Examining the N400 semantic context effect item-by-item: relationship to corpus-based measures of word co-occurrence

Understanding words in context: A naturalistic EEG study of children's lexical processing

When listening to speech, adults rely on context to anticipate upcoming words. Evidence for this comes from studies demonstrating that the N400, an event-related potential (ERP) that indexes ease of lexical-semantic processing, is influenced by the predictability of a word in context. We know far less about the role of context in children's speech comprehension. The present study explored lexical processing in adults and 5-10-year-old children as they listened to a story. ERPs time-locked to the onset of every word were recorded. Each content word was coded for frequency, semantic association, and predictability. In both children and adults, N400s reflect word predictability, even when controlling for frequency and semantic association. These findings suggest that both adults and children use top-down constraints from context to anticipate upcoming words when listening to stories.

Using Event-Related Potentials to Evidence the Visual and Semantic Impact: A Pilot Study with N400 Effect and Food Packaging

Packaging design is pivotal in motivating consumer decisions, as a key communication tool from creation to purchase. Currently, the interpretation and evaluation of packaging's impact are shifting toward non-traditional methods. This pilot study evaluated the packaging perception of York Ham and Turkey Breast products. The event-related potential (ERP) technique, the methodology priming words (positive and negative), and target images (original and modified packaging) were applied. A total of 23 participants were sampled using a 32-channels scalp elastic electrode cap and viewed 200 trials of word-image matching. Participants responded whether the images and adjectives matched or not, using the two groups of images. The results demonstrate an N400 effect in the parietal area. This region was observed to show evidence of cognitive processing related to congruency or incongruency, by contrasting the priming and target of this study. The evaluation positioned the York Ham packaging as the best rated. The findings show a relevant contribution to ERPs and research related to the food packaging perception.

Strong Prediction: Language Model Surprisal Explains Multiple N400 Effects

Theoretical accounts of the N400 are divided as to whether the amplitude of the N400 response to a stimulus reflects the extent to which the stimulus was predicted, the extent to which the stimulus is semantically similar to its preceding context, or both. We use state-of-the-art machine learning tools to investigate which of these three accounts is best supported by the evidence. GPT-3, a neural language model trained to compute the conditional probability of any word based on the words that precede it, was used to operationalize contextual predictability. In particular, we used an information-theoretic construct known as surprisal (the negative logarithm of the conditional probability). Contextual semantic similarity was operationalized by using two high-quality co-occurrence-derived vector-based meaning representations for words: GloVe and fastText. The cosine between the vector representation of the sentence frame and final word was used to derive contextual cosine similarity estimates. A series of regression models were constructed, where these variables, along with cloze probability and plausibility ratings, were used to predict single trial N400 amplitudes recorded from healthy adults as they read sentences whose final word varied in its predictability, plausibility, and semantic relationship to the likeliest sentence completion. Statistical model comparison indicated GPT-3 surprisal provided the best account of N400 amplitude and suggested that apparently disparate N400 effects of expectancy, plausibility, and contextual semantic similarity can be reduced to variation in the predictability of words. The results are argued to support predictive coding in the human language network.

Ignoring the alternatives: The N400 is sensitive to stimulus preactivation alone

The N400 component of the event-related brain potential is a neural signal of processing difficulty. In the language domain, it is widely believed to be sensitive to the degree to which a given word or its semantic features have been preactivated in the brain based on the preceding context. However, it has also been shown that the brain often preactivates many words in parallel. It is currently unknown whether the N400 is also affected by the preactivations of alternative words other than the stimulus that is actually presented. This leaves a weak link in the derivation chain-how can we use the N400 to understand the mechanisms of preactivation if we do not know what it indexes? This study directly addresses this gap. We estimate the extent to which all words in a lexicon are preactivated in a given context using the predictions of contemporary large language models. We then directly compare two competing possibilities: that the amplitude of the N400 is sensitive only to the extent to which the stimulus is preactivated, and that it is also sensitive to the preactivation states of the alternatives. We find evidence of the former. This result allows for better grounded inferences about the mechanisms underlying the N400, lexical preactivation in the brain, and language processing more generally.

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

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

Meaning above (and in) the head: Combinatorial visual morphology from comics and emoji

Compositionality is a primary feature of language, but graphics can also create combinatorial meaning, like with items above faces (e.g., lightbulbs to mean inspiration). We posit that these “upfixes” (i.e., upwards affixes) involve a productive schema enabling both stored and novel face–upfix dyads. In two experiments, participants viewed either conventional (e.g., lightbulb) or unconventional (e.g., clover-leaves) upfixes with faces which either matched (e.g., lightbulb/smile) or mismatched (e.g., lightbulb/frown). In Experiment 1, matching dyads sponsored higher comprehensibility ratings and faster response times, modulated by conventionality. In Experiment 2, event-related brain potentials (ERPs) revealed conventional upfixes, regardless of matching, evoked larger N250s, indicating perceptual expertise, but mismatching and unconventional dyads elicited larger semantic processing costs (N400) than conventional-matching dyads. Yet mismatches evoked a late negativity, suggesting congruent novel dyads remained construable compared with violations. These results support that combinatorial graphics involve a constrained productive schema, similar to the lexicon of language.

The electrophysiological correlates of word pre-activation during associative word learning

Human beings continuously make use of learned associations to generate predictions about future occurrences in the environment. Such memory-related predictive processes provide a scaffold for learning in that mental representations of foreseeable events can be adjusted or strengthened based on a specific outcome. Learning the meaning of novel words through picture-word associations constitutes a prime example of associative learning because pictures preceding words can trigger word prediction through the pre-activation of the related mnemonic representations. In the present electroencephalography (EEG) study, we used event-related potentials (ERPs) to compare neural indices of word pre-activation between a word learning condition with maximal prediction likelihood and a non-learning control condition with low prediction. Results revealed that prediction-related N400 amplitudes in response to pictures decreased over time at central electrodes as a function of word learning, whereas late positive component (LPC) amplitudes increased. Notably, N400 but not LPC changes were also predictive of word learning performance, suggesting that the N400 component constitutes a sensitive marker of word pre-activation during associative word learning.

Dynamic branching in a neural network model for probabilistic prediction of sequences

An important function of the brain is to predict which stimulus is likely to occur based on the perceived cues. The present research studied the branching behavior of a computational network model of populations of excitatory and inhibitory neurons, both analytically and through simulations. Results show how synaptic efficacy, retroactive inhibition and short-term synaptic depression determine the dynamics of selection between different branches predicting sequences of stimuli of different probabilities. Further results show that changes in the probability of the different predictions depend on variations of neuronal gain. Such variations allow the network to optimize the probability of its predictions to changing probabilities of the sequences without changing synaptic efficacy.

N400 differences between physical and mental metaphors: The role of Theories of Mind

Whether Theory of Mind (ToM) contributes to metaphor understanding has been largely investigated in language acquisition and decay. Yet we know very little about the role of ToM in real-time processing of metaphors in neurotypical adults. Here, we tested the relationship between ToM and metaphor through Event Related Potentials (ERPs) by capitalizing on the difference between metaphors inviting inferences on physical (Boxers are pandas) vs. mental aspects (Teachers are books). Physical metaphors involved a larger and sustained negativity compared to mental ones. This pattern resembled concreteness effects and suggests that physical metaphors may benefit from both verbal and perceptual information. Moreover, higher scores in the Reading the Mind in the Eyes Test (RMET), but not in the Animation task, were associated with a reduction of the N400 amplitude for both physical and mental metaphors. When exploring the ERP temporal trajectory with Generalized Additive Mixed Modeling, earlier differences between metaphors characterized individuals with higher RMET scores. Among the various ToM components, thus, emotion recognition seems to be involved in the processing of metaphors in general, with an earlier impact on the mental type. These findings highlight the multifaceted nature of metaphor, at the crossroad of language, social and perceptual experience.

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.

English Speakers' Implicit Gender Concepts Influence Their Processing of French Grammatical Gender: Evidence for Semantically Mediated Cross-Linguistic Influence

Second language (L2) learners often show influence from their first language (L1) in all domains of language. This cross-linguistic influence could, in some cases, be mediated by semantics. The purpose of the present study was to test whether implicit English gender connotations affect L1 English speakers’ judgments of the L2 French gender of objects. We hypothesized that gender estimates derived from word embedding models that measure similarity of word contexts in English would affect accuracy and response time on grammatical gender (GG) decision in L2 French. L2 French learners were asked to identify the GG of French words estimated to be either congruent or incongruent with the implicit gender in English. The results showed that they were more accurate with words that were congruent with English gender connotations than words that were incongruent, suggesting that English gender connotations can influence grammatical judgments in French. Response times showed the same pattern. The results are consistent with semantics-mediated cross-linguistic influence.

Pitches that Wire Together Fire Together: Scale Degree Associations Across Time Predict Melodic Expectations

The ongoing generation of expectations is fundamental to listeners' experience of music, but research into types of statistical information that listeners extract from musical melodies has tended to emphasize transition probabilities and n-grams, with limited consideration given to other types of statistical learning that may be relevant. Temporal associations between scale degrees represent a different type of information present in musical melodies that can be learned from musical corpora using expectation networks, a computationally simple method based on activation and decay. Expectation networks infer the expectation of encountering one scale degree followed in the near (but not necessarily immediate) future by another given scale degree, with previous work suggesting that scale degree associations learned by expectation networks better predict listener ratings of pitch similarity than transition probabilities. The current work outlines how these learned scale degree associations can be combined to predict melodic continuations and tests the resulting predictions on a dataset of listener responses to a musical cloze task previously used to compare two other models of melodic expectation, a variable-order Markov model (IDyOM) and Temperley's music-theoretically motivated model. Under multinomial logistic regression, all three models explain significant unique variance in human melodic expectations, with coefficient estimates highest for expectation networks. These results suggest that generalized scale degree associations informed by both adjacent and nonadjacent relationships between melodic notes influence listeners' melodic predictions above and beyond n-gram context, highlighting the need to consider a broader range of statistical learning processes that may underlie listeners' expectations for upcoming musical events.

How 'rational' is semantic prediction? A critique and re-analysis of

In a recent article in Cognition, Delaney-Busch et al. (2019) claim evidence for 'rational', Bayesian adaptation of semantic predictions, using ERP data from Lau, Holcomb, and Kuperberg (2013). Participants read associatively related and unrelated prime-target word pairs in a first block with only 10% related trials and a second block with 50%. Related words elicited smaller N400s than unrelated words, and this difference was strongest in the second block, suggesting greater engagement in predictive processing. Using a rational adaptor model, Delaney-Busch et al. argue that the stronger N400 reduction for related words in the second block developed as a function of the number of related trials, and concluded therefore that participants predicted related words more strongly when their predictions were fulfilled more often. In this critique, I discuss two critical flaws in their analyses, namely the confounding of prediction effects with those of lexical frequency and the neglect of data from the first block. Re-analyses suggest a different picture: related words by themselves did not yield support for their conclusion, and the effect of relatedness gradually strengthened in othe two blocks in a similar way. Therefore, the N400 did not yield evidence that participants rationally adapted their semantic predictions. Within the framework proposed by Delaney-Busch et al., presumed semantic predictions may even be thought of as 'irrational'. While these results yielded no evidence for rational or probabilistic prediction, they do suggest that participants became increasingly better at predicting target words from prime words.

Semantic reversal anomalies under the microscope: Task and modality influences on language-associated event-related potentials

Semantic reversal anomalies (SRAs)-sentences where an implausibility is created by reversing participant roles-have attracted much attention in the literature on the electrophysiology of language. In spite of being syntactically well formed but semantically implausible, these sentences unexpectedly elicited a monophasic P600 effect in English and Dutch rather than an N400 effect. Subsequent research revealed variability in the presence/absence of an N400 effect to SRAs depending on the language examined and the choice of verb type in English. However, most previous studies employed the same presentation modality (visual) and task (acceptability judgement). Here, we conducted two experiments and three statistical analyses to investigate the influence of stimulus modality, task demand and statistical choices on event-related potential (ERP) response patterns to SRAs in English. We reproduced a previous study's procedure and analysis (N. Bourguignon et al. (2012) Brain and Language, 122, 179-189) and further introduced between-subjects factors of task type and modality, using mixed-effects modelling to analyse the data. We observed an N400 effect to typical English SRAs (agent subject verbs, e.g. "the fries will eat the boys"), which contrasts existing literature and was not predicted by existing theories that account for SRA processing. Task demand modulated the ERPs elicited by SRAs, while auditory presentation led to increased comprehension accuracy and a more broadly distributed ERP. Finally, the statistical methods used influenced the presence/absence of ERP effects. Our results suggest a sensitivity of language-related ERP patterns to methodological parameters, and we conclude that future experiments should take this into careful consideration.

Dissociable effects of prediction and integration during language comprehension: evidence from a large-scale study using brain potentials

Composing sentence meaning is easier for predictable words than for unpredictable words. Are predictable words genuinely predicted, or simply more plausible and therefore easier to integrate with sentence context? We addressed this persistent and fundamental question using data from a recent, large-scale (n = 334) replication study, by investigating the effects of word predictability and sentence plausibility on the N400, the brain's electrophysiological index of semantic processing. A spatio-temporally fine-grained mixed-effect multiple regression analysis revealed overlapping effects of predictability and plausibility on the N400, albeit with distinct spatio-temporal profiles. Our results challenge the view that the predictability-dependent N400 reflects the effects of either prediction or integration, and suggest that semantic facilitation of predictable words arises from a cascade of processes that activate and integrate word meaning with context into a sentence-level meaning. This article is part of the theme issue 'Towards mechanistic models of meaning composition'.

Measuring brand association strength with EEG: A single-trial N400 ERP study

Companies need to ensure that customers perceive their brands as intended, with strong and unique associations, when facing a competitive market. Traditionally, brand associations are measured using conventional techniques such as surveys and questionnaires albeit both conscious and unconscious factors can influence the collected data and the outcome of a campaign. Neuromarketing can shed light on how the customer’s brain processes marketing stimuli. We report here on an EEG study aimed at gauging mental associations with brands. We focus on the N400 event-related potential, an EEG component most strongly elicited in response to a concept unrelated to a preceding concept. We considered two video on demand brands, Netflix and Rex&Rio, and selected a set of words grouped in 4 categories that were either related (Television, Relaxation, and Price), in varying degrees, or unrelated (Unrelated) to the said brands. The experiment started with both brands’ TV commercials, as a common reference for our participants. We then applied a semantic priming paradigm in which a brand logo (“prime”) was followed by a word (“target”), and the strength of the N400 response to the word used as an inverted measure of the association strength with the brand logo. We clustered N400 responses to identify, for each brand, natural groups of associated words. As a result, for Netflix the cluster with the smallest N400 responses (i.e., strongest associations) consisted of words related to Television but for Rex&Rio it consisted of words related to Relaxation. We also evaluated the relationship between the two brands and determined which associations they share or which ones not. It turned out that associations related to Relaxation and Television distinguish the two brands. Interestingly, survey data did not show any difference between the two brands as they were equally associated with Television and Relaxation. These findings show that our N400 technique can reveal brand associations, and natural categories thereof, that would otherwise go unnoticed when using conventional surveys.

What does "it" mean, anyway? Examining the time course of semantic activation in reference resolution

Pronouns serve a critical referential function, yet the cognitive processes engaged during pronoun comprehension remain incompletely understood. One view is that encountering a pronoun leads the comprehender to reactivate the semantic features of its antecedent. We examined this by manipulating the concreteness of a noun antecedent and assessing whether an Event Related Potential (ERP) concreteness effect was elicited at a downstream pronoun. We observed a robust concreteness effect at the noun, but no similar effect at the pronoun. We also examined whether N400 semantic priming from the antecedent would increase on content words shortly following the pronoun, relative to those preceding it. Again, although we observed semantic priming following the noun, it did not increase following the pronoun. Our data suggest that pronouns do not induce the activation of (much) new semantic information in long-term memory, perhaps instead triggering an attentional shift towards their antecedents' extant representations within working memory.

Congruency of Separable Affix Verb Combinations Is Linearly Indexed by the N400

Separable affix verbs consist of a stem and a derivational affix, which, in some languages can appear together or in discontinuous, distributed form, e.g., German “aufgreifen” and “greifen … auf” [“up-pick(ing)” and “pick … up”]. Certain stems can combine with only certain affixes. However, many such combinations are evaluated not as clearly correct or incorrect, but frequently take an intermediate status with participants rating them ambiguously. Here, we mapped brain responses to combinations of verb stems and affixes realized in short sentences, including more and less common particle verbs, borderline acceptable combinations and clear violations. Event-related potential responses to discontinuous particle verbs were obtained for five affixes re-combined with 10 verb stems, situated within short, German sentences, i.e., “sie <stem>en es <affix>,” English: “they <stem> it <affix>.” The congruity of combinations was assessed both with behavioral ratings of the stimuli and corpus-derived probability measures. The size of a frontal N400 correlated with the degree of incongruency between stem and affix, as assessed by both measures. Behavioral ratings performed better than corpus-derived measures in predicting N400 magnitudes, and a combined model performed best of all. No evidence for a discrete, right/wrong effect was found. We discuss methodological implications and integrate the results into past research on the N400 and neurophysiological studies on separable-affix verbs, generally.

Contextual constraints on lexico-semantic processing in aging: Evidence from single-word event-related brain potentials

The current study reports the effects of accumulating contextual constraints on neural indices of lexico-semantic processing (i.e., effects of word frequency and orthographic neighborhood) as a function of normal aging. Event-related brain potentials were measured from a sample of older adults as they read sentences that were semantically congruent, provided only syntactic constraints (syntactic prose), or were random word strings. A linear mixed-effects modeling approach was used to probe the effects of accumulating contextual constraints on N400 responses to individual words. Like young adults in prior work, older adults exhibited a classic word position context effect on the N400 in congruent sentences, although the magnitude of the effect was reduced in older relative to younger adults. Moreover, by modeling single-word variability in N400 responses, we observed robust effects of orthographic neighborhood density that were larger in older adults than the young, and preserved effects word frequency. Importantly, in older adults, frequency effects were not modulated by accumulating contextual constraints, unlike in the young. Collectively, these findings indicate that older adults are less likely (or able) to use accumulating top-down contextual constraints, and therefore rely more strongly on bottom-up lexical features to guide semantic access of individual words during sentence comprehension.

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.

Co-occurrence frequency evaluated with large language corpora boosts semantic priming effects

In recent decades, many computational techniques have been developed to analyse the contextual usage of words in large language corpora. The present study examined whether the co-occurrence frequency obtained from large language corpora might boost purely semantic priming effects. Two experiments were conducted: one with conscious semantic priming, the other with subliminal semantic priming. Both experiments contrasted three semantic priming contexts: an unrelated priming context and two related priming contexts with word pairs that are semantically related and that co-occur either frequently or infrequently. In the conscious priming presentation (166-ms stimulus-onset asynchrony, SOA), a semantic priming effect was recorded in both related priming contexts, which was greater with higher co-occurrence frequency. In the subliminal priming presentation (66-ms SOA), no significant priming effect was shown, regardless of the related priming context. These results show that co-occurrence frequency boosts pure semantic priming effects and are discussed with reference to models of semantic network.

Latching dynamics in neural networks with synaptic depression

Prediction is the ability of the brain to quickly activate a target concept in response to a related stimulus (prime). Experiments point to the existence of an overlap between the populations of the neurons coding for different stimuli, and other experiments show that prime-target relations arise in the process of long term memory formation. The classical modelling paradigm is that long term memories correspond to stable steady states of a Hopfield network with Hebbian connectivity. Experiments show that short term synaptic depression plays an important role in the processing of memories. This leads naturally to a computational model of priming, called latching dynamics; a stable state (prime) can become unstable and the system may converge to another transiently stable steady state (target). Hopfield network models of latching dynamics have been studied by means of numerical simulation, however the conditions for the existence of this dynamics have not been elucidated. In this work we use a combination of analytic and numerical approaches to confirm that latching dynamics can exist in the context of a symmetric Hebbian learning rule, however lacks robustness and imposes a number of biologically unrealistic restrictions on the model. In particular our work shows that the symmetry of the Hebbian rule is not an obstruction to the existence of latching dynamics, however fine tuning of the parameters of the model is needed.

Semantic integration by pattern priming: experiment and cortical network model

Neural network models describe semantic priming effects by way of mechanisms of activation of neurons coding for words that rely strongly on synaptic efficacies between pairs of neurons. Biologically inspired Hebbian learning defines efficacy values as a function of the activity of pre- and post-synaptic neurons only. It generates only pair associations between words in the semantic network. However, the statistical analysis of large text databases points to the frequent occurrence not only of pairs of words (e.g., "the way") but also of patterns of more than two words (e.g., "by the way"). The learning of these frequent patterns of words is not reducible to associations between pairs of words but must take into account the higher level of coding of three-word patterns. The processing and learning of pattern of words challenges classical Hebbian learning algorithms used in biologically inspired models of priming. The aim of the present study was to test the effects of patterns on the semantic processing of words and to investigate how an inter-synaptic learning algorithm succeeds at reproducing the experimental data. The experiment manipulates the frequency of occurrence of patterns of three words in a multiple-paradigm protocol. Results show for the first time that target words benefit more priming when embedded in a pattern with the two primes than when only associated with each prime in pairs. A biologically inspired inter-synaptic learning algorithm is tested that potentiates synapses as a function of the activation of more than two pre- and post-synaptic neurons. Simulations show that the network can learn patterns of three words to reproduce the experimental results.

ERP Response Unveils Effect of Second Language Manipulation on First Language Processing

Lexical access in bilinguals has been considered either selective or non-selective and evidence exists in favor of both hypotheses. We conducted a linguistic experiment to assess whether a bilingual’s language mode influences the processing of first language information. We recorded event related potentials during a semantic priming paradigm with a covert manipulation of the second language (L2) using two types of stimulus presentations (short and long). We observed a significant facilitation of word pairs related in L2 in the short version reflected by a decrease in N400 amplitude in response to target words related to the English meaning of an inter-lingual homograph (homograph-unrelated group). This was absent in the long version, as the N400 amplitude for this group was similar to the one for the control-unrelated group. We also interviewed the participants whether they were aware of the importance of L2 in the experiment. We conclude that subjects participating in the long and short versions were in different language modes: closer to monolingual mode for the long and closer to bilingual mode for the short version; and that awareness about covert manipulation of L2 can influence the language mode, which in its turn influences the processing of the first language.

Predicting Lexical Priming Effects from Distributional Semantic Similarities: A Replication with Extension

In two experiments, we attempted to replicate and extend findings by Günther et al. (2016) that word similarity measures obtained from distributional semantics models—Latent Semantic Analysis (LSA) and Hyperspace Analog to Language (HAL)—predict lexical priming effects. To this end, we used the pseudo-random method to generate item material while systematically controlling for word similarities introduced by Günther et al. (2016) which was based on LSA cosine similarities (Experiment 1) and HAL cosine similarities (Experiment 2). Extending the original study, we used semantic spaces created from far larger corpora, and implemented several additional methodological improvements. In Experiment 1, we only found a significant effect of HAL cosines on lexical decision times, while we found significant effects for both LSA and HAL cosines in Experiment 2. As further supported by an analysis of the pooled data from both experiments, this indicates that HAL cosines are a better predictor of priming effects than LSA cosines. Taken together, the results replicate the finding that priming effects can be predicted from distributional semantic similarity measures.

Revisiting the incremental effects of context on word processing: Evidence from single-word event-related brain potentials

The amplitude of the N400-an event-related potential (ERP) component linked to meaning processing and initial access to semantic memory-is inversely related to the incremental buildup of semantic context over the course of a sentence. We revisited the nature and scope of this incremental context effect, adopting a word-level linear mixed-effects modeling approach, with the goal of probing the continuous and incremental effects of semantic and syntactic context on multiple aspects of lexical processing during sentence comprehension (i.e., effects of word frequency and orthographic neighborhood). First, we replicated the classic word-position effect at the single-word level: Open-class words showed reductions in N400 amplitude with increasing word position in semantically congruent sentences only. Importantly, we found that accruing sentence context had separable influences on the effects of frequency and neighborhood on the N400. Word frequency effects were reduced with accumulating semantic context. However, orthographic neighborhood was unaffected by accumulating context, showing robust effects on the N400 across all words, even within congruent sentences. Additionally, we found that N400 amplitudes to closed-class words were reduced with incrementally constraining syntactic context in sentences that provided only syntactic constraints. Taken together, our findings indicate that modeling word-level variability in ERPs reveals mechanisms by which different sources of information simultaneously contribute to the unfolding neural dynamics of comprehension.