A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedural-learning-based) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior cingulate and prefrontal cortices are critical to the verbal system. In addition to making predictions for normal human adults, the theory makes specific predictions for children, elderly people, and patients suffering from Parkinson's disease, Huntington's disease, major depression, amnesia, or lesions of the prefrontal cortex. Two separate formal descriptions of the theory are also provided. One describes trial-by-trial learning, and the other describes global dynamics. The theory is tested on published neuropsychological data and on category learning data with normal adults.

Active vision systems, and especially foveated vision systems, depend on efficient attentional mechanisms. We propose that machine visual attention should consist of both high level, context dependent components, and low level, context free components. As a basis for the context free component, we present an attention operator based on the intuitive notion of symmetry, which generalizes many of the existing methods for detecting regions of interest. It is a low level operator that can be applied successfully without a priori knowledge of the world. The resulting symmetry edge map can be applied in various low, intermediate and high level tasks, such as extraction of interest points, grouping and object recognition. In particular, we have implemented an algorithm which locates interest points in real time, and can be incorporated in active and purposive vision systems. The results agree with some psychophysical findings concerning symmetry as well as evidence concerning selection of fixation points...

Modern economic theory ignores the influence of emotions on decision-making. Emerging neuroscience evidence suggests that sound and rational decision making, in fact, depends on prior accurate emotional processing. The somatic marker hypothesis provides a systems-level neuroanatomical and cognitive framework for decision-making and its influence by emotion. The key idea of this hypothesis is that decision-making is a process that is influenced by marker signals that arise in bioregulatory processes, including those that express themselves in emotions and feelings. This influence can occur at multiple levels of operation, some of which occur consciously, and some of which occur non-consciously. Here we review studies that confirm various predictions from the hypothesis, and propose a neural model for economic decision, in which emotions are a major factor in the interaction between environmental conditions and human decision processes, with these emotional systems providing valuable implicit or explicit knowledge for making fast and advantageous decisions. © 2004 Elsevier Inc. All rights reserved.

Two tasks were used to evaluate the grain of visual attention, the minimum spacing at which attention can select individual items. First, observers performed a tracking task at many viewing distances. Performance dropped to chance levels at small display sizes even though, in all conditions, observers could easily resolve the items and their motions. The limiting size for selection was roughly the same whether tracking one or three targets, suggesting that the resolution limit acts independently of the capacity limit of attention. Second, the closest spacing that still allowed individuation of single items in dense, static displays was examined. This critical spacing was about 50% coarser in the radial direction compared to the tangential direction, and was coarser in the upper as opposed to the lower visual field. The results suggest that no more than about 72 items can be arrayed in the central 30 degrees of the visual field while still allowing attentional access to each individuall...

erations, respectively; and (iii) left posterior/ventral (Broca's area) and bilateral posterior/dorsal areas were more activated during WM than during ER, possibly reflecting phonological and generic WM operations, respectively. Second, hippocampal and parahippocampal regions were activated not only for ER but also for WM. This result suggests that indexing operations mediated by the medial temporal lobes apply to both long-term and short-term memory traces. Overall, our results show that direct cross-function comparisons are critical to understand the role of different brain regions in various cognitive functions. 2002 Elsevier Science (USA) INTRODUCTION During the past decade, numerous positron emission tomography (PET) and functional MRI (fMRI) studies have investigated the neural correlates of different cognitive functions (for a review, see Cabeza and Nyberg, 2000). Although most studies have focused on a single function (see however, LaBar et al., 1999; Braver et al., 2001; Ny

An object-based analog very large-scale integration (VLSI) model of selective attentional processing has been implemented using a standard 2.0-m CMOS process. This chip extends previous work on modeling a saliency-map-based selection and scanning mechanism to incorporate the ability to group pixels into objects. This grouping, or segmentation, couples the circuitry of the object's pixels to act as a single, larger pixel. The grouping of pixels is dynamic, driven solely by the segmentation criterion at the input. In this demonstration circuit, image intensity has been chosen for the input saliency map and the segmentation is based on spatial low-pass filtering followed by an intensity threshold. We present experimental results from a one-dimensional implementation of the object-based analog VLSI selective-attention system.

This paper argues for an explanation of the mechanistic (computational) basis of consciousness that is based on the distinction between localist (symbolic) representation and distributed representation, the ideas of which have been put forth in the connectionist literature. A model is developed to substantiate and test this approach. The paper also explores the issue of the functional roles of consciousness, in relation to the proposed mechanistic explanation of consciousness. The model, embodying the representational difference, is able to account for the functional role of consciousness, in the form of the synergy between the conscious and the unconscious. The fit between the model and various cognitive phenomena and data (documented in the psychological literatures) is discussed to accentuate the plausibility of the model and its explanation of consciousness. Comparisons with existing models of consciousness are made in the end.

We present an approach to attention in active computer vision. The notion of attention plays an important role in biological vision. In recent years, and especially with the emerging interest in active vision, computer vision researchers have been increasingly concerned with attentional mechanisms as well, see [26, 30] for a few examples. The basic principles behind these efforts are greatly influenced by psychophysical research. That is the case also in the work presented here, which adapts to the model of Treisman [25], with an early parallel stage with preattentive cues followed by a later serial stage where the cues are integrated. The contributions in our approach are (i) the incorporation of depth information from stereopsis, (ii) the simple implementation of low level modules such as disparity and flow by local phase, and (iii) the cue integration along pursuit and saccade mode that allows us a proper target selection based on nearness and motion. We demonstrate the technique by experiments in which a moving observer selectively masks out different moving objects in real scenes. Keywords: Attention, stereoscopic depth, image flow, motion, cue integration, pursuit, saccade, target selection # This report describes research done while the author was with CVAP, the Royal Institute of Technology (KTH). Present address: TOSHIBA Kansai Research Laboratories 8-6-26 Motoyama-Minami-Cho, Higashinada-ku, Kobe, 658 Japan. 1 2.

Brain imaging methods, such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), provide a unique opportunity to study the neurobiology of human memory. Since these methods can measure most of the brain, it is possible to examine the operations of large-scale neural systems and their relation to cognition. Two neuroimaging studies, one concerning working memory and the other episodic memory retrieval, serve as examples of application of two analytic methods that are optimized for the quantification of neural systems, structural equation modeling and partial least squares. Structural equation modeling was used to explore shifting prefrontal and limbic interactions from the right to the left hemisphere in a delayed match-to-sample task for faces. A feature of the functional network for short delays was strong right hemisphere interactions between hippocampus, inferior prefrontal, and anterior cingulate cortices. At longer delays, these same three areas were strongly linked, but in the left hemisphere, which was interpreted as reflecting change in task strategy from perceptual to elaborate encoding with increasing delay. The primary manipulation in the memory retrieval study was different levels of retrieval success. Partial least squares was used to determine whether the image-wide pattern of covariances of Brodmann areas 10 and 45/47 in right prefrontal cortex (RPFC) and the left hippocampus (LGH) could be mapped on to retrieval levels. Area 10 and LGH showed an opposite pattern of functional connectivity with a large expanse of bilateral limbic cortices that was equivalent for all levels of retrieval as well as the baseline task. However, only during high retrieval area 45/47 was included in this pattern. The results suggest that activ...

this article shouldbe addressed to T. S. Braver, Department of Psychology, Washington University, Campus Box 1125, One BrookingsDrive, St. Louis, MO 63130 (e-mail: tbraver@artsci.wustl.edu)