RT Journal Article
SR Electronic
T1 An image-computable model of human visual shape similarity
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.01.10.901876
DO 10.1101/2020.01.10.901876
A1 Yaniv Morgenstern
A1 Frieder Hartmann
A1 Filipp Schmidt
A1 Henning Tiedemann
A1 Eugen Prokott
A1 Guido Maiello
A1 Roland W. Fleming
YR 2020
UL http://biorxiv.org/content/early/2020/01/11/2020.01.10.901876.abstract
AB Shape is a defining feature of objects. Yet, no image-computable model accurately predicts how similar or different shapes appear to human observers. To address this, we developed a model (‘ShapeComp’), based on over 100 shape features (e.g., area, compactness, Fourier descriptors). When trained to capture the variance in a database of >25,000 animal silhouettes, ShapeComp predicts human shape similarity judgments almost perfectly (r2>0.99) without fitting any parameters to human data. To test the model, we created carefully selected arrays of complex novel shapes using a Generative Adversarial Network trained on the animal silhouettes, which we presented to observers in a wide range of tasks. Our findings show that human shape perception is inherently multidimensional and optimized for comparing natural shapes. ShapeComp outperforms conventional metrics, and can also be used to generate perceptually uniform stimulus sets, making it a powerful tool for investigating shape and object representations in the human brain.