A fundamental question is whether people have available one category learning system, or many. Most multiple systems advocates postulate one explicit and one implicit system. Although there is much agreement about the nature of the explicit system, there is less agreement about the nature of the implicit system. In this article, we review a dual systems theory of category learning called competition between verbal and implicit systems (COVIS) developed by Ashby et al. (1998). The explicit system dominates the learning of verbalizable, rule-based category structures and is mediated by frontal brain areas such as the anterior cingulate, prefrontal cortex (PFC), and head of the caudate nucleus. The implicit system, which uses procedural learning, dominates the learning of non-verbalizable, information-integration category structures, and is mediated by the tail of the caudate nucleus and a dopamine-mediated reward signal. We review nine studies that test six a priori predictions from COVIS, each of which is supported by the data.

Two imaging studies were performed -- one of an algebraic transformation task studied by Anderson, Reder, and Lebiere (1996) and the other of an abstraction symbol manipulation task studied by Blessing and Anderson (1996). ACT-R models exist that carefully model the latency patterns in these tasks. These models require activity of an imaginal buffer to represent changes in the problem representation, in a retrieval buffer to hold information from declarative memory, and in a manual buffer to hold information about motor behavior. A general theory is described about how to map activity in these buffers onto the fMRI bold response. This theory claims that the BOLD response is integrated over the duration a buffer is active and can be used to predict the observed BOLD function. Activity in the imaginal buffer is shown to predict the BOLD response in a left, posterior parietal region; activity in the retrieval buffer is shown to predict the BOLD response in a left DLPFC region; and activity in the manual buffer is shown to predict activity in a motor region. Cognitive models have been increasingly successful at accounting for complex data sets on problem-solving (Anderson & Lebiere, 1998; Meyer & Kieras, 1997; Pew & Mavor, 1998). Largely, these cognitive models have focused on reaction time and accuracy and usually only final times and accuracies. These models often specify rather complex sequences of unseen processes taking place over many seconds. Even when the pattern of data they fit is correspondingly complex, one is naturally wary about a chain of inferences about unseen processes. It would be better if we could have data about these intervening processes. Basically, more converging data would be better. This paper will demonstrate the potential of functional magnetic...

This paper presents a computational theory of developmental mental architectures for artificial and natural systems, motivated by neuroscience. The work is an attempt to approximately model biological mental architectures using mathematical tools. Six types of architecture are presented, beginning with the observation-driven Markov decision process as Type-1. From Type-1 to Type-6, the architecture progressively becomes more complete toward the necessary functions of autonomous mental development. Properties of each type are presented. Experiments are discussed with emphasis on their architectures. r 2007 Published by Elsevier B.V.

smart people can be so stupid (pp. 124-158). New Haven, CT: Yale

this article I had in mind when trying to find a name for the model that I will describe in this chapter. Although at a different level, I too want to make explicit goals of connectionism, to state some fundamentals and to describe its relation to other participants within the cognitive field. The `level of entry' is different, however, since the model describes in a methodological way the manner in which connectionist progress goes and can therefore be taken to be a `meta'-view. Smolensky fills in important parts (i.e. the introduction of subsymbols) in the contents of the connectionist approach. I want to state some fundamental hypotheses about the nature of progress within this field and I will use the model to look at the differences between connectionism and symbolism. Together with the shape the model has taken, `Stagewise Treatment of Connectionism' (STC) seemed to be a logical choice. What will follow is a first introduction of these stages and the way they are connected. To give an indication of why these four stages are appropriate, quotes from several `leading figures' will be given to indicate on what basis the concept of these stages has been developed. In the next section, this first approximation of the model will be expanded to give a complete picture of the several ways in which artificial neural networks are currently applied to gain insights into the workings of brains. This will be followed by a simple example of the application of STC. So let's take a look at the way it works. Stage 1 Modelling on the basis of assumptions about the brain. The first of the four stages is the generation of network models based on some very basic assumptions about the brain, i.e. there are elements which are connected with each other through connections which can be mo...