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For the last decade, convolutional neural networks (CNNs) have vastlysuperseded their predecessors in nearly all vision tasks in artificialintelligence, including object recognition. However, despite abundantadvancements, they continue to pale in comparison to biological vision. Thischasm has prompted the development of biologically-inspired models that haveattempted to mimic the human visual system, primarily at a neural level, whichis evaluated using standard dataset benchmarks. However, more work is needed tounderstand how these models perceive the visual world. This article proposes astate-of-the-art procedure that generates a new metric, Psychophysical-Score,which is grounded in visual psychophysics and is capable of reliably estimatingperceptual responses across numerous models -- representing a large range incomplexity and biological inspiration. We perform the procedure on twelvemodels that vary in degree of biological inspiration and complexity, we comparethe results against the aggregated results of 2,390 Amazon Mechanical Turkworkers who together provided ~2.7 million perceptual responses. Each model'sPsychophysical-Score is compared against the state-of-the-art neuralactivity-based metric, Brain-Score. Our study indicates that models with a highcorrelation to human perceptual behavior also have a high correlation with thecorresponding neural activity.

Psychophysical-Score: A Behavioral Measure for Assessing the Biological Plausibility of Visual Recognition Models