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Comparing representations of complex stimuli in neural network layers tohuman brain representations or behavioral judgments can guide modeldevelopment. However, even qualitatively distinct neural network models oftenpredict similar representational geometries of typical stimulus sets. Wepropose a Bayesian experimental design approach to synthesizing stimulus setsfor adjudicating among representational models efficiently. We apply our methodto discriminate among candidate neural network models of behavioral facedissimilarity judgments. Our results indicate that a neural network trained toinvert a 3D-face-model graphics renderer is more human-aligned than the samearchitecture trained on identification, classification, or autoencoding. Ourproposed stimulus synthesis objective is generally applicable to designingexperiments to be analyzed by representational similarity analysis for modelcomparison.

Distinguishing representational geometries with controversial stimuli:  Bayesian experimental design and its application to face dissimilarity  judgments