Biology subjects

Majaj, N. J.

Publications and source records attributed to Majaj, N. J..

12 recordsLinked to original sources

bioRxiv2026

Curvature tuning in areas V2 and V4 of the developing macaque

Visual areas V2 and V4 are critical for the perception of visual forms in primates. Neurons in area V4 of macaque monkeys are often sensitive to the curvature of specific boundary segments within shapes, but it is unknown how curvature tuning is represented in the developing brain. To address this, we recorded multiunit neural activity from areas V2 and V4 of two macaque monkeys, at both 30 and 58 weeks of age, in response to shape stimuli which primarily varied in curvature along a single segment. Observable curvature tuning was adult-like from 30 weeks of age in both V2 and V4. We compared the tuning of sites to shapes presented at multiple positions. We saw evidence of position-invariant tuning in V4, but not in V2. Position invariance in V4 was stable from 30 weeks of age. Finally, we fit two models - a stimulus-centric model of boundary curvature tuning, and a simple linear model based on the spike-triggered average response to all stimuli. We found many sites in both V2 and V4 whose responses could be captured by one or both models, but no evidence of age-related changes in curvature tuning within the space of either model. Our results suggest that the neural representation of curvature in both areas reaches maturity soon after birth, and that object-centric representations of curvature first emerge in V4, not V2.

neuroscience↗

bioRxiv2026

Temporal Structure of Reward Availability and Sensory Uncertainty Modulate Allocation Dynamics in Naturalistic Foraging

Adaptive foraging requires animals to combine uncertain sensory cues with predictions about when rewards are likely to occur. While theoretical models describe how animals should allocate their effort under variable-interval reward schedules, it remains unclear how the timing or rewards and the reliability of sensory cues affects behavior. We developed a continuous foraging task in which freely moving macaque monkeys navigated among three reward patches. Rewards became available at unpredictable times, with their availability signaled by a visual cue of varying reliability. We also varied the schedule of reward availability: in some conditions, rewards were equally likely to become available at any moment (exponentially distributed intervals), while in others the interval distribution was more concentrated around a particular mean (gamma-distributed intervals) which increased the cost of premature responses. Under exponential schedules, monkeys eventually allocated their time at different patches according to reward schedules, and cue reliability had only modest effects. Under gamma-distributed intervals, monkeys more quickly learned to differentiate between patches. Their choices were more strongly dependent on predicted reward timing, particularly when sensory cues were highly reliable. These results show that both the timing of rewards and reliability of sensory cues shape how animals allocate their time and effort in continuous naturalistic foraging tasks.

animal behavior and cognition↗

bioRxiv2026

Object recognition along the ventral stream: effects of visual noise

How does the visual system recognize objects in natural environments? Here, we investigate the neural computations underlying robust object recognition along the ventral stream. We adapted psychophysical critical-band masking to fMRI, measuring BOLD responses along the ventral stream to natural images perturbed by bandpass noise. We found that, along this stream, the overall BOLD response to noise alone is sensitive to an increasingly wide range of stimulus spatial frequency, from 2 octaves in V1 to 5 octaves in ventral temporal cortex (VTC). However, when we assess the effect of the same noise on the accuracy of decoding scene images from the BOLD response, we obtain a different result: Recognition bandwidth is conserved along the ventral stream at about 2 octaves, close to the 1.5-octave behavioral channel. Though the recognition band is conserved, its noise tolerance increases steadily along the ventral stream, approaching behavioral levels in VTC. These findings suggest that V1 sets the bandwidth of the object-recognition channel, while downstream areas progressively denoise the signal, establishing the channels noise tolerance. This biological architecture--early channel isolation followed by successive denoising--may explain why human vision remains more robust than current machine vision.

neuroscience↗

bioRxiv2025

Emergence of a contrast-invariant representation of naturalistic texture in macaque visual cortex

Sensory stimuli vary across a variety of dimensions, like contrast, orientation, or texture. The brain must rely on population representations to distinguish changes in one dimension from changes in another. To understand how the visual system might extract separable stimulus representations, we recorded multiunit neuronal responses to texture images varying along two dimensions: contrast, a property represented as early as the retina, and naturalistic statistical structure, a property that modulates neuronal responses in V2 and V4, but not in V1. We measured how sites in these 3 cortical areas responded to variation in both dimensions. Contrast modulated responses in all areas. In V2 and V4, the presence of naturalistic structure both modulated responses and increased contrast sensitivity. Tuning for naturalistic structure was both strongest and most dispersed in V4. We measured how well populations in each area could support the linear readout of both dimensions. Populations in V2 and V4 could support the linear readout of naturalistic structure, but in V4, this readout was more robust to variations in contrast. Significance StatementTo support flexible behavior, the brain must simultaneously represent different stimulus dimensions. Single neurons are typically modulated by multiple dimensions, and so cannot distinguish them - they must be extracted by decoding neural populations. We studied neuronal responses in three cortical visual areas - V1, V2, V4 - using texture images varying in both contrast and naturalistic image structure. We used population decoders to read out each dimension. In all areas, contrast was well decoded independently of image structure. On the other hand, V1 could not decode image structure independent of contrast, while V2 and V4 could. V4 decoding was greatly superior, because the selectivity of individual sites for texture was more diverse than in V1 or V2.

neuroscience↗

bioRxiv2024

Conservation of cortical crowding distance across individuals in human V4

Visual recognition is limited by both object size (acuity) and spacing. The spacing limit, called "crowding", is the failure to recognize an object in the presence of other objects. Here, we take advantage of individual differences in crowding to investigate its biological basis. Crowding distance, the minimum object spacing needed for recognition, varies 2-fold among healthy adults. We test the conjecture that this variation in psychophysical crowding distance is due to variation in cortical map size. To test this, we made paired measurements of brain and behavior in 50 observers. We used psychophysics to measure crowding distance and calculate{lambda} , the number of letters that fit into each observers visual field without crowding. In the same observers, we used fMRI to measure the surface area A (mm{superscript 2}) of retinotopic maps V1, V2, V3, and V4. Across observers,{lambda} is proportional to the surface area of V4 but is uncorrelated with the surface area of V1 to V3. The proportional relationship of{lambda} to area of V4 indicates conservation of cortical crowding distance across individuals: letters can be recognized if they are spaced by at least 1.4 mm on the V4 map, irrespective of map size and psychophysical crowding distance. We conclude that the size of V4 predicts the spacing limit of visual perception.

neuroscience↗

bioRxiv2024

Developmentally stable representations of naturalistic image structure in macaque visual cortex

We studied visual development in macaque monkeys using texture stimuli, matched in local spectral content but varying in "naturalistic" structure. In adult monkeys, naturalistic textures preferentially drive neurons in areas V2 and V4, but not V1. We paired behavioral measurements of naturalness sensitivity with separately-obtained neuronal population recordings from neurons in areas V1, V2, V4, and inferotemporal cortex (IT). We made behavioral measurements from 16 weeks of age and physiological measurements as early as 20 weeks, and continued through 56 weeks. Behavioral sensitivity reached half of maximum at roughly 25 weeks of age. Neural sensitivities remained stable from the earliest ages tested. As in adults, neural sensitivity to naturalistic structure increased from V1 to V2 to V4. While sensitivities in V2 and IT were similar, the dimensionality of the IT representation was more similar to V4s than to V2s.

neuroscience↗

bioRxiv2024

Development of radial frequency pattern perception in macaque monkeys

Infant primates see poorly, and most perceptual functions mature steadily beyond early infancy. Behavioral studies on human and macaque infants show that global form perception, as measured by the ability to integrate contour information into a coherent percept, improves dramatically throughout the first several years after birth. However, it is unknown when sensitivity to curvature and shape emerges in early life. We studied the development of shape sensitivity in eighteen macaques, aged 2 months to 10 years. Using radial frequency stimuli (RFS), circular targets whose radii are modulated sinusoidally, we tested monkeys ability to discriminate RFS from circles as a function of the depth and frequency of sinusoidal modulation. We implemented a new 4-choice oddity task and compared the resulting data with that from a traditional 2-alternative task. Behavioral performance at all radial frequencies improved with age. Performance was better for higher radial frequencies, suggesting the developing visual system prioritizes processing of fine visual details that are ecologically relevant. By utilizing two complementary methods, we were able to capture a comprehensive developmental trajectory for shape perception.

neuroscience↗

bioRxiv2023

EasyEyes - Accurate fixation for online vision testing of crowding and beyond

Online methods allow testing of larger, more diverse populations, with much less effort than in-lab testing. However, many psychophysical measurements, including visual crowding, require accurate eye fixation, which is classically achieved by testing only experienced observers who have learned to fixate reliably, or by using a gaze tracker to restrict testing to moments when fixation is accurate. Alas, both approaches are impractical online since online observers tend to be inexperienced, and online gaze tracking, using the built-in webcam, has a low precision ({+/-}4 deg, Papoutsaki et al., 2016). The EasyEyes open-source software reliably measures peripheral thresholds online with accurate fixation achieved in a novel way, without gaze tracking. EasyEyes tells observers to use the cursor to track a moving crosshair. At a random time during successful tracking, a brief target is presented in the periphery. The observer responds by identifying the target. To evaluate EasyEyes fixation accuracy and thresholds, we tested 12 naive observers in three ways in a counterbalanced order: first, in the lab, using gaze-contingent stimulus presentation (Kurzawski et al., 2023; Pelli et al., 2016); second, in the lab, using EasyEyes while independently monitoring gaze; third, online at home, using EasyEyes. We find that crowding thresholds are consistent (no significant differences in mean and variance of thresholds across ways) and individual differences are conserved. The small root mean square (RMS) fixation error (0.6 deg) during target presentation eliminates the need for gaze tracking. Thus, EasyEyes enables fixation-dependent measurements online, for easy testing of larger and more diverse populations.

neuroscience↗

bioRxiv2022

Sensitivity to naturalistic texture relies primarily on high spatial frequencies

Natural images contain information at multiple spatial scales. Although we understand how early visual mechanisms split multi-scale images into distinct spatial frequency channels, we do not know how the outputs of these channels are processed further by mid-level visual mechanisms. We have recently developed a naturalness discrimination task that uses synthesized, multi-scale textures to isolate these mid-level mechanisms (Freeman et. al. 2013). Here, we use three experimental manipulations (image blur, image rescaling, and eccentric viewing) to show that naturalness sensitivity is strongly dependent on image features at high object spatial frequencies (measured in cycles/image). As a result, sensitivity depends on a texture acuity limit, a property of the visual system that sets the highest retinal spatial frequency (measured in cycles/degree) that can be used to solve the task. A model observer analysis shows that high object spatial frequencies carry more task-relevant information than low object spatial frequencies. Comparing the outcome of this analysis with human performance reveals that human observers efficiency is similar for all object spatial frequencies. We conclude that the mid-level mechanisms that underlie naturalness sensitivity effectively extract information from all image features below the texture acuity limit, regardless of their retinal and object spatial frequency.

neuroscience↗

bioRxiv2021

An enhanced Bouma model fits a hundred people's visual crowding

AbstractCrowding is the failure to recognize an object due to surrounding clutter. Our visual crowding survey measured 13 crowding distances (or "critical spacings") twice in each of 50 observers. The survey included three eccentricities (0, 5, and 10 deg), four cardinal meridians, two orientations (radial and tangential), and two fonts (Sloan and Pelli). The survey also tested foveal acuity, twice. Remarkably, fitting a two-parameter model, the well- known Bouma law -- crowding distance grows linearly with eccentricity -- explains 82% of the variance for all 13 x 50 measured log crowding distances, cross-validated. An enhanced Bouma law, with factors for meridian, crowding orientation, target kind, and observer, explains 94% of the variance, again cross-validated. These additional factors reveal several asymmetries, consistent with previous reports, which can be expressed as crowding- distance ratios: 0.62 horizontal:vertical, 0.79 lower:upper, 0.78 right:left, 0.55 tangential:radial, and 0.78 Sloan font:Pelli font. Across our observers, peripheral crowding is independent of foveal crowding and acuity. Evaluation of the Bouma factor b (the slope of the Bouma law) as a biomarker of visual health would be easier if there were a way to compare results across crowding studies that use different methods. We define a standardized Bouma factor b that corrects for differences from Boumas 25 choice alternatives, 75% threshold criterion, and linearly symmetric flanker placement. For radial crowding on the right meridian, the standardized Bouma factor b is 0.24 for this study, 0.35 for Bouma (1970), and 0.30 for the geometric mean across five representative modern studies, including this one, showing good agreement across labs, including Boumas. We found that guaranteeing fixation by gaze-contingent display halved the standard deviation across observers of the estimated log b. The reduction in standard deviation is explained by a "peeking" model in which the observer looked near an anticipated target location in 50% of unmonitored-fixation trials. Individual differences are robust, as evidenced by the much larger 0.08 SD of log b across observers than the 0.03 SD of test-retest within observers. Crowdings ease of measurement enhances its promise as a biomarker for dyslexia and visual health.

neuroscience↗

bioRxiv2018

Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?

The internal representations of early deep artificial neural networks (ANNs) were found to be remarkably similar to the internal neural representations measured experimentally in the primate brain. Here we ask, as deep ANNs have continued to evolve, are they becoming more or less brain-like? ANNs that are most functionally similar to the brain will contain mechanisms that are most like those used by the brain. We therefore developed Brain-Score - a composite of multiple neural and behavioral benchmarks that score any ANN on how similar it is to the brains mechanisms for core object recognition - and we deployed it to evaluate a wide range of state-of-the-art deep ANNs. Using this scoring system, we here report that: (1) DenseNet-169, CORnet-S and ResNet-101 are the most brain-like ANNs. (2) There remains considerable variability in neural and behavioral responses that is not predicted by any ANN, suggesting that no ANN model has yet captured all the relevant mechanisms. (3) Extending prior work, we found that gains in ANN ImageNet performance led to gains on Brain-Score. However, correlation weakened at [≥] 70% top-1 ImageNet performance, suggesting that additional guidance from neuroscience is needed to make further advances in capturing brain mechanisms. (4) We uncovered smaller (i.e. less complex) ANNs that are more brain-like than many of the best-performing ImageNet models, which suggests the opportunity to simplify ANNs to better understand the ventral stream. The scoring system used here is far from complete. However, we propose that evaluating and tracking model-benchmark correspondences through a Brain-Score that is regularly updated with new brain data is an exciting opportunity: experimental benchmarks can be used to guide machine network evolution, and machine networks are mechanistic hypotheses of the brains network and thus drive next experiments. To facilitate both of these, we release Brain-Score.org: a platform that hosts the neural and behavioral benchmarks, where ANNs for visual processing can be submitted to receive a Brain-Score and their rank relative to other models, and where new experimental data can be naturally incorporated.

neuroscience↗

bioRxiv2017

Deep learning: Using machine learning to study biological vision

Today many vision-science presentations employ machine learning and especially \"deep learning\", one of the more recent and successful variants. Many neuroscientists use machine learning to decode neural responses. Many perception scientists try to understand how living organisms recognize objects. To them, deep neural networks offer several benchmark accuracies for recognition of learned stimuli. Originally machine learning was inspired by the brain. Today, machine learning is used as a statistical tool to decode brain activity. Tomorrow, deep neural networks might become our best model of brain function. This brief overview of the use of machine learning in biological vision touches on its strengths, weaknesses, milestones, controversies, and current directions. Here, we hope to help vision scientists assess what role machine learning should play in their research.

neuroscience↗