this report. Ninety-eight of these cells were recorded from the monkey performing the ABBA task, and 47 were recorded from the monkey performing the standard task.

Spatial attention: Visual selection and deployment over space The attentional spotlight and spatial cueing Attentional shifts, splits, and resolution Object-based Selection The visual search paradigm Top-down and bottom-up control of attention Inhibitory mechanisms of attention Invalid cueing Negative priming Inhibition of return Temporal attention: Visual selection and deployment over time Single target search Attentional blink and attentional dwell time Repetition blindness NEURAL MECHANISMS OF SELECTION Single-cell physiological method Event-related potentials Functional imaging: PET and fMRI

When an observer detects a target in a rapid stream of visual stimuli, there is a brief period of time during which the detection of subsequent targets is impaired. In this study, event-related potentials (ERPs) were recorded from normal adult observers to determine whether this "attentional blink " reflects a suppression of perceptual processes or an impairment in postperceptual processes. No suppression was observed during the attentional blink interval for ERP components corresponding to sensory processing (the P1 and N1 components) or semantic analysis (the N400 component). However, complete suppression was observed for an ERP component that has been hypothesized to reflect the updating of working memory (the P3 component). Results indicate that the attentional blink reflects an impairment in a postperceptual stage of processing. Over the past several decades, the vast majority of studies of visual attention have examined the operation of attention across space. In the visual search task, for example, a target item must be detected within an array of distractor items that are presented at different locations from the target. In recent

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Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff formulated four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural ‘blackboard ’ architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception. 2 Content

Displacement of a visual target during a saccadic eye movement is normally detected only at a high threshold, implying that high-quality information about target position is not stored in the nervous system across the saccade. We show that blanking the target for 50-300 msec after a saccade restores sensitivity to the displacement. With blanking, subjects reliably detect displacements as small as 0.33 deg across 6 deg eye movements, with correspondingly steep psychophysical functions. Performance with blanking in a fixation control is inferior, evidence for a saccadic enhancement of sensitivity to image displacement. If blanking is delayed so that the target is visible immediately after the saccade in its displaced position, performance declines to non-blanking levels. Blanking the target before the saccade, and restoring it during the saccade, yields a similar but weaker effect. We interpret these results with a model in which the visual system searches for the r postsaccadic goal target within a restricted spatiotemporal window. If it is not found, the assumption of stationarity of the world is broken and the system makes use of other information such as extraretinal signals for calibrating location.

this paper indicate that the coordinates of the object in space that is to be the target for action are passed to the motor system by virtue of the facts that the object represented in the inferior temporal cortex in complex scenes is at the fovea and that the dorsal visual system that executes the actions has information about eye gaze position (cf. Ballard, 1991; Rolls and Deco, 2002)

Recent neurophysiological experiments have led to a promising “biased competition hypothesis” of the neural basis of attention. According to this hypothesis, attention appears as a sometimes non-linear property that results from a top-down biassing effect that influences the competitive and cooperative interactions that work both within cortical areas and between cortical areas. In this paper we describe a detailed dynamical analysis of the synaptic and neuronal spiking mechanisms underlying biased competition. We perform a detailed analysis of the dynamical capabilities of the system by exploring the stationary attractors in the parameter space via a mean field reduction consistent with the underlying synaptic and spiking dynamics. The nonstationary dynamical behaviour, as measured in neuronal recording experiments, is studied via an integrate-and-fire model with realistic dynamics. This elucidates the role of cooperation and competition in the dynamics of biased competition; and shows why feedback connections between cortical areas need optimally to be weaker by a factor of approximately 2.5 than the feedforward connections in an attentional network. We modelled the interaction between top-down attention and bottom up stimulus contrast effects

This paper describes a parallel neural net architecture for efficient and robust attentive visual selection in generic gray level images. Objects are represented through flexible star type planar arrangements of binary local features, which are in turn star type planar arrangements of oriented edges. Candidate locations are detected over a range of scales and other deformations. The flexibility of the arrangements provides the required invariance. Training involves selecting a small number of stable local features, from a predefined pool, which are well localized on registered examples of the object. Training therefore requires only small data sets and can be implemented through Hebbian learning in a central `memory module'. No changes need to be made to the network for detecting new objects except for learning their representation in the memory module. Analogies with the visual system are discussed. 1 Introduction The issue at hand is attentive visual selection in gray ...

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