The basal ganglia play a central role in cognition and are involved in such general functions as action selection and reinforcement learning. Here, we present a model exploring the hypothesis that the basal ganglia implement a conditional information-routing system. The system directs the transmission of cortical signals between pairs of regions by manipulating separately the selection of sources and destinations of information transfers. We suggest that such a mechanism provides an account for several cognitive functions of the basal ganglia. The model also incorporates a possible mechanism by which subsequent transfers of information control the release of dopamine. This signal is used to produce novel stimulus–response associations by internalizing transferred cortical representations in the striatum. We discuss how the model is related to production systems and cognitive architectures. A series of simulations is presented to illustrate how the model can perform simple stimulus–response tasks, develop automatic behaviors, and provide an account of impairments in Parkinson’s and Huntington’s diseases.

In this chapter, we review computer models of cognition that have focused on the use of neural networks. These architectures were inspired by research into how computation works in the brain and subsequent work has produced models of cognition with a distinctive flavor. Processing is characterized by patterns of

Abstract. Stimuli and activations of mental states usually induce emotional responses that are experienced by their associated feelings. On the one hand the experienced emotions often have their effect in turn on activations of other mental states and on behaviour, and on the other hand these experiences have an adaptive effect on the strength of such emotional responses in the future. This paper concentrates on the latter type of dependency: the strength by which emotional responses are induced depends on previous experiences to which adaptation has taken place. The paper presents a comparative analysis of three adaptive dynamic modelling approaches addressing how these induction strengths are adapted over time. As a basis a dynamical model for emotional responses and associated feelings is taken, based on a recursive as-if body loop. Next three dynamical models are presented for adaptation of the strength of the emotional response based on experiences. Example simulation results are shown and commonalities and differences between the models are discussed. 1

An agent's beliefs usually depend on informational or cognitive factors such as observation or received communication or reasoning, but also affective factors may play a role. In this paper, by adopting neurological theories on the role of emotions and feelings, an agent model is introduced incorporating the interaction between cognitive and affective factors in believing. The model describes how the strength of a belief may not only depend on information obtained, but also on the emotional responses on the belief. For feeling emotions a recursive body loop between preparations for emotional responses and feelings is assumed. The model introduces a second feedback loop for the interaction between feeling and belief. The strength of a belief and of the feeling both result from the converging dynamic pattern modelled by the combination of the two loops. For some specific cases it is described, for example, how for certain personal characteristics an optimistic world view is generated in the agent’s beliefs, or, for other characteristics, a pessimistic world view. Moreover, the paper shows how such affective effects on beliefs can emerge and become stronger over time due to experiences obtained. It is shown how based on Hebbian learning a connection from feeling to belief can develop. As these connections affect the strenghts of future beliefs, in this way an effect of judgment ‘by experience built up in the past ’ or ‘by gut feeling ’ can be obtained. Some example simulation results and a mathematical analysis of the equilibria are presented. 1

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