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People vary in their general ability to compare, identify, and remember objects. Research using latent variable modeling identifies a domain-general visual recognition ability (called o) that reflects correlations among different visual tasks and categories. We measure associations between a psychometrically-sensitive measure of o and a neurometrically-sensitive measure of visual sensitivity to shape. We report evidence for distributed neural correlates of o using functional and anatomical regions-of-interest (ROIs) as well as whole brain analyses. Neural selectivity to shape is associated with o in several regions of the ventral pathway, as well as additional foci in parietal and premotor cortex. Multivariate analyses suggest the distributed effects in ventral cortex reflect a common mechanism. The network of brain areas where neural selectivity predicts o is similar to that evoked by the most informative features for object recognition in prior work, showing convergence of 2 different approaches on identifying areas that support the best object recognition performance. Because o predicts performance across many visual tasks for both novel and familiar objects, we propose that o could predict the magnitude of neural changes in task-relevant areas following experience with specific task and object category.

Measurement of domain-general object recognition ability (o) requires minimization of domain-specific variance. One approach is to model o as a latent variable explaining performance on a battery of tests which differ in task demand and stimuli; however, time and sample requirements may be prohibitive. Alternatively, an aggregate measure of o can be obtained by averaging z-scores across tests. Using data from Sunday et al. (2022), we demonstrate that aggregate scores from just two such object recognition tests provide a good approximation (r = .79) of factor scores calculated from a model using a much larger set of tests. Some test combinations produced correlations of up to r = .87 with factor scores. We then revise these tests to reduce testing time, and develop an odd-one-out task, using a unique object category on each trial, to increase task and stimuli diversity. To test our measures, 163 participants completed the object recognition tests on two occasions, one month apart. Providing the first evidence that o is stable over time, our short aggregate o measure demonstrated good test-retest reliability (r = .77). The stability of o could not be completely accounted for by intelligence, perceptual speed, and early visual ability. Structural equation modeling suggests our tests load significantly onto the same latent variable, and demonstrates that as a latent variable, o is highly stable (r = .93). Aggregation is an efficient method to estimate o, allowing investigation of individual differences in object recognition ability to be more accessible in future studies.

Amygdala volume and face activation activity have been linked to social network size. In the current study, these relationships were examined through analysis of Magnetic Resonance Imaging and survey data of 116 subjects, with a more detailed analysis of a smaller sub-sample of 20 subjects. Amygdalae structural volumes were analyzed using Freesurfer, functional activation was analyzed using Statistical Parametric Mapping, and ultra-high-resolution susceptibility-weighted images were analyzed using in-house MATLAB scripts. Mean amygdala volumes obtained in this experiment are in line with those reported in previous research. Left amygdala volume was correlated with social network scores in males, and both left and right hippocampus volume were correlated with the square root of the size of one’s social network in females. Significant correlations were also observed between left amygdala face activation levels and social network measures in the smaller sub-sample (n = 20).

There is recent evidence for a domain-general object recognition ability, called O, which is distinct from general intelligence and other cognitive and personality constructs. We extend the study of O by characterizing how it generalizes to the ability to recognize familiar objects and to the ability to make judgments of the average identity of ensembles of objects. We applied latent variable modeling to data collected from a sample of adults (N = 284) in three different tasks and for six different object domains (three novel and three familiar). The results replicated prior work in finding that on average 88% of the variance of lower-order factors could be accounted by O for novel objects. The latent constructs recruited by the higher-order factor for novel objects and for familiar objects were almost perfectly correlated and therefore functionally identical. A latent factor for ensemble perception shared about 42% of the variance with O, suggesting at least strong overlap between abilities supporting judgments about individual objects and ensemble of objects. This work extends the theoretical reach of O by showing generalization across two dimensions (familiar vs. novel objects; individual vs. ensemble object perception). With respect to the structure of individual differences in high-level vision, researchers would benefit from accounting for the contribution of O when seeking to understand various domain-specific abilities. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Visual object recognition depends in large part on a domain-general ability (Richler et al. Psychol Rev 126(2): 226–251, 2019). Given evidence pointing towards shared mechanisms for object perception across vision and touch, we ask whether individual differences in haptic and visual object recognition are related. We use existing validated visual tests to estimate visual object recognition ability and relate it to performance on two novel tests of haptic object recognition ability (n = 66). One test includes complex objects that participants chose to explore with a hand grasp. The other test uses a simpler stimulus set that participants chose to explore with just their fingertips. Only performance on the haptic test with complex stimuli correlated with visual object recognition ability, suggesting a shared source of variance across task structures, stimuli, and modalities. A follow-up study using a visual version of the haptic test with simple stimuli shows a correlation with the original visual tests, suggesting that the limited complexity of the stimuli did not limit correlation with visual object recognition ability. Instead, we propose that the manner of exploration may be a critical factor in whether a haptic test relates to visual object recognition ability. Our results suggest a perceptual ability that spans at least across vision and touch, however, it may not be recruited during just fingertip exploration.

People can make judgments about statistical properties of visual features within groups of objects, such as the average size, size variability, or numerosity of circles. Emerging from recent work is the view that these kinds of visual estimations, collectively dubbed ensemble perception, rely on independent abilities that are specific to the statistical property being estimated (mean, variance, range, numerosity). Here we revisit evidence for the claim that different statistical judgments (i.e., average and variability) for the same object feature are based on independent abilities. We tested a large sample of people, using a pre-registered open-ended sequential design to avoid ending up with weak evidence. We estimated the shared variance in ability across different ensemble judgments, with common constraints for the different tasks. We found that the abilities to judge the average size and the size variability for an array of circles are positively correlated, even after controlling for the ability to discriminate the size of single circles. Our results refute the idea that judgments of average and variability for the same object feature rely on completely independent abilities.

Holistic processing is inferred from a number of effects, many of which suggest that people generally find it difficult to process face parts independently. The study of holistic processing using faces has revealed many failures of convergence across different measures, as well as very poor reliability. New tasks designed for individual-differences measurement of holistic processing are more reliable. But other challenges to the study of individual differences in holistic processing require a different approach, in particular the use of nonface objects. Observers’ experiences with faces may be so extensive that it cannot be quantified. In addition, it is difficult to manipulate experience with faces to study causes and mechanisms underlying holistic effects. Recent work has combined an individual-differences approach with a parametric manipulation of experience to reveal that holistic processing arises from domain-specific experience. Other work has revealed that learned attention to parts is sufficient to result in holistic processing, consistent with a mechanism rooted in category-specific learned attention.

Holistic processing refers to the processing of objects as wholes rather than in a piecemeal, part-based fashion. Despite a suggested link between expertise and holistic processing, the role of experience in determining holistic processing of both faces and objects has been questioned. Here, we combine an individual differences approach with an experimental training study and parametrically manipulate experience with novel objects to examine the determinants of holistic processing. We also measure object-recognition ability. Our results show that although domain-general visual ability is a predictor of the ability to match object parts, it is the amount of experience people have individuating objects of a category that determines the extent to which they process new objects of this category in a holistic manner. This work highlights the benefits of dissociating the influences of domain-general ability and domain-specific experience, typically confounded in measures of performance or "expertise." Our findings are consistent with those in recent work with faces showing that variability specific to experience is a better predictor of domain-specific effects than is variability in performance. We argue that individual differences in holistic processing arise from domain-specific experience and that these effects are related to similar effects of experience on other measures of selective attention. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

When seeing groups of objects, various features can be extracted to form an ensemble representation, including low-level features such as orientation and higher-level features like facial expression. Past research proposed distinct abilities for ensemble coding of high-level versus low-level visual features, but the only complex objects used were faces. Here, we examine evidence for a shared ability supporting ensemble representations for complex objects from different object categories. In 2 experiments, participants completed an ensemble mean judgment task for an array of 4 objects, including planes and birds (Experiment 1) or cars and birds (Experiment 2). We also measured and controlled for domain-specific recognition ability. Across the 2 experiments, performance on ensemble judgments with different objects were correlated, even after controlled for domain-specific recognition abilities. These findings provide the strongest evidence to date of a domain-general ability involved in complex object ensemble coding. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Musical practice may benefit not only domain-specific abilities, such as pitch discrimination and music performance, but also domain-general abilities, like executive functioning and memory. Behavioral and neural changes in visual processing have been associated with music-reading experience. However, it is still unclear whether there is a domain-specific visual ability to process musical notation. This study investigates the specificity of the visual skills relevant to simple decisions about musical notation. Ninety-six participants varying in music-reading experience answered a short survey to quantify experience with musical notation and completed a test battery that assessed musical notation reading fluency and accuracy at the level of individual note or note sequence. To characterize how this ability may relate to domain-general abilities, we also estimated general intelligence (as measured with the Raven's Progressive Matrices) and general object-recognition ability (as measure by a recently proposed construct o). We obtained reliable measurements on our various tasks and found evidence for a domain-specific ability of the perception of musical notation. This music-reading ability and domain-general abilities were found to contribute to performance on specific tasks differently, depending on the level of experience reading music.

People can make judgments about statistical properties of visual features within groups of objects, such as the average size, size variability, or numerosity of circles. Emerging from recent work is the view that these kinds of visual estimations, collectively dubbed ensemble perception, rely on independent abilities that are specific to the statistical property being estimated (mean, variance, range, numerosity). Here we revisit evidence for the claim that different statistical judgments (i.e., average and variability) for the same object feature are based on independent abilities. We tested a large sample of people, using a pre-registered open-ended sequential design to avoid ending up with weak evidence. We estimated the shared variance in ability across different ensemble judgments, with common constraints for the different tasks. We found that the abilities to judge the average size and the size variability for an array of circles are positively correlated, even after controlling for the ability to discriminate the size of single circles. Our results refute the idea that judgments of average and variability for the same object feature rely on completely independent abilities.

Several studies have found that individuals from small hometowns show diminished face recognition ability as compared with individuals from larger hometowns. We further this line of research by relating six measures of face recognition ability to hometown density. We predicted that the three face recognition ability measures which included a learning component would relate to hometown density whereas the three measures which did not include such a learning component would not. Instead, we found that none of the six measures related to hometown density. Interestingly, we found interactions between gender and hometown population density on many of these measures and on a general index of face recognition, with females from small hometowns outperforming males from small hometowns but no such differences in the large hometown group. In a follow-up re-analysis of a previous study, we found a similar interaction in one of two face recognition ability measures. Together, these results reveal a pattern of gender differences modulated by hometown population density. If indeed experience with faces in one's hometown influences face recognition ability, understanding these effects may require more than a quantification of the environment. Men and women growing up in the same environment likely have different experiences, which likely modulates effects on visual abilities.

Recent reports have shown that individuals from small hometowns show relatively poor face recognition ability as measured by the Cambridge Face Memory Test or CFMT (Balas & Saville, 2015, 2017), suggesting that the number of faces present in an individual’s visual environment relates to that individual’s face recognition ability. We replicate this finding in a sample from a different region (Nebraska) and with more variable age distribution. We extend the study by using another test of face recognition ability that does not require learning over trials, and with non-face object recognition tests that share the learning format with the CFMT. We find no hometown effect in these other tests, although more power would be required to show the CFMT effect is significantly larger. We use the same dataset to explore whether experience with more faces and cars in larger hometowns leads to specialization of these abilities. We find strong and substantial support for the hypothesis that the recognition abilities for faces and for cars are more independent from general object recognition in people from larger hometowns. This suggests that experience may be critical to the specialization of these abilities.

Visual arts require the ability to process, categorize, recognize, and understand a variety of visual inputs. These challenges may engage and even influence mechanisms that are also relevant for visual object recognition beyond visual arts. A domain-general object recognition ability that applies broadly across a range of visual tasks was recently discovered. Here, we ask whether experience with visual arts is correlated with this domain-general ability. We developed a new survey to measure general visual arts experience and use it to measure arts experience in 142 individuals in whom we also estimated domain-general object recognition ability. Despite our measures demonstrating high reliability in a large sample size, we found substantial evidence (BF01 = 9.52) for no correlation between visual arts experience and general object recognition ability. This suggests that experience in visual arts has little influence on object recognition skills or vice versa, at least in our sample ranging from low to moderately high levels of arts experience. Our methods can be extended to other populations and our results should be replicated, as they suggest some limitations for the generalization of programs targeting visual literacy beyond the visual arts.

Object representations from convolutional neural network (CNN) models of computer vision (LeCun, Bengio, & Hinton, 2015) were used to drive a cognitive model of decision making, the linear ballistic accumulator (LBA) model (Brown & Heathcote, 2008), to predict errors and response times (RTs) in a novel object recognition task in humans. CNNs have become very successful at visual tasks like classifying objects in real-world images (e.g., He, Zhang, Ren, & Sun, 2015; Krizhevsky, Sutskever, & Hinton, 2012). We asked whether object representations learned by CNNs previously trained on a large corpus of natural images could be used to predict performance recognizing novel objects the network has never been trained on; we used novel Greebles, Ziggerins, and Sheinbugs that have been used in a number of previous object recognition studies. We specifically investigated whether a model combining high-level CNN representations of these novel objects could be used to drive an LBA model of decision making to account for errors and RTs in a same-different matching task (from Richler et al., 2019). Combining linearly transformed CNN object representations with the LBA provided reasonable accounts of performance not only on average, but at the individual-participant level and the item level as well. We frame the findings in the context of growing interest in using CNN models to understand visual object representations and the promise of using CNN representations to extend cognitive models to explain more complex aspects of human behavior. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

When objects from two categories of expertise (e.g., faces and cars in dual car/face experts) are processed simultaneously, competition occurs across a variety of tasks. Here, we investigate whether competition between face and car processing also occurs during ensemble coding. The relationship between single object recognition and ensemble coding is debated, but if ensemble coding relies on the same ability as object recognition, we expect cars to interfere with ensemble coding of faces as a function of car expertise. We measured the ability to judge the variability in identity of arrays of faces, in the presence of task-irrelevant distractors (cars or novel objects). On each trial, participants viewed two sequential arrays containing four faces and four distractors, judging which array was the more diverse in terms of face identity. We measured participants’ car expertise, object recognition ability, and face recognition ability. Using Bayesian statistics, we found evidence against competition as a function of car expertise during ensemble coding of faces. Face recognition ability predicted ensemble judgments for faces, regardless of the category of task-irrelevant distractors. The result suggests that ensemble coding is not susceptible to competition between different domains of similar expertise, unlike single-object recognition.

People can relatively easily report summary properties for ensembles of objects, suggesting that this information can enrich visual experience and increase the efficiency of perceptual processing. Here, we ask whether the ability to judge diversity within object arrays improves with experience. We surmised that ensemble judgments would be more accurate for commonly experienced objects, and perhaps even more for objects of expertise like faces. We also expected improvements in ensemble processing with practice with a novel category, and perhaps even more with repeated experience with specific exemplars. We compared the effect of experience on diversity judgments for arrays of objects, with participants being tested with either a small number of repeated exemplars or with a large number of exemplars from the same object category. To explore the role of more prolonged experience, we tested participants with completely novel objects (random blobs), with objects familiar at the category level (cars), and with objects with which observers are experts at subordinate-level recognition (faces). For objects that are novel, participants showed evidence of improved ability to distribute attention. In contrast, for object categories with long-term experience, i.e., faces and cars, performance improved during the experiment but not necessarily due to improved ensemble processing. Practice with specific exemplars did not result in better diversity judgments for all object categories. Considered together, these results suggest that ensemble processing improves with experience. However, experience operates rapidly, the role of experience does not rely on exemplar-level knowledge and may not benefit from subordinate-level expertise.

Viewers can summarize redundant features in groups of objects into an ensemble percept. There appears to be separate mechanisms underlying ensemble perception of low- and high-level visual features, but it is unclear whether ensemble perception of different low-level features is supported by common mechanisms. Yoruk and Boduroglu, in Attention, Perception, & Psychophysics 82 (2020) 852–864, investigated whether length and orientation summarization tap common mechanisms by examining the correlation between errors on length- and orientation-averaging tasks and concluded that because they did not find any correlations, the two features are summarized by different, feature-specific mechanisms. However, their study was conducted with a small sample size and included sources of individual performance variance that may have diminished correlations. We report two studies that tested the correlation between performance in the length- and orientation-averaging tasks, with larger samples and modifications that sought to reduce the sources of variance. Study 1 used ensembles that varied in both feature dimensions and Study 2 used ensembles that only varied in the task-relevant dimension. Both studies showed that errors in length- and orientation-averaging are correlated, suggesting that ensemble perception of these low-level features is supported, at least to some extent, by a common ability.

People with superior face recognition have relatively thin cortex in face-selective brain areas, while those with superior vehicle recognition have relatively thick cortex in the same areas. We suggest that these opposite correlations reflect distinct mechanisms influencing cortical thickness (CT) for abilities acquired at different points in development. We explore a new prediction regarding the specificity of these effects through the depth of the cortex: that face recognition selectively and negatively correlates with thickness of the deepest laminar subdivision in face-selective areas. With ultra-high resolution MRI at 7T, we estimated the thickness of three laminar subdivisions, which we term MR layers, in the right fusiform face area (rFFA) in 14 adult male humans. Face recognition was negatively associated with the thickness of deep MR layers, while vehicle recognition was positively related to the thickness of all layers. Regression model comparisons provided overwhelming support for a model specifying that the magnitude of the association between face recognition and CT differs across MR layers (deep vs. superficial/middle) while the magnitude of the association between vehicle recognition and CT is invariant across layers. The total CT of rFFA accounted for 69% of the variance in face recognition, and thickness of the deep layer alone accounted for 84% of this variance. Our findings demonstrate the functional validity of MR laminar estimates in FFA. Studying the structural basis of individual differences for multiple abilities in the same cortical area can reveal effects of distinct mechanisms that are not apparent when studying average variation or development.Significance StatementFace and object recognition vary in the normal population and are only modestly related to each other. The recognition of faces and vehicles are both positively related to neural responses in the fusiform face area (FFA), but show different relations to the cortical thickness of FFA. Here, we use very high-resolution MRI, and find that face recognition ability (a skill acquired early in life) is negatively correlated with thickness of FFA’s deepest MR-defined layers, whereas recognition of vehicles (a skill acquired later in life) is positively related to thickness at of all cortical layers. Our methods can be used in the future to characterize sources of variability in human abilities and relate them to distinct mechanisms of neural plasticity.