  For the last decade, convolutional neural networks (CNNs) have vastly
superseded their predecessors in nearly all vision tasks in artificial
intelligence, including object recognition. However, despite abundant
advancements, they continue to pale in comparison to biological vision. This
chasm has prompted the development of biologically-inspired models that have
attempted to mimic the human visual system, primarily at a neural level, which
is evaluated using standard dataset benchmarks. However, more work is needed to
understand how these models perceive the visual world. This article proposes a
state-of-the-art procedure that generates a new metric, Psychophysical-Score,
which is grounded in visual psychophysics and is capable of reliably estimating
perceptual responses across numerous models -- representing a large range in
complexity and biological inspiration. We perform the procedure on twelve
models that vary in degree of biological inspiration and complexity, we compare
the results against the aggregated results of 2,390 Amazon Mechanical Turk
workers who together provided ~2.7 million perceptual responses. Each model's
Psychophysical-Score is compared against the state-of-the-art neural
activity-based metric, Brain-Score. Our study indicates that models with a high
correlation to human perceptual behavior also have a high correlation with the
corresponding neural activity.
