Musical prediction error responses similarly reduced by predictive uncertainty in musicians and non-musicians

Quiroga-Martinez, David Ricardo; Hansen, Niels Chr.; Højlund, Andreas; Pearce, Marcus T.; Brattico, Elvira; Vuust, Peter

doi:10.1101/754333

Cited by 3 publications

(4 citation statements)

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“…The latency of the MMNm was significantly longer (t = 35.68, p < .001) and its amplitude was significantly larger (t = 8.37, p < .001) than those of the N1m, in the mean and peak amplitude analyses. Moreover, expertise effects were found for the MMNm (p < .001), thus reproducing in the magnetometer space the differences previously reported for combined gradiometers (Quiroga-Martinez et al, 2019a). Regarding source analyses, one-sample t-tests suggested that the main generators of both the MMNm and the N1m were located in the surroundings of primary auditory cortex, as expected (Figure 9).…”

Section: Comparison Between N1m and Mmnmsupporting

confidence: 87%

“…The stimuli were delivered in three blocks lasting around seven minutes each and were part of a longer experimental session that included other conditions addressing questions beyond the scope of this study. Data from these conditions have been (Quiroga-Martinez et al, 2019a, 2019b and will be published elsewhere. Individual tones were created using a piano sample and had a duration of 250 ms.…”

Section: Stimulimentioning

confidence: 99%

“…The preprocessing steps for the measurement of the MMNm were the same as for the N1m, with the difference that, instead of surprise levels, an ERF was computed for only two conditions: standards and deviants. The MMNm is the difference between these two conditions, which was assessed here for magnetometers through paired-samples t-tests in mass-univariate analyses (note that statistical analyses for the MMNm had previously been conducted for gradiometers in Quiroga-Martinez et al 2019a, 2019b. The deviants analyzed here correspond to out-of-tune tones (i.e., tones with pitches outside the musical tuning system that cannot normally be played on a piano).…”

Section: Comparison With the Mmnmmentioning

confidence: 99%

“…Second, we compared-in sensor and source space-the N1m modulation with the magnetic counterpart of the mismatch negativity (MMNm), which is a well-studied brain response to the violation of auditory regularities (Bendixen, SanMiguel, & Schröger, 2012;Garrido, Kilner, Stephan, & Friston, 2009;Näätänen, Gaillard, & Mäntysalo, 1978;Näätänen, Paavilainen, Rinne, & Alho, 2007). Since our dataset was recorded employing a novel MMN experimental paradigm with realistic non-repetitive melodies as stimuli (Quiroga-Martinez et al, 2019b, 2019a, it provided a valuable opportunity to compare these responses in the same subjects. This comparison aimed to determine whether the N1m modulation could be better interpreted as an MMNm, something that was not clear from the results in Omigie et al, (2013).…”

Section: Introductionmentioning

confidence: 99%

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Decomposing neural responses to melodic surprise in musicians and non-musicians: evidence for a hierarchy of predictions in the auditory system

Quiroga-Martinez¹,

Hansen

Hoejlund

et al. 2019

Preprint

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AbstractNeural responses to auditory surprise are typically studied with highly unexpected, disruptive sounds. Consequently, little is known about auditory prediction in everyday contexts that are characterized by fine-grained, non-disruptive fluctuations of auditory surprise. To address this issue, we used IDyOM, a computational model of auditory expectation, to obtain continuous surprise estimates for a set of newly composed melodies. Our main goal was to assess whether the neural correlates of non-disruptive surprising sounds in a musical context are affected by musical expertise. Using magnetoencephalography (MEG), auditory responses were recorded from musicians and non-musicians while they listened to the melodies. Consistent with a previous study, the amplitude of the N1m component increased with higher levels of computationally estimated surprise. This effect, however, was not different between the two groups. Further analyses offered an explanation for this finding: Pitch interval size itself, rather than probabilistic prediction, was responsible for the modulation of the N1m, thus pointing to low-level sensory adaptation as the underlying mechanism. In turn, the formation of auditory regularities and proper probabilistic prediction were reflected in later components: the mismatch negativity (MMNm) and the P3am, respectively. Overall, our findings reveal a hierarchy of expectations in the auditory system and highlight the need to properly account for sensory adaptation in research addressing statistical learning.Highlights- In melodies, sound expectedness (modeled with IDyOM) is associated with the amplitude of the N1m.- This effect is not different between musicians and non-musicians.- Sensory adaptation related to melodic pitch intervals explains better the N1m effect.- Auditory regularities and the expectations captured by IDyOM are reflected in the MMNm and P3am.- Evidence for a hierarchy of auditory predictions during melodic listening.

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Section: Comparison Between N1m and Mmnmsupporting

confidence: 87%

Section: Stimulimentioning

confidence: 99%

Section: Comparison With the Mmnmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decomposing neural responses to melodic surprise in musicians and non-musicians: evidence for a hierarchy of predictions in the auditory system

Quiroga-Martinez¹,

Hansen

Hoejlund

et al. 2019

Preprint

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Listeners with congenital amusia are sensitive to context uncertainty in melodic sequences

Quiroga-Martinez¹,

Tillmann

Brattico

et al. 2020

Preprint

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AbstractIn typical listeners, the perceptual salience of a surprising auditory event depends on the uncertainty of its context. For example, in melodies, pitch deviants are more easily detected and generate larger neural responses when the context is highly predictable than when it is less so. However, it is not known whether amusic listeners with abnormal pitch processing are sensitive to the degree of uncertainty of pitch sequences and, if so, whether they are to a different extent than typical listeners. To answer this question, we manipulated the uncertainty of short melodies while participants with and without congenital amusia underwent EEG recordings in a passive listening task. Uncertainty was manipulated by presenting melodies with different levels of complexity and familiarity, under the assumption that simpler and more familiar patterns would enhance pitch predictability. We recorded mismatch negativity (MMN) responses to pitch, intensity, timbre, location, and rhythm deviants as a measure of auditory surprise. We found reduced MMN amplitudes and longer peak latencies for all sound features with increasing levels of complexity, and putative familiarity effects only for intensity deviants. No significant group-by-complexity or group-by-familiarity interactions were detected. However, in amusics, pitch MMN responses peaked later and were disrupted in high complexity and unfamiliar melodies. Our results indicate that amusics are sensitive to the uncertainty of melodic sequences and hint at pitch-specific impairments in this population when uncertainty is high. As previous research has linked amusia with abnormal frontotemporal connectivity, our findings potentially suggest that processing pitch under high uncertainty conditions requires an intact frontotemporal loop.

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Musicianship and melodic predictability enhance neural gain in auditory cortex during pitch deviance detection

Quiroga-Martinez

Hansen

Højlund

et al. 2021

Preprint

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When listening to music, pitch deviations are more salient and elicit stronger prediction error responses when the melodic context is predictable and when the listener is a musician. Yet, the neuronal dynamics and changes in synaptic efficacy underlying such effects remain unclear. Here, we employed dynamic causal modeling (DCM) to investigate whether the magnetic mismatch negativity response (MMNm), and its modulation by context predictability and musical expertise, are associated with enhanced neural gain of auditory areas, as a plausible mechanism for encoding precision-weighted prediction errors. Using Bayesian model comparison, we asked whether models with intrinsic connections within primary auditory cortex (A1) and superior temporal gyrus (STG) (typically related to gain control) or extrinsic connections between A1 and STG (typically related to propagation of prediction and error signals) better explained magnetoencephalography (MEG) responses. We found that, compared to regular sounds, out-of-tune pitch deviations were associated with lower intrinsic (inhibitory) connectivity in A1 and STG, and lower backward (inhibitory) connectivity from STG to A1, consistent with disinhibition and enhanced neural gain in these auditory areas. More predictable melodies were associated with disinhibition in right A1, while musicianship was associated with disinhibition in left A1 and reduced connectivity from STG to left A1. These results indicate that musicianship and melodic predictability, as well as pitch deviations themselves, enhance neural gain in auditory cortex during deviance detection. Our findings are consistent with predictive processing theories suggesting that precise and informative error signals are selected by the brain for subsequent hierarchical processing.

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Musical prediction error responses similarly reduced by predictive uncertainty in musicians and non-musicians

Cited by 3 publications

References 50 publications

Decomposing neural responses to melodic surprise in musicians and non-musicians: evidence for a hierarchy of predictions in the auditory system

Decomposing neural responses to melodic surprise in musicians and non-musicians: evidence for a hierarchy of predictions in the auditory system

Listeners with congenital amusia are sensitive to context uncertainty in melodic sequences

Musicianship and melodic predictability enhance neural gain in auditory cortex during pitch deviance detection

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