ML Anthology

A Maximum-Likelihood Approach to Modeling Multisensory Enhancement

H. Colonius, A. Diederich
NeurIPS 2001 pp. 181-187
/neurips/2001/colonius2001neurips-maximumlikelihood/

Abstract

Multisensory response enhancement (MRE) is the augmentation of the response of a neuron to sensory input of one modality by si(cid:173) multaneous input from another modality. The maximum likelihood (ML) model presented here modifies the Bayesian model for MRE (Anastasio et al.) by incorporating a decision strategy to maximize the number of correct decisions. Thus the ML model can also deal with the important tasks of stimulus discrimination and identifi(cid:173) cation in the presence of incongruent visual and auditory cues. It accounts for the inverse effectiveness observed in neurophysiolog(cid:173) ical recording data, and it predicts a functional relation between uni- and bimodal levels of discriminability that is testable both in neurophysiological and behavioral experiments.

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Text

Colonius and Diederich. "A Maximum-Likelihood Approach to Modeling Multisensory Enhancement." Neural Information Processing Systems, 2001.

Markdown

[Colonius and Diederich. "A Maximum-Likelihood Approach to Modeling Multisensory Enhancement." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/colonius2001neurips-maximumlikelihood/)

BibTeX

@inproceedings{colonius2001neurips-maximumlikelihood,
  title     = {{A Maximum-Likelihood Approach to Modeling Multisensory Enhancement}},
  author    = {Colonius, H. and Diederich, A.},
  booktitle = {Neural Information Processing Systems},
  year      = {2001},
  pages     = {181-187},
  url       = {https://mlanthology.org/neurips/2001/colonius2001neurips-maximumlikelihood/}
}