The role of prefrontal cortex in the control of feature attention in area V4

Bichot, Narcisse P.; Xu, Rui; Ghadooshahy, Azriel; Williams, Michael L.; Desimone, Robert

doi:10.1038/s41467-019-13761-7

Cited by 69 publications

(83 citation statements)

References 67 publications

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“…Our results are consistent with a single shared computational mechanism of attention implemented by a different neural architecture depending on the feature (Cohen & Maunsell, 2011). This view is supported by the similarity of neural effects for different forms of attention (Cohen & Maunsell, 2011;Treue & Martinez-Trujillo, 1999;Patzwahl & Treue, 2009;Ling, Jehee, & Pestilli, 2015;Jehee et al, 2011;Martinez-Trujillo & Treue, 2004), the additive or multiplicative advantage of combining multiple cues (Hayden & Gallant, 2009;White et al, 2015;Andersen et al, 2011;Goddard et al, 2019), and the similar top-down sources in prefrontal cortex from which attention signals are thought to originate (Corbetta & Shulman, 2002;Moore & Armstrong, 2003;Zhou & Desimone, 2011;Bichot, Xu, Ghadooshahy, Williams, & Desimone, 2019;Liu & Hou, 2013). From this perspective, spatial location is just another feature and spatial attention is a special form of feature-based attention (Treue & Martinez-Trujillo, 1999).…”

supporting

confidence: 84%

Sensitivity enhancement and selection are shared mechanisms for spatial and feature-based attention

Birman

Gardner

2021

Preprint

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Human observers use cues to guide visual attention to the most behaviorally relevant parts of the visual world. Cues are often separated into two forms: those that rely on spatial location and those that use features, such as motion or color. These forms of cueing are known to rely on different populations of neurons. Despite these differences in neural implementation, attention may rely on shared computational principles, enhancing and selecting sensory representations in a similar manner for all types of cues. Here we examine whether evidence for shared computational mechanisms can be obtained from how attentional cues enhance performance in estimation tasks. In our tasks, observers were cued either by spatial location or feature to two of four dot patches. They then estimated the color or motion direction of one of the cued patches, or averaged them. In all cases we found that cueing improved performance. We decomposed the effects of the cues on behavior into model parameters that separated sensitivity enhancement from sensory selection and found that both were important to explain improved performance. We found that a model which shared parameters across forms of cueing was favored by our analysis, suggesting that observers have equal sensitivity and likelihood of making selection errors whether cued by location or feature. Our perceptual data support theories in which a shared computational mechanism is re-used by all forms of attention.Significance StatementCues about important features or locations in visual space are similar from the perspective of visual cortex, both allow relevant sensory representations to be enhanced while irrelevant ones can be ignored. Here we studied these attentional cues in an estimation task designed to separate different computational mechanisms of attention. Despite cueing observers in three different ways, to spatial locations, colors, or motion directions, we found that all cues led to similar perceptual improvements. Our results provide behavioral evidence supporting the idea that all forms of attention can be reconciled as a single repeated computational motif, re-implemented by the brain in different neural architectures for many different visual features.

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supporting

confidence: 84%

Sensitivity enhancement and selection are shared mechanisms for spatial and feature-based attention

Birman

Gardner

2021

Preprint

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“…1 in Schall, 2015). In the studies by Bichot et al (2015, 2019), the authors identified a ventral prearcuate region (VPA) that was proposed as the human IFJ homologue and that overlapped with areas 46v, 45A and 12. Based on their injection sites, three regions that may correspond to the VPA were highlighted on the Yerkes19 atlas (b) (the regions 45A, 9/46v and 12r′ from the composite PFC parcellation by Donahue et al, 2018; PS, principal sulcus; AS, arcuate sulcus; both (a) and (b) were adapted from the datasets available in BALSA at https://balsa.wustl.edu/; Van Essen et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Structure, function and connectivity fingerprints of the frontal eye field versus the inferior frontal junction: A comprehensive comparison

Bedini

Baldauf

2021

Eur J of Neuroscience

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The human prefrontal cortex contains two prominent areas, the frontal eye field and the inferior frontal junction, that are crucially involved in the orchestrating functions of attention, working memory and cognitive control. Motivated by comparative evidence in non‐human primates, we review the human neuroimaging literature, suggesting that the functions of these regions can be clearly dissociated. We found remarkable differences in how these regions relate to sensory domains and visual topography, top‐down and bottom‐up spatial attention, spatial versus non‐spatial (i.e., feature‐ and object‐based) attention and working memory and, finally, the multiple‐demand system. Functional magnetic resonance imaging (fMRI) studies using multivariate pattern analysis reveal the selectivity of the frontal eye field and inferior frontal junction to spatial and non‐spatial information, respectively. The analysis of functional and effective connectivity provides evidence of the modulation of the activity in downstream visual areas from the frontal eye field and inferior frontal junction and sheds light on their reciprocal influences. We therefore suggest that future studies should aim at disentangling more explicitly the role of these regions in the control of spatial and non‐spatial selection. We propose that the analysis of the structural and functional connectivity (i.e., the connectivity fingerprints) of the frontal eye field and inferior frontal junction may be used to further characterize their involvement in a spatial (‘where’) and a non‐spatial (‘what’) network, respectively, highlighting segregated brain networks that allow biasing visual selection and working memory performance to support goal‐driven behaviour.

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“…In target search, the comparison is between objects in the display and a target representation; in popout search, the comparison is between objects in the display and one another. In both cases, the representations depend on ventral-stream visual areas like V4 and IT (Zhou & Desimone, 2011;Westerberg, Maier, & Schall, 2020) as well as prefrontal areas (Bichot et al, 2015;Bichot, Xu, Ghadooshahy, Williams, & Desimone, 2019). The processing involved in popout is likely related to the identification-based feedforward inhibition we already identified as important for target selection.…”

Section: Extensionsmentioning

confidence: 88%

Salience by Competitive and Recurrent Interactions: Bridging Neural Spiking and Computation in Visual Attention

Ge¹,

Palmeri²,

Gd³

et al. 2021

Preprint

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Decisions about where to move the eyes depend on neurons in Frontal Eye Field (FEF). Movement neurons in FEF accumulate salience evidence derived from FEF visual neurons to select the location of a saccade target among distractors. How visual neurons achieve this salience representation is unknown. We present a neuro-computational model of target selection called Salience by Competitive and Recurrent Interactions (SCRI), based on the Competitive Interaction model of attentional selection and decision making (Smith & Sewell, 2013). SCRI selects targets by synthesizing localization and identification information to yield a dynamically evolving representation of salience across the visual field. SCRI accounts for neural spiking of individual FEF visual neurons, explaining idiosyncratic differences in neural dynamics with specific parameters. Many visual neurons resolve the competition between search items through feedforward inhibition between signals representing different search items, some also require lateral inhibition, and many act as recurrent gates to modulate the incoming flow of information about stimulus identity. SCRI was tested further by using simulated spiking representations of visual salience as input to the Gated Accumulator Model of FEF movement neurons (Purcell et al., 2010; Purcell, Schall, Logan, & Palmeri, 2012). Predicted saccade response times fit those observed for search arrays of different set size and different target-distractor similarity, and accumulator trajectories replicated movement neuron discharge rates. These findings offer new insights into visual decision making through converging neuro-computational constraints and provide a novel computational account of the diversity of FEF visual neurons.

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The role of prefrontal cortex in the control of feature attention in area V4

Cited by 69 publications

References 67 publications

Sensitivity enhancement and selection are shared mechanisms for spatial and feature-based attention

Sensitivity enhancement and selection are shared mechanisms for spatial and feature-based attention

Structure, function and connectivity fingerprints of the frontal eye field versus the inferior frontal junction: A comprehensive comparison

Salience by Competitive and Recurrent Interactions: Bridging Neural Spiking and Computation in Visual Attention

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