Biology subjects

Rabovsky, M.

Publications and source records attributed to Rabovsky, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Semantic Update as a Predictor of Reading Time: Moving Beyond Word-Level Surprisal

This study explores the role of semantic prediction error in shaping reading behavior, as reflected in reading time during naturalistic reading. We operationalize semantic prediction error using semantic update (SU), a measure derived from the Sentence Gestalt (SG) model, which simulates incremental predictive comprehension at the level of sentence meaning. While SU has been previously linked to the N400 ERP component - an index of semantic processing - here we test its behavioral relevance by examining how it predicts total word-fixation durations in an eye-tracking corpus. To contextualize the role of semantic prediction error, we compare SU with lexical prediction error, quantified as next-word surprisal estimated from a language model (LM) trained on the same corpus as the SG model. Our results show that SU significantly contributes to explaining total word-fixation durations, even after accounting for LM-based surprisal and lower-level lexical factors. We also find that this predictive relationship holds primarily for content words, but not for function words, which parallels findings for lexical surprisal. These findings support the view that semantic prediction error plays a unique and measurable role in guiding real-time reading behavior, complementary to lexical prediction.

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bioRxiv2025

No effect of continuous transcutaneous auricular vagus nerve stimulation on the P3 and the P600 in an oddball and sentence comprehension task

The ERP components P3 and P600 have been proposed to reflect phasic activity of the locus coeruleus norepinephrine (LC/NE) system in response to deviant and task-relevant stimuli across cognitive domains. Yet causal evidence for this link remains limited. Here, we used continuous transcutaneous auricular vagus nerve stimulation (taVNS), a non-invasive method proposed to modulate LC/NE activity, to test whether these components are indeed sensitive to NE manipulation. Forty participants completed both an active visual oddball task and a sentence processing task including both syntactic and semantic violations, while receiving continuous taVNS at the cymba conchae in one session and sham stimulation at the earlobe in another session. We observed robust P3 and P600 effects. Crucially though, taVNS had no effect on P3 or P600 amplitude. The physiological NE markers salivary alpha amylase level and baseline pupil size were also unaffected by the stimulation, suggesting that the taVNS protocol and/or task may not have been sufficient to successfully engage the LC/NE system. Beyond the stimulation, however, exploratory analyses revealed correlations between the syntactic P600 and both the P3 and salivary alpha amylase levels, supporting the idea that the P600 might be related to both the P3 and NE. Overall, our findings do not allow for theoretical implications concerning a potential causal link between the two components and NE but highlight the need for more standardized taVNS protocols.

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bioRxiv2025

Interindividual differences in predicting words versus sentence meaning:Explaining N400 amplitudes using large-scale neural network models

Prediction error, both at the level of sentence meaning and at the level of the next presented word, has been shown to successfully account for N400 amplitudes. Here we address the question of whether people differ in the representational level at which they implicitly predict upcoming language. To this end, we compute a measure of prediction error at the level of sentence meaning (magnitude of change in hidden layer activation, termed semantic update, in a neural network model of sentence comprehension, the Sentence Gestalt model) and a measure of prediction error at the level of the next presented word (surprisal from a next word prediction language model). When using both measures to predict N400 amplitudes during the reading of naturalistic texts, results showed that both measures significantly accounted for N400 amplitudes even when the other measure was controlled for. Most important for current purposes, both effects were significantly negatively correlated such that people with a reversed or weak surprisal effect showed the strongest influence of semantic update on N400 amplitudes, and random-effects model comparison showed that individuals differ in whether their N400 amplitudes are driven by semantic update only, by surprisal only, or by both, and that the most common model in the population was either semantic update or the combined model but clearly not the pure surprisal model. The current approach of combining large-scale models implementing different theoretical accounts with advanced model comparison techniques enables fine-grained investigations into the computational processes underlying N400 amplitudes, including interindividual differences.

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bioRxiv2025

When Meaning Matters Most: Rethinking Cloze Probability in N400 Research

The N400 component of ERPs is modulated by how predictable a word is, but predictability is usually quantified with lexical cloze--the probability that readers supply that exact word in offline sentence completion tasks. This form-based metric is at odds with decades of evidence that the N400 is primarily sensitive to meaning. Here, we asked whether a measure of semantic feature predictability can better account for N400 amplitude modulation. We reanalysed two independent EEG datasets (N = 26 and N = 334), computing lexical and semantic cloze for each critical word. Across both datasets, semantic cloze emerged as a better predictor of the N400 data than lexical cloze. Using the same materials, we then compared semantic and lexical cloze with probabilities from four large language models (GPT-2, GPT-2.7b, RoBERTa, ALBERT). None of the LLM-derived predictors outperformed semantic cloze. Our findings support the view that the N400 primarily reflects semantic--not exact-word--processing. Methodologically, we argue that replacing lexical cloze with semantic cloze can substantially increase the explanatory power of N400 studies, and caution against substituting human norms with raw LLM probabilities.

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bioRxiv2024

Bayesian semantic surprise based on different types of regularities predicts the N400 and P600 brain signals

The brains remarkable ability to extract patterns from sequences of events has been demonstrated across cognitive domains and is a central assumption of predictive processing theories. While predictions shape language processing at the level of meaning, little is known about the underlying learning mechanism. Here, we investigated how continuous statistical inference in a semantic sequence influences the neural response. 60 participants were presented with a semantic oddball-like roving paradigm, consisting of sequences of nouns from different semantic categories. Unknown to the participants, the overall sequence contained an additional manipulation of transition probability between categories. Two Bayesian sequential learner models that captured different aspects of probabilistic learning were used to derive theoretical surprise levels for each trial and investigate online probabilistic semantic learning. The N400 ERP component was primarily modulated by increased probability with repeated exposure to the categories throughout the experiment, which essentially represents repetition suppression. This N400 repetition suppression likely prevented sizeable influences of more complex predictions such as those based on transition probability, as any incoming information was already continuously active in semantic memory. In contrast, the P600 was associated with semantic surprise in a transition probability model over recent observations, possibly indicating a working memory update in response to violations of these conditional dependencies. The results support probabilistic predictive processing of semantic information and demonstrate that continuous update of distinct statistics differentially influences language related ERPs.

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bioRxiv2024

The P600 during sentence reading predicts behavioral and neural markers of recognition memory

The P600 ERP component is elicited by a wide range of anomalies and ambiguities during sentence comprehension and remains important for neurocognitive models of language processing. It has been proposed that the P600 is a more domain-general component, signaling phasic norepinephrine release from the locus coeruleus in response to salient stimuli that require attention and behavioral adaptation. Since such norepinephrine release promotes explicit memory formation, we here investigated whether the P600 during sentence reading (encoding) is thus predictive of such explicit memory formation using a subsequent old/new word recognition task. Indeed, the P600 amplitude during our encoding task was related to behavioral recognition effects in the memory task on a trial-by-trial basis, though only for one type of violation. Recognition performance was better for semantically, but not syntactically violated words that had previously elicited a larger P600. However, the P600 to both types of violations during encoding was positively related to a more subtle, neural marker of recognition, namely the amplitude of the recollection ERP component in response to old words. In sum, we find that the P600 predicts later recognition memory both on the behavioral and neural level. Such explicit memory effects further link the late positivity to norepinephrine activity, suggesting a more domain-general nature of the component. The connection between the P600 and later recognition indicates that the neurocognitive processes that deal with salient and anomalous aspects in the linguistic input in the moment will also be involved in keeping this event available for later recognition.

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bioRxiv2023

Error-based implicit learning in language: the effect of sentence context and constraint in a repetition paradigm

Prediction errors drive implicit learning in language, but the specific mechanisms underlying these effects remain debated. This issue was addressed in an electroencephalogram (EEG) study manipulating the context of a repeated unpredictable word (repetition of the complete sentence or repetition of the word in a new sentence context) and sentence constraint. For the manipulation of sentence constraint, unexpected words were presented either in high constraint (eliciting a precise prediction) or low constraint sentences (not eliciting any specific prediction). Repetition induced reduction of N400 amplitudes and of power in the alpha/beta frequency band was larger for words repeated with their sentence context as compared to words repeated in a new low constraint context, suggesting that implicit learning happens not only at the level of individual items but additionally improves sentence-based predictions. These processing benefits for repeated sentences did not differ between constraint conditions, suggesting that sentence-based prediction update might be proportional to the amount of unpredicted semantic information, rather than to the precision of the prediction that was violated. Additionally, the consequences of high constraint prediction violations, as reflected in a frontal positivity and increased theta band power, were reduced with repetition. Overall, our findings suggest a powerful and specific adaptation mechanism that allows the language system to quickly adapt its predictions when unexpected semantic information is processed, irrespective of sentence constraint, and to reduce potential costs of strong predictions that were violated.

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bioRxiv2023

The P600 and P3 ERP components are linked to the task-evoked pupillary response as a correlate of norepinephrine activity

During language comprehension, anomalies and ambiguities in the input typically elicit the P600 event-related potential component. Although traditionally interpreted as a specific signal of combinatorial operations in sentence processing, the component has alternatively been proposed to be a variant of the oddball-sensitive, domain-general P3 component. In particular, both components might reflect phasic norepinephrine release from the locus coeruleus (LC/NE) to motivationally significant stimuli. In this preregistered study, we tested this hypothesis by relating both components to the task-evoked pupillary response, a putative biomarker of LC/NE activity. 36 participants completed a sentence comprehension task (containing 25% morphosyntactic violations) and a non-linguistic oddball task (containing 20% oddballs), while the EEG and pupil size were co-registered. Our results showed that the task-evoked pupillary response and the ERP amplitudes of both components were similarly affected by both experimental tasks. Crucially, the size of the pupillary response - both pupil size and its temporal derivative - predicted the amplitude of both ERP components on a trial- by-trial basis. This pattern of results supports the idea that both, the P3 and the P600, might rely on a shared neural generator and, more specifically, that they may both be linked to phasic NE release. Generally, our findings further stimulate the debate on whether language- related ERPs are indeed specific to linguistic processes or shared across cognitive domains. In the case of the P600, the present results indicate that anomalies during language comprehension might rather initiate transient NE activity in response to rare and motivationally significant stimuli more generally.

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bioRxiv2022

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, Hansen, and McClelland (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, Otten, Galli, & Vigliocco, 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, St. John, & Taraban, 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 SG 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.

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bioRxiv2022

Semantic Surprise Predicts the N400 Brain Potential

Language is central to human life; however, how our brains derive meaning from language is still not well understood. A commonly studied electrophysiological measure of on-line meaning related processing is the N400 component, the computational basis of which is still actively debated. Here, we test one of the recently proposed, computationally explicit hypotheses on the N400 - namely, that it reflects surprise with respect to a probabilistic representation of the semantic features of the current stimulus in a given context. We devise a Bayesian sequential learner model to derive trial-by-trial semantic surprise in a semantic oddball like roving paradigm experiment, where single nouns from different semantic categories are presented in sequences. Using experimental data from 40 subjects, we show that model-derived semantic surprise significantly predicts the N400 amplitude, substantially outperforming a non-probabilistic baseline model. Investigating the temporal signature of the effect, we find that the effect of semantic surprise on the EEG is restricted to the time window of the N400. Moreover, comparing the topography of the semantic surprise effect to a conventional ERP analysis of predicted vs. unpredicted words, we find that the semantic surprise closely replicates the N400 topography. Our results make a strong case for the role of probabilistic semantic representations in eliciting the N400, and in language comprehension in general. Significance StatementWhen we read or listen to a sentence, our brain continuously analyses its meaning and updates its understanding of it. The N400 brain potential, measured with electrophysiology, is modulated by on-line, meaning related processing. However, its computational underpinnings are still under debate. Inspired by studies of mismatch potentials in perception, here we test the hypothesis that the N400 indexes the surprise of a Bayesian observer of semantic features. We show that semantic surprise predicts the N400 amplitude to single nouns in an oddball like roving paradigm with nouns from different semantic categories. Moreover, the semantic surprise predicts the N400 to a much larger extent than a non-probabilistic baseline model. Our results thus yield further support to the Bayesian brain hypothesis.

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bioRxiv2021

The P600, but not N400, is modulated by sustained attention

The functional significance of the two prominent language-related ERP components N400 and P600 is still under debate. It has recently been suggested that one important dimension along which the two vary is in terms of automaticity versus attentional control, with N400 amplitudes reflecting more automatic and P600 amplitudes reflecting more controlled aspects of sentence comprehension. The availability of executive resources necessary for controlled processes depends on sustained attention, which fluctuates over time. Here, we thus tested whether P600 and N400 amplitudes depend on the level of sustained attention. We re-analyzed EEG and behavioral data from a sentence processing task by Sassenhagen & Bornkessel-Schlesewsky (2015, Cortex), which included sentences with morphosyntactic and semantic violations. Participants read sentences phrase by phrase and indicated whether a sentence contained any type of anomaly as soon as they had the relevant information. To quantify the varying degree of sustained attention, we extracted a moving reaction time coefficient of variation over the entire course of the task. We found that the P600 amplitude was significantly larger during periods of low reaction time variability (high sustained attention) than in periods of high reaction time variability (low sustained attention). In contrast, the amplitude of the N400 was not affected by reaction time variability. These results thus suggest that the P600 component is sensitive to sustained attention while the N400 component is not, which provides independent evidence for accounts suggesting that P600 amplitudes reflect more controlled and N400 amplitudes more automatic aspects of sentence comprehension.

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bioRxiv2021

Predicting the N400 ERP component using the Sentence Gestalt model trained on a large scale corpus

The N400 component of the event related brain potential is widely used to investigate language and meaning processing. However, despite much research the components functional basis remains actively debated. Recent work showed that the update of the predictive representation of sentence meaning (semantic update, or SU) generated by the Sentence Gestalt model (McClelland, St. John, & Taraban, 1989) consistently displayed a similar pattern to the N400 amplitude in a series of conditions known to modulate this event-related potential. These results led Rabovsky, Hansen, and McClelland (2018) to suggest that the N400 might reflect change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error. However, a limitation of this work is that the model was trained on a small artificial training corpus and thus could not be presented with the same naturalistic stimuli presented in empirical experiments. In the present study, we overcome this limitation and directly model the amplitude of the N400 elicited during naturalistic sentence processing by using as predictor the SU generated by a Sentence Gestalt model trained on a large corpus of texts. The results reported in this paper corroborate the hypothesis that the N400 component reflects the change in a probabilistic representation of meaning after every word presentation. Further analyses demonstrate that the SU of the Sentence Gestalt model and the amplitude of the N400 are influenced similarly by the stochastic and positional properties of the linguistic input.

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bioRxiv2021

The N400 ERP component reflects a learning signal during language comprehension

The functional significance of the N400 ERP component is still actively debated. Based on neural network modeling it was recently proposed that the N400 component can be interpreted as the change in a probabilistic representation corresponding to an internal temporal-difference prediction error at the level of meaning that drives adaptation in language processing. These computational modeling results imply that increased N400 amplitudes should correspond to greater adaptation. To investigate this model derived hypothesis, the current study manipulated expectancy in a sentence reading task, which influenced N400 amplitudes, and critically also later implicit memory for the manipulated word: reaction times in a perceptual identification task were significantly faster for previously unexpected words. Additionally, it could be demonstrated that this adaptation seems to specifically depend on the process underlying N400 amplitudes, as participants with larger N400 differences also exhibited a larger implicit memory benefit. These findings support the interpretation of the N400 as an implicit learning signal in language processing.

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bioRxiv2021

Distrust Before First Sight: Knowledge- and Appearance-Based Effects of Trustworthiness on the Visual Consciousness of Faces

The present EEG study with 32 healthy participants investigated whether affective knowledge about a person influences the visual awareness of their face, additionally considering the impact of facial appearance. Faces differing in perceived trustworthiness based on appearance were associated with negative or neutral social information and shown as target stimuli in an attentional blink task. As expected, participants showed enhanced awareness of faces associated with negative compared to neutral social information. On the neurophysiological level, this effect was connected to differences in the time range of the early posterior negativity (EPN)--a component associated with enhanced attention and facilitated processing of emotional stimuli. The findings indicate that the social-affective relevance of a face based on emotional knowledge is accessed during a phase of attentional enhancement for conscious perception and can affect prioritization for awareness. In contrast, no clear evidence for influences of facial trustworthiness during the attentional blink was found.

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bioRxiv2021

Graded Visual Consciousness During the Attentional Blink

One of the ongoing debates about visual consciousness is whether it can be considered as an all-or-none or a graded phenomenon. This may depend on the experimental paradigm and the task used to investigate this question. The present event-related potential study (N = 32) focuses on the attentional blink paradigm for which so far only little and mixed evidence is available. Detection of T2 face targets during the attentional blink was assessed via an objective accuracy measure (reporting the faces gender), subjective visibility on a perceptual awareness scale (PAS) as well as event-related potentials time-locked to T2 onset (components P1, N1, N2, and P3). The behavioral results indicate a graded rather than an all-or-none pattern of visual awareness. Corresponding graded differences in the N1, N2, and P3 components were observed for the comparison of visibility levels. These findings suggest that conscious perception during the attentional blink can occur in a graded fashion.

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bioRxiv2019

Implicit semantic prediction error can account for N400 effects on articles that do not differ in meaning: A neural network model

N400 effects on indefinite articles (a/an) compatible or incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, we simulate these effects using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training, providing a possible explanation for the small size of the effects in empirical studies. The model accounts for article induced N400 effects without assuming pre-activation of word forms, and instead simulates these effects as the stimulus-induced change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error.

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bioRxiv2018

Object naming is facilitated by semantic richness but inhibited by semantic density: Electrophysiological and behavioral evidence

Language production ultimately aims to convey meaning. Yet, words differ widely in the richness and density of their semantic representations and these differences impact conceptual and lexical processes during speech planning. Here, we replicate the recent finding that semantic richness, measured as the number of associated semantic features according to semantic feature production norms, facilitates object naming. In contrast, intercorrelational semantic feature density, measured as the degree of intercorrelation of a concepts features, presumably resulting in the coactivation of closely related concepts, has an inhibitory influence. We replicate the behavioral effects and investigate their relative time course and electrophysiological correlates. Both the facilitatory effect of high semantic richness and the inhibitory influence of high feature density were reflected in an increased posterior positivity starting at about 250 ms, in line with previous reports of posterior positivities in paradigms employing contextual manipulations to induce semantic interference during language production. Furthermore, amplitudes at the same posterior electrode sites were positively correlated with object naming times between about 230 and 380 ms. The observed effects follow naturally from the assumption of conceptual facilitation and simultaneous lexical competition, and are difficult to explain by language production theories dismissing lexical competition.

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bioRxiv2018

Attentional modulation of orthographic neighborhood effects during reading: Evidence from event-related brain potentials in a psychological refractory period paradigm

It is often assumed that word reading proceeds automatically. Here, we tested this assumption by recording event-related potentials during a psychological refractory period (PRP) paradigm, requiring lexical decisions about written words. Specifically, we selected words differing in their orthographic neighborhood size - the number of words that can be obtained from a target by exchanging a single letter - and investigated how influences of this variable depend on the availability of central attention. As expected, when attentional resources for lexical decisions were unconstrained, words with many orthographic neighbors elicited larger N400 amplitudes than those with few neighbors. However, under conditions of high temporal overlap with a high priority primary task, the N400 effect disappeared. This finding indicates strong attentional influences on the incidental processing of orthographic neighbors during word reading, providing novel evidence against the automaticity of processes involved in word reading. Furthermore, in conjunction with the observation of an underadditive interaction between stimulus onset asynchrony (SOA) and orthographic neighborhood size in lexical decision performance, commonly taken to indicate automaticity, our results raise issues concerning the standard logic of cognitive slack in the PRP paradigm.

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