Mark R. Saddler

Mark R. Saddler

@msaddler.bsky.social

Postdoc at DTU Hearing Systems doing computational research in auditory neuroscience

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Mark R. Saddler
@msaddler.bsky.social

The model does not require internal noise to reproduce human perceptual limits. These results raise the possibility that absolute discrimination thresholds are largely determined by linear separability in representations optimized for natural behavior, rather than by intrinsic neural noise. [5/6]

Mark R. Saddler
@msaddler.bsky.social

Our preprint adds to the growing evidence that optimization for ecological tasks can explain human behavior beyond the tasks on which a model is trained, including a surprisingly comprehensive swath of classic psychoacoustic phenomena. [6/6]

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Mark R. Saddler
@msaddler.bsky.social

Despite never being fit to human data or trained on psychophysical tasks, the model's thresholds closely matched human thresholds across a large battery of classic experiments spanning auditory sensitivity (1632 thresholds across 31 experiments!) [3/6]

Mark R. Saddler
@msaddler.bsky.social

This match depended on whether the model was optimized for natural behavior. If the model was not optimized, its thresholds were worse than those of humans. And if the same architecture was optimized for individual psychophysical tasks, it exhibited superhuman sensitivity. [4/6]

Mark R. Saddler
@msaddler.bsky.social

We trained a deep neural network to jointly localize and recognize words, voices, and environmental sounds from simulated auditory nerve input. We then measured the model's psychophysical thresholds using linear classifiers operating on the learned representations. [2/6]

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