More Like this

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

   https://doi.org/10.1162/jocn_a_01551  

   McGugin, Rankin W.; Newton, Allen T.; Tamber-Rosenau, Benjamin; Tomarken, Andrew; Gauthier, Isabel (July 2020, Journal of Cognitive Neuroscience)

   null (Ed.)

   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. 

   more » « less
2. Car expertise does not compete with face expertise during ensemble coding

   https://doi.org/10.3758/s13414-020-02188-8  

   Sun, Jisoo; Gauthier, Isabel (January 2020, Attention, Perception, & Psychophysics)

   null (Ed.)

   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. 

   more » « less
3. Gray Matter Structure Is Associated with Reading Skill in Typically Developing Young Readers

   https://doi.org/10.1093/cercor/bhaa126  

   Perdue, Meaghan V; Mednick, Joshua; Pugh, Kenneth R; Landi, Nicole (June 2020, Cerebral Cortex)

   Abstract Research using functional and structural magnetic resonance imaging has identified areas of reduced brain activation and gray matter volume in children and adults with reading disability, but associations between cortical structure and individual differences in reading in typically developing children remain underexplored. Furthermore, the majority of research linking gray matter structure to reading ability quantifies gray matter in terms of volume, and cannot specify unique contributions of cortical surface area and thickness to these relationships. Here, we applied a continuous analytic approach to investigate associations between distinct surface-based properties of cortical structure and individual differences in reading-related skills in a sample of typically developing young children. Correlations between cortical structure and reading-related skills were conducted using a surface-based vertex-wise approach. Cortical thickness in the left superior temporal cortex was positively correlated with word and pseudoword reading performance. The observed positive correlation between cortical thickness in the left superior temporal cortex and reading may have implications for the patterns of brain activation that support reading. 

   more » « less
4. Modeling naturalistic face processing in humans with deep convolutional neural networks

   https://doi.org/10.1073/pnas.2304085120  

   Jiahui, Guo; Feilong, Ma; Visconti di Oleggio Castello, Matteo; Nastase, Samuel A.; Haxby, James V.; Gobbini, M. Ida (October 2023, Proceedings of the National Academy of Sciences)

   Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent across architectures, cognitive representational geometries were consistent across raters in a behavioral arrangement task, and neural representational geometries in face areas were consistent across brains. Representational geometries in late, fully connected DCNN layers, which are optimized for individuation, were much more weakly correlated with cognitive and neural geometries than were geometries in late-intermediate layers. The late-intermediate face-DCNN layers successfully matched cognitive representational geometries, as measured with a behavioral arrangement task that primarily reflected categorical attributes, and correlated with neural representational geometries in known face-selective topographies. Our study suggests that current DCNNs successfully capture neural cognitive processes for categorical attributes of faces but less accurately capture individuation and dynamic features. 

   more » « less
5. Domain-specific experience determines individual differences in holistic processing

   https://doi.org/10.1037/xge0000628  

   Chua, K.-W.; Gauthier, I. (January 2020, Journal of experimental psychology)

   null (Ed.)

   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. 

   more » « less