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The Faces of Predictive Coding
Open accessAbstract
Recent neurophysiological accounts of predictive coding hypothesized that a mismatch of prediction and sensory evidence-a prediction error (PE)-should be signaled by increased gamma-band activity (GBA) in the cortical area where prediction and evidence are compared. This hypothesis contrasts with alternative accounts where violated predictions should lead to reduced neural responses. We tested these hypotheses by violating predictions about face orientation and illumination direction in a Mooney face-detection task, while recording magnetoencephalographic responses in a large sample of 48 human subjects. The investigated predictions, acquired via lifelong experience, are known to be processed at different time points and brain regions during face recognition.Behavioral responses confirmed the induction of PEs by our task. Beamformer source analysis revealed an early PE signal for unexpected orientation in visual brain areas followed by a PE signal for unexpected illumination in areas involved in 3D shape from shading and spatial working memory. Both PE signals were reflected by increases in high-frequency (68-140 Hz) GBA. In high-frequency GBA we also observed a late interaction effect in visual brain areas, probably corresponding to a high-level PE signal. In addition, increased high-frequency GBA for expected illumination was observed in brain areas involved in attention to internal representations. Our results strongly support the hypothesis that increased GBA signals PEs. Additionally, GBA may represent attentional effects.
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“But there is mounting evidence that oscillatory dynamics play a role in regulating cortical processing and thus can also play a role, especially the gamma (40 to 90 Hz) and alpha/beta (10 to 30 Hz) bands (1, 18–25).”
From Layer and rhythm specificity for predictive routing (2020) · cites this paper as [21]
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Citations
Cites
The assessment and analysis of handedness: The Edinburgh inventory
Oldfield, R. C. (1971) · Neuropsychologia · doi:10.1016/0028-3932(71)90067-4 · Cited by 35,900
A Simple Sequentially Rejective Multiple Test Procedure
(1979) · Scandinavian Journal of Statistics · doi:10.2307/4615733 · Cited by 21,526
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/156869 · Cited by 10,323
(1966) · Medical Entomology and Zoology · Cited by 10,293
Signal detection theory and psychophysics
Ingleby, D. (1967) · Journal of Sound and Vibration · doi:10.1016/0022-460x(67)90197-6 · Cited by 8,616
Nonparametric statistical testing of EEG- and MEG-data
Maris, E., & Oostenveld, R. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.03.024 · Cited by 8,496
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120 · cited 2×
“…mechanisms underlying the integration of experience-based information and sensory evidence during the perceptual process are still a matter of debate (Mumford, 1992; Rao and Ballard, 1999; Kersten et al., 2004; Friston, 2005, 2010; Grossberg, 2007, 2013; Spratling, 2008; Kay and Phillips, 2011).”
BackgroundMethodPower failure: why small sample size undermines the reliability of neuroscience
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3475 · Cited by 7,641 · cited 2×
“The large sample size of 48 subjects was chosen to reduce the risk of false positives, as suggested by Button et al. (2013).”
BackgroundWhatever next? Predictive brains, situated agents, and the future of cognitive science
Clark, A. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12000477 · Cited by 5,924 · cited 1×
“The view of the brain as a “predictive machine” has gained considerable popularity in the last decade (Hawkins and Blakeslee, 2005; Clark, 2013; Hohwy, 2013).”
BackgroundRao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483 · cited 8×
“…mechanisms underlying the integration of experience-based information and sensory evidence during the perceptual process are still a matter of debate (Mumford, 1992; Rao and Ballard, 1999; Kersten et al., 2004; Friston, 2005, 2010; Grossberg, 2007, 2013; Spratling, 2008; Kay and Phillips, 2011).”
BackgroundInfluential
Research works printed but not linked: 1
- Cavanagh P (1991) What's up in top-down processing? In: Representations of vision: trends and tacit assumptions in vision research (Gorea A, ed), pp 295-304. Cambridge, UK: Cambridge UP.
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“There is accumulating evidence that prediction and prediction error signals oscillate at different frequencies within the brain (e.g. Arnal and Giraud, 2012; Bressler and Richter, 2015; Brodski et al. , 2015).”
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“There is accumulating evidence that prediction and prediction error signals oscillate at different frequencies within the brain (e.g. Arnal and Giraud, 2012; Bressler and Richter, 2015; Brodski et al., 2015).”
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“This is supported by human electro/magnetoencephalography (MEG/EEG) studies and a recent monkey electrocorticography study (Arnal and Giraud, 2012; Auksztulewicz and Friston, 2016; Bauer et al., 2006; Brodski et al., 2015; Chao et al., 2018; Mayer et al., 2016; van Pelt et al., 2016; Todorovic et al., 2011).”
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Metadata
- Authors
- Brodski, A., Paasch, G.-F., Helbling, S., & Wibral, M.
- Affiliations
- Goethe University Frankfurt (DE)
- Venue
- Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2015-06-17
- Volume
- 35
- Issue
- 24
- Pages
- 8997-9006
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6605164
- OpenAlex
- W1590203034
- Cited by
- 99
- References
- 81
- Citations by year
- 2025: 6 · 2024: 2 · 2023: 8 · 2022: 4 · 2021: 7 · 2020: 28 · 2019: 4 · 2018: 21 · 2017: 10 · 2016: 7 · 2015: 2
- FWCI
- 8.02
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Max Planck Digital Library (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- predictive coding, prediction error, gamma oscillations, face recognition (cognition), Mooney faces, face orientation, light direction, spatial working memory, attention, magnetoencephalography
- MeSH terms
- Adult, Brain, Brain Mapping, Face, Female, Forecasting, Humans, Male, Orientation, Pattern Recognition, Visual, Photic Stimulation, Reaction Time, Magnetoencephalography, Gamma Rhythm
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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