Regression-based estimation of ERP waveforms: I. The rERP framework

Common neural choice signals can emerge artefactually amid multiple distinct value signals

Previous work has identified characteristic neural signatures of value-based decision-making, including neural dynamics that closely resemble the ramping evidence accumulation process believed to underpin choice. Here we test whether these signatures of the choice process can be temporally dissociated from additional, choice-'independent' value signals. Indeed, EEG activity during value-based choice revealed distinct spatiotemporal clusters, with a stimulus-locked cluster reflecting affective reactions to choice sets and a response-locked cluster reflecting choice difficulty. Surprisingly, 'neither' of these clusters met the criteria for an evidence accumulation signal. Instead, we found that stimulus-locked activity can 'mimic' an evidence accumulation process when aligned to the response. Re-analysing four previous studies, including three perceptual decision-making studies, we show that response-locked signatures of evidence accumulation disappear when stimulus-locked and response-locked activity are modelled jointly. Collectively, our findings show that neural signatures of value can reflect choice-independent processes and look deceptively like evidence accumulation.

Reward positivity affects temporal interval production in a continuous timing task

The neural circuits of reward processing and interval timing (including the perception and production of temporal intervals) are functionally intertwined, suggesting that it might be possible for momentary reward processing to influence subsequent timing behavior. Previous animal and human studies have mainly focused on the effect of reward on interval perception, whereas its impact on interval production is less clear. In this study, we examined whether feedback, as an example of performance-contingent reward, biases interval production. We recorded EEG from 20 participants while they engaged in a continuous drumming task with different realistic tempos (1728 trials per participant). Participants received color-coded feedback after each beat about whether they were correct (on time) or incorrect (early or late). Regression-based EEG analysis was used to unmix the rapid occurrence of a feedback response called the reward positivity (RewP), which is traditionally observed in more slow-paced tasks. Using linear mixed modeling, we found that RewP amplitude predicted timing behavior for the upcoming beat. This performance-biasing effect of the RewP was interpreted as reflecting the impact of fluctuations in reward-related anterior cingulate cortex activity on timing, and the necessity of continuous paradigms to make such observations was highlighted.

Neural mechanism underlying preview effects and masked priming effects in visual word processing

Two classic experimental paradigms - masked repetition priming and the boundary paradigm - have played a pivotal role in understanding the process of visual word recognition. Traditionally, these paradigms have been employed by different communities of researchers, with their own long-standing research traditions. Nevertheless, a review of the literature suggests that the brain-electric correlates of word processing established with both paradigms may show interesting similarities, in particular with regard to the location, timing, and direction of N1 and N250 effects. However, as of yet, no direct comparison has been undertaken between the two paradigms. In the current study, we used combined eye-tracking/EEG to perform such a within-subject comparison using the same materials (single Chinese characters) as stimuli. To facilitate direct comparisons, we used a simplified version of the boundary paradigm - the single word boundary paradigm. Our results show the typical early repetition effects of N1 and N250 for both paradigms. However, repetition effects in N250 (i.e., a reduced negativity following identical-word primes/previews as compared to different-word primes/previews) were larger with the single word boundary paradigm than with masked priming. For N1 effects, repetition effects were similar across the two paradigms, showing a larger N1 after repetitions as compared to alternations. Therefore, the results indicate that at the neural level, a briefly presented and masked foveal prime produces qualitatively similar facilitatory effects on visual word recognition as a parafoveal preview before a single saccade, although such effects appear to be stronger in the latter case.

Language prediction in monolingual and bilingual speakers: an EEG study

Prediction of upcoming words is thought to be crucial for language comprehension. Here, we are asking whether bilingualism entails changes to the electrophysiological substrates of prediction. Prior findings leave it open whether monolingual and bilingual speakers predict upcoming words to the same extent and in the same manner. We address this issue with a naturalistic approach, employing an information-theoretic metric, surprisal, to predict and contrast the N400 brain potential in monolingual and bilingual speakers. We recruited 18 Iranian Azeri-Persian bilingual speakers and 22 Persian monolingual speakers. Subjects listened to a story in Persian while their electroencephalogram (EEG) was recorded. Bayesian item-level analysis was used. While in monolingual speakers N400 was sensitive to information-theoretic properties of both the current and previous words, in bilingual speakers N400 reflected the properties of the previous word only. Our findings show evidence for a processing delay in bilingual speakers which is consistent with prior research.

A Deep Learning Approach to Analyzing Continuous-Time Cognitive Processes

The dynamics of the mind are complex. Mental processes unfold continuously in time and may be sensitive to a myriad of interacting variables, especially in naturalistic settings. But statistical models used to analyze data from cognitive experiments often assume simplistic dynamics. Recent advances in deep learning have yielded startling improvements to simulations of dynamical cognitive processes, including speech comprehension, visual perception, and goal-directed behavior. But due to poor interpretability, deep learning is generally not used for scientific analysis. Here, we bridge this gap by showing that deep learning can be used, not just to imitate, but to analyze complex processes, providing flexible function approximation while preserving interpretability. To do so, we define and implement a nonlinear regression model in which the probability distribution over the response variable is parameterized by convolving the history of predictors over time using an artificial neural network, thereby allowing the shape and continuous temporal extent of effects to be inferred directly from time series data. Our approach relaxes standard simplifying assumptions (e.g., linearity, stationarity, and homoscedasticity) that are implausible for many cognitive processes and may critically affect the interpretation of data. We demonstrate substantial improvements on behavioral and neuroimaging data from the language processing domain, and we show that our model enables discovery of novel patterns in exploratory analyses, controls for diverse confounds in confirmatory analyses, and opens up research questions in cognitive (neuro)science that are otherwise hard to study.

(Micro)saccade-related potentials during face recognition: A study combining EEG, eye-tracking, and deconvolution modeling

Under natural viewing conditions, complex stimuli such as human faces are typically looked at several times in succession, implying that their recognition may unfold across multiple eye fixations. Although electrophysiological (EEG) experiments on face recognition typically prohibit eye movements, participants still execute frequent (micro)saccades on the face, each of which generates its own visuocortical response. This finding raises the question of whether the fixation-related potentials (FRPs) evoked by these tiny gaze shifts also contain psychologically valuable information about face processing. Here, we investigated this question by corecording EEG and eye movements in an experiment with emotional faces (happy, angry, neutral). Deconvolution modeling was used to separate the stimulus ERPs to face onset from the FRPs generated by subsequent microsaccades-induced refixations on the face. As expected, stimulus ERPs exhibited typical emotion effects, with a larger early posterior negativity (EPN) for happy/angry compared with neutral faces. Eye tracking confirmed that participants made small saccades in 98% of the trials, which were often aimed at the left eye of the stimulus face. However, while each saccade produced a strong response over visual areas, this response was unaffected by the face's emotional expression, both for the first and for subsequent (micro)saccades. This finding suggests that the face's affective content is rapidly evaluated after stimulus onset, leading to only a short-lived sensory enhancement by arousing stimuli that does not repeat itself during immediate refixations. Methodologically, our work demonstrates how eye tracking and deconvolution modeling can be used to extract several brain responses from each EEG trial, providing insights into neural processing at different latencies after stimulus onset.

The neural correlates of continuous feedback processing

Feedback processing is commonly studied by analyzing the brain's response to discrete rather than continuous events. Such studies have led to the hypothesis that rapid phasic midbrain dopaminergic activity tracks reward prediction errors (RPEs), the effects of which are measurable at the scalp via electroencephalography (EEG). Although studies using continuous feedback are sparse, recent animal work suggests that moment-to-moment changes in reward are tracked by slowly ramping midbrain dopaminergic activity. Some have argued that these ramping signals index state values rather than RPEs. Our goal here was to develop an EEG measure of continuous feedback processing in humans, then test whether its behavior could be accounted for by the RPE hypothesis. Participants completed a stimulus-response learning task in which a continuous reward cue gradually increased or decreased over time. A regression-based unmixing approach revealed EEG activity with a topography and time course consistent with the stimulus-preceding negativity (SPN), a scalp potential previously linked to reward anticipation and tonic dopamine release. Importantly, this reward-related activity depended on outcome expectancy: as predicted by the RPE hypothesis, activity for expected reward cues was reduced compared to unexpected reward cues. These results demonstrate the possibility of using human scalp-recorded potentials to track continuous feedback processing, and test candidate hypotheses of this activity.

Intonation Units in Spontaneous Speech Evoke a Neural Response

Spontaneous speech is produced in chunks called intonation units (IUs). IUs are defined by a set of prosodic cues and presumably occur in all human languages. Recent work has shown that across different grammatical and sociocultural conditions IUs form rhythms of ∼1 unit per second. Linguistic theory suggests that IUs pace the flow of information in the discourse. As a result, IUs provide a promising and hitherto unexplored theoretical framework for studying the neural mechanisms of communication. In this article, we identify a neural response unique to the boundary defined by the IU. We measured the EEG of human participants (of either sex), who listened to different speakers recounting an emotional life event. We analyzed the speech stimuli linguistically and modeled the EEG response at word offset using a GLM approach. We find that the EEG response to IU-final words differs from the response to IU-nonfinal words even when equating acoustic boundary strength. Finally, we relate our findings to the body of research on rhythmic brain mechanisms in speech processing. We study the unique contribution of IUs and acoustic boundary strength in predicting delta-band EEG. This analysis suggests that IU-related neural activity, which is tightly linked to the classic Closure Positive Shift (CPS), could be a time-locked component that captures the previously characterized delta-band neural speech tracking.SIGNIFICANCE STATEMENT Linguistic communication is central to human experience, and its neural underpinnings are a topic of much research in recent years. Neuroscientific research has benefited from studying human behavior in naturalistic settings, an endeavor that requires explicit models of complex behavior. Usage-based linguistic theory suggests that spoken language is prosodically structured in intonation units. We reveal that the neural system is attuned to intonation units by explicitly modeling their impact on the EEG response beyond mere acoustics. To our understanding, this is the first time this is demonstrated in spontaneous speech under naturalistic conditions and under a theoretical framework that connects the prosodic chunking of speech, on the one hand, with the flow of information during communication, on the other.

Neural speech tracking shifts from the syllabic to the modulation rate of speech as intelligibility decreases

The most prominent acoustic features in speech are intensity modulations, represented by the amplitude envelope of speech. Synchronization of neural activity with these modulations supports speech comprehension. As the acoustic modulation of speech is related to the production of syllables, investigations of neural speech tracking commonly do not distinguish between lower-level acoustic (envelope modulation) and higher-level linguistic (syllable rate) information. Here we manipulated speech intelligibility using noise-vocoded speech and investigated the spectral dynamics of neural speech processing, across two studies at cortical and subcortical levels of the auditory hierarchy, using magnetoencephalography. Overall, cortical regions mostly track the syllable rate, whereas subcortical regions track the acoustic envelope. Furthermore, with less intelligible speech, tracking of the modulation rate becomes more dominant. Our study highlights the importance of distinguishing between envelope modulation and syllable rate and provides novel possibilities to better understand differences between auditory processing and speech/language processing disorders.

Quantifying decision-making in dynamic, continuously evolving environments

During perceptual decision-making tasks, centroparietal electroencephalographic (EEG) potentials report an evidence accumulation-to-bound process that is time locked to trial onset. However, decisions in real-world environments are rarely confined to discrete trials; they instead unfold continuously, with accumulation of time-varying evidence being recency-weighted towards its immediate past. The neural mechanisms supporting recency-weighted continuous decision-making remain unclear. Here, we use a novel continuous task design to study how the centroparietal positivity (CPP) adapts to different environments that place different constraints on evidence accumulation. We show that adaptations in evidence weighting to these different environments are reflected in changes in the CPP. The CPP becomes more sensitive to fluctuations in sensory evidence when large shifts in evidence are less frequent, and the potential is primarily sensitive to fluctuations in decision-relevant (not decision-irrelevant) sensory input. A complementary triphasic component over occipito-parietal cortex encodes the sum of recently accumulated sensory evidence, and its magnitude covaries with parameters describing how different individuals integrate sensory evidence over time. A computational model based on leaky evidence accumulation suggests that these findings can be accounted for by a shift in decision threshold between different environments, which is also reflected in the magnitude of pre-decision EEG activity. Our findings reveal how adaptations in EEG responses reflect flexibility in evidence accumulation to the statistics of dynamic sensory environments.

Nonadditive integration of visual information in ensemble processing

Statistically summarizing information from a stimulus array into an ensemble representation (e.g., the mean) improves the efficiency of visual processing. However, little is known about how the brain computes the ensemble statistics. Here, we propose that ensemble processing is realized by nonadditive integration, rather than linear averaging, of individual items. We used a linear regression model approach to extract EEG responses to three levels of information: the individual items, their local interactions, and their global interaction. The local and global interactions, representing nonadditive integration of individual items, elicited rapid and independent neural responses. Critically, only the neural representation of the global interaction predicted the precision of the ensemble perception at the behavioral level. Furthermore, spreading attention over the global pattern to enhance ensemble processing directly promoted rapid neural representation of the global interaction. Taken together, these findings advocate a global, nonadditive mechanism of ensemble processing in the brain.

The P600 as a continuous index of integration effort

The integration of word meaning into an unfolding utterance representation is a core operation of incremental language comprehension. There is considerable debate, however, as to which component of the ERP signal-the N400 or the P600-directly reflects integrative processes, with far reaching consequences for the temporal organization and architecture of the comprehension system. Multi-stream models maintaining the N400 as integration crucially rely on the presence of a semantically attractive plausible alternative interpretation to account for the absence of an N400 effect in response to certain semantic anomalies, as reported in previous studies. The single-stream Retrieval-Integration account posits the P600 as an index of integration, further predicting that its amplitude varies continuously with integrative effort. Here, we directly test these competing hypotheses using a context manipulation design in which a semantically attractive alternative is either available or not, and target word plausibility is varied across three levels. An initial self-paced reading study revealed graded reading times for plausibility, suggesting differential integration effort. A subsequent ERP study showed no N400 differences across conditions, and that P600 amplitude is graded for plausibility. These findings are inconsistent with the interpretation of the N400 as an index of integration, as no N400 effect emerged even in the absence of a semantically attractive alternative. By contrast, the link between plausibility, reading times, and P600 amplitude supports the view that the P600 is a continuous index of integration effort. More generally, our results support a single-stream architecture and eschew the need for multi-stream accounts.

Better Together: Integrating Multivariate with Univariate Methods, and MEG with EEG to Study Language Comprehension

We used MEG and EEG to examine the effects of Plausibility (anomalous vs. plausible) and Animacy (animate vs. inanimate) on activity to incoming words during language comprehension. We conducted univariate event-related and multivariate spatial similarity analyses on both datasets. The univariate and multivariate results converged in their time course and sensitivity to Plausibility. However, only the spatial similarity analyses detected effects of Animacy. The MEG and EEG findings largely converged between 300-500ms, but diverged in their univariate and multivariate responses to the anomalies between 600-1000ms. We interpret the full set of results within a predictive coding framework. In addition to the theoretical significance of these findings, we discuss the methodological implications of the convergence and divergence between the univariate and multivariate results, as well as between the MEG and EEG results. We argue that a deeper understanding of language processing can be achieved by integrating different analysis approaches and techniques.

Episodic memory formation in unrestricted viewing

The brain systems of episodic memory and oculomotor control are tightly linked, suggesting a crucial role of eye movements in memory. But little is known about the neural mechanisms of memory formation across eye movements in unrestricted viewing behavior. Here, we leverage simultaneous eye tracking and EEG recording to examine episodic memory formation in free viewing. Participants memorized multi-element events while their EEG and eye movements were concurrently recorded. Each event comprised elements from three categories (face, object, place), with two exemplars from each category, in different locations on the screen. A subsequent associative memory test assessed participants' memory for the between-category associations that specified each event. We used a deconvolution approach to overcome the problem of overlapping EEG responses to sequential saccades in free viewing. Brain activity was time-locked to the fixation onsets, and we examined EEG power in the theta and alpha frequency bands, the putative oscillatory correlates of episodic encoding mechanisms. Three modulations of fixation-related EEG predicted high subsequent memory performance: (1) theta increase at fixations after between-category gaze transitions, (2) theta and alpha increase at fixations after within-element gaze transitions, (3) alpha decrease at fixations after between-exemplar gaze transitions. Thus, event encoding with unrestricted viewing behavior was characterized by three neural mechanisms, manifested in fixation-locked theta and alpha EEG activity that rapidly turned on and off during the unfolding eye movement sequences. These three distinct neural mechanisms may be the essential building blocks that subserve the buildup of coherent episodic memories during unrestricted viewing behavior.

Developmental differences in EEG oscillations supporting the identification of novel word meaning from context

Implicit learning about new words by picking up on associative information in the contexts they appear in is an important aspect of vocabulary growth. The current study investigated the neural correlates that underlie how school-aged children and adolescents identify the meaning of novel words embedded within sentence contexts. Importantly, we examine how differences in the brain response to novel words and their context differ as a function of 1) explicit learning success, i.e., whether novel word meanings can be correctly estimated in isolation after a learning opportunity, and 2) individual differences in offline language aptitude as well as age across our cohort (N = 82; 8-16 years). Using a regression-based analysis, we identified the unique influence of these individuals difference metrics by using both measures within the same series of models. The most notable finding from our analysis was a frequency-specific dissociation between the way age and language abilities held relationships with task-relevant oscillatory activity during the novel word meaning task: language abilities associated with task-relevant changes in beta band activity during sentence processing, while age associated with task-relevant changes in theta band activity during pseudoword processing. These effects reflect the how the neural correlates of mapping semantic meaning from sentence contexts-an important skill for word learning-is uniquely influenced by the maturity of language abilities as well as age.

Late dominance of the right hemisphere during narrative comprehension

PET and fMRI studies suggest that auditory narrative comprehension is supported by a bilateral multilobar cortical network. The superior temporal resolution of magnetoencephalography (MEG) makes it an attractive tool to investigate the dynamics of how different neuroanatomic substrates engage during narrative comprehension. Using beta-band power changes as a marker of cortical engagement, we studied MEG responses during an auditory story comprehension task in 31 healthy adults. The protocol consisted of two runs, each interleaving 7 blocks of the story comprehension task with 15 blocks of an auditorily presented math task as a control for phonological processing, working memory, and attention processes. Sources at the cortical surface were estimated with a frequency-resolved beamformer. Beta-band power was estimated in the frequency range of 16-24 Hz over 1-sec epochs starting from 400 msec after stimulus onset until the end of a story or math problem presentation. These power estimates were compared to 1-second epochs of data before the stimulus block onset. The task-related cortical engagement was inferred from beta-band power decrements. Group-level source activations were statistically compared using non-parametric permutation testing. A story-math contrast of beta-band power changes showed greater bilateral cortical engagement within the fusiform gyrus, inferior and middle temporal gyri, parahippocampal gyrus, and left inferior frontal gyrus (IFG) during story comprehension. A math-story contrast of beta power decrements showed greater bilateral but left-lateralized engagement of the middle frontal gyrus and superior parietal lobule. The evolution of cortical engagement during five temporal windows across the presentation of stories showed significant involvement during the first interval of the narrative of bilateral opercular and insular regions as well as the ventral and lateral temporal cortex, extending more posteriorly on the left and medially on the right. Over time, there continued to be sustained right anterior ventral temporal engagement, with increasing involvement of the right anterior parahippocampal gyrus, STG, MTG, posterior superior temporal sulcus, inferior parietal lobule, frontal operculum, and insula, while left hemisphere engagement decreased. Our findings are consistent with prior imaging studies of narrative comprehension, but in addition, they demonstrate increasing right-lateralized engagement over the course of narratives, suggesting an important role for these right-hemispheric regions in semantic integration as well as social and pragmatic inference processing.

Temporal scaling of human scalp-recorded potentials

Much of human behavior is governed by common processes that unfold over varying timescales. Standard event-related potential analysis assumes fixed-duration responses relative to experimental events. However, recent single-unit recordings in animals have revealed neural activity scales to span different durations during behaviors demanding flexible timing. Here, we employed a general linear modeling approach using a combination of fixed-duration and variable-duration regressors to unmix fixed-time and scaled-time components in human magneto-/electroencephalography (M/EEG) data. We use this to reveal consistent temporal scaling of human scalp-recorded potentials across four independent electroencephalogram (EEG) datasets, including interval perception, production, prediction, and value-based decision making. Between-trial variation in the temporally scaled response predicts between-trial variation in subject reaction times, demonstrating the relevance of this temporally scaled signal for temporal variation in behavior. Our results provide a general approach for studying flexibly timed behavior in the human brain.

A hierarchy of linguistic predictions during natural language comprehension

Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction to guide the interpretation of incoming input. However, the role of prediction in language processing remains disputed, with disagreement about both the ubiquity and representational nature of predictions. Here, we address both issues by analyzing brain recordings of participants listening to audiobooks, and using a deep neural network (GPT-2) to precisely quantify contextual predictions. First, we establish that brain responses to words are modulated by ubiquitous predictions. Next, we disentangle model-based predictions into distinct dimensions, revealing dissociable neural signatures of predictions about syntactic category (parts of speech), phonemes, and semantics. Finally, we show that high-level (word) predictions inform low-level (phoneme) predictions, supporting hierarchical predictive processing. Together, these results underscore the ubiquity of prediction in language processing, showing that the brain spontaneously predicts upcoming language at multiple levels of abstraction.

A hierarchy of linguistic predictions during natural language comprehension

Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction to guide the interpretation of incoming input. However, the role of prediction in language processing remains disputed, with disagreement about both the ubiquity and representational nature of predictions. Here, we address both issues by analyzing brain recordings of participants listening to audiobooks, and using a deep neural network (GPT-2) to precisely quantify contextual predictions. First, we establish that brain responses to words are modulated by ubiquitous predictions. Next, we disentangle model-based predictions into distinct dimensions, revealing dissociable neural signatures of predictions about syntactic category (parts of speech), phonemes, and semantics. Finally, we show that high-level (word) predictions inform low-level (phoneme) predictions, supporting hierarchical predictive processing. Together, these results underscore the ubiquity of prediction in language processing, showing that the brain spontaneously predicts upcoming language at multiple levels of abstraction.

Researcher-practitioner partnerships and in-school laboratories facilitate translational research in reading

Educational neuroscience approaches have helped to elucidate the brain basis of Reading Disability (RD) and of reading intervention response; however, there is often limited translation of this knowledge to the broader scientific and educational communities. Moreover, this work is traditionally lab-based, and thus the underlying theories and research questions are siloed from classroom practices. With growing awareness of the neurobiological origins of RD and increasing popularity of putative "brain-based" approaches in clinics and classrooms, it is imperative that we create more direct and bidirectional communication between scientists and practitioners. Such direct collaborations can help dispel neuromyths, and lead to increased understanding of the promises and pitfalls of neuroscience approaches. Moreover, direct partnerships between researchers and practitioners can lead to greater ecological validity in study designs to improve upon the translational potential of findings. To this end, we have forged collaborative partnerships, and built cognitive neuroscience laboratories within independent reading disabilities schools. This approach affords frequent and ecologically valid neurobiological assessment as children's reading improves in response to intervention. It also permits the creation of dynamic models of leading and lagging relationships of students' learning, and identification of individual-level predictors of intervention response. The partnerships also provide in-depth knowledge of student characteristics and classroom practices, which, when combined with the data we acquire, may facilitate optimization of instructional approaches. In this commentary, we discuss the creation of our partnerships, the scientific problem we are addressing (variable response to reading intervention), and the epistemological significance of researcher-practitioner bi-directional learning.

Visual Motion and Decision-Making in Dyslexia: Reduced Accumulation of Sensory Evidence and Related Neural Dynamics

Children with and without dyslexia differ in their behavioral responses to visual information, particularly when required to pool dynamic signals over space and time. Importantly, multiple processes contribute to behavioral responses. Here we investigated which processing stages are affected in children with dyslexia when performing visual motion processing tasks, by combining two methods that are sensitive to the dynamic processes leading to responses. We used a diffusion model which decomposes response time and accuracy into distinct cognitive constructs, and high-density EEG. Fifty children with dyslexia (24 male) and 50 typically developing children (28 male) 6-14 years of age judged the direction of motion as quickly and accurately as possible in two global motion tasks (motion coherence and direction integration), which varied in their requirements for noise exclusion. Following our preregistered analyses, we fitted hierarchical Bayesian diffusion models to the data, blinded to group membership. Unblinding revealed reduced evidence accumulation in children with dyslexia compared with typical children for both tasks. Additionally, we identified a response-locked EEG component which was maximal over centro-parietal electrodes which indicated a neural correlate of reduced drift rate in dyslexia in the motion coherence task, thereby linking brain and behavior. We suggest that children with dyslexia tend to be slower to extract sensory evidence from global motion displays, regardless of whether noise exclusion is required, thus furthering our understanding of atypical perceptual decision-making processes in dyslexia.SIGNIFICANCE STATEMENT Reduced sensitivity to visual information has been reported in dyslexia, with a lively debate about whether these differences causally contribute to reading difficulties. In this large preregistered study with a blind modeling approach, we combine state-of-the art methods in both computational modeling and EEG analysis to pinpoint the stages of processing that are atypical in children with dyslexia in two visual motion tasks that vary in their requirement for noise exclusion. We find reduced evidence accumulation in children with dyslexia across both tasks, and identify a neural marker, allowing us to link brain and behavior. We show that children with dyslexia exhibit general difficulties with extracting sensory evidence from global motion displays, not just in tasks that require noise exclusion.

Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience

Expectations shape our experience of music. However, the internal model upon which listeners form melodic expectations is still debated. Do expectations stem from Gestalt-like principles or statistical learning? If the latter, does long-term experience play an important role, or are short-term regularities sufficient? And finally, what length of context informs contextual expectations? To answer these questions, we presented human listeners with diverse naturalistic compositions from Western classical music, while recording neural activity using MEG. We quantified note-level melodic surprise and uncertainty using various computational models of music, including a state-of-the-art transformer neural network. A time-resolved regression analysis revealed that neural activity over fronto-temporal sensors tracked melodic surprise particularly around 200ms and 300-500ms after note onset. This neural surprise response was dissociated from sensory-acoustic and adaptation effects. Neural surprise was best predicted by computational models that incorporated long-term statistical learning-rather than by simple, Gestalt-like principles. Yet, intriguingly, the surprise reflected primarily short-range musical contexts of less than ten notes. We present a full replication of our novel MEG results in an openly available EEG dataset. Together, these results elucidate the internal model that shapes melodic predictions during naturalistic music listening.

Neural correlates of intra-saccadic motion perception

Retinal motion of the visual scene is not consciously perceived during ocular saccades in normal everyday conditions. It has been suggested that extra-retinal signals actively suppress intra-saccadic motion perception to preserve stable perception of the visual world. However, using stimuli optimized to preferentially activate the M-pathway, Castet and Masson (2000) demonstrated that motion can be perceived during a saccade. Based on this psychophysical paradigm, we used electroencephalography and eye-tracking recordings to investigate the neural correlates related to the conscious perception of intra-saccadic motion. We demonstrated the effective involvement during saccades of the cortical areas V1-V2 and MT-V5, which convey motion information along the M-pathway. We also showed that individual motion perception was related to retinal temporal frequency.

Retrieval (N400) and integration (P600) in expectation-based comprehension

Expectation-based theories of language processing, such as Surprisal theory, are supported by evidence of anticipation effects in both behavioural and neurophysiological measures. Online measures of language processing, however, are known to be influenced by factors such as lexical association that are distinct from—but often confounded with—expectancy. An open question therefore is whether a specific locus of expectancy related effects can be established in neural and behavioral processing correlates. We address this question in an event-related potential experiment and a self-paced reading experiment that independently cross expectancy and lexical association in a context manipulation design. We find that event-related potentials reveal that the N400 is sensitive to both expectancy and lexical association, while the P600 is modulated only by expectancy. Reading times, in turn, reveal effects of both association and expectancy in the first spillover region, followed by effects of expectancy alone in the second spillover region. These findings are consistent with the Retrieval-Integration account of language comprehension, according to which lexical retrieval (N400) is facilitated for words that are both expected and associated, whereas integration difficulty (P600) will be greater for unexpected words alone. Further, an exploratory analysis suggests that the P600 is not merely sensitive to expectancy violations, but rather, that there is a continuous relation. Taken together, these results suggest that the P600, like reading times, may reflect a meaning-centric notion of Surprisal in language comprehension.

Referential Chains Reveal Predictive Processes and Form-to-Function Mapping: An Electroencephalographic Study Using Naturalistic Story Stimuli

In discourse pragmatics, different referential forms are claimed to be indicative of the cognitive status of a referent in the current discourse. Referential expressions thereby possess a double function: They point back to an (existing) referent (form-to-function mapping), and they are used to derive predictions about a referent’s subsequent recurrence in discourse. Existing event-related potential (ERP) research has mainly focused on the form-to-function mapping of referential expression. In the present ERP study, we explore the relationship of form-to-function mapping and prediction derived from the antecedent of referential expressions in naturalistic auditory language comprehension. Specifically, the study investigates the relationship between the form of a referential expression (pronoun vs. noun) and the form of its antecedent (pronoun vs. noun); i.e., it examines the influence of the interplay of predictions derived from an antecedent (forward-looking function) and the form-to-function mapping of an anaphor (backward-looking function) on the ERPs time-locked to anaphoric expressions. The results in the time range of the P300 and N400 allow for a dissociation of these two functions during online language comprehension.

Single-trial modeling separates multiple overlapping prediction errors during reward processing in human EEG

Learning signals during reinforcement learning and cognitive control rely on valenced reward prediction errors (RPEs) and non-valenced salience prediction errors (PEs) driven by surprise magnitude. A core debate in reward learning focuses on whether valenced and non-valenced PEs can be isolated in the human electroencephalogram (EEG). We combine behavioral modeling and single-trial EEG regression to disentangle sequential PEs in an interval timing task dissociating outcome valence, magnitude, and probability. Multiple regression across temporal, spatial, and frequency dimensions characterized a spatio-tempo-spectral cascade from early valenced RPE value to non-valenced RPE magnitude, followed by outcome probability indexed by a late frontal positivity. Separating negative and positive outcomes revealed the valenced RPE value effect is an artifact of overlap between two non-valenced RPE magnitude responses: frontal theta feedback-related negativity on losses and posterior delta reward positivity on wins. These results reconcile longstanding debates on the sequence of components representing reward and salience PEs in the human EEG.

fitgrid: A Python package for multi-channel event-related time series regression modeling

Electrical brain activity related to external stimulation and internal mental events can be measured at the scalp as tiny time-varying electric potential waveforms (electroencephalogram; EEG), typically a few tens of microvolts peak to peak (Berger, 1930). Even tinier brain responses, too small to be seen by naked eye in the EEG, can be detected by repeating the stimulation, aligning the EEG recordings to the triggering event and averaging them at each time point (Dawson, 1951, 1954). Under assumptions that the brain response (signal) is the same in each recording and the ongoing background EEG (noise) varies randomly, averaging improves the estimate of the "true" brain response at each time point as the random variation cancels. The average event-related brain potential (ERP) and its counterpart for event-related magnetic fields (ERFs) are cornerstones of experimental brain research in human sensation, perception, and cognition (Luck & Kappenman, 2013). Smith and Kutas pointed out that the average ERP at each time t is mathematically identical to the estimated constant β ^ 0 ( t ) for the regression model y(t) = β ₀(t) + ε(t), fit by minimizing squared error (Smith & Kutas, 2015a). The average ERP can be viewed as a time series of model parameter estimates. Generalizing to more complex models such as multiple regression y = β ₀ + β ₁ X ₁ + … + β _pX_p + ε, likewise produces time series of estimates for the constant and each regressor coefficient, the β ^ 0 ( t ) , β ^ 1 ( t ) , … , β ^ p ( t ) dubbed regression ERP (rERP) waveforms (see Smith & Kutas, 2015a, 2015b for discussion of related approaches). This holds for straight-line fits ("slope" rERPs) as well as models of curvilinear relationships such as spline regression (Smith & Kutas, 2015b). Besides the estimated coefficient rERPs, the approach also produces time series for all the basic and derived quantities of the fitted model: coefficient standard errors, residuals, likelihood, Akaike information criterion (AIC), and so forth. With the shift from averaging to regression modeling, however, comes a new problem: fitting, diagnosing, comparing, evaluating and interpreting large numbers of regression models.

Decoding EEG Brain Activity for Multi-Modal Natural Language Processing

Until recently, human behavioral data from reading has mainly been of interest to researchers to understand human cognition. However, these human language processing signals can also be beneficial in machine learning-based natural language processing tasks. Using EEG brain activity for this purpose is largely unexplored as of yet. In this paper, we present the first large-scale study of systematically analyzing the potential of EEG brain activity data for improving natural language processing tasks, with a special focus on which features of the signal are most beneficial. We present a multi-modal machine learning architecture that learns jointly from textual input as well as from EEG features. We find that filtering the EEG signals into frequency bands is more beneficial than using the broadband signal. Moreover, for a range of word embedding types, EEG data improves binary and ternary sentiment classification and outperforms multiple baselines. For more complex tasks such as relation detection, only the contextualized BERT embeddings outperform the baselines in our experiments, which raises the need for further research. Finally, EEG data shows to be particularly promising when limited training data is available.

When components collide: Spatiotemporal overlap of the N400 and P600 in language comprehension

The problem of spatiotemporal overlap between event-related potential (ERP) components is generally acknowledged in language research. However, its implications for the interpretation of experimental results are often overlooked. In a previous experiment on the functional interpretation of the N400 and P600, it was argued that a P600 effect to implausible words was largely obscured - in one of two implausible conditions - by an overlapping N400 effect of semantic association. In the present ERP study, we show that the P600 effect of implausibility is uncovered when the critical condition is tested against a proper baseline condition which elicits a similar N400 amplitude, while it is obscured when tested against a baseline condition producing an N400 effect. Our findings reveal that component overlap can result in the apparent absence or presence of an effect in the surface signal and should therefore be carefully considered when interpreting ERP patterns. Importantly, we show that, by factoring in the effects of spatiotemporal overlap between the N400 and P600 on the surface signal, which we reveal using rERP analysis, apparent inconsistencies in previous findings are easily reconciled, enabling us to draw unambiguous conclusions about the functional interpretation of the N400 and P600 components. Overall, our results provide compelling evidence that the N400 reflects lexical retrieval processes, while the P600 indexes compositional integration of word meaning into the unfolding utterance interpretation.

Dissociable electrophysiological measures of natural language processing reveal differences in speech comprehension strategy in healthy ageing

Healthy ageing leads to changes in the brain that impact upon sensory and cognitive processing. It is not fully clear how these changes affect the processing of everyday spoken language. Prediction is thought to play an important role in language comprehension, where information about upcoming words is pre-activated across multiple representational levels. However, evidence from electrophysiology suggests differences in how older and younger adults use context-based predictions, particularly at the level of semantic representation. We investigate these differences during natural speech comprehension by presenting older and younger subjects with continuous, narrative speech while recording their electroencephalogram. We use time-lagged linear regression to test how distinct computational measures of (1) semantic dissimilarity and (2) lexical surprisal are processed in the brains of both groups. Our results reveal dissociable neural correlates of these two measures that suggest differences in how younger and older adults successfully comprehend speech. Specifically, our results suggest that, while younger and older subjects both employ context-based lexical predictions, older subjects are significantly less likely to pre-activate the semantic features relating to upcoming words. Furthermore, across our group of older adults, we show that the weaker the neural signature of this semantic pre-activation mechanism, the lower a subject’s semantic verbal fluency score. We interpret these findings as prediction playing a generally reduced role at a semantic level in the brains of older listeners during speech comprehension and that these changes may be part of an overall strategy to successfully comprehend speech with reduced cognitive resources.

Electroencephalographic correlates of temporal Bayesian belief updating and surprise

The brain predicts the timing of forthcoming events to optimize responses to them. Temporal predictions have been formalized in terms of the hazard function, which integrates prior beliefs on the likely timing of stimulus occurrence with information conveyed by the passage of time. However, how the human brain updates prior temporal beliefs is still elusive. Here we investigated electroencephalographic (EEG) signatures associated with Bayes-optimal updating of temporal beliefs. Given that updating usually occurs in response to surprising events, we sought to disentangle EEG correlates of updating from those associated with surprise. Twenty-six participants performed a temporal foreperiod task, which comprised a subset of surprising events not eliciting updating. EEG data were analyzed through a regression-based massive approach in the electrode and source space. Distinct late positive, centro-parietally distributed, event-related potentials (ERPs) were associated with surprise and belief updating in the electrode space. While surprise modulated the commonly observed P3b, updating was associated with a later and more sustained P3b-like waveform deflection. Results from source analyses revealed that neural encoding of surprise comprises neural activity in the cingulo-opercular network (CON) and parietal regions. These data provide evidence that temporal predictions are computed in a Bayesian manner, and that this is reflected in P3 modulations, akin to other cognitive domains. Overall, our study revealed that analyzing P3 modulations provides an important window into the Bayesian brain. Data and scripts are shared on OSF: https://osf.io/ckqa5/.

Neurobehavioral Correlates of Surprisal in Language Comprehension: A Neurocomputational Model

Expectation-based theories of language comprehension, in particular Surprisal Theory, go a long way in accounting for the behavioral correlates of word-by-word processing difficulty, such as reading times. An open question, however, is in which component(s) of the Event-Related brain Potential (ERP) signal Surprisal is reflected, and how these electrophysiological correlates relate to behavioral processing indices. Here, we address this question by instantiating an explicit neurocomputational model of incremental, word-by-word language comprehension that produces estimates of the N400 and the P600-the two most salient ERP components for language processing-as well as estimates of "comprehension-centric" Surprisal for each word in a sentence. We derive model predictions for a recent experimental design that directly investigates "world-knowledge"-induced Surprisal. By relating these predictions to both empirical electrophysiological and behavioral results, we establish a close link between Surprisal, as indexed by reading times, and the P600 component of the ERP signal. The resultant model thus offers an integrated neurobehavioral account of processing difficulty in language comprehension.

Selective adaptation in sentence comprehension: Evidence from event-related brain potentials

In this study, two event-related potential experiments were conducted to investigate whether readers adapt their expectations to morphosyntactically (Experiment 1) or semantically (Experiment 2) anomalous sentences when they are repeatedly exposed to them. To address this issue, we experimentally manipulated the probability of occurrence of grammatical sentences and syntactically and semantically anomalous sentences through experiments. For the low probability block, anomalous sentences were presented less frequently than grammatical sentences (with a ratio of 1 to 4), while they were presented as frequently as grammatical sentences in the equal probability block. Experiment 1 revealed a smaller P600 effect for morphosyntactic violations in the equal probability block than in the low probability block. Linear mixed-effects models were used to examine how the size of the P600 effect changed as the experiment went along. The results showed that the smaller P600 effect of the equal probability block resulted from an amplitude's decline in morphosyntactically violated sentences over the course of the experiment, suggesting an adaptation to morphosyntactic violations. In Experiment 2, semantically anomalous sentences elicited a larger N400 effect than their semantically natural counterparts regardless of probability manipulation. Little evidence was found in favour of adaptation to semantic violations in that the processing cost associated with the N400 did not decrease over the course of the experiment. Therefore, a dynamic aspect of language-processing system was demonstrated in this study. We will discuss why the language-processing system shows a selective adaptation to morphosyntactic violations.

Regression-based analysis of combined EEG and eye-tracking data: Theory and applications

Fixation-related potentials (FRPs), neural responses aligned to the end of saccades, are a promising tool for studying the dynamics of attention and cognition under natural viewing conditions. In the past, four methodological problems have complicated the analysis of such combined eye-tracking/electroencephalogram experiments: (1) the synchronization of data streams, (2) the removal of ocular artifacts, (3) the condition-specific temporal overlap between the brain responses evoked by consecutive fixations, and (4) the fact that numerous low-level stimulus and saccade properties also influence the postsaccadic neural responses. Although effective solutions exist for the first two problems, the latter two are only beginning to be addressed. In the current paper, we present and review a unified regression-based framework for FRP analysis that allows us to deconvolve overlapping potentials while also controlling for both linear and nonlinear confounds on the FRP waveform. An open software implementation is provided for all procedures. We then demonstrate the advantages of this proposed (non)linear deconvolution modeling approach for data from three commonly studied paradigms: face perception, scene viewing, and reading. First, for a traditional event-related potential (ERP) face recognition experiment, we show how this technique can separate stimulus ERPs from overlapping muscle and brain potentials produced by small (micro)saccades on the face. Second, in natural scene viewing, we model and isolate multiple nonlinear effects of saccade parameters on the FRP. Finally, for a natural sentence reading experiment using the boundary paradigm, we show how it is possible to study the neural correlates of parafoveal preview after removing spurious overlap effects caused by the associated difference in average fixation time. Our results suggest a principal way of measuring reliable eye movement-related brain activity during natural vision.

No Evidence for a Role of Spatially Modulated α-Band Activity in Tactile Remapping and Short-Latency, Overt Orienting Behavior

Oscillatory α-band activity is commonly associated with spatial attention and multisensory prioritization. It has also been suggested to reflect the automatic transformation of tactile stimuli from a skin-based, somatotopic reference frame into an external one. Previous research has not convincingly separated these two possible roles of α-band activity. Previous experimental paradigms have used artificially long delays between tactile stimuli and behavioral responses to aid relating oscillatory activity to these different events. However, this strategy potentially blurs the temporal relationship of α-band activity relative to behavioral indicators of tactile-spatial transformations. Here, we assessed α-band modulation with massive univariate deconvolution, an analysis approach that disentangles brain signals overlapping in time and space. Thirty-one male and female human participants performed a delay-free, visual search task in which saccade behavior was unrestricted. A tactile cue to uncrossed or crossed hands was either informative or uninformative about visual target location. α-Band suppression following tactile stimulation was lateralized relative to the stimulated hand over central-parietal electrodes but relative to its external location over parieto-occipital electrodes. α-Band suppression reflected external touch location only after informative cues, suggesting that posterior α-band lateralization does not index automatic tactile transformation. Moreover, α-band suppression occurred at the time of, or after, the production of the saccades guided by tactile stimulation. These findings challenge the idea that α-band activity is directly involved in tactile-spatial transformation and suggest instead that it reflects delayed, supramodal processes related to attentional reorienting.SIGNIFICANCE STATEMENT Localizing a touch in space requires integrating somatosensory information about skin location and proprioceptive or visual information about posture. The automatic remapping between skin-based tactile information to a location in external space has been proposed to rely on the modulation of oscillatory brain activity in the α-band range, across the multiple cortical areas that are involved in tactile, multisensory, and spatial processing. We report two findings that are inconsistent with this view. First, α-band activity reflected the remapped stimulus location only when touch was task relevant. Second, α-band modulation occurred too late to account for spatially directed behavioral responses and, thus, only after remapping must have taken place. These characteristics contradict the idea that α-band directly reflects automatic tactile remapping processes.

Splitting event-related potentials: Modeling latent components using regression-based waveform estimation

Event-related potentials (ERPs) provide a multidimensional and real-time window into neurocognitive processing. The typical Waveform-based Component Structure (WCS) approach to ERPs assesses the modulation pattern of components-systematic, reoccurring voltage fluctuations reflecting specific computational operations-by looking at mean amplitude in predetermined time-windows. This WCS approach, however, often leads to inconsistent results within as well as across studies. It has been argued that at least some inconsistencies may be reconciled by considering spatiotemporal overlap between components; that is, components may overlap in both space and time, and given their additive nature, this means that the WCS may fail to accurately represent its underlying latent component structure (LCS). We employ regression-based ERP (rERP) estimation to extend traditional approaches with an additional layer of analysis, which enables the explicit modeling of the LCS underlying WCS. To demonstrate its utility, we incrementally derive an rERP analysis of a recent study on language comprehension with seemingly inconsistent WCS-derived results. Analysis of the resultant regression models allows one to derive an explanation for the WCS in terms of how relevant regression predictors combine in space and time, and crucially, how individual predictors may be mapped onto unique components in LCS, revealing how these spatiotemporally overlap in the WCS. We conclude that rERP estimation allows for investigating how scalp-recorded voltages derive from the spatiotemporal combination of experimentally manipulated factors. Moreover, when factors can be uniquely mapped onto components, rERPs may offer explanations for seemingly inconsistent ERP waveforms at the level of their underlying latent component structure.

Characterizing the brain's dynamical response from scalp-level neural electrical signals: a review of methodology development

The brain displays dynamical system behaviors at various levels that are functionally and cognitively relevant. Ample researches have examined how the dynamical properties of brain activity reflect the neural cognitive working mechanisms. A prevalent approach in this field is to extract the trial-averaged brain electrophysiological signals as a representation of the dynamical response of the complex neural system to external stimuli. However, the responses are intrinsically variable in latency from trial to trial. The variability compromises the accuracy of the detected dynamical response pattern based on trial-averaged approach, which may mislead subsequent modelling works. More accurate characterization of the brain's dynamical response incorporating single trial variability information is of profound significance in deepening our understanding of neural cognitive dynamics and brain's working principles. Various methods have been attempted to address the trial-to-trial asynchrony issue in order to achieve an improved representation of the dynamical response. We review the latest development of methodology in this area and the contribution of latency variability-based decomposition and reconstruction of dynamical response to neural cognitive researches.

An exploratory data analysis of word form prediction during word-by-word reading

In 2005, we reported evidence indicating that upcoming phonological word forms-e.g., kite vs. airplane-were predicted during reading. We recorded brainwaves (electroencephalograms [EEGs]) as people read word-by-word and then correlated the predictability in context of indefinite articles that preceded nouns ( a kite vs. an airplane) with the average event-related brain potentials (ERPs) they elicited [K. A. DeLong, T. P. Urbach, M. Kutas, Nat. Neurosci. 8, 1117-1121 (2005)]. Amid a broader controversy about the role of word-form prediction in comprehension, those findings were recently challenged by a failed putative direct replication attempt [M. S. Nieuwland et al., eLife 7, e33468 (2018); nine labs, one experiment, and 2.6e4 observations]. To better understand the empirical justification for positing an association between prenominal article predictability and scalp potentials, we conducted a wide-ranging exploratory data analysis (EDA), pooling our original data with extant data from two followup studies (one lab, three experiments, and 1.2e4 observations). We modeled the time course of article predictability in the single-trial data by fitting linear mixed-effects regression (LMER) models at each time point and scalp location spanning a 3-s interval before, during, and after the article. Model comparisons based on Akaike information criteria (AIC) and slope-regression ERPs [rERPs; N. J. Smith, M. Kutas, Psychophysiology 52, 157-168 (2015)] provide substantial empirical support for a small positive association between article predictability and scalp potentials approximately 300 to 500 ms after article onset, predominantly over bilateral posterior scalp. We think this effect may reasonably be attributed to prediction of upcoming word forms.

Attention allocation in a language with post-focal prominences

Accentuation influences selective attention and the depth of semantic processing during online speech comprehension. We investigated the processing of semantically congruent and incongruent words in a language that presents cues to prosodic prominences in the region of the utterance occurring after the focussed information (the post-focal region). This language is Italian, in particular the variety spoken in Bari. In this variety, questions have a compressed, post-focal accent, whereas in statements there is a low-level pitch in this position. Using event-related potentials, we investigated the processing of congruent and incongruent target words with two prosodic realizations (focussed with accentuation, post-focal realization) and in two-sentence modalities (statement, question). Results indicate an N400 congruence effect that was modulated by position (focal, post-focal) and modality (statement, question): processing was deeper for questions in narrow focus than in post-focal position, while statements showed similar pronounced N400 effects across positions. The attenuated N400 difference for post-focal targets in questions was accompanied by a more enhanced late positivity when they were incongruent, indicating that attentional resources are allocated during updating of speech act information.

Neural dynamics of sentiment processing during naturalistic sentence reading

When we read, our eyes move through the text in a series of fixations and high-velocity saccades to extract visual information. This process allows the brain to obtain meaning, e.g., about sentiment, or the emotional valence, expressed in the written text. How exactly the brain extracts the sentiment of single words during naturalistic reading is largely unknown. This is due to the challenges of naturalistic imaging, which has previously led researchers to employ highly controlled, timed word-by-word presentations of custom reading materials that lack ecological validity. Here, we aimed to assess the electrical neural correlates of word sentiment processing during naturalistic reading of English sentences. We used a publicly available dataset of simultaneous electroencephalography (EEG), eye-tracking recordings, and word-level semantic annotations from 7129 words in 400 sentences (Zurich Cognitive Language Processing Corpus; Hollenstein et al., 2018). We computed fixation-related potentials (FRPs), which are evoked electrical responses time-locked to the onset of fixations. A general linear mixed model analysis of FRPs cleaned from visual- and motor-evoked activity showed a topographical difference between the positive and negative sentiment condition in the 224-304 ​ms interval after fixation onset in left-central and right-posterior electrode clusters. An additional analysis that included word-, phrase-, and sentence-level sentiment predictors showed the same FRP differences for the word-level sentiment, but no additional FRP differences for phrase- and sentence-level sentiment. Furthermore, decoding analysis that classified word sentiment (positive or negative) from sentiment-matched 40-trial average FRPs showed a 0.60 average accuracy (95% confidence interval: [0.58, 0.61]). Control analyses ruled out that these results were based on differences in eye movements or linguistic features other than word sentiment. Our results extend previous research by showing that the emotional valence of lexico-semantic stimuli evoke a fast electrical neural response upon word fixation during naturalistic reading. These results provide an important step to identify the neural processes of lexico-semantic processing in ecologically valid conditions and can serve to improve computer algorithms for natural language processing.

Eye contact in active and passive viewing: Event-related brain potential evidence from a combined eye tracking and EEG study

Eye contact is a salient social cue, which is assumed to influence already early neural correlates of face perception. Specifically, the N170 component of the event-related potential (ERP) has often been found to be larger for faces with an averted gaze as compared to faces that directly look at the observer. In most existing ERP studies, effects of eye contact were investigated under comparatively artificial conditions where participants were instructed to maintain a steady fixation while they passively observed gaze changes in the stimulus face. It is therefore unclear to what extent neural correlates of eye contact generalize to more naturalistic situations that involve a continuous interplay between directed and averted gaze between the communication partners. To start bridging this gap, the present study compared the passive viewing of gaze changes to an active condition in which the participant's own gaze (measured online with an eye tracker) interacted with the gaze position of a continuously presented stimulus face. We also investigated whether eye contact effects were modulated by the face's emotional expression. In both the passive and the active viewing condition, N170 amplitudes were larger when the gaze of the stimulus faces was averted rather than directed towards the participant. Furthermore, eye contact decreased P300 amplitudes in both conditions. The emotional expression of the face also modulated the N170, but this effect did not interact with that of gaze direction. We conclude that the neural correlates of gaze perception during active gaze interactions are comparable to those found during passive viewing, encouraging the further study of eye contact effects in more naturalistic settings.

ReSync: Correcting the trial-to-trial asynchrony of event-related brain potentials to improve neural response representation

BACKGROUND

For various reasons, the brain response activities in electroencephalography (EEG) signals are not perfectly synchronized between trials with respect to event markers-a problem commonly referred to as latency jitter. Experimental technologies have been greatly advanced to reduce technical timing errors and thereby reduce jitter. However, there remain intrinsic sources of jitter that are difficult to remove. The problem becomes more complicated when multiple sub-components possess different degrees and features of jitter. The jitter issue renders trial-averaged ERP inaccurate and even misleading. Effective methods for correcting ERP distortion due to latency jitter are needed.

NEW METHOD

This study developed a simple and easy-to-use method and toolbox for correcting ERP jitter based on simple signal processing theories, named ReSync. ReSync can be used to correct multiple overlapping ERP sub-components with different degrees of jitter (including static sub-components) without their affecting each other.

RESULTS

The theories, principles, technical details, and limitations of ReSync are presented in this paper, along with a series of simulation and real data examples used to evaluate and validate the method.

COMPARISON WITH EXISTING METHODS

ReSync was conceptually compared with previous methods in the literature that are related to tackling of the jitter issue from theoretical, methodological, and technical perspectives.

CONCLUSIONS

Providing a novel approach to latency jitter estimation with automatic dominant frequency identification and integrated decomposition and reconstruction, the ReSync method was validated using both simulation and empirical data, and demonstrated to be an effective jitter-correction approach with implementational simplicity.

Fixation-related Brain Potentials during Semantic Integration of Object–Scene Information

In vision science, a particularly controversial topic is whether and how quickly the semantic information about objects is available outside foveal vision. Here, we aimed at contributing to this debate by coregistering eye movements and EEG while participants viewed photographs of indoor scenes that contained a semantically consistent or inconsistent target object. Linear deconvolution modeling was used to analyze the ERPs evoked by scene onset as well as the fixation-related potentials (FRPs) elicited by the fixation on the target object (t) and by the preceding fixation (t − 1). Object–scene consistency did not influence the probability of immediate target fixation or the ERP evoked by scene onset, which suggests that object–scene semantics was not accessed immediately. However, during the subsequent scene exploration, inconsistent objects were prioritized over consistent objects in extrafoveal vision (i.e., looked at earlier) and were more effortful to process in foveal vision (i.e., looked at longer). In FRPs, we demonstrate a fixation-related N300/N400 effect, whereby inconsistent objects elicit a larger frontocentral negativity than consistent objects. In line with the behavioral findings, this effect was already seen in FRPs aligned to the pretarget fixation t − 1 and persisted throughout fixation t, indicating that the extraction of object semantics can already begin in extrafoveal vision. Taken together, the results emphasize the usefulness of combined EEG/eye movement recordings for understanding the mechanisms of object–scene integration during natural viewing.

Face Selective Neural Activity: Comparisons Between Fixed and Free Viewing

Event Related Potentials (ERPs) are widely used to study category-selective EEG responses to visual stimuli, such as the face-selective N170 component. Typically, this is done by flashing stimuli at the point of static gaze fixation. While allowing for good experimental control, these paradigms ignore the dynamic role of eye-movements in natural vision. Fixation-related potentials (FRPs), obtained using simultaneous EEG and eye-tracking, overcome this limitation. Various studies have used FRPs to study processes such as lexical processing, target detection and attention allocation. The goal of this study was to carefully compare face-sensitive activity time-locked to an abrupt stimulus onset at fixation, with that time-locked to a self-generated fixation on a stimulus. Twelve participants participated in three experimental conditions: Free-viewing (FRPs), Cued-viewing (FRPs) and Control (ERPs). We used a multiple regression approach to disentangle overlapping activity components. Our results show that the N170 face-effect is evident for the first fixation on a stimulus, whether it follows a self-generated saccade or stimulus appearance at fixation point. The N170 face-effect has similar topography across viewing conditions, but there were major differences within each stimulus category. We ascribe these differences to an overlap of the fixation-related lambda response and the N170. We tested the plausibility of this account using dipole simulations. Finally, the N170 exhibits category-specific adaptation in free viewing. This study establishes the comparability of the free-viewing N170 face-effect with the classic event-related effect, while highlighting the importance of accounting for eye-movement related effects.

Lumos!: Electrophysiological tracking of (wizarding) world knowledge use during reading

In Troyer and Kutas (2018), individual differences in knowledge of the world of Harry Potter (HP) rapidly modulated individuals' average electrical brain potentials to contextually supported words in sentence endings. Using advances in single-trial electroencephalogram analysis, we examined whether this relationship is strictly a result of domain knowledge mediating the proportion of facts each participant knew; we find it is not. Participants read sentences ending in a contextually supported word, reporting online whether they had known each fact. Participants' reports correlated with HP domain knowledge and reliably modulated event-related brain potentials to sentence-final words within 250 ms. Critically, domain knowledge had a dissociable influence in the same time window for endings that participants reported not having known and/or were less likely to be known/remembered across participants. We hypothesize that knowledge impacts written word processing primarily by affecting the neural processes of (implicit) retrieval from long-term memory (LTM): Greater knowledge eases otherwise difficult retrieval processes. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Eye Movements and Fixation-Related Potentials in Reading: A Review

The present review is addressed to researchers in the field of reading and psycholinguistics who are both familiar with and new to co-registration research of eye movements (EMs) and fixation related-potentials (FRPs) in reading. At the outset, we consider a conundrum relating to timing discrepancies between EM and event related potential (ERP) effects. We then consider the extent to which the co-registration approach might allow us to overcome this and thereby discriminate between formal theoretical and computational accounts of reading. We then describe three phases of co-registration research before evaluating the existing body of such research in reading. The current, ongoing phase of co-registration research is presented in comprehensive tables which provide a detailed summary of the existing findings. The thorough appraisal of the published studies allows us to engage with issues such as the reliability of FRP components as correlates of cognitive processing in reading and the advantages of analysing both data streams (i.e., EMs and FRPs) simultaneously relative to each alone, as well as the current, and limited, understanding of the relationship between EM and FRP measures. Finally, we consider future directions and in particular the potential of analytical methods involving deconvolution and the potential of measurement of brain oscillatory activity.

Post-Saccadic Face Processing Is Modulated by Pre-Saccadic Preview: Evidence from Fixation-Related Potentials

Humans actively sample their environment with saccadic eye movements to bring relevant information into high-acuity foveal vision. Despite being lower in resolution, peripheral information is also available before each saccade. How the pre-saccadic extrafoveal preview of a visual object influences its post-saccadic processing is still an unanswered question. The current study investigated this question by simultaneously recording behavior and fixation-related brain potentials while human subjects made saccades to face stimuli. We manipulated the relationship between pre-saccadic "previews" and post-saccadic images to explicitly isolate the influences of the former. Subjects performed a gender discrimination task on a newly foveated face under three preview conditions: scrambled face, incongruent face (different identity from the foveated face), and congruent face (same identity). As expected, reaction times were faster after a congruent-face preview compared with a scrambled-face preview. Importantly, intact face previews (either incongruent or congruent) resulted in a massive reduction of post-saccadic neural responses. Specifically, we analyzed the classic face-selective N170 component at occipitotemporal electroencephalogram electrodes, which was still present in our experiments with active looking. However, the post-saccadic N170 was strongly attenuated following intact-face previews compared with the scrambled condition. This large and long-lasting decrease in evoked activity is consistent with a trans-saccadic mechanism of prediction that influences category-specific neural processing at the start of a new fixation. These findings constrain theories of visual stability and show that the extrafoveal preview methodology can be a useful tool to investigate its underlying mechanisms.SIGNIFICANCE STATEMENT Neural correlates of object recognition have traditionally been studied by flashing stimuli to the central visual field. This procedure differs in fundamental ways from natural vision, where viewers actively sample the environment with eye movements and also obtain a low-resolution preview of soon-to-be-fixated objects. Here we show that the N170, a classic electrophysiological marker of the structural encoding of faces, also occurs during a more natural viewing condition but is strongly reduced due to extrafoveal preprocessing (preview benefit). Our results therefore highlight the importance of peripheral vision during trans-saccadic processing in building a coherent and stable representation of the world around us.