Neural Scaling Laws for an Uncertain World

Autonomous neural systems must efficiently process information in a wide range of novel environments which may have very different statistical properties. We consider the problem of how to optimally distribute receptors along a 1-dimensional continuum consistent with the following design principles....

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Bibliographic Details
Published in:Psychological review 2018-01, Vol.125 (1), p.47-58
Main Authors: Howard, Marc W., Shankar, Karthik H.
Format: Article
Language:English
Subjects:
Animals
Cognition
Cognition & reasoning
Derivation
Differential Threshold
Humans
Laws
Models, Psychological
Neural Networks
Numerosity Perception
Perceptions
Psychology
Psychophysics
Statistics
Time
Uncertainty
Visual Perception - physiology
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Summary:Autonomous neural systems must efficiently process information in a wide range of novel environments which may have very different statistical properties. We consider the problem of how to optimally distribute receptors along a 1-dimensional continuum consistent with the following design principles. First, neural representations of the world should obey a neural uncertainty principle-making as few assumptions as possible about the statistical structure of the world. Second, neural representations should convey, as much as possible, equivalent information about environments with different statistics. The results of these arguments resemble the structure of the visual system and provide a natural explanation of the behavioral Weber-Fechner law, a foundational result in psychology. Because the derivation is extremely general, this suggests that similar scaling relationships should be observed not only in sensory continua, but also in neural representations of "cognitive" 1-dimensional quantities such as time or numerosity.
ISSN:0033-295X
1939-1471
DOI:10.1037/rev0000081