Modelling trial-by-trial changes in the mismatch negativity

Lieder, Falk; Daunizeau, Jean; Garrido, Marta I.; Friston, Karl J.; Stephan, Klaas E.

doi:10.1371/journal.pcbi.1002911

Cited by 168Neural dynamics and brain functionNeuroscience and Music PerceptionEmbodied and Extended Cognition

Modelling trial-by-trial changes in the mismatch negativity

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Lieder, Falk, Daunizeau, Jean, Garrido, Marta I., Friston, Karl J., Stephan, Klaas E.·2013·PLoS Computational Biology

DOI 10.1371/journal.pcbi.1002911PMC PMC3578779

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Abstract

The mismatch negativity (MMN) is a differential brain response to violations of learned regularities. It has been used to demonstrate that the brain learns the statistical structure of its environment and predicts future sensory inputs. However, the algorithmic nature of these computations and the underlying neurobiological implementation remain controversial. This article introduces a mathematical framework with which competing ideas about the computational quantities indexed by MMN responses can be formalized and tested against single-trial EEG data. This framework was applied to five major theories of the MMN, comparing their ability to explain trial-by-trial changes in MMN amplitude. Three of these theories (predictive coding, model adjustment, and novelty detection) were formalized by linking the MMN to different manifestations of the same computational mechanism: approximate Bayesian inference according to the free-energy principle. We thereby propose a unifying view on three distinct theories of the MMN. The relative plausibility of each theory was assessed against empirical single-trial MMN amplitudes acquired from eight healthy volunteers in a roving oddball experiment. Models based on the free-energy principle provided more plausible explanations of trial-by-trial changes in MMN amplitude than models representing the two more traditional theories (change detection and adaptation). Our results suggest that the MMN reflects approximate Bayesian learning of sensory regularities, and that the MMN-generating process adjusts a probabilistic model of the environment according to prediction errors.

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Electrophysiological studies investigating neural responses to deviant stimuli, such as the mismatch negativity, suggest a hierarchical organization of prediction and prediction error signaling.
“Electrophysiological studies investigating neural responses to deviant stimuli, such as the mismatch negativity (MMN), suggest a hierarchical organization of prediction and PE signaling (32–34).”
From The Predictive Coding Account of Psychosis (2018) · cites this paper as [34]
Predictive coding theory provides a more plausible model than the sensory memory hypothesis or the adaptation hypothesis.
“Several studies have shown that the predictive coding theory not only explains MMN (22) but also provides a more plausible model than the sensory memory hypothesis or the adaptation hypothesis (23).”
From A Predictive Coding Perspective on Mismatch Negativity Impairment in Schizophrenia (2020) · cites this paper as [23]

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A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743 · see citing works
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120 · cited 1× · see citing works
“These posterior expectations might be encoded by the average activities of neuronal populations, and the precision parameters that determine the relative weight assigned to prior beliefs and sensory evidence could be encoded by the strength of the recurrent connections of prediction error units [80] (see also Modellin…”
Background
Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483 · cited 1× · see citing works
“These models appeal to predictive coding [5] formulations of free energy minimization that rest upon hierarchical message passing between representational and prediction error units .”
Background
A theory of cortical responses.
Friston, K. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1622 · Cited by 4,578 · see citing works
Early selective-attention effect on evoked potential reinterpreted
Näätänen, R., Gaillard, A. W. K., & Mäntysalo, S. (1978) · Acta Psychologica · doi:10.1016/0001-6918(78)90006-9 · Cited by 2,725 · see citing works
The mismatch negativity (MMN) in basic research of central auditory processing: A review
Näätänen, R., Paavilainen, P., Rinne, T., & Alho, K. (2007) · Clinical Neurophysiology · doi:10.1016/j.clinph.2007.04.026 · Cited by 2,644 · see citing works
The Bayesian brain: The role of uncertainty in neural coding and computation
Knill, D. C., & Pouget, A. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.10.007 · Cited by 2,588 · see citing works
Attention and Brain Function
Risto, N. (2018) · Routledge eBooks · doi:10.4324/9780429487354 · Cited by 2,035 · see citing works
Handbuch der physiologischen Optik
Helmholtz, H. L. F. (2011) · DFG Viewer (DFG Viewer) · Cited by 1,947 · see citing works
The free-energy principle: a rough guide to the brain?
Friston, K. (2009) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2009.04.005 · Cited by 1,831 · see citing works
Synaptic computation
Abbott, L. F., & Regehr, W. G. (2004) · Nature · doi:10.1038/nature03010 · Cited by 1,684 · see citing works
Predictive coding under the free-energy principle
Friston, K., & Kiebel, S. (2009) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2008.0300 · Cited by 1,579 · see citing works
Hierarchical Bayesian inference in the visual cortex
Lee, T. S., & Mumford, D. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.001434 · Cited by 1,473 · see citing works
Bayesian inference with probabilistic population codes
Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006) · Nature Neuroscience · doi:10.1038/nn1790 · Cited by 1,457 · see citing works
The mismatch negativity: a review of underlying mechanisms
Garrido, M. I., Kilner, J. M., Stephan, K. E., & Friston, K. J. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.11.029 · Cited by 1,441 · cited 2× · see citing works
“The MMN is usually expressed most strongly at fronto-central electrodes, and its peak latency varies between 100 and 250 milliseconds after deviance onset, depending on the specific paradigm and type of regularity that is violated [7,8].”
Background
Bayesian model selection for group studies
Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.025 · Cited by 1,423 · see citing works
A free energy principle for the brain
Friston, K., Kilner, J., & Harrison, L. (2006) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2006.10.001 · Cited by 1,369 · see citing works
Object perception as Bayesian inference
Kersten, D., Mamassian, P., & Yuille, A. (2004) · Annual Review of Psychology · doi:10.1146/annurev.psych.55.090902.142005 · Cited by 1,269 · see citing works
A Scale for the Measurement of the Psychological Magnitude Pitch
Stevens, S. S., Volkmann, J., & Newman, E. B. (1937) · The Journal of the Acoustical Society of America · doi:10.1121/1.1915893 · Cited by 1,107 · cited 1× · see citing works
“We assume that the subjects’ initial tone categories approximately correspond to musical notes, because for pure tones subjects’ auditory representations are likely to be shaped by musical experience, and pitch perception becomes increasingly logarithmic for frequencies above 500 Hz [47].”
Background
On the computational architecture of the neocortex: II The role of cortico-cortical loops
Mumford, D. (1992) · Biological Cybernetics · doi:10.1007/bf00198477 · Cited by 1,074 · see citing works
Elemente der Psychophysik
Fechner, G. T. (1907) · doi:10.3931/e-rara-10879 · Cited by 1,031 · see citing works
On the computational architecture of the neocortex: I. The role of the thalamo-cortical loop
Mumford, D. (1991) · Biological Cybernetics · doi:10.1007/bf00202389 · Cited by 929 · see citing works
Vision as Bayesian inference: analysis by synthesis?
Yuille, A., & Kersten, D. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.002 · Cited by 849 · see citing works
Synaptic plasticity and dysconnection in schizophrenia
Stephan, K. E., Baldeweg, T., & Friston, K. J. (2006) · Biological Psychiatry · doi:10.1016/j.biopsych.2005.10.005 · Cited by 841 · see citing works
‘Primitive intelligence’ in the auditory cortex
Näätänen, R., Tervaniemi, M. H., Sussman, E. S., Paavilainen, P., & Winkler, I. (2001) · Trends in Neurosciences · doi:10.1016/s0166-2236(00)01790-2 · Cited by 827 · see citing works
Ten simple rules for dynamic causal modeling
Stephan, K. E., Penny, W. D., Moran, R. J., den Ouden, H. E. M., Daunizeau, J., & Friston, K. J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.015 · Cited by 803 · see citing works
Self-citation
Mismatch negativity in schizophrenia: a meta-analysis
Umbricht, D., & Krljes, S. (2005) · Schizophrenia Research · doi:10.1016/j.schres.2004.12.002 · Cited by 742 · see citing works
Learning and inference in the brain
Friston, K. (2003) · Neural Networks · doi:10.1016/j.neunet.2003.06.005 · Cited by 724 · see citing works
The mismatch negativity (MMN): towards the optimal paradigm
Näätänen, R., Pakarinen, S., Rinne, T., & Takegata, R. (2003) · Clinical Neurophysiology · doi:10.1016/j.clinph.2003.04.001 · Cited by 721 · see citing works
Multiple Time Scales of Adaptation in Auditory Cortex Neurons
Ulanovsky, N., Las, L., Farkas, D., & Nelken, I. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.1905-04.2004 · Cited by 711 · see citing works
Comparing families of dynamic causal models
Penny, W. D., Stephan, K. E., Daunizeau, J., Rosa, M. J., Friston, K. J., Schofield, T. M., & Leff, A. P. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000709 · Cited by 688 · see citing works
Self-citation
Role of cortical N-methyl-D-aspartate receptors in auditory sensory memory and mismatch negativity generation: implications for schizophrenia.
Javitt, D. C., Steinschneider, M., Schroeder, C. E., & Arezzo, J. C. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.21.11962 · Cited by 662 · see citing works
Representation of cochlea within primary auditory cortex in the cat
Merzenich, M. M., Knight, P. L., & Roth, G. (1975) · Journal of Neurophysiology · doi:10.1152/jn.1975.38.2.231 · Cited by 638 · see citing works
Attentive novelty detection in humans is governed by pre-attentive sensory memory
Tiitinen, H., May, P., Reinikainen, K., & Näätänen, R. (1994) · Nature · doi:10.1038/372090a0 · Cited by 637 · see citing works
Ketamine-Induced Deficits in Auditory and Visual Context-Dependent Processing in Healthy Volunteers
Umbricht, D., Schmid, L., Koller, R., Vollenweider, F. X., Hell, D., & Javitt, D. C. (2000) · Archives of General Psychiatry · doi:10.1001/archpsyc.57.12.1139 · Cited by 583 · see citing works
Interpreting the Mismatch Negativity
Winkler, I. (2007) · Journal of Psychophysiology · doi:10.1027/0269-8803.21.34.147 · Cited by 574 · see citing works
Probabilistic models of cognition: exploring representations and inductive biases
Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.05.004 · Cited by 568 · see citing works
Mismatch negativity (MMN), the deviance-elicited auditory deflection, explained
May, P. J. C., & Tiitinen, H. (2010) · Psychophysiology · doi:10.1111/j.1469-8986.2009.00856.x · Cited by 549 · see citing works
Differential Contribution of Frontal and Temporal Cortices to Auditory Change Detection: fMRI and ERP Results
Opitz, B., Rinne, T., Mecklinger, A., von Cramon, D. Y., & Schröger, E. (2002) · NeuroImage · doi:10.1006/nimg.2001.0970 · Cited by 513 · see citing works
Human posterior auditory cortex gates novel sounds to consciousness
Jääskeläinen, I. P., Ahveninen, J., Bonmassar, G., Dale, A. M., Ilmoniemi, R. J., Levänen, S., Lin, F., May, P., Melcher, J. R., Stufflebeam, S. M., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0303760101 · Cited by 464 · see citing works
The functional anatomy of the MMN: a DCM study of the roving paradigm
Garrido, M. I., Friston, K. J., Kiebel, S. J., Stephan, K. E., Baldeweg, T., & Kilner, J. M. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.05.018 · Cited by 458 · see citing works
The cochlear frequency map for the cat: Labeling auditory-nerve fibers of known characteristic frequency
Liberman, M. C. (1982) · The Journal of the Acoustical Society of America · doi:10.1121/1.388677 · Cited by 445 · see citing works
I NFERENCE AND C OMPUTATION WITH P OPULATION C ODES
Pouget, A., Dayan, P., & Zemel, R. S. (2003) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.26.041002.131112 · Cited by 429 · see citing works
When a good fit can be bad
Pitt, M. A., & Myung, I. J. (2002) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(02)01964-2 · Cited by 419 · see citing works
Memory prerequisites of mismatch negativity in the auditory event-related potential (ERP).
Cowan, N., Winkler, I., Teder, W. A., & Näätänen, R. (1993) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.19.4.909 · Cited by 359 · see citing works
Tonotopic organization of the human auditory cortex.
Romani, G., Williamson, S. J., & Kaufman, L. (1982) · Science · doi:10.1126/science.7079770 · Cited by 341 · see citing works
Adaptive modeling of the unattended acoustic environment reflected in the mismatch negativity event-related potential
Winkler, I., Karmos, G., & Näätänen, R. (1996) · Brain Research · doi:10.1016/s0006-8993(96)01008-6 · Cited by 338 · see citing works
A tutorial on variational Bayesian inference
Fox, C. W., & Roberts, S. (2011) · Artificial Intelligence Review · doi:10.1007/s10462-011-9236-8 · Cited by 329 · see citing works
Mismatch negativity potentials and cognitive impairment in schizophrenia
Baldeweg, T., Klugman, A., Gruzelier, J., & Hirsch, S. R. (2004) · Schizophrenia Research · doi:10.1016/j.schres.2003.09.009 · Cited by 321 · see citing works
Event-Related Brain Potential Correlates of Human Auditory Sensory Memory-Trace Formation
Haenschel, C., Vernon, D. J., Dwivedi, P., Gruzelier, J. H., & Baldeweg, T. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.1227-05.2005 · Cited by 294 · see citing works
Role of Mismatch Negativity and Novelty-P3 in Involuntary Auditory Attention
Escera, C., & Corral, M. J. (2007) · Journal of Psychophysiology · doi:10.1027/0269-8803.21.34.251 · Cited by 270 · see citing works
Trial-by-Trial Fluctuations in the Event-Related Electroencephalogram Reflect Dynamic Changes in the Degree of Surprise
Mars, R. B., Debener, S., Gladwin, T. E., Harrison, L. M., Haggard, P., Rothwell, J. C., & Bestmann, S. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.2925-08.2008 · Cited by 262 · see citing works
Short‐Term Habituation and Dishabituation of the Mismatch Negativity of the ERP
Sams, M. E., Alho, K., & Näätänen, R. (1984) · Psychophysiology · doi:10.1111/j.1469-8986.1984.tb00223.x · Cited by 260 · see citing works
Sequential effects on the ERP in discriminating two stimuli
Sams, M. E. (1983) · Biological Psychology · doi:10.1016/0301-0511(83)90065-0 · Cited by 257 · see citing works
Bayesian Computation in Recurrent Neural Circuits
Rao, R. P. N. (2004) · Neural Computation · doi:10.1162/08997660460733976 · Cited by 252 · see citing works
Evidence from auditory and visual event-related potential (ERP) studies of deviance detection (MMN and vMMN) linking predictive coding theories and perceptual object representations
Winkler, I., & Czigler, I. (2011) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2011.10.001 · Cited by 249 · see citing works
Impaired mismatch negativity (MMN) generation in schizophrenia as a function of stimulus deviance, probability, and interstimulus/interdeviant interval
Javitt, D. C., Grochowski, S., Shelley, A.-M., & Ritter, W. (1998) · Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section · doi:10.1016/s0168-5597(97)00073-7 · Cited by 241 · see citing works
Repetition suppression and plasticity in the human brain
Garrido, M. I., Kilner, J. M., Kiebel, S. J., Stephan, K. E., Baldeweg, T., & Friston, K. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.06.034 · Cited by 223 · see citing works
The Frontal Generator of the Mismatch Negativity Revisited
Deouell, L. Y. (2007) · Journal of Psychophysiology · doi:10.1027/0269-8803.21.34.188 · Cited by 220 · see citing works
Mismatch Negativity and Stimulus-Specific Adaptation in Animal Models
Nelken, I., & Ulanovsky, N. (2007) · Journal of Psychophysiology · doi:10.1027/0269-8803.21.34.214 · Cited by 210 · see citing works
Dynamic Causal Modeling of the Response to Frequency Deviants
Garrido, M. I., Kilner, J. M., Kiebel, S. J., & Friston, K. J. (2009) · Journal of Neurophysiology · doi:10.1152/jn.90291.2008 · Cited by 208 · see citing works
Mismatch negativity in dichotic listening: Evidence for interhemispheric differences and multiple generators
Deouell, L. Y., Shlomo Bentin, A., & Giard, M. (1998) · Psychophysiology · doi:10.1111/1469-8986.3540355 · Cited by 194 · see citing works
Frequency Change Detection in Human Auditory Cortex
May, P., Tiitinen, H., Ilmoniemi, R. J., Nyman, G., Taylor, J. G., & Näätänen, R. (1999) · Journal of Computational Neuroscience · doi:10.1023/a:1008896417606 · Cited by 175 · see citing works
A Scale for the Measurement of the Psychological Magnitude Pitch
Volkmann, J. E., Stevens, S. S., & Newman, E. B. (1937) · The Journal of the Acoustical Society of America · doi:10.1121/1.1901999 · Cited by 174 · see citing works
Cortical circuits for perceptual inference
Friston, K. J., & Kiebel, S. J. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.07.023 · Cited by 170 · see citing works
Observing the observer (I): meta-bayesian models of learning and decision-making
Daunizeau, J., den Ouden, H. E. M., Pessiglione, M., Kiebel, S. J., Stephan, K. E., & Friston, K. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015554 · Cited by 167 · see citing works
Mismatch negativity generation in the human 5HT2A agonist and NMDA antagonist model of psychosis
Heekeren, K., Daumann, J., Neukirch, A., Stock, C., Kawohl, W., Norra, C., Waberski, T. D., & Gouzoulis‐Mayfrank, E. (2008) · Psychopharmacology · doi:10.1007/s00213-008-1129-4 · Cited by 152 · see citing works
Regularity Extraction and Application in Dynamic Auditory Stimulus Sequences
Bendixen, A., Roeber, U., & Schröger, E. (2007) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2007.19.10.1664 · Cited by 147 · see citing works
Evidence for neural encoding of Bayesian surprise in human somatosensation
Ostwald, D., Spitzer, B., Guggenmos, M., Schmidt, T. T., Kiebel, S. J., & Blankenburg, F. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.04.050 · Cited by 120 · see citing works
Cerebral mechanisms underlying orienting of attention towards auditory frequency changes
Yago, E., Escera, C., Alho, K., & Giard, M. (2001) · Neuroreport · doi:10.1097/00001756-200108080-00058 · Cited by 111 · see citing works
Processing of Abstract Rule Violations in Audition
Schröger, E., Bendixen, A., Trujillo‐Barreto, N. J., & Roeber, U. (2007) · PLoS ONE · doi:10.1371/journal.pone.0001131 · Cited by 111 · see citing works
Perception and hierarchical dynamics
Kiebel, S. J. (2009) · Frontiers in Neuroinformatics · doi:10.3389/neuro.11.020.2009 · Cited by 106 · see citing works
Interactions between Transient and Long-Term Auditory Memory as Reflected by the Mismatch Negativity
Winkler, I., Cowan, N., Csépe, V., Czigler, I., & Näätänen, R. (1996) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1996.8.5.403 · Cited by 103 · see citing works
Mismatch negativity
Winkler, I., & Czigler, I. (1998) · Neuroreport · doi:10.1097/00001756-199812010-00008 · Cited by 101 · see citing works
Presentation rate and magnitude of stimulus deviance effects on human pre-attentive change detection
Schröger, E., & Winkler, I. (1995) · Neuroscience Letters · doi:10.1016/0304-3940(95)11696-t · Cited by 99 · see citing works
Determinants of the auditory mismatch response
Imada, T., Hari, R., Loveless, N. E., McEvoy, L. K., & Sams, M. E. (1993) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(93)90120-k · Cited by 98 · see citing works
The Mismatch Negativity (MMN)
Näätänen, R., & Kreegipuu, K. (2011) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780195374148.013.0081 · Cited by 92 · see citing works
A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences
Mill, R. W., Coath, M., Wennekers, T., & Denham, S. L. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002117 · Cited by 87 · see citing works
Direct evidence for differential roles of temporal and frontal components of auditory change detection
Shalgi, S., & Deouell, L. Y. (2007) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2006.11.023 · Cited by 86 · see citing works
Memory trace formation for abstract auditory features and its consequences in different attentional contexts
Bendixen, A., & Schröger, E. (2008) · Biological Psychology · doi:10.1016/j.biopsycho.2008.03.005 · Cited by 72 · see citing works
Differential changes in frontal and sub-temporal components of mismatch negativity
Baldeweg, T., Williams, J. D., & Gruzelier, J. H. (1999) · International Journal of Psychophysiology · doi:10.1016/s0167-8760(99)00026-4 · Cited by 68 · see citing works
Cortical Auditory Adaptation in the Awake Rat and the Role of Potassium Currents
Abolafia, J. M., Vergara, R. O., ARNOLD, M. M., Reig, R., & Sanchez-Vives, M. V. (2010) · Cerebral Cortex · doi:10.1093/cercor/bhq163 · Cited by 58 · see citing works
Evidence for the different additivity of the temporal and frontal generators of mismatch negativity: a human auditory event-related potential study
Paavilainen, P., Mikkonen, M., Kilpeläinen, M., Lehtinen, R., Kujala, M. V., & Tapola, L. (2003) · Neuroscience Letters · doi:10.1016/s0304-3940(03)00787-0 · Cited by 56 · see citing works
Optimizing Experimental Design for Comparing Models of Brain Function
Jean Daunizeau, A., Preuschoff, K., Friston, K. J., & Stephan, K. E. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002280 · Cited by 48 · see citing works
ATTRACTORS IN SONG
Friston, K. J., & Kiebel, S. J. (2009) · New Mathematics and Natural Computation · doi:10.1142/s1793005709001209 · Cited by 9 · see citing works
Book Review
Storey, E. (2002) · International Psychogeriatrics · doi:10.1017/s1041610202218529 · Cited by 2 · see citing works
Mismatch negativity of the event-related brain potential as an indicator of automatic information processing
Näätänen, R. (1985) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(85)90315-3 · Cited by 1 · see citing works

Flagged first, then by citations

Research works printed but not linked: 3

[46]Greenstein B, Greenstein A (2000) Color Atlas of Neuroscience: Neuroanatomy and Neurophysiology (Thieme Flexibook). Thieme
[80]Friston K (2010) The free-energy principle: a unified brain theory? Nature Reviews Neuroscience 11: 127– 138. 20068583
[82]Näätänen R, Tervaniemi M, Sussman E, Paavilainen P, Winkler I (2001) “Primitive intelligence” in the auditory cortex. Trends in Neurosciences 24: 283– 288. 11311381

Sorted by citations

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“This could encompass generative models of trial-wise M/EEG responses where, for example, trial by trial amplitudes are predicted as a linear mixture of prediction errors (Lieder et al., 2013).”
Background
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Vuust, P., Heggli, O. A., Friston, K. J., & Kringelbach, M. L. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00578-5 · Cited by 416 · see citing works
Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns
Barascud, N., Pearce, M. T., Griffiths, T. D., Friston, K. J., & Chait, M. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508523113 · Cited by 296 · see citing works
Visual mismatch negativity: a predictive coding view
Stefanics, G., Kremláček, J., & Czigler, I. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00666 · Cited by 338 · see citing works
Expectation and Attention in Hierarchical Auditory Prediction
Chennu, S., Noreika, V., Gueorguiev, D., Blenkmann, A., Kochen, S., Ibanez, A., Owen, A. M., & Bekinschtein, T. A. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.0114-13.2013 · Cited by 299 · see citing works
Computational approaches to psychiatry
Stephan, K. E., & Mathys, C. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.12.007 · Cited by 260 · see citing works
The mismatch-negativity (MMN) component of the auditory event-related potential to violations of abstract regularities: A review
Paavilainen, P. (2013) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2013.03.015 · Cited by 236 · see citing works
An objective index of individual face discrimination in the right occipito-temporal cortex by means of fast periodic oddball stimulation
Liu‐Shuang, J., Norcia, A. M., & Rossion, B. (2013) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2013.10.022 · Cited by 229 · see citing works
Translational perspectives for computational neuroimaging
Stephan, K. E., Iglesias, S., Heinzle, J., & Diaconescu, A. O. (2015) · Neuron · doi:10.1016/j.neuron.2015.07.008 · Cited by 169 · see citing works
Computational neuroimaging strategies for single patient predictions
Stephan, K. E., Schlagenhauf, F., Huys, Q. J. M., Raman, S., Aponte, E. A., Brodersen, K. H., Rigoux, L., Moran, R., Jean Daunizeau, A., Dolan, R. J., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.038 · Cited by 166 · see citing works
Human Inferences about Sequences: A Minimal Transition Probability Model
Meyniel, F., Maheu, M., & Dehaene, S. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005260 · Cited by 137 · see citing works
An information-theoretic perspective on the costs of cognition
Zénon, A., Solopchuk, O., & Pezzulo, G. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2018.09.013 · Cited by 142 · see citing works
Visual Mismatch and Predictive Coding: A Computational Single-Trial ERP Study
Stefanics, G., Heinzle, J., Horvath, A. A., & Stephan, K. E. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.3365-17.2018 · Cited by 124 · see citing works
A Neurocomputational Model of the Mismatch Negativity
Lieder, F., Stephan, K. E., Jean Daunizeau, A., Garrido, M. I., & Friston, K. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003288 · Cited by 119 · see citing works
Anterior insula coordinates hierarchical processing of tactile mismatch responses
Allen, M., Fardo, F., Dietz, M. J., Hillebrandt, H. F., Friston, K. J., Rees, G. E., & Roepstorff, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.030 · Cited by 117 · see citing works
A computational analysis of the neural bases of Bayesian inference
Kolossa, A., Kopp, B., & Fingscheidt, T. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.11.007 · Cited by 114 · see citing works
Mismatch Negativity as a “Translatable” Brain Marker Toward Early Intervention for Psychosis: A Review
Nagai, T., Tada, M., Kirihara, K., Araki, T., Jinde, S., & Kasai, K. (2013) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2013.00115 · Cited by 114 · see citing works
Understanding human individuation of unfamiliar faces with oddball fast periodic visual stimulation and electroencephalography
Rossion, B., Retter, T. L., & Liu-Shuang, J. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.14865 · Cited by 91 · see citing works
Investigating bottom-up auditory attention
Kaya, E. M., & Elhilali, M. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00327 · Cited by 101 · see citing works
Decision-making in schizophrenia: A predictive-coding perspective
Sterzer, P., Voss, M., Schlagenhauf, F., & Heinz, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.074 · Cited by 100 · see citing works
Brain signatures of a multiscale process of sequence learning in humans
Maheu, M., Dehaene, S., & Meyniel, F. (2019) · eLife · doi:10.7554/elife.41541 · Cited by 98 · see citing works
Predictive Regularity Representations in Violation Detection and Auditory Stream Segregation: From Conceptual to Computational Models
Schröger, E., Bendixen, A., Denham, S. L., Mill, R. W., Bõhm, T. M., & Winkler, I. (2013) · Brain Topography · doi:10.1007/s10548-013-0334-6 · Cited by 87 · see citing works
Laminar fMRI and computational theories of brain function
Stephan, K. E., Petzschner, F. H., Kasper, L., Bayer, J., Wellstein, K. V., Stefanics, G., Pruessmann, K. P., & Heinzle, J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.001 · Cited by 82 · see citing works
Hierarchical Organization of Frontotemporal Networks for the Prediction of Stimuli across Multiple Dimensions
Phillips, H. N., Blenkmann, A. O., Hughes, L. E., Bekinschtein, T. A., & Rowe, J. B. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.5095-14.2015 · Cited by 80 · see citing works
Single-Trial Event-Related Potential Correlates of Belief Updating
Bennett, D., Murawski, C., & Bode, S. (2015) · eNeuro · doi:10.1523/eneuro.0076-15.2015 · Cited by 78 · see citing works
Human Pavlovian fear conditioning conforms to probabilistic learning
Tzovara, A., Korn, C. W., & Bach, D. R. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006243 · Cited by 75 · see citing works
Differential Effects of Awareness and Task Relevance on Early and Late ERPs in a No-Report Visual Oddball Paradigm
Schloßmacher, I., Dellert, T., Pitts, M. A., Bruchmann, M., & Straube, T. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2077-19.2020 · Cited by 76 · see citing works
Unexpected abrupt onsets can override a top-down set for color.
Folk, C. L., & Remington, R. W. (2015) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/xhp0000084 · Cited by 76 · see citing works
Ketamine Affects Prediction Errors about Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity
Weber, L. A. E., Diaconescu, A. O., Mathys, C. D., Schmidt, A., Kometer, M., Vollenweider, F. X., & Stephan, K. E. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.3069-19.2020 · Cited by 73 · see citing works
Expectation violation and attention to pain jointly modulate neural gain in somatosensory cortex
Fardo, F., Auksztulewicz, R., Allen, M., Dietz, M. J., Roepstorff, A., & Friston, K. J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.041 · Cited by 59 · see citing works
Neurons in Macaque Inferior Temporal Cortex Show No Surprise Response to Deviants in Visual Oddball Sequences
Kaliukhovich, D. A., & Vogels, R. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.2154-14.2014 · Cited by 69 · see citing works
Mismatch negativity (MMN) to pitch change is susceptible to order-dependent bias
Todd, J., Heathcote, A., Whitson, L., Mullens, D., Provost, A. L., & Winkler, I. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00180 · Cited by 55 · see citing works
A taxonomy of surprise definitions
Modirshanechi, A., Brea, J., & Gerstner, W. (2022) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2022.102712 · Cited by 60 · see citing works
The contemplative exercise through the lenses of predictive processing: A promising approach
Pagnoni, G. (2019) · Progress in brain research · doi:10.1016/bs.pbr.2018.10.022 · Cited by 58 · see citing works
Mismatch Negativity in Recent-Onset and Chronic Schizophrenia: A Current Source Density Analysis
Fulham, W. R., Michie, P. T., Ward, P. B., Rasser, P. E., Todd, J., Johnston, P., Thompson, P. M., & Schall, U. (2014) · PLoS ONE · doi:10.1371/journal.pone.0100221 · Cited by 56 · see citing works
A Predictive Coding Perspective on Mismatch Negativity Impairment in Schizophrenia
Kirihara, K., Tada, M., Koshiyama, D., Fujioka, M., Usui, K., Araki, T., & Kasai, K. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00660 · Cited by 51 · see citing works
“Several studies have shown that the predictive coding theory not only explains MMN (22) but also provides a more plausible model than the sensory memory hypothesis or the adaptation hypothesis (23).”
Background
Differential effects of non-dual and focused attention meditations on the formation of automatic perceptual habits in expert practitioners
Fucci, E., Abdoun, O., Caclin, A., Francis, A. D., Dunne, J. D., Ricard, M., Davidson, R. J., & Antoine Lutz, A. (2018) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2018.07.025 · Cited by 52 · see citing works
Brain dynamics for confidence-weighted learning
Meyniel, F. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007935 · Cited by 49 · see citing works
Neuronal responses to omitted tones in the auditory brain: A neuronal correlate for predictive coding
Lao-Rodríguez, A. B., Przewrocki, K., Pérez‐González, D., Alishbayli, A., Yılmaz, E. O., Malmierca, M. S., & Englitz, B. (2023) · Science Advances · doi:10.1126/sciadv.abq8657 · Cited by 51 · see citing works
Limited Evidence for Sensory Prediction Error Responses in Visual Cortex of Macaques and Humans
Solomon, S. S., Tang, H., Sussman, E. S., & Kohn, A. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab014 · Cited by 50 · see citing works
Beta oscillatory power modulation reflects the predictability of pitch change
Chang, A., Bosnyak, D. J., & Trainor, L. J. (2018) · Cortex · doi:10.1016/j.cortex.2018.06.008 · Cited by 47 · see citing works
The Adaptation Model Offers a Challenge for the Predictive Coding Account of Mismatch Negativity
May, P. J. C. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.721574 · Cited by 43 · see citing works
Neural surprise in somatosensory Bayesian learning
Gijsen, S. C. J., Grundei, M., Lange, R. T., Ostwald, D., & Blankenburg, F. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008068 · Cited by 44 · see citing works
What controls gain in gain control? Mismatch negativity (MMN), priors and system biases
Todd, J., Heathcote, A., Mullens, D., Whitson, L., Provost, A. L., & Winkler, I. (2013) · Brain Topography · doi:10.1007/s10548-013-0344-4 · Cited by 41 · see citing works
Learning in Volatile Environments With the Bayes Factor Surprise
Liakoni, V., Modirshanechi, A., Wulfram Gerstner, B., & Brea, J. (2021) · Neural Computation · doi:10.1162/neco_a_01352 · Cited by 36 · see citing works
Computational framework for investigating predictive processing in auditory perception
Skerritt-Davis, B., & Elhilali, M. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109177 · Cited by 37 · see citing works
Neurocomputational Underpinnings of Expected Surprise
Lecaignard, F., Bertrand, O., Caclin, A., & Mattout, J. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0601-21.2021 · Cited by 37 · see citing works
Automatic Sensory Predictions: A Review of Predictive Mechanisms in the Brain and Their Link to Conscious Processing
Tivadar, R. I., Knight, R. T., & Tzovara, A. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.702520 · Cited by 28 · see citing works
Neural sources of prediction errors detect unrealistic VR interactions
Gehrke, L., Lopes, P., Klug, M., Akman, S., & Gramann, K. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac69bc · Cited by 31 · see citing works
Auditory Event-related Potentials
Winkler, I., Denham, S. L., & Escera, C. (2013) · Springer eBooks · doi:10.1007/978-1-4614-7320-6_99-1 · Cited by 30 · see citing works
Feature-specific prediction errors for visual mismatch
Stefanics, G., Stephan, K. E., & Heinzle, J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.020 · Cited by 26 · see citing works
A tutorial on variational Bayes for latent linear stochastic time-series models
Ostwald, D., Kirilina, E. P., Starke, L., & Blankenburg, F. (2014) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2014.04.003 · Cited by 28 · see citing works
Mismatch Negativity of Sad Syllables Is Absent in Patients with Major Depressive Disorder
Pang, X., Xu, J., Chang, Y., Tang, D., Zheng, Y., Liu, Y., & Sun, Y. (2014) · PLoS ONE · doi:10.1371/journal.pone.0091995 · Cited by 28 · see citing works
Assimilation of L2 vowels to L1 phonemes governs L2 learning in adulthood: a behavioral and ERP study
Grimaldi, M., Sisinni, B., Fivela, B. G., Invitto, S., Resta, D., Alku, P., & Brattico, E. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00279 · Cited by 28 · see citing works
EEG mismatch responses in a multimodal roving stimulus paradigm provide evidence for probabilistic inference across audition, somatosensation, and vision
Grundei, M., Schröder, P., Gijsen, S. C. J., & Blankenburg, F. (2023) · Human Brain Mapping · doi:10.1002/hbm.26303 · Cited by 26 · see citing works
Auditory mismatch responses are differentially sensitive to changes in muscarinic acetylcholine versus dopamine receptor function
Weber, L. A. E., Tomiello, S., Schöbi, D., Wellstein, K. V., Mueller, D. R., Iglesias, S., & Stephan, K. E. (2022) · eLife · doi:10.7554/elife.74835 · Cited by 26 · see citing works
Model-based prediction of muscarinic receptor function from auditory mismatch negativity responses
Schöbi, D., Homberg, F., Frässle, S., Endepols, H., Moran, R. J., Friston, K. J., Tittgemeyer, M., Heinzle, J., & Stephan, K. E. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118096 · Cited by 25 · see citing works
Self-citation
The relationship between mismatch response and the acoustic change complex in normal hearing infants
Uhler, K. M., Hunter, S. K., Tierney, E., & Gilley, P. M. (2018) · Clinical Neurophysiology · doi:10.1016/j.clinph.2018.02.132 · Cited by 25 · see citing works
A quantitative model reveals a frequency ordering of prediction and prediction-error signals in the human brain
Chao, Z. C., Huang, Y. T., & Wu, C. (2022) · Communications Biology · doi:10.1038/s42003-022-04049-6 · Cited by 24 · see citing works
Perceptual learning of tone patterns changes the effective connectivity between Heschl's gyrus and planum temporale
Lumaca, M., Dietz, M. J., Hansen, N. C., Quiroga‐Martinez, D. R., & Vuust, P. (2020) · Human Brain Mapping · doi:10.1002/hbm.25269 · Cited by 25 · see citing works
Preferred music listening is associated with perceptual learning enhancement at the expense of self-focused attention
Sarasso, P., Barbieri, P., Fante, E. D., Bechis, L., Neppi-Mòdona, M., Sacco, K., & Ronga, I. (2022) · Psychonomic Bulletin & Review · doi:10.3758/s13423-022-02127-8 · Cited by 23 · see citing works
Trial-by-trial surprise-decoding model for visual and auditory binary oddball tasks
Modirshanechi, A., Kiani, M. M., & Aghajan, H. K. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.028 · Cited by 22 · see citing works
Reduced Primacy Bias in Autism during Early Sensory Processing
Goris, J., Braem, S., Herck, S. V., Simoens, J., Deschrijver, E., Wiersema, J. R., Paton, B., Brass, M., & Todd, J. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.3088-20.2022 · Cited by 23 · see citing works
Timing of repetition suppression of event‐related potentials to unattended objects
Stefanics, G., Heinzle, J., Czigler, I., Valentini, E., & Stephan, K. E. (2018) · European Journal of Neuroscience · doi:10.1111/ejn.13972 · Cited by 21 · see citing works
Mismatch negativity and neural adaptation: Two sides of the same coin. Response: Commentary: Visual mismatch negativity: a predictive coding view
Stefanics, G., Kremláček, J., & Czigler, I. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00013 · Cited by 21 · see citing works
A multimodal cortical network of sensory expectation violation revealed by fMRI
Grundei, M., Schmidt, T. T., & Blankenburg, F. (2023) · Human Brain Mapping · doi:10.1002/hbm.26482 · Cited by 18 · see citing works
Biased relevance filtering in the auditory system: A test of confidence-weighted first-impressions
Mullens, D., Winkler, I., Damaso, K. A. M., Heathcote, A., Whitson, L., Provost, A. L., & Todd, J. (2016) · Biological Psychology · doi:10.1016/j.biopsycho.2016.01.018 · Cited by 19 · see citing works
Convolutional neural networks for decoding electroencephalography responses and visualizing trial by trial changes in discriminant features
Aellen, F. M., Göktepe-Kavis, P., Apostolopoulos, S., & Tzovara, A. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109367 · Cited by 18 · see citing works
Beauty and Uncertainty as Transformative Factors: A Free Energy Principle Account of Aesthetic Diagnosis and Intervention in Gestalt Psychotherapy
Sarasso, P., Francesetti, G., Roubal, J., Gecele, M., Ronga, I., Neppi-Mòdona, M., & Sacco, K. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.906188 · Cited by 20 · see citing works
Standard Tone Stability as a Manipulation of Precision in the Oddball Paradigm: Modulation of Prediction Error Responses to Fixed-Probability Deviants
SanMiguel, I., Costa‐Faidella, J., Lugo, Z., Vilella, E., & Escera, C. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.734200 · Cited by 16 · see citing works
Toward a New Application of Real-Time Electrophysiology: Online Optimization of Cognitive Neurosciences Hypothesis Testing
Sanchez, G., Jean Daunizeau, A., Maby, E., Bertrand, O., Bompas, A., & Mattout, J. (2014) · Brain Sciences · doi:10.3390/brainsci4010049 · Cited by 16 · see citing works
Spectral-temporal EEG dynamics of speech discrimination processing in infants during sleep
Gilley, P. M., Uhler, K. M., Watson, K., & Yoshinaga‐Itano, C. (2017) · BMC Neuroscience · doi:10.1186/s12868-017-0353-4 · Cited by 15 · see citing works
Brain activation during the expectations of sensory experience for cutaneous electrical stimulation
Jung, W.-M., Ryu, Y., Park, H., Lee, H., & Chae, Y. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.06.022 · Cited by 15 · see citing works
Disentangling “Bayesian brain” theories of autism spectrum disorderPreprint
Schneebeli, M., Haker, H., Rüesch, A., Zahnd, N., Marino, S., Paolini, G., Iglesias, S., Petzschner, F. H., & Stephan, K. E. (2022) · medRxiv · doi:10.1101/2022.02.07.22270242 · Cited by 16 · see citing works
Decoding of single-trial auditory mismatch responses for online perceptual monitoring and neurofeedback
Brandmeyer, A., Sadakata, M., Spyrou, L., McQueen, J. M., & Desain, P. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00265 · Cited by 14 · see citing works
Temporal dynamics of repetition suppression to individual faces presented at a fast periodic rate
Nemrodov, D., Jacques, C., & Rossion, B. (2015) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2015.06.006 · Cited by 14 · see citing works
Differential effects of prediction error and adaptation along the auditory cortical hierarchy during deviance processing
Schloßmacher, I., Dilly, J., Protmann, I., Hofmann, D., Dellert, T., Roth-Paysen, M.-L., Moeck, R., Bruchmann, M., & Straube, T. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119445 · Cited by 13 · see citing works
Erroneous predictive coding across brain hierarchies in a non-human primate model of autism spectrum disorder
Chao, Z. C., Komatsu, M., Matsumoto, M., Iijima, K., Nakagaki, K., & Ichinohe, N. (2024) · Communications Biology · doi:10.1038/s42003-024-06545-3 · Cited by 13 · see citing works
Brain signals of a Surprise-Actor-Critic model: Evidence for multiple learning modules in human decision making
Liakoni, V., Lehmann, M. P., Modirshanechi, A., Brea, J., Lutti, A., Gerstner, W., & Preuschoff, K. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118780 · Cited by 14 · see citing works
Does mismatch negativity have utility for NMDA receptor drug development in depression?
Murphy, N., Lijffijt, M., Ramakrishnan, N., Vo-Le, B., Vo‐Le, B., Iqbal, S., Iqbal, T., O’Brien, B., Smith, M. A., Swann, A. C., et al. (2021) · Brazilian Journal of Psychiatry · doi:10.1590/1516-4446-2020-1685 · Cited by 12 · see citing works
Late N1 and postimperative negative variation analysis depending on the previous trial history in paradigms of increasing auditory complexity
Ruiz‐Martínez, F. J., Ortíz, M. M., & Gómez, C. M. (2022) · Journal of Neurophysiology · doi:10.1152/jn.00313.2021 · Cited by 11 · see citing works
Patients with schizophrenia demonstrate reduced cortical sensitivity to auditory oddball regularities
Bridwell, D. A., Kiehl, K. A., Pearlson, G. D., & Calhoun, V. D. (2014) · Schizophrenia Research · doi:10.1016/j.schres.2014.06.037 · Cited by 12 · see citing works
Auditory evoked fields measured noninvasively with small-animal MEG reveal rapid repetition suppression in the guinea pig
Christianson, G. B., Maria Chait, A., Alain De Cheveigné, A., & Linden, J. F. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00189.2014 · Cited by 11 · see citing works
Brain signatures of surprise in EEG and MEG dataPreprint
Mousavi, Z., Kiani, M. M., & Aghajan, H. K. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.06.895664 · Cited by 11 · see citing works
Physical but not virtual presence of others potentiates implicit and explicit learning
Sarasso, P., Ronga, I., Fante, E. D., Barbieri, P., Lozzi, I., Rosaia, N., Cicerale, A., Neppi-Mòdona, M., & Sacco, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-25273-4 · Cited by 11 · see citing works
Examining predictive coding accounts of typical and autistic neurocognitive development
Rapaport, H., & Sowman, P. F. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105905 · Cited by 15 · see citing works
Effects of awareness and task relevance on neurocomputational models of mismatch negativity generation
Schloßmacher, I., Lucka, F. F., Peters, A., Bruchmann, M., & Straube, T. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119530 · Cited by 11 · see citing works
Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response
Poublan‐Couzardot, A., Lecaignard, F., Fucci, E., Davidson, R. J., Mattout, J., Antoine Lutz, A., & Abdoun, O. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010557 · Cited by 11 · see citing works
Shared attention in virtual immersive reality enhances electrophysiological correlates of implicit sensory learning
Sarasso, P., Ronga, I., Piovesan, F., Barbieri, P., Fante, E. D., Luca, D. D., Bechis, L., Osello, A., & Sacco, K. (2024) · Scientific Reports · doi:10.1038/s41598-024-53937-w · Cited by 11 · see citing works
EEG artifact correction strategies for online trial-by-trial analysis
de Freitas, A. M., Sanchez, G., Lecaignard, F., Maby, E., Soares, A. B., & Mattout, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab581d · Cited by 11 · see citing works
Initial Uncertainty Impacts Statistical Learning in Sound Sequence Processing
Todd, J., Provost, A., Whitson, L., & Mullens, D. (2018) · Neuroscience · doi:10.1016/j.neuroscience.2018.05.011 · Cited by 10 · see citing works
Beyond the Adult Mind: A Developmental Framework for Predictive Processing in Infancy
Ward, E. K., Rutar, D., Zaadnoordijk, L., Poli, F., & Hunnius, S. (2025) · Topics in Cognitive Science · doi:10.1111/tops.70028 · Cited by 8 · see citing works
Bayesian Model Selection Maps for Group Studies Using M/EEG Data
Harris, C. D., Rowe, E., Randeniya, R., & Garrido, M. I. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00598 · Cited by 9 · see citing works
A neural network model for exogenous perceptual alternations of the Necker cube
Araki, O., Tsuruoka, Y., & Urakawa, T. (2019) · Cognitive Neurodynamics · doi:10.1007/s11571-019-09565-6 · Cited by 10 · see citing works
Decoding violated sensory expectations from the auditory cortex of anaesthetised mice: Hierarchical recurrent neural network depicts separate ‘danger’ and ‘safety’ units
O’Reilly, J. A., Angsuwatanakul, T., & Wehrman, J. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15736 · Cited by 9 · see citing works
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Authors: Lieder, F., Daunizeau, J., Garrido, M. I., Friston, K. J., & Stephan, K. E.
Affiliations: University of Zurich (CH)
The University of Queensland (AU)
Wellcome Centre for Human Neuroimaging (GB)
Corresponding author: Lieder, Falk
ORCID: Lieder, Falk
Garrido, Marta I.
Friston, Karl J.
Stephan, Klaas E.
Venue: PLoS Computational Biology
Publisher: Public Library of Science (PLoS)
Published: 2013-02-21
Volume: 9
Issue: 2
Pages: e1002911
ISSN: 1553-734X (ISSN-L)
1553-7358
DOI: 10.1371/journal.pcbi.1002911
PMC: PMC3578779
OpenAlex: W1975232326
Cited by: 168
References: 87
Citations by year: 2026: 11 · 2025: 11 · 2024: 17 · 2023: 11 · 2022: 21 · 2021: 16 · 2020: 13 · 2019: 11 · 2018: 11 · 2017: 7 · 2016: 10 · 2015: 6 · 2014: 14 · 2013: 9
FWCI: 7.10
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS Computational Biology (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
UCL Discovery (University College London) (submittedVersion)
Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
ETH Zürich Research Collection (cc-by)
Zurich Open Repository and Archive (University of Zurich)
Funders: Wellcome Trust (091593, 088130)
SystemsX.ch
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience and Music Perception
Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
Keywords: mismatch negativity, predictive coding, model modification, novelty detection, free energy principle, approximate Bayesian inference, statistical learning, prediction error, single-trial EEG, change detection, sensory adaptation
MeSH terms: Acoustic Stimulation, Adult, Bayes Theorem, Electroencephalography, Evoked Potentials, Auditory, Female, Humans, Learning, Male, Models, Neurological, Neurons, Signal Processing, Computer-Assisted, Models, Statistical

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