Visual attention mediated by biased competition in extrastriate visual cortex.

By Robert Desimone. 1998

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Citations in the corpus

Generalization Through the Recurrent Interaction of Episodic Memories (2012, background): “…in mediating selective attention and cognitive control, through an influence on local excitatory and inhibitory processes in lower level brain regions, which allows relevant target representations to dominate over competing alternatives (Desimone, 1998; Duncan, 2001; Miller & Cohen, 2001).”
Top-Down Neural Attention by Excitation Backprop (2016, background): “ioral goals can affect the feature and location expectancy in visual processing (Wolfe 1994;Anne 1980;Koch andUllman 1987; Desimone and Duncan 1995), and bias the competition among the neurons (Reynolds and Heeger 2009; Tsotsos et al. 1995; Desimone and Duncan 1995; Desimone 1998; Diane 2009).”
Separate neural definitions of visual consciousness and visual attention; a case for phenomenal awareness (2004, background): “Attentional selection (in one way or another, see above), may resolve this competition (Desimone, 1998).”
Limited attention: the constraint underlying search image (2001, background): “These studies have revealed that focusing attention on a given task is associated with increased activation of the neurons processing this task, suppressed activity of other neurons, and enhanced behavioral performance on the attended task (e.g., Behrmann and Haimson, 1999; Corbetta et al., 1990; De”
Attention, Uncertainty, and Free-Energy (2010, background): “…attention is directed toward one of two competing stimuli in a receptive field, the mutually suppressive effect disappears and the response of the cell emulates the response to the attended stimulus alone (Moran and Desimone, 1985; Chelazzi et al., 1993; Treue and Maunsell, 1996; Desimone, 1998).”
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution (2020, background): “To detect objects, human visual perception selectively enhances and suppresses neuron activation by passing high-level semantic information through feedback connections [2,19,20].”
Computing with a Canonical Neural Circuits Model with Pool Normalization and Modulating Feedback (2014, background): “One such framework proposes that FF sensory activations are amplified by matching FB signals such that subsequent competition between neurons yields a competitive advantage, and thus gain enhancement, for those cells that have an increased gain through the action of modulating FB for matching featur”
Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world (2013, methodology): “ART accomplishes these properties by proposing how topdown expectations focus attention on salient combinations of cues, and characterizes how attention may operate via a form of self-normalizing ‘‘biased competition’’ (Desimone, 1998).”
The time course of perceptual choice: the leaky, competing accumulator model. (2001, background): “This pattern of results is highly consistent with the use of lateral inhibition for selection (Desimone 1998; Reynolds, Chelazzi, & Desimone, 1999).”
Object-based visual attention for computer vision (2003, background): “In fact, the ART Matching Rules suggested in his model tends to produce later selection of attention and is partly similar to Duncan’s integrated competition hypothesis [22] which is an object-based attention theory and different to the above models.”
Integrated Neural Processes for Defining Potential Actions and Deciding between Them: A Computational Model (2006, methodology): “This is related to the biased competition mechanism used in theories of visual attention (Desimone, 1998; Boynton, 2005).”
A Cortical Mechanism for Triggering Top-Down Facilitation in Visual Object Recognition (2003, unknown): “indicate that top-down mechanisms may play a central role in visual processing (Bullier, 2001; Engel, Fries, & Singer, 2001; Gilbert, Sigman, & Crist, 2001; Moore & Engel, 2001; Pascual-Leone & Walsh, 2001; Hopfinger, Buonocore, & Mangun, 2000; Miyashita & Hayashi, 2000; Siegel, Kording, & König, 20”
A mechanistic cortical microcircuit of attention for amplification, normalization and suppression. (2015, background): “A very influential milestone has been the discovery of interactions between representations of stimuli that are placed within a common receptive field – coined as biased competition (Desimone, 1998; Desimone & Duncan, 1995).”
The cost of limited attention in blue jays (2000, unknown): “Consequently, subjects that pay more attention to a central difficult task compensate by devoting less attention to a secondary task (Desimone, 1998; Desimone and Duncan, 1995; Hillyard et al., 1998; Kastner et al., 1998).”
Dynamic Computational Time for Visual Attention (2017, background): “Cognitive science explains this as the “Biased Competition Theory” [3, 4] that human visual cortex is enhanced by top-down guidance during feedback loops.”
A Feedback Model of Attention Explains the Diverse Effects of Attention on Neural Firing Rates and Receptive Field Structure (2016, background): “Attention also biases the competition between different stimuli occurring within the receptive field (RF) of a given cell, in such a way that the actual response is more similar to that elicited by the selected stimulus in isolation [8].”