Challenges in replication: Does amygdala gray matter volume relate to social network size?

In this work, we tried to replicate and extend prior research on the relationship between social network size and the volume of the amygdala. We focused on the earliest evidence for this relationship (Bickart et al., Nature Neuroscience 14(2), 163-164, 2011) and another methodologically unique study that often is cited as a replication (Kanai et al., Proceedings of the Royal Society B: Biological Sciences, 279(1732), 1327-1334, 2012). Despite their tight link in the literature, we argue that Kanai et al. (Proceedings of the Royal Society B: Biological Sciences, 279(1732), 1327-1334, 2012) is not a replication of Bickart et al. Nature Neuroscience 14(2), 163-164 (2011), because it uses different morphometric measurements. We collected data from 128 participants on a 7-Tesla MRI and examined variations in gray matter volume (GMV) in the amygdala and its nuclei. We found inconclusive support for a correlation between measures of real-world social network and amygdala GMV, with small effect sizes and only anecdotal evidence for a positive relationship. We found support for the absence of a correlation between measures of online social network and amygdala GMV. We discuss different challenges faced in replication attempts for small effects, as initially reported in these two studies, and suggest that the results would be most helpful in the context of estimation and future meta-analytical efforts. Our findings underscore the value of a narrow approach in replication of brain-behavior relationships, one that is focused enough to investigate the specifics of what is measured. This approach can provide a complementary perspective to the more popular "thematic" alternative, in which conclusions are often broader but where conclusions may become disconnected from the evidence.

The neural correlates of domain-general visual ability.

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.

How general is ensemble perception?

People can summarize features of groups of objects (e.g., the mean size of apples). Claims of dissociations or common mechanisms supporting such ensemble perception (EP) judgments have generally been made on the basis of correlations between pairs of tasks. These correlations can be inflated because they use the same stimuli, summary statistics and/or task format. Performance on EP tasks also correlates with that on object recognition (OR) tasks. Here, we seek evidence for a general EP ability that is also distinct from OR ability. Two-hundred participants completed three tasks that did not overlap in stimuli, summary statistic or task format. Participants performed a diversity comparison for arrays of nonsense blobs, a mean identity judgment with ensembles of Transformer toys, and the novel object memory task with novel objects (NOMT-Greeble). We hypothesized that EP contributes to the first two of these tasks, while OR contributes only to the last two. Performance on the two tasks suggested to tap an EP ability were correlated after controlling for the third task. Confirmatory factor analysis was used to test our predictions without the confound of measurement error. Correlations between factors assumed to share influence from EP or from OR were higher than that between the factors that we expect did not share these influences. The results provide the first clear evidence for a domain-general EP ability distinct from OR. We argue that understanding such a general ability will require a change in designs and analytical approaches in the study if individual differences in EP.

Measuring object recognition ability: Reliability, validity, and the aggregate z-score approach.

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 demands 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., Journal of Experimental Psychology: General, 151, 676-694, (2022), we demonstrated 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 revised these tests to reduce testing time, and developed an odd one out task, using a unique object category on nearly every trial, to increase task and stimuli diversity. To validate 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 suggested that our tests load significantly onto the same latent variable, and revealed that as a latent variable, o is highly stable (r = .93). Aggregation is an efficient method for estimating o, allowing investigation of individual differences in object recognition ability to be more accessible in future studies.

Haptic object recognition based on shape relates to visual object recognition ability.

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.

Visual object recognition ability is not related to experience with visual arts.

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.

Thickness of Deep Layers in the Fusiform Face Area Predicts Face Recognition.

People with superior face recognition have relatively thin cortex in face-selective brain areas, whereas 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) as abilities are 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 ultrahigh resolution MRI at 7T, we estimated the thickness of three laminar subdivisions, which we term "MR layers," in the right fusiform face area (FFA) in 14 adult male humans. Face recognition was negatively associated with the thickness of deep MR layers, whereas 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) whereas the magnitude of the association between vehicle recognition and CT is invariant across layers. The total CT of right FFA 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.

The Role of Experience in the Face-Selective Response in Right FFA.

The expertise hypothesis suggests the fusiform face area (FFA) is more responsive to faces than to other categories because of experience individuating faces. Accordingly, individual differences in FFA's selectivity for faces should relate to differences in behavioral face-recognition ability. However, previous studies have not demonstrated this, while the comparable association is often observed with nonface objects. We created a training paradigm with conditions sufficient to observe the same effect with faces. First, we selected subjects with a wide range of behavioral face-recognition abilities, then we manipulated experience with an artificial race of faces based on subjects' pretraining ability, maximizing variability in face individuation. Neural selectivity was measured for Caucasian faces and artificial-race faces relative to control objects. Selecting subjects for greater variability in face-recognition ability revealed an association between behavior and FFA selectivity for Caucasian faces, with an effect exclusive to the middle right FFA (FFA2). Manipulating experience with artificial-race faces led to stronger brain-behavior correlation for artificial-race faces, also in right FFA2. Group analyses showed an overlap of these effects for Caucasian and artificial-race faces in right FFA2. The right FFA2 appears particularly sensitive to experience with faces just as it is for nonface objects.

High-resolution Functional Magnetic Resonance Imaging Reveals Configural Processing of Cars in Right Anterior Fusiform Face Area of Car Experts.

Visual object expertise correlates with neural selectivity in the fusiform face area (FFA). Although behavioral studies suggest that visual expertise is associated with increased use of holistic and configural information, little is known about the nature of the supporting neural representations. Using high-resolution 7-T functional magnetic resonance imaging, we recorded the multivoxel activation patterns elicited by whole cars, configurally disrupted cars, and car parts in individuals with a wide range of car expertise. A probabilistic support vector machine classifier was trained to differentiate activation patterns elicited by whole car images from activation patterns elicited by misconfigured car images. The classifier was then used to classify new combined activation patterns that were created by averaging activation patterns elicited by individually presented top and bottom car parts. In line with the idea that the configuration of parts is critical to expert visual perception, car expertise was negatively associated with the probability of a combined activation pattern being classified as a whole car in the right anterior FFA, a region critical to vision for categories of expertise. Thus, just as found for faces in normal observers, the neural representation of cars in right anterior FFA is more holistic for car experts than car novices, consistent with common mechanisms of neural selectivity for faces and other objects of expertise in this area.

Age-related differential item functioning in tests of face and car recognition ability.

The presence of differential item functioning (DIF) in a test suggests bias that could disadvantage members of a certain group. Previous work with tests of visual learning abilities found significant DIF related to age groups in a car test (Lee, Cho, McGugin, Van Gulick, & Gauthier, 2015), but not in a face test (Cho et al., 2015). The presence of age DIF is a threat to the validity of the test even for studies where aging is not of interest. Here, we assessed whether this pattern of age DIF for cars and not faces would also apply to new tests targeting the same abilities with a new matching task that uses two studied items per trial. We found evidence for DIF in matching tests for faces and for cars, though with encouragingly small effect sizes. Even though the age DIF was small enough at the test level to be acceptable for most uses, we also asked whether the specific format of our matching tasks may induce some age-related DIF regardless of domain. We decomposed the face matching task into its components, and using new data from subjects performing these simpler tasks, found evidence that the age DIF was driven by the similarity of the two faces presented at study on each trial. Overall, our results suggest that using a matching format, especially for cars, reduces age-related DIF, and that a simpler matching task with only one study item per trial could reduce age DIF further.

Domain-specific reports of visual imagery vividness are not related to perceptual expertise.

Just as people vary in their perceptual expertise with a given domain, they also vary in their abilities to imagine objects. Visual imagery and perception share common mechanisms. However, it is unclear whether domain-specific expertise is relevant to visual imagery. Although the vividness of visual imagery is typically measured as a domain-general construct, a component of this vividness may be domain-specific. For example, individuals who have gained perceptual expertise with a specific domain might experience clearer mental images within this domain. Here we investigated whether perceptual expertise for cars relates to visual imagery vividness in the same domain, by assessing the correlations between a widely used domain-general measure of visual imagery vividness (the Vividness of Visual Imagery Questionnaire; Marks in British Journal of Psychology, 64, 17-24, 1973), a new measure of visual imagery vividness specific to cars, and behavioral tests of car expertise. We found that domain-specific imagery relates most strongly to general imagery vividness and less strongly to self-reported expertise, while it does not relate to perceptual or semantic expertise.

Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration.

Are face and object recognition abilities independent? Although it is commonly believed that they are, Gauthier et al. [Gauthier, I., McGugin, R. W., Richler, J. J., Herzmann, G., Speegle, M., & VanGulick, A. E. Experience moderates overlap between object and face recognition, suggesting a common ability. Journal of Vision, 14, 7, 2014] recently showed that these abilities become more correlated as experience with nonface categories increases. They argued that there is a single underlying visual ability, v, that is expressed in performance with both face and nonface categories as experience grows. Using the Cambridge Face Memory Test and the Vanderbilt Expertise Test, they showed that the shared variance between Cambridge Face Memory Test and Vanderbilt Expertise Test performance increases monotonically as experience increases. Here, we address why a shared resource across different visual domains does not lead to competition and to an inverse correlation in abilities? We explain this conundrum using our neurocomputational model of face and object processing ["The Model", TM, Cottrell, G. W., & Hsiao, J. H. Neurocomputational models of face processing. In A. J. Calder, G. Rhodes, M. Johnson, & J. Haxby (Eds.), The Oxford handbook of face perception. Oxford, UK: Oxford University Press, 2011]. We model the domain general ability v as the available computational resources (number of hidden units) in the mapping from input to label and experience as the frequency of individual exemplars in an object category appearing during network training. Our results show that, as in the behavioral data, the correlation between subordinate level face and object recognition accuracy increases as experience grows. We suggest that different domains do not compete for resources because the relevant features are shared between faces and objects. The essential power of experience is to generate a "spreading transform" for faces (separating them in representational space) that generalizes to objects that must be individuated. Interestingly, when the task of the network is basic level categorization, no increase in the correlation between domains is observed. Hence, our model predicts that it is the type of experience that matters and that the source of the correlation is in the fusiform face area, rather than in cortical areas that subserve basic level categorization. This result is consistent with our previous modeling elucidating why the FFA is recruited for novel domains of expertise [Tong, M. H., Joyce, C. A., & Cottrell, G. W. Why is the fusiform face area recruited for novel categories of expertise? A neurocomputational investigation. Brain Research, 1202, 14-24, 2008].

Cortical Thickness in Fusiform Face Area Predicts Face and Object Recognition Performance.

The fusiform face area (FFA) is defined by its selectivity for faces. Several studies have shown that the response of FFA to nonface objects can predict behavioral performance for these objects. However, one possible account is that experts pay more attention to objects in their domain of expertise, driving signals up. Here, we show an effect of expertise with nonface objects in FFA that cannot be explained by differential attention to objects of expertise. We explore the relationship between cortical thickness of FFA and face and object recognition using the Cambridge Face Memory Test and Vanderbilt Expertise Test, respectively. We measured cortical thickness in functionally defined regions in a group of men who evidenced functional expertise effects for cars in FFA. Performance with faces and objects together accounted for approximately 40% of the variance in cortical thickness of several FFA patches. Whereas participants with a thicker FFA cortex performed better with vehicles, those with a thinner FFA cortex performed better with faces and living objects. The results point to a domain-general role of FFA in object perception and reveal an interesting double dissociation that does not contrast faces and objects but rather living and nonliving objects.

Expertise Effects in Face-Selective Areas are Robust to Clutter and Diverted Attention, but not to Competition.

Expertise effects for nonface objects in face-selective brain areas may reflect stable aspects of neuronal selectivity that determine how observers perceive objects. However, bottom-up (e.g., clutter from irrelevant objects) and top-down manipulations (e.g., attentional selection) can influence activity, affecting the link between category selectivity and individual performance. We test the prediction that individual differences expressed as neural expertise effects for cars in face-selective areas are sufficiently stable to survive clutter and manipulations of attention. Additionally, behavioral work and work using event related potentials suggest that expertise effects may not survive competition; we investigate this using functional magnetic resonance imaging. Subjects varying in expertise with cars made 1-back decisions about cars, faces, and objects in displays containing one or 2 objects, with only one category attended. Univariate analyses suggest car expertise effects are robust to clutter, dampened by reducing attention to cars, but nonetheless more robust to manipulations of attention than competition. While univariate expertise effects are severely abolished by competition between cars and faces, multivariate analyses reveal new information related to car expertise. These results demonstrate that signals in face-selective areas predict expertise effects for nonface objects in a variety of conditions, although individual differences may be expressed in different dependent measures depending on task and instructions.

Measuring nonvisual knowledge about object categories: The Semantic Vanderbilt Expertise Test.

How much do people differ in their abilities to recognize objects, and what is the source of these differences? To address the first question, psychologists have created visual learning tests including the Cambridge Face Memory Test (Duchaine & Nakayama, 2006) and the Vanderbilt Expertise Test (VET; McGugin et al., 2012). The second question requires consideration of the influences of both innate potential and experience, but experience is difficult to measure. One solution is to measure the products of experience beyond perceptual knowledge-specifically, nonvisual semantic knowledge. For instance, the relation between semantic and perceptual knowledge can help clarify the nature of object recognition deficits in brain-damaged patients (Barton, Hanif, & Ashraf, Brain, 132, 3456-3466, 2009). We present a reliable measure of nonperceptual knowledge in a format applicable across categories. The Semantic Vanderbilt Expertise Test (SVET) measures knowledge of relevant category-specific nomenclature. We present SVETs for eight categories: cars, planes, Transformers, dinosaurs, shoes, birds, leaves, and mushrooms. The SVET demonstrated good reliability and domain-specific validity. We found partial support for the idea that the only source of domain-specific shared variance between the VET and SVET is experience with a category. We also demonstrated the utility of the SVET-Bird in experts. The SVET can facilitate the study of individual differences in visual recognition.

High-resolution imaging of expertise reveals reliable object selectivity in the fusiform face area related to perceptual performance.

The fusiform face area (FFA) is a region of human cortex that responds selectively to faces, but whether it supports a more general function relevant for perceptual expertise is debated. Although both faces and objects of expertise engage many brain areas, the FFA remains the focus of the strongest modular claims and the clearest predictions about expertise. Functional MRI studies at standard-resolution (SR-fMRI) have found responses in the FFA for nonface objects of expertise, but high-resolution fMRI (HR-fMRI) in the FFA [Grill-Spector K, et al. (2006) Nat Neurosci 9:1177-1185] and neurophysiology in face patches in the monkey brain [Tsao DY, et al. (2006) Science 311:670-674] reveal no reliable selectivity for objects. It is thus possible that FFA responses to objects with SR-fMRI are a result of spatial blurring of responses from nonface-selective areas, potentially driven by attention to objects of expertise. Using HR-fMRI in two experiments, we provide evidence of reliable responses to cars in the FFA that correlate with behavioral car expertise. Effects of expertise in the FFA for nonface objects cannot be attributed to spatial blurring beyond the scale at which modular claims have been made, and within the lateral fusiform gyrus, they are restricted to a small area (200 mm(2) on the right and 50 mm(2) on the left) centered on the peak of face selectivity. Experience with a category may be sufficient to explain the spatially clustered face selectivity observed in this region.

About-face on face recognition ability and holistic processing.

Previous work found a small but significant relationship between holistic processing measured with the composite task and face recognition ability measured by the Cambridge Face Memory Test (CFMT; Duchaine & Nakayama, 2006). Surprisingly, recent work using a different measure of holistic processing (Vanderbilt Holistic Face Processing Test [VHPT-F]; Richler, Floyd, & Gauthier, 2014) and a larger sample found no evidence for such a relationship. In Experiment 1 we replicate this unexpected result, finding no relationship between holistic processing (VHPT-F) and face recognition ability (CFMT). A key difference between the VHPT-F and other holistic processing measures is that unique face parts are used on each trial in the VHPT-F, unlike in other tasks where a small set of face parts repeat across the experiment. In Experiment 2, we test the hypothesis that correlations between the CFMT and holistic processing tasks are driven by stimulus repetition that allows for learning during the composite task. Consistent with our predictions, CFMT performance was correlated with holistic processing in the composite task when a small set of face parts repeated over trials, but not when face parts did not repeat. A meta-analysis confirms that relationships between the CFMT and holistic processing depend on stimulus repetition. These results raise important questions about what is being measured by the CFMT, and challenge current assumptions about why faces are processed holistically.

Differential item functioning analysis of the Vanderbilt Expertise Test for cars.

The Vanderbilt Expertise Test for cars (VETcar) is a test of visual learning for contemporary car models. We used item response theory to assess the VETcar and in particular used differential item functioning (DIF) analysis to ask if the test functions the same way in laboratory versus online settings and for different groups based on age and gender. An exploratory factor analysis found evidence of multidimensionality in the VETcar, although a single dimension was deemed sufficient to capture the recognition ability measured by the test. We selected a unidimensional three-parameter logistic item response model to examine item characteristics and subject abilities. The VETcar had satisfactory internal consistency. A substantial number of items showed DIF at a medium effect size for test setting and for age group, whereas gender DIF was negligible. Because online subjects were on average older than those tested in the lab, we focused on the age groups to conduct a multigroup item response theory analysis. This revealed that most items on the test favored the younger group. DIF could be more the rule than the exception when measuring performance with familiar object categories, therefore posing a challenge for the measurement of either domain-general visual abilities or category-specific knowledge.

Reliability of composite-task measurements of holistic face processing.

There is growing interest in the study of individual differences in face recognition, including one of its hallmarks, holistic processing, which can be defined as a failure of selective attention to parts. These efforts demand that researchers be aware of, and try to maximize, the reliability of their measurements. Here we report on the reliability of measurements using the composite task (complete design), a measure of holistic processing that has been shown to have relatively good validity. Several studies have used the composite task to investigate individual differences, yet only one study has discussed its reliability. We investigate the reliability of composite-task measurements in eight data sets from five different samples of subjects. In general, we found reliability to be fairly low, but there was substantial variability across experiments. Researchers should keep in mind that reliability is a property of measurements, not of a task, and think about the ways in which measurements in a particular task may be improved before embarking on individual differences research.

Perceptual expertise and top-down expectation of musical notation engages the primary visual cortex.

Most theories of visual processing propose that object recognition is achieved in higher visual cortex. However, we show that category selectivity for musical notation can be observed in the first ERP component called the C1 (measured 40-60 msec after stimulus onset) with music-reading expertise. Moreover, the C1 note selectivity was observed only when the stimulus category was blocked but not when the stimulus category was randomized. Under blocking, the C1 activity for notes predicted individual music-reading ability, and behavioral judgments of musical stimuli reflected music-reading skill. Our results challenge current theories of object recognition, indicating that the primary visual cortex can be selective for musical notation within the initial feedforward sweep of activity with perceptual expertise and with a testing context that is consistent with the expertise training, such as blocking the stimulus category for music reading.