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Population receptive field (pRF) models fit to fMRI data are used to non-invasively measure retinotopic maps in human visual cortex, and these maps are a fundamental component of visual neuroscience experiments. Here, we examined the reproducibility of retinotopic maps across two datasets: a newly acquired retinotopy dataset from New York University (NYU) (n = 44) and a public dataset from the Human Connectome Project (HCP) (n = 181). Our goal was to assess the degree to which pRF properties are similar across datasets, despite substantial differences in their experimental protocols. The two datasets simultaneously differ in their stimulus apertures, participant pool, fMRI protocol, MRI field strength, and preprocessing pipeline. We assessed the cross-dataset reproducibility of the two datasets in terms of the similarity of vertex-wise pRF estimates and in terms of large-scale polar angle asymmetries in cortical magnification. Within V1, V2, V3, and hV4, the group-median NYU and HCP vertex-wise polar angle estimates were nearly identical. Both eccentricity and pRF size estimates were also strongly correlated between the two datasets, but with a slope different from 1; the eccentricity and pRF size estimates were systematically greater in the NYU data. Next, to compare large-scale map properties, we quantified two polar angle asymmetries in V1 cortical magnification previously identified in the HCP data. The NYU dataset confirms earlier reports that more cortical surface area represents horizontal than vertical visual field meridian, and lower than upper vertical visual field meridian. Together, our findings show that the retinotopic properties of V1, V2, V3, and hV4 can be reliably measured across two datasets, despite numerous differences in their experimental design. fMRI-derived retinotopic maps are reproducible because they rely on an explicit computational model of the fMRI response. In the case of pRF mapping, the model is grounded in physiological evidence of how visual receptive fields are organized, allowing one to quantitatively characterize the BOLD signal in terms of stimulus properties (i.e., location and size). The new NYU Retinotopy Dataset will serve as a useful benchmark for testing hypotheses about the organization of visual areas and for comparison to the HCP 7T Retinotopy Dataset.

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 make paired measurements of brain and behavior in 49 observers. We use psychophysics to measure crowding distance and calculate λ , the number of letters that fit into each observer’s visual field without crowding. In the same observers, we use functional magnetic resonance imaging (fMRI) to measure the surface area A of retinotopic maps V1, V2, V3, and V4. Across observers, λ is proportional to the surface area of V4 but is uncorrelated with the surface area of V1 to V3. The proportional relationship of λ 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. Across 49 observers, we found large variations in crowding distance and retinotopic map size. These measures covary, conserving a 1.4-mm cortical crowding distance in the human V4 map. This links the spacing limit of visual recognition to V4 size.

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 as online observers tend to be inexperienced, and online gaze tracking, using the built-in webcam, has a low precision (±4 deg). EasyEyes open-source software reliably measures peripheral thresholds online with accurate fixation achieved in a novel way, without gaze tracking. It 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 laboratory, using gaze-contingent stimulus presentation; second, in the laboratory, using EasyEyes while independently monitoring gaze using EyeLink 1000; third, online at home, using EasyEyes. We find that crowding thresholds are consistent 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, this method enables fixation-dependent measurements online, for easy testing of larger and more diverse populations.

Sensory cortical mechanisms combine auditory or visual features into perceived objects. This is difficult in noisy or cluttered environments. Knowing that individuals vary greatly in their susceptibility to clutter, we wondered whether there might be a relation between an individual’s auditory and visual susceptibilities to clutter. In auditory masking , background sound makes spoken words unrecognizable. When masking arises due to interference at central auditory processing stages, beyond the cochlea, it is called informational masking. A strikingly similar phenomenon in vision, called visual crowding , occurs when nearby clutter makes a target object unrecognizable, despite being resolved at the retina. We here compare susceptibilities to auditory informational masking and visual crowding in the same participants. Surprisingly, across participants, we find a negative correlation ( R = –0.7) between susceptibility to informational masking and crowding: Participants who have low susceptibility to auditory clutter tend to have high susceptibility to visual clutter, and vice versa. This reveals a tradeoff in the brain between auditory and visual processing.

This perspective article proposes a general law (Bouma law), which states that a visual object is crowded (and therefore cannot be perceived) when spacing between multiple objects is less than a critical spacing value. Crucially, this value is independent of the object. It is now emerging that vision is usually limited by object spacing rather than size. The visual system recognizes an object by detecting and then combining its features. 'Crowding' occurs when objects are too close together and features from several objects are combined into a jumbled percept. Here, we review the explosion of studies on crowding—in grating discrimination, letter and face recognition, visual search, selective attention, and reading—and find a universal principle, the Bouma law. The critical spacing required to prevent crowding is equal for all objects, although the effect is weaker between dissimilar objects. Furthermore, critical spacing at the cortex is independent of object position, and critical spacing at the visual field is proportional to object distance from fixation. The region where object spacing exceeds critical spacing is the 'uncrowded window'. Observers cannot recognize objects outside of this window and its size limits the speed of reading and search.

Research in object recognition has tried to distinguish holistic recognition from recognition by parts. One can also guess an object from its context. Words are objects, and how we recognize them is the core question of reading research. Do fast readers rely most on letter-by-letter decoding (i.e., recognition by parts), whole word shape, or sentence context? We manipulated the text to selectively knock out each source of information while sparing the others. Surprisingly, the effects of the knockouts on reading rate reveal a triple dissociation. Each reading process always contributes the same number of words per minute, regardless of whether the other processes are operating.

To understand why human sensitivity for complex objects is so low, we study how word identification combines eye and ear or parts of a word (features, letters, syllables). Our observers identify printed and spoken words presented concurrently or separately. When researchers measure threshold (energy of the faintest visible or audible signal) they may report either sensitivity (one over the human threshold) or efficiency (ratio of the best possible threshold to the human threshold). When the best possible algorithm identifies an object (like a word) in noise, its threshold is independent of how many parts the object has. But, with human observers, efficiency depends on the task. In some tasks, human observers combine parts efficiently, needing hardly more energy to identify an object with more parts. In other tasks, they combine inefficiently, needing energy nearly proportional to the number of parts, over a 60∶1 range. Whether presented to eye or ear, efficiency for detecting a short sinusoid (tone or grating) with few features is a substantial 20%, while efficiency for identifying a word with many features is merely 1%. Why? We show that the low human sensitivity for words is a cost of combining their many parts. We report a dichotomy between inefficient combining of adjacent features and efficient combining across senses. Joining our results with a survey of the cue-combination literature reveals that cues combine efficiently only if they are perceived as aspects of the same object. Observers give different names to adjacent letters in a word, and combine them inefficiently. Observers give the same name to a word's image and sound, and combine them efficiently. The brain's machinery optimally combines only cues that are perceived as originating from the same object. Presumably such cues each find their own way through the brain to arrive at the same object representation.

Neuroaesthetics is a rapidly developing interdisciplinary field of research that aims to understand the neural substrates of aesthetic experience: While understanding aesthetic experience has been an objective of philosophers for centuries, it has only more recently been embraced by neuroscientists. Recent work in neuroaesthetics has revealed that aesthetic experience with static visual art engages visual, reward and default-mode networks. Very little is known about the temporal dynamics of these networks during aesthetic appreciation. Previous behavioral and brain imaging research suggests that critical aspects of aesthetic experience have slow dynamics, taking more than a few seconds, making them amenable to study with fMRI. Here, we identified key aspects of the dynamics of aesthetic experience while viewing art for various durations. In the first few seconds following image onset, activity in the DMN (and high-level visual and reward regions) was greater for very pleasing images; in the DMN this activity counteracted a suppressive effect that grew longer and deeper with increasing image duration. In addition, for very pleasing art, the DMN response returned to baseline in a manner time-locked to image offset. Conversely, for non-pleasing art, the timing of this return to baseline was inconsistent. This differential response in the DMN may therefore reflect the internal dynamics of the participant's state: The participant disengages from art-related processing and returns to stimulus-independent thought. These dynamics suggest that the DMN tracks the internal state of a participant during aesthetic experience.

Recall memory and sequential dependence threaten the independence of successive beauty ratings. Such independence is usually assumed when using repeated measures to estimate the intrinsic variance of a rating. We call “intrinsic” the variance of all possible responses that the participant could give on a trial. Variance arises within and across participants. In attributing the measured variance to sources, the first step is to assess how much is intrinsic. In seven experiments, we measure how much of the variability across beauty ratings can be attributed to recall memory and sequential dependence. With a set size of one, memory is a problem and contributes half the measured variance. However, we showed that for both beauty and ellipticity, with set size of nine or more, recall memory causes a mere 10% increase in the variance of repeated ratings. Moreover, we showed that as long as the stimuli are diverse (i.e., represent different object categories), sequential dependence does not affect the variance of beauty ratings. Lastly, the variance of beauty ratings increases in proportion to the 0.15 power of stimulus set size. We show that the beauty rating of a stimulus in a diverse set is affected by the stimulus set size and not the value of other stimuli. Overall, we conclude that the variance of repeated ratings is a good way to estimate the intrinsic variance of a beauty rating of a stimulus in a diverse set.

Unless we fixate directly on it, it is hard to see an object among other objects. This breakdown in object recognition, called crowding , severely limits peripheral vision. The effect is more severe when objects are more similar. When observers mistake the identity of a target among flanker objects, they often report a flanker. Many have taken these flanker reports as evidence of internal substitution of the target by a flanker. Here, we ask observers to identify a target letter presented in between one similar and one dissimilar flanker letter. Simple substitution takes in only one letter, which is often the target but, by unwitting mistake, is sometimes a flanker. The opposite of substitution is pooling, which takes in more than one letter. Having taken only one letter, the substitution process knows only its identity, not its similarity to the target. Thus, it must report similar and dissimilar flankers equally often. Contrary to this prediction, the similar flanker is reported much more often than the dissimilar flanker, showing that rampant flanker substitution cannot account for most flanker reports. Mixture modeling shows that simple substitution can account for, at most, about half the trials. Pooling and nonpooling (simple substitution) together include all possible models of crowding. When observers are asked to identify a crowded object, at least half of their reports are pooled, based on a combination of information from target and flankers, rather than being based on a single letter.