Shape-specific preparatory activity mediates attention to targets in human visual cortex

The mechanisms of attention prioritize sensory input for efficient perceptual processing. Influential theories suggest that attentional biases are mediated via preparatory activation of task-relevant perceptual representations in visual cortex, but the neural evidence for a preparatory coding model of attention remains incomplete. In this experiment, we tested core assumptions underlying a preparatory coding model for attentional bias. Exploiting multivoxel pattern analysis of functional neuroimaging data obtained during a non-spatial attention task, we examined the locus, time-course, and functional significance of shape-specific preparatory attention in the human brain. Following an attentional cue, yet before the onset of a visual target, we observed selective activation of target-specific neural subpopulations within shape-processing visual cortex (lateral occipital complex). Target-specific modulation of baseline activity was sustained throughout the duration of the attention trial and the degree of target specificity that characterized preparatory activation patterns correlated with perceptual performance. We conclude that top-down attention selectively activates target-specific neural codes, providing a competitive bias favoring task-relevant representations over competing representations distributed within the same subregion of visual cortex.     

Task-dependent influences of attention on the activation of human primary visual cortex

There has been a good deal of controversy over whether attention influences area V1—the first cortical area onto which information from the retina is projected. Attention to motion has been found to modulate monkey area MT and the human homolog of MT/MST. Here we show that activation of V1 by attention to motion is task dependent. Our stimulus consisted of a group of translating random dots superimposed over another group of random dots executing expansion motion. Subjects were instructed to pay attention selectively to the translation, expansion, or neither in particular (passive condition). The activity in the human MT/MST homolog measured by functional magnetic resonance imaging (fMRI) was significantly higher in both the translation and the expansion conditions than in the passive condition, while the activity in area V1 was significantly higher only in the translation condition. These results show that attention to motion modulates area V1, and more interestingly that high-level cognitive processing such as attention may directly or indirectly determine the retroactive extent of feedback within the motion pathway in a manner dependent on the type of motion attended.     

A unified selection signal for attention and reward in primary visual cortex

Stimuli associated with high rewards evoke stronger neuronal activity than stimuli associated with lower rewards in many brain regions. It is not well understood how these reward effects influence activity in sensory cortices that represent low-level stimulus features. Here, we investigated the effects of reward information in the primary visual cortex (area V1) of monkeys. We found that the reward value of a stimulus relative to the value of other stimuli is a good predictor of V1 activity. Relative value biases the competition between stimuli, just as has been shown for selective attention. The neuronal latency of this reward value effect in V1 was similar to the latency of attentional influences. Moreover, V1 neurons with a strong value effect also exhibited a strong attention effect, which implies that relative value and top–down attention engage overlapping, if not identical, neuronal selection mechanisms. Our findings demonstrate that the effects of reward value reach down to the earliest sensory processing levels of the cerebral cortex and imply that theories about the effects of reward coding and top–down attention on visual representations should be unified.     

Distinctive compartmental organization of human primary visual cortex

In the primary visual area of macaques and other monkeys, layer 4A is a mosaic of separate tissue compartments related to the parvocellular (P) and magnocellular (M) layers of the lateral geniculate nucleus. This mosaic resembles a honeycomb, with thin walls that receive direct P inputs and cores consisting of columns of dendrites and cell bodies ascending from layer 4B, a layer that receives indirect M inputs. To determine whether apes and humans have a macaque-like layer 4A, we examined the primary visual area in humans, chimpanzees, an orangutan, Old World monkeys, and New World monkeys. Apes and humans lacked the dense band of cytochrome oxidase staining in layer 4A that marks the stratum of P-geniculate afferents in monkeys. Furthermore, humans displayed a unique arrangement of presumed M-related cells and dendrites in layer 4A, as revealed with antibodies against nonphosphorylated neurofilaments and microtubule-associated protein 2. Human 4A contained a large amount of M-like tissue distributed in a complex, mesh-like pattern rather than in simple vertical arrays as in other anthropoid primates. Our results suggest that (i) the direct P-geniculate projection to layer 4A was reduced early in the evolution of the ape-human group, (ii) the M component of layer 4A was subsequently modified (and possibly enhanced) in the human lineage, and (iii) the honeycomb model does not adequately characterize human layer 4A. This is the first demonstration of a difference in the cortical architecture of humans and apes, the animals most closely related to humans.     

Figure-ground activity in primary visual cortex is suppressed by anesthesia

By means of their small receptive fields (RFs), neurons in primary visual cortex perform highly localized analyses of the visual scene, far removed from our normal unified experience of vision. Local image elements coded by the RF are put into more global context, however, by means of modulation of the responses of the V1 neurons. Contextual modulation has been shown to follow closely the perceptual interpretation of the scene as a whole. This would suggest that some aspects of contextual modulation can be recorded only in awake and perceiving animals. In this study, multi-unit activity was recorded with implanted electrodes from primary visual cortex of awake, fixating monkeys viewing textured displays in which figure and ground regions were segregated by differences in either orientation or motion. Contextual modulation was isolated from local RF processing, by keeping RF stimulation identical across trials while sampling responses for various positions of the RF relative to figure and ground. Contextual modulation was observed to unfold spatially and temporally in a way that closely resembles the figure-ground percept. When recording was repeated, but with the animals anesthetized, the figure-ground related modulatory activity was selectively suppressed. RF tuning properties, however, remained unaffected. The results show that the modulatory activity is functionally distinct from the RF properties. V1 thus hosts distinct regimes of activity that are mediated by separate mechanisms and that depend differentially on the animal being awake or anesthetized.     

Novel stimuli evoke excess activity in the mouse primary visual cortex

To explore how neural circuits represent novel versus familiar inputs, we presented mice with repeated sets of images with novel images sparsely substituted. Using two-photon calcium imaging to record from layer 2/3 neurons in the mouse primary visual cortex, we found that novel images evoked excess activity in the majority of neurons. This novelty response rapidly emerged, arising with a time constant of 2.6 ± 0.9 s. When a new image set was repeatedly presented, a majority of neurons had similarly elevated activity for the first few presentations, which decayed to steady state with a time constant of 1.4 ± 0.4 s. When we increased the number of images in the set, the novelty response’s amplitude decreased, defining a capacity to store ∼15 familiar images under our conditions. These results could be explained quantitatively using an adaptive subunit model in which presynaptic neurons have individual tuning and gain control. This result shows that local neural circuits can create different representations for novel versus familiar inputs using generic, widely available mechanisms.

Because the behavioral consequences of a sensory stimulus can depend on whether that stimulus is novel or familiar, sensory systems can benefit from employing different representations of novel versus familiar stimuli. At the level of human psychophysics, stimulus novelty can enhance salience and capture attention (1–3), while familiarity can speed visual search (4). Novelty also affects aversive conditioning (5–7) and fear conditioning (8, 9). In human brain imaging, novel stimuli have been shown to generate the mismatch negativity (MMN) (10, 11) while repeated stimuli lead to repetition suppression (12). Explicit representation of novelty has been shown at higher stages of the sensory hierarchy, such as in the hippocampus (13) and inferotemporal cortex (14–16), and has been interpreted as a possible substrate of recognition memory (17). Lower in sensory hierarchies, the representation of novelty can be enhanced by stimulus-specific adaptation (SSA) (18–21) as well as by gain control (22, 23). Novelty signals are also prominently present in midbrain dopamine neurons (24).Explicit representation of stimulus novelty is also related to theories of predictive coding, in which neural circuits carry out computations that emphasize novel or surprising information. Theories of predictive coding have had a long history, starting with ideas about how the receptive field structure of retinal ganglion cells more efficiently encodes natural visual scenes by removing redundant data (25–28) and including the idea that active adaptation may aid in this process (18). Theories of predictive coding in the neocortex have typically focused on the idea that feedback from higher cortical areas encodes a prediction about lower-level sensory data (29) that is subtracted from the lower-level representation, so that the signals traveling up the cortical hierarchy represent surprise or novelty (30, 31). However, a recent study failed to find these signatures of predictive coding (32).Here, we investigate novelty processing in the mouse primary visual cortex. We repeatedly presented a set of images, each composed of a random superposition of Gabor functions, and then occasionally presented novel images drawn from the same ensemble. Using two-photon imaging of the Ca²⁺ sensor GCaMP6f to measure neural activity in layer 2/3 of awake, head-fixed mice (33), we found that the majority of neurons exhibited excess activity in response to a novel image. This distinction between novel versus familiar images was quickly reached, emerging with a time constant of 2.6 ± 0.9 s. Similarly, when we began presenting a new set of images, a majority of the neurons exhibited elevated firing that relaxed to a steady state with a time constant of 1.4 ± 0.4 s. When we presented novel images within larger image sets, the amplitude of novelty response decreased, defining a capacity of the system to encode ∼15 familiar images. All of these findings could be explained qualitatively using an adaptive subunit model in which neurons presynaptic to a recorded neuron have both individual tuning to visual stimuli and adaptive gain control.     

Laminar analysis of visually evoked activity in the primary visual cortex

Studying the laminar pattern of neural activity is crucial for understanding the processing of neural signals in the cerebral cortex. We measured neural population activity [multiunit spike activity (MUA) and local field potential, LFP] in Macaque primary visual cortex (V1) in response to drifting grating stimuli. Sustained visually driven MUA was at an approximately constant level across cortical depth in V1. However, sustained, visually driven, local field potential power, which was concentrated in the γ-band (20-60 Hz), was greatest at the cortical depth corresponding to cortico-cortical output layers 2, 3, and 4B. γ-band power also tends to be more sustained in the output layers. Overall, cortico-cortical output layers accounted for 67% of total γ-band activity in V1, whereas 56% of total spikes evoked by drifting gratings were from layers 2, 3, and 4B. The high-resolution layer specificity of γ-band power, the laminar distribution of MUA and γ-band activity, and their dynamics imply that neural activity in V1 is generated by laminar-specific mechanisms. In particular, visual responses of MUA and γ-band activity in cortico-cortical output layers 2, 3, and 4B seem to be strongly influenced by laminar-specific recurrent circuitry and/or feedback.     

Lightness constancy in primary visual cortex

When the illumination of a visual scene changes, the quantity of light reflected from objects is altered. Despite this, the perceived lightness of the objects generally remains constant. This perceptual lightness constancy is thought to be important behaviorally for object recognition. Here we show that interactions from outside the classical receptive fields of neurons in primary visual cortex modulate neural responses in a way that makes them immune to changes in illumination, as is perception. This finding is consistent with the hypothesis that the responses of neurons in primary visual cortex carry information about surface lightness in addition to information about form. It also suggests that lightness constancy, which is sometimes thought to involve "higher-level" processes, is manifest at the first stage of visual cortical processing.     

Top-down attention switches coupling between low-level and high-level areas of human visual cortex

Top-down attention is an essential cognitive ability, allowing our finite brains to process complex natural environments by prioritizing information relevant to our goals. Previous evidence suggests that top-down attention operates by modulating stimulus-evoked neural activity within visual areas specialized for processing goal-relevant information. We show that top-down attention also has a separate influence on the background coupling between visual areas: adopting different attentional goals resulted in specific patterns of noise correlations in the visual system, whereby intrinsic activity in the same set of low-level areas was shared with only those high-level areas relevant to the current goal. These changes occurred independently of evoked activity, persisted without visual stimulation, and predicted behavioral success in deploying attention better than the modulation of evoked activity. This attentional switching of background connectivity suggests that attention may help synchronize different levels of the visual processing hierarchy, forming state-dependent functional pathways in human visual cortex to prioritize goal-relevant information.     

Functional MRI reveals spatially specific attentional modulation in human primary visual cortex

Selective visual attention can strongly influence perceptual processing, even for apparently low-level visual stimuli. Although it is largely accepted that attention modulates neural activity in extrastriate visual cortex, the extent to which attention operates in the first cortical stage, striate visual cortex (area V1), remains controversial. Here, functional MRI was used at high field strength (3 T) to study humans during attentionally demanding visual discriminations. Similar, robust attentional modulations were observed in both striate and extrastriate cortical areas. Functional mapping of cortical retinotopy demonstrates that attentional modulations were spatially specific, enhancing responses to attended stimuli and suppressing responses when attention was directed elsewhere. The spatial pattern of modulation reveals a complex attentional window that is consistent with object-based attention but is inconsistent with a simple attentional spotlight. These data suggest that neural processing in V1 is not governed simply by sensory stimulation, but, like extrastriate regions, V1 can be strongly and specifically influenced by attention.     

Feature-selective attention enhances color signals in early visual areas of the human brain

We used an electrophysiological measure of selective stimulus processing (the steady-state visual evoked potential, SSVEP) to investigate feature-specific attention to color cues. Subjects viewed a display consisting of spatially intermingled red and blue dots that continually shifted their positions at random. The red and blue dots flickered at different frequencies and thereby elicited distinguishable SSVEP signals in the visual cortex. Paying attention selectively to either the red or blue dot population produced an enhanced amplitude of its frequency-tagged SSVEP, which was localized by source modeling to early levels of the visual cortex. A control experiment showed that this selection was based on color rather than flicker frequency cues. This signal amplification of attended color items provides an empirical basis for the rapid identification of feature conjunctions during visual search, as proposed by "guided search" models.     

Adaptive shape processing in primary visual cortex

The ability to derive meaning from complex sensory input requires the integration of information over space and time, as well as cognitive mechanisms to shape that integration. We studied these processes in the primary visual cortex (V1), where neurons are thought to integrate visual inputs along contours defined by an association field (AF). We recorded extracellularly from single cells in macaque V1 to map the AF, by using an optimization algorithm to find the contours that maximally activated individual cells. We combined the algorithm with a delayed-match-to-sample task, to test how the optimal contours might be molded by the monkey's expectation for particular cue shapes. We found that V1 neurons were selective for complex shapes, a property previously ascribed to higher cortical areas. Furthermore, the shape selectivity was reprogrammed by perceptual task: Over the whole network, the optimal modes of geometric selectivity shifted between distinct subsets of the AF, alternately representing different stimulus features known to predominate in natural scenes. Our results suggest a general model of cortical function, whereby horizontal connections provide a broad domain of potential associations, and top-down inputs dynamically gate these linkages to task switch the function of a network.     

Subtask sequencing in the primary visual cortex

Complex visual tasks can usually be decomposed into a number of simpler subtasks. Whether such subtasks are solved serially or in parallel is subject to considerable debate. Here we investigate how subtasks are coordinated in time by recording from the primary visual cortex of macaque monkeys. The animals were trained to perform both a simple and a composite task. In the simple task, they had to mentally trace a target curve while ignoring a distractor curve. Neuronal responses in the primary visual cortex to the target curve were enhanced relative to responses to the distractor curve 130 ms after stimulus appearance. In the composite task, the monkeys searched for a colored marker and traced a curve that was attached to this marker. In an initial phase of the trials, neuronal responses reflected visual search, and the response enhancement due to curve tracing now occurred after 230 ms, 100 ms later than in the simple task. We conclude that subtasks of the composite task are carried out in a structured and sequential manner that can be monitored in the primary visual cortex.     

The primary visual cortex fills in color

One of the most important goals of visual processing is to reconstruct adequate representations of surfaces in a scene. It is thought that surface representation is produced mainly in the midlevel vision and that area V1 (the primary visual cortex) activity is solely due to feedback from the midlevel stage. Here, we measured functional MRI signals corresponding to "neon color spreading": an illusory transparent surface with long-range color filling-in, one of the important mediums in reconstructing a surface. The experiment was conducted with careful controls of attention, which can send feedback signals from higher visual areas. Activity for filling-in was observed only in V1, whereas activity for illusory contours was observed in multiple visual areas. These results indicate that surface representation is produced by multiple rather than single processing.     

Frequency preference and attention effects across cortical depths in the human primary auditory cortex

Columnar arrangements of neurons with similar preference have been suggested as the fundamental processing units of the cerebral cortex. Within these columnar arrangements, feed-forward information enters at middle cortical layers whereas feedback information arrives at superficial and deep layers. This interplay of feed-forward and feedback processing is at the core of perception and behavior. Here we provide in vivo evidence consistent with a columnar organization of the processing of sound frequency in the human auditory cortex. We measure submillimeter functional responses to sound frequency sweeps at high magnetic fields (7 tesla) and show that frequency preference is stable through cortical depth in primary auditory cortex. Furthermore, we demonstrate that—in this highly columnar cortex—task demands sharpen the frequency tuning in superficial cortical layers more than in middle or deep layers. These findings are pivotal to understanding mechanisms of neural information processing and flow during the active perception of sounds.Auditory perception starts in our ears, where hair cells at different places in the cochlea respond to different sound frequencies. The spatially ordered arrangement of neural responses to frequencies (tonotopy) that arises from this transduction mechanism is preserved in subcortical (1, 2) and cortical stages of processing, where neuronal populations form multiple tonotopic maps (3, 4). At the cortical level, tonotopic maps describe systematic changes along the surface. In the orthogonal direction (i.e., perpendicular to the cortical surface), aggregates of neurons with parallel axons have been reported (5, 6). These anatomical observations of cortical microcolumns inspired invasive electrophysiological investigations in cats (7), demonstrating that frequency preference is constant across cortical depth (i.e., frequency columns). Since this early study, frequency columns have been observed in a variety of animals (3, 8–10), and a columnar organization has been suggested for other acoustic properties (10, 11). Despite this anatomical and physiological evidence from animal models, the role of cortical columns in auditory perception is not understood (6, 12, 13). To unravel intracolumnar computations, it is of fundamental importance to analyze the transformation of information across cortical depths. Differences in cell types and in patterns of input and output projections suggest a distinct role of cortical layers in neural information processing (5). In particular, behavioral demands and ongoing brain states can modulate the functional properties of layer 2/3 neurons, suggesting that supragranular neuronal populations may be of fundamental relevance for the processing of sensory information in a context-dependent manner (14). Recordings in the primary auditory cortex of animals have shown differences across layers in response latency (15, 16), in frequency selectivity (i.e., tuning width) (8, 16), and in the complexity of neuronal preference to acoustic information (i.e., receptive field) (17). However, the reports are not concordant across species. Moreover, most of the knowledge regarding auditory columnar processing has been obtained in anesthetized animals, making its relation to human behavior unclear.To date, there is no functional evidence for a columnar organization and for the layer-dependent processing of sound frequency in the human auditory cortex from either invasive or noninvasive recordings. In this study, we address this question noninvasively using high magnetic field (7 tesla) functional magnetic resonance imaging (fMRI) at high spatial resolution and specificity (18, 19). We acquired functional images in the primary auditory cortex (PAC) of five healthy volunteers and estimated the best frequency (BF) responses voxel-by-voxel. Then, by analyzing the 3D spatial variations of these responses, we identified the PAC regions with a stable arrangement of BF across cortical depths. The term “column” has been used with multiple meanings in the past (13). Here, we refer to columnar region as the cortical region where the variation of frequency with depth is significantly smaller compared with the frequency variation orthogonal to depth (i.e., across the surface) (SI Text). Further, we examined the functional differences across cortical layers by estimating the cortical depth-dependent changes in frequency tuning during an auditory and a control visual task. We hypothesized that additional top-down processing engaged by the auditory task would modulate the frequency tuning of fMRI responses in the PAC in a cortical depth-dependent manner. Finally, we simulated how the observed changes of frequency tuning across layers and tasks may result in behaviorally relevant changes of neuronal population-based sound representations.     

Simultaneous selection by object-based attention in visual and frontal cortex

Models of visual attention hold that top-down signals from frontal cortex influence information processing in visual cortex. It is unknown whether situations exist in which visual cortex actively participates in attentional selection. To investigate this question, we simultaneously recorded neuronal activity in the frontal eye fields (FEF) and primary visual cortex (V1) during a curve-tracing task in which attention shifts are object-based. We found that accurate performance was associated with similar latencies of attentional selection in both areas and that the latency in both areas increased if the task was made more difficult. The amplitude of the attentional signals in V1 saturated early during a trial, whereas these selection signals kept increasing for a longer time in FEF, until the moment of an eye movement, as if FEF integrated attentional signals present in early visual cortex. In erroneous trials, we observed an interareal latency difference because FEF selected the wrong curve before V1 and imposed its erroneous decision onto visual cortex. The neuronal activity in visual and frontal cortices was correlated across trials, and this trial-to-trial coupling was strongest for the attended curve. These results imply that selective attention relies on reciprocal interactions within a large network of areas that includes V1 and FEF.Visual scenes are usually too complex for all information to be analyzed at once. Selective attention selects a subset of the objects in the visual scene for detailed analysis at the expense of other items. Visual objects compete for selection, and the outcome of this competition depends on bottom-up cues such as saliency and perceptual organization and top-down cues that signal the objects’ behavioral relevance (1). It is not well understood how these different cues interact and which brain areas take the lead in visual selection.The top-down mechanisms for attentional selection are tightly linked to those for the selection of actions (2), and accordingly, cortical areas related to action planning influence the deployment of visual attention. The frontal eye fields (FEF) is one such area that is involved in visual processing, shifts of visual attention (2, 3), and also in the control of eye movements (4, 5). Area FEF contains different types of cells. Visual processing relies on visual and visuomovement cells, whereas the programming of eye movements relies on the activity of visuomovement and movement cells (6, 7). There are several lines of evidence that also implicate FEF in attentional control. First, FEF inactivation impairs attention shifts toward the contralateral visual field (8, 9). Second, subthreshold FEF microstimulation enhances neuronal activity in visual cortex in a manner that is reminiscent of selective attention (10, 11). Third, a role of FEF in the top-down guidance of attention is supported by studies on visual search. In search, selection signals in frontal cortex precede those in area V4 by 50 ms, suggesting that the frontal cortex determines selection and then provides feedback to visual cortex (12, 13). A comparable interareal delay in attentional effects was observed between the lateral intraparietal area and the motion sensitive middle temporal area (14). Thus, the parietal and frontal cortices appear to take the lead in attentional selection and to provide top-down signals to visual cortex. Within the visual cortex, such a reverse hierarchy (15) of attentional effects was observed in a task that required shifts of spatial attention (16) and also in a task demanding shifts between visual and auditory attention (17). Attentional signals in area V4 preceded signals in V2 by 50–250 ms, which in turn preceded attentional effects in the primary visual cortex (V1) by 50–400 ms.However, top-down factors are not the only ones that guide attention. Attention can be object-based, implying that the visual stimulus itself influences the distribution of attention too. If attention is directed to a feature, attention tends to coselect visually related features on the basis of perceptual grouping cues (18) so that entire objects rather than isolated features are attended (19, 20). The influence of perceptual grouping on attentional selection can be investigated with a curve-tracing task that requires grouping of the contour elements of a single curve (21, 22). Attention in this task is directed to the entire curve, implying that the curve’s shape itself influences the distribution of attention (22). Indeed, a traced curve evokes stronger activity in primary visual cortex than an irrelevant curve, revealing a neuronal correlate of object-based attention (23). However, it is not known if the coselection of all image elements of a single object is determined within early visual cortex or is guided by the frontal cortex, just as was shown for other tasks.Here we compare selection signals in areas FEF and V1 in the curve-tracing task with simultaneous recordings in the two areas. A priori, several possibilities exist for the interaction between V1 and FEF. First, the frontal cortex might select the relevant curve and then feed a guiding signal back to visual cortex (24, 25) as in the other tasks described above. If so, attentional selection signals in V1 might arise tens to hundreds of milliseconds later than in FEF. However, the chain of events in the curve-tracing task might differ because visual shape has a profound influence on the distribution of attention (26). Thus, a second possibility is that the visual cortex determines selection so that the attentional modulation in visual cortex precedes that in frontal cortex. A third possibility is that visual and frontal areas jointly determine what is relevant and what is not. In this situation, the selection signals are expected to occur in both areas at approximately the same time. It is also possible that the order of selection in different areas depends on the difficulty of the task. For example, the reverse hierarchy theory of visual perception (15) proposed that easy tasks are usually solved by higher visual areas, whereas lower visual areas are recruited when the picture has to be scrutinized. We therefore varied the difficulty of the curve-tracing task.     

Coinciding early activation of the human primary visual cortex and anteromedial cuneus

Proper understanding of processes underlying visual perception requires information on the activation order of distinct brain areas. We measured dynamics of cortical signals with magnetoencephalography while human subjects viewed stimuli at four visual quadrants. The signals were analyzed with minimum current estimates at the individual and group level. Activation emerged 55-70 ms after stimulus onset both in the primary posterior visual areas and in the anteromedial part of the cuneus. Other cortical areas were active after this initial dual activation. Comparison of data between species suggests that the anteromedial cuneus either comprises a homologue of the monkey area V6 or is an area unique to humans. Our results show that visual stimuli activate two cortical areas right from the beginning of the cortical response. The anteromedial cuneus has the temporal position needed to interact with the primary visual cortex V1 and thereby to modify information transferred via V1 to extrastriate cortices.     

Correspondence of presaccadic activity in the monkey primary visual cortex with saccadic eye movements

We continuously scan the visual world via rapid or saccadic eye movements. Such eye movements are guided by visual information, and thus the oculomotor structures that determine when and where to look need visual information to control the eye movements. To know whether visual areas contain activity that may contribute to the control of eye movements, we recorded neural responses in the visual cortex of monkeys engaged in a delayed figure-ground detection task and analyzed the activity during the period of oculomotor preparation. We show that approximately 100 ms before the onset of visually and memory-guided saccades neural activity in V1 becomes stronger where the strongest presaccadic responses are found at the location of the saccade target. In addition, in memory-guided saccades the strength of presaccadic activity shows a correlation with the onset of the saccade. These findings indicate that the primary visual cortex contains saccade-related responses and participates in visually guided oculomotor behavior.