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Perceiving the surrounding environment in terms of objects is useful for any general purpose intelligent agent. In this paper, we investigate a fundamental mechanism making object perception possible, namely the identification of spatio-temporally invariant structures in the sensorimotor experience of an agent. We take inspiration from the Sensorimotor Contingencies Theory to define a computational model of this mechanism through a sensorimotor, unsupervised and predictive approach. Our model is based on processing the unsupervised interaction of an artificial agent with its environment. We show how spatio-temporally invariant structures in the environment induce regularities in the sensorimotor experience of an agent, and how this agent, while building a predictive model of its sensorimotor experience, can capture them as densely connected subgraphs in a graph of sensory states connected by motor commands. Our approach is focused on elementary mechanisms, and is illustrated with a set of simple experiments in which an agent interacts with an environment. We show how the agent can build an internal model of moving but spatio-temporally invariant structures by performing a Spectral Clustering of the graph modeling its overall sensorimotor experiences. We systematically examine properties of the model, shedding light more globally on the specificities of the paradigm with respect to methods based on the supervised processing of collections of static images.

When two individuals interact in a collaborative task, such as carrying a sofa or a table, usually spatiotemporal coordination of individual motor behavior will emerge. In many cases, interpersonal coordination can arise independently of verbal communication, based on the observation of the partners' movements and/or the object's movements. In this study, we investigate how social coupling between two individuals can emerge in a collaborative task under different modes of perceptual information. A visual reference condition was compared with three different conditions with new types of additional auditory feedback provided in real time: effect-based auditory feedback, performance-based auditory feedback, and combined effect/performance-based auditory feedback. We have developed a new paradigm in which the actions of both participants continuously result in a seamlessly merged effect on an object simulated by a tablet computer application. Here, participants should temporally synchronize their movements with a 90° phase difference and precisely adjust the finger dynamics in order to keep the object (a ball) accurately rotating on a given circular trajectory on the tablet. Results demonstrate that interpersonal coordination in a joint task can be altered by different kinds of additional auditory information in various ways.

When two individuals interact in a collaborative task, such as carrying a sofa or a table, usually spatiotemporal coordination of individual motor behavior will emerge. In many cases, interpersonal coordination can arise independently of verbal communication, based on the observation of the partners' movements and/or the object's movements. In this study, we investigate how social coupling between two individuals can emerge in a collaborative task under different modes of perceptual information. A visual reference condition was compared with three different conditions with new types of additional auditory feedback provided in real time: effect-based auditory feedback, performance-based auditory feedback, and combined effect/performance-based auditory feedback. We have developed a new paradigm in which the actions of both participants continuously result in a seamlessly merged effect on an object simulated by a tablet computer application. Here, participants should temporally synchronize their movements with a 90° phase difference and precisely adjust the finger dynamics in order to keep the object (a ball) accurately rotating on a given circular trajectory on the tablet. Results demonstrate that interpersonal coordination in a joint task can be altered by different kinds of additional auditory information in various ways. [ABSTRACT FROM AUTHOR]

Perceiving the surrounding environment in terms of objects is useful for any general purpose intelligent agent. In this paper, we investigate a fundamental mechanism making object perception possible, namely the identification of spatio-temporally invariant structures in the sensorimotor experience of an agent. We take inspiration from the Sensorimotor Contingencies Theory to define a computational model of this mechanism through a sensorimotor, unsupervised and predictive approach. Our model is based on processing the unsupervised interaction of an artificial agent with its environment. We show how spatio-temporally invariant structures in the environment induce regularities in the sensorimotor experience of an agent, and how this agent, while building a predictive model of its sensorimotor experience, can capture them as densely connected subgraphs in a graph of sensory states connected by motor commands. Our approach is focused on elementary mechanisms, and is illustrated with a set of simple experiments in which an agent interacts with an environment. We show how the agent can build an internal model of moving but spatio-temporally invariant structures by performing a Spectral Clustering of the graph modeling its overall sensorimotor experiences. We systematically examine properties of the model, shedding light more globally on the specificities of the paradigm with respect to methods based on the supervised processing of collections of static images., (Copyright © 2018 Le Hir, Sigaud and Laflaquière.)