Arguments for a Computational Model for Forms of Selective Attention Based on Cognitive and Affective Feelings

We present arguments for a computational model of selective attention that relies on the assumption that uncertain, surprising and motive congruent/incongruent information demands attention from an intelligent agent. This computational model that we propose has been integrated into the architecture of a Belief-Desire-Intention artificial agent so that this can autonomously select relevant, interesting information of the (external or internal) environment while ignoring other less relevant information. The advantage is that the agent can communicate only that interesting, selective information to its processing resources (focus of the senses, decision-making, etc.) or to its human owner´s processing resources so that these resources can be allocated more effectively. We provide both theoretical and empirical arguments for that computational model.