Enhanced deviant responses in patterned relative to random sound sequences

Southwell, Rosy; Chait, Maria

doi:10.1016/j.cortex.2018.08.032

Cited by 83 publications

(93 citation statements)

References 74 publications

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“…Unexpected orientations led to increased orientation selectivity soon after stimulus presentation and this effect reemerged at later time points, consistent with an updating of sensory expectations. Overall, this evidence is consistent with PP's contention that the brain constructs dynamic representations of regularities in the sensory stream and that these representations are intimately involved in subsequent sensory processing 144,154,159 …”

Section: Hypothesis 2: Top‐down Signals Represent Sensory Predictionssupporting

confidence: 84%

“…In line with PP's precision‐weighting mechanism, there is also evidence from auditory studies in humans that the inherent predictability of a stimulus stream moderates this process of prediction generation 144,149,150 and subsequent ES 151–153 . For example, Southwell and Chait 154 found that the neural response to frequency outliers was enhanced when the deviant tones were presented in the context of a regular pattern compared with a random pattern even when the pattern was presented too rapidly to allow conscious detection of the regularity.…”

Section: Hypothesis 2: Top‐down Signals Represent Sensory Predictionsmentioning

confidence: 87%

“…A particularly perplexing set of results in this literature comes from a series of studies reporting that the transition from random to regular sequences of auditory stimuli is accompanied by a sustained increase in neural activity measured with MEG, EEG, and fMRI, 144,154,155 in stark contrast to ES studies showing dampened neural responses to predictable stimuli 78,81,156,157 . One interpretation of this result is that the enhanced activity reflects the high precision‐weighting afforded to low variance input 155 .…”

Section: Hypothesis 2: Top‐down Signals Represent Sensory Predictionsmentioning

confidence: 99%

“…Humans are capable of rapidly extracting regularities from the sensory environment 144,158,160,161 and there is strong evidence that the resultant expectations influence sensory processing 154 . It is a substantial achievement to begin to isolate this predictive activity in recordings of neural activity spanning human, monkey, and rodent research.…”

Section: Hypothesis 2: Top‐down Signals Represent Sensory Predictionsmentioning

confidence: 99%

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Evaluating the neurophysiological evidence for predictive processing as a model of perception

Walsh

McGovern

Clark

et al. 2020

Annals of the New York Academy of Sciences

205

172

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For many years, the dominant theoretical framework guiding research into the neural origins of perceptual experience has been provided by hierarchical feedforward models, in which sensory inputs are passed through a series of increasingly complex feature detectors. However, the long‐standing orthodoxy of these accounts has recently been challenged by a radically different set of theories that contend that perception arises from a purely inferential process supported by two distinct classes of neurons: those that transmit predictions about sensory states and those that signal sensory information that deviates from those predictions. Although these predictive processing (PP) models have become increasingly influential in cognitive neuroscience, they are also criticized for lacking the empirical support to justify their status. This limited evidence base partly reflects the considerable methodological challenges that are presented when trying to test the unique predictions of these models. However, a confluence of technological and theoretical advances has prompted a recent surge in human and nonhuman neurophysiological research seeking to fill this empirical gap. Here, we will review this new research and evaluate the degree to which its findings support the key claims of PP.

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Section: Hypothesis 2: Top‐down Signals Represent Sensory Predictionssupporting

confidence: 84%

Section: Hypothesis 2: Top‐down Signals Represent Sensory Predictionsmentioning

confidence: 87%

Section: Hypothesis 2: Top‐down Signals Represent Sensory Predictionsmentioning

confidence: 99%

Section: Hypothesis 2: Top‐down Signals Represent Sensory Predictionsmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating the neurophysiological evidence for predictive processing as a model of perception

Walsh

McGovern

Clark

et al. 2020

Annals of the New York Academy of Sciences

205

172

View full text Add to dashboard Cite

show abstract

“…It is commonly observed that responses to repeated standard sounds decrease over time more for regular than for random sequences, while deviant responses remain constant for both (Arnal & Giraud, ; Knolle, Schröger, Baess, & Kotz, ). Thus, the relative increase in positivity of standard responses for regular sequences might largely or even solely account for larger MMNs observed for those, leaving the contribution of deviant‐evoked activity unclear (see Southwell & Chait, , for a N1 account).…”

Section: Introductionmentioning

confidence: 99%

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

Lumaca

Haumann

Brattico

et al. 2019

Eur J of Neuroscience

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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.

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Neurophysiological and functional neuroanatomical coding of statistical and deterministic rule information during sequence learning

Takács

Kóbor

Kardos

et al. 2021

Human Brain Mapping

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Humans are capable of acquiring multiple types of information presented in the same information stream. It has been suggested that at least two parallel learning processes are important during learning of sequential patterns—statistical learning and rule‐based learning. Yet, the neurophysiological underpinnings of these parallel learning processes are not fully understood. To differentiate between the simultaneous mechanisms at the single trial level, we apply a temporal EEG signal decomposition approach together with sLORETA source localization method to delineate whether distinct statistical and rule‐based learning codes can be distinguished in EEG data and can be related to distinct functional neuroanatomical structures. We demonstrate that concomitant but distinct aspects of information coded in the N2 time window play a role in these mechanisms: mismatch detection and response control underlie statistical learning and rule‐based learning, respectively, albeit with different levels of time‐sensitivity. Moreover, the effects of the two learning mechanisms in the different temporally decomposed clusters of neural activity also differed from each other in neural sources. Importantly, the right inferior frontal cortex (BA44) was specifically implicated in visuomotor statistical learning, confirming its role in the acquisition of transitional probabilities. In contrast, visuomotor rule‐based learning was associated with the prefrontal gyrus (BA6). The results show how simultaneous learning mechanisms operate at the neurophysiological level and are orchestrated by distinct prefrontal cortical areas. The current findings deepen our understanding on the mechanisms of how humans are capable of learning multiple types of information from the same stimulus stream in a parallel fashion.

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Enhanced deviant responses in patterned relative to random sound sequences

Cited by 83 publications

References 74 publications

Evaluating the neurophysiological evidence for predictive processing as a model of perception

Evaluating the neurophysiological evidence for predictive processing as a model of perception

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

Neurophysiological and functional neuroanatomical coding of statistical and deterministic rule information during sequence learning

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