Modeling nonlinear relationships in ERP data using mixed-effects regression with R examples

Assessment of the excitation-inhibition ratio in the Fmr1 KO2 mouse using neuronal oscillation dynamics

In vitro and ex vivo studies have shown consistent indications of hyperexcitability in the Fragile X Messenger Ribonucleoprotein 1 (Fmr1) knockout mouse model of autism spectrum disorder. We recently introduced a method to quantify network-level functional excitation-inhibition ratio from the neuronal oscillations. Here, we used this measure to study whether the implicated synaptic excitation-inhibition disturbances translate to disturbances in network physiology in the Fragile X Messenger Ribonucleoprotein 1 (Fmr1) gene knockout model. Vigilance-state scoring was used to extract segments of inactive wakefulness as an equivalent behavioral condition to the human resting-state and, subsequently, we performed high-frequency resolution analysis of the functional excitation-inhibition biomarker, long-range temporal correlations, and spectral power. We corroborated earlier studies showing increased high-frequency power in Fragile X Messenger Ribonucleoprotein 1 (Fmr1) knockout mice. Long-range temporal correlations were higher in the gamma frequency ranges. Contrary to expectations, functional excitation-inhibition was lower in the knockout mice in high frequency ranges, suggesting more inhibition-dominated networks. Exposure to the Gamma-aminobutyric acid (GABA)-agonist clonazepam decreased the functional excitation-inhibition in both genotypes, confirming that increasing inhibitory tone results in a reduction of functional excitation-inhibition. In addition, clonazepam decreased electroencephalogram power and increased long-range temporal correlations in both genotypes. These findings show applicability of these new resting-state electroencephalogram biomarkers to animal for translational studies and allow investigation of the effects of lower-level disturbances in excitation-inhibition balance.

Flexible multi-step hypothesis testing of human ECoG data using cluster-based permutation tests with GLMEs

BACKGROUND

Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types.

METHODS

We first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data.

RESULTS

First, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of "suboptimal" data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data.

CONCLUSIONS

We propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data.

Surprisal From Language Models Can Predict ERPs in Processing Predicate-Argument Structures Only if Enriched by an Agent Preference Principle

Language models based on artificial neural networks increasingly capture key aspects of how humans process sentences. Most notably, model-based surprisals predict event-related potentials such as N400 amplitudes during parsing. Assuming that these models represent realistic estimates of human linguistic experience, their success in modeling language processing raises the possibility that the human processing system relies on no other principles than the general architecture of language models and on sufficient linguistic input. Here, we test this hypothesis on N400 effects observed during the processing of verb-final sentences in German, Basque, and Hindi. By stacking Bayesian generalised additive models, we show that, in each language, N400 amplitudes and topographies in the region of the verb are best predicted when model-based surprisals are complemented by an Agent Preference principle that transiently interprets initial role-ambiguous noun phrases as agents, leading to reanalysis when this interpretation fails. Our findings demonstrate the need for this principle independently of usage frequencies and structural differences between languages. The principle has an unequal force, however. Compared to surprisal, its effect is weakest in German, stronger in Hindi, and still stronger in Basque. This gradient is correlated with the extent to which grammars allow unmarked NPs to be patients, a structural feature that boosts reanalysis effects. We conclude that language models gain more neurobiological plausibility by incorporating an Agent Preference. Conversely, theories of human processing profit from incorporating surprisal estimates in addition to principles like the Agent Preference, which arguably have distinct evolutionary roots.

Combining EEG and 3D-eye-tracking to study the prediction of upcoming speech in naturalistic virtual environments: A proof of principle

EEG and eye-tracking provide complementary information when investigating language comprehension. Evidence that speech processing may be facilitated by speech prediction comes from the observation that a listener's eye gaze moves towards a referent before it is mentioned if the remainder of the spoken sentence is predictable. However, changes to the trajectory of anticipatory fixations could result from a change in prediction or an attention shift. Conversely, N400 amplitudes and concurrent spectral power provide information about the ease of word processing the moment the word is perceived. In a proof-of-principle investigation, we combined EEG and eye-tracking to study linguistic prediction in naturalistic, virtual environments. We observed increased processing, reflected in theta band power, either during verb processing - when the verb was predictive of the noun - or during noun processing - when the verb was not predictive of the noun. Alpha power was higher in response to the predictive verb and unpredictable nouns. We replicated typical effects of noun congruence but not predictability on the N400 in response to the noun. Thus, the rich visual context that accompanied speech in virtual reality influenced language processing compared to previous reports, where the visual context may have facilitated processing of unpredictable nouns. Finally, anticipatory fixations were predictive of spectral power during noun processing and the length of time fixating the target could be predicted by spectral power at verb onset, conditional on the object having been fixated. Overall, we show that combining EEG and eye-tracking provides a promising new method to answer novel research questions about the prediction of upcoming linguistic input, for example, regarding the role of extralinguistic cues in prediction during language comprehension.

Trial-by-trial fluctuations of pre-stimulus alpha power predict language ERPs

Anticipatory mechanisms are known to play a key role in language, but they have been mostly investigated with violation paradigms, which only consider what happens after predictions have been (dis)confirmed. Relatively few studies focused on the pre-stimulus interval and found that stronger expectations are associated with lower pre-stimulus alpha power. However, alpha power also fluctuates spontaneously, in the absence of experimental manipulations; and in the attention and perception domains, spontaneously low pre-stimulus power is associated with better behavioral performance and with event-related potential (ERPs) with shorter latencies and higher amplitudes. Importantly, little is known about the role of alpha fluctuations in other domains, as it is in language. To this aim, we investigated whether spontaneous fluctuations in pre-stimulus alpha power modulate language-related ERPs in a semantic congruence task. Electrophysiology data were analyzed using Generalized Additive Mixed Models to model nonlinear interactions between pre-stimulus alpha power and EEG amplitude, at the single-trial level. We found that the N400 and the late posterior positivity/P600 were larger in the case of lower pre-stimulus alpha power. Still, while the N400 was observable regardless of the level of pre-stimulus power, a late posterior positivity/P600 effect was only observable for low pre-stimulus alpha power. We discuss these findings in light of the different, albeit connected, functional interpretations of pre-stimulus alpha and the ERPs according to both a nonpredictive interpretation focused on attentional mechanisms and under a predictive processing framework.

An Agent-First Preference in a Patient-First Language During Sentence Comprehension

The language comprehension system preferentially assumes that agents come first during incremental processing. While this might reflect a biologically fixed bias, shared with other domains and other species, the evidence is limited to languages that place agents first, and so the bias could also be learned from usage frequency. Here, we probe the bias with electroencephalography (EEG) in Äiwoo, a language that by default places patients first, but where sentence-initial nouns are still locally ambiguous between patient or agent roles. Comprehenders transiently interpreted nonhuman nouns as patients, eliciting a negativity when disambiguation was toward the less common agent-initial order. By contrast and against frequencies, human nouns were transiently interpreted as agents, eliciting an N400-like negativity when the disambiguation was toward patient-initial order. Consistent with the notion of a fixed property, the agent bias is robust against usage frequency for human referents. However, this bias can be reversed by frequency experience for nonhuman referents.

Effects of aging on cortical representations of continuous speech

Understanding speech in a noisy environment is crucial in day-to-day interactions and yet becomes more challenging with age, even for healthy aging. Age-related changes in the neural mechanisms that enable speech-in-noise listening have been investigated previously; however, the extent to which age affects the timing and fidelity of encoding of target and interfering speech streams is not well understood. Using magnetoencephalography (MEG), we investigated how continuous speech is represented in auditory cortex in the presence of interfering speech in younger and older adults. Cortical representations were obtained from neural responses that time-locked to the speech envelopes with speech envelope reconstruction and temporal response functions (TRFs). TRFs showed three prominent peaks corresponding to auditory cortical processing stages: early (∼50 ms), middle (∼100 ms), and late (∼200 ms). Older adults showed exaggerated speech envelope representations compared with younger adults. Temporal analysis revealed both that the age-related exaggeration starts as early as ∼50 ms and that older adults needed a substantially longer integration time window to achieve their better reconstruction of the speech envelope. As expected, with increased speech masking envelope reconstruction for the attended talker decreased and all three TRF peaks were delayed, with aging contributing additionally to the reduction. Interestingly, for older adults the late peak was delayed, suggesting that this late peak may receive contributions from multiple sources. Together these results suggest that there are several mechanisms at play compensating for age-related temporal processing deficits at several stages but which are not able to fully reestablish unimpaired speech perception.NEW & NOTEWORTHY We observed age-related changes in cortical temporal processing of continuous speech that may be related to older adults' difficulty in understanding speech in noise. These changes occur in both timing and strength of the speech representations at different cortical processing stages and depend on both noise condition and selective attention. Critically, their dependence on noise condition changes dramatically among the early, middle, and late cortical processing stages, underscoring how aging differentially affects these stages.

Flexible Multi-Step Hypothesis Testing of Human ECoG Data using Cluster-based Permutation Tests with GLMEs

Background

Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types.

Methods

We first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data.

Results

First, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of "suboptimal" data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data.

Conclusions

We propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power and accuracy leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data.

Spoken word recognition in a second language: The importance of phonetic details

Spoken word recognition depends on variations in fine-grained phonetics as listeners decode speech. However, many models of second language (L2) speech perception focus on units such as isolated syllables, and not on words. In two eye-tracking experiments, we investigated how fine-grained phonetic details (i.e. duration of nasalization on contrastive and coarticulatory nasalized vowels in Canadian French) influenced spoken word recognition in an L2, as compared to a group of native (L1) listeners. Results from L2 listeners (English-native speakers) indicated that fine-grained phonetics impacted the recognition of words, i.e. they were able to use nasalization duration variability in a way similar to L1-French listeners, providing evidence that lexical representations can be highly specified in an L2. Specifically, L2 listeners were able to distinguish minimal word pairs (differentiated by the presence of phonological vowel nasalization in French) and were able to use variability in a way approximating L1-French listeners. Furthermore, the robustness of the French "nasal vowel" category in L2 listeners depended on age of exposure. Early bilinguals displayed greater sensitivity to some ambiguity in the stimuli than late bilinguals, suggesting that early bilinguals had greater sensitivity to small variations in the signal and thus better knowledge of the phonetic cue associated with phonological vowel nasalization in French, similarly to L1 listeners.

Neural Mechanisms of the Acceptable Noise Level

PURPOSE

The present brain-behavior study examined whether sensory registration or neural inhibition processes explained variability in the behavioral most comfortable level (MCL) and background noise level (BNL) components of the acceptable noise level (ANL) measure.

METHOD

A traditional auditory gating paradigm was used to evoke neural responses to pairs of pure-tone stimuli in 32 adult listeners with normal hearing. Relationships between behavioral ANL, MCL, and BNL components and cortical responses to each of the paired stimuli were analyzed using linear mixed-effects regression analyses.

RESULTS

Neural responses elicited by Stimulus 2 in the gating paradigm significantly predicted the computed ANL response. The MCL component was significantly associated with responses elicited by Stimulus 1 of the pair. The BNL component of the ANL was significantly associated with neural responses to both Stimulus 1 and Stimulus 2.

CONCLUSIONS

The results suggest neural processes related to neural inhibition support the ANL and BNL component while neural stimulus registration properties are associated with the MCL a listener chooses. These findings suggest that differential neural mechanisms underlie the separate MCL and BNL components of the ANL response.

Very early and late form-to-meaning computations during visual word recognition as revealed by electrophysiology

We used a large-scale data-driven approach to investigate the role of word form in accessing semantics. By using distributional semantic methods and taking advantage of an ERP lexical decision mega-study, we investigated the exact time dynamic of semantic access from printed words as driven by orthography-semantics consistency (OSC) and phonology-semantics consistency (PSC). Generalized Additive Models revealed very early and late OSC-by-PSC interactions, visible at 100 and 400 msec, respectively. This pattern suggests that, during visual word recognition: a) meaning is accessed by means of two distinct and interactive paths - the orthography-to-meaning and the orthography-to-phonology-to-meaning path -, which mutually contribute to recognition since early stages; b) the system may exploit a dual mechanism for semantic access, with early and late effects associated to a fast-coarse and a slow-fine grained semantic analysis, respectively. The results also highlight the high sensitivity of the visual word recognition system to arbitrary form-meaning relations.

Do emotions influence safe browsing? Toward an electroencephalography marker of affective responses to cybersecurity notifications

Cybersecurity notifications play an important role in encouraging users to use computers safely. Emotional reactions to such notifications are known to positively influence users’ adherence to these notifications, though it is challenging for researchers to identify and quantify users’ emotional reactions. In this study, we explored electroencephalography (EEG) signals that were elicited by the presentation of various emotionally charged image stimuli provided by the International Affective Picture System (IAPS) and compared signals to those elicited by images of cybersecurity notifications and other computer-related stimuli. Participants provided behavioral assessments of valence and arousal elicited by the images which were used to cross-reference the results. We found that EEG amplitudes corresponding to the late positive potential (LPP) were elevated in reaction to images of cybersecurity notifications as well as IAPS images known to elicit strong positive and negative valence, when compared to neutral valence or other computer-related stimuli. These findings suggest that the LPP may account for emotional deliberation about cybersecurity notifications, which could be a useful measure when conducting future studies into the role such emotional reactions play in encouraging safe computer behavior.

Developmental features of sleep electrophysiology in family dogs

Age-related differences in dog sleep and the age at which dogs reach adulthood as indexed by sleep electrophysiology are unknown. We assessed, in (1) a Juvenile sample (n = 60) of 2-14-month-old dogs (weight range: 4-68 kg), associations between age, sleep macrostructure, and non-rapid eye movement (NREM) EEG power spectrum, whether weight moderates associations, and (2) an extended sample (n = 91) of 2-30-months-old dogs, when sleep parameters stabilise. In Juvenile dogs, age was positively associated with time in drowsiness between 2 and 8 months, and negatively with time in rapid eye movement (REM) sleep between 2 and 6 months. Age was negatively associated with delta and positively with theta and alpha power activity, between 8 and 14 months. Older dogs exhibited greater sigma and beta power activity. Larger, > 8-month-old dogs had less delta and more alpha and beta activity. In extended sample, descriptive data suggest age-related power spectrum differences do not stabilise by 14 months. Drowsiness, REM, and delta power findings are consistent with prior results. Sleep electrophysiology is a promising index of dog neurodevelopment; some parameters stabilise in adolescence and some later than one year. Determination of the effect of weight and timing of power spectrum stabilisation needs further inquiry. The dog central nervous system is not fully mature by 12 months of age.

Individual Differences in Political Ideology and Disgust Sensitivity Affect Real-Time Spoken Language Comprehension

Individuals' moral views have been shown to affect their event-related potentials (ERP) response to spoken statements, and people's political ideology has been shown to guide their sentence completion behavior. Using pupillometry, we asked whether political ideology and disgust sensitivity affect online spoken language comprehension. 60 native speakers of English listened to spoken utterances while their pupil size was tracked. Some of those utterances contained grammatical errors, semantic anomalies, or socio-cultural violations, statements incongruent with existing gender stereotypes and perceived speaker identity, such as "I sometimes buy my bras at Hudson's Bay," spoken by a male speaker. An individual's disgust sensitivity is associated with the Behavioral Immune System, and may be correlated with socio-political attitudes, for example regarding out-group stigmatization. We found that more disgust-sensitive individuals showed greater pupil dilation with semantic anomalies and socio-cultural violations. However, political views differently affected the processing of the two types of violations: whereas more conservative listeners showed a greater pupil response to socio-cultural violations, more progressive listeners engaged more with semantic anomalies, but this effect appeared much later in the pupil record.

A Shift in the Direction of the Production Effect in Children Aged 2-6 Years

Research has found mixed evidence for the production effect in childhood. Some studies have found a positive effect of production on word recognition and recall, while others have found the reverse. This paper takes a developmental approach to investigate the production effect. Children aged 2-6 years (n = 150) from a predominantly white population in Ottawa, Canada were trained on familiar words which were either seen, heard or produced, followed by a recall task. Results showed a developmental shift: younger participants showed a reverse production effect, recalling more words that were heard during training, while older children showed the typical production effect, recalling more produced words. The effect of production on recall is not unidirectional and varies by age.

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.

Perceptual Asymmetries and Auditory Processing of Estonian Quantities

Similar to visual perception, auditory perception also has a clearly described “pop-out” effect, where an element with some extra feature is easier to detect among elements without an extra feature. This phenomenon is better known as auditory perceptual asymmetry. We investigated such asymmetry between shorter or longer duration, and level or falling of pitch of linguistic stimuli that carry a meaning in one language (Estonian), but not in another (Russian). For the mismatch negativity (MMN) experiment, we created four different types of stimuli by modifying the duration of the first vowel [ɑ] (170, 290 ms) and pitch contour (level vs. falling pitch) of the stimuli words (‘SATA,’ ‘SAKI’). The stimuli were synthesized from Estonian words (‘SATA,’ ‘SAKI’) and follow the Estonian language three-way quantity system, which incorporates tonal features (falling pitch contour) together with temporal patterns. This made the meaning of the word dependent on the combination of both features and allows us to compare the relative contribution of duration and pitch contour in discrimination of language stimuli in the brain via MMN generation. The participants of the experiment were 12 Russian native speakers with little or no experience in Estonian and living in Estonia short-term, and 12 Estonian native speakers (age 18–27 years). We found that participants’ perception of the linguistic stimuli differed not only according to the physical features but also according to their native language, confirming that the meaning of the word interferes with the early automatic processing of phonological features. The GAMM and ANOVA analysis of the reversed design results showed that the deviant with longer duration among shorter standards elicited a MMN response with greater amplitude than the short deviant among long standards, while changes in pitch contour (falling vs. level pitch) produced neither strong MMN nor asymmetry. Thus, we demonstrate the effect of language background on asymmetric perception of linguistic stimuli that aligns with those of previous studies (Jaramillo et al., 2000), and contributes to the growing body of knowledge supporting auditory perceptual asymmetry.

Using multilevel models for the analysis of event-related potentials

Multilevel modeling (MLM) is becoming increasingly accessible and popular in the analysis of event-related potentials (ERPs). In this article, we review the benefits of MLM for analyzing psychophysiological data, which often contains repeated observations within participants, and introduce some of the decision-making points in the analytic process, including how to set up the data set, specify the model, conduct hypothesis tests, and visualize the model estimates. We highlight how the use of MLM can extend the types of theoretical questions that can be answered using ERPs, including investigations of how ERPs vary meaningfully across trials within a testing session. We also address reporting practices and provide tools to calculate effect sizes and simulate power curves. Ultimately, we hope this review contributes to emerging best practices for the use of MLM with psychophysiological data.

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.

Mobile Brain/Body Imaging of cognitive-motor impairment in multiple sclerosis: Deriving EEG-based neuro-markers during a dual-task walking study

OBJECTIVE

Individuals with a diagnosis of multiple sclerosis (MS) often present with cognitive and motor deficits, and thus the ability to perform tasks that rely on both domains may be particularly impaired. Yet, dual-task walking studies yield mixed results. Individual variance in the ability to cope with brain insult and mobilize additional brain resources may contribute to mixed findings.

METHODS

To test this hypothesis, we acquired event-related potentials (ERP) in individuals with MS and healthy controls (HCs) performing a Go/NoGo task while sitting (i.e., single task) or walking (i.e., dual-task) and looked at the relationship between task related modulation of the brain response and performance.

RESULTS

On the Go/NoGo task the MS group showed dual-task costs when walking, whereas HCs showed a dual-task benefit. Further, whereas the HC group showed modulation of the brain response as a function of task load, this was not the case in the MS group. Analysis for the pooled sample revealed a positive correlation between load-related ERP effects and dual-task performance.

CONCLUSIONS

These data suggest a neurophysiological marker of cognitive-motor dysfunction in MS.

SIGNIFICANCE

Understanding neural processes underlying dual-task walking will help identify objective brain measurements of real-world issues and may improve assessment of MS.

Electrophysiological responses to images ranging in motivational salience: Attentional abnormalities associated with schizophrenia-spectrum disorder risk

Individuals at risk for schizophrenia-spectrum disorders display abnormalities related to motivational salience, or the ability of stimuli to elicit attention due to associations with rewards or punishments. However, the nature of these abnormalities is unclear because most focus on responses to stimuli from broad “pleasant” and “unpleasant” categories and ignore the variation of motivational salience within these categories. In two groups at risk for schizophrenia-spectrum disorders—a Social Anhedonia group and a Psychotic-like Experiences group—and a control group, the current study examined event-related potential components sensitive to motivational salience—the Early Posterior Negativity (EPN), reflecting earlier selective attention, and the Late Positive Potential (LPP), reflecting sustained attention. Compared to controls, the Social Anhedonia group showed smaller increases in the EPN in response to erotica and smaller increases in the LPP as the motivational salience of pleasant images increased (exciting<affiliative<erotica). In contrast, the Psychotic-like Experiences group had larger increases in LPP amplitudes as the motivational salience of pleasant images increased. Also, both at-risk groups showed larger increases in the LPP to threatening images but smaller increases to mutilation images. These findings suggest that examining abnormalities beyond those associated with broad categories may be a way to identify mechanisms of dysfunction.

(Early) context effects on event-related potentials over natural inputs

Language understanding requires the integration of the input with preceding context. Event-related potentials (ERPs) have contributed significantly to our understanding of what contextual information is accessed and when. Much of this research has, however, been limited to experimenter-designed stimuli with highly atypical lexical and context statistics. This raises questions about the extent to which previous findings generalize to everyday language processing of natural stimuli with typical linguistic statistics. We ask whether context can affect ERPs over natural stimuli early, before the N400 time window. We re-analyzed a data set of ERPs over ~700 visually presented content words in sentences from English novels. To increase power, we employed linear mixed effects regression simultaneously modeling random variance by subject and by item. To reduce concerns about Type I error inflation common to any type of time series analysis, we introduced a simple approach to model and discount auto-correlations at multiple, empirically determined, time lags. We compared this approach to Bonferroni correction. Planned follow-up analyses used Generalized Additive Mixture Models to assess the linearity of contextual effects, including lexical surprisal, found within the N400 time window. We found that contextual information affects ERPs in both early (~200ms after word onset) and late (N400) time windows, supporting a cascading, interactive account of lexical access.

Dynamic effects of habituation and novelty detection on newborn event-related potentials

Newborns habituate to repeated auditory stimuli, and discriminate syllables, generating opportunities for early language learning. This study investigated trial-by-trial changes in newborn electrophysiological responses to auditory speech syllables as an index of habituation and novelty detection. Auditory event-related potentials (ERPs) were recorded from 16 term newborn infants, aged 1-3 days, in response to monosyllabic speech syllables presented during habituation and novelty detection tasks. Multilevel models demonstrated that newborns habituated to repeated auditory syllables, as ERP amplitude attenuated for a late-latency component over successive trials. Subsequently, during the novelty detection task, early- and late-latency component amplitudes decreased over successive trials for novel syllables only, indicating encoding of the novel speech syllable. We conclude that newborns dynamically encoded novel syllables over relatively short time periods, as indicated by a systematic change in response patterns with increased exposure. These results have important implications for understanding early precursors of learning and memory in newborns.

Evaluation of Self-generated Behavior: Untangling Metacognitive Readout and Error Detection

When producing a duration, for instance, by pressing a key for 1 sec, the brain relies on self-generated neuronal dynamics to monitor the “flow of time.” Evidence has suggested that the brain can also monitor itself monitoring time, the so-called self-evaluation. How are temporal errors inferred on the basis of purely internally driven brain dynamics with no external reference for time? Although studies have shown that participants can reliably detect temporal errors when generating a duration, the neural bases underlying the evaluation of this self-generated temporal behavior are unknown. Theories of psychological time have also remained silent about such self-evaluation abilities. We assessed the contributions of an error-detection mechanism, in which error detection results from the ability to estimate the latency of motor actions, and of a readout mechanism, in which errors would result from inferring the state of a duration representation. Error detection predicts a V-shape association between neural activity and self-evaluation at the offset of a produced interval, whereas the readout predicts a linear association. Here, human participants generated a time interval and evaluated the magnitude of their timing (first- and second-order behavioral judgments, respectively). Focusing on the MEG/EEG signatures after the termination of the self-generated duration, we found several cortical sources involved in performance monitoring displaying a linear association between the power of alpha (α = 8–14 Hz) oscillations and self-evaluation. Altogether, our results support the readout hypothesis and indicate that duration representation may be integrated for the evaluation of self-generated behavior.

tDCS to the left DLPFC modulates cognitive and physiological correlates of executive function in a state-dependent manner

BACKGROUND

The use of transcranial Direct Current Stimulation (tDCS) to study anatomical and physiological dynamics and circuits supporting cognition and executive functions in particular has dramatically increased in recent years. However, its mechanisms of action remain only partially understood.

OBJECTIVE

In this study we assess the cognitive and physiological effects of anodal tDCS to the DLPFC on executive function in order to understand (1) the role of DLPFC laterality, (2) the physiological dynamics sustaining the modulation of executive function by tDCS, and (3) the impact of state-dependent dynamics.

METHODS

In a randomized, placebo-controlled, cross-over study, we applied anodal tDCS targeting the left vs. right DLPFC vs. sham in 20 healthy individuals (10 males, 10 females). Immediately before and after tDCS, subjects performed the Flanker Task while we measured behavioral (reaction time and accuracy) and neurophysiological (ERP) responses. Specifically, the amplitude of N200, P300, ERN and Pe is compared before and after stimulation.

RESULTS

Anodal tDCS to the left DLPFC lead to a significant improvement in reaction time, an increase in P300 amplitude and a decrease in N200 amplitude in a state-dependent manner: baseline ERP amplitudes conditioned the effects of tDCS.

CONCLUSION

Given the role of these ERPs in conflict-related tasks, we speculate that tDCS is modulating the subconstructs of selective attention, conflict monitoring and response inhibition. These findings contribute to a further understanding of the role of left DLPFC in the modulation of executive function, and shed light into the mechanisms of action and the state dependent nature of tDCS.

How much baseline correction do we need in ERP research? Extended GLM model can replace baseline correction while lifting its limits

Baseline correction plays an important role in past and current methodological debates in ERP research (e.g., the Tanner vs. Maess debate in the Journal of Neuroscience Methods), serving as a potential alternative to strong high-pass filtering. However, the very assumptions that underlie traditional baseline also undermine it, implying a reduction in the signal-to-noise ratio. In other words, traditional baseline correction is statistically unnecessary and even undesirable. Including the baseline interval as a predictor in a GLM-based statistical approach allows the data to determine how much baseline correction is needed, including both full traditional and no baseline correction as special cases. This reduces the amount of variance in the residual error term and thus has the potential to increase statistical power.

A functional generalized F-test for signal detection with applications to event-related potentials significance analysis

Motivated by the analysis of complex dependent functional data such as event-related brain potentials (ERP), this paper considers a time-varying coefficient multivariate regression model with fixed-time covariates for testing global hypotheses about population mean curves. Based on a reduced-rank modeling of the time correlation of the stochastic process of pointwise test statistics, a functional generalized F-test is proposed and its asymptotic null distribution is derived. Our analytical results show that the proposed test is more powerful than functional analysis of variance testing methods and competing signal detection procedures for dependent data. Simulation studies confirm such power gain for data with patterns of dependence similar to those observed in ERPs. The new testing procedure is illustrated with an analysis of the ERP data from a study of neural correlates of impulse control.

Analyzing the Time Course of Pupillometric Data

This article provides a tutorial for analyzing pupillometric data. Pupil dilation has become increasingly popular in psychological and psycholinguistic research as a measure to trace language processing. However, there is no general consensus about procedures to analyze the data, with most studies analyzing extracted features from the pupil dilation data instead of analyzing the pupil dilation trajectories directly. Recent studies have started to apply nonlinear regression and other methods to analyze the pupil dilation trajectories directly, utilizing all available information in the continuously measured signal. This article applies a nonlinear regression analysis, generalized additive mixed modeling, and illustrates how to analyze the full-time course of the pupil dilation signal. The regression analysis is particularly suited for analyzing pupil dilation in the fields of psychological and psycholinguistic research because generalized additive mixed models can include complex nonlinear interactions for investigating the effects of properties of stimuli (e.g., formant frequency) or participants (e.g., working memory score) on the pupil dilation signal. To account for the variation due to participants and items, nonlinear random effects can be included. However, one of the challenges for analyzing time series data is dealing with the autocorrelation in the residuals, which is rather extreme for the pupillary signal. On the basis of simulations, we explain potential causes of this extreme autocorrelation, and on the basis of the experimental data, we show how to reduce their adverse effects, allowing a much more coherent interpretation of pupillary data than possible with feature-based techniques.

Temporal Metacognition as the Decoding of Self-Generated Brain Dynamics

Metacognition, the ability to know about one’s thought process, is self-referential. Here, we combined psychophysics and time-resolved neuroimaging to explore metacognitive inference on the accuracy of a self-generated behavior. Human participants generated a time interval and evaluated the signed magnitude of their temporal production. We show that both self-generation and self-evaluation relied on the power of beta oscillations (β; 15–40 Hz) with increases in early β power predictive of increases in duration. We characterized the dynamics of β power in a low-dimensional space (β state-space trajectories) as a function of timing and found that the more distinct trajectories, the more accurate metacognitive inferences were. These results suggest that β states instantiate an internal variable determining the fate of the timing network’s trajectory, possibly as release from inhibition. Altogether, our study describes oscillatory mechanisms for timing, suggesting that temporal metacognition relies on inferential processes of self-generated dynamics.

Exploratory structural equation modeling for event-related potential data-An all-in-one approach?

ERP data are characterized by high dimensionality and a mixture of constituting signals and are thus challenging for researchers to analyze. To address these challenges, exploratory factor analysis (EFA) has been used to provide estimates of the unobserved factors and to use these estimates for further statistical analyses (e.g., analyses of group effects). However, the EFA approach is prone to biases due to assigning individual factor scores to each observation as an intermediate step and does not properly consider participants, electrodes, and groups/conditions as differentiable sources of factor variance, with the consequence that factor correlations are inaccurately estimated. Here, we suggest exploratory structural equation modeling (ESEM) as a potential approach to address these limitations. ESEM may handle the complexity of ERP data more appropriately because multiple sources of variance can be formally taken into consideration. We demonstrate the application of ESEM to ERP data (in comparison with EFA) with an illustrative example and report the results of a small simulation study in which ESEM clearly outperformed EFA with respect to accurate estimation of the population factor loadings, population factor correlations, and group differences. We discuss how robust statistical inference can be conducted within the ESEM approach. We conclude that ESEM naturally extends the current EFA approach for ERP data and that it can provide a coherent and flexible analysis framework for all kinds of ERP research questions.

Refixation control in free viewing: a specialized mechanism divulged by eye-movement-related brain activity

In free viewing, the eyes return to previously visited locations rather frequently, even though the attentional and memory-related processes controlling eye-movement show a strong antirefixation bias. To overcome this bias, a special refixation triggering mechanism may have to be recruited. We probed the neural evidence for such a mechanism by combining eye tracking with EEG recording. A distinctive signal associated with refixation planning was observed in the EEG during the presaccadic interval: the presaccadic potential was reduced in amplitude before a refixation compared with normal fixations. The result offers direct evidence for a special refixation mechanism that operates in the saccade planning stage of eye movement control. Once the eyes have landed on the revisited location, acquisition of visual information proceeds indistinguishably from ordinary fixations. NEW & NOTEWORTHY A substantial proportion of eye fixations in human natural viewing behavior are revisits of recently visited locations, i.e., refixations. Our recently developed methods enabled us to study refixations in a free viewing visual search task, using combined eye movement and EEG recording. We identified in the EEG a distinctive refixation-related signal, signifying a control mechanism specific to refixations as opposed to ordinary eye fixations.

Presaccadic EEG activity predicts visual saliency in free-viewing contour integration

While viewing a scene, the eyes are attracted to salient stimuli. We set out to identify the brain signals controlling this process. In a contour integration task, in which participants searched for a collinear contour in a field of randomly oriented Gabor elements, a previously established model was applied to calculate a visual saliency value for each fixation location. We studied brain activity related to the modeled saliency values, using coregistered eye tracking and EEG. To disentangle EEG signals reflecting salience in free viewing from overlapping EEG responses to sequential eye movements, we adopted generalized additive mixed modeling (GAMM) to single epochs of saccade-related EEG. We found that, when saliency at the next fixation location was high, amplitude of the presaccadic EEG activity was low. Since presaccadic activity reflects covert attention to the saccade target, our results indicate that larger attentional effort is needed for selecting less salient saccade targets than more salient ones. This effect was prominent in contour-present conditions (half of the trials), but ambiguous in the contour-absent condition. Presaccadic EEG activity may thus be indicative of bottom-up factors in saccade guidance. The results underscore the utility of GAMM for EEG-eye movement coregistration research.

Regularization and a general linear model for event-related potential estimation

The usual event-related potential (ERP) estimation is the average across epochs time-locked on stimuli of interest. These stimuli are repeated several times to improve the signal-to-noise ratio (SNR) and only one evoked potential is estimated inside the temporal window of interest. Consequently, the average estimation does not take into account other neural responses within the same epoch that are due to short inter stimuli intervals. These adjacent neural responses may overlap and distort the evoked potential of interest. This overlapping process is a significant issue for the eye fixation-related potential (EFRP) technique in which the epochs are time-locked on the ocular fixations. The inter fixation intervals are not experimentally controlled and can be shorter than the neural response's latency. To begin, the Tikhonov regularization, applied to the classical average estimation, was introduced to improve the SNR for a given number of trials. The generalized cross validation was chosen to obtain the optimal value of the ridge parameter. Then, to deal with the issue of overlapping, the general linear model (GLM), was used to extract all neural responses inside an epoch. Finally, the regularization was also applied to it. The models (the classical average and the GLM with and without regularization) were compared on both simulated data and real datasets from a visual scene exploration in co-registration with an eye-tracker, and from a P300 Speller experiment. The regularization was found to improve the estimation by average for a given number of trials. The GLM was more robust and efficient, its efficiency actually reinforced by the regularization.

Applying Functional Data Analysis to Assess Tele-Interpersonal Psychotherapy's Efficacy to Reduce Depression

The use of parametric linear mixed models and generalized linear mixed models to analyze longitudinal data collected during randomized control trials (RCT) is conventional. The application of these methods, however, is restricted due to various assumptions required by these models. When the number of observations per subject is sufficiently large, and individual trajectories are noisy, functional data analysis (FDA) methods serve as an alternative to parametric longitudinal data analysis techniques. However, the use of FDA in randomized control trials, is rare. In this paper, the effectiveness of FDA and linear mixed models was compared by analyzing data from rural persons living with HIV and comorbid depression enrolled in a depression treatment randomized clinical trial. Interactive voice response (IVR) systems were used for weekly administrations of the 10-item Self-Administered Depression Scale (SADS) over 41 weeks. Functional principal component analysis and functional regression analysis methods detected a statistically significant difference in SADS between telphone-administered interpersonal psychotherapy (tele-IPT) and controls but, linear mixed effects model results did not. Additional simulation studies were conducted to compare FDA and linear mixed models under a different nonlinear trajectory assumption. In this clinical trial with sufficient per subject measured outcomes and individual trajectories that are noisy and nonlinear, we found functional data analysis methods to be a better alternative to linear mixed models.

An electrophysiological megastudy of spoken word recognition

This study used electrophysiological recordings to a large sample of spoken words to track the time-course of word frequency, phonological neighbourhood density, concreteness and stimulus duration effects in two experiments. Fifty subjects were presented more than a thousand spoken words during either a go/no go lexical decision task (Experiment 1) or a go/no go semantic categorisation task (Experiment 2) while EEG was collected. Linear mixed effects modelling was used to analyze the data. Effects of word frequency were found on the N400 and also as early as 100 ms in Experiment 1 but not Experiment 2. Phonological neighbourhood density produced an early effect around 250 ms and the typical N400 effect. Concreteness elicited effects in later epochs on the N400. Stimulus duration affected all epochs and its influence reflected changes in the timing of the ERP components. Overall the results support cascaded interactive models of spoken word recognition.

Temporal dynamics of reactive cognitive control as revealed by event-related brain potentials

Reactive cognitive control refers to a complementary set of cognitive operations by which individuals monitor for and detect the presence of goal-interfering conflict (i.e., conflict monitoring/evaluation) and, subsequently, initiate attention-focusing and response selection processes to bolster goal-directed action in the face of such conflict (regulative control). The purpose of the current study was to characterize the nature of conflict adaptation in both components of this dynamic process across sequences of trials and, more broadly, across time as participants complete a cognitive control task. Fifty-two young adults completed a standard arrow flanker task while behavioral and ERP data were recorded. Multilevel modeling of sequences of compatible and incompatible trials over time showed that, whereas response time data demonstrated a typical conflict adaptation effect throughout the task, N2 and frontal slow wave (FSW) indices of conflict monitoring and regulative control, respectively, demonstrated significant conflict adaptation only during the early part of the task. Moreover, although differential change in N2 and FSW over time suggested that conflict monitoring and regulative control were dissociable, a reciprocal relation between them was maintained throughout the task and was not present in a component theoretically unrelated to conflict adaptation (visual attention-related N1). Findings are discussed in terms of compensatory processes that help to maintain goal-directed performance even as control-related neural responses become fatigued.

Using trial-level data and multilevel modeling to investigate within-task change in event-related potentials

EEG data, and specifically the ERP, provide psychologists with the power to examine quickly occurring cognitive processes at the native temporal resolution at which they occur. Despite the advantages conferred by ERPs to examine processes at different points in time, ERP researchers commonly ignore the trial-to-trial temporal dimension by collapsing across trials of similar types (i.e., the signal averaging approach) because of constraints imposed by repeated measures ANOVA. Here, we present the advantages of using multilevel modeling (MLM) to examine trial-level data to investigate change in neurocognitive processes across the course of an experiment. Two examples are presented to illustrate the usefulness of this technique. The first demonstrates decreasing differentiation in N170 amplitude to faces of different races across the course of a race categorization task. The second demonstrates attenuation of the ERN as participants commit more errors within a task designed to measure implicit racial bias. Although the examples presented here are within the realm of social psychology, the use of MLM to analyze trial-level EEG data has the potential to contribute to a number of different theoretical domains within psychology.

Electrophysiology Reveals the Neural Dynamics of Naturalistic Auditory Language Processing: Event-Related Potentials Reflect Continuous Model Updates

The recent trend away from ANOVA-based analyses places experimental investigations into the neurobiology of cognition in more naturalistic and ecologically valid designs within reach. Using mixed-effects models for epoch-based regression, we demonstrate the feasibility of examining event-related potentials (ERPs), and in particular the N400, to study the neural dynamics of human auditory language processing in a naturalistic setting. Despite the large variability between trials during naturalistic stimulation, we replicated previous findings from the literature: the effects of frequency, animacy, and word order and find previously unexplored interaction effects. This suggests a new perspective on ERPs, namely, as a continuous modulation reflecting continuous stimulation instead of a series of discrete and essentially sequential processes locked to discrete events.

Estimation of overlapped Eye Fixation Related Potentials: The General Linear Model, a more flexible framework than the ADJAR algorithm

The Eye Fixation Related Potential (EFRP) estimation is the average of EEG signals across epochs at ocular fixation onset. Its main limitation is the overlapping issue. Inter Fixation Intervals (IFI) - typically around 300 ms in the case of unrestricted eye movement- depend on participants’ oculomotor patterns, and can be shorter than the latency of the components of the evoked potential. If the duration of an epoch is longer than the IFI value, more than one fixation can occur, and some overlapping between adjacent neural responses ensues. The classical average does not take into account either the presence of several fixations during an epoch or overlapping. The Adjacent Response algorithm (ADJAR), which is popular for event-related potential estimation, was compared to the General Linear Model (GLM) on a real dataset from a conjoint EEG and eye-tracking experiment to address the overlapping issue. The results showed that the ADJAR algorithm was based on assumptions that were too restrictive for EFRP estimation. The General Linear Model appeared to be more robust and efficient. Different configurations of this model were compared to estimate the potential elicited at image onset, as well as EFRP at the beginning of exploration. These configurations took into account the overlap between the event-related potential at stimulus presentation and the following EFRP, and the distinction between the potential elicited by the first fixation onset and subsequent ones. The choice of the General Linear Model configuration was a tradeoff between assumptions about expected behavior and the quality of the EFRP estimation: the number of different potentials estimated by a given model must be controlled to avoid erroneous estimations with large variances.

The iterative nature of person construal: Evidence from event-related potentials

Recently, a dynamic-interactive model of person construal (DI model) has been proposed, whereby the social categories a person represents are determined on the basis of an iterative integration of bottom-up and top-down influences. The current study sought to test this model by leveraging the high temporal resolution of event-related brain potentials (ERPs) as 65 participants viewed male faces that varied by race (White vs Black), fixating either between the eyes or on the forehead. Within face presentations, the effect of fixation, meant to vary bottom-up visual input, initially was large but decreased across early latency neural responses identified by a principal components analysis (PCA). In contrast, the effect of race, reflecting a combination of top-down and bottom-up factors, initially was small but increased across early latency principal components. These patterns support the DI model prediction that bottom-up and top-down processes are iteratively integrated to arrive at a stable construal within 230 ms. Additionally, exploratory multilevel modeling of single trial ERP responses representing a component linked to outgroup categorization (the P2) suggests change in effects of the manipulations over the course of the experiment. Implications of the findings for the DI model are considered.

Sweet-cheeks vs. pea-brain: embodiment, valence, and task all influence the emotional salience of language

Previous research has found that more embodied insults (e.g. numbskull) are identified faster and more accurately than less embodied insults (e.g. idiot). The linguistic processing of embodied compliments has not been well explored. In the present study, participants completed two tasks where they identified insults and compliments, respectively. Half of the stimuli were more embodied than the other half. We examined the late positive potential (LPP) component of event-related potentials in early (400-500 ms), middle (500-600 ms), and late (600-700 ms) time windows. Increased embodiment resulted in improved response accuracy to compliments in both tasks, whereas it only improved accuracy for insults in the compliment detection task. More embodied stimuli elicited a larger LPP than less embodied stimuli in the early time window. Insults generated a larger LPP in the late time window in the insult task; compliments generated a larger LPP in the early window in the compliment task. These results indicate that electrophysiological correlates of emotional language perception are sensitive to both top-down and bottom-up processes.

Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies

Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take into account repeated measures and multivariate predictor variables are integral and essential to successful data analysis and interpretation. This work implements and compares conventional Pearson correlations and linear mixed-effects (LME) regression models using data from two recently published auditory electrophysiology studies. For the specific research questions in both studies, the Pearson correlation test is inappropriate for determining strengths between the behavioral responses for speech-in-noise recognition and the multiple neurophysiological measures as the neural responses across listening conditions were simply treated as independent measures. In contrast, the LME models allow a systematic approach to incorporate both fixed-effect and random-effect terms to deal with the categorical grouping factor of listening conditions, between-subject baseline differences in the multiple measures, and the correlational structure among the predictor variables. Together, the comparative data demonstrate the advantages as well as the necessity to apply mixed-effects models to properly account for the built-in relationships among the multiple predictor variables, which has important implications for proper statistical modeling and interpretation of human behavior in terms of neural correlates and biomarkers.

An electrophysiological investigation of emotional abnormalities in groups at risk for schizophrenia-spectrum personality disorders

Both extreme levels of social anhedonia (SocAnh) and perceptual aberration/magical ideation (PerMag) are associated with risk for schizophrenia-spectrum disorders and with emotional abnormalities. Yet, the nature of any psychophysiological-measured affective abnormality, including the role of automatic/controlled processes, is unclear. We examined the late positive potential (LPP) during passive viewing (to assess automatic processing) and during cognitive reappraisal (to assess controlled processing) in three groups: SocAnh, PerMag, and controls. The SocAnh group exhibited an increased LPP when viewing negative images. Further, SocAnh exhibited greater reductions in the LPP for negative images when told to use strategies to alter negative emotion. Similar to SocAnh, PerMag exhibited an increased LPP when viewing negative images. However, PerMag also exhibited an increased LPP when viewing positive images as well as an atypical decreased LPP when increasing positive emotion. Overall, these results suggest that at-risk groups are associated with shared and unique automatic and controlled abnormalities.