ERP studies of lexical processing typically involve individuals perceiving isolated words or sentences. However, much of language processing occurs in conversation with others. Here, we used EEG hyperscanning while pairs of acquaintances either engaged in a scripted conversation or passively viewed a recording of the scripted conversation. Our primary goals were to replicate the established N400 frequency effect (a greater negativity between ∼300 and 600 msec for low-frequency words) and compare this effect during scripted conversation to passive viewing, which more closely resembled conventional paradigms. Target words of high and low lexical frequency were embedded in the dialogues, and the onsets of these words were identified post hoc from audio synchronized with the EEG data. Both groups exhibited a significantly greater N400 response when hearing low-frequency words compared with high-frequency words, replicating previous findings. However, the N400 frequency effect was significantly larger in the control group than in the scripted conversation group across midline central-parietal channels. This attenuation in the scripted conversation condition may reflect differences in lexical processing in the context of active social interaction compared with passive comprehension. Specifically, the rapport created by engaging in a conversation with another person may facilitate lexical access and reduce the greater processing associated with low-frequency words. These findings demonstrate that ERP experiments can be conducted using conversational stimuli, opening new opportunities to study real-time language processing in interactive social contexts.

SIGNIFICANCE:Functional near-infrared spectroscopy (fNIRS) offers several advantages for neuroimaging in children, yet its use in research on cognitive development, particularly reading, remains limited. AIM:This scoping review focuses on fNIRS studies examining reading development in school-aged children (6-12 years), with four goals: (1) characterize methodological parameters, including stimuli and tasks; (2) identify brain regions showing fNIRS signal changes during reading and across development; (3) document technical fNIRS methods; (4) assess adherence to best practice in fNIRS reporting. APPROACH:We searched PubMed, Embase, PsycINFO, Scopus, gray literature, and reference lists. RESULTS:Reading-related fNIRS activation was most frequently observed in the inferior frontal gyrus, superior temporal gyrus, and middle frontal gyrus. However, only seven studies directly examined developmental changes in reading using fNIRS. A consistent issue across studies was underreporting of technical methods and incomplete adherence to recommended best practices for fNIRS data collection and analysis. CONCLUSIONS:To advance the field, future fNIRS research on reading development should (1) follow established reporting guidelines, (2) ensure adequate brain region coverage when designing probe arrays, and (3) prioritize longitudinal and developmental investigations to better capture changes in brain function during reading development.

Advances in generative artificial intelligence (AI) have made virtual agents ubiquitous, leading to widespread disruption of higher education. Many are asking whether these agents will replace educators altogether. In this paper, we explore some of the literature on virtual agents and past work from the educational technology literature to outline one of the key limitations of educational virtual agents: their inability to generate social presence. We then provide reasons why the N400 event-related potential (ERP) may be sensitive to aspects of social presence and thus reflect associated perceptions of virtual teachers and virtual agents broadly. We conclude with a proposal for an experiment which could establish cognitive differences, as measured by N400 amplitude differences and their relationship to Cloze probability of phrase endings. This would also suggest that user expectations are important considerations in the effective design of virtual teachers.

Single-word reading depends on multiple types of information processing: readers must process low-level visual properties of the stimulus, form orthographic and phonological representations of the word, and retrieve semantic content from memory. Reading aloud introduces an additional type of processing wherein readers must execute an appropriate sequence of articulatory movements necessary to produce the word. To date, cognitive and neural differences between aloud and silent reading have mainly been ascribed to articulatory processes. However, it remains unclear whether articulatory information is used to discriminate unique words, at the neural level, during aloud reading. Moreover, very little work has investigated how other types of information processing might differ between the two tasks. The current work used representational similarity analysis (RSA) to interrogate fMRI data collected while participants read single words aloud or silently. RSA was implemented using a whole-brain searchlight procedure to characterise correspondence between neural data and each of five models representing a discrete type of information. Both conditions elicited decodability of visual, orthographic, phonological, and articulatory information, though to different degrees. Compared with reading silently, reading aloud elicited greater decodability of visual, phonological, and articulatory information. By contrast, silent reading elicited greater decodability of orthographic information in right anterior temporal lobe. These results support an adaptive view of reading whereby information is weighted according to its task relevance, in a manner that best suits the reader's goals.

Standards of patient care require that comprehensive pain assessments be conducted at routine intervals. Infants and children with and at risk for intellectual disabilities, who are at high risk for experiencing pain, receive significantly less representation in the literature to inform pain measurement practice. The objectives of this review include (1) review and discuss the current literature surrounding pain measurement in infants and children with and at risk for intellectual disabilities, (2) define pain assessment tools, scales, and measures that are being used in infants and children with and at risk for intellectual disabilities, (3) discuss the strengths and limitations of the pain assessment tools, scales, and measures, (4) make recommendations for future pain research focused on this population. A narrative review of the literature regarding pain measures in infants and children with and at risk for intellectual disabilities was conducted using PubMed. A search strategy was created in consultation with a librarian scientist. There were no date limiters applied to the search. Pain measures can be classified as self-report, behavioral (e.g., cry, facial expressions), physiological (e.g., heart rate, biomarkers, oxygen saturation, respiratory rate), and neurophysiological (electroencephalogram, functional magnetic resonance imaging, near infrared spectroscopy). There is a considerable dearth in the literature surrounding pain measures and pain indicators in this population, along with small sample sizes and inconsistent findings reported across studies. Future research is needed to compare pain responses across different age groups and intellectual disability diagnoses to neurotypical peers.

BACKGROUND:Mood disorders, including depressive and bipolar disorders, begin in late adolescence to early adulthood, tend to run in families, and present early with subthreshold symptoms. They have been associated with differential connectivity in 3 core networks: the default mode network (DMN), cognitive executive network (CEN), and salience network (SN), but it remains unclear whether differences in connectivity in the DMN, CEN, and SN are associated with familial risk for mood disorders. METHODS:We recruited youth aged 9-19 years, including offspring of parents with major depressive or bipolar disorders (familial high risk [FHR]) and offspring of parents with no mood disorder (controls) for a resting-state functional magnetic resonance imaging study. We tested associations between family history of major mood disorders and connectivity within and between the DMN, CEN, and SN. RESULTS:We included 215 youth: 126 at FHR with a mean age of 13.38 (standard deviation [SD] 2.91) years and 79 controls with a mean age of 13.17 (SD 2.67) years. Mean connectivity in the DMN (β = 0.003, 95% confidence interval [CI] -0.023 to 0.029), CEN (β = -0.009, 95% CI, -0.070 to 0.089), and SN (β = -0.010, 95% CI -0.071 to 0.051) in the FHR group was similar to that of controls. Moreover, DMN, CEN, and SN connectivity was not significantly associated with depressive symptoms. LIMITATIONS:Given that brain connectivity changes over the developmental period, longitudinal studies would improve understanding of how this change occurs in familial risk groups to identify critical time periods for intervention or prevention of mood disorders. CONCLUSION:Connectivity within and between the DMN, CEN, and SN is not a neural indicator of familial risk for major mood disorders.

Virtual influencers have received significant recent research attention. Past work has investigated users’ perceptions of their human-likeness, uncanniness, trust, and ability to persuade. However, the findings are mixed, motivating new theoretical approaches and investigations into the antecedents of the typically utilized variables. We thus took an exploratory, inductive approach by conducting two neuroimaging experiments with complementary brain imaging techniques and then derived theoretical explanations based on the findings. We discovered three key antecedents that impact human and virtual influencer evaluations: (1) expectancy violation, (2) emotion, and (3) cognitive effort. To validate their explanatory power, we tested their effects on the intention to follow influencer recommendations using uncanniness, trust and distrust as serial mediators in a third behavioral study. Results confirm our interpretation of neural results and reveal three explanatory paths towards following intentions with human and virtual influencers: expectancy violation → uncanniness; emotion → trust/distrust; and cognitive effort → follow intentions. Given the lack of theorizing on expectancy violation, emotion, and cognitive effort in the existing research on virtual influencers, we offer a significant theoretical contribution to the field by showing how these features fundamentally predict further evaluations. Our results can guide design theories for the creation of virtual influencer accounts, help companies to better evaluate the predictors for successful virtual influencer marketing, and inform future information systems studies interested in taking an exploratory, inductive approach using neurophysiological data.

Background: Mnemonic discrimination (MD) involves distinguishing new stimuli from memories of highly similar “lure” items or events, and is a putative indirect probe of dentate gyrus functioning. MD is impaired in the elderly and in individuals with hippocampal lesions, schizophrenia, major depressive disorder, and Alzheimer’s disease. The gold-standard MD test, called the mnemonic similarity task (MST), is rarely used in clinical research. We therefore aimed to validate a novel analysis method that extracts information about MD and recognition memory in widely clinically used recognition memory tests which do not have categorical distinctions between “lures” and “foils.”Methods: By fitting a logistic function to the relationship between stimulus interference and the probability of classifying a stimulus as novel, at the single participant level, we derived participant-level indices of MD (λ) and overall recognition memory performance (Δ). We applied the novel measures to MST data from two independent datasets (N=18; N=67). Using linear mixed-effects modelling, we sought to confirm that λ predicts the MST’s lure discrimination index (LDI), while Δ predicts the MST’s overall recognition memory index (REC). Results: Across both datasets, λ predicted LDI (β=0.76, 95% CI [0.62-0.91], p<0.001), but not REC (β=-0.06, 95% CI [-0.20-0.09], p=0.438), while Δ predicted REC (β=0.93, 95% CI [0.83-1.02], p<0.001), but not LDI (β=0.06, 95% CI [-0.03-0.15], p=0197). The λ and Δ indices were not correlated.Conclusion: Our novel measure accurately indexes MD, without correlating with overall recognition memory performance. Future studies should apply it to large clinical datasets with widely used recognition memory tests, such as the California Verbal Learning Test.

Political misinformation is a growing problem for democracies, partly due to the rise of widely accessible artificial intelligence-generated content (AIGC). In response, social media platforms are increasingly considering explicit AI content labeling, though the evidence to support the effectiveness of this approach has been mixed. In this paper, we discuss two studies which shed light on antecedent cognitive processes that help explain why and how AIGC labeling impacts user evaluations in the specific context of AI-generated political images. In the first study, we conducted a neurophysiological experiment with 26 participants using EEG event-related potentials (ERPs) and self-report measures to gain deeper insights into the brain processes associated with the evaluations of artificially generated political images and AIGC labels. In the second study, we embedded some of the stimuli from the EEG study into replica YouTube recommendations and administered them to 276 participants online. The results from the two studies suggest that AI-generated political images are associated with heightened attentional and emotional processing. These responses are linked to perceptions of humanness and trustworthiness. Importantly, trustworthiness perceptions can be impacted by effective AIGC labels. We found effects traceable to the brain’s late-stage executive network activity, as reflected by patterns of the P300 and late positive potential (LPP) components. Our findings suggest that AIGC labeling can be an effective approach for addressing online misinformation when the design is carefully considered. Future research could extend these results by pairing more photorealistic stimuli with ecologically valid social-media tasks and multimodal observation techniques to refine label design and personalize interventions across demographic segments.

Recent work surrounding the neural correlates of episodic memory retrieval has focussed on the decodability of neural activation patterns elicited by unique stimuli. Research in this area has revealed two distinct phenomena: (i) neural pattern reactivation, which describes the fidelity of activation patterns between encoding and retrieval; (ii) neural pattern transformation, which describes systematic changes to these patterns. This study used fMRI to investigate the roles of these two processes in the context of the production effect, which is a robust episodic memory advantage for words read aloud compared to words read silently. Twenty-five participants read words either aloud or silently, and later performed old-new recognition judgements on all previously seen words. We applied multivariate analysis to compare measures of reactivation and transformation between the two conditions. We found that, compared with silent words, successful recognition of aloud words was associated with reactivation in the left insula and transformation in the left precuneus. By contrast, recognising silent words (compared to aloud) was associated with relatively more extensive reactivation, predominantly in left ventral temporal and prefrontal areas. We suggest that recognition of aloud words might depend on retrieval and metacognitive evaluation of speech-related information that was elicited during the initial encoding experience, while recognition of silent words is more dependent on reinstatement of visual-orthographic information. Overall, our results demonstrate that different encoding conditions may give rise to dissociable neural mechanisms supporting single word recognition. ### Competing Interest Statement The authors have declared no competing interest.

Research focused on children with intellectual disabilities has been of increasing interest over the last two decades. However, a considerable lag in the amount of research that is representative and generalizable to this population in comparison to neurotypical children remains, largely attributed to issues with participant engagement and recruitment. Challenges and barriers associated with engaging and recruiting this population include lack of research to provide a sound foundation of knowledge, ethical considerations, parental attitudes, family commitments, and organizational gatekeeping. Researchers can engage children and their families using participatory research methods, honouring the child’s right to assent, and collaborating with parents. Recruitment strategies include partnering with organizations, working with parent and patient partners, and using remote methods. Employing evidence-informed engagement and recruitment strategies may provide substantial social and scientific value to the research field by ensuring that this underrepresented population benefits equitably from research findings.

IntroductionAs children become independent readers, they regularly encounter new words whose meanings they must infer from context, and whose spellings must be learned for future recognition. The self-teaching hypothesis proposes orthographic learning skills are critical in the transition to fluent reading, while the lexical quality hypothesis further emphasizes the importance of semantics. Event-related potential (ERP) studies of reading development have focused on effects related to the N170 component—print tuning (letters vs. symbols) and lexical tuning (real words vs. consonant strings)—as well as the N400 reflecting semantic processing, but have not investigated the relationship of these components to word learning during independent reading.MethodsIn this study, children in grade 3 independently read short stories that introduced novel words, then completed a lexical decision task from which ERPs were derived.ResultsLike real words, newly-learned novel words evoked a lexical tuning effect, indicating rapid establishment of orthographic representations. Both real and novel words elicited significantly smaller N400s than pseudowords, suggesting that semantic representations of the novel words were established. Further, N170 print tuning predicted accuracy on identifying the spellings of the novel words, while the N400 effect for novel words was associated with reading comprehension.DiscussionExposure to novel words during self-directed reading rapidly establishes neural markers of orthographic and semantic processing. Furthermore, the ability to rapidly filter letter strings from symbols is predictive of orthographic learning, while rapid establishment of semantic representations of novel words is associated with stronger reading comprehension.

Family history and early depressive symptoms are the most established predictors of major mood disorders, including depressive and bipolar disorders. One way the familial and clinical risk may lead to the onset of mood disorders is through affecting the coordination of brain function, as reflected in the default mode network (DMN) connectivity. Previous studies have reported either increased or no differences in the DMN connectivity of youth at high familial risk for mood disorders. The purpose of the current study was to investigate DMN connectivity in a sample of youth at high familial risk of developing major mood disorders, and characterize the correlation between DMN connectivity and depressive symptoms.

Aging is associated with changes in cognitive function, including declines in learning, memory, and executive function. Prism adaptation (PA) is a useful paradigm to measure changes in explicit and implicit mechanisms of visuo-motor learning with age, but the neural correlates are not well understood. In the present study, we used PA to investigate visuo-motor learning and error processing in older adults. Twenty older adults (56-85 yrs) and 20 younger adults (18-33 yrs) underwent a goal-oriented reaching task while wearing prism goggles as continuous EEG was recorded to examine neural correlates of error detection. We examined behavioural mea-sures of PA, as well as ERP components previously found associated with the early and late phases of adaptation to visual distortion caused by the prism goggles. Our results indicate important age-related behavioural and neurophysiological differences. Older adults reached more slowly than younger adults but showed the same accuracy throughout the prism exposure. Older adults also displayed larger aftereffects, indicating preserved visuomotor adaptation. EEG results indicated similar initial error processing in older and younger adults, as measured by the feedback error related negativity (FRN). As seen previously in young adults, the P3a and P3b declined over the prism exposure phase in both groups. Older adults displayed reduced P3a amplitude compared to the younger group in the early phase of adaptation, however, suggesting reduced attentional orienting. Finally, the older group exhibited a greater P3b amplitude compared to the younger group in the later phases of adaptation, potentially a marker of enhanced context updating underlying spatial realignment, leading to their larger aftereffect. Implications for age-related learning differences and clinical applications are discussed.

Virtual influencers (VI) are on the rise on Instagram, and companies increasingly cooperate with them for marketing campaigns. This has motivated an increasing number of studies, which investigate our perceptions of these influencers. Most studies propose that VI are often rated lower in perceived trust and higher in uncanniness. Yet, we still lack a deeper understanding as to why this is the case. We conduct 2 studies: 1) a questionnaire with 150 participants to get the general perception for the included influencers, and 2) an electroencephalography (EEG) study to get insights into the underlying neural mechanisms of influencer perception. Our results support findings from related works regarding lower trust and higher uncanniness associated with VI. Interestingly, the EEG components N400 and LPP did not modulate perceived trust, but rather perceived humanness, uncanniness, and intentions to follow recommendations. This provides a fruitful beginning for future research on virtual humans.

Background: Mnemonic discrimination (MD) involves distinguishing new stimuli from memories of highly similar “lure” items or events, and is a putative indirect probe of dentate gyrus functioning. MD is impaired in the elderly and in individuals with hippocampal lesions, schizophrenia, major depressive disorder, and Alzheimer’s disease. The gold-standard MD test, called the mnemonic similarity task (MST), is rarely used in clinical research. We therefore aimed to validate a novel analysis method that extracts information about MD and recognition memory in widely clinically used recognition memory tests which do not have categorical distinctions between “lures” and “foils.” Methods: By fitting a logistic function to the relationship between stimulus interference and the probability of classifying a stimulus as novel, at the single participant level, we derived participant-level indices of MD (λ) and overall recognition memory performance (Δ). We applied the novel measures to MST data from two independent datasets (N=18; N=67). Using linear mixed-effects modelling, we sought to confirm that λ predicts the MST’s lure discrimination index (LDI), while Δ predicts the MST’s overall recognition memory index (REC). Results: Across both datasets, λ predicted LDI (β=0.76, 95% CI [0.62-0.91], p&lt;0.001), but not REC (β=-0.06, 95% CI [-0.20-0.09], p=0.438), while Δ predicted REC (β=0.93, 95% CI [0.83-1.02], p&lt;0.001), but not LDI (β=0.06, 95% CI [-0.03-0.15], p=0197). The λ and Δ indices were not correlated. Conclusion: Our novel measure accurately indexes MD, without correlating with overall recognition memory performance. Future studies should apply it to large clinical datasets with widely used recognition memory tests, such as the California Verbal Learning Test.

EEG hyperscanning refers to recording electroencephalographic (EEG) data from multiple participants simultaneously. Many hyperscanning experimental designs seek to mimic naturalistic behavior, relying on unpredictable participant-generated stimuli. The majority of this research has focused on neural oscillatory activity that is quantified over hundreds of milliseconds or more. This contrasts with traditional event-related potential (ERP) research in which analysis focuses on transient responses, often only tens of milliseconds in duration. Deriving ERPs requires precise time-locking between stimuli and EEG recordings, and thus typically relies on pre-set stimuli that are presented to participants by a system that controls stimulus timing and synchronization with an EEG system. EEG hyperscanning methods typically use separate EEG amplifiers for each participant, increasing cost and complexity — including challenges in synchronizing data between systems. Here, we describe a method that allows for simultaneous acquisition of EEG data from a pair of participants engaged in conversation, using a single EEG system with simultaneous audio data collection that is synchronized with the EEG recording. This allows for the post-hoc insertion of trigger codes so that it is possible to analyze ERPs time-locked to specific events. We further demonstrate methods for deriving ERPs elicited by another person's spontaneous speech, using this setup. • EEG hyperscanning method using a single EEG amplifier • EEG hyperscanning method allowing simultaneous recording of audio data directly into the EEG data file for perfect synchronization • EEG method for naturalistic language and human interaction studies that allows the study of event-related potentials time-locked to spontaneous speech

With current trends in population migration, international mobility, and connectedness, an understanding of the factors that lead to optimal second language acquisition is increasingly important. Based on Helen Neville’s work, this chapter discusses some of the neurocognitive research on second language processing with a focus on studies utilizing event-related potentials (ERP). The chapter is structured around phonology, semantics, and syntax. For each of these subsystems of language, there is a focus on three factors important for second language processing: age of acquisition (AoA), proficiency, and cross-linguistic influence. We argue for a shift in ERP research from a focus on AoA as a sole factor for describing differences in processing languages to a more comprehensive approach, including proficiency and cross-linguistic influence.

NeuroIS researchers have become increasingly interested in the design of new types of information systems that leverage neurophysiological data. In this paper we describe the results of machine learning analysis which validates a method for the passive detection of mind wandering. Following the presentation of the results, we describe ways that this technique could be applied to create a neuroadaptive online learning and virtual meeting tool which may improve users’ retention of information by providing auditory feedback.