Ezequiel A. Di Paolo

Can social interaction constitute social cognition

An important shift is taking place in social cognition research, away from a focus on the individual mind and toward embodied and participatory aspects of social understanding. Empirical results already imply that social cognition is not reducible to the workings of individual cognitive mechanisms. To galvanize this interactive turn, we provide an operational definition of social interaction and distinguish the different explanatory roles - contextual, enabling and constitutive - it can play in social cognition. We show that interactive processes are more than a context for social cognition: they can complement and even replace individual mechanisms. This new explanatory power of social interaction can push the field forward by expanding the possibilities of scientific explanation beyond the individual.

Evolutionary Robotics: A New Scientific Tool for Studying Cognition

We survey developments in artificial neural networks, in behavior-based robotics, and in evolutionary algorithms that set the stage for evolutionary robotics (ER) in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments, which is an essential aspect of real cognition that is often either bypassed or modeled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion, the origins of learning, and the ontogenetic acquisition of entrainment.

Defining Agency: Individuality, Normativity, Asymmetry, and Spatio-temporality in Action

The concept of agency is of crucial importance in cognitive science and artificial intelligence, and it is often used as an intuitive and rather uncontroversial term, in contrast to more abstract and theoretically heavily weighted terms such as intentionality , rationality, or mind. However, most of the available definitions of agency are too loose or unspecific to allow for a progressive scientific research program. They implicitly and unproblematically assume the features that characterize agents, thus obscuring the full potential and challenge of modeling agency. We identify three conditions that a system must meet in order to be considered as a genuine agent: (a) a system must define its own individuality, (b) it must be the active source of activity in its environment (interactional asymmetry), and (c) it must regulate this activity in relation to certain norms (normativity). We find that even minimal forms of proto-cellular systems can already provide a paradigmatic example of genuine agency. By abstracting away some specific details of minimal models of living agency we define the kind of organization that is capable of meeting the required conditions for agency (which is not restricted to living organisms). On this basis, we define agency as an autonomous organization that adaptively regulates its coupling with its environment and contributes to sustaining itself as a consequence. We find that spatiality and temporality are the two fundamental domains in which agency spans at different scales. We conclude by giving an outlook for the road that lies ahead in the pursuit of understanding, modeling, and synthesizing agents.

The interactive brain hypothesis

Enactive approaches foreground the role of interpersonal interaction in explanations of social understanding. This motivates, in combination with a recent interest in neuroscientific studies involving actual interactions, the question of how interactive processes relate to neural mechanisms involved in social understanding. We introduce the Interactive Brain Hypothesis (IBH) in order to help map the spectrum of possible relations between social interaction and neural processes. The hypothesis states that interactive experience and skills play enabling roles in both the development and current function of social brain mechanisms, even in cases where social understanding happens in the absence of immediate interaction. We examine the plausibility of this hypothesis against developmental and neurobiological evidence and contrast it with the widespread assumption that mindreading is crucial to all social cognition. We describe the elements of social interaction that bear most directly on this hypothesis and discuss the empirical possibilities open to social neuroscience. We propose that the link between coordination dynamics and social understanding can be best grasped by studying transitions between states of coordination. These transitions form part of the self-organization of interaction processes that characterize the dynamics of social engagement. The patterns and synergies of this self-organization help explain how individuals understand each other. Various possibilities for role-taking emerge during interaction, determining a spectrum of participation. This view contrasts sharply with the observational stance that has guided research in social neuroscience until recently. We also introduce the concept of readiness to interact to describe the practices and dispositions that are summoned in situations of social significance (even if not interactive). This latter idea links interactive factors to more classical observational scenarios.

behavioral coordination structural congruence and entrainment in a simulation of acoustically coupled agents

er for as long as possible using noisy continuous acoustic interaction. Evolved dynamical recurrent neural networks are used as the control architecture. Acoustic coupling poses nontrivial problems like discriminating ’self’ from ’non-self’ and structuring production of signals in time so as to minimize interference. Detailed observation of the most frequently evolved behavioral strategy shows that interacting agents perform rhythmic signals leading to the coordination of movement. During coordination, signals become entrained in an anti-phase mode that resembles turn-taking. Perturbation techniques show that signalling behavior not only performs an external function, but it is also integrated into the movement of the producing agent, thus showing the difficulty of separating behavior into social and non-social classes. Structural congruence between agents is shown by exploring internal dynamics as well as the response of single agents in the presence of signalling beacons that reproduce the signal patterns of the interacting agents. Lack of entrain-

How (not) to model autonomous behaviour

Autonomous systems are the result of self-sustaining processes of constitution of an identity under precarious circumstances. They may transit through different modes of dynamical engagement with their environment, from committed ongoing coping to open susceptibility to external demands. This paper discusses these two statements and presents examples of models of autonomous behaviour using methods in evolutionary robotics. A model of an agent capable of issuing self-instructions demonstrates the fragility of modelling autonomy as a function rather than as a property of a systems organization. An alternative model of behavioural preference based on homeostatic adaptation avoids this problem by establishing a mutual constraining between lower-level processes (neural dynamics and sensorimotor interaction) and higher-level metadynamics (experience-dependent, homeostatic triggering of local plasticity and re-organization). The results of these models are lessons about how strong autonomy should be approached: neither as a function, nor as a matter of external vs. internal determination.

A dynamical systems account of sensorimotor contingencies

According to the sensorimotor approach, perception is a form of embodied know-how, constituted by lawful regularities in the sensorimotor flow or in sensorimotor contingencies (SMCs) in an active and situated agent. Despite the attention that this approach has attracted, there have been few attempts to define its core concepts formally. In this paper, we examine the idea of SMCs and argue that its use involves notions that need to be distinguished. We introduce four distinct kinds of SMCs, which we define operationally. These are the notions of sensorimotor environment (open-loop motor-induced sensory variations), sensorimotor habitat (closed-loop sensorimotor trajectories), sensorimotor coordination (reliable sensorimotor patterns playing a functional role), and sensorimotor strategy (normative organization of sensorimotor coordinations). We make use of a minimal dynamical model of visually guided categorization to test the explanatory value of the different kinds of SMCs. Finally, we discuss the impact of our definitions on the conceptual development and empirical as well as model-based testing of the claims of the sensorimotor approach.

Rhythmic and non-rhythmic attractors in asynchronous random Boolean networks

In multi-component, discrete systems, such as Boolean networks and cellular automata, the scheme of updating of the individual elements plays a crucial role in determining their dynamic properties and their suitability as models of complex phenomena. Many interesting properties of these systems rely heavily on the use of synchronous updating of the individual elements. Considerations of parsimony have motivated the claim that, if the natural systems being modelled lack any clear evidence of synchronously driven elements, then random asynchronous updating should be used by default. The introduction of a random element precludes the possibility of strictly cyclic behaviour. In principle, this poses the question of whether asynchronously driven Boolean networks, cellular automata, etc., are inherently bad choices at the time of modelling rhythmic phenomena. This paper focuses on this subsidiary issue for the case of Asynchronous Random Boolean Networks (ARBNs). It defines measures of pseudo-periodicity between states and sufficiently relaxed statistical constraints. These measures are used to guide a genetic algorithm to find appropriate examples. Success in this search for a number of cases, and the subsequent statistical analysis lead to the conclusion that ARBNs can indeed be used as models of co-ordinated rhythmic phenomena, which may be stronger precisely because of their in-built asynchrony. The same technique is used to find non-stationary attractors that show no rhythm. Evidence suggests that the latter are more abundant than rhythmic attractor. The methodology is flexible, and allows for more demanding statistical conditions for defining pseudo-periodicity, and constraining the evolutionary search.

An investigation into the evolution of communication

This article presents a theoretical criticism of current approaches to the study of the evolution of communication. In particular, two very common preconceptions about the subject are analyzed: the role of natural selection in the definition of the phenomenon of communication and the metaphor of communication as information exchange. An alternative characterization is presented in terms of autopoietic theory, which avoids the mentioned preconceptions. In support of this view, the evolution of coordinated activity is studied in a population of artificial agents playing an interactional game. Dynamical modeling of this evolutionary process based on game-theoretical considerations shows the existence of an evolutionarily stable strategy in the total lack of coordinated activity which, however, may be unreachable due to the presence of a periodic attractor. In a computational model of the same game, action coordination evolves even with individual costs against it, due to the presence of spatial structuring processes. A detailed explanation of this phenomenon, which does not require kin selection, is presented. In an extended game, recursive coordination evolves nontrivially when the participants share all the relevant information, demonstrating that the metaphor of information exchange can be misleading. It is shown that agents engaged in this sort of interaction are able to perform beyond their individual capabilities.This article presents a theoretical criticism of current approaches to the study of the evolution of communication. In particular, two very common preconceptions about the subject are analyzed: the role of natural selection in the definition of the phenomenon of communication and the metaphor of communication as information exchange. An alternative characterization is presented in terms of autopoietic theory, which avoids the mentioned preconceptions. In support of this view, the evolution of coordinated activity is studied in a population of artificial agents playing an interactional game. Dynamical modeling of this evolutionary process based on game-theoretical considerations shows the existence of an evolutionarily stable strategy in the total lack of coordinated activity which, however, may be unreachable due to the presence of a periodic attractor. In a computational model of the same game, action coordination evolves even with individual costs against it, due to the presence of spatial structuring processes. A detailed explanation of this phenomenon, which does not require kin selection, is presented. In an extended game, recursive coordination evolves nontrivially when the participants share all the relevant information, demonstrating that the metaphor of information exchange can be misleading. It is shown that agents engaged in this sort of interaction are able to perform beyond their individual capabilities.

Modelling social interaction as perceptual crossing: an investigation into the dynamics of the interaction process

This paper continues efforts to establish a mutually informative dialogue between psychology and evolutionary robotics in order to investigate the dynamics of social interaction. We replicate a recent simulation model of a minimalist experiment in perceptual crossing and confirm the results with significantly simpler artificial agents. A series of psycho-physical tests of their behaviour informs a hypothetical circuit model of their internal operation. However, a detailed study of the actual internal dynamics reveals this circuit model to be unfounded, thereby offering a tale of caution for those hypothesising about sub-personal processes in terms of behavioural observations. In particular, it is shown that the behaviour of the agents largely emerges out of the interaction process itself rather than being an individual achievement alone. We also extend the original simulation model in two novel directions in order to test further the extent to which perceptual crossing between agents can self-organise in a robust manner. These modelling results suggest new hypotheses that can become the basis for further psychological experiments.