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1. 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. 

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2. Investigating Online versus Face-to-Face Course Dropout: Why Do Students Say They Are Leaving?

   https://doi.org/10.3390/educsci13111122  

   Hachey, Alyse C; Wladis, Claire; Conway, Katherine M (November 2023, Education Sciences)

   Despite more focused attention in the wake of the COVID-19 pandemic, high online attrition remains both a concern and a mystery; gaps in our knowledge exist as to why students so often do not complete online courses. Pre-pandemic, and using a sample of 780 students who dropped out of fully online courses (or the same course face-to-face) from a large university system in the Northeast U.S., students were explicitly asked about their specific reasons for course withdrawal. All students enrolled in a fully online course (or a face-to-face section of the same course) at the City University of New York (CUNY) in fall 2015 were invited to take the online survey from which this study data was taken. Results indicate that there were distinct differences in the patterns of reasons given by online and face-to-face students: although the perceived quality of the instructor/instruction was deemed important to student persistence in both modalities, it seemed to be of greater importance face-to-face than online. Furthermore, issues related to time were found to be more prominent reasons for dropping for online learners than face-to-face learners. Findings from this study shed new light on the impetus for online attrition, with implications for online policy and course design in a post-pandemic era. 

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3. Thickness of deep layers in FFA predicts face recognition performance

   Gauthier, I; McGugin, R.W.; Tamber-Rosenau, B.; Newton, A. (January 2017, Journal of vision)

   Individual differences in expertise with non-face objects has been positively related to neural selectivity for these objects in several brain regions, including in the fusiform face area (FFA). Recently, we reported that FFA’s cortical thickness is also positively correlated with expertise for non-living objects, while FFA’s cortical thickness is negatively correlated with face recognition ability. These opposite relations between structure and visual abilities, obtained in the same subjects, were postulated to reflect the earlier experience with faces relative to cars, with different mechanisms of plasticity operating at these different developmental times. Here we predicted that variability for faces, presumably reflecting pruning, would be found selectively in deep cortical layers. In 13 men selected to vary in their performance with faces, we used ultra-high field imaging (7 Tesla), we localized the FFA functionally and collected and averaged 6 ultra-high resolution susceptibility weighed images (SWI). Voxel dimensions were 0.194x0.194x1.00mm, covering 20 slices with 0.1mm gap. Images were then processed by two operators blind to behavioral results to define the gray matter/white matter (deep) and gray matter/CSF (superficial) cortical boundaries. Internal boundaries between presumed deep, middle and superficial cortical layers were obtained with an automated method based on image intensities. We used an extensive battery of behavioral tests to quantify both face and object recognition ability. We replicate prior work with face and non-living object recognition predicting large and independent parts of the variance in cortical thickness of the right FFA, in different directions. We also find that face recognition is specifically predicted by the thickness of the deep cortical layers in FFA, whereas recognition of vehicles relates to the thickness of all cortical layers. Our results represent the most precise structural correlate of a behavioral ability to date, linking face recognition ability to a specific layer of a functionally-defined area. 

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4. The Role of Unimodal Feedback Pathways in Gender Perception During Activation of Voice and Face Areas

   https://doi.org/10.3389/fnsys.2021.669256  

   Abbatecola, Clement; Gerardin, Peggy; Beneyton, Kim; Kennedy, Henry; Knoblauch, Kenneth (May 2021, Frontiers in Systems Neuroscience)

   Cross-modal effects provide a model framework for investigating hierarchical inter-areal processing, particularly, under conditions where unimodal cortical areas receive contextual feedback from other modalities. Here, using complementary behavioral and brain imaging techniques, we investigated the functional networks participating in face and voice processing during gender perception, a high-level feature of voice and face perception. Within the framework of a signal detection decision model, Maximum likelihood conjoint measurement (MLCM) was used to estimate the contributions of the face and voice to gender comparisons between pairs of audio-visual stimuli in which the face and voice were independently modulated. Top–down contributions were varied by instructing participants to make judgments based on the gender of either the face, the voice or both modalities ( N = 12 for each task). Estimated face and voice contributions to the judgments of the stimulus pairs were not independent; both contributed to all tasks, but their respective weights varied over a 40-fold range due to top–down influences. Models that best described the modal contributions required the inclusion of two different top–down interactions: (i) an interaction that depended on gender congruence across modalities (i.e., difference between face and voice modalities for each stimulus); (ii) an interaction that depended on the within modalities’ gender magnitude. The significance of these interactions was task dependent. Specifically, gender congruence interaction was significant for the face and voice tasks while the gender magnitude interaction was significant for the face and stimulus tasks. Subsequently, we used the same stimuli and related tasks in a functional magnetic resonance imaging (fMRI) paradigm ( N = 12) to explore the neural correlates of these perceptual processes, analyzed with Dynamic Causal Modeling (DCM) and Bayesian Model Selection. Results revealed changes in effective connectivity between the unimodal Fusiform Face Area (FFA) and Temporal Voice Area (TVA) in a fashion that paralleled the face and voice behavioral interactions observed in the psychophysical data. These findings explore the role in perception of multiple unimodal parallel feedback pathways. 

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5. Physics through Algebra for Preservice Elementary Teachers: A Comparison of Asynchronous and Hybrid/Face-to-Face Learning

   Kurz, T.L.; Meltzer, D.E.; Nation, M. (November 2022, Proceedings of EdMedia + Innovate Learning Online 2022)

   At a large Hispanic-Serving Institution in the southwestern United States, physics was integrated into an algebra content course for preservice teachers. Due to the pandemic, content was asynchronously taught during the fall 2020 semester. During the fall 2021 semester, content was taught in both hybrid and face-to-face format. This research question was: Regarding preservice teachers’ leaning and academic experiences, what were the differences between perceptions of preservice teachers’ learning on the subject of physics-based algebra in an asynchronous format compared to a hybrid or face-to-face format? Data were gathered from both preservice teachers and their instructors. Data from preservice teachers showed that the in-person and hybrid instruction cohort saw a significant change in Personal Mathematics Teaching Efficacy from pre- to post-test when testing at the .05 level, whereas this was not the case for the online instruction cohort. There was no statistical difference in the post Personal Mathematics Teaching Efficacy and post Mathematics Teaching Outcome Expectancy scores between the face-to-face and hybrid preservice teachers and the asynchronous preservice teachers. Their instructors felt that experiences were more fruitful in a face to face format. 

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