from one or several demonstrations provided by a human teacher, possibly through tele-operation or imitation. This implies controllers that can address both (1) the feature selection problem related to a great amount of mostly irrelevant sensory data and (2) dealing with temporal sequences of demonstrations. Echo State Networks have been proposed recently for time series prediction and have been shown to perform remarkably well on this kind of data. In this paper, we introduce ESN to robot behavior acquisition in the scope of a mobile robot performing navigation tasks. ESN actually show comparable and even better performance with that of other algorithms from the literature in similar experimental conditions. Moreover, some properties regarding dynamics of ESN in the context of learning by demonstration are investigated

A discrete computational model of sensorimotor contingencies for object perception and control of behavior

Abstract-According to Sensorimotor Contingency Theory (SCT), visual awareness in humans emerges from the specific properties of the relation between the actions an agent performs on an object and the resulting changes in sensory stimulation. The main consequence of this approach is that perception is based not only on information coming from the sensory system, but requires knowledge about the actions that caused this input. For the development of autonomous artificial agents the conclusion is that consideration of the actions, that cause changes in sensory measurements, could result in a more human-like performance in object recognition and manipulation than ever more sophisticated analyses of the sensory signal in isolation, an approach that has not been fully explored yet. We present the first results of a modeling study elucidating computational mechanisms implied by adopting SCT for robot control, and demonstrate the concept in two artificial agents. The model is given in abstract, probabilistic terms that lead to straightforward implementations on a computer, but also allow for a neurophysiological grounding. After demonstrating the emergence of object-following behavior in a computer simulation of the model, we present results on object perception in a real robot controlled by the model. We show how the model accounts for aspects of the robot's embodiment, and discuss the role of memory, behavior, and value systems with respect to SCT as a cognitive control architecture

Opinions and Outlooks on Morphological Computation

Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals – e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the muscle-tendon system – and plants, but it has also been observed at the cellular and even at the molecular level – as seen, for example, in spontaneous self-assembly. The concept of morphological computation has served as an inspirational resource to build bio-inspired robots, design novel approaches for support systems in health care, implement computation with natural systems, but also in art and architecture. As a consequence, the field is highly interdisciplinary, which is also nicely reflected in the wide range of authors that are featured in this e-book. We have contributions from robotics, mechanical engineering, health, architecture, biology, philosophy, and others

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review