However, the great majority of these impressive results rely on black-box techniques that achieve their goals without considering the nature of information representation and processing; correspondingly, scientific investigation of how humans process faces has remained largely qualitative and ad hoc.
Combining vision techniques with statistical modling
Right now there is a timely opportunity to combine the best machine learning/vision techniques with advanced statistical modeling, in order to gain a mathematically rigorous and theoretically grounded understanding of human face processing.
Such an understanding is important not only because face processing is essential for human social life, reproductive success, and even survival (e.g. discriminating a foe versus a friend), but may also usher in a whole new level of understanding by revisiting many classical cognitive functions (perception, attention, learning, memory, decision-making, social cognition) through the use of face stimuli as much more complex and higher-dimensional experimental probes than found in traditional experiments.
Having more complex behavioral experimental paradigms that are nevertheless computationally interpretable makes it possible to study the neural bases of cognitive functions in ways that were hitherto impossible with simpler, artificial experimental stimuli and paradigms. With our parametric, vector-space model of human psychological face space, we are able to have the cake and eat it too: retaining interpretability while probing the brain with high-dimensional, complex, ecologically important face stimuli.
Related Papers
- Ryali, C, Yu, A J (2018). Beauty-in-averageness and its contextual modulations: A Bayesian statistical account, Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA.
- Guan, J, Ryali, C, Yu, A J (2018). Computational modeling of social face perception in humans: Leveraging the active appearance model, bioRxiv.
- Huang, S J, Ryali, C, K, Liu J, Guo, D, Guan, J, Li, Y, Yu, A J (2019). A Model-Based Investigation of the Biological Origin of Human Social Perception of Faces. Proceedings of the Cognitive Science Society Conference.
- Ryali, C, Wang, X, Yu, A J (2020). Leveraging computer vision face representation to understand human face representation. Proceedings of the Cognitive Science Society Conference.
- Ryali, C K, Goffin, S, Winkielman, P, Yu, A J (2020). From likely to likable: The role of statistical typicality in human social assessment of faces. Proceedings of the National Academy of Sciences, 117 (47).