According to the motor theories of perception, the motor systems of an observer are actively involved in the perception of actions when these are performed by a demonstrator. In this paper we review our computational architecture, HAMMER (Hierarchical Attentive Multiple Models for Execution and Recognition), where the motor control systems of a robot are organised in a hierarchical, distributed manner, and can be used in the dual role of (a) competitively selecting and executing an action, and (b) perceiving it when perfomed by a demonstrator. We subsequently demonstrate that such arrangement can provide a principled method for the top-down control of attention during action perception, resulting in significant performance gains. We assess these performance gains under a variety of resource allocation strategies.

Cleeremans: The search for the computational correlates of consciousness

We present a connectionist architecture that can learn a model of the relations between perceptions and actions and use this model for behavior planning. State representations are learned with a growing selforganizing layer which is directly coupled to a perception and a motor layer. Knowledge about possible state transitions is encoded in the lateral connectivity. Motor signals modulate this lateral connectivity and a dynamic field on the layer organizes a planning process. All mechanisms are local and adaptation is based on Hebbian ideas. The model is continuous in the action, perception, and time domain.

Computer games are being approached from a wide range of perspectives, but the activity of playing games, with the player and her actions in focus has, so far, not received much attention in academic research. Approaching games from a cognitive science perspective, however, it is argued in this paper that theories on embodied and situated cognition provide a strong basis for research on this particular issue since game play is a socially embodied and situated activity, shaped by the player’s bodily experience and her interactions with the game environment.

To be published in Behavioral and Brain Sciences (in press)

Awareness and autonomous interaction with the environment in a robotic system is the base of the new discipline of machine consciousness. In this paper we present the results of a first attempt in order to engineer these robotic systems by applying a Situational Method Engineering approach that extends PASSI and to create a model for conscious systems.

Several scientists suggested that certain perceptual qualities are based on sensorimotor anticipation: for example, the softness of a sponge is perceived by anticipating the sensations resulting from a grasping movement. For the perception of spatial arrangements, this article demonstrates that this concept can be realized in a mobile robot. The robot first learned to predict how its visual input changes under movement commands. With this ability, two perceptual tasks could be solved: judging the distance to an obstacle in front by ‘mentally ’ simulating a movement toward the obstacle, and recognizing a dead end by simulating either an obstacle-avoidance algorithm or a recursive search for an exit. A simulated movement contained a series of prediction steps. In each step, a multi-layer perceptron anticipated the next image, which, however, became increasingly noisy. To denoise an image, it was split into patches, and each patch was projected onto a manifold obtained by modeling the density of the distribution of training patches with a mixture of Gaussian functions. Key words: sensorimotor anticipation, vision, forward model, mobile robot, perception, multi-layer perceptron, image denoising, Gaussian mixture model ⋆ I am grateful to Ralf Möller for introducing me to the area of perception through sensorimotor anticipation, for numerous discussions, and for comments on the manuscript. Bruno Lara, Helmut Radrich, Karl-Heinz Honsberg, and Fiorello Banci helped setting up the robot. Furthermore, I thank the two anonymous reviewers, whose comments helped to improve the manuscript.

This paper proposes a brain-inspired cognitive architecture that incorporates approximations to the concepts of consciousness, emotion, and imagination. To emulate the empirically established cognitive efficacy of conscious as opposed to unconscious information processing in the mammalian brain, the architecture adopts a model of information flow from global workspace theory. Cognitive functions such as anticipation and planning are realised through internal simulation of interaction with the environment. Action selection, in both actual and internally simulated interaction with the environment, is mediated by affect. An implementation of the architecture is described which is based on weightless neurons and is used to control a simulated robot. 1

Abstract. In this paper an adaptive decision model based on predictive loops through feeling states is analysed from the perspective of rationality. Four different variations of Hebbian learning are considered for different types of connections in the decision model. To assess the extent of rationality, a measure is introduced reflecting the environment’s behaviour. Simulation results and the extents of rationality of the different models over time are presented and analysed.

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