Temporal window of integration revealed by MMN to sound omission

Yabe, Hirooki; Tervaniemi, Mari; Reinikainen, K.; Näätänen, Risto

doi:10.1097/00001756-199705260-00035

Cited by 265 publications

(215 citation statements)

References 7 publications

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“…Our results confirm earlier findings that the omission of an expected tone leads to a time-locked brain response, which is easily detectable by MEG and EEG and has a topography similar to the original evoked response (16)(17)(18). Furthermore, our design tested the prediction, unique to the hierarchical predictive coding framework, that the omission response should vary with the context.…”

Section: Discussionsupporting

confidence: 88%

“…It is a rather remarkable fact that the auditory cortex generates extensive responses locked to the absence of a predictable sound. This omission response can be detected by a variety of methods, including event-related potentials (ERPs) (16), magnetoencephalography (MEG) (17), and intracranial recordings (18). Omission responses fit quite naturally within the predictive coding framework: If stimulus-evoked brain activity indexes the difference between a sensory signal and its top-down prediction, then, when the sensory signal is omitted, the evoked activity should reflect the pure prediction signal within the same cortical area (8,19,20).…”

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confidence: 99%

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Evidence for a hierarchy of predictions and prediction errors in human cortex

Wacongne

Labyt

Wassenhove

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

486

543

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According to hierarchical predictive coding models, the cortex constantly generates predictions of incoming stimuli at multiple levels of processing. Responses to auditory mismatches and omissions are interpreted as reflecting the prediction error when these predictions are violated. An alternative interpretation, however, is that neurons passively adapt to repeated stimuli. We separated these alternative interpretations by designing a hierarchical auditory novelty paradigm and recording human EEG and magnetoencephalographic (MEG) responses to mismatching or omitted stimuli. In the crucial condition, participants listened to frequent series of four identical tones followed by a fifth different tone, which generates a mismatch response. Because this response itself is frequent and expected, the hierarchical predictive coding hypothesis suggests that it should be cancelled out by a higher-order prediction. Three consequences ensue. First, the mismatch response should be larger when it is unexpected than when it is expected. Second, a perfectly monotonic sequence of five identical tones should now elicit a higher-order novelty response. Third, omitting the fifth tone should reveal the brain's hierarchical predictions. The rationale here is that, when a deviant tone is expected, its omission represents a violation of two expectations: a local prediction of a tone plus a hierarchically higher expectation of its deviancy. Thus, such an omission should induce a greater prediction error than when a standard tone is expected. Simultaneous EEE-magnetoencephalographic recordings verify those predictions and thus strongly support the predictive coding hypothesis. Higher-order predictions appear to be generated in multiple areas of frontal and associative cortices. mismatch negativity | P300 component

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Section: Discussionsupporting

confidence: 88%

mentioning

confidence: 99%

Evidence for a hierarchy of predictions and prediction errors in human cortex

Wacongne

Labyt

Wassenhove

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

486

543

View full text Add to dashboard Cite

show abstract

“…Critically, this and other previous studies using tone omissions [e.g., Yabe et al (1997Yabe et al ( , 1998Yabe et al ( , 2001, Shinozaki et al (2003), and Winkler et al (2005)] did not manipulate the sequential predictability of the omitted tone. The present study reveals that initial processing similarities are only observed with full predictability of the omitted tone, but not with partial predictability as given in the restorable and control conditions.…”

Section: Discussionmentioning

confidence: 47%

“…Note that the current stimulus paradigm and analyses were not optimized toward testing the omission-related MMN, and that the predictability effect was found substantially earlier than the expected latency range of the omission-related MMN [100 -150 ms from the onset of the expected tone (cf. Yabe et al, 1997Yabe et al, , 1998…”

Section: Discussionmentioning

confidence: 99%

I Heard That Coming: Event-Related Potential Evidence for Stimulus-Driven Prediction in the Auditory System

Bendixen

Schröger

Winkler³

2009

Journal of Neuroscience

193

154

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The auditory system has been shown to detect predictability in a tone sequence, but does it use the extracted regularities for actually predicting the continuation of the sequence? The present study sought to find evidence for the generation of such predictions. Predictability was manipulated in an isochronous series of tones in which every other tone was a repetition of its predecessor. The existence of predictions was probed by occasionally omitting either the first (unpredictable) or the second (predictable) tone of a same-frequency tone pair. Event-related electrical brain activity elicited by the omission of an unpredictable tone differed from the response to the actual tone right from the tone onset. In contrast, early electrical brain activity elicited by the omission of a predictable tone was quite similar to the response to the actual tone. This suggests that the auditory system preactivates the neural circuits for expected input, using sequential predictions to specifically prepare for future acoustic events.

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“…One of the most remarkable properties of the auditory system is that it can generate evoked responses to an absent but expected stimulus (Joutsiniemi and Hari, 1989;Raij et al, 1997;Yabe et al, 1997;Hughes et al, 2001;Todorovic et al, 2011;Wacongne et al, 2011). We similarly tested the response of our network to the omission of an expected sound.…”

Section: Mmn To Omissionmentioning

confidence: 99%

A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity

Wacongne¹,

Changeux²,

Dehaene³

2012

J. Neurosci.

487

423

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The mismatch negativity (MMN) is thought to index the activation of specialized neural networks for active prediction and deviance detection. However, a detailed neuronal model of the neurobiological mechanisms underlying the MMN is still lacking, and its computational foundations remain debated. We propose here a detailed neuronal model of auditory cortex, based on predictive coding, that accounts for the critical features of MMN. The model is entirely composed of spiking excitatory and inhibitory neurons interconnected in a layered cortical architecture with distinct input, predictive, and prediction error units. A spike-timing dependent learning rule, relying upon NMDA receptor synaptic transmission, allows the network to adjust its internal predictions and use a memory of the recent past inputs to anticipate on future stimuli based on transition statistics. We demonstrate that this simple architecture can account for the major empirical properties of the MMN. These include a frequency-dependent response to rare deviants, a response to unexpected repeats in alternating sequences (ABABAA. . . ), a lack of consideration of the global sequence context, a response to sound omission, and a sensitivity of the MMN to NMDA receptor antagonists. Novel predictions are presented, and a new magnetoencephalography experiment in healthy human subjects is presented that validates our key hypothesis: the MMN results from active cortical prediction rather than passive synaptic habituation.

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Temporal window of integration revealed by MMN to sound omission

Cited by 265 publications

References 7 publications

Evidence for a hierarchy of predictions and prediction errors in human cortex

Evidence for a hierarchy of predictions and prediction errors in human cortex

I Heard That Coming: Event-Related Potential Evidence for Stimulus-Driven Prediction in the Auditory System

A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity

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