Tracking the Time Course of Bayesian Inference With Event-Related Potentials:A Study Using the Central Cue Posner Paradigm

Event-Related Spectral Perturbations differences analyzed in standard-deviant tone sequences presented in passive and active conditions

The predictive coding theory, although a well-supported framework for understanding brain processing, remains elusive regarding how different brain rhythms contribute to error prediction and modify the a priori probabilities of predictive events. This study addresses this issue by analyzing Event-Related Spectral Perturbations (ERSP) generated during an auditory oddball paradigm presented in both a passive and active condition. The design involved sequences of four tones, where the last tone was either predictable (standard, S), completing the scale, or less predictable (deviant, D) when the first tone was occasionally repeated. In the passive condition, participants were instructed to ignore the sounds, whereas, in the active condition, they were asked to press the up or down arrow on a keyboard depending on whether the last tone of the sequence presented a higher or lower frequency than the previous one. This experimental design aimed to bias cognitive processing towards predictable (S) or unpredictable scenarios (D) in two different conditions: passive and attentional. EEG data from 13 channels were analyzed with Morlet wavelets, revealing event-related synchronization (ERS) and desynchronization (ERD) induced by the stimuli. Early theta activity was key in computing prediction errors and updating next-trial expectations. In the active condition, theta responses were higher in D than in S trials, indicating enhanced prediction error processing with attention. Early beta activity also increased during D, likely reflecting motor adjustments. These findings emphasize the critical role of early theta rhythms and the amplifying effect of attention on prediction error processing.

Bayesian prior uncertainty and surprisal elicit distinct neural patterns during sound localization in dynamic environments

Estimating the location of a stimulus is a key function in sensory processing, and widely considered to result from the integration of prior information and sensory input according to Bayesian principles. A deviation of sensory input from the prior elicits surprisal, depending on the uncertainty of the prior. While this mechanism is increasingly understood in the visual domain, much less is known about its implementation in audition, especially regarding spatial localization. Here, we combined human EEG with computational modeling to study auditory spatial inference in a noisy, volatile environment and analyzed behavioral and neural patterns associated with prior uncertainty and surprisal. First, our results demonstrate that participants indeed used prior information during periods of stable environmental statistics, but showed evidence of surprisal and discarded prior information following environmental changes. Second, we observed distinct EEG activity patterns associated with prior uncertainty and surprisal in both the time- and time-frequency domain, which are in line with previous studies using visual tasks. Third, these EEG activity patterns were predictive of our participants' sound localization error, response uncertainty, and prior bias on a trial-by-trial basis. In summary, our work provides novel behavioral and neural evidence for Bayesian inference during dynamic auditory localization.

Predictive brain activity related to auditory information is associated with performance in speech comprehension tasks in noisy environments

Introduction

Understanding speech in noisy environments is challenging even for individuals with normal hearing, and it poses a significant challenge for those with hearing impairments or listening difficulties. There are limitations associated with the current methods of evaluating speech comprehension in such environments, especially in individuals with peripheral hearing impairments. According to the predictive coding model, speech comprehension is an active inference process that integrates sensory information through the interaction of bottom-up and top-down processing. Therefore, in this study, we aimed to examine the role of prediction in speech comprehension using an electrophysiological marker of anticipation: stimulus-preceding negativity (SPN).

Methods

We measured SPN amplitude in young adults with normal hearing during a time-estimation task with auditory feedback under both quiet and noisy conditions.

Results

The results showed that SPN amplitude significantly increased in noisy environments. Moreover, individual differences in SPN amplitude correlated with performance in a speech-in-noise test.

Discussion

The increase in SPN amplitude was interpreted as reflecting the increased requirement for attentional resources for accurate prediction of speech information. These findings suggest that SPN could serve as a noninvasive neural marker for assessing individual differences in top-down processing involved in speech comprehension in noisy environments.

Enhancing EEG-Based MI-BCIs with Class-Specific and Subject-Specific Features Detected by Neural Manifold Analysis

This paper presents an innovative approach leveraging Neuronal Manifold Analysis of EEG data to identify specific time intervals for feature extraction, effectively capturing both class-specific and subject-specific characteristics. Different pipelines were constructed and employed to extract distinctive features within these intervals, specifically for motor imagery (MI) tasks. The methodology was validated using the Graz Competition IV datasets 2A (four-class) and 2B (two-class) motor imagery classification, demonstrating an improvement in classification accuracy that surpasses state-of-the-art algorithms designed for MI tasks. A multi-dimensional feature space, constructed using NMA, was built to detect intervals that capture these critical characteristics, which led to significantly enhanced classification accuracy, especially for individuals with initially poor classification performance. These findings highlight the robustness of this method and its potential to improve classification performance in EEG-based MI-BCI systems.

Early and late contingent negative variation (CNV) reflect different aspects of deficits in schizophrenia

Abnormal reward processing and psychomotor slowing are well-known in schizophrenia (SZ). As a slow frontocentral potential, contingent negative variation (CNV) is associated with anticipatory attention, motivation and motor planning. The present study aims to evaluate the early and late amplitude and latencies of CNV in patients with SZ compared to healthy controls during a reward processing task and to show its association with clinical symptoms. We recruited 21 patients with SZ and 22 healthy controls to compare early and late CNV amplitude and latency values during a Monetary Incentive Delay (MID) Task between groups. Patients' symptom severity, levels of negative symptoms and depressive symptoms were assessed. Clinical features of the patients were further examined for their relation with CNV components. In conclusion, we found decreased early CNV amplitudes in SZ during the reward condition. They also displayed diminished and shortened late CNV responses for incentive cues, specifically at the central location. Furthermore, early CNV amplitudes exhibited a significant correlation with positive symptoms. Both CNV latencies were linked with medication dosage and the behavioural outcomes of the MID task. We revealed that early and late CNV exhibit different functions in neurophysiology and correspond to various facets of the deficits observed in patients. Our findings also emphasized that slow cortical potentials are indicative of deficient motivational processes as well as impaired reaction preparation in SZ. To gain a deeper understanding of the cognitive and motor impairments associated with psychosis, future studies must compare the effects of CNV in the early and late phases.

Systemic neurophysiological signals of auditory predictive coding

Predictive coding framework posits that our brain continuously monitors changes in the environment and updates its predictive models, minimizing prediction errors to efficiently adapt to environmental demands. However, the underlying neurophysiological mechanisms of these predictive phenomena remain unclear. The present study aimed to explore the systemic neurophysiological correlates of predictive coding processes during passive and active auditory processing. Electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and autonomic nervous system (ANS) measures were analyzed using an auditory pattern-based novelty oddball paradigm. A sample of 32 healthy subjects was recruited. The results showed shared slow evoked potentials between passive and active conditions that could be interpreted as automatic predictive processes of anticipation and updating, independent of conscious attentional effort. A dissociated topography of the cortical hemodynamic activity and distinctive evoked potentials upon auditory pattern violation were also found between both conditions, whereas only conscious perception leading to imperative responses was accompanied by phasic ANS responses. These results suggest a systemic-level hierarchical reallocation of predictive coding neural resources as a function of contextual demands in the face of sensory stimulation. Principal component analysis permitted to associate the variability of some of the recorded signals.

Neurophysiological signatures of prediction in language: A critical review of anticipatory negativities

Recent event-related potential (ERP) studies in language comprehension converge in finding anticipatory negativities preceding words or word segments that can be pre-activated based on either sentence contexts or phonological cues. We review these findings from different paradigms in the light of evidence from other cognitive domains in which slow negative potentials have long been associated with anticipatory processes and discuss their potential underlying mechanisms. We propose that this family of anticipatory negativities captures common mechanisms associated with the pre-activation of linguistic information both within words and within sentences. Future studies could utilize these anticipatory negativities in combination with other, well-established ERPs, to simultaneously track prediction-related processes emerging at different time intervals (before and after the perception of pre-activated input) and with distinct time courses (shorter-lived and longer-lived cognitive operations).

Alpha oscillations encode Bayesian belief updating underlying attentional allocation in dynamic environments

In a dynamic environment, expectations of the future constantly change based on updated evidence and affect the dynamic allocation of attention. To further investigate the neural mechanisms underlying attentional expectancies, we employed a modified Central Cue Posner Paradigm in which the probability of cues being valid (that is, accurately indicated the upcoming target location) was manipulated. Attentional deployment to the cued location (α), which was governed by precision of predictions on previous trials, was estimated using a hierarchical Bayesian model and was included as a regressor in the analyses of electrophysiological (EEG) data. Our results revealed that before the target appeared, alpha oscillations (8∼13 Hz) for high-predictability cues (88 % valid) were significantly predicted by precision-dependent attention (α). This relationship was not observed under low-predictability conditions (69 % and 50 % valid cues). After the target appeared, precision-dependent attention (α) correlated with alpha band oscillations only in the valid cue condition and not in the invalid condition. Further analysis under conditions of significant attentional modulation by precision suggested a separate effect of cue orientation. These results provide new insights on how trial-by-trial Bayesian belief updating relates to alpha band encoding of environmentally-sensitive allocation of visual spatial attention.

Emotion regulation strategies differentially modulate neural activity across affective prediction stages: An HD-EEG investigation

Emotion regulation (ER) strategies can influence how affective predictions are constructed by the brain (generation stage) to prearrange action (implementation stage) and update internal models according to incoming stimuli (updating stage). However, neurocomputational mechanisms by which this is achieved are unclear. We investigated through high-density EEG if different ER strategies (expressive suppression vs. cognitive reappraisal) predicted event-related potentials (ERPs) and brain source activity across affective prediction stages, as a function of contextual uncertainty. An S1-S2 paradigm with emotional faces and pictures as S1s and S2s was presented to 36 undergraduates. Contextual uncertainty was manipulated across three blocks with 100, 75, or 50% S1-S2 affective congruency. The effects of ER strategies, as assessed through the Emotion Regulation Questionnaire, on ERP and brain source activity were tested for each prediction stage through linear mixed-effects models. No ER strategy affected prediction generation. During implementation, in the 75% block, a higher tendency to suppress emotions predicted higher activity in the left supplementary motor area at 1,500-2,000 ms post-stimulus, and smaller amplitude of the Contingent Negative Variation at 2,000-2,500 ms. During updating, in the 75% block, a higher tendency to cognitively reappraise emotions predicted larger P2, Late Positive Potential, and right orbitofrontal cortex activity. These results suggest that both ER strategies interact with the levels of contextual uncertainty by differently modulating ERPs and source activity, and that different strategies are deployed in a moderately predictive context, supporting the efficient updating of affective predictive models only in the context in which model updating occurs.

Unbalanced functional connectivity at rest affects the ERP correlates of affective prediction in high intolerance of uncertainty individuals: A high density EEG investigation

In a recent study we outlined the link between Intolerance of Uncertainty (IU) and the neural correlates of affective predictions, as constructed by the brain (generation stage) to prepare to relevant stimuli (implementation stage), and update predictive models according to incoming stimuli (updating stage). In this study we further explored whether the brain's functional organization at rest can modulate neural activity elicited within an emotional S1-S2 paradigm as a function of IU and uncertainty of S1-S2 contingencies. We computed resting state functional connectivity (RS-FC) from a 3-min resting period recorded with high density EEG, and we tested whether RS graph theory nodal measures (i.e., strength, clustering coefficient, betweenness centrality) predicted in-task ERP modulation as a function of IU. We found that RS-FC differently predicted in-task ERPs within the generation and updating stages. Higher IU levels were associated to altered RS-FC patterns within both domain-specific (i.e., right superior temporal sulcus) and domain-general regions (i.e., right orbitofrontal cortex), predictive of a reduced modulation of in-task ERPs in the generation and updating stages. This is presumably ascribable to an unbalancing between synchronization and integration within these regions, which may disrupt the exchange of information between top-down and bottom-up pathways. This altered RS-FC pattern may in turn result in the construction of less efficient affective predictions and a reduced ability to deal with contextual uncertainty in individuals high in IU.

Late N1 and Post Imperative Negative Variation analysis depending on the previous trial history in paradigms of increasing auditory complexity

Predictive coding reflects the ability of the human brain to extract environmental patterns in order to reformulate previous expectations. The present report analyzes through the late N1 auditory component and the post imperative negative variation (PINV) the updating of predictions regarding the characteristics of a new trial, depending on the previous trial history, complexity, and type of trial (standard or deviant). Data were obtained from 31 healthy subjects recorded in a previous study, based on two paradigms composed of stimulus sequences of decreasing or increasing frequencies intermingled with the sporadic appearance of unexpected tone endings. Our results showed a higher amplitude for the most complex condition and deviant trials for both the late N1 and the PINV components. Additionally, the N1 and PINV presented a different amplitude response to the standard and deviant trials as a function of previous trial history, suggesting a continuous updating of trial categorization. The results suggest that the late N1 and PINV components are involved in the generation of an internal model about the rules of external auditory stimulation.

Impaired selective attention in patients with severe primary monosymptomatic nocturnal enuresis: An event-related potential study

Objectives

Primary monosymptomatic nocturnal enuresis (PMNE) is a very common problem in school age children. It is thought that PMNE represents a maturational lag in the central nervous system of those children. We did this case control study to assess the selective attention and resource allocation in those children using the P300 wave of the Event-Related Potentials (ERPs) and its relation to disease severity.

Methods

Forty four patients with PMNE and twenty three healthy controls were included in this study. Patients were diagnosed according to the criteria of international children's continence society and were classified into two groups; patients with frequent wetting (≥4 episodes/week), and patients with infrequent wetting (<4 episodes/week). ERPs were recorded at Fz, Cz, and Pz locations using odd-ball paradigm. N200 and P300 peak latencies (ms), and N200/P300 peak to peak amplitudes (µV) were measured.

Results

We found significant increase of P300 and N200/P300 interpeak latencies, and significant decrease of P300 amplitudes in frequent wetting group "severe" PMNE compared to healthy controls and infrequent wetting group.

Conclusion

Abnormal selective attention and resource allocation were found in patients with severe PMNE. Measures to improve selective attention might be helpful in treatment of patients with severe PMNE.

Significance

Impaired selective attention might play a role in pathogenesis of severe PMNE and the need for the various measures to improve selective attention may be further studied as a therapeutic tool for patients with severe PMNE.

Dealing with uncertainty: A high-density EEG investigation on how intolerance of uncertainty affects emotional predictions

Intolerance of uncertainty (IU) can influence emotional predictions, constructed by the brain (generation stage) to prearrange action (implementation stage), and update internal models according to incoming stimuli (updating stage). However, neurocomputational mechanisms by which IU affects emotional predictions are unclear. This high-density EEG study investigated if IU predicted event-related potentials (ERPs) and brain sources activity developing along the stages of emotional predictions, as a function of contextual uncertainty. Thirty-six undergraduates underwent a S1-S2 paradigm, with emotional faces and pictures as S1s and S2s, respectively. Contextual uncertainty was manipulated across three blocks, each with 100%, 75%, or 50% S1-S2 emotional congruency. ERPs, brain sources and their relationship with IU scores were analyzed for each stage. IU did not affect prediction generation. During prediction implementation, higher IU predicted larger Contingent Negative Variation in the 75% block, and lower left anterior cingulate cortex and supplementary motor area activations. During prediction updating, as IU increased P2 to positive S2s decreased, along with P2 and Late Positive Potential in the 75% block, and right orbito-frontal cortex activity to emotional S2s. IU was therefore associated with altered uncertainty assessment and heightened attention deployment during implementation, and to uncertainty avoidance, reduced attention to safety cues and disrupted access to emotion regulation strategies during prediction updating.

Mismatch Negativity and Stimulus-Preceding Negativity in Paradigms of Increasing Auditory Complexity: A Possible Role in Predictive Coding

The auditory mismatch negativity (MMN) has been considered a preattentive index of auditory processing and/or a signature of prediction error computation. This study tries to demonstrate the presence of an MMN to deviant trials included in complex auditory stimuli sequences, and its possible relationship to predictive coding. Additionally, the transfer of information between trials is expected to be represented by stimulus-preceding negativity (SPN), which would possibly fit the predictive coding framework. To accomplish these objectives, the EEG of 31 subjects was recorded during an auditory paradigm in which trials composed of stimulus sequences with increasing or decreasing frequencies were intermingled with deviant trials presenting an unexpected ending. Our results showed the presence of an MMN in response to deviant trials. An SPN appeared during the intertrial interval and its amplitude was reduced in response to deviant trials. The presence of an MMN in complex sequences of sounds and the generation of an SPN component, with different amplitudes in deviant and standard trials, would support the predictive coding framework.

What's next? Neural correlates of emotional predictions: A high-density EEG investigation

Emotions were recently reconsidered as predictions, constructed by the brain (generation stage) to prearrange action (implementation stage), and update internal models according to incoming stimuli (updating stage). However, it is unclear how emotional predictions are shaped by stimuli predictability. This study investigated the role of stimuli predictability on emotional predictions through high-density EEG. Twenty-six undergraduates underwent a S1-S2 paradigm, with emotional faces as S1s and emotional pictures as S2s. Stimuli predictability was manipulated across three blocks, in which S1 valence was predictive of S2 in the 100%, 75%, or 50% of trials. ERPs and brain sources were analyzed for each stage. During prediction generation, a larger N170/superior temporal sulcus activity emerged to fearful faces in blocks with full (100%) and medium (75%) predictive ratios. During implementation, the random block (50%) elicited a valence-independent pre-allocation of resources, reflected by a larger CNV and activation of a wide left network. In the updating stage, emotional pictures always elicited a larger LPP, while a larger P2 to neutral stimuli and a higher activity of the orbitofrontal cortex signaled early valence-dependent and late block-dependent prediction errors. These findings provide the first evidence of how stimuli predictability shape each neurocomputational stage of emotional predictions construction.

Learning to predict: Neuronal signatures of auditory expectancy in human event-related potentials

Learning to anticipate future states of the world based on statistical regularities in the environment is a key component of perception and is vital for the survival of many organisms. Such statistical learning and prediction are crucial for acquiring language and music appreciation. Importantly, learned expectations can be implicitly derived from exposure to sensory input, without requiring explicit information regarding contingencies in the environment. Whereas many previous studies of statistical learning have demonstrated larger neuronal responses to unexpected versus expected stimuli, the neuronal bases of the expectations themselves remain poorly understood. Here we examined behavioral and neuronal signatures of learned expectancy via human scalp-recorded event-related brain potentials (ERPs). Participants were instructed to listen to a series of sounds and press a response button as quickly as possible upon hearing a target noise burst, which was either reliably or unreliably preceded by one of three pure tones in low-, mid-, and high-frequency ranges. Participants were not informed about the statistical contingencies between the preceding tone ‘cues’ and the target. Over the course of a stimulus block, participants responded more rapidly to reliably cued targets. This behavioral index of learned expectancy was paralleled by a negative ERP deflection, designated as a neuronal contingency response (CR), which occurred immediately prior to the onset of the target. The amplitude and latency of the CR were systematically modulated by the strength of the predictive relationship between the cue and the target. Re-averaging ERPs with respect to the latency of behavioral responses revealed no consistent relationship between the CR and the motor response, suggesting that the CR represents a neuronal signature of learned expectancy or anticipatory attention. Our results demonstrate that statistical regularities in an auditory input stream can be implicitly learned and exploited to influence behavior. Furthermore, we uncover a potential ‘prediction signal’ that reflects this fundamental learning process.

P300 response modulation reflects breaches of non-probabilistic expectations

In oddball paradigms, infrequent stimuli elicit larger P300 event related potentials (ERPs) than frequent ones. One hypothesis is that P300 modulations reflect the degree of “surprise” associated with unexpected stimuli. That is the P300 represents how unlikely the stimulus is and this signal is then used to update the observer’s expectations. It could be hypothesized that P300 is modulated by any factor affecting an observer’s expectations, not only target probability. Alternatively, the P300 may reflect an evaluative process engaged whenever a discrepancy between task context and sensory inputs arises, irrespective of the latter probability. In previous ERP studies, stimulus probability was often the only determinant of task set confounding the effects of stimulus probability and set stimulus discrepancy. In this study, we used a speeded luminance detection task. The target was preceded by a central cue that predicted its location. The probability that the target was valid, i.e. would appear at the cued location was manipulated by varying the reliability of the cue. Reaction times were modulated by probabilistic expectations based on cue reliability and target validity while P300 was affected by target validity only. We conclude that increased P300 amplitude reflects primarily breaches of non-probabilistic expectations, rather than target probability.