Context is everything: How context shapes modulations of responses to unattended sound

Todd, Juanita; Frost, Jade D.; Yeark, Mattsen; Paton, Bryan

doi:10.1016/j.heares.2020.107975

Cited by 7 publications

(8 citation statements)

References 54 publications

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“…An important aspect that computational models of perception have put forward is the influence of the acoustic context onto sound processing in oddball paradigms, that is, as we shall see, explicitly formalized in popular predictive coding implementation ( Friston, 2005 ; Spratling, 2016 ). Interestingly, this is in line with recent MMN findings which emphasized the importance of the ordering of experimental conditions ( Fitzgerald and Todd, 2020 ; Todd et al, 2021 ), pointing out the need for refining our understanding of mismatch responses.…”

Section: Introductionsupporting

confidence: 87%

“…As a consequence, averaging methods like traditional event-related potential (ERP) approaches will likely be unable to reveal the contribution of their respective dynamics onto brain activity, even if these dynamics become separable due to a predictability manipulation. To circumvent this issue, attempts have been made that consisted of comparing the mismatch responses obtained in the beginning and end of oddball sequences ( Fitzgerald and Todd, 2018 ; Todd et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals

Lecaignard

Bertrand

Brunner

et al. 2022

Front. Hum. Neurosci.

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Recent computational models of perception conceptualize auditory oddball responses as signatures of a (Bayesian) learning process, in line with the influential view of the mismatch negativity (MMN) as a prediction error signal. Novel MMN experimental paradigms have put an emphasis on neurophysiological effects of manipulating regularity and predictability in sound sequences. This raises the question of the contextual adaptation of the learning process itself, which on the computational side speaks to the mechanisms of gain-modulated (or precision-weighted) prediction error. In this study using electrocorticographic (ECoG) signals, we manipulated the predictability of oddball sound sequences with two objectives: (i) Uncovering the computational process underlying trial-by-trial variations of the cortical responses. The fluctuations between trials, generally ignored by approaches based on averaged evoked responses, should reflect the learning involved. We used a general linear model (GLM) and Bayesian Model Reduction (BMR) to assess the respective contributions of experimental manipulations and learning mechanisms under probabilistic assumptions. (ii) To validate and expand on previous findings regarding the effect of changes in predictability using simultaneous EEG-MEG recordings. Our trial-by-trial analysis revealed only a few stimulus-responsive sensors but the measured effects appear to be consistent over subjects in both time and space. In time, they occur at the typical latency of the MMN (between 100 and 250 ms post-stimulus). In space, we found a dissociation between time-independent effects in more anterior temporal locations and time-dependent (learning) effects in more posterior locations. However, we could not observe any clear and reliable effect of our manipulation of predictability modulation onto the above learning process. Overall, these findings clearly demonstrate the potential of trial-to-trial modeling to unravel perceptual learning processes and their neurophysiological counterparts.

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Section: Introductionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals

Lecaignard

Bertrand

Brunner

et al. 2022

Front. Hum. Neurosci.

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“…Should the observed effect be confidently attributed to tone order, there follows the question of to what extent it generalizes across sequence structures, tone types, and deviations. As noted, Figure 2A is derived from sequences in which the two sounds differ in duration (30 and 60 ms) and the same pattern of MMN amplitude modulation is obtained for the two block types whether the long tone or the short tone is rare in the first context (i.e., it is order-dependent not feature-dependent [(15), see also (210)]. Certainly, there is also evidence that similar modulation patterns can be observed using frequency deviants (7) and spatial deviants (208) offering support to the notion that it is a general order-driven effect.…”

Section: Experience Matters: An Order-driven Effectmentioning

confidence: 82%

Making Sense of Mismatch Negativity

2020

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Evoked potentials provide valuable insight into brain processes that are integral to our ability to interact effectively and efficiently in the world. The mismatch negativity (MMN) component of the evoked potential has proven highly informative on the ways in which sensitivity to regularity contributes to perception and cognition. This review offers a compendium of research on MMN with a view to scaffolding an appreciation for its use as a tool to explore the way regularities contribute to predictions about the sensory environment over many timescales. In compiling this work, interest in MMN as an index of sensory encoding and memory are addressed, as well as attention. Perspectives on the possible underlying computational processes are reviewed as well as recent observations that invite consideration of how MMN relates to how we learn, what we learn, and why.

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“…In the alternating contexts experiment (Experiment 3), another group of newborns were presented with the same tones as those used in the control contexts, but organized into an alternating sequence, switching back and forth between periods of context 1 and 2 modelled on sequences used in adults 41 , 49 , 50 (see Fig. 1 ).…”

Section: Resultsmentioning

confidence: 99%

“…The study was designed to determine whether the differential precision-weighting based on large scale structure seen in adults 41 – 44 might also be observed in neonates. Here we provide evidence that the brain of newborn infants can indeed form internal models that differentiate sounds based on probability, and weight that response based on precision, but that the propensity to do this depends on contextual factors that may be related to a determination of the inferred value of tracking information over time.…”

Section: Introductionmentioning

confidence: 99%

Relevance to the higher order structure may govern auditory statistical learning in neonates

Todd

Háden

Winkler

2022

Sci Rep

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Hearing is one of the earliest senses to develop and is quite mature by birth. Contemporary theories assume that regularities in sound are exploited by the brain to create internal models of the environment. Through statistical learning, internal models extrapolate from patterns to predictions about subsequent experience. In adults, altered brain responses to sound enable us to infer the existence and properties of these models. In this study, brain potentials were used to determine whether newborns exhibit context-dependent modulations of a brain response that can be used to infer the existence and properties of internal models. Results are indicative of significant context-dependence in the responsivity to sound in newborns. When common and rare sounds continue in stable probabilities over a very long period, neonates respond to all sounds equivalently (no differentiation). However, when the same common and rare sounds at the same probabilities alternate over time, the neonate responses show clear differentiations. The context-dependence is consistent with the possibility that the neonate brain produces more precise internal models that discriminate between contexts when there is an emergent structure to be discovered but appears to adopt broader models when discrimination delivers little or no additional information about the environment.

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Context is everything: How context shapes modulations of responses to unattended sound

Cited by 7 publications

References 54 publications

Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals

Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals

Making Sense of Mismatch Negativity

Relevance to the higher order structure may govern auditory statistical learning in neonates

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