Predictive coding across the left fronto-temporal hierarchy during language comprehension.

We used magnetoencephalography (MEG) and event-related potentials (ERPs) to track the time-course and localization of evoked activity produced by expected, unexpected plausible, and implausible words during incremental language comprehension. We suggest that the full pattern of results can be explained within a hierarchical predictive coding framework in which increased evoked activity reflects the activation of residual information that was not already represented at a given level of the fronto-temporal hierarchy ("error" activity). Between 300 and 500 ms, the three conditions produced progressively larger responses within left temporal cortex (lexico-semantic prediction error), whereas implausible inputs produced a selectively enhanced response within inferior frontal cortex (prediction error at the level of the event model). Between 600 and 1,000 ms, unexpected plausible words activated left inferior frontal and middle temporal cortices (feedback activity that produced top-down error), whereas highly implausible inputs activated left inferior frontal cortex, posterior fusiform (unsuppressed orthographic prediction error/reprocessing), and medial temporal cortex (possibly supporting new learning). Therefore, predictive coding may provide a unifying theory that links language comprehension to other domains of cognition.

Neural Evidence for the Prediction of Animacy Features during Language Comprehension: Evidence from MEG and EEG Representational Similarity Analysis.

It has been proposed that people can generate probabilistic predictions at multiple levels of representation during language comprehension. We used magnetoencephalography (MEG) and electroencephalography (EEG), in combination with representational similarity analysis, to seek neural evidence for the prediction of animacy features. In two studies, MEG and EEG activity was measured as human participants (both sexes) read three-sentence scenarios. Verbs in the final sentences constrained for either animate or inanimate semantic features of upcoming nouns, and the broader discourse context constrained for either a specific noun or for multiple nouns belonging to the same animacy category. We quantified the similarity between spatial patterns of brain activity following the verbs until just before the presentation of the nouns. The MEG and EEG datasets revealed converging evidence that the similarity between spatial patterns of neural activity following animate-constraining verbs was greater than following inanimate-constraining verbs. This effect could not be explained by lexical-semantic processing of the verbs themselves. We therefore suggest that it reflected the inherent difference in the semantic similarity structure of the predicted animate and inanimate nouns. Moreover, the effect was present regardless of whether a specific word could be predicted, providing strong evidence for the prediction of coarse-grained semantic features that goes beyond the prediction of individual words.SIGNIFICANCE STATEMENT Language inputs unfold very quickly during real-time communication. By predicting ahead, we can give our brains a "head start," so that language comprehension is faster and more efficient. Although most contexts do not constrain strongly for a specific word, they do allow us to predict some upcoming information. For example, following the context of "they cautioned the…," we can predict that the next word will be animate rather than inanimate (we can caution a person, but not an object). Here, we used EEG and MEG techniques to show that the brain is able to use these contextual constraints to predict the animacy of upcoming words during sentence comprehension, and that these predictions are associated with specific spatial patterns of neural activity.

A Tale of Two Positivities and the N400: Distinct Neural Signatures Are Evoked by Confirmed and Violated Predictions at Different Levels of Representation.

It has been proposed that hierarchical prediction is a fundamental computational principle underlying neurocognitive processing. Here, we ask whether the brain engages distinct neurocognitive mechanisms in response to inputs that fulfill versus violate strong predictions at different levels of representation during language comprehension. Participants read three-sentence scenarios in which the third sentence constrained for a broad event structure, for example, {Agent caution animate-Patient}. High constraint contexts additionally constrained for a specific event/lexical item, for example, a two-sentence context about a beach, lifeguards, and sharks constrained for the event, {Lifeguards cautioned Swimmers}, and the specific lexical item swimmers. Low constraint contexts did not constrain for any specific event/lexical item. We measured ERPs on critical nouns that fulfilled and/or violated each of these constraints. We found clear, dissociable effects to fulfilled semantic predictions (a reduced N400), to event/lexical prediction violations (an increased late frontal positivity), and to event structure/animacy prediction violations (an increased late posterior positivity/P600). We argue that the late frontal positivity reflects a large change in activity associated with successfully updating the comprehender's current situation model with new unpredicted information. We suggest that the late posterior positivity/P600 is triggered when the comprehender detects a conflict between the input and her model of the communicator and communicative environment. This leads to an initial failure to incorporate the unpredicted input into the situation model, which may be followed by second-pass attempts to make sense of the discourse through reanalysis, repair, or reinterpretation. Together, these findings provide strong evidence that confirmed and violated predictions at different levels of representation manifest as distinct spatiotemporal neural signatures.

Spatiotemporal Signatures of Lexical-Semantic Prediction.

Although there is broad agreement that top-down expectations can facilitate lexical-semantic processing, the mechanisms driving these effects are still unclear. In particular, while previous electroencephalography (EEG) research has demonstrated a reduction in the N400 response to words in a supportive context, it is often challenging to dissociate facilitation due to bottom-up spreading activation from facilitation due to top-down expectations. The goal of the current study was to specifically determine the cortical areas associated with facilitation due to top-down prediction, using magnetoencephalography (MEG) recordings supplemented by EEG and functional magnetic resonance imaging (fMRI) in a semantic priming paradigm. In order to modulate expectation processes while holding context constant, we manipulated the proportion of related pairs across 2 blocks (10 and 50% related). Event-related potential results demonstrated a larger N400 reduction when a related word was predicted, and MEG source localization of activity in this time-window (350-450 ms) localized the differential responses to left anterior temporal cortex. fMRI data from the same participants support the MEG localization, showing contextual facilitation in left anterior superior temporal gyrus for the high expectation block only. Together, these results provide strong evidence that facilitatory effects of lexical-semantic prediction on the electrophysiological response 350-450 ms postonset reflect modulation of activity in left anterior temporal cortex.

Automatic semantic facilitation in anterior temporal cortex revealed through multimodal neuroimaging.

A core property of human semantic processing is the rapid, facilitatory influence of prior input on extracting the meaning of what comes next, even under conditions of minimal awareness. Previous work has shown a number of neurophysiological indices of this facilitation, but the mapping between time course and localization-critical for separating automatic semantic facilitation from other mechanisms-has thus far been unclear. In the current study, we used a multimodal imaging approach to isolate early, bottom-up effects of context on semantic memory, acquiring a combination of electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) measurements in the same individuals with a masked semantic priming paradigm. Across techniques, the results provide a strikingly convergent picture of early automatic semantic facilitation. Event-related potentials demonstrated early sensitivity to semantic association between 300 and 500 ms; MEG localized the differential neural response within this time window to the left anterior temporal cortex, and fMRI localized the effect more precisely to the left anterior superior temporal gyrus, a region previously implicated in semantic associative processing. However, fMRI diverged from early EEG/MEG measures in revealing semantic enhancement effects within frontal and parietal regions, perhaps reflecting downstream attempts to consciously access the semantic features of the masked prime. Together, these results provide strong evidence that automatic associative semantic facilitation is realized as reduced activity within the left anterior superior temporal cortex between 300 and 500 ms after a word is presented, and emphasize the importance of multimodal neuroimaging approaches in distinguishing the contributions of multiple regions to semantic processing.

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.

Eye movements modulate the spatiotemporal dynamics of word processing.

Active reading requires coordination between frequent eye movements (saccades) and short fixations in text. Yet, the impact of saccades on word processing remains unknown, as neuroimaging studies typically employ constant eye fixation. Here we investigate eye-movement effects on word recognition processes in healthy human subjects using anatomically constrained magnetoencephalography, psychophysical measurements, and saccade detection in real time. Word recognition was slower and brain responses were reduced to words presented early versus late after saccades, suggesting an overall transient impairment of word processing after eye movements. Response reductions occurred early in visual cortices and later in language regions, where they colocalized with repetition priming effects. Qualitatively similar effects occurred when words appeared early versus late after background movement that mimicked saccades, suggesting that retinal motion contributes to postsaccadic inhibition. Further, differences in postsaccadic and background-movement effects suggest that central mechanisms also contribute to postsaccadic modulation. Together, these results suggest a complex interplay between visual and central saccadic mechanisms during reading.

Dissociating N400 effects of prediction from association in single-word contexts.

When a word is preceded by a supportive context such as a semantically associated word or a strongly constraining sentence frame, the N400 component of the ERP is reduced in amplitude. An ongoing debate is the degree to which this reduction reflects a passive spread of activation across long-term semantic memory representations as opposed to specific predictions about upcoming input. We addressed this question by embedding semantically associated prime-target pairs within an experimental context that encouraged prediction to a greater or lesser degree. The proportion of related items was used to manipulate the predictive validity of the prime for the target while holding semantic association constant. A semantic category probe detection task was used to encourage semantic processing and to preclude the need for a motor response on the trials of interest. A larger N400 reduction to associated targets was observed in the high than the low relatedness proportion condition, consistent with the hypothesis that predictions about upcoming stimuli make a substantial contribution to the N400 effect. We also observed an earlier priming effect (205-240 msec) in the high-proportion condition, which may reflect facilitation because of form-based prediction. In summary, the results suggest that predictability modulates N400 amplitude to a greater degree than the semantic content of the context.

Dissociating the pre-activation of word meaning and form during sentence comprehension: Evidence from EEG representational similarity analysis.

During language comprehension, the processing of each incoming word is facilitated in proportion to its predictability. Here, we asked whether anticipated upcoming linguistic information is actually pre-activated before new bottom-up input becomes available, and if so, whether this pre-activation is limited to the level of semantic features, or whether extends to representations of individual word-forms (orthography/phonology). We carried out Representational Similarity Analysis on EEG data while participants read highly constraining sentences. Prior to the onset of the expected target words, sentence pairs predicting semantically related words (financial "bank" - "loan") and form-related words (financial "bank" - river "bank") produced more similar neural patterns than pairs predicting unrelated words ("bank" - "lesson"). This provides direct neural evidence for item-specific semantic and form predictive pre-activation. Moreover, the semantic pre-activation effect preceded the form pre-activation effect, suggesting that top-down pre-activation is propagated from higher to lower levels of the linguistic hierarchy over time.

Natural Language Processing Markers for Psychosis and Other Psychiatric Disorders: Emerging Themes and Research Agenda From a Cross-Linguistic Workshop.

This workshop summary on natural language processing (NLP) markers for psychosis and other psychiatric disorders presents some of the clinical and research issues that NLP markers might address and some of the activities needed to move in that direction. We propose that the optimal development of NLP markers would occur in the context of research efforts to map out the underlying mechanisms of psychosis and other disorders. In this workshop, we identified some of the challenges to be addressed in developing and implementing NLP markers-based Clinical Decision Support Systems (CDSSs) in psychiatric practice, especially with respect to psychosis. Of note, a CDSS is meant to enhance decision-making by clinicians by providing additional relevant information primarily through software (although CDSSs are not without risks). In psychiatry, a field that relies on subjective clinical ratings that condense rich temporal behavioral information, the inclusion of computational quantitative NLP markers can plausibly lead to operationalized decision models in place of idiosyncratic ones, although ethical issues must always be paramount.

It's All About You: an ERP study of emotion and self-relevance in discourse.

Accurately communicating self-relevant and emotional information is a vital function of language, but we have little idea about how these factors impact normal discourse comprehension. In an event-related potential (ERP) study, we fully crossed self-relevance and emotion in a discourse context. Two-sentence social vignettes were presented either in the third or the second person (previous work has shown that this influences the perspective from which mental models are built). ERPs were time-locked to a critical word toward the end of the second sentence which was pleasant, neutral, or unpleasant (e.g., A man knocks on Sandra's/your hotel room door. She/You see(s) that he has agift/tray/gunin his hand.). We saw modulation of early components (P1, N1, and P2) by self-relevance, suggesting that a self-relevant context can lead to top-down attentional effects during early stages of visual processing. Unpleasant words evoked a larger late positivity than pleasant words, which evoked a larger positivity than neutral words, indicating that, regardless of self-relevance, emotional words are assessed as motivationally significant, triggering additional or deeper processing at post-lexical stages. Finally, self-relevance and emotion interacted on the late positivity: a larger late positivity was evoked by neutral words in self-relevant, but not in non-self-relevant, contexts. This may reflect prolonged attempts to disambiguate the emotional valence of ambiguous stimuli that are relevant to the self. More broadly, our findings suggest that the assessment of emotion and self-relevance are not independent, but rather that they interactively influence one another during word-by-word language comprehension.

(Pea)nuts and bolts of visual narrative: structure and meaning in sequential image comprehension.

Just as syntax differentiates coherent sentences from scrambled word strings, the comprehension of sequential images must also use a cognitive system to distinguish coherent narrative sequences from random strings of images. We conducted experiments analogous to two classic studies of language processing to examine the contributions of narrative structure and semantic relatedness to processing sequential images. We compared four types of comic strips: (1) Normal sequences with both structure and meaning, (2) Semantic Only sequences (in which the panels were related to a common semantic theme, but had no narrative structure), (3) Structural Only sequences (narrative structure but no semantic relatedness), and (4) Scrambled sequences of randomly-ordered panels. In Experiment 1, participants monitored for target panels in sequences presented panel-by-panel. Reaction times were slowest to panels in Scrambled sequences, intermediate in both Structural Only and Semantic Only sequences, and fastest in Normal sequences. This suggests that both semantic relatedness and narrative structure offer advantages to processing. Experiment 2 measured ERPs to all panels across the whole sequence. The N300/N400 was largest to panels in both the Scrambled and Structural Only sequences, intermediate in Semantic Only sequences and smallest in the Normal sequences. This implies that a combination of narrative structure and semantic relatedness can facilitate semantic processing of upcoming panels (as reflected by the N300/N400). Also, panels in the Scrambled sequences evoked a larger left-lateralized anterior negativity than panels in the Structural Only sequences. This localized effect was distinct from the N300/N400, and appeared despite the fact that these two sequence types were matched on local semantic relatedness between individual panels. These findings suggest that sequential image comprehension uses a narrative structure that may be independent of semantic relatedness. Altogether, we argue that the comprehension of visual narrative is guided by an interaction between structure and meaning.

We both say tomato: Intact lexical alignment in schizophrenia and bipolar disorder.

In people with schizophrenia and related disorders, impairments in communication and social functioning can negatively impact social interactions and quality of life. In the present study, we investigated the cognitive basis of a specific aspect of linguistic communication-lexical alignment-in people with schizophrenia and bipolar disorder. We probed lexical alignment as participants played a collaborative picture-naming game with the experimenter, in which the two players alternated between naming a dual-name picture (e.g., rabbit/bunny) and listening to their partner name a picture. We found evidence of lexical alignment in all three groups, with no differences between the patient groups and the controls. We argue that these typical patterns of lexical alignment in patients were supported by preserved-and in some cases increased-bottom-up mechanisms, which balanced out impairments in top-down perspective-taking.

Establishing causal coherence across sentences: an ERP study.

This study examined neural activity associated with establishing causal relationships across sentences during on-line comprehension. ERPs were measured while participants read and judged the relatedness of three-sentence scenarios in which the final sentence was highly causally related, intermediately related, and causally unrelated to its context. Lexico-semantic co-occurrence was matched across the three conditions using a Latent Semantic Analysis. Critical words in causally unrelated scenarios evoked a larger N400 than words in both highly causally related and intermediately related scenarios, regardless of whether they appeared before or at the sentence-final position. At midline sites, the N400 to intermediately related sentence-final words was attenuated to the same degree as to highly causally related words, but otherwise the N400 to intermediately related words fell in between that evoked by highly causally related and intermediately related words. No modulation of the late positivity/P600 component was observed across conditions. These results indicate that both simple and complex causal inferences can influence the earliest stages of semantically processing an incoming word. Further, they suggest that causal coherence, at the situation level, can influence incremental word-by-word discourse comprehension, even when semantic relationships between individual words are matched.

Slow and steady: sustained effects of lexico-semantic associations can mediate referential impairments in schizophrenia.

The present study investigated the contribution of lexico-semantic associations to impairments in establishing reference in schizophrenia. We examined event-related potentials as schizophrenia patients and healthy, demographically matched controls read five-sentence scenarios. Sentence 4 introduced a noun that referred back to three possible referents introduced in Sentences 1-3. These referents were contextually appropriate, contextually inappropriate but lexico-semantically associated, and contextually inappropriate and lexico-semantically nonassociated. In order to determine whether participants had correctly linked the anaphor to its referent, the final sentence reintroduced each referent, and participants indicated whether the last two sentences referred to the same entity. Results indicated that between 300 and 400 ms, patients, like healthy controls, used discourse context to link the noun with its preceding referent. However, between 400 and 500 ms, neural activity in patients was modulated only by lexico-semantic associations, rather than by discourse context. Moreover, patients were also more likely than controls to incorrectly link the noun with contextually inappropriate but lexico-semantically associated referents. These results suggest that at least some types of referential impairments may be driven by sustained activation of contextually inappropriate lexico-semantic associations.

Tea With Milk? A Hierarchical Generative Framework of Sequential Event Comprehension.

To make sense of the world around us, we must be able to segment a continual stream of sensory inputs into discrete events. In this review, I propose that in order to comprehend events, we engage hierarchical generative models that "reverse engineer" the intentions of other agents as they produce sequential action in real time. By generating probabilistic predictions for upcoming events, generative models ensure that we are able to keep up with the rapid pace at which perceptual inputs unfold. By tracking our certainty about other agents' goals and the magnitude of prediction errors at multiple temporal scales, generative models enable us to detect event boundaries by inferring when a goal has changed. Moreover, by adapting flexibly to the broader dynamics of the environment and our own comprehension goals, generative models allow us to optimally allocate limited resources. Finally, I argue that we use generative models not only to comprehend events but also to produce events (carry out goal-relevant sequential action) and to continually learn about new events from our surroundings. Taken together, this hierarchical generative framework provides new insights into how the human brain processes events so effortlessly while highlighting the fundamental links between event comprehension, production, and learning.

Word predictability effects are linear, not logarithmic: Implications for probabilistic models of sentence comprehension.

During language comprehension, we routinely use information from the prior context to help identify the meaning of individual words. While measures of online processing difficulty, such as reading times, are strongly influenced by contextual predictability, there is disagreement about the mechanisms underlying this lexical predictability effect, with different models predicting different linking functions - linear (Reichle, Rayner & Pollatsek, 2003) or logarithmic (Levy, 2008). To help resolve this debate, we conducted two highly-powered experiments (self-paced reading, N = 216; cross-modal picture naming, N = 36), and a meta-analysis of prior eye-tracking while reading studies (total N = 218). We observed a robust linear relationship between lexical predictability and word processing times across all three studies. Beyond their methodological implications, these findings also place important constraints on predictive processing models of language comprehension. In particular, these results directly contradict the empirical predictions of surprisal theory, while supporting a proportional pre-activation account of lexical prediction effects in comprehension.

Electrophysiological correlates of complement coercion.

This study examined the electrophysiological correlates of complement coercion. ERPs were measured as participants read and made acceptability judgments about plausible coerced sentences, plausible noncoerced sentences, and highly implausible animacy-violated sentences ("The journalist began/wrote/astonished the article before his coffee break"). Relative to noncoerced complement nouns, the coerced nouns evoked an N400 effect. This effect was not modulated by the number of possible activities implied by the coerced nouns (e.g., began reading the article; began writing the article) and did not differ either in magnitude or scalp distribution from the N400 effect evoked by the animacy-violated complement nouns. We suggest that the N400 modulation to both coerced and animacy-violated complement nouns reflected different types of mismatches between the semantic restrictions of the verb and the semantic properties of the incoming complement noun. This is consistent with models holding that a verb's semantic argument structure is represented and stored at a distinct level from its syntactic argument structure. Unlike the coerced complement noun, the animacy-violated nouns also evoked a robust P600 effect, which may have been triggered by the judgments of the highly implausible (syntactically determined) meanings of the animacy-violated propositions. No additional ERP effects were seen in the coerced sentences until the sentence-final word that, relative to sentence-final words in the noncoerced sentences, evoked a sustained anteriorly distributed positivity. We suggest that this effect reflected delayed attempts to retrieve the specific event(s) implied by coerced complement nouns.

Building coherence: A framework for exploring the breakdown of links across clause boundaries in schizophrenia.

Clinically, patients with schizophrenia show prominent abnormalities at the discourse level, with production characterized by tangential and illogical relationships between ideas and unclear references. Despite these clinical manifestations, most studies of language in schizophrenia have focused on semantic relationships between single words and the build-up of meaning within single-clause sentences. The present paper discusses the few studies that have gone beyond clause boundaries to fully understand language impairments in schizophrenia. We also give an overview of a relevant literature that considers the neurocognitive mechanisms by which coherence links are established across clauses in healthy adults, providing a framework that may guide future research in this area.

Impairments in Probabilistic Prediction and Bayesian Learning Can Explain Reduced Neural Semantic Priming in Schizophrenia.

It has been proposed that abnormalities in probabilistic prediction and dynamic belief updating explain the multiple features of schizophrenia. Here, we used electroencephalography (EEG) to ask whether these abnormalities can account for the well-established reduction in semantic priming observed in schizophrenia under nonautomatic conditions. We isolated predictive contributions to the neural semantic priming effect by manipulating the prime's predictive validity and minimizing retroactive semantic matching mechanisms. We additionally examined the link between prediction and learning using a Bayesian model that probed dynamic belief updating as participants adapted to the increase in predictive validity. We found that patients were less likely than healthy controls to use the prime to predictively facilitate semantic processing on the target, resulting in a reduced N400 effect. Moreover, the trial-by-trial output of our Bayesian computational model explained between-group differences in trial-by-trial N400 amplitudes as participants transitioned from conditions of lower to higher predictive validity. These findings suggest that, compared with healthy controls, people with schizophrenia are less able to mobilize predictive mechanisms to facilitate processing at the earliest stages of accessing the meanings of incoming words. This deficit may be linked to a failure to adapt to changes in the broader environment. This reciprocal relationship between impairments in probabilistic prediction and Bayesian learning/adaptation may drive a vicious cycle that maintains cognitive disturbances in schizophrenia.