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1. Using population receptive field models to elucidate spatial integration in high-level visual cortex

   https://doi.org/https://doi.org/10.32470/CCN.2019.1178-0  

   Poltoratski, Sonia Kay (September 2019, 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany)

   While spatial information and biases have been consistently reported in high-level face regions, the functional contribution of this information toward face recognition behavior is unclear. Here, we propose that spatial integration of information plays a critical role in a hallmark phenomenon of face perception: holistic processing, or the tendency to process all features of a face concurrently rather than independently. We sought to gain insight into the neural basis of face recognition behavior by using a voxelwise encoding model of spatial selectivity to characterize the human face network using both typical face stimuli, and stimuli thought to disrupt normal face perception. We mapped population receptive fields (pRFs) using 3T fMRI in 6 participants using upright as well as inverted faces, which are thought to disrupt holistic processing. Compared to upright faces, inverted faces yielded substantial differences in measured pRF size, position, and amplitude. Further, these differences increased in magnitude along the face network hierarchy, from IOG- to pFus- and mFus-faces. These data suggest that pRFs in high-level regions reflect complex stimulus- dependent neural computations that underlie variations in recognition performance. 

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2. Visual Psychophysics for Making Face Recognition Algorithms More Explainable

   https://doi.org/10.1007/978-3-030-01267-0  

   Webster, Brandon R; Kwon, So Yon; Clarizio, Christopher; Anthony, Samuel E; Scheirer, Walter J (July 2018, European Conference on Computer Vision 2018)

   Abstract. Scientific fields that are interested in faces have developed their own sets of concepts and procedures for understanding how a tar- get model system (be it a person or algorithm) perceives a face under varying conditions. In computer vision, this has largely been in the form of dataset evaluation for recognition tasks where summary statistics are used to measure progress. While aggregate performance has continued to improve, understanding individual causes of failure has been difficult, as it is not always clear why a particular face fails to be recognized, or why an impostor is recognized by an algorithm. Importantly, other fields studying vision have addressed this via the use of visual psychophysics: the controlled manipulation of stimuli and careful study of the responses they evoke in a model system. In this paper, we suggest that visual psychophysics is a viable methodology for making face recognition algo- rithms more explainable. A comprehensive set of procedures is developed for assessing face recognition algorithm behavior, which is then deployed over state-of-the-art convolutional neural networks and more basic, yet still widely used, shallow and handcrafted feature-based approaches. 

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3. Tuning Attention to Object Categories: Spatially Global Effects of Attention to Faces in Visual Processing

   https://doi.org/10.1162/jocn_a_01400  

   Störmer, Viola S.; Cohen, Michael A.; Alvarez, George A. (July 2019, Journal of Cognitive Neuroscience)

   Feature-based attention is known to enhance visual processing globally across the visual field, even at task-irrelevant locations. Here, we asked whether attention to object categories, in particular faces, shows similar location-independent tuning. Using EEG, we measured the face-selective N170 component of the EEG signal to examine neural responses to faces at task-irrelevant locations while participants attended to faces at another task-relevant location. Across two experiments, we found that visual processing of faces was amplified at task-irrelevant locations when participants attended to faces relative to when participants attended to either buildings or scrambled face parts. The fact that we see this enhancement with the N170 suggests that these attentional effects occur at the earliest stage of face processing. Two additional behavioral experiments showed that it is easier to attend to the same object category across the visual field relative to two distinct categories, consistent with object-based attention spreading globally. Together, these results suggest that attention to high-level object categories shows similar spatially global effects on visual processing as attention to simple, individual, low-level features. 

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4. Face identity coding in the deep neural network and primate brain

   https://doi.org/10.1038/s42003-022-03557-9  

   Wang, Jinge; Cao, Runnan; Brandmeir, Nicholas_J; Li, Xin; Wang, Shuo (June 2022, Communications Biology)

   Abstract A central challenge in face perception research is to understand how neurons encode face identities. This challenge has not been met largely due to the lack of simultaneous access to the entire face processing neural network and the lack of a comprehensive multifaceted model capable of characterizing a large number of facial features. Here, we addressed this challenge by conducting in silico experiments using a pre-trained face recognition deep neural network (DNN) with a diverse array of stimuli. We identified a subset of DNN units selective to face identities, and these identity-selective units demonstrated generalized discriminability to novel faces. Visualization and manipulation of the network revealed the importance of identity-selective units in face recognition. Importantly, using our monkey and human single-neuron recordings, we directly compared the response of artificial units with real primate neurons to the same stimuli and found that artificial units shared a similar representation of facial features as primate neurons. We also observed a region-based feature coding mechanism in DNN units as in human neurons. Together, by directly linking between artificial and primate neural systems, our results shed light on how the primate brain performs face recognition tasks. 

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5. Shared neural codes for visual and semantic information about familiar faces in a common representational space

   https://doi.org/10.1073/pnas.2110474118  

   Visconti di Oleggio Castello, Matteo; Haxby, James V.; Gobbini, M. Ida (November 2021, Proceedings of the National Academy of Sciences)

   Processes evoked by seeing a personally familiar face encompass recognition of visual appearance and activation of social and person knowledge. Whereas visual appearance is the same for all viewers, social and person knowledge may be more idiosyncratic. Using between-subject multivariate decoding of hyperaligned functional magnetic resonance imaging data, we investigated whether representations of personally familiar faces in different parts of the distributed neural system for face perception are shared across individuals who know the same people. We found that the identities of both personally familiar and merely visually familiar faces were decoded accurately across brains in the core system for visual processing, but only the identities of personally familiar faces could be decoded across brains in the extended system for processing nonvisual information associated with faces. Our results show that personal interactions with the same individuals lead to shared neural representations of both the seen and unseen features that distinguish their identities. 

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