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Noise characteristics and prior expectations in human visual speed perception
Alan A Stocker1, Eero P Simoncelli
1Howard Hughes Medical Institute, Center for Neural Science and Courant Institute of Mathematical Sciences, New York University, 4 Washington Place Rm 809, New York, New York 10003, USA. alan.stocker@nyu.edu
Nature Neuroscience
|March 21, 2006
Summary
Human visual speed perception aligns with a Bayesian model favoring lower speeds. This study infers internal noise and prior expectations from psychophysical data, revealing non-Gaussian priors and speed-dependent noise.
Area of Science:
- Visual perception
- Computational neuroscience
- Psychophysics
Background:
- Human visual speed perception is modeled as a Bayesian observer combining noisy sensory data with a prior bias for lower speeds.
- Quantitative validation is hindered by unknown internal noise characteristics and prior expectations.
Purpose of the Study:
- To develop an augmented observer model to account for response variability in speed discrimination tasks.
- To infer internal noise characteristics and prior probability distributions directly from psychophysical data.
Main Methods:
- Developed an augmented Bayesian observer model.
- Collected psychophysical data from a speed discrimination task.
- Inferred model parameters, including prior shape and noise characteristics, from empirical data.
Main Results:
- The model accurately describes human speed discrimination data across various stimulus parameters.
- Inferred prior distributions exhibit heavier tails than Gaussian distributions.
- Internal noise amplitude is proportional to stimulus speed and inversely related to stimulus contrast.
Conclusions:
- The augmented observer model provides a robust framework for understanding human speed perception.
- The inferred non-Gaussian priors and specific noise characteristics offer new insights into visual processing.
- The generalizable framework has potential applications in other perceptual domains.