@article {5542, title = {Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition}, journal = {Proceedings of the National Academy of Sciences}, volume = {120}, year = {2023}, month = {07/2023}, abstract = {

Significance

For decades, cognitive scientists have collected behavioral signatures of face recognition. Here, we move beyond the mere curation of behavioral phenomena to ask why the human face system works the way it does. We find that many classic signatures of human face perception emerge spontaneously in convolutional neural networks (CNNs) trained on face discrimination, but not in CNNs trained on object classification (or on both object classification and face detection), suggesting that these long-documented properties of the human face perception system reflect optimizations for face recognition, not by-products of a generic visual categorization system. This work further illustrates how CNN models can be synergistically linked to classic behavioral findings in vision research, thereby providing psychological insights into human perception.

Abstract

Human face recognition is highly accurate and exhibits a number of distinctive and well-documented behavioral {\textquotedblleft}signatures{\textquotedblright} such as the use of a characteristic representational space, the disproportionate performance cost when stimuli are presented upside down, and the drop in accuracy for faces from races the participant is less familiar with. These and other phenomena have long been taken as evidence that face recognition is {\textquotedblleft}special{\textquotedblright}. But why does human face perception exhibit these properties in the first place? Here, we use deep convolutional neural networks (CNNs) to test the hypothesis that all of these signatures of human face perception result from optimization for the task of face recognition. Indeed, as predicted by this hypothesis, these phenomena are all found in CNNs trained on face recognition, but not in CNNs trained on object recognition, even when additionally trained to detect faces while matching the amount of face experience. To test whether these signatures are in principle specific to faces, we optimized a CNN on car discrimination and tested it on upright and inverted car images. As we found for face perception, the car-trained network showed a drop in performance for inverted vs. upright cars. Similarly, CNNs trained on inverted faces produced an inverted face inversion effect. These findings show that the behavioral signatures of human face perception reflect and are well explained as the result of optimization for the task of face recognition, and that the nature of the computations underlying this task may not be so special after all.

}, issn = {0027-8424}, doi = {10.1073/pnas.2220642120}, url = {https://pnas.org/doi/10.1073/pnas.2220642120}, author = {Dobs, Katharina and Yuan, Joanne and Martinez, Julio and Kanwisher, Nancy} } @article {5313, title = {CNNs reveal the computational implausibility of the expertise hypothesis}, journal = {iScience}, volume = {26}, year = {2023}, month = {02/2023}, pages = {105976}, abstract = {

Face perception has long served as a classic example of domain specificity of mind and brain. But an alternative {\textquotedblleft}expertise{\textquotedblright} 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.

}, issn = {25890042}, doi = {10.1016/j.isci.2023.105976}, url = {https://linkinghub.elsevier.com/retrieve/pii/S2589004223000536}, author = {Kanwisher, Nancy and Gupta, Pranjul and Dobs, Katharina} } @article {5320, title = {Using artificial neural networks to ask {\textquoteleft}why{\textquoteright} questions of minds and brains}, journal = {Trends in Neurosciences}, volume = {46}, year = {2023}, month = {03/2023}, pages = {240 - 254}, abstract = {

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 {\textquoteleft}why{\textquoteright} 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 {\textquoteleft}why{\textquoteright} 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.

}, issn = {01662236}, doi = {10.1016/j.tins.2022.12.008}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0166223622002624}, author = {Kanwisher, Nancy and Khosla, Meenakshi and Dobs, Katharina} } @article {5085, title = {Brain-like functional specialization emerges spontaneously in deep neural networks}, journal = {Science Advances}, volume = {8}, year = {2022}, month = {03/2023}, abstract = {

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.

}, doi = {10.1126/sciadv.abl8913}, url = {https://www.science.org/doi/10.1126/sciadv.abl8913}, author = {Dobs, Katharina and Julio Martinez-Trujillo and Kell, Alexander J. E. and Nancy Kanwisher} } @conference {4525, title = {Using task-optimized neural networks to understand why brains have specialized processing for faces }, booktitle = {Computational and Systems Neurosciences}, year = {2020}, month = {02/2020}, address = {Denver, CO, USA}, author = {Dobs, Katharina and Alexander J. E. Kell and Julio Martinez-Trujillo and Michael Cohen and Nancy Kanwisher} } @conference {4524, title = {Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks }, booktitle = {Conference on Cognitive Computational Neuroscience}, year = {2020}, month = {09/2020}, address = {Berlin, Germany}, author = {Dobs, Katharina and Alexander J. E. Kell and Julio Martinez-Trujillo and Michael Cohen and Nancy Kanwisher} } @conference {4533, title = {Effects of Face Familiarity in Humans and Deep Neural Networks }, booktitle = {European Conference on Visual Perception}, year = {2019}, month = {09/2019}, address = {Leuven, Belgium}, author = {Dobs, Katharina and Ian A Palmer and Joanne Yuan and Yalda Mohsenzadeh and Aude Oliva and Nancy Kanwisher} } @article {4180, title = {How face perception unfolds over time}, journal = {Nature Communications}, volume = {10}, year = {2019}, month = {01/2019}, abstract = {

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.

}, doi = {10.1038/s41467-019-09239-1}, url = {http://www.nature.com/articles/s41467-019-09239-1}, author = {Dobs, Katharina and Leyla Isik and Pantazis, Dimitrios and Nancy Kanwisher} }