Discovering Cognitive Structure using Large-Scale Social Data and Artificial Intelligence

What can we learn about the structure of individual minds, human or artificial, using large-scale social data, such as the textual or visual data flowing through search engines and social media platforms? In this talk, I present a range of studies showing that large-scale social data can reveal striking insights into the mind, ranging from the structure of embodied cognition to the psychological biases that underlie stereotypes. I will give special attention to presenting the results of a study we recently published in Nature which demonstrates how combining large-scale image and text data from online sources, analyzed via artificial intelligence, can reveal the latent multimodal structure of gender stereotypes. I will then share ongoing work that builds on these results by revealing the multimodal structure of intersectional stereotypes (e.g., gendered ageism) not only in human minds, but also in the judgments and associations formed by generative AI. Throughout, I will emphasize that big data and artificial intelligence are useful not only for testing existing theories about cognitive structure, but also for discovering and testing new theories. As an example, I will briefly describe ongoing work that harnesses this suite of algorithmic methodologies to unveil deep connections between the representational structure of gender and the concreteness and abstractness of concepts across domains, using visual and textual data, as well as behavioral outputs from AI. Opportunities for further advancing the integration of computer science, cognitive science, and cultural sociology will be discussed.

Speaker: Douglas Guilbeault, Stanford University

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Room 126