Journal of Vision

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.

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.

Visual attention is shaped both by low-level perceptual features and by the semantic properties of scene regions. However, compared to the salience of low-level image features, the characterization of image properties that lead to scene understanding has been more difficult. The semantic properties of scene regions have been operationalized via meaning maps, proposed by Henderson and Hayes (2017), by having human observers rate the informativeness of scene patches. However, this rating procedure is labor-intensive and limits the method's versatility. An automated alternative, the DeepMeaning model, reduces rating costs but still requires human training data from a similar class of images, limiting generalizability. Here, we tested whether instruction-following vision-language models (VLMs) can generate human-like ratings in a zero-shot manner: using the original human-rating instructions as a single prompt without any task-specific training. We obtained meaningfulness ratings from both open-weight and proprietary VLMs for scene patches across three meaning-map datasets, and compared the resulting zero-shot AI meaning maps (AIMMs) to human meaning maps and maps obtained from the DeepMeaning model. Zero-shot AIMMs closely approximate human meaning maps, and maps from the task-specific DeepMeaning model, showing that VLMs can replicate human semantic judgments without any training. Zero-shot AIMMs demonstrate utility and validity, matching human meaning maps in their ability to predict human fixations and reproducing the same pattern of relative performance against other gaze-prediction models. We provide open-source code that allows researchers to use this zero-shot approach to quickly and flexibly explore meaning maps across spatial scales, prompts, and scene categories.

The visual environment contains more information than can be fully processed. To cope with this information, the visual system selects, integrates, and compresses sensory inputs. These processes are particularly evident in peripheral vision, where nearby items strongly limit access to individual elements, as in crowding and redundancy masking. In redundancy masking, repeated items are compressed into fewer items than are physically present, for example, three spatially separated items presented in the periphery are often perceived as only two items. Whether these interactions among items merely limit access to visual information or serve a functional role in perception remains unknown. Here, we investigated whether redundancy masking, despite reducing the number of perceived items, improves feature discrimination. In two experiments, participants viewed arrays of identical bars in peripheral vision, reported the number of bars they perceived, and adjusted a foveal probe to match perceived width (Experiment 1) or width and spacing (Experiment 2). Strong redundancy masking occurred in both experiments. When participants perceived fewer bars than were presented, perceived width was closer to the presented bar width than when all bars were perceived, where width was systematically underestimated. In Experiment 2, the perceived spacing between the (fewer) perceived bars increased. We modeled feature discrimination under redundancy masking and found that the observed improvement could not be explained by averaging features of the lost and perceived bars. These results suggest that the visual system efficiently compresses redundant information, reducing the number of perceived items while improving feature discrimination.

When objects are embedded in a complex lighting environment, the illumination they receive depends on their geometric relationship to the environment. We measured performance in an odd-one-out color constancy task, with objects rendered in complex lighting environments. Observers identified the odd object among four objects, with two rendered under each of two lighting environments, sampled from a wide range of object orientations and camera positions. Constancy was high. The presence of a surrounding context increased accuracy. Gloss provided no measurable benefit. We also tested a condition in which the global color impression provided misleading information about the lighting environment, but this impaired performance only slightly. These findings suggest that human color constancy remains robust even under complex lighting environments, contrary to predictions of many simple models of illuminant discounting.

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.

Rigorously tracking eye and head behavior in space is key to building realistic models of the stimulus that reaches our retina. The motion structure of this stimulus or retinal flow - the substrate for self and object motion processing - is created by the relative movement of the eyes with respect to the world. Characterizing this stimulus requires tracking the eyes three degrees of freedom in the head and the heads six degrees of freedom in the world. While vertical and horizontal eye rotations have been described during locomotion in the context of gaze stabilization (Moore et al, 2001), the component around the line of sight - torsion - has remained difficult to quantify, and how all three rotational components jointly contribute to retinal flow during self-motion remains largely unexplored. Here, we leveraged head-mounted technology to estimate eye torsion in ten subjects as they walked towards a distant target in a fast and slow condition (from 14 to 4 meters away from the target, see Fig. 1A). More specifically, we combined automatic feature tracking with gaze-constrained simulations of eye rotations and camera projection to recover torsion from image data. We then estimated flow curl in head and retina centered frames in two scenarios: torsion as estimated from our data and with no torsion. We show that the eyes torsional component compensates for the roll component of heads angular displacement, altering the incoming visual flow in ways that are relevant for the extraction of self-motion parameters from retinal flow. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/743586v1_fig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@391b9eorg.highwire.dtl.DTLVardef@1444510org.highwire.dtl.DTLVardef@1121e16org.highwire.dtl.DTLVardef@754b6a_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig 1.C_FLOATNO A. Top. Custom-made head-mounted device combining the Neon eye tracker (Pupil Labs), an RGB camera and a dimmable light. Bottom. Four 3D frames of reference (FoR) are relevant for this study, two static (world and locomotion) and two subject centered (head and eye). The Z axis of the locomotion, head and eye FoRs are approximately aligned throughout the trial. For the locomotion FoR the Z axis is fixed in the world and points forward (towards the target). The heads Z axis moves with the head but - as subjects are fixating a target along their path -, it also points approximately forward. The eyes Z axis also moves with the head and its exact forward orientation will depend on compensatory eye movements. B. Left. Blue dots represent the Z component of the heads orientation vector (on the locomotion frame) on the X axis, and the sum of all three components on the Y axis; for each frame for all corpus data. Blue contour is the 75th percentile 2D density distribution of the blue dots. Red and violet contours represent the 75th percentile for the X and Y components of head orientation, respectively. Right. Same logic but applied to the heads velocity vector. C. Left. Two examples showing the mean rotation of iris features over the course of a slow (top) and fast (bottom) trial. Colored lines show each of the 561 simulated cameras for a given scenario (one color per scenario); the black line shows the camera from the empirical data. Right. Trial-level mean fit score of each scenario with the empirical data is represented as a function of each subjects fitted gain. 20 dots represent 10 subjects x 2 trials. On the rightmost column, all values are aligned vertically to show the mean fit score across trials for the three scenarios. Size indicates the 75th percentile of head z component for each trial. C_FIG

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.

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

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.

Color vision deficiencies affect color perception and discrimination, but their consequences for the structure and stability of color categories remain less clear. We examined how trichromats, anomalous trichromats, and dichromats assigned 450 physical Munsell color samples to eleven basic color categories and measured the stability of these assignments in a retest two hours later. In each session, participants sorted a randomly mixed set of chips by hand and selected one representative chip for each category. We quantified agreement within and between color-vision groups as well as test-retest reliability within participants. For the 217 chips that all trichromats assigned to the same category, anomalous trichromats used the same category label for 85% of chips, and their category assignments were less stable across sessions as those of trichromats. Within anomalous trichromats poorer red-green discrimination, indexed by higher RG-CAD scores, was associated with lower sorting consistency. Dichromats showed lower agreement with trichromats and anomalous trichromats and greater prototype instability, but their color categories remained systematically organized rather than random. Information-theoretic analyses revealed a graded increase in categorical uncertainty from trichromats through anomalous trichromats to dichromats together with increasing reliance on lightness. Thus, the principal effect of color vision deficiency was not a collapse of color categories, but a graded reduction in the precision with which different shades were assigned to otherwise recognizable categories. In a separate subset of nine anomalous trichromats, EnChroma glasses produced no measurable immediate shift toward trichromatic category structures. Overall, our findings suggest that categorical color representations in anomalous trichromats and dichromats remain remarkably robust despite substantial sensory deficits.

Our visual system compensates for its limited information processing capacity by creating summary representations of stimulus ensembles. A longstanding debate concerns whether summary statistics are computed by integrating information across the entire ensemble or by sampling only a subset of its items. Subsampling is intuitively appealing because 1) integrating over a small subset presumably reduces processing resource expenditure and 2) observer models employing this strategy can effectively reproduce ensemble coding performance. Here, we systematically evaluate both aspects. We first show that the apparent success of subsampling models depends on a problematic assumption: that the sensory encoding noise of each item remains constant as set size increases. This assumption entails that total coding resources, quantified as Fisher information, grow unconstrained with set size. Under the realistic assumption that total coding resources are limited, such that the encoding precision for each item decreases with increasing set size, we demonstrate that an ideal observer integrating the entire ensemble achieves the same accuracy with a strictly smaller total resource budget for every set size. We further show that this resource-constrained full-integration model provides a more parsimonious, yet equally good, account of ensemble coding behavior across multiple existing datasets as well as newly collected data. Our findings call into question the necessity of the subsampling hypothesis, as restricting integration to a subset of ensemble items confers neither a theoretical nor an explanatory benefit over resource-rational full integration.

Eye movements have been shown to reveal the cognitive processes underlying spatial ability, but most studies investigating gaze behaviour in spatial cognition use a single, screen-based task. Therefore, it is unknown whether these gaze behaviours are preserved across naturalistic task environments, or whether their relationship with spatial performance is domain-general or task-specific. The present study tested whether established gaze behaviours are preserved and generalizable across diverse, naturalistic spatial tasks. N = 30 young adults completed three naturalistic spatial tasks varying in perceptual, cognitive, and visuomotor demands: Q-bitz, the Brick Building Task (BBT), and the Mental Rotation Test (MRT). Gaze was measured using the Pupil Labs Neon eye tracker. Within-task analyses were conducted to assess whether gaze behaviours were preserved, and cross-task analyses were conducted to assess the generalizability of preserved gaze behaviours. Within-task analyses identified source inspection and transition rate as gaze behaviours that were consistently sensitive to changing task demands. Cross-task analyses showed that source fixation duration was a domain-general predictor of performance, whereas the relationship between transition rate and performance differed by task. Exploratory sex-separated analyses also suggested a more consistent gaze-performance relationship in males. These findings show that source fixation duration may represent a common cognitive process underlying performance across naturalistic spatial tasks.

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.

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.

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.

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.

Attentional selection not only increases behavioural performance and neural information for attended stimuli but, in some instances, induces changes in stimulus appearance. While reported appearance changes have mostly concerned the effects of attending to a particular spatial location, here we report a shift in appearance that occurs when attention is directed to one component of a stimulus rapidly alternating at a single spatial location. Our stimuli included two frames of different colors, which alternated at 3.75Hz or 7.5Hz. Participants reported the perceived dominance of one frame's color in the reference stimulus, by comparison with a target stimulus. We found that when one stimulus frame was more salient than the other, with a greater likelihood of capturing exogenous attention, there was an illusory perception that the frame was physically present for a greater proportion of time. When the frames were matched in salience, a task encouraging participants to endogenously direct their attention to one frame produced a smaller shift in appearance. Our results add to the body of literature demonstrating that attention can change appearance, in this case altering the appearance of attended versus unattended stimuli that are presented at the same spatial location. Furthermore, our results demonstrate that at the 'temporal transparent' alternation rate of 7.5Hz, where rapidly alternating stimuli are perceived as two translucent surfaces, attentional selection of one surface can account for the apparent paradox that feature conjunctions are perceived at stimulus alternation rates higher than predicted by the temporal limits of feature binding for other stimuli.

The attentional blink is a well-known phenomenon illustrating the limitations of human attention: When two visual targets are presented in rapid succession, identification of the second target is often impaired. While the attentional blink is known to attenuate when targets share perceptual features or category membership, real-world objects are also linked through contextual associations, shaped by objects typically occurring within the same environments. Here, we devised an attentional blink experiment in which we orthogonally manipulated contextual and categorical relationships between the two targets while controlling for their perceptual similarity. As the key result, contextual associations facilitated identification of the second target but impaired identification of the first target. These findings suggest that contextual associations yield distinct benefits and costs for visual cognition, where enhanced attentional access to subsequent targets is traded off against increased interference between targets.

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.