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

Abstract

The human brain's ability to extract and encode temporal regularities and to predict the timing of upcoming events is critical for music and speech perception. This work addresses how these mechanisms deal with different levels of temporal complexity, here the number of distinct durations in rhythmic patterns. We use electroencephalography (EEG) to relate the mismatch negativity (MMN), a proxy of neural prediction error, to a measure of information content of rhythmic sequences, the Shannon entropy. Within each of three conditions, participants listened to repeatedly presented standard rhythms of five tones (four inter‐onset intervals) and of a given level of entropy: zero (isochronous), medium entropy (two distinct interval durations), or high entropy (four distinct interval durations). Occasionally, the fourth tone was moved forward in time that is it occurred 100 ms (small deviation) or 300 ms early (large deviation). According to the predictive coding framework, high‐entropy stimuli are more difficult to model for the brain, resulting in less confident predictions and yielding smaller prediction errors for deviant sounds. Our results support this hypothesis, showing a gradual decrease in MMN amplitude as a function of entropy, but only for small timing deviants. For large timing deviants, in contrast, a modulation of activity in the opposite direction was observed for the earlier N1 component, known to also be sensitive to sudden changes in directed attention. Our results suggest the existence of a fine‐grained neural mechanism that weights neural prediction error to the complexity of rhythms and that mostly manifests in the absence of directed attention.

Reduced prediction error responses in high- as compared to low-uncertainty musical contexts

“…They are also in agreement with studies that found differences in sustained tonic activity-as opposed to phasic responses such as the MMN-when comparing low-and high-entropy contexts (Auksztulewicz et al, 2017;Barascud et al, 2016;Nastase, Iacovella, & Hasson, 2014;Overath et al, 2007). Closer to assessing the effect of precision in a musical context, recent research shows that the entropy of short rhythmic sequences modulates MMN responses (Lumaca, Haumann, Brattico, Grube, & Vuust, 2019). However, in this study rhythms were presented as repeated short patterns, which makes them less akin to actual musical stimuli than our HE sequences.…”

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

“…Despite these limitations, our work provides further evidence for the effect of uncertainty-or precisionon prediction error, which is consistent with an increasing number of empirical findings (Garrido et al, 2013;Hsu et al, 2015;Lumaca et al, 2019;Sedley et al, 2016;Sohoglu & Chait, 2016;Southwell & Chait, 2018), theories of predictive processing and models of music perception (Clark, 2016;Feldman & Friston, 2010;Hohwy, 2013;Ross & Hansen, 2016;Vuust et al, 2018). Furthermore, our findings confirm that MMNm responses can be reliably recorded in realistic paradigms where sounds constantly change, which constitutes a methodological improvement on existing approaches.…”

“…The ensuing precision-weighted prediction error would ensure that primarily reliable sensory signals drive learning and behavior. While a growing body of research already provides evidence for this phenomenon in the auditory modality (Garrido, Sahani, & Dolan, 2013;Hsu, Bars, Hämäläinen, & Waszak, 2015;Lumaca, Haumann, Brattico, Grube, & Vuust, 2019;Sedley et al, 2016;Sohoglu & Chait, 2016;Southwell & Chait, 2018), our study was the first to show its presence in a more ecologically valid setting such as music listening. Furthermore, the findings also pointed to a feature-selective effect in which only prediction error responses related to the manipulated auditory feature-pitch, in our case-are modulated by uncertainty.…”

Neurocomputational underpinnings of expected surprise

“…These theoretical views of the brain have already succeeded in providing new and testable hypotheses, leading to new or revisited experimental paradigms to address how the brain adapts under uncertainty [6][7][8][9][10] . A striking example is the growing interest in how stimulus predictability modulates some long-established sensory responses [11][12][13] . However, beyond the great insights that those theories have already provided, the precise mental processes and neurophysiological mechanisms that subsume perceptual learning remain elusive.…”