Brain Signals Related to Change Detection

2017 ◽  
pp. 262-276
Author(s):  
Riitta Hari ◽  
Aina Puce
Keyword(s):  
Change Detection ◽  
Mismatch Negativity ◽  
Predictive Coding ◽  
Evoked Responses ◽  
Stimulus Probability ◽  
Error Related Negativity ◽  
Brain Signals ◽  
Low Probability

This chapter discusses, in the context of the predictive-coding framework, evoked responses to various changes in the environment and describes how the responses are related to variations in stimulus probability and the subject’s expectations. The focus is on three well-known responses: (a) the mismatch negativity peaking at 100 to 250 ms and elicited to changes in stimulus attributes, even when the stimuli are not attended to, (b) the P300 response peaking about 300 ms after attended low-probability “oddball” stimuli, and (c) the N400 peaking about 400 ms after semantic or lexical violations of sentences presented either visually or auditorily. Continent negative variation and error-related negativity are introduced as well.

scholarly journals Standard Tone Stability as a Manipulation of Precision in the Oddball Paradigm: Modulation of Prediction Error Responses to Fixed-Probability Deviants

2021 ◽  
Vol 15 ◽  
Author(s):  
Iria SanMiguel ◽  
Jordi Costa-Faidella ◽  
Zulay R. Lugo ◽  
Elisabet Vilella ◽  
Carles Escera
Keyword(s):  
Correlation Analysis ◽  
Mismatch Negativity ◽  
Prediction Error ◽  
Pure Tone ◽  
Predictive Coding ◽  
Multiple Comparisons ◽  
Oddball Paradigm ◽  
High Confidence ◽  
Low Probability ◽  
Electroencephalogram Eeg

Electrophysiological sensory deviance detection signals, such as the mismatch negativity (MMN), have been interpreted from the predictive coding framework as manifestations of prediction error (PE). From a frequentist perspective of the classic oddball paradigm, deviant stimuli are unexpected because of their low probability. However, the amount of PE elicited by a stimulus can be dissociated from its probability of occurrence: when the observer cannot make confident predictions, any event holds little surprise value, no matter how improbable. Here we tested the hypothesis that the magnitude of the neural response elicited to an improbable sound (D) would scale with the precision of the prediction derived from the repetition of another sound (S), by manipulating repetition stability. We recorded the Electroencephalogram (EEG) from 20 participants while passively listening to 4 types of isochronous pure tone sequences differing in the probability of the S tone (880 Hz) while holding constant the probability of the D tone [1,046 Hz; p(D) = 1/11]: Oddball [p(S) = 10/11]; High confidence (7/11); Low confidence (4/11); and Random (1/11). Tones of 9 different frequencies were equiprobably presented as fillers [p(S) + p(D) + p(F) = 1]. Using a mass-univariate non-parametric, cluster-based correlation analysis controlling for multiple comparisons, we found that the amplitude of the deviant-elicited ERP became more negative with increasing S probability, in a time-electrode window consistent with the MMN (ca. 120–200 ms; frontal), suggesting that the strength of a PE elicited to an improbable event indeed increases with the precision of the predictive model.

scholarly journals Dynamic Causal Modeling of the Response to Frequency Deviants

2009 ◽  
Vol 101 (5) ◽  
pp. 2620-2631 ◽  
Author(s):  
Marta I. Garrido ◽  
James M. Kilner ◽  
Stefan J. Kiebel ◽  
Karl J. Friston
Keyword(s):  
Mismatch Negativity ◽  
Sensory Input ◽  
Model Comparison ◽  
Memory Trace ◽  
Effective Connectivity ◽  
Predictive Coding ◽  
Causal Modeling ◽  
Evoked Responses ◽  
Dynamic Causal Modeling ◽  
The Brain

This article describes the use of dynamic causal modeling to test hypotheses about the genesis of evoked responses. Specifically, we consider the mismatch negativity (MMN), a well-characterized response to deviant sounds and one of the most widely studied evoked responses. There have been several mechanistic accounts of how the MMN might arise. It has been suggested that the MMN results from a comparison between sensory input and a memory trace of previous input, although others have argued that local adaptation, due to stimulus repetition, is sufficient to explain the MMN. Thus the precise mechanisms underlying the generation of the MMN remain unclear. This study tests some biologically plausible spatiotemporal dipole models that rest on changes in extrinsic top-down connections (that enable comparison) and intrinsic changes (that model adaptation). Dynamic causal modeling suggested that responses to deviants are best explained by changes in effective connectivity both within and between cortical sources in a hierarchical network of distributed sources. Our model comparison suggests that both adaptation and memory comparison operate in concert to produce the early (N1 enhancement) and late (MMN) parts of the response to frequency deviants. We consider these mechanisms in the light of predictive coding and hierarchical inference in the brain.

scholarly journals A Predictive Coding Perspective on Mismatch Negativity Impairment in Schizophrenia

2020 ◽  
Vol 11 ◽  
Author(s):  
Kenji Kirihara ◽  
Mariko Tada ◽  
Daisuke Koshiyama ◽  
Mao Fujioka ◽  
Kaori Usui ◽  
...  
Keyword(s):  
Mismatch Negativity ◽  
Predictive Coding

Effects of sleep deprivation on auditory change detection: a N1-Mismatch Negativity study

2011 ◽  
Vol 81 (3) ◽  
pp. 312-316 ◽  
Author(s):  
Marta Bortoletto ◽  
Giuliano De Min Tona ◽  
Simona Scozzari ◽  
Simone Sarasso ◽  
Luciano Stegagno
Keyword(s):  
Sleep Deprivation ◽  
Change Detection ◽  
Mismatch Negativity ◽  
Auditory Change Detection

scholarly journals Short- and long-term habituation of auditory event-related potentials in the rat

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 182
Author(s):  
Kestutis Gurevicius ◽  
Arto Lipponen ◽  
Rimante Minkeviciene ◽  
Heikki Tanila
Keyword(s):  
Mismatch Negativity ◽  
Standard Stimulus ◽  
Specific Effect ◽  
Event Related Potentials ◽  
Auditory Evoked Potential ◽  
Oddball Paradigm ◽  
Evoked Responses ◽  
Auditory Oddball ◽  
Long Duration ◽  
Related Potentials

An auditory oddball paradigm in humans generates a long-duration cortical negative potential, often referred to as mismatch negativity. Similar negativity has been documented in monkeys and cats, but it is controversial whether mismatch negativity also exists in awake rodents. To this end, we recorded cortical and hippocampal evoked responses in rats during alert immobility under a typical passive oddball paradigm that yields mismatch negativity in humans. The standard stimulus was a 9 kHz tone and the deviant either 7 or 11 kHz tone in the first condition. We found no evidence of a sustained potential shift when comparing evoked responses to standard and deviant stimuli. Instead, we found repetition-induced attenuation of the P60 component of the combined evoked response in the cortex, but not in the hippocampus. The attenuation extended over three days of recording and disappeared after 20 intervening days of rest. Reversal of the standard and deviant tones resulted is a robust enhancement of the N40 component not only in the cortex but also in the hippocampus. Responses to standard and deviant stimuli were affected similarly. Finally, we tested the effect of scopolamine in this paradigm. Scopolamine attenuated cortical N40 and P60 as well as hippocampal P60 components, but had no specific effect on the deviant response. We conclude that in an oddball paradigm the rat demonstrates repetition-induced attenuation of mid-latency responses, which resembles attenuation of the N1-component of human auditory evoked potential, but no mismatch negativity.

scholarly journals Can expectation suppression be explained by reduced attention to predictable stimuli?

2020 ◽  
Author(s):  
Arjen Alink ◽  
Helen Blank
Keyword(s):  
Predictive Coding ◽  
Additional Research ◽  
Prediction Errors ◽  
Evoked Responses ◽  
Suppression Effect ◽  
Attention Effect ◽  
Neural Representations ◽  
Stimulus Features

AbstractThe expectation-suppression effect – reduced stimulus-evoked responses to expected stimuli – is widely considered to be an empirical hallmark of reduced prediction errors in the framework of predictive coding. Here we challenge this notion by proposing that this phenomenon can also be explained by a reduced attention effect. Specifically, we argue that reduced responses to predictable stimuli can also be explained by a reduced saliency-driven allocation of attention. To resolve whether expectation suppression is best explained by attention or predictive coding, additional research is needed to determine whether attention effects precede the encoding of expectation violations (or vice versa) and to reveal how expectations change neural representations of stimulus features.

scholarly journals Weighting of neural prediction error by rhythmic complexity: A predictive coding account using mismatch negativity

2019 ◽  
Vol 49 (12) ◽  
pp. 1597-1609 ◽  
Author(s):  
Massimo Lumaca ◽  
Niels Trusbak Haumann ◽  
Elvira Brattico ◽  
Manon Grube ◽  
Peter Vuust
Keyword(s):  
Mismatch Negativity ◽  
Prediction Error ◽  
Predictive Coding

Mismatch Negativity and Stimulus-Specific Adaptation in Animal Models

2007 ◽  
Vol 21 (3-4) ◽  
pp. 214-223 ◽  
Author(s):  
Israel Nelken ◽  
Nachum Ulanovsky
Keyword(s):  
Animal Models ◽  
Auditory Cortex ◽  
Change Detection ◽  
Neural Activity ◽  
Mismatch Negativity ◽  
Single Neuron ◽  
Neural Processing ◽  
Sensory Coding ◽  
Methodological Issues ◽  
Specific Adaptation

Animal models of MMN may serve both to further our understanding of neural processing beyond pure sensory coding and for unraveling the neural and pharmacological processes involved in the generation of MMN. We start this review by discussing the methodological issues that are especially important when pursuing a single-neuron correlate of MMN. Correlates of MMN have been studied in mice, rats, cats, and primates. Whereas essentially all of these studies demonstrated the presence of stimulus-specific adaptation, in the sense that responses to deviant tones are larger than the responses to standard tones, the presence of real MMN has been established only in a few. We argue for the use of more and better controls in order to clarify the situation. Finally, we discuss in detail the relationships between stimulus-specific adaptation of single-neuron responses, as established in the cat auditory cortex, and MMN. We argue that this is currently the only fully established correlate of true change detection, and hypothesize that it precedes and probably induces the neural activity that is eventually measured as MMN.

The Frontal Generator of the Mismatch Negativity Revisited

2007 ◽  
Vol 21 (3-4) ◽  
pp. 188-203 ◽  
Author(s):  
Leon Y. Deouell
Keyword(s):  
Change Detection ◽  
Mismatch Negativity ◽  
Current Source ◽  
Rare Event ◽  
Attention Shift ◽  
Source Imaging ◽  
Consistent Finding ◽  
Acoustic Environment ◽  
Eeg Source Imaging ◽  
Adopted Model

The mismatch negativity (MMN) is an event-related brain potential elicited by the occurrence of a rare event (deviance) in an otherwise regular acoustic environment, and is assumed to reflect a preattentive mechanism for change detection. A widely adopted model holds that MMN has main generators in the superior temporal planes bilaterally, which are responsible for the sensory memory part of change detection, as well as frontal lobe sources responsible for triggering an attention shift upon change detection. Whereas the temporal sources have been documented in numerous studies across species and methodologies, much less is known about the frontal sources. The present review examines the current state of the evidence for their existence, location, and possible function. It confirms that the frontal generator is still a less consistent finding in MMN research than the temporal generator. There is clear evidence from scalp EEG and, especially, current source density studies for the existence of an MMN generator that is functionally distinct from the main supratemporal generator of the MMN. Evidence from fMRI, PET, optical imaging, EEG source imaging, and lesion studies implicates mainly the inferior frontal and possibly also the medial frontal cortex. However, these results should be taken with caution because of the paucity of support from more direct measures like intracranial recordings and MEG, and the negative findings from several fMRI and PET, as well as EEG source imaging studies. Recent studies also raise questions about the exact role of the frontal generator in triggering an attention shift. Delineating the exact cortical locations of frontal MMN generators, the conditions under which they are activated and, consequently, their function, remains an acute challenge.

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