Neural correlates of perceptual similarity masking in primate V1.

Visual detection is a fundamental natural task. Detection becomes more challenging as the similarity between the target and the background in which it is embedded increases, a phenomenon termed 'similarity masking'. To test the hypothesis that V1 contributes to similarity masking, we used voltage sensitive dye imaging (VSDI) to measure V1 population responses while macaque monkeys performed a detection task under varying levels of target-background similarity. Paradoxically, we find that during an initial transient phase, V1 responses to the target are enhanced, rather than suppressed, by target-background similarity. This effect reverses in the second phase of the response, so that in this phase V1 signals are positively correlated with the behavioral effect of similarity. Finally, we show that a simple model with delayed divisive normalization can qualitatively account for our findings. Overall, our results support the hypothesis that a nonlinear gain control mechanism in V1 contributes to perceptual similarity masking.

Effects of target-amplitude and background-contrast uncertainty predicted by a normalized template-matching observer.

When detecting targets under natural conditions, the visual system almost always faces multiple, simultaneous, dimensions of extrinsic uncertainty. This study focused on the simultaneous uncertainty about target amplitude and background contrast. These dimensions have a large effect on detection and vary greatly in natural scenes. We measured the human performance for detecting a sine-wave target in white noise and natural-scene backgrounds for two levels of prior probability of the target being present. We derived and tested the ideal observer for white-noise backgrounds, a special case of a template-matching observer that dynamically moves its criterion with the background contrast (the DTM observer) and two simpler models with a fixed criterion: the template-matching (TM) observer and the normalized template-matching (NTM) observer that normalizes template response by background contrast. Simulations show that, when the target prior is low, the performance of the NTM observer is near optimal and the TM observer is near chance, suggesting that manipulating the target prior is valuable for distinguishing among models. Surprisingly, we found that the NTM and DTM observers better explain human performance than the TM observer for both target priors in both background types. We argue that the visual system most likely exploits contrast normalization, rather than dynamic criterion adjustment, to deal with simultaneous background contrast and target amplitude uncertainty. Finally, our findings show that the data collected under high levels of uncertainty have a rich structure capable of discriminating between models, providing an alternative approach for studying high dimensions of uncertainty.

Phase-dependent asymmetry of pattern masking in natural images explained by intrinsic position uncertainty.

A number of recent studies have been directed at measuring and modeling detection of targets at specific locations in natural backgrounds, a key subtask of visual search in natural environments. A useful approach is to bin natural background patches into joint histograms with bins along specific background dimensions. By measuring psychometric functions in a sparse subset of these bins, it is possible to estimate how the included dimensions jointly affect detectability over the whole space of natural backgrounds. In previous studies, we found that threshold is proportional to the product of the background luminance, contrast, and similarity; a result predicted by a simple template-matching observer with divisive normalization along each of the dimensions. The measure of similarity was the cosine similarity of the amplitude spectra of the target and background (SA)-a phase-invariant measure. Here, we investigated the effect of the cosine similarity of the target and background images (SI|A)-a phase-dependent measure. We found that threshold decreases monotonically with SI|A in agreement with a recent study (Rideaux et al., 2022). In contrast, the template-matching observer predicts threshold to be a U-shaped function of SI|A reaching a minimum when the target and background are orthogonal (SI|A = 0). Surprisingly, when the template-matching observer includes a small amount of intrinsic position uncertainty (measured in a separate experiment) the pattern of thresholds is explained.

NEURAL CORRELATES OF PERCEPTUAL SIMILARITY MASKING IN PRIMATE V1.

Visual detection is a fundamental natural task. Detection becomes more challenging as the similarity between the target and the background in which it is embedded increases, a phenomenon termed "similarity masking". To test the hypothesis that V1 contributes to similarity masking, we used voltage sensitive dye imaging (VSDI) to measure V1 population responses while macaque monkeys performed a detection task under varying levels of target-background similarity. Paradoxically, we find that during an initial transient phase, V1 responses to the target are enhanced, rather than suppressed, by target-background similarity. This effect reverses in the second phase of the response, so that in this phase V1 signals are positively correlated with the behavioral effect of similarity. Finally, we show that a simple model with delayed divisive normalization can qualitatively account for our findings. Overall, our results support the hypothesis that a nonlinear gain control mechanism in V1 contributes to perceptual similarity masking.

Spatiotemporal defocus sensitivity function of the human visual system.

Tunable lenses make it possible to measure visual sensitivity to rapid changes in optical power, surpassing the limits imposed by mechanical elements. Using a tunable lens system, we measured, for the first time, the spatiotemporal defocus sensitivity function (STDSF), and the limits of human defocus perception. Specifically, we measured defocus sensitivity using a QUEST adaptive psychophysical procedure for different stimuli (Gabor patches of different spatial frequencies, natural images, and edges) and we developed descriptive models of defocus perception. For Gabor patches, we found on average (across seven subjects) that the maximum sensitivity to defocus is 0.22 D at 14 cpd and 10 Hz, and that the upper limits of sensitivity are 40 cpd and 40 Hz. Our results suggest that accommodation remains fixed while performing the defocus flicker-detection task. These results have implications for new technologies whose working principles make use of fast changes to defocus.

Stereo slant discrimination of planar 3D surfaces: Frontoparallel versus planar matching.

Binocular stereo cues are important for discriminating 3D surface orientation, especially at near distances. We devised a single-interval task where observers discriminated the slant of a densely textured planar test surface relative to a textured planar surround reference surface. Although surfaces were rendered with correct perspective, the stimuli were designed so that the binocular cues dominated performance. Slant discrimination performance was measured as a function of the reference slant and the level of uncorrelated white noise added to the test-plane images in the left and right eyes. We compared human performance with an approximate ideal observer (planar matching [PM]) and two subideal observers. The PM observer uses the image in one eye and back projection to predict a test image in the other eye for all possible slants, tilts, and distances. The estimated slant, tilt, and distance are determined by the prediction that most closely matches the measured image in the other eye. The first subideal observer (local planar matching [LPM]) applies PM over local neighborhoods and then pools estimates across the test plane. The second suboptimal observer (local frontoparallel matching [LFM]) uses only location disparity. We find that the ideal observer (PM) and the first subideal observer (LPM) outperforms the second subideal observer (LFM), demonstrating the additional benefit of pattern disparities. We also find that all three model observers can account for human performance, if two free parameters are included: a fixed small level of internal estimation noise, and a fixed overall efficiency scalar on slant discriminability.

Detection of targets in filtered noise: whitening in space and spatial frequency.

Most studies of detection in complex backgrounds have measured and modeled human performance for statistically uniform (stationary) backgrounds. However, natural and medical images have statistical properties that vary over space. We measured detection of various target shapes presented in Gaussian 1/f noise backgrounds that were statistically uniform over space, and in ones that modulated in contrast over space. We find that the pattern of human thresholds is not consistent with the ideal observer but is consistent with a suboptimal observer that performs partial whitening in spatial frequency and whitening (reliability-weighting) in space, and has a small level of intrinsic position uncertainty.

Similar neural and perceptual masking effects of low-power optogenetic stimulation in primate V1.

Can direct stimulation of primate V1 substitute for a visual stimulus and mimic its perceptual effect? To address this question, we developed an optical-genetic toolkit to 'read' neural population responses using widefield calcium imaging, while simultaneously using optogenetics to 'write' neural responses into V1 of behaving macaques. We focused on the phenomenon of visual masking, where detection of a dim target is significantly reduced by a co-localized medium-brightness mask (Cornsweet and Pinsker, 1965; Whittle and Swanston, 1974). Using our toolkit, we tested whether V1 optogenetic stimulation can recapitulate the perceptual masking effect of a visual mask. We find that, similar to a visual mask, low-power optostimulation can significantly reduce visual detection sensitivity, that a sublinear interaction between visual- and optogenetic-evoked V1 responses could account for this perceptual effect, and that these neural and behavioral effects are spatially selective. Our toolkit and results open the door for further exploration of perceptual substitutions by direct stimulation of sensory cortex.

Efficient allocation of attentional sensitivity gain in visual cortex reduces foveal sensitivity in visual search.

The human visual system has a high-resolution fovea and a low-resolution periphery. When actively searching for a target, humans perform a covert search during each fixation, and then shift fixation (the fovea) to probable target locations. Previous studies of covert search under carefully controlled conditions provide strong evidence that for simple and small search displays, humans process all potential target locations with the same efficiency that they process those locations when individually cued on each trial. Here, we extend these studies to the case of large displays, in which the target can appear anywhere within the display. These more natural conditions reveal an attentional effect in which sensitivity in the fovea and parafovea is greatly diminished. We show that this "foveal neglect" is the expected consequence of efficiently allocating a fixed total attentional sensitivity gain across the retinotopic map in the visual cortex. We present a formal theory that explains our findings and the previous findings.

A method to integrate and classify normal distributions.

Univariate and multivariate normal probability distributions are widely used when modeling decisions under uncertainty. Computing the performance of such models requires integrating these distributions over specific domains, which can vary widely across models. Besides some special cases where these integrals are easy to calculate, there exist no general analytical expressions, standard numerical methods, or software for these integrals. Here we present mathematical results and open-source software that provide (a) the probability in any domain of a normal in any dimensions with any parameters; (b) the probability density, cumulative distribution, and inverse cumulative distribution of any function of a normal vector; (c) the classification errors among any number of normal distributions, the Bayes-optimal discriminability index, and relation to the receiver operating characteristic (ROC); (d) dimension reduction and visualizations for such problems; and (e) tests for how reliably these methods may be used on given data. We demonstrate these tools with vision research applications of detecting occluding objects in natural scenes and detecting camouflage.