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this article are also gratefully acknowledged

this paper. The cortex on the orbital surface of the frontal lobe includes area 13 caudally and area 14 medially, and the cortex on the inferior convexity includes area 12 caudally and area 11 anteriorly (Fig. 1) (Carmichael and Price, 1994; Petrides and Pandya, 1994; Price et al., 1996). This brain region is well developed in primates, including humans, but poorly developed in rodents, with homologies to areas found in primates uncertain, so that care must be used in interpretation of the term `orbitofrontal ' when applied to rodents (Uylings and van Eden, 1990). To understand the function of this brain region in humans, the majority of the studies described were therefore performed with macaques or with humans

The contribution of the striatum to category learning was examined by having patients with Parkinson's disease (PD) and matched controls solve categorization problems in which the optimal rule was linear or nonlinear using the perceptual categorization task. Traditional accuracy-based analyses, as well as quantitative model-based analyses were performed. Unlike accuracy-based analyses, the model-based analyses allow one to quantify and separate the effects of categorization rule learning from variability in the trial-by-trial application of the participant's rule. When the categorization rule was linear, PD patients showed no accuracy, categorization rule learning, or rule application variability deficits. Categorization accuracy for the PD patients was associated with their performance on a test believed to be sensitive to frontal lobe functioning. In contrast, when the categorization rule was nonlinear, the PD patients showed accuracy, categorization rule learning, and rule application variability deficits. Furthermore, categorization accuracy was not associated with performance on the test of frontal lobe functioning. Implications for neuropsychological theories of categorization learning are discussed. (JINS, 2001, 7, 710 --727.) Keywords: Categorization, Parkinson's disease, Striatum, Memory, Learning

Although perseveration---the inappropriate repetition of previous responses---is quite common among patients with neurological damage, relatively few detailed computational accounts of its various forms have been put forth. A particularly well-documented variety involves the pattern of errors made by "optic aphasic" patients, who have a selective deficit in naming visually-presented objects. Based on our previous work in modeling impaired reading for meaning in deep dyslexia, we develop a connectionist simulation of visual object naming. The major extension in the present work is the incorporation of short-term correlational weights that bias the network towards reproducing patterns of activity that have occurred on recently preceding trials. Under damage, the network replicates the complex semantic and perseverative effects found in the optic aphasic error pattern. Further analysis reveals that the perseverative effects are strongest when the lesions are near or within semanti...

switching Cognitive behaviour requires complex context-dependent mapping between sensory stimuli and actions. The same stimulus can lead to different behaviours depending on the situation, or the same behaviour may be elicited by different cueing stimuli. Neurons in the primate prefrontal cortex show task-speci®c ®ring activity during working memory delay periods. These neurons provide a neural substrate for mapping stimulus and response in a ¯exible, context- or rule-dependent, fashion. We describe here an integrate-and-®re network model to explain and investigate the different types of working-memory-related neuronal activity observed. The model contains different populations (or pools) of neurons (as found neurophysiologically) in attractor networks which respond in the delay period to the stimulus object, the stimulus position (`sensory pools'), to combinations of the stimulus sensory properties (e.g. the object identity or object location) and the response (`intermediate pools'), and to the response required (left or right) (`premotor pools'). The pools are arranged hierarchically, are linked by associative synaptic connections, and have global inhibition through inhibitory interneurons to implement competition. It is shown that a biasing attentional input to de®ne the current rule applied to the intermediate pools enables the system to select the correct response in what is a biased competition model of attention. The integrate-and-®re model not only produces realistic spiking dynamicals very similar to the neuronal data but also shows how dopamine could weaken and shorten the persistent neuronal activity in the delay period; and allows us to predict more response errors when dopamine is elevated because there

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dynamic task switching

The dominant cognitive theory of working memory (WM) postulates a strict architectural segregation between components responsible for the short-term active maintenance of information and those responsible for the control and coordination of that information. Cognitive neuroscience research has provided strong evidence that the prefrontal cortex (PFC) serves as an important neural substrate of WM. However, the literature is mixed as to whether PFC should be considered a storage or control component. A theory is presented that attempts to resolve this conflict by postulating that PFC represents and actively maintains context information. These maintained representations provide a mechanism of control by serving as a top-down bias on the local competitive interactions that occur during processing. As such, it is suggested that storage and control functions are integrated within PFC. This theory is implemented as connectionist computational model. Simulation studies are described which demonstrate that the model can account for a wide range of behavioral data associated with performance of a simple task paradigm that probes both the storage and control functions of WM. Two neuroimaging studies are then presented which directly test the predictions of the model regarding the role of PFC in context processing. Taken together, the results provide new insights into the relationship between storage and control in WM, and the role of PFC in subserving these functions. i Working Memory and Prefrontal Cortex

INTRODUCTION Although our brains have developed exquisite mechanisms for recording speci# c experiences, it is not always advantageous for us to take the world too literally. A brain limited to storing an independent record of each experience would require a prodigious amount of storage and burden us with unnecessary details. Instead, we have evolved the ability to detect the commonalities among experiences and store them as abstract concepts, general principles and rules. This is an ef# cient way to deal with a complex world and allows the navigation of many different situations with a minimal amount of storage. It also allows us to deal with novelty. By extracting the essential elements from our experiences, we can generalize to future situations that share some elements but may, on the surface, appear very different. For example, consider the concept `camera'. We do not have to learn anew about every camera that we may encounter. Just knowing that the item is a camera communicates