Publications by authors named "Wilson S Geisler"

Article Synopsis
  • Visual search is a key ability for humans and animals, and the study explored how humans decide when scanning briefly shown displays with distinct potential target locations.
  • Compared to the Bayesian-optimal decision process, humans surprisingly performed slightly better, despite having less sensitivity in their central vision (foveal neglect).
  • The research identified three reasons for this: simple decision rules can lead to near-optimal performance, foveal neglect mainly impacts the center target, and neural noise can enhance search performance beyond what is expected from independently operating signals.
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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Visual systems evolve to process the stimuli that arise in the organism's natural environment, and hence, to fully understand the neural computations in the visual system, it is important to measure behavioral and neural responses to natural visual stimuli. Here, we measured psychometric and neurometric functions in the macaque monkey for detection of a windowed sine-wave target in uniform backgrounds and in natural backgrounds of various contrasts. The neurometric functions were obtained by near-optimal decoding of voltage-sensitive-dye-imaging (VSDI) responses at the retinotopic scale in primary visual cortex (V1).

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Detection of target objects in the surrounding environment is a common visual task. There is a vast psychophysical and modeling literature concerning the detection of targets in artificial and natural backgrounds. Most studies involve detection of additive targets or of some form of image distortion.

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A fundamental natural visual task is the identification of specific target objects in the environments that surround us. It has long been known that some properties of the background have strong effects on target visibility. The most well-known properties are the luminance, contrast, and similarity of the background to the target.

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Humans have remarkable scale-invariant visual capabilities. For example, our orientation discrimination sensitivity is largely constant over more than two orders of magnitude of variations in stimulus spatial frequency (SF). Orientation-selective V1 neurons are likely to contribute to orientation discrimination.

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Humans and other primates sample the visual environment using saccadic eye movements that shift a high-resolution fovea toward regions of interest to create a clear perception of a scene across fixations. Many mammals, however, like mice, lack a fovea, which raises the question of why they make saccades. Here we describe and test the hypothesis that saccades are matched to natural scene statistics and to the receptive field sizes and adaptive properties of neural populations.

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How do cortical responses to local image elements combine to form a spatial pattern of population activity in primate V1? Here, we used voltage-sensitive dye imaging, which measures summed membrane potential activity, to examine the rules that govern lateral interactions between the representations of two small local-oriented elements in macaque () V1. We find strong subadditive and mostly orientation-independent interactions for nearby elements [2-4 mm interelement cortical distance (IED)] that gradually become linear at larger separations (>6 mm IED). These results are consistent with a population gain control model describing nonlinear V1 population responses to single oriented elements.

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A long-term goal of visual neuroscience is to develop and test quantitative models that account for the moment-by-moment relationship between neural responses in early visual cortex and human performance in natural visual tasks. This review focuses on efforts to address this goal by measuring and perturbing the activity of primary visual cortex (V1) neurons while nonhuman primates perform demanding, well-controlled visual tasks. We start by describing a conceptual approach-the decoder linking model (DLM) framework-in which candidate decoding models take neural responses as input and generate predicted behavior as output.

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Little is known about distance discrimination in real scenes, especially at long distances. This is not surprising given the logistical difficulties of making such measurements. To circumvent these difficulties, we collected 81 stereo images of outdoor scenes, together with precisely registered range images that provided the ground-truth distance at each pixel location.

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An extension of the signal-detection theory framework is described and demonstrated for two-alternative identification tasks. The extended framework assumes that the subject and an arbitrary model (or two subjects, or the same subject on two occasions) are performing the same task with the same stimuli, and that on each trial they both compute values of a decision variable. Thus, their joint performance is described by six fundamental quantities: two levels of intrinsic discriminability (d'), two values of decision criterion, and two decision-variable correlations (DVCs), one for each of the two categories of stimuli.

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This theoretical note describes a simple equation that closely approximates the psychometric functions of template-matching observers with arbitrary levels of position and orientation uncertainty. We show that the approximation is accurate for detection of targets in white noise, 1/f noise, and natural backgrounds. In its simplest form, this equation, which we call the uncertain normal integral (UNI) function, has two parameters: one that varies only with the level of uncertainty and one that varies only with the other properties of the stimuli.

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A fundamental everyday visual task is to detect target objects within a background scene. Using relatively simple stimuli, vision science has identified several major factors that affect detection thresholds, including the luminance of the background, the contrast of the background, the spatial similarity of the background to the target, and uncertainty due to random variations in the properties of the background and in the amplitude of the target. Here we use an experimental approach based on constrained sampling from multidimensional histograms of natural stimuli, together with a theoretical analysis based on signal detection theory, to discover how these factors affect detection in natural scenes.

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Estimating three-dimensional (3D) surface orientation (slant and tilt) is an important first step toward estimating 3D shape. Here, we examine how three local image cues from the same location (disparity gradient, luminance gradient, and dominant texture orientation) should be combined to estimate 3D tilt in natural scenes. We collected a database of natural stereoscopic images with precisely co-registered range images that provide the ground-truth distance at each pixel location.

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Understanding the neural basis of behaviour requires studying brain activity in behaving subjects using complementary techniques that measure neural responses at multiple spatial scales, and developing computational tools for understanding the mapping between these measurements. Here we report the first results of widefield imaging of genetically encoded calcium indicator (GCaMP6f) signals from V1 of behaving macaques. This technique provides a robust readout of visual population responses at the columnar scale over multiple mm(2) and over several months.

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Accurate perception of motion depends critically on accurate estimation of retinal motion speed. Here we first analyse natural image movies to determine the optimal space-time receptive fields (RFs) for encoding local motion speed in a particular direction, given the constraints of the early visual system. Next, from the RF responses to natural stimuli, we determine the neural computations that are optimal for combining and decoding the responses into estimates of speed.

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