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Adaptive information processing, comprised of local computations and their efficient routing, is crucial for flexible brain function. Spatial attention is a quintessential example of this adaptive process. It is critical for recognizing and interacting with behaviorally relevant objects in a cluttered environment1. Object recognition is mediated by ensembles of computational units distributed across the ventral visual hierarchy. How the deployment of spatial attention aids these hierarchical computations is unclear. Based on pairwise correlation analysis, two key mechanisms have been proposed: First is an improvement in the efficacy of unique information directed from one encoding stage to another, suggested by evidence along the visual hierarchy2–6. Based on the theoretical results that even weak correlated variability can substantially limit the encoding capacity of a neuronal pool7, a second proposal is an improvement in the sensory information capacity of an encoding stage through a reduction in shared fluctuations8,9. However, pairwise analyses capture both unique and shared components of fluctuations, and therefore cannot disambiguate the proposed mechanisms. To test these proposals, it is crucial to estimate the attentional modulation of unique information flow across and shared information within the stages of the visual hierarchy. We investigated this in the multi-stage laminar network of visual area V4, an area strongly modulated by attention10–12. Using network-based statistical modeling, we estimated the strength of inter-layer information flow by measuring statistical dependencies that reflect how the cortical layersuniquelydrive each other’s neural activity. We quantified their modulation across attention conditions (attend-in vs. attend-away) in a change detection task and found that deployment of attention indeed strengthened unique dependencies between the input and superficial layers. Using the partial information decomposition framework13,14, we estimated modulation of shared dependencies and found that they are reduced within laminar populations, specifically the putative excitatory subpopulations. Surprisingly, we found a strengthening of unique dependencies within the laminar populations, a finding not previously predicted. Crucially, these modulation patterns were also observed across behavioral outcomes (hit vs. miss) that are thought to be mediated by endogenous state fluctuations15–17. By “decomposing” the modulation of dependency components and in combination with prior theoretical work7, our results suggest the following computational model of optimal sensory states that are attained by either task demands or endogenous fluctuations in brain state: enhanced information flowbetweenand improved information capacitywithinencoding stages.

Identical stimuli can be perceived or go unnoticed across successive presentations, producing divergent behavioral readouts despite similarities in sensory input. We hypothesized that fluctuations in neurophysiological states in the sensory neocortex, which could alter cortical processing at the level of neural subpopulations, underlies this perceptual variability. We analyzed cortical layer-specific electrophysiological activity in visual area V4 during a cued attention task. We find that hit trials are characterized by a larger pupil diameter and lower incidence of microsaccades, indicative of a behavioral state with increased arousal and perceptual stability. Target stimuli presented at perceptual threshold evoke elevated multi-unit activity in V4 neurons in hit trials compared to miss trials, across all cortical layers. Putative excitatory and inhibitory neurons are strongly positively modulated in the input (IV) and deep (V & VI) layers of the cortex during hit trials. Excitatory neurons in the superficial cortical layers exhibit lower variability in hit trials. Deep layer neurons are less phase-locked to low frequency rhythms in hits. Hits are also characterized by greater interlaminar coherence between the superficial and deep layers in the pre-stimulus period, and a complementary pattern between the input layer and both the superficial and deep layers in the stimulus-evoked period. Taken together, these results indicate that a state of elevated levels of arousal and perceptual stability allow enhanced processing of sensory stimuli, which contributes to hits at perceptual threshold.

Contrast is a key feature of the visual scene that aids object recognition. Attention has been shown to selectively enhance the responses to low contrast stimuli in visual area V4, a critical hub that sends projections both up and down the visual hierarchy. Veridical encoding of contrast information is a key computation in early visual areas, while later stages encode higher level features that benefit from improved sensitivity to low contrast. How area V4 meets these distinct information processing demands in the attentive state is not known. We found that attentional modulation of contrast responses in area V4 is cortical layer and cell-class specific. Putative excitatory neurons in the superficial output layers that project to higher areas show enhanced boosting of low contrast information. On the other hand, putative excitatory neurons of deep output layers that project to early visual areas exhibit contrast-independent scaling. Computational modeling revealed that such layer-wise differences may result from variations in spatial integration extent of inhibitory neurons. These findings reveal that the nature of interactions between attention and contrast in V4 is highly compartmentalized, in alignment with the demands of the visual processing hierarchy.

Saccades are a ubiquitous and crucial component of our visual system, allowing for the efficient deployment of the fovea and its accompanying neural resources. Initiation of a saccade is known to cause saccadic suppression, a temporary reduction in visual sensitivity1, 2 and visual cortical firing rates3–6. While saccadic suppression has been well characterized at the level of perception and single neurons, relatively little is known about the visual cortical networks governing this phenomenon. Here we examine the effects of saccadic suppression on distinct neural subpopulations within visual area V4. We find cortical layer- and cell type-specific differences in the magnitude and timing of peri-saccadic modulation. Neurons in the input layer show changes in firing rate and inter-neuronal correlations prior to saccade onset, indicating that this layer receives information about impending saccades. Putative inhibitory interneurons in the input layer elevate their firing rate during saccades, suggesting they play a role in suppressing the activity of other cortical subpopulations. A computational model of this circuit recapitulates our empirical observations and demonstrates that an input layer-targeting pathway can initiate saccadic suppression by enhancing local inhibitory activity. Collectively, our results provide a mechanistic understanding of how eye movement signaling interacts with cortical circuitry to enforce visual stability.

Deployment of covert attention to a spatial location can cause large decreases in low-frequency correlated variability among neurons in macaque area V4 whose receptive-fields lie at the attended location. It has been estimated that this reduction accounts for a substantial fraction of the attention-mediated improvement in sensory processing. These estimates depend on assumptions about how population signals are decoded and the conclusion that correlated variability impairs perception, is purely hypothetical. Here we test this proposal directly by optogenetically inducing low-frequency fluctuations, to see if this interferes with performance in an attention-demanding task. We find that low-frequency optical stimulation of neurons in V4 elevates correlations among pairs of neurons and impairs the animal’s ability to make fine sensory discriminations. Stimulation at higher frequencies does not impair performance, despite comparable modulation of neuronal responses. These results support the hypothesis that attention-dependent reductions in correlated variability contribute to improved perception of attended stimuli.

Attention is critical to perception, serving to select behaviorally relevant information for privileged processing. To understand the neural mechanisms of attention, we must discern how attentional modulation varies by cell type and across cortical layers. Here, we test whether attention acts non-selectively across cortical layers or whether it engages the laminar circuit in specific and selective ways. We find layer- and cell-class-specific differences in several different forms of attentional modulation in area V4. Broad-spiking neurons in the superficial layers exhibit attention-mediated increases in firing rate and decreases in variability. Spike count correlations are highest in the input layer and attention serves to reduce these correlations. Superficial and input layer neurons exhibit attention-dependent decreases in low-frequency (

Deployment of covert attention to a spatial location can cause large decreases in low-frequency correlated variability among neurons in macaque area V4 whose receptive-fields lie at the attended location. It has been estimated that this reduction accounts for a substantial fraction of the attention-mediated improvement in sensory processing. These estimates depend on assumptions about how population signals are decoded and the conclusion that correlated variability impairs perception, is purely hypothetical. Here we test this proposal directly by optogenetically inducing low-frequency fluctuations, to see if this interferes with performance in an attention-demanding task. We find that low‐ frequency optical stimulation of neurons in V4 elevates correlations among pairs of neurons and impairs the animal’s ability to make fine sensory discriminations. Stimulation at higher frequencies does not impair performance, despite comparable modulation of neuronal responses. These results support the hypothesis that attention-dependent reductions in correlated variability contribute to improved perception of attended stimuli.

SummaryPrevious studies have shown that neurons in area V4 are involved in the processing of shapes of intermediate complexity and are sensitive to curvature. These studies also suggest that curvature-tuned neurons are position invariant. We sought to examine the mechanisms that endow V4 neurons with these properties. Consistent with previous studies, we found that response rank order to the most- and least-preferred stimuli was preserved throughout the receptive field. However, a fine-grained analysis of shape tuning revealed a surprising result: V4 neurons tuned to highly curved shapes exhibit very limited translation invariance. At a fine spatial scale, these neurons exhibit local variation in orientation. In contrast, neurons that prefer straight contours exhibit spatially invariant orientation-tuning and homogenous fine-scale orientation maps. Both of these patterns are consistent with a simple orientation-pooling model, with tuning for straight or curved shapes resulting, respectively, from pooling of homogenous or heterogeneous orientation signals inherited from early visual areas.

Crowding impairs the perception of form in peripheral vision. It is likely to be a key limiting factor of form vision in patients without central vision. Crowding has been extensively studied in normally sighted individuals, typically with a stimulus duration of a few hundred milliseconds to avoid eye movements. These restricted testing conditions do not reflect the natural behavior of a patient with central field loss. Could unlimited stimulus duration and unrestricted eye movements change the properties of crowding in any fundamental way? We studied letter identification in the peripheral vision of normally sighted observers in three conditions: (i) a fixation condition with a brief stimulus presentation of 250ms, (ii) another fixation condition but with an unlimited viewing time, and (iii) an unrestricted eye movement condition with an artificial central scotoma and an unlimited viewing time. In all conditions, contrast thresholds were measured as a function of target-to-flanker spacing, from which we estimated the spatial extent of crowding in terms of critical spacing. We found that presentation duration beyond 250ms had little effect on critical spacing with stable gaze. With unrestricted eye movements and a simulated central scotoma, we found a large variability in critical spacing across observers, but more importantly, the variability in critical spacing was well correlated with the variability in target eccentricity. Our results assure that the large body of findings on crowding made with briefly presented stimuli remains relevant to conditions where viewing time is unconstrained. Our results further suggest that impaired oculomotor control associated with central vision loss can confound peripheral form vision beyond the limits imposed by crowding.

To deepen our understanding of object recognition, it is critical to understand the nature of transformations that occur in intermediate stages of processing in the ventral visual pathway, such as area V4. Neurons in V4 are selective to local features of global shape, such as extended contours. Previously, we found that V4 neurons selective for curved elements exhibit a high degree of spatial variation in their preference. If spatial variation in curvature selectivity was also marked by distinct temporal response patterns at different spatial locations, then it might be possible to untangle this information in subsequent processing based on temporal responses. Indeed, we find that V4 neurons whose receptive fields exhibit intricate selectivity also show variation in their temporal responses across locations. A computational model that decodes stimulus identity based on population responses benefits from using this temporal information, suggesting that it could provide a multiplexed code for spatio-temporal features.

Processing of shape information in human peripheral visual fields is impeded beyond what can be expected by poor spatial resolution. Visual crowding, the inability to identify objects in clutter, has been shown to be the primary factor limiting shape perception in peripheral vision. Despite the well-documented effects of crowding, its underlying causes remain poorly understood. Given that spatial attention both facilitates learning of image statistics and directs saccadic eye movements, we propose that the acquisition of image statistics in peripheral visual fields is confounded by eye-movement artifacts. Specifically, the image statistics acquired under a peripherally deployed spotlight of attention are systematically biased by saccade-induced image displacements. These erroneously represented image statistics lead to inappropriate contextual interactions in the periphery and cause crowding.

Objects in natural scenes are spatially broadband; in contrast, feature detectors in the early stages of visual processing are narrowly tuned in spatial frequency. Earlier studies of feature integration using gratings suggested that integration across spatial frequencies is suboptimal. Here we re-examined this conclusion using a letter identification task at the fovea and at 10 deg in the lower visual field. We found that integration across narrow-band (1-octave) spatial frequency components of letter stimuli is optimal in the fovea. Surprisingly, this optimality is preserved in the periphery, even though feature integration is known to be deficient in the periphery from studies of other form-vision tasks such as crowding. A model that is otherwise a white-noise ideal observer except for a limited spatial resolution defined by the human contrast sensitivity function and using internal templates slightly wider in bandwidth than the stimuli is able to account for the human data. Our findings suggest that deficiency in feature integration found in peripheral vision is not across spatial frequencies.

Classification image and other similar noise-driven linear methods have found increasingly wider applications in revealing psychophysical receptive field structures or perceptual templates. These techniques are relatively easy to deploy, and the results are simple to interpret. However, being a linear technique, the utility of the classification-image method is believed to be limited. Uncertainty about the target stimuli on the part of an observer will result in a classification image that is the superposition of all possible templates for all the possible signals. In the context of a well-established uncertainty model, which pools the outputs of a large set of linear frontends with a max operator, we show analytically, in simulations, and with human experiments that the effect of intrinsic uncertainty can be limited or even eliminated by presenting a signal at a relatively high contrast in a classification-image experiment. We further argue that the subimages from different stimulus-response categories should not be combined, as is conventionally done. We show that when the signal contrast is high, the subimages from the error trials contain a clear high-contrast image that is negatively correlated with the perceptual template associated with the presented signal, relatively unaffected by uncertainty. The subimages also contain a "haze" that is of a much lower contrast and is positively correlated with the superposition of all the templates associated with the erroneous response. In the case of spatial uncertainty, we show that the spatial extent of the uncertainty can be estimated from the classification subimages. We link intrinsic uncertainty to invariance and suggest that this signal-clamped classification-image method will find general applications in uncovering the underlying representations of high-level neural and psychophysical mechanisms.