It is well established that attention modulates visual processing in extrastriate cortex. However, the underlying neural mechanisms are unknown. A consistent observation is that attention has its greatest impact on neuronal responses when multiple stimuli appear together within a cell’s receptive field. One way to explain this is to assume that multiple stimuli activate competing populations of neurons and that attention biases this competition in favor of the attended stimulus. In the absence of competing stimuli, there is no competition to be resolved. Accordingly, attention has a more limited effect on the neuronal response to a single stimulus. To test this interpretation, we measured the responses of neurons in macaque areas V2 and V4 using a behavioral paradigm that allowed us to isolate automatic sensory processing mechanisms from attentional effects. First, we measured each cell’s response to a single

We examined how attention affected the orientation tuning of 262 isolated neurons in extrastriate area V4 and 135 neurons in area V1 of two rhesus monkeys. The animals were trained to perform a delayed match-to-sample task in which oriented stimuli were presented in the receptive field of the neuron being recorded. On some trials the animals were instructed to pay attention to those stimuli, and on other trials they were instructed to pay attention to other stimuli outside the receptive field. In this way, orientation-tuning curves could be constructed from neuronal responses collected in two behavioral states: one in which those stimuli were attended by the animal and one in which those stimuli were ignored by the animal. We fit Gaussians to the neuronal responses to twelve different orientations for each behavioral state. Although attention enhanced the responses of V4 neurons (median 26 % increase)

This article describes the use of Bayes factors for comparing Dynamic Causal Models (DCMs). DCMs are used to make inferences about effective connectivity from functional Magnetic Resonance Imaging (fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is, the connectivity pattern between the regions included in the model. Given the current lack of detailed knowledge on anatomical connectivity in the human brain, there are often considerable degrees of freedom when defining the connectional structure of DCMs. In addition, many plausible scientific hypotheses may exist about which connections are changed by experimental manipulation, and a formal procedure for directly comparing these competing hypotheses is highly desirable. In this article, we show how Bayes factors can be used to guide choices about model structure, both with regard to the intrinsic connectivity pattern and the contextual modulation of individual connections. The combined use of Bayes factors and DCM thus allows one to evaluate competing scientific theories about the architecture of large-scale neural networks and the neuronal interactions that mediate perception and cognition.

Recent neurophysiological studies have shown that primary visual cortex, or V1, does more than passively process image features using the feedforward filters suggested by Hubel and Wiesel. It also uses horizontal interactions to group features preattentively into object representations, and feedback interactions to selectively attend to these groupings. All neocortical areas, including V1, are organized into layered circuits. We present a neural model showing how the layered circuits in areas V1 and V2 enable feedforward, horizontal, and feedback interactions to complete perceptual groupings over positions that do not receive contrastive visual inputs, even while attention can only modulate or prime positions that do not receive such inputs. Recent neurophysiological data about how grouping and attention occur and interact in V1 are simulated and explained, and testable predictions are made. These simulations show how attention can selectively propagate along an object grouping and protect it from competitive masking, and how contextual stimuli can enhance or suppress groupings in a contrast-sensitive manner.

... This article presents one alternative to the computer metaphor suggesting that brains are organized into independent modules. Evidence is reviewed that brains are organized into parallel processing streams with complementary properties. Hierarchical interactions within each stream and parallel interactions between streams create coherent behavioral representations that overcome the complementary deficiencies of each stream and support unitary conscious experiences. This perspective suggests how brain design reflects the organization of the physical world with which brains interact. Examples from perception, learning, cognition, and action are described, and theoretical concepts and mechanisms by which complementarity is accomplished are presented.

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

& The processing required to decide whether a briefly flashed natural scene contains an animal can be achieved in 150 msec (Thorpe, Fize, & Marlot, 1996). Here we report that extensive training with a subset of photographs over a 3-week period failed to increase the speed of the processing underlying such rapid visual categorizations: Completely novel scenes could be categorized just as fast as highly familiar ones. Such data imply that the visual system processes new stimuli at a speed and with a number of stages that cannot be compressed. This rapid processing mode was seen with a wide range of visual complex images challenging the idea that short reaction times can only be seen with simple visual stimuli and implying that highly automatic feed-forward mechanisms underlie a far greater proportion of the sophisticated image analysis needed for everyday vision than is generally assumed. & Both humans and monkeys are able to categorize natural images accurately and very rapidly (Fabre-Thorpe, Richard, & Thorpe, 1998; Thorpe, Fize, & Marlot, 1996). The nature of the underlying mechanisms is currently

Abstract not found

Attentional selection of an object for recognition is often modeled using all-or-nothing switching of neuronal connection pathways from the attended region of the retinal input to the recognition units.