What controls gain in gain control? Mismatch negativity (MMN), priors and system biases

Todd, Juanita; Heathcote, Andrew; Mullens, Daniel; Whitson, Lisa R.; Provost, Alexander; Winkler, István

doi:10.1007/s10548-013-0344-4

Cited by 36 publications

(45 citation statements)

References 52 publications

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“…However, context effects on the MMN or adaptation can be experimentally studied by manipulating different sources of expectation, which may depend on dissociable neural mechanisms. For example, Todd et al (39,40) found that, when the identities of the standard and deviants were switched during the experiment, the MMN for the initial order was larger than for the subsequent order, especially when longer sequences were played before the switch. This "primacy effect" suggests a long-term memory of the initial standard.…”

Section: Discussionmentioning

confidence: 99%

Hierarchy of prediction errors for auditory events in human temporal and frontal cortex

Dürschmid

Edwards

Reichert

et al. 2016

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

139

160

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Predictive coding theories posit that neural networks learn statistical regularities in the environment for comparison with actual outcomes, signaling a prediction error (PE) when sensory deviation occurs. PE studies in audition have capitalized on low-frequency event-related potentials (LF-ERPs), such as the mismatch negativity. However, local cortical activity is well-indexed by higher-frequency bands [high-γ band (Hγ): 80-150 Hz]. We compared patterns of human Hγ and LF-ERPs in deviance detection using electrocorticographic recordings from subdural electrodes over frontal and temporal cortices. Patients listened to trains of task-irrelevant tones in two conditions differing in the predictability of a deviation from repetitive background stimuli (fully predictable vs. unpredictable deviants). We found deviance-related responses in both frequency bands over lateral temporal and inferior frontal cortex, with an earlier latency for Hγ than for LF-ERPs. Critically, frontal Hγ activity but not LF-ERPs discriminated between fully predictable and unpredictable changes, with frontal cortex sensitive to unpredictable events. The results highlight the role of frontal cortex and Hγ activity in deviance detection and PE generation.predictive coding | prediction error | mismatch negativity | frontal cortex | high γ-activity

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Section: Discussionmentioning

confidence: 99%

Hierarchy of prediction errors for auditory events in human temporal and frontal cortex

Dürschmid

Edwards

Reichert

et al. 2016

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

139

160

View full text Add to dashboard Cite

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“…Hence, being able to dissociate different types of uncertainty enables inference of new (higher order) causes in environment. Note that, apart from these types of explicit learning tasks, the mismatch negativity signal could also be exploited to infer the learning of these types of regularities from the amplitude (gain) of the mismatch responses (for an example in a typical population, see Todd et al, 2014).…”

Section: Learning In Unstable Environmentsmentioning

confidence: 99%

Disentangling signal and noise in autism spectrum disorder

Cruys

Hallen

Wagemans

2017

Brain and Cognition

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a b s t r a c tPredictive coding has recently been welcomed as a fruitful framework to understand autism spectrum disorder. Starting from an account centered on deficient differential weighting of prediction errors (based in so-called precision estimation), we illustrate that individuals with autism have particular difficulties with separating signal from noise, across different tasks. Specifically, we discuss how deficient precision-setting is detrimental for learning in unstable environments, for context-dependent assignment of salience to inputs, and for robustness in perception, as illustrated in coherent motion paradigms.

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“…The results of several studies employing duration as the deviating feature have revealed that the assumption that higher stability of the standard/deviant configuration leads to higher MMN amplitudes is only partially correct (Todd et al, 2011, 2013a,b; Mullens et al, 2014). MMN elicited to the sound first encountered as the devian t (that is, the sound that is rare when the sequence begins; hereafter called first deviant ) is indeed larger in slow than faster changing sequences.…”

Section: Introductionmentioning

confidence: 99%

“…In a closer inspection of the phenomenon, Todd et al (2013b) split the data from Todd et al (2013a) into halves for each block of the slow and fast alternating sequences to explore how MMN amplitude changed from the beginning to end of blocks making up each sequence. Based on the assumption that confidence/precision affects the MMN amplitude, it should increase across the duration of a block as confidence builds up with the increased stability in the pattern.…”

Section: Introductionmentioning

confidence: 99%

“…That is, the MMN amplitude pattern found for the slow alternation sequences reversed in the fast alternation sequences. This means that the differential sequence effect on MMN amplitude, which defines primacy bias (slow MMN > fast MMN for the first deviant and slow MMN = fast MMN for the second deviant) was driven by differences in MMN amplitude at transition points in the sequence when the roles of the sounds had recently switched (Todd et al, 2013b). …”

Section: Introductionmentioning

confidence: 99%

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Mismatch negativity (MMN) to pitch change is susceptible to order-dependent bias

et al. 2014

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Pattern learning facilitates prediction about upcoming events. Within the auditory system such predictions can be studied by examining effects on a component of the auditory-evoked potential known as mismatch negativity (MMN). MMN is elicited when sound does not conform to the characteristics inferred from statistical probabilities derived from the recent past. Stable patterning in sequences elevates confidence in automatically generated perceptual inferences about what sound should come next and when. MMN amplitude should be larger when sequence is highly stable compared to when it is more volatile. This expectation has been tested using a multi-timescale paradigm. In this study, two sounds of different duration alternate roles as a predictable repetitive “standard” and rare MMN-eliciting “deviation.” The paradigm consists of sound sequences that differ in the rate at which the roles of two tones alternate, varying from slowly changing (high stability) to rapidly alternating (low stability). Previous studies using this paradigm discovered a “primacy bias” affecting how stability in patterning impacts MMN amplitude. The primacy bias refers to the observation that the effect of longer-term stability within sequences only appears to impact MMN to the sound first encountered as deviant (the sound that is rare when the sequence commences). This study determines whether this order-driven bias generalizes to sequences that contain two tones differing in pitch. By manipulating (within-subjects) the order in which sounds are encountered as deviants the data demonstrate the two defining characteristics of primacy bias: (1) sequence stability only ever impacts MMN amplitude to the first-deviant sound; and (2) within higher stability sequences, MMN is significantly larger when a sound is the first compared to when it is the second deviant. The results are consistent with a general order-driven bias exerting modulating effects on MMN amplitude over a longer timescale.

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What controls gain in gain control? Mismatch negativity (MMN), priors and system biases

Cited by 36 publications

References 52 publications

Hierarchy of prediction errors for auditory events in human temporal and frontal cortex

Hierarchy of prediction errors for auditory events in human temporal and frontal cortex

Disentangling signal and noise in autism spectrum disorder

Mismatch negativity (MMN) to pitch change is susceptible to order-dependent bias

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