Analysis Of Event-related Potentials Research Articles

Neural mechanisms of attentional bias to emotional faces in patients with chronic insomnia disorder

ObjectiveThis study used event-related potential (ERP) and resting-state functional connectivity (rs-FC) approaches to investigate the neural mechanisms underlying the emotional attention bias in patients with chronic insomnia disorder (CID). MethodsTwenty-five patients with CID and thirty-three demographically matched healthy controls (HCs) completed clinical questionnaires and underwent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) scans. EEG analysis examined the group differences in terms of reaction times, P3 amplitudes, event-related spectral perturbations, and inter-trial phase synchrony. Subsequently, seed-based rs-FC analysis of the amygdala nuclei (including the central-medial amygdala [CMA] and basolateral amygdala [BLA]) was performed. The relationship between P3 amplitude, rs-FC and clinical symptom severity in patients with CID was further investigated by correlation analysis. ResultsCID patients exhibited shorter reaction times than HCs in both standard and deviant stimuli, with the abnormalities becoming more pronounced as attention allocation increased. Compared to HCs, ERP analysis revealed increased P3 amplitude, theta wave power, and inter-trial synchrony in CID patients. The rs-FC analysis showed increased connectivity of the BLA-occipital pole, CMA-precuneus, and CMA-angular gyrus and decreased connectivity of the CMA-thalamus in CID patients. Notably, correlation analysis of the EEG and fMRI measurements showed a significant positive correlation between the P3 amplitude and the rs-FC of the CMA-PCU. ConclusionThis study confirms an emotional attention bias in CID, specifically in the neural mechanisms of attention processing that vary depending on the allocation of attentional resources. Abnormal connectivity in the emotion-cognition networks may constitute the neural basis of the abnormal scalp activation pattern.

Rhythmic temporal prediction enhances neural representations of movement intention for brain–computer interface

Objective. Detecting movement intention is a typical use of brain–computer interfaces (BCI). However, as an endogenous electroencephalography (EEG) feature, the neural representation of movement is insufficient for improving motor-based BCI. This study aimed to develop a new movement augmentation BCI encoding paradigm by incorporating the cognitive function of rhythmic temporal prediction, and test the feasibility of this new paradigm in optimizing detections of movement intention. Methods. A visual-motion synchronization task was designed with two movement intentions (left vs. right) and three rhythmic temporal prediction conditions (1000 ms vs. 1500 ms vs. no temporal prediction). Behavioural and EEG data of 24 healthy participants were recorded. Event-related potentials (ERPs), event-related spectral perturbation induced by left- and right-finger movements, the common spatial pattern (CSP) and support vector machine, Riemann tangent space algorithm and logistic regression were used and compared across the three temporal prediction conditions, aiming to test the impact of temporal prediction on movement detection. Results. Behavioural results showed significantly smaller deviation time for 1000 ms and 1500 ms conditions. ERP analyses revealed 1000 ms and 1500 ms conditions led to rhythmic oscillations with a time lag in contralateral and ipsilateral areas of movement. Compared with no temporal prediction, 1000 ms condition exhibited greater beta event-related desynchronization (ERD) lateralization in motor area (P< 0.001) and larger beta ERD in frontal area (P< 0.001). 1000 ms condition achieved an averaged left–right decoding accuracy of 89.71% using CSP and 97.30% using Riemann tangent space, both significantly higher than no temporal prediction. Moreover, movement and temporal information can be decoded simultaneously, achieving 88.51% four-classification accuracy. Significance. The results not only confirm the effectiveness of rhythmic temporal prediction in enhancing detection ability of motor-based BCI, but also highlight the dual encodings of movement and temporal information within a single BCI paradigm, which is promising to expand the range of intentions that can be decoded by the BCI.

Effects of fear of missing out on inhibitory control in social media context: evidence from event-related potentials.

The present study aimed to investigate the impact of fear of missing out (FoMO) on inhibitory control in social media context. The present study used a two-choice oddball task combined with event-related potentials (ERPs) technology to measure inhibitory control. Based on the Fear of Missing Out Scale, participants with varying degrees of FoMO were recruited to complete two studies. A total of 78 participants in Study 1 completed a two-choice oddball task (stimuli "W" or "M"). The results showed that FoMO did not have a significant impact on general inhibitory control at both the behavioral and electrophysiological levels. To further examine the effect of FoMO in social media context. In Study 2, 72 participants completed a modified two-choice oddball task with three types of pictures (high and low social media-related and neutral). The behavioral results revealed that as FoMO scores increased, inhibitory control decreased. ERP analysis revealed that with higher FoMO scores, social media-related pictures elicited larger N2 amplitude and smaller P3 amplitude, but not for neutral pictures. This suggests that FoMO undermines inhibitory control by consuming more cognitive resources in the early conflict detection stage and leading to insufficient cognitive resources in the later stages of the inhibitory process. These findings suggest that FoMO can undermine inhibitory control in the social media context. Considering the indispensable use of social media in the digital age, addressing and understanding the influence of FoMO on inhibitory control could be essential for promoting healthy digital behaviors and cognitive functions.

Acousto-optic stimuli to promote coherent 40-Hz frequency entrainment effect.

Research has shown that a fundamental frequency of 40 Hz in continuous neural oscillation is indicative of normal brain activity; in Alzheimer disease (AD) patients, these oscillations either disappear or are significantly interrupted. Research has also indicated that the degenerative impacts of AD in mice were mitigated by the synchronization of 40-Hz acousto-optic stimulation (AOS). To examine the impact of employing a 40-Hz AOS intervention on the induction of a substantial 40-Hz frequency entrainment and improvement in working memory performance among a sample of young individuals in good health. We conduct an analysis of event-related potentials (ERPs) derived from electroencephalogram (EEG) data following the presentation of AOS. We recruited 20 healthy volunteers (median age: 25 years; 8 female subjects). Following the administration of various stimuli, including no stimuli, 40-Hz AOS, pink noise, and 40Hz acoustic stimuli (AS), the participants were required to complete a working memory task. A total of 62 electrodes were used to record EEG data, which was subsequently analyzed to investigate the impact of AOS on the activity of working memory. We also aimed to determine if AOS lead to a more pronounced 40-Hz frequency entrainment. Following the administration of AOS, a notable enhancement in the 40-Hz power of pertinent cerebral areas was observed, accompanied by a substantial improvement in the performance of the subjects on working memory tests subsequent to the stimulation. The findings unequivocally establish the efficacy of using AOS to enhance the 40-Hz power and working memory.

Sensory Emotion in Words: Evidence From an Event-related Potential (ERP) Study in Light of the Emotioncy Model.

Delving into the prominent role of emotions and senses in language is not something new in the field. Thereupon, the newly developed notion of emotioncy has been introduced to foreign language education to underscore the role of sense-induced emotions in the language learning and teaching process. The present study implemented event-related potentials (ERPs) to provide evidence of the significance of employing emosensory instructional strategies in teaching vocabulary items. Hence, 18 female participants were randomly instructed on six English nouns toward which they had no prior knowledge and received no instruction for the other three words. Then, while the participants' electroencephalogram (EEG) was being recorded, they took a sentence comprehension task. Behavioral results demonstrated significant differences among the avolved, the exvolved, and the involved nouns. However, ERP analyses of target words indicated the modulations of N100 and N480 components while no significant effect was observed at P200. Further, the analysis of sensory N100 for the critical words revealed no significant effect. In conclusion, emotioncy-based language instruction can affect neural correlates of emotional word comprehension from the early stages of EEG recording. The results of this study can clarify the importance of including senses and emotions in language teaching, learning, and testing, along with materials development. Direct emosensory involvement can affect word semantic processing.Indirect emosensory involvement can affect sensorial comprehension of the word.Direct sensory involvement may enhance the accuracy of the responses.Direct sensory involvement may reduce the response time. Nowadays, learning a foreign language is considered one of the challenges in our lives. It is believed that including senses and emotions in education can foster learning new words in a foreign language. One concept that focuses on the use of sensory emotions is called emotioncy. This study employed brain imaging to analyze the effects of teaching through emotioncy on related neural modulations. Based on the results of the study, higher levels of employing senses and emotions can affect the processing and comprehension of words.

Total Sleep Deprivation Triggers a Compensatory Mechanism During Conflict Monitoring Process: Evidence From Event-Related Potentials.

Sleep deprivation impairs cognitive function and is accompanied by a simultaneous compensatory effect, one of the brain's capacities to maintain function in emergency situations. However, the time course of the compensatory effect is unclear. In this study, 22 male participants completed a pronunciation working memory task that included congruent and incongruent stimuli trials with EEG recordings before and after total sleep deprivation (TSD). Behavioral performance analysis showed that after TSD, the participants' reaction time (RT) was shortened, but accuracy was reduced significantly. Analysis of event-related potential (ERP) results showed that the amplitude of N2 (an early visual ERP) was larger (i.e., more negative) after TSD than at baseline. A significant interaction between congruency and sleep condition was seen. Compared to that before TSD, the increase in amplitude of P3 (a stimulus-induced positive deflection component) under an incongruent stimulus was larger than that under a congruent stimulus after TSD. Moreover, a significant negative correlation was found between P3 amplitude and RT. Our results suggest that TSD impairs cognitive function. Meanwhile, the brain activates a compensatory mechanism after TSD, which is comprehensive during the conflict-detection and information-updating stages. This study provides a fresh viewpoint for understanding how TSD affects cognitive function.

Neural mechanisms of working memory dysfunction in patients with chronic insomnia disorder

ObjectiveThis study aimed to investigate the neural mechanisms underlying working memory impairment in patients with chronic insomnia disorder (CID) using event-related potentials (ERP) and resting-state functional connectivity (rsFC) approaches. MethodsParticipants, including CID patients and healthy controls (HCs), completed clinical scales and underwent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). EEG analysis compared reaction times, P3 amplitudes, event-related spectral perturbations (ERSP), and inter-trial phase synchronisation (ITPS) between CID patients and HCs. Subsequently, frontal regions (i.e., the Superior Frontal Gyrus [SFG] and Middle Frontal Gyrus [MFG]) corresponding to the EEG were selected as seeds for rsFC analysis. Correlation analyses were conducted to further investigate the relationship between functional connectivity abnormalities in brain regions and clinical symptom severity and P3 amplitude in CID patients. ResultsCompared to HCs, CID patients exhibited slower reaction times across all working memory conditions, with the deficits becoming more pronounced as memory load increased. ERP analysis revealed increased P3 amplitude, theta wave power, and reduced inter-trial synchrony in CID patients. rsFC analysis showed decreased connectivity of SFG-posterior cingulated cortex (PCC), SFG-MFG, and MFG-frontal pole (FP), and increased connectivity of MFG- Middle Temporal Gyrus (MTG)in CID patients. Importantly, a significant correlation was found between the rsFC of SFG-MTG and P3 amplitude during 1-back. ConclusionThis study confirms deficits in working memory capacity in patients with CID, specifically in the neural mechanisms of cognitive processing that vary depending on the level of cognitive load. Alterations in connectivity patterns within and between the frontal and temporal regions may be the neural basis of the cognitive impairment.

Enhanced average for event-related potential analysis using dynamic time warping

Electroencephalography (EEG) provides a way to understand, and evaluate neurotransmission. In this context, time-locked EEG activity or event-related potentials (ERPs) are often used to capture neural activity related to specific mental processes. Normally, they are considered on the basis of averages across a number of trials. However, there exist notable variability in latency jitter, jitter, and amplitude, across trials, and, also, across users; this causes the average ERP waveform to blur, and, furthermore, diminish the amplitude of underlying waves.For these reasons, a strategy is proposed for obtaining ERP waveforms based on dynamic time warping (DTW) to adapt, and adjust individual trials to the averaged ERP, previously calculated, to build an enhanced average by making use of these warped signals. At the sight of the experiments carried out on the behaviour of the proposed scheme using publicly available datasets, this strategy reduces the attenuation in amplitude of ERP components thanks to the reduction of the influence of variability of latency and jitter, and, thus, improves the averaged ERP waveforms.

Vestibular mapping of the naturalistic head-centered motion spectrum.

Naturalistic head accelerations can be used to elicit vestibular evoked potentials (VestEPs). These potentials allow for analysis of cortical vestibular processing and its multi-sensory integration with a high temporal resolution. We report the results of two experiments in which we compared the differential VestEPs elicited by randomized translations, rotations, and tilts in healthy subjects on a motion platform. An event-related potential (ERP) analysis revealed that established VestEPs were verifiable in all three acceleration domains (translations, rotations, tilts). A further analysis of the VestEPs showed a significant correlation between rotation axes (yaw, pitch, roll) and the amplitude of the evoked potentials. We found increased amplitudes for rotations in the roll compared to the pitch and yaw plane. A distributed source localization analysis showed that the activity in the cingulate sulcus visual (CSv) area best explained direction-dependent amplitude modulations of the VestEPs, but that the same cortical network (posterior insular cortex, CSv) is involved in processing vestibular information, regardless of the motion direction. The results provide evidence for an anisotropic, direction-dependent processing of vestibular input by cortical structures. The data also suggest that area CSv plays an integral role in ego-motion perception and interpretation of spatial features such as acceleration direction and intensity.

Post-stimulus encoding of decision confidence in EEG: toward a brain–computer interface for decision making

Objective. When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain–computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI. Approach. We investigate the neural correlates of confidence by collecting high-density electroencephalography (EEG) during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap. Main results. We perform event-related potential and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-locked activity from confounding stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high. Significance. Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.

Machine learning and EEG can classify passive viewing of discrete categories of visual stimuli but not the observation of pain

Previous studies have demonstrated the potential of machine learning (ML) in classifying physical pain from non-pain states using electroencephalographic (EEG) data. However, the application of ML to EEG data to categorise the observation of pain versus non-pain images of human facial expressions or scenes depicting pain being inflicted has not been explored. The present study aimed to address this by training Random Forest (RF) models on cortical event-related potentials (ERPs) recorded while participants passively viewed faces displaying either pain or neutral expressions, as well as action scenes depicting pain or matched non-pain (neutral) scenarios. Ninety-one participants were recruited across three samples, which included a model development group (n = 40) and a cross-subject validation group (n = 51). Additionally, 25 participants from the model development group completed a second experimental session, providing a within-subject temporal validation sample. The analysis of ERPs revealed an enhanced N170 component in response to faces compared to action scenes. Moreover, an increased late positive potential (LPP) was observed during the viewing of pain scenes compared to neutral scenes. Additionally, an enhanced P3 response was found when participants viewed faces displaying pain expressions compared to neutral expressions. Subsequently, three RF models were developed to classify images into faces and scenes, neutral and pain scenes, and neutral and pain expressions. The RF model achieved classification accuracies of 75%, 64%, and 69% for cross-validation, cross-subject, and within-subject classifications, respectively, along with reasonably calibrated predictions for the classification of face versus scene images. However, the RF model was unable to classify pain versus neutral stimuli above chance levels when presented with subsequent tasks involving images from either category. These results expand upon previous findings by externally validating the use of ML in classifying ERPs related to different categories of visual images, namely faces and scenes. The results also indicate the limitations of ML in distinguishing pain and non-pain connotations using ERP responses to the passive viewing of visually similar images.

Open access dataset integrating EEG and fNIRS during Stroop tasks

Conflict monitoring and processing are crucial components of the human cognitive system, with significant implications for daily life and the diagnosis of cognitive disorders. The Stroop task, combined with brain function detection technology, has been widely employed as a classical paradigm for investigating conflict processing. However, there remains a lack of public datasets that integrate Electroencephalogram (EEG) and functional Near-infrared Spectroscopy (fNIRS) to simultaneously record brain activity during a Stroop task. We introduce a dual-modality Stroop task dataset incorporating 34-channel EEG (sampling frequency is 1000 Hz) and 20-channel high temporal resolution fNIRS (sampling frequency is 100 Hz) measurements covering the whole frontal cerebral cortex from 21 participants (9 females/12 males, aged 23.0 ± 2.3 years). Event-related potential analysis of EEG recordings and activation analysis of fNIRS recordings were performed to show the significant Stroop effect. We expected that the data provided would be utilized to investigate multimodal data processing algorithms during cognitive processing.

Electrophysiological correlates of semantic pain processing in the affective priming.

Pain plays a fundamental role in the well-being of the individual, and its semantic content may have specific properties compared to other negative domains (i.e., fear and anger) which allows the cognitive system to detect it with priority. Considering the influence of the affective context in which stimuli (targets) are evaluated, it is possible that their valence could be differentially processed if preceded by negative stimuli (primes) associated with pain than negative stimuli not associated with pain. Thus, the present study aims to investigate the electrophysiological correlates of the implicit processing of words with pain content by using an affective priming paradigm. Event-related potentials (ERPs) were recorded while participants were presented with positive and negative word targets (not associated with pain) that were preceded by positive, negative (not associated with pain), and pain word primes. Participants were asked to judge the valence of the target word. Results showed faster reaction times (RTs) in congruent conditions, especially when the negative target was preceded by a pain prime rather than a positive one. ERPs analyses showed no effect of pain at an early-stage processing (N400), but a larger waveform when the pain prime preceded the positive prime on the LPP. These results reaffirm the importance that valence has in establishing the priority with which stimuli are encoded in the environment and highlight the role that pain has in the processing of stimuli, supporting the hypothesis according to which the valence and the semantics of a stimulus interact with each other generating a specific response for each type of emotion.

With or without Feedback?-How the Presence of Feedback Affects Processing in Children with Developmental Language Disorder.

Language acquisition depends on the ability to process and learn probabilistic information, often through the integration of performance feedback. Children with developmental language disorder (DLD) have demonstrated weaknesses in both probabilistic learning and feedback processing, but the individual effects of each skill are poorly understood in this population. This study examined school-aged children with DLD (n = 29) and age- and gender-matched children with typical development (TD; n = 44) on a visual probabilistic classification learning task presented with and without feedback. In the feedback-based version of the task, children received performance feedback on a trial-by-trial basis during the training phase of the task. In the feedback-free version, children responded after seeing the correct choice marked with a green border and were not presented with feedback. Children with TD achieved higher accuracy than children with DLD following feedback-based training, while the two groups achieved similar levels of accuracy following feedback-free training. Analyses of event-related potentials (ERPs) provided insight into stimulus encoding processes. The feedback-free task was dominated by a frontal slow wave (FSW) and a late parietal component (LPC) which were not different between the two groups. The feedback-based task was dominated by a parietal slow wave (PSW) and an LPC, both of which were found to be larger in the TD than in the DLD group. In combination, results suggest that engagement with feedback boosts learning in children with TD, but not in children with DLD. When the need to process feedback is eliminated, children with DLD demonstrate behavioral and neurophysiological responses similar to their peers with TD.

Stimulus expectations do not modulate visual event-related potentials in probabilistic cueing designs

Humans and other animals can learn and exploit repeating patterns that occur within their environments. These learned patterns can be used to form expectations about future sensory events. Several influential predictive coding models have been proposed to explain how learned expectations influence the activity of stimulus-selective neurons in the visual system. These models specify reductions in neural response measures when expectations are fulfilled (termed expectation suppression) and increases following surprising sensory events. However, there is currently scant evidence for expectation suppression in the visual system when confounding factors are taken into account. Effects of surprise have been observed in blood oxygen level dependent (BOLD) signals, but not when using electrophysiological measures. To provide a strong test for expectation suppression and surprise effects we performed a predictive cueing experiment while recording electroencephalographic (EEG) data. Participants (n=48) learned cue-face associations during a training session and were then exposed to these cue-face pairs in a subsequent experiment. Using univariate analyses of face-evoked event-related potentials (ERPs) we did not observe any differences across expected (90% probability), neutral (50%) and surprising (10%) face conditions. Across these comparisons, Bayes factors consistently favoured the null hypothesis throughout the time-course of the stimulus-evoked response. When using multivariate pattern analysis we did not observe above-chance classification of expected and surprising face-evoked ERPs. By contrast, we found robust within- and across-trial stimulus repetition effects. Our findings do not support predictive coding-based accounts that specify reduced prediction error signalling when perceptual expectations are fulfilled. They instead highlight the utility of other types of predictive processing models that describe expectation-related phenomena in the visual system without recourse to prediction error signalling.

Second Order Blind Identification of Event Related Potentials Sources.

Event-related potentials (ERPs) recorded on the surface of the head are a mixture of signals from many sources in the brain due to volume conductions. As a result, the spatial resolution of the ERPs is quite low. Blind source separation can help to recover source signals from multichannel ERP records. In this study, we present a novel implementation of a method for decomposing multi-channel ERP into components, which is based on the modeling of second-order statistics of ERPs. We also report a new implementation of Bayesian Information Criteria (BIC), which is used to select the optimal number of hidden signals (components) in the original ERPs. We tested these methods using both synthetic datasets and real ERPs data arrays. Testing has shown that the ERP decomposition method can reconstruct the source signals from their mixture with acceptable accuracy even when these signals overlap significantly in time and the presence of noise. The use of BIC allows us to determine the correct number of source signals at the signal-to-noise ratio commonly observed in ERP studies. The proposed approach was compared with conventionally used methods for the analysis of ERPs. It turned out that the use of this new method makes it possible to observe such phenomena that are hidden by other signals in the original ERPs. The proposed method for decomposing a multichannel ERP into components can be useful for studying cognitive processes in laboratory settings, as well as in clinical studies.

Real and Deepfake Face Recognition: An EEG Study on Cognitive and Emotive Implications.

The human brain's role in face processing (FP) and decision making for social interactions depends on recognizing faces accurately. However, the prevalence of deepfakes, AI-generated images, poses challenges in discerning real from synthetic identities. This study investigated healthy individuals' cognitive and emotional engagement in a visual discrimination task involving real and deepfake human faces expressing positive, negative, or neutral emotions. Electroencephalographic (EEG) data were collected from 23 healthy participants using a 21-channel dry-EEG headset; power spectrum and event-related potential (ERP) analyses were performed. Results revealed statistically significant activations in specific brain areas depending on the authenticity and emotional content of the stimuli. Power spectrum analysis highlighted a right-hemisphere predominance in theta, alpha, high-beta, and gamma bands for real faces, while deepfakes mainly affected the frontal and occipital areas in the delta band. ERP analysis hinted at the possibility of discriminating between real and synthetic faces, as N250 (200-300 ms after stimulus onset) peak latency decreased when observing real faces in the right frontal (LF) and left temporo-occipital (LTO) areas, but also within emotions, as P100 (90-140 ms) peak amplitude was found higher in the right temporo-occipital (RTO) area for happy faces with respect to neutral and sad ones.

Event-Related Potentials (ERP) evidence of predictive coding account of time perception in the sub-second range oddball tasks

ABSTRACT This paper aims to examine the controversial effects of repetition suppression, predictability, and pitch of an auditory stimulus on its perceived duration and Event-Related Potentials (ERP). Behavioral results indicated that the repetition suppression and pitch increase lead to time overestimation. ERP analysis showed that the amplitude of N1, P2, N2, and P3 components decreased with each repetition of the standard stimulus. Whereas they increased after the oddball stimulus. Furthermore, a significant increase in N1 and N2 amplitude evoked by the unpredictable stimulus was observed. P2 and P3 (occurring about 200ms after the tone onset and offset, respectively) were significantly affected by tone pitch and duration, respectively. The predictive coding theory provides an account for the above effects. Adaptation causes a reduction in the amplitude of all components. At N1 and N2, sensory input information and predictions are compared, but pieces of evidence are accumulated 200ms later at P2 and P3.

The Levels of Auditory Processing during Emotional Perception in Children with Autism.

The perception of basic emotional sounds, such as crying and laughter is associated with effective interpersonal communication. Difficulties with the perception and analysis of sounds that complicate understanding emotions at an early development age may contribute to communication deficits. This study focused on auditory nonverbal emotional perception including emotional vocalizations with opposite valences (crying and laughter) and neutral sound (phoneme "Pᴂ"). We conducted event-related potential analysis and compared peak alpha frequencies (PAFs) for different conditions in children with autism spectrum disorder (ASD) and typically developing (TD) children aged 4 to 6 years old (N = 25 for each group). Children with ASD had a higher amplitude of P100 and lower amplitude of N200 for all types of sounds and higher P270 in response to neutral phoneme. During the perception of emotional sounds, children with ASD demonstrated a single P270 electroencephalography (EEG) component instead of a P200-P300 complex specific to TD children. However, the most significant differences were associated with a response to emotional valences of stimuli. The EEG differences between crying and laughter were expressed as a lower amplitude of N400 and higher PAF for crying compared to laughter and were found only in TD children. Children with ASD have shown not just abnormal acoustical perception but altered emotional analysis of affective sounds as well.

Complain like you mean it! How prosody conveys suffering even about innocuous events

When complaining, speakers can use their voice to convey a feeling of pain, even when describing innocuous events. Rapid detection of emotive and identity features of the voice may constrain how the semantic content of complaints is processed, as indexed by N400 and P600 effects evoked by the final, pain-related word. Twenty-six participants listened to statements describing painful and innocuous events expressed in a neutral or complaining voice, produced by ingroup and outgroup accented speakers. Participants evaluated how hurt the speaker felt under EEG monitoring. Principal Component Analysis of Event-Related Potentials from the final word onset demonstrated N400 and P600 increases when complainers described innocuous vs. painful events in a neutral voice, but these effects were altered when utterances were expressed in a complaining voice. Independent of prosody, N400 amplitudes increased for complaints spoken in outgroup vs. ingroup accents. Results demonstrate that prosody and accent constrain the processing of spoken complaints as proposed in a parallel-constraint-satisfaction model.