### Abstract

The juxtaposition of established signal detection theory models of perception and more recent claims about the encoding of uncertainty in perception is a rich source of confusion. Are the latter simply a rehash of the former? Here, we make an attempt to distinguish precisely between optimal and probabilistic computation. In optimal computation, the observer minimizes the expected cost under a posterior probability distribution. In probabilistic computation, the observer uses higher moments of the likelihood function of the stimulus on a trial-by-trial basis. Computation can be optimal without being probabilistic, and vice versa. Most signal detection theory models describe optimal computation. Behavioral data only provide evidence for a neural representation of uncertainty if they are best described by a model of probabilistic computation. We argue that single-neuron activity sometimes suffices for optimal computation, but never for probabilistic computation. A population code is needed instead. Not every population code is equally suitable, because nuisance parameters have to be marginalized out. This problem is solved by Poisson-like, but not by Gaussian variability. Finally, we build a dictionary between signal detection theory quantities and Poisson-like population quantities.

Original language | English (US) |
---|---|

Pages (from-to) | 2308-2319 |

Number of pages | 12 |

Journal | Vision Research |

Volume | 50 |

Issue number | 22 |

DOIs | |

State | Published - Oct 28 2010 |

### Fingerprint

### Keywords

- Bayesian inference
- Population coding
- Signal detection theory
- Single neurons

### ASJC Scopus subject areas

- Ophthalmology
- Sensory Systems

### Cite this

**Signal detection theory, uncertainty, and Poisson-like population codes.** / Ma, Wei Ji.

Research output: Contribution to journal › Article

*Vision Research*, vol. 50, no. 22, pp. 2308-2319. https://doi.org/10.1016/j.visres.2010.08.035

}

TY - JOUR

T1 - Signal detection theory, uncertainty, and Poisson-like population codes

AU - Ma, Wei Ji

PY - 2010/10/28

Y1 - 2010/10/28

N2 - The juxtaposition of established signal detection theory models of perception and more recent claims about the encoding of uncertainty in perception is a rich source of confusion. Are the latter simply a rehash of the former? Here, we make an attempt to distinguish precisely between optimal and probabilistic computation. In optimal computation, the observer minimizes the expected cost under a posterior probability distribution. In probabilistic computation, the observer uses higher moments of the likelihood function of the stimulus on a trial-by-trial basis. Computation can be optimal without being probabilistic, and vice versa. Most signal detection theory models describe optimal computation. Behavioral data only provide evidence for a neural representation of uncertainty if they are best described by a model of probabilistic computation. We argue that single-neuron activity sometimes suffices for optimal computation, but never for probabilistic computation. A population code is needed instead. Not every population code is equally suitable, because nuisance parameters have to be marginalized out. This problem is solved by Poisson-like, but not by Gaussian variability. Finally, we build a dictionary between signal detection theory quantities and Poisson-like population quantities.

AB - The juxtaposition of established signal detection theory models of perception and more recent claims about the encoding of uncertainty in perception is a rich source of confusion. Are the latter simply a rehash of the former? Here, we make an attempt to distinguish precisely between optimal and probabilistic computation. In optimal computation, the observer minimizes the expected cost under a posterior probability distribution. In probabilistic computation, the observer uses higher moments of the likelihood function of the stimulus on a trial-by-trial basis. Computation can be optimal without being probabilistic, and vice versa. Most signal detection theory models describe optimal computation. Behavioral data only provide evidence for a neural representation of uncertainty if they are best described by a model of probabilistic computation. We argue that single-neuron activity sometimes suffices for optimal computation, but never for probabilistic computation. A population code is needed instead. Not every population code is equally suitable, because nuisance parameters have to be marginalized out. This problem is solved by Poisson-like, but not by Gaussian variability. Finally, we build a dictionary between signal detection theory quantities and Poisson-like population quantities.

KW - Bayesian inference

KW - Population coding

KW - Signal detection theory

KW - Single neurons

UR - http://www.scopus.com/inward/record.url?scp=77957754433&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77957754433&partnerID=8YFLogxK

U2 - 10.1016/j.visres.2010.08.035

DO - 10.1016/j.visres.2010.08.035

M3 - Article

C2 - 20828581

AN - SCOPUS:77957754433

VL - 50

SP - 2308

EP - 2319

JO - Vision Research

JF - Vision Research

SN - 0042-6989

IS - 22

ER -