P300 amplitude variations, prior probabilities, and likelihoods: a Bayesian ERP study
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The capability of the human brain for Bayesian inference was assessed by manipulating probabilistic contingencies in an urn-ball task. Event-related potentials (ERPs) were recorded in response to stimuli that differed in their relative frequency of occurrence (.18 to .82). A veraged ERPs with sufficient signal-to-noise ratio (relative frequency of occurrence > .5) were used for further analysis. Research hypotheses about relationships between probabilistic contingencies and ERP amplitude variations were formalized as (in-)equality constrained hypotheses. Conducting Bayesian model comparisons, we found that manipulations of prior probabilities and likelihoods were associated with separately modifiable and distinct ERP responses. P3a amplitudes were sensitive to the degree of prior certainty such that higher prior probabilities were related to larger frontally distributed P3a waves. P3b amplitudes were sensitive to the degree of likelihood certainty such that lower likelihoods were associated with larger parietally distributed P3b waves. These ERP data suggest that these antecedents of Bayesian inference (prior probabilities and likelihoods) are coded by the human brain.
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It is possible that the brain represents probability distributions over small collections of variables or variational approximations to probability distributions, but this would not affect the key argument, which stems from the unavoidable difficulty of finding isolated peaks in, and calculating the volume of, complex probability distributions.
“Of course, it is possible that the brain represents probability distributions over small collections of variables [90,91], or variational approximations to probability distributions [92], but this would not affect our key argument, which stems from the unavoidable difficulty of finding isolated peaks in, and calculating the volume of, complex probability distributions.”
From Bayesian Brains without Probabilities (2016) · cites this paper as [90]
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- Authors
- Kopp, B., Seer, C., Lange, F., Kluytmans, A., Kolossa, A., Fingscheidt, T., & Hoijtink, H.
- Affiliations
- Medizinische Hochschule Hannover (DE)
- Utrecht University (NL)
- Technische Universität Braunschweig (DE)
- Corresponding author
- Kopp, Bruno
- Venue
- Cognitive, Affective, & Behavioral Neuroscience
- Published
- 2016-07-12
- Volume
- 16
- Issue
- 5
- Pages
- 911-928
- ISSN
- 1530-7026 (ISSN-L)
- 1531-135X
- OpenAlex
- W2473011566
- Cited by
- 60
- References
- 86
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 5 · 2023: 5 · 2022: 5 · 2021: 9 · 2020: 10 · 2019: 6 · 2018: 7 · 2016: 2
- FWCI
- 3.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cognitive Affective & Behavioral Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Funders
- Deutsche Forschungsgemeinschaft
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (406-12-001)
- Studienstiftung des Deutschen Volkes
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Keywords
- Bayesian inference, P300, P3a, P3b, prior probability, likelihood, event-related potentials, Bayesian model selection
- MeSH terms
- Adult, Bayes Theorem, Brain, Electroencephalography, Female, Humans, Male, Middle Aged, Neuropsychological Tests, Signal Processing, Computer-Assisted, Likelihood Functions, Event-Related Potentials, P300, Young Adult, Anticipation, Psychological
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