Pre-attentive Mismatch Response and Involuntary Attention Switching to a Deviance in an Earlier-Than-Usual Auditory Stimulus: An ERP Study

Ungan, Pekcan; Karşılar, Hakan; Yağcıoğlu, Süha

doi:10.3389/fnhum.2019.00058

Cited by 13 publications

(12 citation statements)

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“…At the scalp level and in intracortical electrodes, slow electrical oscillations do seem to anticipatorily track the structure of periodic auditory stimuli [87,88], and this tracking is associated with the subjective passage of time [89]; these oscillations could be explored as possible estimates of mean underlying phase, with particular focus on those in motor areas. Ideally, timing prediction errors could be observed in the evoked EEG response to events (the ERP), allowing a direct measurement of event expectancy at each event time, and there are indeed indicators that the ERP is sensitive to temporal predictability (e.g., [90,91]); however, the sensitivity of the ERP to recent stimulus history makes this approach unpromising. However, timing prediction errors may be observable in EEG/MEG through their effect on gamma oscillations [92,93].…”

Section: Pippet In the Brainmentioning

confidence: 99%

Expectancy-based rhythmic entrainment as continuous Bayesian inference

Cannon

2021

PLoS Comput Biol

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When presented with complex rhythmic auditory stimuli, humans are able to track underlying temporal structure (e.g., a “beat”), both covertly and with their movements. This capacity goes far beyond that of a simple entrained oscillator, drawing on contextual and enculturated timing expectations and adjusting rapidly to perturbations in event timing, phase, and tempo. Previous modeling work has described how entrainment to rhythms may be shaped by event timing expectations, but sheds little light on any underlying computational principles that could unify the phenomenon of expectation-based entrainment with other brain processes. Inspired by the predictive processing framework, we propose that the problem of rhythm tracking is naturally characterized as a problem of continuously estimating an underlying phase and tempo based on precise event times and their correspondence to timing expectations. We present two inference problems formalizing this insight: PIPPET (Phase Inference from Point Process Event Timing) and PATIPPET (Phase and Tempo Inference). Variational solutions to these inference problems resemble previous “Dynamic Attending” models of perceptual entrainment, but introduce new terms representing the dynamics of uncertainty and the influence of expectations in the absence of sensory events. These terms allow us to model multiple characteristics of covert and motor human rhythm tracking not addressed by other models, including sensitivity of error corrections to inter-event interval and perceived tempo changes induced by event omissions. We show that positing these novel influences in human entrainment yields a range of testable behavioral predictions. Guided by recent neurophysiological observations, we attempt to align the phase inference framework with a specific brain implementation. We also explore the potential of this normative framework to guide the interpretation of experimental data and serve as building blocks for even richer predictive processing and active inference models of timing.

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Section: Pippet In the Brainmentioning

confidence: 99%

Expectancy-based rhythmic entrainment as continuous Bayesian inference

Cannon

2021

PLoS Comput Biol

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“…In addition to MMN and P300, a further late ERP component is referred to index prediction errors in the brain. This component, known as the reorienting negativity or RON, appears as a negative deflection at 400-600ms in response to reorientation of attention to the predictive/standard stimuli after it has been switched (indexed by P300) towards the preceding deviant stimuli (Correa-Jaraba et al, 2016; Ungan et al, 2019). Even though our study could not cover this ERP since it did not come out as a prominent peak in the subtraction waveforms ( Figure 1 ), especially in the audio only modality, we argue that RON too would, being a late component, have common brain generators like P300 across modalities and it is also shown to be centred upon fronto-central regions in extant literature (Correa-Jaraba et al, 2016; Justo-Guillén et al, 2019; Ungan et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…We found that the processing of prediction error becomes amodal at the P300 level itself and is not something that is attained at further later stages during the reorienting negativity (RON). The RON in the brain appears as a negative deflection at 400-600ms in response to the reorientation of attention to the predictive/standard stimuli after it has been switched (indexed by P300) towards the preceding deviant stimuli (Correa-Jaraba et al, 2016; Ungan et al, 2019). Even though in our analysis we could not cover this ERP since it did not come out as a prominent peak in the subtraction waveforms ( Figure 1 ), we argue that RON too would, being an even later component, have common brain generators across modalities, probably more frontal, as also reported in recent studies (Correa-Jaraba et al, 2016; Justo-Guillén et al, 2019; Ungan et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal mapping of the neural markers of prediction error processing across multisensory and unisensory modalities

Talwar

Ghosh

Banerjee

2022

Preprint

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Prediction errors in the brain are indexed by two event related potentials - MMN and P300 which are elicited whenever there is a violation of regularity in occurrence of a repetitive stimulus. While, MMN reflects the brain's ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory error detection, P300 is associated with cognitive processes such as attention and memory. Till date, there has been extensive research on the roles of MMN and P300 individually, because of their potential to be used as clinical markers of consciousness and attention, respectively. However, the relationship between these two ERPs, in context of prediction error propagation is an open question. Secondly, whether these events represent modality specific or modality independent information processing needs to be tested in unisensory and multisensory environments. Our EEG study on 22 healthy human volunteers shows that multisensory prediction errors are temporally facilitated by the constituent unisensory modality that has shorter processing latencies at any stage of prediction error processing. Employing source localization using individual participant's MRI scan, our study reveals that the corresponding ERP generators underlying the temporal hierarchy of prediction mismatch signals in the brain transition from being modality sensitive to modality independent across both unisensory and multisensory contexts. Such knowledge can be of immense value in clinical research for determining the stages of various treatments in aging, schizophrenia and depression, and their efficacy on cognitive function.

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“…At the scalp level and in intracortical electrodes, slow electrical oscillations do seem to anticipatorily track the structure of periodic auditory stimuli [85, 86], and this tracking is associated with the subjective passage of time [87]; these oscillations could be explored as possible estimates of mean underlying phase, with particular focus on those in motor areas. Ideally, timing prediction errors could be observed in the evoked EEG response to events (the ERP), allowing a direct measurement of event expectancy at each event time, and there are indeed indicators that the ERP is sensitive to temporal predictability (e.g., [88, 89]); however, the sensitivity of the ERP to recent stimulus history makes this approach unpromising. However, timing prediction errors may be observable in EEG/MEG through their effect on gamma oscillations [90, 91].…”

Section: Discussionmentioning

confidence: 99%

Expectancy-based rhythmic entrainment as continuous Bayesian inference

Cannon

2020

Preprint

View full text Add to dashboard Cite

When presented with complex rhythmic auditory stimuli, humans are able to track underlying temporal structure (e.g., a “beat”), both covertly and with their movements. This capacity goes far beyond that of a simple entrained oscillator, drawing on contextual and enculturated timing expectations and adjusting rapidly to perturbations in event timing, phase, and tempo. Here we propose that the problem of rhythm tracking is most naturally characterized as a problem of continuously estimating an underlying phase and tempo based on precise event times and their correspondence to timing expectations. We formalize this problem as a case of inferring a distribution on a hidden state from point process data in continuous time: either Phase Inference from Point Process Event Timing (PIPPET) or Phase And Tempo Inference (PATIPPET). This approach to rhythm tracking generalizes to non-isochronous and multi-voice rhythms. We demonstrate that these inference problems can be approximately solved using a variational Bayesian method that generalizes the Kalman-Bucy filter to point-process data. These solutions reproduce multiple characteristics of overt and covert human rhythm tracking, including period-dependent phase corrections, illusory contraction of unexpectedly empty intervals, and failure to track excessively syncopated rhythms, and could could be plausibly approximated in the brain. PIPPET can serve as the basis for models of performance on a wide range of timing and entrainment tasks and opens the door to even richer predictive processing and active inference models of rhythmic timing.

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Pre-attentive Mismatch Response and Involuntary Attention Switching to a Deviance in an Earlier-Than-Usual Auditory Stimulus: An ERP Study

Cited by 13 publications

References 70 publications

Expectancy-based rhythmic entrainment as continuous Bayesian inference

Expectancy-based rhythmic entrainment as continuous Bayesian inference

Spatiotemporal mapping of the neural markers of prediction error processing across multisensory and unisensory modalities

Expectancy-based rhythmic entrainment as continuous Bayesian inference

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