Keynotes | K&Ts | GACs | Talks | Posters | Search

Contributed Talk Session: Wednesday, August 5, 2:00 – 3:00 pm, Skirball Theater
Poster C in Poster Session C: Wednesday, August 5, 9:30 – 11:15 am, Kimmel Center, Shorin & Rosenthal Rooms

Concept Manifold Geometry Explains Asymmetry in Model–Brain Bidirectional Predictivity

Hyewon Willow Han¹, Qingqing Yang², Yalda Mohsenzadeh¹; ¹University of Western Ontario, ²Ohio State University, Columbus

Presenter: Hyewon Willow Han

Strong prediction of brain responses by model features does not guarantee that models capture the true structure of biological visual representations. Moreover, model–brain alignment is not fully characterized by one-directional comparisons; asymmetry between bidirectional mappings may provide a more informative measure. Using activations of deep neural networks and human fMRI data, we systematically examined the bidirectional mappings between the models and human brains, and found forward predictivity consistently exceeding reverse predictivity. Furthermore, we tested whether geometric properties of model concept manifolds account for the variation in forward, reverse predictivity, and their asymmetry. Critically, we found that manifold geometric properties explained substantially more variance in reverse predictivity and asymmetry than in forward predictivity, with Effective Dimensionality, Signal, and Correlation emerging as key predictors. These findings suggest that bidirectional mapping provides a more complete and diagnostic measure of alignment, and manifold geometry accounts for the extent to which the representational properties of models and brains are recoverable from each other. Together, this framework offers a principled path toward models that are more faithfully aligned with the human visual system.

Topic Area: Computational Models of Vision & Visual Cortex