Corpus ID: 214692960

Can you hear me $\textit{now}$? Sensitive comparisons of human and machine perception

  title={Can you hear me \$\textit\{now\}\$? Sensitive comparisons of human and machine perception},
  author={Michael A. Lepori and Chaz Firestone},
  journal={arXiv: Audio and Speech Processing},
The rise of sophisticated machine-recognition systems has brought with it a rise in comparisons between human and machine perception. But such comparisons face an asymmetry: Whereas machine perception of some stimulus can often be probed through direct and explicit measures, much of human perceptual knowledge is latent, incomplete, or embedded in unconscious mental processes that may not be available for explicit report. Here, we show how this asymmetry can cause such comparisons to… Expand
1 Citations
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  • Medicine, Psychology
  • Proceedings of the National Academy of Sciences
  • 2020
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  • M. Hutson
  • Medicine, Computer Science
  • Science
  • 2018
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