  Recent trends in the machine learning community show that models with
fidelity toward human perceptual measurements perform strongly on vision tasks.
Likewise, human behavioral measurements have been used to regularize model
performance. But can we transfer latent knowledge gained from this across
different learning objectives? In this work, we introduce PERCEP-TL (Perceptual
Transfer Learning), a methodology for improving transfer learning with the
regularization power of psychophysical labels in models. We demonstrate which
models are affected the most by perceptual transfer learning and find that
models with high behavioral fidelity -- including vision transformers --
improve the most from this regularization by as much as 1.9\% Top@1 accuracy
points. These findings suggest that biologically inspired learning agents can
benefit from human behavioral measurements as regularizers and psychophysical
learned representations can be transferred to independent evaluation tasks.
