Beta- and gamma-band activity reflect predictive coding in the processing of causal events
Open accessAbstract
In daily life, complex events are perceived in a causal manner, suggesting that the brain relies on predictive processes to model them. Within predictive coding theory, oscillatory beta-band activity has been linked to top-down predictive signals and gamma-band activity to bottom-up prediction errors. However, neurocognitive evidence for predictive coding outside lower-level sensory areas is scarce. We used magnetoencephalography to investigate neural activity during probability-dependent action perception in three areas pivotal for causal inference, superior temporal sulcus, temporoparietal junction and medial prefrontal cortex, using bowling action animations. Within this network, Granger-causal connectivity in the beta-band was found to be strongest for backward top-down connections and gamma for feed-forward bottom-up connections. Moreover, beta-band power in TPJ increased parametrically with the predictability of the action kinematics-outcome sequences. Conversely, gamma-band power in TPJ and MPFC increased with prediction error. These findings suggest that the brain utilizes predictive-coding-like computations for higher-order cognition such as perception of causal events.
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Direct evidence for alpha/beta and gamma in predictive coding per se comes from observations of increased gamma power to stimuli that are prediction errors.
“Direct evidence for alpha/beta and gamma in predictive coding per se comes from observations of increased gamma power to stimuli that are prediction errors (22, 24, 25).”
From Layer and rhythm specificity for predictive routing (2020) · cites this paper as [24]
Oscillatory dynamics play a role in regulating cortical processing, especially the gamma (40 to 90 Hz) and alpha/beta (10 to 30 Hz) bands.
“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 [24]
Cites
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120 · cited 5× · see citing works
“with incoming sensory inputs to infer causes of sensory inputs (Friston, 2010; Bastos et al., 2012).”
InfluentialDistributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783 · see citing works
Event-related EEG/MEG synchronization and desynchronization: basic principles
Pfurtscheller, G., & Lopes da Silva, F. H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(99)00141-8 · Cited by 7,328 · see citing works
Whatever 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 · see citing works
Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential
Leski, S., Lindén, H., Tetzlaff, T., Pettersen, K. H., & Einevoll, G. T. (2013) · PubMed Central · doi:10.18154/rwth-conv-084442 · Cited by 3,662 · see citing works
Preface: Cerebral Cortex Has Come of Age
Goldman, P. S., & RAKIC, P. T. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1-a · Cited by 3,412 · see citing works
Ongur, D. (2000) · Cerebral Cortex · doi:10.1093/cercor/10.3.206 · Cited by 2,627 · see citing works
Canonical Microcircuits for Predictive Coding
Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.038 · Cited by 2,421 · cited 6× · see citing works
“with incoming sensory inputs to infer causes of sensory inputs (Friston, 2010; Bastos et al., 2012).”
InfluentialMeasurement of Linear Dependence and Feedback between Multiple Time Series
Geweke, J. (1982) · Journal of the American Statistical Association · doi:10.1080/01621459.1982.10477803 · Cited by 2,060 · see citing works
Hohwy, J. (2013) · doi:10.1093/acprof:oso/9780199682737.001.0001 · Cited by 1,914 · see citing works
How to grow a mind: Statistics, structure, and abstraction
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science · doi:10.1126/science.1192788 · Cited by 1,815 · see citing works
Visual areas exert feedforward and feedback influences through distinct frequency channels
Bastos, A. M., Vezoli, J., Bosman, C. A., Schoffelen, J.-M., Oostenveld, R., Dowdall, J. R., De Weerd, P., Kennedy, H., & Fries, P. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.018 · Cited by 1,320 · see citing works
Castelli, F., Happe, F. G. E., Uta Frith, A., & Frith, C. (2000) · NeuroImage · doi:10.1006/nimg.2000.0612 · Cited by 1,299 · see citing works
A unifying computational framework for motor control and social interaction
Wolpert, D. M., Doya, K., & Kawato, M. (2003) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.2002.1238 · Cited by 1,228 · see citing works
When the world becomes ‘too real’: a Bayesian explanation of autistic perception
Pellicano, E., & Burr, D. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.08.009 · Cited by 1,156 · see citing works
Decety, J., & Lamm, C. (2007) · The Neuroscientist · doi:10.1177/1073858407304654 · Cited by 1,044 · see citing works
Cross-frequency coupling between neuronal oscillations
Jensen, O., & Colgin, L. L. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.05.003 · Cited by 991 · see citing works
Kamiński, M., Ding, M., Truccolo, W., & Bressler, S. L. (2001) · Biological Cybernetics · doi:10.1007/s004220000235 · Cited by 990 · see citing works
Activation of human primary motor cortex during action observation: A neuromagnetic study
Hari, R., Forss, N., Avikainen, S., Kirveskari, E., Salenius, S., & Rizzolatti, G. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.15061 · Cited by 957 · see citing works
Granger causality analysis in neuroscience and neuroimaging
Seth, A. K., Barrett, A. B., & Barnett, L. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.4399-14.2015 · Cited by 928 · see citing works
Expectation in perceptual decision making: neural and computational mechanisms
Summerfield, C., & de Lange, F. P. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3838 · Cited by 832 · see citing works
Measures of Conditional Linear Dependence and Feedback Between Time Series
Geweke, J. F. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10477110 · Cited by 769 · see citing works
Anatomy of hierarchy: Feedforward and feedback pathways in macaque visual cortex
Markov, N. T., Vezoli, J., Chameau, P., Falchier, A., Quilodran, R., Huissoud, C., Lamy, C., Misery, P., Giroud, P., Ullman, S., et al. (2014) · Journal of Comparative Neurology · doi:10.1002/cne.23458 · Cited by 759 · see citing works
Attentional stimulus selection through selective synchronization between monkey visual areas
Bosman, C. A., Schoffelen, J.-M., Brunet, N., Oostenveld, R., Bastos, A. M., Womelsdorf, T., Rubehn, B., Stieglitz, T., De Weerd, P., & Fries, P. (2012) · Neuron · doi:10.1016/j.neuron.2012.06.037 · Cited by 720 · see citing works
Action understanding and active inference
Friston, K., Mattout, J., & Kilner, J. (2011) · Biological Cybernetics · doi:10.1007/s00422-011-0424-z · Cited by 665 · see citing works
Visual, presaccadic, and cognitive activation of single neurons in monkey lateral intraparietal area
Colby, C. L., Duhamel, J. R., & Goldberg, M. E. (1996) · Journal of Neurophysiology · doi:10.1152/jn.1996.76.5.2841 · Cited by 631 · see citing works
Laminar differences in gamma and alpha coherence in the ventral stream
Buffalo, E. A., Fries, P., Landman, R., Buschman, T. J., & Desimone, R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1011284108 · Cited by 606 · see citing works
Evidence for a hierarchy of predictions and prediction errors in human cortex
Wacongne, C., Labyt, E., van Wassenhove, V., Bekinschtein, T., Naccache, L., & Dehaene, S. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1117807108 · Cited by 560 · see citing works
A neuronal model of predictive coding accounting for the mismatch negativity.
Wacongne, C., Changeux, J.-P., & Dehaene, S. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.5003-11.2012 · Cited by 542 · see citing works
Theory of Mind: A Neural Prediction Problem
Koster-Hale, J., & Saxe, R. (2013) · Neuron · doi:10.1016/j.neuron.2013.08.020 · Cited by 513 · see citing works
Neural correlates of depth of strategic reasoning in medial prefrontal cortex
Coricelli, G., & Nagel, R. (2009) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0807721106 · Cited by 385 · see citing works
Transitions in neural oscillations reflect prediction errors generated in audiovisual speech
Arnal, L. H., Wyart, V., & Giraud, A.-L. (2011) · Nature Neuroscience · doi:10.1038/nn.2810 · Cited by 353 · see citing works
Estimating Granger Causality from Fourier and Wavelet Transforms of Time Series Data
Dhamala, M., Rangarajan, G., & Ding, M. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.018701 · Cited by 346 · see citing works
Mapping the information flow from one brain to another during gestural communication
Schippers, M. B., Roebroeck, A., Renken, R., Nanetti, L., & Keysers, C. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1001791107 · Cited by 323 · see citing works
The mirror-neuron system: a Bayesian perspective
Kilner, J. M., Friston, K. J., & Frith, C. (2007) · Neuroreport · doi:10.1097/wnr.0b013e3281139ed0 · Cited by 287 · see citing works
den Ouden, H. E. M., Uta Frith, A., Frith, C., & Blakemore, S. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.05.001 · Cited by 279 · see citing works
Embodied Cognition and the Simulation of Action to Understand Others
Grafton, S. T. (2009) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2009.04425.x · Cited by 273 · see citing works
Robust Gamma Coherence between Macaque V1 and V2 by Dynamic Frequency Matching
Roberts, M. J., Lowet, E., Brunet, N. M., Wal, M. T., Tiesinga, P. H. E., Fries, P., & Weerd, P. D. (2013) · Neuron · doi:10.1016/j.neuron.2013.03.003 · Cited by 253 · see citing works
Adolescent development of the neural circuitry for thinking about intentions
Blakemore, S.-J., den Ouden, H., Choudhury, S., & Frith, C. (2007) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsm009 · Cited by 250 · see citing works
Wheatley, T. P., Milleville, S. C., & Martin, A. D. (2007) · Psychological Science · doi:10.1111/j.1467-9280.2007.01923.x · Cited by 240 · see citing works
Attentional Modulation of Alpha/Beta and Gamma Oscillations Reflect Functionally Distinct Processes
Bauer, M., Stenner, M.-P., Friston, K. J., & Dolan, R. J. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.3474-13.2014 · Cited by 224 · see citing works
Interareal oscillatory synchronization in top-down neocortical processing
Bressler, S. L., & Richter, C. G. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.08.010 · Cited by 195 · see citing works
LFP and oscillations—what do they tell us?
Friston, K. J., Bastos, A. M., Pinotsis, D. A., & Vladimir Litvak, A. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.05.004 · Cited by 187 · see citing works
Top-Down Beta Rhythms Support Selective Attention via Interlaminar Interaction: A Model
Lee, J. H., Whittington, M. A., & Kopell, N. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003164 · Cited by 166 · see citing works
Bastos, A. M., Litvak, V., Moran, R., Bosman, C. A., Fries, P., & Friston, K. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.081 · Cited by 152 · see citing works
The role of interoceptive inference in theory of mind
Ondobaka, S., Kilner, J. M., & Friston, K. J. (2015) · Brain and Cognition · doi:10.1016/j.bandc.2015.08.002 · Cited by 150 · see citing works
Self-citationProcessing social aspects of human gaze: A combined fMRI-DTI study
Ethofer, T., Gschwind, M., & Vuilleumier, P. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.033 · Cited by 147 · see citing works
Xing, D., Shen, Y., Burns, S. P., Yeh, C.-I., Shapley, R. M., & Li, W. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.5644-11.2012 · Cited by 123 · see citing works
Brain mechanisms underlying perceptual causality
Fugelsang, J. A., Roser, M. E., Corballis, P. M., Gazzaniga, M. S., & Dunbar, K. N. (2005) · Cognitive Brain Research · doi:10.1016/j.cogbrainres.2004.12.001 · Cited by 123 · see citing works
How the brain perceives causality: an event-related fMRI study
Blakemore, S., Fonlupt, P., Pachot‐Clouard, M., Darmon, C., Boyer, P., Meltzoff, A. N., Segebarth, C. M., & Jean Decety, A. (2001) · Neuroreport · doi:10.1097/00001756-200112040-00027 · Cited by 117 · see citing works
Modeling Human Plan Recognition Using Bayesian Theory of Mind
Baker, C. L., & Tenenbaum, J. B. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-398532-3.00007-5 · Cited by 114 · see citing works
The Faces of Predictive Coding
Brodski, A., Paasch, G.-F., Helbling, S., & Wibral, M. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1529-14.2015 · Cited by 99 · see citing works
Paying Attention to Social Meaning: An fMRI Study
Tavares, P., Lawrence, A. D., & Barnard, P. J. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhm212 · Cited by 81 · see citing works
The role of immediate and final goals in action planning: An fMRI study
Majdandžić, J., Grol, M. J., van Schie, H. V., Verhagen, L., Toni, I., & Bekkering, H. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.071 · Cited by 81 · see citing works
Self-citationMurdaugh, D. L., Nadendla, K. D., & Kana, R. K. (2014) · Neuroscience Letters · doi:10.1016/j.neulet.2014.03.051 · Cited by 43 · see citing works
Launching the Effect: Representations of Causal Movements are Influenced by what they Lead to
Kerzel, D., Harold Bekkering, A., Wohlschläger, A., & Prinz, W. (2000) · The Quarterly Journal of Experimental Psychology Section A · doi:10.1080/713755938 · Cited by 21 · see citing works
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- Authors
- van Pelt, S., Heil, L., Kwisthout, J., Ondobaka, S., van Rooij, I., & Bekkering, H.
- Affiliations
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands and
- Radboud University Nijmegen (NL)
- Venue
- Social Cognitive and Affective Neuroscience
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-02-12
- Volume
- 11
- Issue
- 6
- Pages
- 973-980
- ISSN
- 1749-5016 (ISSN-L)
- 1749-5024
- PMC
- PMC4884316
- OpenAlex
- W2255704194
- Cited by
- 98
- References
- 56
- Citations by year
- 2026: 13 · 2025: 10 · 2024: 12 · 2023: 12 · 2022: 12 · 2021: 8 · 2020: 10 · 2019: 9 · 2018: 6 · 2017: 4 · 2016: 2
- FWCI
- 11.01
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Social Cognitive and Affective Neuroscience (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (407‐11‐040, 446-14-002)
- Topics
- Social Sciences › Psychology › Social Psychology › Action Observation and Synchronization
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Keywords
- predictive coding, causal inference, beta oscillations, gamma oscillations, prediction error, top-down prediction, superior temporal sulcus, temporoparietal junction, medial prefrontal cortex, action perception
- MeSH terms
- Adult, Beta Rhythm, Cerebral Cortex, Humans, Motor Activity, Probability, Thinking, Visual Perception, Magnetoencephalography, Gamma Rhythm
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