Attentional modulation of visual processing.

By John H. Reynolds, Leonardo Chelazzi. Annual Review of Neuroscience, 2004

1,178 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Sensory Systems

View Paper: https://www.semanticscholar.org/paper/bd1dad58964790cf2e0045b2bc2a07d275f37789

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Citations in the corpus

Rhythms For Cognition: Communication Through Coherence (2015, positive): “Stimulus contrast and selective attention have closely related effects on neuronal processing (Reynolds and Chelazzi, 2004; Reynolds and Heeger, 2009), with attention increasing the effective contrast of an attended stimulus by about 50% (Reynolds et al., 2000).”
Cortical Information Flow During Flexible Sensorimotor Decisions (2015, neutral): “First, there is a clear record of layers (18, 19) in 9P/Tempel 1 and 67P/C-G and possibly also in 19P/Borrelly and 81P/Wild 2.”
Attentional Stimulus Selection through Selective Synchronization between Monkey Visual Areas (2012, neutral): “It is intriguing, because attention to a stimulus is similar to an increase in stimulus contrast (Reynolds and Chelazzi, 2004), and higher contrast induces higher gamma-band frequencies in monkey area V1 (Figure S5A) (Ray andMaunsell, 2010).”
Visual attention: The past 25 years (2011, background): “These ostensibly contradictory findings have been taken to represent alternative models of attention (Boynton, 2009; Lee & Maunsell, 2009; Martinez-Trujillo & Treue, 2002; McAdams & Maunsell, 1999; Pestilli et al., 2009; Reynolds & Chelazzi, 2004; Reynolds et al., 2000).”
Expectation and Surprise Determine Neural Population Responses in the Ventral Visual Stream (2010, methodology): “…two feature attention models, one incorporating an additive baseline-shift parameter (Luck et al., 1997; Kastner et al., 1999; Boynton, 2009), and the other incorporating a multiplicative gain parameter (Reynolds and Chelazzi, 2004; Williford and Maunsell, 2006) into the feature detection model.”
Multiple Intelligences, the Mozart Effect, and Emotional Intelligence: A Critical Review (2006, background): “…perceptual processes such as vision, hearing, smell, and taste, but these processes have not been determined to be a seeing intelligence, smelling intelligence, tactile intelligence, or the like (Born & Bradley, 2005; Eibenstein et al., 2005; Goodwin & Wheat, 2004; J. H. Reynolds & Chelazzi, 2004).”
Differential attention-dependent response modulation across cell classes in macaque visual area V4. (2007, background): “The most well-established form of attention-dependent response modulation is an increase in firing rate when attention is directed to a stimulus within a neuron’s RF (for reviews, see Treue, 2003; Reynolds and Chelazzi, 2004).”
Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. (2009, background): “Several models of attention have proposed that attention either directly scales neuronal firing rates (McAdams and Maunsell, 1999) or scales the inputs to a normalization circuit (Reynolds et al., 1999; Reynolds and Chelazzi, 2004; Ghose and Maunsell, 2008; Reynolds and Heeger, 2009; Lee and Maunsel”
The Frontoparietal Attention Network of the Human Brain (2012, background): “…of attentional priority proposed in this article goes substantially beyond previous descriptions of saliency or priority computations in the LIP (Bisley and Goldberg 2010), FEF (Fecteau and Munoz 2006), or ventral stream areas such as V4 (Mazer and Gallant 2003; Reynolds and Chelazzi 2004).”
When size matters: attention affects performance by contrast or response gain (2010, methodology): “As a complementary way of testing the predictions of the model, we used nested hypothesis tests (nested F-tests) to compare the full model fit with four restricted models: (1) normalization model prediction, constraining c50 to be constant across cue conditions (valid, neutral, invalid) for large st”
Network Oscillations: Emerging Computational Principles (2006, background): “An increase in the local coherence in a population of neurons can boost their firing rates as well as the downstream neurons, giving them a competitive advantage (Reynolds and Chelazzi, 2004), as shown in Figure 2.”