Abstract not found

A computational model of the context processing is presented. It is shown in computer simulations how a stable context representation can be learned from a dynamic sequence of attentional shifts between various stimuli in the environment. The mechanism can automatically create the required context representations, store memories of stimuli and bind them to locations. The model also shows how an explicit matching between expected and actual stimuli can be used for novelty detection.

We present an attention system that models the dynamics that occur in memory in response to stimuli, which includes habituation, novelty detection, and forgetting. We demonstrate how such an attention system can be used as a trigger for learning perception-action mappings. We discuss the value of social situatedhess in the form demonstrator-learner interactions, and show results fi'om both simulations and robot-human experiments of a simple wall-following task.

Abstract—Selective visual attention in a natural environment can be seen as the interaction between the external visual stimulus and task specific knowledge of the required behavior. This interaction between the bottom-up stimulus and the top-down, task-related knowledge is crucial for what is selected in the space and time within the scene. In this paper, we propose a computational model for selective attention for a visual search task. We go beyond simple saliency-based attention models to model selective attention guided by top-down visual cues, which are dynamically integrated with the bottom-up information. In this way, selection of a location is accomplished by interaction between bottom-up and top-down information. First, the general structure of our model is briefly introduced and followed by a description of the top-down processing of task-relevant cues. This is then followed by a description of the processing of the external images to give three feature maps that are combined to give an overall bottom-up map. Second, the development of the formalism for our novel interactive spiking neural network (ISNN) is given, with the interactive activation rule that calculates the integration map. The learning rule for both bottom-up and top-down weight parameters are given, together with some further analysis of the properties of the resulting ISNN. Third, the model is applied to a face detection task to search for the location of a specific face that is cued. The results show that the trajectories of attention are dramatically changed by interaction of information and variations of cues, giving an appropriate, task-relevant search pattern. Finally, we discuss ways in which these results can be seen as compatible with existing psychological evidence. Index Terms—Attention, bottom-up map, computer vision, cueguided search, top-down map.

We describe a behavioural experiment with the hawkmoth Deilephila elpenor and show how its behaviour in the experimental situation can be reproduced by a computational model. The aim of the model is to investigate what learning strategies are necessary to produce the behaviour observed in the experiment. Since very little is known about the nervous system of the animal, the model is mainly based on behavioural data and the sensitivities of its photoreceptors. The model consists of a number of interacting behaviour systems that are triggered by specific stimuli and control specific behaviours. The ability of the moth to learn the colours of different flowers and the adaptive processes involved in the choice between stimulus-approach and place-approach strategies is also modelled. The behavioural choices of the simulated model closely parallel those of the real animal. The model has implications both for the ecology of the animal and for robotic systems.

This dissertation presents Behaviour-Based Learning (BBL), a methodology, for developing rapidly adapting behaviours in Behaviour-Based (BB) robots. BBL deals with a set of current issues related to learning in robots, in particular: speed of adaptation, the use of domain knowledge, problem restriction, robustness and integration of programmability and adaptivity.

A computational model with three interacting components for context sensitive reinforcement learning, context processing and automation can autonomously learn a focus attention and a shift attention task. The performance of the model is similar to that of normal children, and when a single parameter is changed, the performance on the two tasks approaches that of autistic children. 1.

A computational model of learning in visual attention is described, and it is shown how it can integrate information over time to form a representation of the visual context.

A bio-inspired computational model of covert attention and learning