Vision Research

Locusts possess a collision-sensitive pathway in their visual system culminating in a neuron, the lobula giant movement detector (LGMD), which preferentially responds to looming stimuli. The LGMD is also notable for its reduced or absent response to other stimuli, such as translating objects, wide-field motion, and incoherent images of looming objects. While experimental and modelling work have both shed light on the various mechanisms underpinning these properties, broad examinations of how these mechanisms interact with each other and with diverse stimulus types have been limited. To address this, we developed a model incorporating mechanisms from recent literature, and subjected to an array of looming stimuli with varying size-to-speed ratio, polarity (OFF and ON), and coherence; wide-field background motion, translating stimuli, and trajectory changes were also examined. The model showed quantitative and qualitative fidelity to biological data in its replication of a wide variety of stimulus responses, with its preference for incoherent visual stimuli being a notable exception. Based on investigations with various inhibition types removed, it was shown that lateral inhibition predominantly suppressed wide-field motion responses. Global inhibition both normalized growing excitation over the course of looming, and improved recognition of looming stimuli against moving backgrounds. Feedforward inhibition was found to have diverse roles, including shaping peak response time and its variability, and improving coherence selectivity. The success of the model in replicating multiple stimuli also showed the plausibility of several underlying assumptions, including sharing of input between the LGMD and multiple other neuron types.

Mental representations are the explanatory construct of the cognitive sciences, but there is no widely accepted characterization of how they cause behavior. Visual representational geometry can be quantified by similarity scores, but almost all methods require an explicit judgment. To examine how representations cause behavior by varying task demands, observers must perform different tasks while continuously reporting similarity, leading to dual-task interference. This study develops scanpaths as an implicit similarity measure, and uses representational similarity analysis to validate it. Observers searched for a target; fixations on distractors may reveal similarity. Similarity was also quantified by an odd-one-out task in the same participants, and ratings from different participants (Jiang et al. 2022). Representational geometries between tasks correlated. A generative model predicted first fixations in novel data. This double validation of the scanpath method opens the door to examining the causality of representations by determining if and how they vary with task demands.

In this paper, we address the problem of quantifying similarity between planar 2D shapes, which is relevant to studies of internal representations in cognitive, developmental, and neurological research. We designed a set of test shapes arranged along a visually defined perceptual similarity gradient and used them to evaluate classical geometric methods for shape comparison, including Procrustes and Chamfer distance, as well as a convolutional neural network (CNN)-inspired feature-based method. Based on the limitations identified for these individual methods, we developed a hybrid Geometric-Feature Similarity (GFS) algorithm that combines geometric alignment, global contour properties, and convolutional feature-based descriptors into a unified weighted similarity score. By combining global geometric information with local structural features, the GFS algorithm more accurately reproduces human perceptual judgments of shape similarity than either geometric or feature-based methods alone. Requiring neither network training nor large labelled datasets, the proposed algorithm provides an efficient and interpretable tool for a broad range of studies involving quantitative shape comparison.

Introduction: Decision making for selecting an object or a course of action from possible alternatives largely depends on our perceptual ability modulated by attention. When multiple stimuli appear close together in time, processing one stimulus can temporarily impair the processing of another due to temporal limitations of attention. Observers frequently fail to detect the second target (T2) presented within a few hundred milliseconds after the first target (T1) in a stream of stimuli, which is commonly known as attentional blink (AB). Existing theories attribute this perceptual lapse to T1 processing, distractor interference, or transient attentional gating; however, the computations underlying suppressive mechanism remains unresolved. We investigated whether pupil-size could reveal the underlying mechanisms of AB and predict conscious perception on a trial-by-trial basis. Methods: Pupil diameter and gaze locations were recorded using an infrared eye tracker. Machine learning techniques were used to classify trials when T2 was detected versus when it was not, after correct identification of T1, during an AB task from the pupil dynamics, which also yielded attentional episode (AE) associated with each element in the stream of visual stimuli when deconvolved. Results: Cross-validating classifiers achieved near-perfect accuracy not only in distinguishing but also predicting perceptual outcomes on a single-trial basis. AEs exhibited greater power when T2 was detected than when it was missed; the differential power in AEs on a logarithmic scale was highly synced with the differential pupil size. Conclusions: Collectively, these findings establish a framework for predicting attention-driven perceptual outcomes from pupil-dynamics at finer time-scale.

Face recognition ability varies enormously across humans. Individuals with face-blindness (prosopagnosia) struggle to recognize even close family members while super-recognizers can identify strangers with exceptional accuracy. Where do ANNs fall within the human face recognition spectrum? Here, we administered the standardized tests used to characterize human face recognition ability to a diverse set of models, allowing us to contextualize a model's performance within the distribution of human behavior. We found that the majority 55% of models to be classified as face-blind and that even the best face-trained models do not cross the human threshold to be considered a super-recognizer. Interrogating the internal representations of these models showed that models that performed well on standardized face recognition tasks were more identity-selective and viewpoint-invariant. We also found that low-performing models did contain some identity information in independent representational subspaces. Removing viewpoint-dependent subspace improved face recognition abilities in 49 of the 53 models tested. Targeted unit ablations further identified opposing contributions, with viewpoint-dependent units disrupting identity coding and viewpoint-tolerant units supporting it. Together, our results show that most AI models are face-blind with worse face recognition ability than humans, and demonstrate how differences across AI models can be used generate testable hypotheses about the computational basis of human face recognition in humans which can then be probed in future studies.

Fly compound eyes pool signals from photoreceptors that sample the same region of visual space through neural superposition in the lamina. The optical axes of photoreceptors projecting to a single lamina cartridge are not perfectly parallel, but instead converge at a point a few millimeters in front of the eye. At short viewing distances (1-10 mm) this leads to distance-dependent differences in receptive field overlap. We explored whether it was possible that flies could sense depth in this "personal space" purely from the geometry of neural superposition. To this end, we combined a computational model of fly eye optics with a disparity-tuned lamina model originally developed for stereoscopic prey capture in praying mantises, and simulated the responses of lamina monopolar cells to moving stimuli at different distances. Across variations in stimulus parameters and lamina models, we found that lamina neuron responses indeed contain a distance-dependent component, which can overall be summarised as an enhanced response due to temporally overlapping receptor responses at a critical distance of 3-4 mm, the convergence distance of photoreceptor axes. Depending on the lamina model and stimulus size, either response amplitude or onset gradient, or both, exhibited this peak. We further show that changes in eye size systematically shift this preferred distance, such that larger flies had a peak response at a greater distance. Our results demonstrate that lamina cell responses may contain a robust, geometry-derived component that is specific to object distance and invariant to other stimulus properties. This suggests that neural superposition, beyond improving sensitivity, may function analogously to a light-field camera system that is effectively "focused" on a behaviorally relevant distance. Author summaryIn this study, we explored how neural superposition in fly compound eyes may lead to an enhanced response to objects at a behaviorally relevant distance. The optical axis of neurally pooled photoreceptors, from neighbouring ommatidia, are not parallel, but converge at a point a few millimeters in front of the eye, which should lead to the strongest correlation of their signals at that distance. This idea was tested by combining a geometric model of the fly eye optics with a computational model of how the lamina cells process the responses of photoreceptors, and evaluating the output for a moving bright dot at different distances. We found that the simulated lamina cell has its fastest response, as measured by the onset gradient, to a stimulus at the distance where the photoreceptors converge (3-4 mm). This distance scales with the simulated eye size and corresponds to behaviorally relevant distances for fly behavior. This suggests that neural superposition may act to enhance the response to objects at a critical distance in the early visual processing of flies.

Changes in pupil size have been widely used as an indirect measure of attention. However, attention engages on a faster timescale than the pupillary response. Therefore, the slow time course of the pupil response makes it difficult to distinguish attentional responses to stimuli presented in close temporal succession. Although signal deconvolution has been used in previous research to estimate latent attentional responses from the pupil signal, existing methods primarily model dilation without considering the visual stimulus evoked constriction phase of the pupil. Here, we introduce a dual-kernel deconvolution model that models constriction and dilation using separate impulse response functions. Using an RSVP paradigm with emotional faces as target stimuli, we demonstrate that the proposed model estimates latent attentional response despite substantial overlap in the measured pupil signal. Importantly, the recovered attentional response resembles the time course of attentional allocation previously predicted by several models of Attentional Blink. Together, these findings demonstrate that the dynamics of latent attentional episodes can be recovered from the pupil response with a temporal resolution previously obscured by the sluggishness of the pupillary response. The proposed method is also provided as a usable software package that serves as a general tool for investigating the temporal dynamics of attention in rapid visual paradigms.

In fully-sighted individuals, pursuit and saccadic eye movements work together to foveate moving targets; pursuit attempts to match target velocity, while catch-up saccades re-align the fovea to the target if it deviates during pursuit. Although saccade-pursuit interactions are well understood in healthy individuals, much less is known about how these mechanisms adapt in the presence of central vision loss. Here, we simulated central vision loss in healthy young adults to examine plasticity in saccade-pursuit mechanisms and investigated whether and how catch-up saccades adapted over five days of visual tracking. Twenty-five fully sighted participants performed a task in which they pursued a moving target that stepped position randomly (designed to elicit catch-up saccades) while viewing with a gaze-contingent central scotoma. The scotoma was shaped like a bitten cookie to encourage use of a specific preferred retinal locus (PRL) corresponding to the peripheral location with the highest acuity. By the end of the five days of repetition, most participants showed catch-up saccade endpoints shifted towards the PRL rather than the fovea. On average, endpoints shifted 0.5 deg towards the PRL between days 1 and 5 (range=0.3-1.5 deg), with significant shifts in 13 of the 25 participants. Adaptation was asymmetric; shifts were largest when the target moved in the direction opposite the scotoma bite and smallest when it moved towards it. In summary, we observed adaptation of saccade-pursuit interactive mechanisms through the re-referencing of catch-up saccades from the fovea to PRL. These results provide a promising avenue for rehabilitation in central vision loss aimed at improving eye movements and encouraging PRL use. Ultimately, such an approach may enhance residual visual function and quality of life.

Prestimulus pupil size is associated with near-threshold detection performance in both the visual and auditory domain, a relationship that is commonly attributed to arousal. However, given that larger pupils also let more light into the eye, in the visual domain this relationship is likely also driven by optical effects. To better understand this, we investigated how pupil size, skin conductance, and electroencephalographic (EEG) measures relate to detection performance in both a visual and an auditory task. We found that larger pupils were associated with higher sensitivity in the visual condition but lower sensitivity in the auditory condition. Skin conductance was negatively related to visual sensitivity, but unrelated to auditory sensitivity. EEG power measures were not related to sensitivity in either condition, though pupil size was positively correlated with alpha and beta power. Together, these results suggest that in visual detection the relationship between pupil size and performance is driven by both arousal and optics, whereas in auditory detection the relationship is driven solely by arousal. More broadly, our findings highlight the pupil as an active and functional component of the visual system.

Even though binocular integration is a foremost example of integrative computations in the brain, it remains unknown how the visual cortex mediates it. Here, we investigated neuronal responses to binocular stimuli in layers 2/3 of mouse primary visual cortex (V1) and lateromedial (LM) area with in vivo two-photon imaging. We presented mice with three types of binocular stimuli: congruent gratings, orthogonal gratings and a flashing Mondrian pattern in binocular conflict with a grating. We looked for congruency-specific responses to the binocular stimuli, but found that most neurons responded homogeneously across stimulus types. Responses of cells with a strong monocular preference for the contralateral eye responded similarly to monocular and binocular stimulation, while responses of cells with a preference for ipsilateral stimuli showed a strong but nonspecific suppression to all binocular stimuli. Only cells without a strong monocular preference (i.e. binocular cells) exhibited linear integration of monocular responses to incongruent stimuli and binocular facilitation to congruent gratings. Responses to ipsilateral components of a binocular display were found to be poor predictors of binocular responses regardless of eye preference of individual cells. We conclude that most neuronal responses to binocular stimuli in mouse V1 & LM are uninformative about the congruency of the monocular components, with the exception of binocular cells which show either linear or supralinear integration.

PurposePupil diameter in daily life depends on both the light reaching the eye and the observers age, but established prediction formulas require laboratory quantities that are rarely measured in natural environments. We developed a compact age-corrected model that predicts pupil diameter from melanopic equivalent daylight illuminance (mEDI). MethodsWe used an existing field dataset in which binocular pupil diameter and near-corneal spectral irradiance were recorded while 83 adults aged 18-87 years moved through indoor and outdoor environments. The analysis included 10,082 valid paired observations. We fitted a bounded sigmoid relating pupil diameter to mEDI and age, with each participant given equal influence, and assessed prediction in participants excluded from model fitting. Performance was compared with simpler models, a flexible generalised additive model (GAM), and Watson-Yellott predictions based on assumed field geometry. ResultsPupil diameter decreased smoothly as mEDI increased. Age primarily reduced the difference between pupils in dim and bright conditions, by 0.768 mm per decade, while the predicted bright-light diameter changed little with age. In held-out participants, the bounded model had a participant-balanced root mean squared error (RMSE) of 0.630 mm and mean absolute error of 0.537 mm. The GAM had a slightly lower point-estimate RMSE of 0.610 mm, but the difference was small and uncertain. The bounded model outperformed the tested log-linear, reduced, age-only, and Watson-Yellott alternatives. ConclusionAge and mEDI are sufficient to provide useful population-average pupil predictions across the observed adult age and real-world light range. The model is transparent, physiologically bounded, and nearly as accurate as a flexible GAM, but predictions approaching darkness remain uncertain because valid mEDI measurements were not available in that range. Key pointsO_LIA compact equation predicts population-average pupil diameter from age and mEDI alone. C_LIO_LIAge mainly compresses the pupils response range by reducing pupil diameter under dimmer conditions. C_LIO_LIPrediction error in unseen participants was close to that of a flexible GAM, without requiring a fitted smooth object. C_LIO_LIThe model is intended for the observed adult age and field-light range, not for extrapolation into darkness. C_LI

The temporal resolution of vision is seriously limited. However, the response to a very brief flash, called the impulse response, is already quite sluggish at the earliest stages of vision, potentially obscuring the true temporal resolution of the rest of the visual system. Faster monitors that produce briefer flashes are subject to diminishing returns because, by definition, they cannot elicit responses that are any briefer than the impulse response. Here, taking inspiration from previous attempts, we develop a novel technique for presenting flashes that elicit 'briefer-than-brief' visual responses. Using a simple deconvolution technique, we reverse-engineer the visual response and estimate the form that the stimulus should take to elicit the response that a faster visual system would produce to a normal flash. Using psychophysics on human observers, we demonstrate that these 'briefer-than-brief' (BTB) flashes partially bypass the temporal limits presumably imposed by the early visual system using two paradigms: one that requires temporal segregation and one that requires temporal integration of sequential flashes. With BTB flashes, human observers successfully isolated two successive flashes at shorter intervals than with conventional flashes, improving temporal resolution by around 16%. We found that BTB stimuli not only improved temporal resolution, but also induced poorer performance on tasks requiring temporal integration, suggesting that the visual responses elicited by BTB flashes overlap less in time due to their briefer duration. In sum, our findings suggest that, using reverse-engineered stimuli, we can alleviate a temporal bottleneck that probably originates from the earliest stages of vision. In doing so, we allow higher visual areas to operate at a higher temporal resolution than previously thought possible.

Resolving the binding problem, i.e., how we integrate perceptual features in space and time to perceive objects, people and events as coherent wholes instead of masses of free-floating features, has been a central goal of cognitive scientists for decades. Conjunction errors occur whenever features are mis-bound leading to illusory percepts that were not actually presented. A specific kind of Rapid Serial Visual Presentation experiment induces temporal illusory conjunctions, called distractor intrusion errors. Despite these errors being well-established, current theories and models provide limited explanations. We present the 2-feature Simultaneous Type/Serial Token (2f-ST2) Model, the latest addition to the family of ST2-models, which simulates the most important behavioural and electrophysiological distractor intrusion findings. These include response distributions, reaction time patterns, and evoked electrophysiology responses. Importantly, we simulate these results across diverse empirical paradigms, implementing a range of stimulus sets, temporal dynamics, and involving multiple responses after each trial, without needing to modify our model's architecture or core parameters. Our findings are discussed in relation to theories of selective attention and working-memory binding. We argue that the 2f-ST2 model is more parsimonious than the leading theory of distractor-intrusions (the focussed-attention/sophisticated-guessing model), since it uses only a single binding mechanism, implying that correct bindings are simply fortunate conjunctions. The 2f-ST2 model further makes predictions for stimulus-competition, subjective-visibility and human electrophysiology. In summary, the ST2-model is a general framework for understanding how perception, temporal attention and working-memory interact, and the 2f-ST2 model extends the framework to distractor intrusions, thus explaining temporal feature binding.

Artists and photographers use diagonals to convey dynamism, tension and instability in naturalistic and abstract images. However, the oblique effect in visual perception refers to a reduced sensitivity to oblique compared to cardinal (vertical/horizontal) orientations, linked to anisotropy in the distribution of preferred orientations of primary visual cortex neurons, which reflect the statistical distribution of edge orientations in natural images. Distal diagonals generically project onto oblique orientations on the retina, creating a conundrum between their perceptual salience in art and their reduced sensitivity in vision. We conjectured that global spatial configurations could shape perceived dynamism overriding local orientations. Inspired by van Doesburgs 1929 Arithmetical Composition, we created 60 distinct linear configurations of four identically-shaped rhombi each, in which global and local-edge orientations were independently manipulated to be cardinal or oblique, and measured their perceived dynamism with behavioral, psychophysical, and psychophysiological methods. First, 250 observers rated oblique configurations as more dynamic, tridimensional, and unstable than cardinal configurations, with wedge shapes and element spacing enhancing dynamism. Second, pairwise comparisons (50 observers) yielded a perceptual dynamism scale that confirmed the dominant role of global orientations. Third, pupillometry (35 observers) showed that pupil dilation correlates systematically with subjective dynamism ratings. Perceived dynamism in static images is thus driven primarily by the orientation and shape of spatial configurations, indicating the importance of estimating orientations and shapes of multielement objects beyond decoding local orientations. By linking visual structure to emotional perceptual experience, this investigation provides empirically grounded principles for creating dynamic percepts in visual art. SIGNIFICANCE STATEMENTDiagonals have been used since Michelangelo to create dynamic compositions in representative and abstract art and photography, but paradoxically humans have been shown to be less sensitive to oblique orientations like those projected on the retina by diagonal edges. We resolve this conflict by distinguishing between global configurations versus local edges and showing through behavioral, psychophysical, and psychophysiological experiments that the global orientation and shape of a multi-element configuration has a much larger effect on perceived dynamism than do the edge orientations of its elements. Neural estimation of orientations and shapes of multi-element configurations is thus essential for image understanding. Our results provide perceptually grounded bases for using diagonal configurations with wedge shapes to create dynamic percepts in visual art.

Neural encoding of sensory information becomes more efficient when distinct stimuli evoke uncorrelated activity patterns. Spatial frequency (SF) information becomes decorrelated early in visual processing in a manner that involves both temporal differentiation in the dorsal lateral geniculate nucleus (dLGN) and firing rate differentiation in the primary visual cortex (V1). However, the specific functional circuit configurations that enable this synergy are not fully understood. To address this gap, we developed a geniculocortical subunit model to identify functional circuit configurations capable of producing a decorrelated V1 population response. We found that decorrelation could be explained by the combined activity of two complementary circuit configurations with distinct functional organization. The first circuit was characterized by temporally organized, late-onset dLGN inputs coupled to weak and delayed intracortical inhibition, whereas the second was characterized by less temporally constrained, early-onset dLGN inputs coupled to strong and early intracortical inhibition. Eliminating SF selectivity in upstream dLGN subunits did not affect SF decorrelation in the modeled V1 population response, indicating that decorrelation tolerates weak SF selectivity and is primarily driven by temporal organization. Together, these results suggest that biologically plausible circuit configurations are likely to produce SF decorrelation in the cortex through the temporal organization of feedforward inputs and intracortical inhibition.

Searching for objects of interest is an essential and pervasive activity in adaptive cognition. In everyday situations, we often search for relevant content in our surroundings and in mind at the same time, yet most experimental tasks consider search behavior in perception or in working memory separately. To address the gap, we designed tasks to investigate perceptual and working-memory search proceeding in tandem. Participants viewed successive arrays of two real-world objects, separated by a delay, and then were prompted to search for a specific object, while the second array remained visible. The probed target was equally likely to be in the previously encoded array, available only in working memory, or in the visible array. Behavioral performance across four experiments with different response demands and delays yielded a consistent pattern of findings. Accuracy was better for detecting perceptual targets than working-memory targets. However, search speed was equivalent for targets in both domains in all cases, suggesting parallel search functions co-existing across the two domains. The final experiment combined continuous color reproduction with eye tracking and neural recordings. Neural measures of posterior alpha lateralization, tracking the neural selection of working-memory and perceptual targets, suggested parallel search functions developing with similar time courses. Intriguingly, oculomotor gaze shifts showed a divergent pattern, with earlier gaze shifts associated with perceptual targets and later shifts with working-memory targets. Overall, the present studies establish a means for investigating search across working-memory and perceptual domains, suggest the co-existence of parallel search mechanisms, and invite new lines of enquiry.

Understanding how our different senses interact to shape our perception is essential to design realistic and immersive virtual and augmented reality (VR/AR) experiences. The present study investigated how roughness perception can be modulated through haptic, visual, and auditory cues in AR using a vibrotactile wristband. Participants compared virtual textures varying in vibration frequency/amplitude, visual grain size, and friction sound. Results revealed strong linear relationships between stimulus parameters and perceived roughness, with haptic frequency and visual cues driving the highest discrimination performance. Adding non-informative sensory feedback reduced perceptual sensitivity, acting as noise. Individual differences emerged: participants who rated haptic as the easiest modality showed greater sensitivity to haptic variations, while visual-reliant participants performed better with visual cues. We conclude that roughness in AR can be systematically manipulated, but is vulnerable to perceptual interference from irrelevant inputs, where our work provides actionable insights for implementing optimized and adaptive AR/VR interfaces.

Cueing paradigms are commonly used to study the neural mechanisms of visual spatial attention control. In these paradigms, each trial starts with an external cue, which instructs the subject to pay covert attention to a spatial location in anticipation of an impending stimulus (instructed attention). Recent work has introduced a new type of cue which prompts the subject to spontaneously decide which spatial location to attend (willed attention). We studied the neural mechanisms of willed attention control by analyzing fMRI and EEG data recorded at two institutions (UF and UC Davis) using the same willed attention paradigm. The findings include: (1) both instructional cues and the choice cue activated the DAN, (2) the choice cue additionally activated a frontoparietal decision network consisting of dorsal anterior cingulate cortex (dACC), anterior insula (AI), anterior prefrontal cortex (APFC), dorsal lateral prefrontal cortex (DLPFC), and inferior parietal lobule (IPL), (3) the decision about where to attend can be decoded in frontoparietal decision network in choice trials but not in instructional trials, and (4) EEG alpha oscillation patterns immediately preceding the choice cue, but not the instructional cues, predicted the postcue direction of attention and the frontoparietal decision network activity. Based on these findings we proposed a model of willed attention control suggesting how the direction of visual spatial attention was decided upon in the absence of external instructions.

The measurement of light received at the cornea of the eye is a paramount consideration for the understanding of the relation between environmental illumination and the non-image-forming effects of light. The field of view (FOV) at the cornea is less than a full hemisphere, because it is partially occluded by human facial morphology. The International Commission on Illumination (CIE) has defined a standard model of human FOV. A suitably designed physical occluder attached to the sensor (of a light meter) has been proposed as a means of incorporating the effect of human FOV when taking measurements. Similarly, when using simulation to predict light received at the cornea, a geometrical description of the occluder at the eye point(s) can be added to the 3D model of the scene. The first occluder model proposed to represent CIE human FOV was enumerated in terms of: the CIE definition; the radius of the occluder; and, the radius of the light sensor disc. We present a simpler model based only on the CIE definition and the occluder radius. Both models were tested using a virtual goniophotometer. Various sensor response functions describing the spatial sensitivity across the sensor disc, including several we characterized through laboratory measurements, were included in the test. For all functions considered, the performance of the simpler occluder model was equivalent to or better than the model first proposed.

In human adults, visual performance varies systematically around the visual field. It is higher along the horizontal than the vertical meridian (horizontal-vertical anisotropy, HVA) and higher at the lower than the upper vertical meridian (vertical-meridian asymmetry, VMA). Although these robust performance fields have been linked to non-uniform neural resources, the system-level computations that translate neural constraints into perceptual asymmetries remain largely unexplored. Here, we used reverse correlation to characterize feature weighting and internal noise during peripheral orientation detection. Reverse correlation revealed non-ideal feature weighting in the joint orientation-spatial-frequency space, which was incorporated into a noisy-observer model jointly constrained by trial-wise detection responses and double-pass consistency. Across observers, the magnitude of the HVA in contrast sensitivity was correlated with individual asymmetries in orientation sensitivity and additive internal noise. In contrast, we found limited evidence that any tested representational or noise components reliably accounted for individual differences in VMA magnitude. Spatial-frequency tuning exhibited substantial individual variability, often peaking below the signals spatial frequency, but did not vary systematically across locations or explain performance asymmetries. These findings suggest that the HVA reflects systematic variation in the feature weighting of task-relevant orientations and internal noise, while constraining which computational components provide robust explanations of polar-angle asymmetries. Moreover, our framework provides a principled approach that links this prevalent perceptual asymmetry to the system-level computations that transform sensory information into perceptual decisions. Author summaryHuman vision is surprisingly uneven across our field of view. At the same distance from where we look, vision is better along the horizontal axis than the vertical axis, and better in the lower than the upper half of the vertical axis. These differences are well established, but little is known about their computational basis. To investigate, we combined visual detection tasks with computational modeling. By analyzing how observers detected faint patterns within noisy images, we measured how they process distinct features--like patterns with different orientations and spatial frequencies at different visual field locations. We then fit a mathematical model to separate distinct sources of internal noise. We found that differences in how the brain processes line orientations predicted the magnitude of the horizontal-vertical asymmetry across individuals, whereas distinct components of internal noise predicted these asymmetries in different ways. Our results show that visual field asymmetries are not driven by a single visual bottleneck, but rather by location-specific combinations of how the brain encodes relevant feature information and neural noise. This study helps explain why human vision is fundamentally uneven across our field of view.