642 research outputs found
Distinguishing representational geometries with controversial stimuli: Bayesian experimental design and its application to face dissimilarity judgments
Comparing representations of complex stimuli in neural network layers to
human brain representations or behavioral judgments can guide model
development. However, even qualitatively distinct neural network models often
predict similar representational geometries of typical stimulus sets. We
propose a Bayesian experimental design approach to synthesizing stimulus sets
for adjudicating among representational models efficiently. We apply our method
to discriminate among candidate neural network models of behavioral face
dissimilarity judgments. Our results indicate that a neural network trained to
invert a 3D-face-model graphics renderer is more human-aligned than the same
architecture trained on identification, classification, or autoencoding. Our
proposed stimulus synthesis objective is generally applicable to designing
experiments to be analyzed by representational similarity analysis for model
comparison
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