### Abstract

It has been demonstrated that basic aspects of human visual motion perception are qualitatively consistent with a Bayesian estimation framework, where the prior probability distribution on velocity favors slow speeds. Here, we present a refined probabilistic model that can account for the typical trial-to-trial variabilities observed in psychophysical speed perception experiments. We also show that data from such experiments can be used to constrain both the likelihood and prior functions of the model. Specifically, we measured matching speeds and thresholds in a two-alternative forced choice speed discrimination task. Parametric fits to the data reveal that the likelihood function is well approximated by a LogNormal distribution with a characteristic contrast-dependent variance, and that the prior distribution on velocity exhibits significantly heavier tails than a Gaussian, and approximately follows a power-law function.

Original language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004 |

Publisher | Neural information processing systems foundation |

ISBN (Print) | 0262195348, 9780262195348 |

State | Published - 2005 |

Event | 18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada Duration: Dec 13 2004 → Dec 16 2004 |

### Other

Other | 18th Annual Conference on Neural Information Processing Systems, NIPS 2004 |
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Country | Canada |

City | Vancouver, BC |

Period | 12/13/04 → 12/16/04 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Networks and Communications
- Information Systems
- Signal Processing

### Cite this

*Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004*Neural information processing systems foundation.

**Constraining a bayesian model of human visual speed perception.** / Stocker, Alan A.; Simoncelli, Eero.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004.*Neural information processing systems foundation, 18th Annual Conference on Neural Information Processing Systems, NIPS 2004, Vancouver, BC, Canada, 12/13/04.

}

TY - GEN

T1 - Constraining a bayesian model of human visual speed perception

AU - Stocker, Alan A.

AU - Simoncelli, Eero

PY - 2005

Y1 - 2005

N2 - It has been demonstrated that basic aspects of human visual motion perception are qualitatively consistent with a Bayesian estimation framework, where the prior probability distribution on velocity favors slow speeds. Here, we present a refined probabilistic model that can account for the typical trial-to-trial variabilities observed in psychophysical speed perception experiments. We also show that data from such experiments can be used to constrain both the likelihood and prior functions of the model. Specifically, we measured matching speeds and thresholds in a two-alternative forced choice speed discrimination task. Parametric fits to the data reveal that the likelihood function is well approximated by a LogNormal distribution with a characteristic contrast-dependent variance, and that the prior distribution on velocity exhibits significantly heavier tails than a Gaussian, and approximately follows a power-law function.

AB - It has been demonstrated that basic aspects of human visual motion perception are qualitatively consistent with a Bayesian estimation framework, where the prior probability distribution on velocity favors slow speeds. Here, we present a refined probabilistic model that can account for the typical trial-to-trial variabilities observed in psychophysical speed perception experiments. We also show that data from such experiments can be used to constrain both the likelihood and prior functions of the model. Specifically, we measured matching speeds and thresholds in a two-alternative forced choice speed discrimination task. Parametric fits to the data reveal that the likelihood function is well approximated by a LogNormal distribution with a characteristic contrast-dependent variance, and that the prior distribution on velocity exhibits significantly heavier tails than a Gaussian, and approximately follows a power-law function.

UR - http://www.scopus.com/inward/record.url?scp=29344434701&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=29344434701&partnerID=8YFLogxK

M3 - Conference contribution

SN - 0262195348

SN - 9780262195348

BT - Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004

PB - Neural information processing systems foundation

ER -