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Optimal speed estimation in natural image movies predicts human performance

Author

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  • Johannes Burge

    (University of Pennsylvania)

  • Wilson S. Geisler

    (Center for Perceptual Systems, University of Texas at Austin)

Abstract

Accurate perception of motion depends critically on accurate estimation of retinal motion speed. Here we first analyse natural image movies to determine the optimal space-time receptive fields (RFs) for encoding local motion speed in a particular direction, given the constraints of the early visual system. Next, from the RF responses to natural stimuli, we determine the neural computations that are optimal for combining and decoding the responses into estimates of speed. The computations show how selective, invariant speed-tuned units might be constructed by the nervous system. Then, in a psychophysical experiment using matched stimuli, we show that human performance is nearly optimal. Indeed, a single efficiency parameter accurately predicts the detailed shapes of a large set of human psychometric functions. We conclude that many properties of speed-selective neurons and human speed discrimination performance are predicted by the optimal computations, and that natural stimulus variation affects optimal and human observers almost identically.

Suggested Citation

  • Johannes Burge & Wilson S. Geisler, 2015. "Optimal speed estimation in natural image movies predicts human performance," Nature Communications, Nature, vol. 6(1), pages 1-11, November.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms8900
    DOI: 10.1038/ncomms8900
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    Cited by:

    1. Johannes Burge & Priyank Jaini, 2017. "Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-32, February.
    2. Seha Kim & Johannes Burge, 2020. "Natural scene statistics predict how humans pool information across space in surface tilt estimation," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-26, June.

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