Many studies suggest that the sustained activation underlying working memory (WM) maintenance is mediated by a distributed network that includes the prefrontal cortex and other structures (e.g., posterior parietal cortex, thalamus, globus pallidus, and the caudate nucleus). A computational model of WM, called FROST (short for FROntal-Striatal-Thalamic), is proposed in which the representation of items and spatial positions is encoded in the lateral prefrontal cortex. During delay intervals, activation in these prefrontal cells is sustained via parallel, prefrontal cortical-thalamic loops. Activation reverberates in these loops because prefrontal cortical excitation of the head of the caudate nucleus leads to disinhibition of the thalamus (via the globus pallidus). FROST successfully accounts for a wide variety of WM data, including single-cell recording data and human behavioral data.

Analogical transfer is the ability to transfer knowledge despite significant changes in the surface features of a problem. In categorization, analogical transfer occurs if a classification strategy learned with one set of stimuli can be transferred to a set of novel, perceptually distinct stimuli. Three experiments investigated analogical transfer in rule-based and information-integration categorization tasks. In rule-based tasks, the optimal strategy is easy to describe verbally, whereas in information-integration tasks, accuracy is maximized only if information from two or more stimulus dimensions is integrated in a way that is difficult or impossible to describe verbally. In all three experiments, analogical transfer was nearly perfect in the rule-based conditions, but no evidence for analogical transfer was found in the information-integration conditions. These results were predicted a priori by the COVIS theory of categorization.

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IntroductionTele-rehabiliation technologies that track human motion could enable physical therapy in the home. To be effective, these systems need to collect critical metrics without PT supervision both in real time and in a store and forward capacity. The first step of this process is to determine if PTs (PTs) are able to accurately assess the quality and quantity of an exercise repetition captured by a tele-rehabilitation platform. The purpose of this pilot project was to determine the level of agreement of quality and quantity of an exercise delivered and assessed by the Virtual Exercise Rehabilitation Assistant (VERA), and seven PTs.MethodsTen healthy subjects were instructed by a PT in how to perform four lower extremity exercises. Subjects then performed each exercises delivered by VERA which counted repetitions and quality. Seven PTs independently reviewed video of each subject’s session and assessed repetitions quality. The percent difference in total repetitions and analysis of the distribution of rating repetition quality was assessed between the VERA and PTs.ResultsThe VERA counted 426 repetitions across 10 subjects performing the four different exercises while the mean repetition count from the PT panel was 426.7 (SD = 0.8). The VERA underestimated the total repetitions performed by 0.16% (SD = 0.03%, 95% CI 0.12 – 0. 22). Chi square analysis across raters was χ2 = 63.17 (df = 6, p<.001), suggesting significant variance in at least one rater.ConclusionThe VERA count of repetitions was accurate in comparison to a seven member panel of PTs. For exercise quality the VERA was able to rate 426 exercise repetitions across 10 patients and four different exercises in a manner consistent with five out of seven experienced PTs.

There is much recent interest in the question of whether people have available a single category learning system or a number of qualitatively different systems. Most proponents of multiple systems have hypothesized an explicit, rule-based system and some type of implicit system. Although there has been general agreement about the nature of the explicit system, there has been disagreement about the exact nature of the implicit system. This chapter explores the question of whether there is implicit category learning, and if there is, what form it might take. First, we examine what the word "implicit" means in the categorization literature. Next, we review some of the evidence that supports the notion that people have available one or more implicit categorization systems. Finally, we consider the nature of implicit categorization by focusing on three alternatives: an exemplar memory-based system, a procedural memory system, and an implicit system that uses the perceptual representation memory system.