Modeling the N400 ERP component as transient semantic over-activation within a neural network model of word comprehension

Evaluating frontoparietal network topography for diagnostic markers of Alzheimer's disease

Numerous prospective biomarkers are being studied for their ability to diagnose various stages of Alzheimer's disease (AD). High-density electroencephalogram (EEG) methods show promise as an accurate, economical, non-invasive approach to measuring the electrical potentials of brains associated with AD. Event-related potentials (ERPs) may serve as clinically useful biomarkers of AD. Through analysis of secondary data, the present study examined the performance and distribution of N4/P6 ERPs across the frontoparietal network (FPN) using EEG topographic mapping. ERP measures and memory as a function of reaction time (RT) were compared between a group of (n = 63) mild untreated AD patients and a control group of (n = 73) healthy age-matched adults. Based on the literature presented, it was expected that healthy controls would outperform patients in peak amplitude and mean component latency across three parameters of memory when measured at optimal N4 (frontal) and P6 (parietal) locations. It was also predicted that the control group would exhibit neural cohesion through FPN integration during cross-modal tasks, thus demonstrating healthy cognitive functioning consistent with older healthy adults. By targeting select frontal and parietal EEG reference channels based on N4/P6 component time windows and positivity, our findings demonstrated statistically significant group variations between controls and patients in N4/P6 peak amplitudes and latencies during cross-modal testing. Our results also support that the N4 ERP might be stronger than its P6 counterpart as a possible candidate biomarker. We conclude through topographic mapping that FPN integration occurs in healthy controls but is absent in AD patients during cross-modal memory tasks.

A predictive coding model of the N400

The N400 event-related component has been widely used to investigate the neural mechanisms underlying real-time language comprehension. However, despite decades of research, there is still no unifying theory that can explain both its temporal dynamics and functional properties. In this work, we show that predictive coding - a biologically plausible algorithm for approximating Bayesian inference - offers a promising framework for characterizing the N400. Using an implemented predictive coding computational model, we demonstrate how the N400 can be formalized as the lexico-semantic prediction error produced as the brain infers meaning from the linguistic form of incoming words. We show that the magnitude of lexico-semantic prediction error mirrors the functional sensitivity of the N400 to various lexical variables, priming, contextual effects, as well as their higher-order interactions. We further show that the dynamics of the predictive coding algorithm provides a natural explanation for the temporal dynamics of the N400, and a biologically plausible link to neural activity. Together, these findings directly situate the N400 within the broader context of predictive coding research. More generally, they raise the possibility that the brain may use the same computational mechanism for inference across linguistic and non-linguistic domains.

Explaining the Sentence Superiority Effect and N400s Elicited by Words and Short Sentences with OB1-Reader

Research into reading has benefitted from the emergence of powerful computational models that account for reading behavior at different levels. Such models become more powerful when the underlying anatomy, architecture or 'physiology' can be linked to the behavior of interest. OB1-reader is a reading model that simulates the processes underlying reading in the human brain. Previous studies showed that OB1-reader can account for various phenomena in the word recognition and text reading literatures. Here we aim to extend OB1's scope, by simulating behavioral performance and evoked EEG activity for two experimental word-recognition tasks: a flanker task in which unrelated flankers generated less accurate responses combined with a larger N400, and a sentence reading task in which words were recognized more accurately at central positions and within intact sentences, than at peripheral positions and in scrambled sentences. OB1 simulated several behavioral findings in both paradigms, including the so-called sentence superiority effect. Moreover, virtual event-related potentials (ERPs) generated from node activity in OB1 were compared to human ERPs. More lexical activity in OB1 predicted the size of the N400 component of human readers in both experiments, but not the N250.

Tracking Lexical and Semantic Prediction Error Underlying the N400 Using Artificial Neural Network Models of Sentence Processing

Recent research has shown that the internal dynamics of an artificial neural network model of sentence comprehension displayed a similar pattern to the amplitude of the N400 in several conditions known to modulate this event-related potential. These results led Rabovsky et al. (2018) to suggest that the N400 might reflect change in an implicit predictive representation of meaning corresponding to semantic prediction error. This explanation stands as an alternative to the hypothesis that the N400 reflects lexical prediction error as estimated by word surprisal (Frank et al., 2015). In the present study, we directly model the amplitude of the N400 elicited during naturalistic sentence processing by using as predictor the update of the distributed representation of sentence meaning generated by a sentence gestalt model (McClelland et al., 1989) trained on a large-scale text corpus. This enables a quantitative prediction of N400 amplitudes based on a cognitively motivated model, as well as quantitative comparison of this model to alternative models of the N400. Specifically, we compare the update measure from the sentence gestalt model to surprisal estimated by a comparable language model trained on next-word prediction. Our results suggest that both sentence gestalt update and surprisal predict aspects of N400 amplitudes. Thus, we argue that N400 amplitudes might reflect two distinct but probably closely related sub-processes that contribute to the processing of a sentence.

Accumulating evidence for myriad alternatives: Modeling the generation of free association

The associative manner by which thoughts follow one another has intrigued scholars for decades. The process by which an association is generated in response to a cue can be explained by classic models of semantic processing through distinct computational mechanisms. Distributed attractor networks implement rich-get-richer dynamics and assume that stronger associations can be reached with fewer steps. Conversely, spreading activation models assume that a cue distributes its activation, in parallel, to all associations at a constant rate. Despite these models' huge influence, their intractability together with the unconstrained nature of free association have restricted their few previous uses to qualitative predictions. To test these computational mechanisms quantitatively, we conceptualize free association as the product of internal evidence accumulation and generate predictions concerning the speed and strength of people's associations. To this end, we first develop a novel approach to mapping the personalized space of words from which an individual chooses an association to a given cue. We then use state-of-the-art evidence accumulation models to demonstrate the function of rich-get-richer dynamics on the one hand and of stochasticity in the rate of spreading activation on the other hand, in preventing an exceedingly slow resolution of the competition among myriad potential associations. Furthermore, whereas our results uniformly indicate that stronger associations require less evidence, only in combination with rich-get-richer dynamics does this explain why weak associations are slow yet prevalent. We discuss implications for models of semantic processing and evidence accumulation and offer recommendations for practical applications and individual-differences research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Spatiotemporal evidence uncovers differential neural activity patterns in cognitive and affective conflict control

Studies have shown that there are overlapping neural bases for cognitive and affective conflict control, but whether the neural activity patterns caused by the two types of conflict are similar remains to be explored. The present study utilizes electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) to temporally and spatially analyze the differences between cognitive and affective conflict control. We employ a semantic conflict task which includes blocks of cognitive and affective judgements primed by conflicting and non-conflicting contexts. The results showed a typical neural conflict effect in the cognitive judgment blocks as reflected by greater amplitudes of P2, N400, and the late positive potential (LPP), as well as greater activation of the left pre-supplementary motor area (pre-SMA) and the right inferior frontal gyrus (IFG) in the conflict condition relative to the non-conflict condition. These patterns did not emerge in the affective judgments, but instead, showed reversed effects of the LPP and in the left SMA. Taken together, these findings suggest that cognitive and affective conflict control result in different neural activity patterns.

Verbal working memory encodes phonological and semantic information differently

Working memory (WM) is often tested through immediate serial recall of word lists. Performance in such tasks is negatively influenced by phonological similarity: People more often get the order of words wrong when they are phonologically similar to each other (e.g., cat, fat, mat). This phonological-similarity effect shows that phonology plays an important role for the representation of serial order in these tasks. By contrast, semantic similarity usually does not impact performance negatively. To resolve and understand this discrepancy, we tested the effects of phonological and semantic similarity for the retention of positional information in WM. Across six experiments (all Ns = 60 young adults), we manipulated between-item semantic and phonological similarity in tasks requiring participants to form and maintain new item-context bindings in WM. Participants were asked to retrieve items from their context, or the contexts from their item. For both retrieval directions, phonological similarity impaired WM for item-context bindings across all experiments. Semantic similarity did not. These results demonstrate that WM encodes phonological and semantic information differently. We propose a WM model accounting for semantic-similarity effects in WM, in which semantic knowledge supports WM through activated long-term memory.

Can you touch the N400? The interactive effects of body-object interaction and task demands on N400 amplitudes and decision latencies

The effects of semantic richness on N400 amplitudes remain unclear. Some studies have reported semantic richness evoking greater N400s, whereas others have reported the opposite effect. Moreover, N400 effects of some semantic richness variables, such as body-object interaction (BOI), have yet to be demonstrated. BOI quantifies the degree to which a word's referent is easy to interact with; words such as bicycle are high-BOI whereas words such as butterfly are low-BOI. We examined BOI effects on N400 amplitudes and decision latencies in two semantic tasks. We found that in a touchable/untouchable task, low-BOI words (e.g., butterfly) evoked greater N400s than high-BOI words (e.g., bicycle), but there was no difference in decision latencies. Conversely, in a concrete/abstract task, high and low-BOI words evoked similar N400s, but decision latencies were shorter for high-BOI than for low-BOI words. Our results show that semantic richness upstream and downstream effects are dissociable and task dependent.

An ERP megastudy of Chinese word recognition

This article reports the first ERP (event-related potential) megastudy in traditional Chinese word recognition. Fifty-one native Chinese undergraduates in Hong Kong, who were native Cantonese speakers, provided ERP data to 1020 two-character words and 204 two-character pseudowords in a go/no-go lexical decision task (go trials: pseudowords). The item list and the ERP data were compiled into a database called "E-MELD" (ERP MEgastudy of Lexical Decision). To illustrate how E-MELD can be utilized in research of traditional Chinese word recognition, a series of linear mixed-effects (LME) models were conducted to examine how properties at word (contextual diversity, number of strokes, and concreteness) and character (contextual diversity, number of homophones, and semantic transparency ratings) levels influenced the ERP amplitudes in different time windows. The results showed that in all time windows, both word and character variables influenced the amplitudes of ERP signals, which argued against the proposal that Chinese two-character words are recognized holistically. At the same time, there was no evidence that character effects preceded word effects (i.e., no evidence of character-mediated word recognition). Overall, the pattern suggests that characters and words are accessed simultaneously in Chinese word recognition. E-MELD is made available online, such that interested researchers can download it and use the data innovatively for their research purpose.

From decomposition to distributed theories of morphological processing in reading

The morphological structure of complex words impacts how they are processed during visual word recognition. This impact varies over the course of reading acquisition and for different languages and writing systems. Many theories of morphological processing rely on a decomposition mechanism, in which words are decomposed into explicit representations of their constituent morphemes. In distributed accounts, in contrast, morphological sensitivity arises from the tuning of finer-grained representations to useful statistical regularities in the form-to-meaning mapping, without the need for explicit morpheme representations. In this theoretically guided review, we summarize research into the mechanisms of morphological processing, and discuss findings within the context of decomposition and distributed accounts. Although many findings fit within a decomposition model of morphological processing, we suggest that the full range of results is more naturally explained by a distributed approach, and discuss additional benefits of adopting this perspective.

Connecting and considering: Electrophysiology provides insights into comprehension

The ability to rapidly and systematically access knowledge stored in long-term memory in response to incoming sensory information-that is, to derive meaning from the world-lies at the core of human cognition. Research using methods that can precisely track brain activity over time has begun to reveal the multiple cognitive and neural mechanisms that make this possible. In this article, I delineate how a process of connecting affords an effortless, continuous infusion of meaning into human perception. In a relatively invariant time window, uncovered through studies using the N400 component of the event-related potential, incoming sensory information naturally induces a graded landscape of activation across long-term semantic memory, creating what might be called "proto-concepts". Connecting can be (but is not always) followed by a process of further considering those activations, wherein a set of more attentionally demanding "active comprehension" mechanisms mediate the selection, augmentation, and transformation of the initial semantic representations. The result is a limited set of more stable bindings that can be arranged in time or space, revised as needed, and brought to awareness. With this research, we are coming closer to understanding how the human brain is able to fluidly link sensation to experience, to appreciate language sequences and event structures, and, sometimes, to even predict what might be coming up next.

Evaluation of N-400 Evoked Response Potential in schizophrenia: An endophenotype or a disease marker?

N400 evoked response potentials (ERP) reliably map key semantic deficits in schizophrenia. Assessing them as endophenotypes might help in better understanding of schizophrenia risk and their use as biomarkers. We aimed to study N400 as an endophenotype marker by comparing schizophrenia (SCZ), unaffected first-degree relatives (FDR) and healthy controls (HC) and, by assessing its ability to discriminate these groups. Drug naïve or free SCZ probands (n=30), their unaffected FDRs (n=30) and HC (n=30), underwent a 40-channel ERP recording while performing a custom-made, Hindi- sentence context paradigm task, containing congruent and incongruent conditions. Fifteen centro-parietal (CP) leads, further classified into three regions-midline (CPM), right (CPR) and left (CPL) were selected as electrodes-of-interest for assessing N400. During the incongruent condition, compared to both FDRs and HC, SCZ showed significantly longer N400 latency, at CPM, CPR and CPL, and significantly lesser (more negative) amplitude, at CPM; no significant difference was noted between FDR and HC groups. On discriminant functional analysis, significant N400 predictors could accurately classify 73.3% SCZ from HC and 75% of SCZ from FDR. We conclude that N400 deficits, elicited by the incongruent condition of the sentence task, could be potential biomarkers to define disease state in schizophrenia; they may not be endophenotype markers.

How well imageability, concreteness, perceptual strength, and action strength predict recognition memory, lexical decision, and reading aloud performance

We examined how well imageability, concreteness, perceptual strength, and action strength predicted recognition memory, lexical decision, and reading aloud performance. We used our imageability estimates [Cortese, M. J., & Fugett, A. (2004). Imageability ratings for 3,000 monosyllabic words. Behavior Methods and Research, Instrumentation, & Computers, 36(3), 384-387. https://doi.org/10.3758/BF03195585; Schock, J., Cortese, M. J., & Khanna, M. M. (2012a). Imageability ratings for 3,000 disyllabic words. Behavior Research Methods, 44(2), 374-379. https://doi.org/10.3758/s13428-011-0162-0], concreteness norms of Brysbaert and colleagues [Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English lemmas. Behavior Research Methods, 46(3), 904-911. https://doi.org/10.3758/s13428-013-0403-5], and perceptual and action strength ratings of Lynott and colleagues [Lynott, D., Connell, L., Brysbaert, M., Brand, J., & Carney, J. (2020). The lancaster sensorimotor norms: Multidimensional measures of perceptual and action strength for 40,000 English words. Behavior Research Methods, 52(3), 1271-1291. https://doi.org/10.3758/s13428-019-01316-z]. Our results indicate imageability is the best predictor, but methodological differences between ratings studies may contribute to the results. Surprisingly, action strength was negatively (albeit weakly) related to recognition memory. Analyses of item zRTs from the English lexicon project indicate these variables were not strong predictors of reading aloud or lexical decision performance. However, there is a small, consistent positive relationship between concreteness and zRTs (i.e., a facilitative abstractness effect). We believe researchers should either employ or control for imageability rather than concreteness, perceptual strength, or action strength when conducting recognition memory experiments. In addition, image-based codes generated at encoding strengthen memory traces but do not provide major inputs into reading aloud and lexical decision processes. Also, the facilitative abstractness effect on lexical decision and reading aloud zRTs may reflect more robust lexical representations for abstract words than concrete words, and that these two constructs are distinct.

An ERP index of real-time error correction within a noisy-channel framework of human communication

Recent evidence suggests that language processing is well-adapted to noise in the input (e.g., spelling or speech errors, misreading or mishearing) and that comprehenders readily correct the input via rational inference over possible intended sentences given probable noise corruptions. In the current study, we probed the processing of noisy linguistic input, asking whether well-studied ERP components may serve as useful indices of this inferential process. In particular, we examined sentences where semantic violations could be attributed to noise-for example, in "The storyteller could turn any incident into an amusing antidote", where the implausible word "antidote" is orthographically and phonologically close to the intended "anecdote". We found that the processing of such sentences-where the probability that the message was corrupted by noise exceeds the probability that it was produced intentionally and perceived accurately-was associated with a reduced (less negative) N400 effect and an increased P600 effect, compared to semantic violations which are unlikely to be attributed to noise ("The storyteller could turn any incident into an amusing hearse"). Further, the magnitudes of these ERP effects were correlated with the probability that the comprehender retrieved a plausible alternative. This work thus adds to the growing body of literature that suggests that many aspects of language processing are optimized for dealing with noise in the input, and opens the door to electrophysiologic investigations of the computations that support the processing of imperfect input.

Age-related dissociation of N400 effect and lexical priming

The use of contextual information is an important capability to facilitate language comprehension. This can be shown by studying behavioral and neurophysiological measures of accelerated word recognition when semantically or phonemically related information is provided in advance, resulting in accompanying attenuation of the respective event-related potential, i.e. the N400 effect. Against the background of age-dependent changes in a broad variety of lexical capacities, we aimed to study whether word priming is accomplished differently in elderly compared to young persons. 19 young (29.9 ± 5.6 years) and 15 older (69.0 ± 7.2 years) healthy adults participated in a primed lexical decision task that required the classification of target stimuli (words or pseudo-words) following related or unrelated prime words. We assessed reaction time, task accuracy and N400 responses. Acceleration of word recognition by semantic and phonemic priming was significant in both groups, but resulted in overall larger priming effects in the older participants. Compared with young adults, the older participants were slower and less accurate in responding to unrelated word-pairs. The expected N400 effect was smaller in older than young adults, particularly during phonemic word and pseudo-word priming, with a rather similar N400 amplitude reduction by semantic relatedness. The observed pattern of results is consistent with preserved or even enhanced lexical context sensitivity in older compared to young adults. This, however, appears to involve compensatory cognitive strategies with higher lexical processing costs during phonological processing in particular, suggested by a reduced N400 effect in the elderly.

Neural Components of Reading Revealed by Distributed and Symbolic Computational Models

Determining how the cognitive components of reading - orthographic, phonological, and semantic representations - are instantiated in the brain has been a longstanding goal of psychology and human cognitive neuroscience. The two most prominent computational models of reading instantiate different cognitive processes, implying different neural processes. Artificial neural network (ANN) models of reading posit non-symbolic, distributed representations. The dual-route cascaded (DRC) model instead suggests two routes of processing, one representing symbolic rules of spelling-sound correspondence, the other representing orthographic and phonological lexicons. These models are not adjudicated by behavioral data and have never before been directly compared in terms of neural plausibility. We used representational similarity analysis to compare the predictions of these models to neural data from participants reading aloud. Both the ANN and DRC model representations corresponded with neural activity. However, ANN model representations correlated to more reading-relevant areas of cortex. When contributions from the DRC model were statistically controlled, partial correlations revealed that the ANN model accounted for significant variance in the neural data. The opposite analysis, examining the variance explained by the DRC model with contributions from the ANN model factored out, revealed no correspondence to neural activity. Our results suggest that ANNs trained using distributed representations provide a better correspondence between cognitive and neural coding. Additionally, this framework provides a principled approach for comparing computational models of cognitive function to gain insight into neural representations.

A Comprehension- or a Production-Based Monitor? Response to Roelofs (2020)

Roelofs (2020) has put forth a rebuttal of the criticisms raised against comprehension-based monitoring and has also raised a number of objections against production-based monitors. In this response, I clarify that the model defended by Roelofs is not a comprehension-based monitor, but belongs to a class of monitoring models which I refer to as production-perception models. I review comprehension-based and production-perception models, highlight the strength of each, and point out the differences between them. I then discuss the limitations of both for monitoring production at higher levels, which has been the motivation for production-based monitors. Next, I address the specific criticisms raised by Roelofs (2020) in light of the current evidence. I end by presenting several lines of arguments that preclude a single monitoring mechanism as meeting all the demands of monitoring in a task as complex as communication. A more fruitful avenue is perhaps to focus on what theories are compatible with the nature of representations at specific levels of the production system and with specific aims of monitoring in language production.

To catch a Snitch: Brain potentials reveal variability in the functional organization of (fictional) world knowledge during reading

We harnessed the temporal sensitivity of event-related brain potentials (ERPs) alongside individual differences in Harry Potter (HP) knowledge to investigate the extent to which the availability and timing of information relevant for real-time written word processing are influenced by variation in domain knowledge. We manipulated meaningful (category, event) relationships between sentence fragments about HP stories and their sentence final words. During word-by-word reading, N400 amplitudes to (a) linguistically supported and (b) unsupported but meaningfully related, but not to (c) unsupported, unrelated sentence endings varied with HP domain knowledge. Single-trial analyses revealed that only the N400s to linguistically supported (but not to either type of unsupported) sentence-final words varied as a function of whether individuals knew (or could remember) the correct (supported) ending for each HP "fact." We conclude that the quick availability of information relevant for word understanding in sentences is a function of individuals' knowledge of both specific facts and the domain to which the facts belong. During written sentence processing, as domain knowledge increases, it is clearly evident that individuals can make use of the relevant knowledge systematically organized around themes, events, and categories in that domain, to the extent they have it.

Neural habituation enhances novelty detection: an EEG study of rapidly presented words

Huber and O'Reilly (2003) proposed that neural habituation aids perceptual processing, separating neural responses to currently viewed objects from recently viewed objects. However, synaptic depression has costs, producing repetition deficits. Prior work confirmed the transition from repetition benefits to deficits with increasing duration of a prime object, but the prediction of enhanced novelty detection was not tested. The current study examined this prediction with a same/different word priming task, using support vector machine (SVM) classification of EEG data, ERP analyses focused on the N400, and dynamic neural network simulations fit to behavioral data to provide a priori predictions of the ERP effects. Subjects made same/different judgements to a response word in relation to an immediately preceding brief target word; prime durations were short (50ms) or long (400ms), and long durations decreased P100/N170 responses to the target word, suggesting that this manipulation increased habituation. Following long duration primes, correct "different" judgments of primed response words increased, evidencing enhanced novelty detection. An SVM classifier predicted trial-by-trial behavior with 66.34% accuracy on held-out data, with greatest predictive power at a time pattern consistent with the N400. The habituation model was augmented with a maintained semantics layer (i.e., working memory) to generate behavior and N400 predictions. A second experiment used response-locked ERPs, confirming the model's assumption that residual activation in working memory is the basis of novelty decisions. These results support the theory that neural habituation enhances novelty detection, and the model assumption that the N400 reflects updating of semantic information in working memory.

The neurocognitive basis of metamemory: Using the N400 to study the contribution of fluency to judgments of learning

Metamemory is crucial for monitoring, evaluating, and optimizing memory performance. The basis of metamemory, however, is a matter of considerable debate. In the present study, we examined the contribution of processing fluency-the ease of processing information during learning-to metamemory judgments. We recorded event-related potentials (ERPs) while participants studied related and unrelated word pairs across two study-test cycles in a judgment of learning (JOL) task. In the first study-test cycle, related pairs were associated with better cued recall, higher JOLs, and a reduced N400 amplitude than unrelated pairs. Crucially, between- and within subject correlational analyses indicated that reduced N400 amplitudes, indexing more fluent processing, were associated with higher JOLs. Furthermore, single-trial N400 mediated a small but significant portion of the relatedness effect on JOLs. In the second study-test cycle, relatedness still increased recall and JOLs. However, related and unrelated pairs did not differ in N400 amplitude. Rather, unrelated pairs elicited a parietal positivity in a later time window that partially mediated the relatedness effect on JOLs. Together, these results suggest that processing fluency, indexed by the N400, contributes to the relatedness effect on JOLs when novel word pairs are learned, but not when previously studied pairs are relearned. Our results also imply that aspects of fluency not captured by the N400 and/or explicit beliefs about memory contribute to JOLs. This study demonstrates the utility of ERPs in gaining new insights into the neurocognitive mechanisms of metamemory.

Quasi-compositional mapping from form to meaning: a neural network-based approach to capturing neural responses during human language comprehension

We argue that natural language can be usefully described as quasi-compositional and we suggest that deep learning-based neural language models bear long-term promise to capture how language conveys meaning. We also note that a successful account of human language processing should explain both the outcome of the comprehension process and the continuous internal processes underlying this performance. These points motivate our discussion of a neural network model of sentence comprehension, the Sentence Gestalt model, which we have used to account for the N400 component of the event-related brain potential (ERP), which tracks meaning processing as it happens in real time. The model, which shares features with recent deep learning-based language models, simulates N400 amplitude as the automatic update of a probabilistic representation of the situation or event described by the sentence, corresponding to a temporal difference learning signal at the level of meaning. We suggest that this process happens relatively automatically, and that sometimes a more-controlled attention-dependent process is necessary for successful comprehension, which may be reflected in the subsequent P600 ERP component. We relate this account to current deep learning models as well as classic linguistic theory, and use it to illustrate a domain general perspective on some specific linguistic operations postulated based on compositional analyses of natural language. This article is part of the theme issue 'Towards mechanistic models of meaning composition'.

Semantic processing in children with cochlear implants: Evidence from event-related potentials

Introduction: Existing research has shown that children with significant hearing loss who use cochlear implants (CIs) perform worse than their hearing peers on behavioral measures of spoken language. The present study sought to examine how children with CIs process lexical-semantic incongruence, as indexed by electrophysiological evidence of the N400 effect. Method: Twelve children with CIs, aged between 6 and 9 years, participated in a spoken word-picture matching task while event-related potentials (ERPs) were recorded. To determine whether the N400 effect elicited in this group deviated from normal, independent samples t tests and analysis of variance (ANOVA) analyses were used to compare the results of children with CIs against those of a similarly aged typically hearing (TH) group (n = 30). Correlational analyses were also conducted within each group to gauge the degree to which the N400 effect related to behavioral measures of spoken language. Results: An N400 effect was elicited in both groups of CI and TH children. The amplitude and latency of the N400 effect did not differ significantly between groups. Despite the similarity in ERP responses, children with CIs scored significantly lower on behavioral measures of spoken word- and sentence-level comprehension. No significant correlations between ERP and behavioral measures were found, although there was a trending relationship between sentence-level spoken language comprehension and the TH group's N400 effect mean amplitude (p = .060). Conclusions: The results suggest that, at a neural level, children with CIs can process lexical-semantic incongruence, and that other underlying processes not measured by the N400 effect contribute to this population's spoken language difficulties.

Toward a Neurobiologically Plausible Model of Language-Related, Negative Event-Related Potentials

Language-related event-related potential (ERP) components such as the N400 have traditionally been associated with linguistic or cognitive functional interpretations. By contrast, it has been considerably more difficult to relate these components to neurobiologically grounded accounts of language. Here, we propose a theoretical framework based on a predictive coding architecture, within which negative language-related ERP components such as the N400 can be accounted for in a neurobiologically plausible manner. Specifically, we posit that the amplitude of negative language-related ERP components reflects precision-weighted prediction error signals, i.e., prediction errors weighted by the relevance of the information source leading to the error. From this perspective, precision has a direct link to cue validity in a particular language and, thereby, to relevance of individual linguistic features for internal model updating. We view components such as the N400 and LAN as members of a family with similar functional characteristics and suggest that latency and topography differences between these components reflect the locus of prediction errors and model updating within a hierarchically organized cortical predictive coding architecture. This account has the potential to unify findings from the full range of the N400 literature, including word-level, sentence-, and discourse-level results as well as cross-linguistic differences.

Exploring the Organization of Semantic Memory through Unsupervised Analysis of Event-related Potentials

Modern multivariate methods have enabled the application of unsupervised techniques to analyze neurophysiological data without strict adherence to predefined experimental conditions. We demonstrate a multivariate method that leverages priming effects on the evoked potential to perform hierarchical clustering on a set of word stimuli. The current study focuses on the semantic relationships that play a key role in the organization of our mental lexicon of words and concepts. The N400 component of the event-related potential is considered a reliable neurophysiological response that is indicative of whether accessing one concept facilitates subsequent access to another (i.e., one "primes" the other). To further our understanding of the organization of the human mental lexicon, we propose to utilize the N400 component to drive a clustering algorithm that can uncover, given a set of words, which particular subsets of words show mutual priming. Such a scheme requires a reliable measurement of the amplitude of the N400 component without averaging across many trials, which was here achieved using a recently developed multivariate analysis method based on beamforming. We validated our method by demonstrating that it can reliably detect, without any prior information about the nature of the stimuli, a well-known feature of the organization of our semantic memory: the distinction between animate and inanimate concepts. These results motivate further application of our method to data-driven exploration of disputed or unknown relationships between stimuli.