Neuroimaging Studies Of Category Learning

Functional magnetic resonance imaging (fMRI) studies also support the idea that there are multiple forms of categorization that depend on different brain systems. Application of explicit rules appears to activate frontal and parietal regions involved in working memory and selective attention. When nonverbal rules appear to guide responses, as in the probabilistic classification task described previously, activation has been reported in the caudate nucleus. Interestingly, this neostriatal activity was accompanied by a decrease in activity in the medial temporal lobe, suggesting that the explicit and implicit learning systems may act in a competitive manner.

Neuroimaging data also support the idea that there are different neural substrates for implicit category learning based on rules vs implicit category learning based on exemplar-based information. In addition to nonverbal rule learning tasks, amnesic patients are able to classify new items based on their similarity to a prototype abstracted from the training exemplars. Both types of category learning can proceed independently of the medial temporal lobe memory system. However, they appear to depend on different systems. Patients with Parkinson's disease are able to learn to classify dot patterns based on a learned prototype despite their impaired performance on nonverbal rule learning. Rather than a neostriatal locus, neuroima-ging evidence suggests that learning about these naturalistic categories may rely on regions involved in perceptual processing. After viewing dot pattern stimuli that are all distortions of a prototype, viewing the prototype dot pattern is accompanied by a decrease in blood flow in occipital regions. This blood flow decrease is similar to what is seen in perceptual priming paradigms. Items that had been previously presented trigger less blood flow in occipital regions than do new items. This blood flow decrease can be understood in terms of a decrease in the resources required to process primed stimuli vs new stimuli. Priming has often been considered to result because of increased ease, or fluency, of processing old stimuli relative to stimuli that have not been recently processed. This is thought to occur because of residual activity in neural representations activated by the initial presentation of the item. Categorization judgments may also be influenced by perceptual fluency. Presentation of a set of exemplars would result in residual activity in the neural representations of each individual exemplar. Because the prototype is the average of all the training exemplars, one might expect that the prototype might enjoy the greatest amount of enhanced processing from the summed residual activity of all the training exemplars. Under this scenario, the prototype is not represented separately from the training exemplars but, rather, emerges as a consequence of the overlapping neural representations of category exemplars.

This process may account for categories such as dot patterns that are defined merely by perceptual features. Can a similar perceptual fluency mechanism account for prototype effects seen in semantic categories? For example, it is a well-known finding that subjects are able to confirm that a robin is a bird compared to confirming that a penguin is a bird. It may be that as exemplars of a category are encountered, a link between the representation of the item and its category is formed. These exemplar representations would partially overlap given the degree to which features were shared. When primed with a category name, items near the prototype would benefit from the fact that they are similar to many individual exemplars that have been linked to the category. Thus, semantic priming would result in stronger semantic priming for even novel items near the prototype because of the wide partial activation induced by these items. Again, this phenomenon would occur as a consequence of overlapping semantic representations of individual exemplars.

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