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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. 

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2. The development of race effects in face processing from childhood through adulthood: Neural and behavioral evidence

   https://doi.org/10.1111/desc.13058  

   Golarai, Golijeh ; Ghahremani, Dara G. ; Greenwood, Anders C. ; Gabrieli, John D. E. ; Eberhardt, Jennifer L. ( December 2020 , Developmental Science)

   Most adults are better at recognizing recently encountered faces of their own race, relative to faces of other races. In adults, this race effect in face recognition is associated with differential neural representations of own‐ and other‐race faces in the fusiform face area (FFA), a high‐level visual region involved in face recognition. Previous research has linked these differential face representations in adults to viewers’ implicit racial associations. However, despite the fact that the FFA undergoes a gradual development which continues well into adulthood, little is known about the developmental time‐course of the race effect in FFA responses. Also unclear is how this race effect might relate to the development of face recognition or implicit associations with own‐ or other‐races during childhood and adolescence. To examine the developmental trajectory of these race effects, in a cross‐sectional study of European American (EA) children (ages 7–11), adolescents (ages 12–16) and adults (ages 18–35), we evaluated responses to adult African American (AA) and EA face stimuli, using functional magnetic resonance imaging and separate behavioral measures outside the scanner. We found that FFA responses to AA and EA faces differentiated during development from childhood into adulthood; meanwhile, the magnitudes of race effects increased in behavioral measures of face‐recognition and implicit racial associations. These three race effects were positively correlated, even after controlling for age. These findings suggest that social and perceptual experiences shape a protracted development of the race effect in face processing that continues well into adulthood.

    

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3. 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. 

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4. 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. 

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5. A Cautionary Note on Predicting Social Judgments from Faces with Deep Neural Networks

   https://doi.org/10.1007/s42761-021-00075-5  

   Keles, Umit ; Lin, Chujun ; Adolphs, Ralph ( September 2021 , Affective Science)

   null (Ed.)

   Abstract People spontaneously infer other people’s psychology from faces, encompassing inferences of their affective states, cognitive states, and stable traits such as personality. These judgments are known to be often invalid, but nonetheless bias many social decisions. Their importance and ubiquity have made them popular targets for automated prediction using deep convolutional neural networks (DCNNs). Here, we investigated the applicability of this approach: how well does it generalize, and what biases does it introduce? We compared three distinct sets of features (from a face identification DCNN, an object recognition DCNN, and using facial geometry), and tested their prediction across multiple out-of-sample datasets. Across judgments and datasets, features from both pre-trained DCNNs provided better predictions than did facial geometry. However, predictions using object recognition DCNN features were not robust to superficial cues (e.g., color and hair style). Importantly, predictions using face identification DCNN features were not specific: models trained to predict one social judgment (e.g., trustworthiness) also significantly predicted other social judgments (e.g., femininity and criminal), and at an even higher accuracy in some cases than predicting the judgment of interest (e.g., trustworthiness). Models trained to predict affective states (e.g., happy) also significantly predicted judgments of stable traits (e.g., sociable), and vice versa. Our analysis pipeline not only provides a flexible and efficient framework for predicting affective and social judgments from faces but also highlights the dangers of such automated predictions: correlated but unintended judgments can drive the predictions of the intended judgments. 

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