Temporal attention, the allocation of attention to a moment in time, improves perception. Here, we examined the computational mechanism by which temporal attention improves perception, under a divisive normalization framework. Under this framework, attention can improve perception of a target signal in three ways: stimulus enhancement (increasing gain across all sensory channels), signal enhancement (selectively increasing gain in channels that encode the target stimulus), or external noise exclusion (reducing the gain in channels that encode irrelevant features). These mechanisms make diverging predictions when a target is embedded in varying levels of noise: stimulus enhancement improves performance only when noise is low, signal enhancement improves performance at all noise intensities, and external noise exclusion improves performance only when noise is high. To date, temporal attention studies have used noise-free displays. Therefore, it is unclear whether temporal attention acts via stimulus enhancement (amplifying both target features and noise) or signal enhancement (selectively amplifying target features) because both mechanisms  predict improved performance in the absence of noise. To tease these mechanisms apart, we manipulated temporal attention using an auditory cue while parametrically varying external noise in a fine-orientation discrimination task. Temporal attention improved perceptual thresholds across all noise levels. Formal model comparisons revealed that this cuing effect was best accounted for by a combination of signal enhancement and stimulus enhancement, suggesting that temporal attention improves perceptual

Neurons within early visual cortex are selective for basic image statistics, including spatial frequency. However, these neurons are thought to act as band-pass filters, with the window of spatial frequency sensitivity varying across the visual field and across visual areas. Although a handful of previous functional MRI studies have examined human spatial frequency sensitivity using conventional designs and analysis methods, these measurements are time consuming and fail to capture the precision of spatial frequency tuning (bandwidth). In this study, we introduce a model-driven approach to fMRI analyses that allows for fast and efficient estimation of popu- lation spatial frequency tuning (pSFT) for individual voxels. Blood oxygen level-dependent (BOLD) responses within early visual cortex were acquired while subjects viewed a series of full-field stimuli that swept through a large range of spatial frequency content. Each stimulus was generated by band-pass filtering white noise with a central frequency that changed periodically between a minimum of 0.5 cycles/degree (cpd) and a maximum of 12 cpd. To estimate the underlying frequency tuning of each voxel, we assumed a log- Gaussian pSFT and optimized the parameters of this function by comparing our model output against the measured BOLD time series. Consistent with previous studies, our results show that an increase in eccentricity within each visual area is accompanied by a drop in the peak spatial frequency of the pSFT. Moreover, we found that pSFT bandwidth depends on eccentricity and is correlated with the pSFT peak; populations with lower peaks possess broader bandwidths in logarithmic scale, whereas in linear scale this relationship is reversed.

Our visual system is tasked with transforming variations in light within our environment into a coherent percept, typically described using properties such as luminance and contrast. Models of vision often downplay the impor- tance of luminance in shaping cortical responses, instead prioritizing representations that do not covary with overall luminance (i.e., con- trast), and yet visuocortical response properties that may reflect luminance encoding remain poorly understood. In this study, we examined whether well-established visuocortical response properties may also reflect luminance encoding, challenging the idea that lumi- nance information itself plays no significant role in supporting visual perception. To do so, we measured functional activity in human visual cortex when presenting stimuli varying in contrast and mean lumi- nance, and found that luminance response functions are strongly contrast dependent between 50 and 250 cd/m2, confirmed with a subsequent experiment. High-contrast stimuli produced linearly in- creasing responses as luminance increased logarithmically for all early visual areas, whereas low-contrast stimuli produced either flat (V1) or assorted positive linear (V2 and V3) response profiles. These results reveal that the mean luminance information of a visual signal persists within visuocortical representations, potentially reflecting an inherent imbalance of excitatory and inhibitory components that can be either contrast dependent (V1 and V2) or contrast invariant (V3). The role of luminance should be considered when the aim is to drive potent visually evoked responses and when activity is compared across studies. More broadly, overall luminance should be weighed heavily as a core feature of the visual system and should play a significant role in cortical models of vision.

Although attention is known to increase the gain of visuocortical responses, its underlying neural computations remain unclear. Here, we use fMRI to test the hypothesis that a neural population’s ability to be modulated by attention is dependent on divisive normalization. To do so, we leverage the feature-tuned properties of normalization and find that visuocortical responses to stimuli sharing features normalize each other more strongly. Comparing these normalization measures to measures of attentional modulation, we demonstrate that sub- populations which exhibit stronger normalization also exhibit larger attentional benefits. In a converging experiment, we reveal that attentional benefits are greatest when a subpopulation is forced into a state of stronger normalization. Taken together, these results suggest that the degree to which a subpopulation exhibits normalization plays a role in dictating its potential for attentional benefits.

When the two eyes’ processing streams meet in visual cortex, two things can happen: sufficiently similar monocular inputs are combined into a fused representation, whereas markedly different inputs engage in rivalry. interestingly, the emergence of rivalry appears to require attention. Withdrawing attention causes the alternating monocular dominance that characterizes rivalry to cease, apparently allowing both monocular signals to be processed simultaneously. What happens to these signals in this case, however, remains something of a mystery; are they fused into an integrated representation? In a set of experiments, we show this not to be the case: visual aftereffects are consistent with the simultaneous yet separate presence of two segregated monocular representations, rather than a joint representation. these results provide evidence that dichoptic vision without attention prompts a third and previously unknown mode, where both eyes’ inputs receive equal processing, but escape interocular fusion.

An adaptive response to threat requires optimized detection of critical sensory cues. This optimization is thought to be aided by freezing - an evolutionarily preserved defensive state of immobility characterized by parasympathetically mediated fear bradycardia and regulated by the amygdala-periaqueductal grey (PAG) circuit. Behavioral observations in humans and animals have suggested that freezing is also a state of enhanced visual sensitivity, particularly for coarse visual information, but the underlying neural mechanisms remain unclear. We induced a freezing-like state in healthy volunteers using threat of electrical shock and measured threat-related changes in both stimulus-independent (baseline) and stimulus-evoked visuocortical activity to low- vs. high-spatial frequency gratings, using functional MRI. As measuring immobility is not feasible in MRI environments, we used fear bradycardia and amygdala- PAG coupling in inferring a freezing-like state. An independent functional localizer and retinotopic mapping were used to assess the retinotopic specificity of visuocortical modulations. We found a threat- induced increase in baseline (stimulus-independent) visuocortical activity that was retinotopically nonspecific, which was accompanied by increased connectivity with the amygdala. A positive correlation between visuocortical activity and fear bradycardia (while controlling for sympathetic activation), and a concomitant increase in amygdala-PAG connectivity, suggest the specificity of these findings for the parasympathetically dominated freezing-like state. Visuocortical responses to gratings were retinotopically specific but did not differ between threat and safe conditions across participants. However, individuals who exhibited better discrimination of low-spatial frequency stimuli showed reduced stimulus-evoked V1 responses under threat. Our findings suggest that a defensive state of freezing involves an integration of preparatory defensive and perceptual changes that is regulated by a common mechanism involving the amygdala.

How distinct are visual memory representations from visual perception? Although evidence suggests that briefly remembered stimuli are represented within early visual cortices, the degree to which these memory traces resemble true visual representations remains something of a mystery. Here, we tested whether both visual memory and perception succumb to a seemingly ubiquitous neural computation: normalization. Participants were asked to remember the contrast of visual stimuli, which were pit against each other to promote normalization either in perception or in visual memory. Our results revealed robust normalization between visual representations in perception, yet no signature of normalization occurring between working memory stores – neither between representations in memory, nor between memory representations and visual inputs. These results provide unique insight into the nature of visual memory representations, illustrating that visual memory representations follow a different set of computational rules, bypassing normalization, a canonical visual computation.