- Oral presentation
- Open Access
Trial-by-trial modeling of electrophysiological signals during inverse Bayesian inference
© Kolossa et al; licensee BioMed Central Ltd. 2014
- Published: 21 July 2014
- Bayesian Inference
- Bayesian Model
- Prospect Theory
- Tops Model
- Electrophysiological Measure
Empirical support for the Bayesian brain hypothesis, although of major theoretical importance for cognitive neuroscience, is surprisingly scarce. The literature still lacks definitive functional neuroimaging evidence that neural activities code and compute Bayesian probabilities. Here, we introduce a new experimental design to relate electrophysiological measures to Bayesian inference. Specifically, an urns-and-balls paradigm was used to study neural underpinnings of probabilistic inverse inference. Event-related potentials (ERPs) were recorded from human participants who performed the urns-and-balls paradigm, and computational modeling was conducted on trial-by-trial electrophysiological signals. Five computational models were compared with respect to their capacity to predict electrophysiological measures. One Bayesian model (BAY) was compared with another Bayesian model which takes potential effects of non-linear probability weighting into account (BAYS). A predictive surprise model (TOPS) of sequential probability revisions was derived from the Bayesian models. A comparison was made with two published models of surprise (DIF  and OST ).
Posterior model probabilities.
ERP waves and electrodes
- Kolossa A, Fingscheidt T, Wessel K, Kopp B: A model-based approach to trial-by-trial P300 amplitude fluctuations. Frontiers in Human Neuroscience. 2012, 6: 359-PubMed CentralPubMedGoogle Scholar
- Ostwald D, Spitzer B, Guggenmos M, Schmidt TT, Kiebel SJ, Blankenburg F: Evidence for neural encoding of Bayesian surprise in human somatosensation. NeuroImage. 2012, 62: 177-188. 10.1016/j.neuroimage.2012.04.050.View ArticlePubMedGoogle Scholar
- Friston KJ, Penny WD, Phillips C, Kiebel SJ, Hinton G, Ashburner J: Classical and Bayesian inference in neuroimaging: theory. NeuroImage. 2002, 16: 465-483. 10.1006/nimg.2002.1090.View ArticlePubMedGoogle Scholar
- Hoijtink H: Informative Hypotheses: theory and practice for behavioral and social scientists. 2012, New York: CRC PressGoogle Scholar
- Kahneman D, Tversky A: Prospect theory: an analysis of decision under risk. Econometrica. 1979, 47: 263-291. 10.2307/1914185.View ArticleGoogle Scholar
- Tversky A, Kahneman D: Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty. 1992, 5: 297-323. 10.1007/BF00122574.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.