The SCCN lab studies how humans recognize other people and acquire knowledge about them, using a combination of behavioral studies, computational models, and neuroimaging. Research is organized along the following main focus areas:
Perceptual identity and the foundations of person knowledge
In previous work, we have identified neural representations that encode information about the identity of a face, and about the identity of a person across different perceptual modalities. With Emily Schwartz, Craig Poskanzer and Katie O’Nell (and with the support of a NSF CAREER grant) we are currently using deep networks to study the computational mechanisms for the joint recognition of person identity and facial expressions. More broadly, we are interested in the perceptual mechanisms that compute the inputs needed for the acquisition of person knowledge.
Structure of person knowledge
Expanding on earlier work on the neural representation of person knowledge, we are studying how knowledge about others is represented. In work with Minjae Kim (in collaboration with Liane Young), we are identifying challenges for existing models of what we know about others and developing new accounts that can address these challenges.
Acquisition of person knowledge
Integrating the two lines of research above, with Minjae Kim, Gordon Kraft-Todd and Kevin Jiang (in collaboration with Liane Young), we are using controlled behavioral paradigms, Bayesian models and neuroimaging to investigate how recognizing individuals and observing their actions in specific contexts can lead to the acquisition of knowledge about them such as their preferences and beliefs. With Mengting Fang, we are modeling the acquisition of person knowledge from more complex stimuli using deep networks.
Multivariate interactions between brain regions
Acquiring knowledge about others requires integrating information across multiple brain regions. For example, we can learn someone’s preference by observing what they order for dinner, integrating a representation of the identity of their face with a representation of the sentence they uttered to the waiter, or of the food they received. We have recently developed methods to study the multivariate interactions between brain regions (MVPD). With Yichen Li (in collaboration with Rebecca Saxe), we are continuing to improve these methods and applying them to the study of individual differences. With Mengting Fang, we are using MVPD to investigate how information from multiple brain regions is integrated.
Autism spectrum disorders
With Aidas Aglinkas and Tony Chen, and with the support of the Simons Foundation Autism Research Initiative, we are investigating individual variability in autism spectrum disorders with fMRI and statistical dependence between brain regions.
Improving methods for fMRI data analysis
With Aidas Aglinkas, Yuting Zhang, and Craig Poskanzer we are working on improving techniques to remove noise and adapt ROI atlases to individual participants.