%0 Journal Article %J iScience %D 2023 %T CNNs reveal the computational implausibility of the expertise hypothesis %A Kanwisher, Nancy %A Gupta, Pranjul %A Dobs, Katharina %X

Face perception has long served as a classic example of domain specificity of mind and brain. But an alternative “expertise” hypothesis holds that putatively face-specific mechanisms are actually domain-general, and can be recruited for the perception of other objects of expertise (e.g., cars for car experts). Here, we demonstrate the computational implausibility of this hypothesis: Neural network models optimized for generic object categorization provide a better foundation for expert fine-grained discrimination than do models optimized for face recognition.

%B iScience %V 26 %P 105976 %8 02/2023 %G eng %U https://linkinghub.elsevier.com/retrieve/pii/S2589004223000536 %N 2 %! iScience %R 10.1016/j.isci.2023.105976 %0 Journal Article %J Trends in Neurosciences %D 2023 %T Using artificial neural networks to ask ‘why’ questions of minds and brains %A Kanwisher, Nancy %A Khosla, Meenakshi %A Dobs, Katharina %X

Neuroscientists have long characterized the properties and functions of the nervous system, and are increasingly succeeding in answering how brains perform the tasks they do. But the question ‘why’ brains work the way they do is asked less often. The new ability to optimize artificial neural networks (ANNs) for performance on human-like tasks now enables us to approach these ‘why’ questions by asking when the properties of networks optimized for a given task mirror the behavioral and neural characteristics of humans performing the same task. Here we highlight the recent success of this strategy in explaining why the visual and auditory systems work the way they do, at both behavioral and neural levels.

%B Trends in Neurosciences %V 46 %P 240 - 254 %8 03/2023 %G eng %U https://linkinghub.elsevier.com/retrieve/pii/S0166223622002624 %N 3 %! Trends in Neurosciences %R 10.1016/j.tins.2022.12.008 %0 Journal Article %J Science Advances %D 2022 %T Brain-like functional specialization emerges spontaneously in deep neural networks %A Dobs, Katharina %A Julio Martinez-Trujillo %A Kell, Alexander J. E. %A Nancy Kanwisher %X

The human brain contains multiple regions with distinct, often highly specialized functions, from recognizing faces to understanding language to thinking about what others are thinking. However, it remains unclear why the cortex exhibits this high degree of functional specialization in the first place. Here, we consider the case of face perception using artificial neural networks to test the hypothesis that functional segregation of face recognition in the brain reflects a computational optimization for the broader problem of visual recognition of faces and other visual categories. We find that networks trained on object recognition perform poorly on face recognition and vice versa and that networks optimized for both tasks spontaneously segregate themselves into separate systems for faces and objects. We then show functional segregation to varying degrees for other visual categories, revealing a widespread tendency for optimization (without built-in task-specific inductive biases) to lead to functional specialization in machines and, we conjecture, also brains.

%B Science Advances %V 8 %8 03/2023 %G eng %U https://www.science.org/doi/10.1126/sciadv.abl8913 %N 11 %! Sci. Adv. %R 10.1126/sciadv.abl8913 %0 Conference Paper %B Computational and Systems Neurosciences %D 2020 %T Using task-optimized neural networks to understand why brains have specialized processing for faces %A Dobs, Katharina %A Alexander J. E. Kell %A Julio Martinez-Trujillo %A Michael Cohen %A Nancy Kanwisher %B Computational and Systems Neurosciences %C Denver, CO, USA %8 02/2020 %G eng %0 Conference Paper %B Conference on Cognitive Computational Neuroscience %D 2020 %T Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks %A Dobs, Katharina %A Alexander J. E. Kell %A Julio Martinez-Trujillo %A Michael Cohen %A Nancy Kanwisher %B Conference on Cognitive Computational Neuroscience %C Berlin, Germany %8 09/2020 %G eng %0 Conference Paper %B European Conference on Visual Perception %D 2019 %T Effects of Face Familiarity in Humans and Deep Neural Networks %A Dobs, Katharina %A Ian A Palmer %A Joanne Yuan %A Yalda Mohsenzadeh %A Aude Oliva %A Nancy Kanwisher %B European Conference on Visual Perception %C Leuven, Belgium %8 09/2019 %G eng %0 Journal Article %J Nature Communications %D 2019 %T How face perception unfolds over time %A Dobs, Katharina %A Leyla Isik %A Pantazis, Dimitrios %A Nancy Kanwisher %X

Within a fraction of a second of viewing a face, we have already determined its gender, age and identity. A full understanding of this remarkable feat will require a characterization of the computational steps it entails, along with the representations extracted at each. Here, we used magnetoencephalography (MEG) to measure the time course of neural responses to faces, thereby addressing two fundamental questions about how face processing unfolds over time. First, using representational similarity analysis, we found that facial gender and age information emerged before identity information, suggesting a coarse-to-fine processing of face dimensions. Second, identity and gender representations of familiar faces were enhanced very early on, suggesting that the behavioral benefit for familiar faces results from tuning of early feed-forward processing mechanisms. These findings start to reveal the time course of face processing in humans, and provide powerful new constraints on computational theories of face perception.

%B Nature Communications %V 10 %8 01/2019 %G eng %U http://www.nature.com/articles/s41467-019-09239-1 %N 1 %! Nat Commun %R 10.1038/s41467-019-09239-1