Prior Context and Individual Alpha Frequency Influence Predictive Processing during Language Comprehension.

The extent to which the brain predicts upcoming information during language processing remains controversial. To shed light on this debate, the present study reanalyzed [Nieuwland, M. S., Politzer-Ahles, S., Heyselaar, E., Segaert, K., Darley, E., Kazanina, N., et al. Large-scale replication study reveals a limit on probabilistic prediction in language comprehension. eLife, 7, e33468, 2018] replication of [DeLong, K. A., Urbach, T. P., & Kutas, M. Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8, 1117-1121, 2005]. Participants (n = 356) viewed sentences containing articles and nouns of varying predictability, while their EEG was recorded. We measured ERPs preceding the critical words (namely, the semantic prediction potential), in conjunction with postword N400 patterns and individual neural metrics. ERP activity was compared with two measures of word predictability: cloze probability and lexical surprisal. In contrast to prior literature, semantic prediction potential amplitudes did not increase as cloze probability increased, suggesting that the component may not reflect prediction during natural language processing. Initial N400 results at the article provided evidence against phonological prediction in language, in line with Nieuwland and colleagues' findings. Strikingly, however, when the surprisal of the prior words in the sentence was included in the analysis, increases in article surprisal were associated with increased N400 amplitudes, consistent with prediction accounts. This relationship between surprisal and N400 amplitude was not observed when the surprisal of the two prior words was low, suggesting that expectation violations at the article may be overlooked under highly predictable conditions. Individual alpha frequency also modulated the relationship between article surprisal and the N400, emphasizing the importance of individual neural factors for prediction. The present study extends upon existing neurocognitive models of language and prediction more generally, by illuminating the flexible and subject-specific nature of predictive processing.

Neural signatures of syntactic variation in speech planning.

Planning to speak is a challenge for the brain, and the challenge varies between and within languages. Yet, little is known about how neural processes react to these variable challenges beyond the planning of individual words. Here, we examine how fundamental differences in syntax shape the time course of sentence planning. Most languages treat alike (i.e., align with each other) the 2 uses of a word like "gardener" in "the gardener crouched" and in "the gardener planted trees." A minority keeps these formally distinct by adding special marking in 1 case, and some languages display both aligned and nonaligned expressions. Exploiting such a contrast in Hindi, we used electroencephalography (EEG) and eye tracking to suggest that this difference is associated with distinct patterns of neural processing and gaze behavior during early planning stages, preceding phonological word form preparation. Planning sentences with aligned expressions induces larger synchronization in the theta frequency band, suggesting higher working memory engagement, and more visual attention to agents than planning nonaligned sentences, suggesting delayed commitment to the relational details of the event. Furthermore, plain, unmarked expressions are associated with larger desynchronization in the alpha band than expressions with special markers, suggesting more engagement in information processing to keep overlapping structures distinct during planning. Our findings contrast with the observation that the form of aligned expressions is simpler, and they suggest that the global preference for alignment is driven not by its neurophysiological effect on sentence planning but by other sources, possibly by aspects of production flexibility and fluency or by sentence comprehension. This challenges current theories on how production and comprehension may affect the evolution and distribution of syntactic variants in the world's languages.

Individual differences in information processing during sleep and wake predict sleep-based memory consolidation of complex rules.

Memory is critical for many cognitive functions, from remembering facts, to learning complex environmental rules. While memory encoding occurs during wake, memory consolidation is associated with sleep-related neural activity. Further, research suggests that individual differences in alpha frequency during wake (∼7 - 13 Hz) modulate memory processes, with higher individual alpha frequency (IAF) associated with greater memory performance. However, the relationship between wake-related EEG individual differences, such as IAF, and sleep-related neural correlates of memory consolidation has been largely unexplored, particularly in a complex rule-based memory context. Here, we aimed to investigate whether wake-derived IAF and sleep neurophysiology interact to influence rule learning in a sample of 35 healthy adults (16 males; mean age = 25.4, range: 18 - 40). Participants learned rules of a modified miniature language prior to either 8hrs of sleep or wake, after which they were tested on their knowledge of the rules in a grammaticality judgement task. Results indicate that sleep neurophysiology and wake-derived IAF do not interact but modulate memory for complex linguistic rules separately. Phase-amplitude coupling between slow oscillations and spindles during non-rapid eye-movement (NREM) sleep also promoted memory for rules that were analogous to the canonical English word order. As an exploratory analysis, we found that rapid eye-movement (REM) sleep theta power at posterior regions interacts with IAF to predict rule learning and proportion of time in REM sleep predicts rule learning differentially depending on grammatical rule type. Taken together, the current study provides behavioural and electrophysiological evidence for a complex role of NREM and REM sleep neurophysiology and wake-derived IAF in the consolidation of rule-based information.

Oscillatory and Aperiodic Neural Activity Jointly Predict Language Learning.

Memory formation involves the synchronous firing of neurons in task-relevant networks, with recent models postulating that a decrease in low-frequency oscillatory activity underlies successful memory encoding and retrieval. However, to date, this relationship has been investigated primarily with face and image stimuli; considerably less is known about the oscillatory correlates of complex rule learning, as in language. Furthermore, recent work has shown that nonoscillatory (1/ƒ) activity is functionally relevant to cognition, yet its interaction with oscillatory activity during complex rule learning remains unknown. Using spectral decomposition and power-law exponent estimation of human EEG data (17 women, 18 men), we show for the first time that 1/ƒ and oscillatory activity jointly influence the learning of word order rules of a miniature artificial language system. Flexible word-order rules were associated with a steeper 1/ƒ slope, whereas fixed word-order rules were associated with a shallower slope. We also show that increased theta and alpha power predicts fixed relative to flexible word-order rule learning and behavioral performance. Together, these results suggest that 1/ƒ activity plays an important role in higher-order cognition, including language processing, and that grammar learning is modulated by different word-order permutations, which manifest in distinct oscillatory profiles.

Mini Pinyin: A modified miniature language for studying language learning and incremental sentence processing.

Artificial grammar learning (AGL) paradigms are used extensively to characterise (neuro)cognitive bases of language learning. However, despite their effectiveness in characterising the capacity to learn complex structured sequences, AGL paradigms lack ecological validity and typically do not account for cross-linguistic differences in sentence comprehension. Here, we describe a new modified miniature language paradigm - Mini Pinyin - that mimics natural language as it is based on an existing language (Mandarin Chinese) and includes both structure and meaning. Mini Pinyin contains a number of cross-linguistic elements, including varying word orders and classifier-noun rules. To evaluate the effectiveness of Mini Pinyin, 76 (mean age = 24.9; 26 female) monolingual native English speakers completed a learning phase followed by a sentence acceptability judgement task. Generalised mixed effects modelling revealed that participants attained a moderate degree of accuracy on the judgement task, with performance scores ranging from 25% to 100% accuracy depending on the word order of the sentence. Further, sentences compatible with the canonical English word order were learned more efficiently than non-canonical word orders. We controlled for inter-individual differences in statistical learning ability, which accounted for ~20% of the variance in performance on the sentence judgement task. We provide stimuli and statistical analysis scripts as open-source resources and discuss how future research can utilise this paradigm to study the neurobiological basis of language learning. Mini Pinyin affords a convenient tool for improving the future of language learning research by building on the parameters of traditional AGL or existing miniature language paradigms.

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.

Predicting "When" in Discourse Engages the Human Dorsal Auditory Stream: An fMRI Study Using Naturalistic Stories.

The hierarchical organization of human cortical circuits integrates information across different timescales via temporal receptive windows, which increase in length from lower to higher levels of the cortical hierarchy (Hasson et al., 2015). A recent neurobiological model of higher-order language processing (Bornkessel-Schlesewsky et al., 2015) posits that temporal receptive windows in the dorsal auditory stream provide the basis for a hierarchically organized predictive coding architecture (Friston and Kiebel, 2009). In this stream, a nested set of internal models generates time-based ("when") predictions for upcoming input at different linguistic levels (sounds, words, sentences, discourse). Here, we used naturalistic stories to test the hypothesis that multi-sentence, discourse-level predictions are processed in the dorsal auditory stream, yielding attenuated BOLD responses for highly predicted versus less strongly predicted language input. The results were as hypothesized: discourse-related cues, such as passive voice, which effect a higher predictability of remention for a character at a later point within a story, led to attenuated BOLD responses for auditory input of high versus low predictability within the dorsal auditory stream, specifically in the inferior parietal lobule, middle frontal gyrus, and dorsal parts of the inferior frontal gyrus, among other areas. Additionally, we found effects of content-related ("what") predictions in ventral regions. These findings provide novel evidence that hierarchical predictive coding extends to discourse-level processing in natural language. Importantly, they ground language processing on a hierarchically organized predictive network, as a common underlying neurobiological basis shared with other brain functions. SIGNIFICANCE STATEMENT: Language is the most powerful communicative medium available to humans. Nevertheless, we lack an understanding of the neurobiological basis of language processing in natural contexts: it is not clear how the human brain processes linguistic input within the rich contextual environments of our everyday language experience. This fMRI study provides the first demonstration that, in natural stories, predictions concerning the probability of remention of a protagonist at a later point are processed in the dorsal auditory stream. Results are congruent with a hierarchical predictive coding architecture assuming temporal receptive windows of increasing length from auditory to higher-order cortices. Accordingly, language processing in rich contextual settings can be explained via domain-general, neurobiological mechanisms of information processing in the human brain.

Where Is the Beat? The Neural Correlates of Lexical Stress and Rhythmical Well-formedness in Auditory Story Comprehension.

While listening to continuous speech, humans process beat information to correctly identify word boundaries. The beats of language are stress patterns that are created by combining lexical (word-specific) stress patterns and the rhythm of a specific language. Sometimes, the lexical stress pattern needs to be altered to obey the rhythm of the language. This study investigated the interplay of lexical stress patterns and rhythmical well-formedness in natural speech with fMRI. Previous electrophysiological studies on cases in which a regular lexical stress pattern may be altered to obtain rhythmical well-formedness showed that even subtle rhythmic deviations are detected by the brain if attention is directed toward prosody. Here, we present a new approach to this phenomenon by having participants listen to contextually rich stories in the absence of a task targeting the manipulation. For the interaction of lexical stress and rhythmical well-formedness, we found one suprathreshold cluster localized between the cerebellum and the brain stem. For the main effect of lexical stress, we found higher BOLD responses to the retained lexical stress pattern in the bilateral SMA, bilateral postcentral gyrus, bilateral middle fontal gyrus, bilateral inferior and right superior parietal lobule, and right precuneus. These results support the view that lexical stress is processed as part of a sensorimotor network of speech comprehension. Moreover, our results connect beat processing in language to domain-independent timing perception.

Sentence understanding depends on contextual use of semantic and real world knowledge.

Human language allows us to express our thoughts and ideas by combining entities, concepts and actions into multi-event episodes. Yet, the functional neuroanatomy engaged in interpretation of such high-level linguistic input remains poorly understood. Here, we used easy to detect and more subtle "borderline" anomalies to investigate the brain regions and mechanistic principles involved in the use of real-world event knowledge in language comprehension. Overall, the results showed that the processing of sentences in context engages a complex set of bilateral brain regions in the frontal, temporal and inferior parietal lobes. Easy anomalies preferentially engaged lower-order cortical areas adjacent to the primary auditory cortex. In addition, the left supramarginal gyrus and anterior temporal sulcus as well as the right posterior middle temporal gyrus contributed to the processing of easy and borderline anomalies. The observed pattern of results is explained in terms of (i) hierarchical processing along a dorsal-ventral axis and (ii) the assumption of high-order association areas serving as cortical hubs in the convergence of information in a distributed network. Finally, the observed modulation of BOLD signal in prefrontal areas provides support for their role in the implementation of executive control processes.

Processing of false belief passages during natural story comprehension: An fMRI study.

The neural correlates of theory of mind (ToM) are typically studied using paradigms which require participants to draw explicit, task-related inferences (e.g., in the false belief task). In a natural setup, such as listening to stories, false belief mentalizing occurs incidentally as part of narrative processing. In our experiment, participants listened to auditorily presented stories with false belief passages (implicit false belief processing) and immediately after each story answered comprehension questions (explicit false belief processing), while neural responses were measured with functional magnetic resonance imaging (fMRI). All stories included (among other situations) one false belief condition and one closely matched control condition. For the implicit ToM processing, we modeled the hemodynamic response during the false belief passages in the story and compared it to the hemodynamic response during the closely matched control passages. For implicit mentalizing, we found activation in typical ToM processing regions, that is the angular gyrus (AG), superior medial frontal gyrus (SmFG), precuneus (PCUN), middle temporal gyrus (MTG) as well as in the inferior frontal gyrus (IFG) billaterally. For explicit ToM, we only found AG activation. The conjunction analysis highlighted the left AG and MTG as well as the bilateral IFG as overlapping ToM processing regions for both implicit and explicit modes. Implicit ToM processing during listening to false belief passages, recruits the left SmFG and billateral PCUN in addition to the "mentalizing network" known form explicit processing tasks.

Augmenting complex and dynamic performance through mindfulness-based cognitive training: An evaluation of training adherence, trait mindfulness, personality and resting-state EEG.

Human performance applications of mindfulness-based training have demonstrated its utility in enhancing cognitive functioning. Previous studies have illustrated how these interventions can improve performance on traditional cognitive tests, however, little investigation has explored the extent to which mindfulness-based training can optimise performance in more dynamic and complex contexts. Further, from a neuroscientific perspective, the underlying mechanisms responsible for performance enhancements remain largely undescribed. With this in mind, the following study aimed to investigate how a short-term mindfulness intervention (one week) augments performance on a dynamic and complex task (target motion analyst task; TMA) in young, healthy adults (n = 40, age range = 18-38). Linear mixed effect modelling revealed that increased adherence to the web-based mindfulness-based training regime (ranging from 0-21 sessions) was associated with improved performance in the second testing session of the TMA task, controlling for baseline performance. Analyses of resting-state electroencephalographic (EEG) metrics demonstrated no change across testing sessions. Investigations of additional individual factors demonstrated that enhancements associated with training adherence remained relatively consistent across varying levels of participants' resting-state EEG metrics, personality measures (i.e., trait mindfulness, neuroticism, conscientiousness), self-reported enjoyment and timing of intervention adherence. Our results thus indicate that mindfulness-based cognitive training leads to performance enhancements in distantly related tasks, irrespective of several individual differences. We also revealed nuances in the magnitude of cognitive enhancements contingent on the timing of adherence, regardless of total volume of training. Overall, our findings suggest that mindfulness-based training could be used in a myriad of settings to elicit transferable performance enhancements.

Surprisal From Language Models Can Predict ERPs in Processing Predicate-Argument Structures Only if Enriched by an Agent Preference Principle.

Language models based on artificial neural networks increasingly capture key aspects of how humans process sentences. Most notably, model-based surprisals predict event-related potentials such as N400 amplitudes during parsing. Assuming that these models represent realistic estimates of human linguistic experience, their success in modeling language processing raises the possibility that the human processing system relies on no other principles than the general architecture of language models and on sufficient linguistic input. Here, we test this hypothesis on N400 effects observed during the processing of verb-final sentences in German, Basque, and Hindi. By stacking Bayesian generalised additive models, we show that, in each language, N400 amplitudes and topographies in the region of the verb are best predicted when model-based surprisals are complemented by an Agent Preference principle that transiently interprets initial role-ambiguous noun phrases as agents, leading to reanalysis when this interpretation fails. Our findings demonstrate the need for this principle independently of usage frequencies and structural differences between languages. The principle has an unequal force, however. Compared to surprisal, its effect is weakest in German, stronger in Hindi, and still stronger in Basque. This gradient is correlated with the extent to which grammars allow unmarked NPs to be patients, a structural feature that boosts reanalysis effects. We conclude that language models gain more neurobiological plausibility by incorporating an Agent Preference. Conversely, theories of human processing profit from incorporating surprisal estimates in addition to principles like the Agent Preference, which arguably have distinct evolutionary roots.

Event Related Brain Responses Reveal the Impact of Spatial Augmented Reality Predictive Cues on Mental Effort.

This article presents the results from a Spatial Augmented Reality (SAR) study which evaluated the cognitive cost of several predictive cues. Participants performed a validated procedural button pressing task, where the predictive cue annotations guided them to the upcoming task. While existing research has evaluated predictive cues based on their performance and self-rated mental effort, actual cognitive cost has yet to be investigated. To measure the user's brain activity, this study utilized electroencephalogram (EEG) recordings. Cognitive load was evaluated by measuring brain responses for a secondary auditory oddball task, with reduced brain responses to oddball tones expected when cognitive load in the primary task is highest. A simple monitor n-back task and procedural task comparing monitor versus SAR were conducted, followed by a version of the procedural task comparing the SAR predictive cues. Results from the brain responses were able to distinguish between performance enhancing cues with a high and low cognitive load. Electrical brain responses also revealed that having an arc or arrow guide towards the upcoming task required the least amount of mental effort.

An Agent-First Preference in a Patient-First Language During Sentence Comprehension.

The language comprehension system preferentially assumes that agents come first during incremental processing. While this might reflect a biologically fixed bias, shared with other domains and other species, the evidence is limited to languages that place agents first, and so the bias could also be learned from usage frequency. Here, we probe the bias with electroencephalography (EEG) in Äiwoo, a language that by default places patients first, but where sentence-initial nouns are still locally ambiguous between patient or agent roles. Comprehenders transiently interpreted nonhuman nouns as patients, eliciting a negativity when disambiguation was toward the less common agent-initial order. By contrast and against frequencies, human nouns were transiently interpreted as agents, eliciting an N400-like negativity when the disambiguation was toward patient-initial order. Consistent with the notion of a fixed property, the agent bias is robust against usage frequency for human referents. However, this bias can be reversed by frequency experience for nonhuman referents.

Multi-Level Precues for Guiding Tasks Within and Between Workspaces in Spatial Augmented Reality.

We explore Spatial Augmented Reality (SAR) precues (predictive cues) for procedural tasks within and between workspaces and for visualizing multiple upcoming steps in advance. We designed precues based on several factors: cue type, color transparency, and multi-level (number of precues). Precues were evaluated in a procedural task requiring the user to press buttons in three surrounding workspaces. Participants performed fastest in conditions where tasks were linked with line cues with different levels of color transparency. Precue performance was also affected by whether the next task was in the same workspace or a different one.

Neural and cognitive correlates of performance in dynamic multi-modal settings.

The endeavour to understand human cognition has largely relied upon investigation of task-related brain activity. However, resting-state brain activity can also offer insights into individual information processing and performance capabilities. Previous research has identified electroencephalographic resting-state characteristics (most prominently: the individual alpha frequency; IAF) that predict cognitive function. However, it has largely overlooked a second component of electrophysiological signals: aperiodic 1/ƒ activity. The current study examined how both oscillatory and aperiodic resting-state EEG measures, alongside traditional cognitive tests, can predict performance in a dynamic and complex, semi-naturalistic cognitive task. Participants' resting-state EEG was recorded prior to engaging in a Target Motion Analysis (TMA) task in a simulated submarine control room environment (CRUSE), which required participants to integrate dynamically changing information over time. We demonstrated that the relationship between IAF and cognitive performance extends from simple cognitive tasks (e.g., digit span) to complex, dynamic measures of information processing. Further, our results showed that individual 1/ƒ parameters (slope and intercept) differentially predicted performance across practice and testing sessions, whereby flatter slopes and higher intercepts were associated with improved performance during learning. In addition to the EEG predictors, we demonstrate a link between cognitive skills most closely related to the TMA task (i.e., spatial imagery) and subsequent performance. Overall, the current study highlights (1) how resting-state metrics - both oscillatory and aperiodic - have the potential to index higher-order cognitive capacity, while (2) emphasising the importance of examining these electrophysiological components within more dynamic settings and over time.

Cross-linguistic differences in case marking shape neural power dynamics and gaze behavior during sentence planning.

Languages differ in how they mark the dependencies between verbs and arguments, e.g., by case. An eye tracking and EEG picture description study examined the influence of case marking on the time course of sentence planning in Basque and Swiss German. While German assigns an unmarked (nominative) case to subjects, Basque specifically marks agent arguments through ergative case. Fixations to agents and event-related synchronization (ERS) in the theta and alpha frequency bands, as well as desynchronization (ERD) in the alpha and beta bands revealed multiple effects of case marking on the time course of early sentence planning. Speakers decided on case marking under planning early when preparing sentences with ergative-marked agents in Basque, whereas sentences with unmarked agents allowed delaying structural commitment across languages. These findings support hierarchically incremental accounts of sentence planning and highlight how cross-linguistic differences shape the neural dynamics underpinning language use.

Task-related, intrinsic oscillatory and aperiodic neural activity predict performance in naturalistic team-based training scenarios.

Effective teams are essential for optimally functioning societies. However, little is known regarding the neural basis of two or more individuals engaging cooperatively in real-world tasks, such as in operational training environments. In this exploratory study, we recruited forty individuals paired as twenty dyads and recorded dual-EEG at rest and during realistic training scenarios of increasing complexity using virtual simulation systems. We estimated markers of intrinsic brain activity (i.e., individual alpha frequency and aperiodic activity), as well as task-related theta and alpha oscillations. Using nonlinear modelling and a logistic regression machine learning model, we found that resting-state EEG predicts performance and can also reliably differentiate between members within a dyad. Task-related theta and alpha activity during easy training tasks predicted later performance on complex training to a greater extent than prior behaviour. These findings complement laboratory-based research on both oscillatory and aperiodic activity in higher-order cognition and provide evidence that theta and alpha activity play a critical role in complex task performance in team environments.

Rapid adaptation of predictive models during language comprehension: Aperiodic EEG slope, individual alpha frequency and idea density modulate individual differences in real-time model updating.

Predictive coding provides a compelling, unified theory of neural information processing, including for language. However, there is insufficient understanding of how predictive models adapt to changing contextual and environmental demands and the extent to which such adaptive processes differ between individuals. Here, we used electroencephalography (EEG) to track prediction error responses during a naturalistic language processing paradigm. In Experiment 1, 45 native speakers of English listened to a series of short passages. Via a speaker manipulation, we introduced changing intra-experimental adjective order probabilities for two-adjective noun phrases embedded within the passages and investigated whether prediction error responses adapt to reflect these intra-experimental predictive contingencies. To this end, we calculated a novel measure of speaker-based, intra-experimental surprisal ("speaker-based surprisal") as defined on a trial-by-trial basis and by clustering together adjectives with a similar meaning. N400 amplitude at the position of the critical second adjective was used as an outcome measure of prediction error. Results showed that N400 responses attuned to speaker-based surprisal over the course of the experiment, thus indicating that listeners rapidly adapt their predictive models to reflect local environmental contingencies (here: the probability of one type of adjective following another when uttered by a particular speaker). Strikingly, this occurs in spite of the wealth of prior linguistic experience that participants bring to the laboratory. Model adaptation effects were strongest for participants with a steep aperiodic (1/f) slope in resting EEG and low individual alpha frequency (IAF), with idea density (ID) showing a more complex pattern. These results were replicated in a separate sample of 40 participants in Experiment 2, which employed a highly similar design to Experiment 1. Overall, our results suggest that individuals with a steep aperiodic slope adapt their predictive models most strongly to context-specific probabilistic information. Steep aperiodic slope is thought to reflect low neural noise, which in turn may be associated with higher neural gain control and better cognitive control. Individuals with a steep aperiodic slope may thus be able to more effectively and dynamically reconfigure their prediction-related neural networks to meet current task demands. We conclude that predictive mechanisms in language are highly malleable and dynamic, reflecting both the affordances of the present environment as well as intrinsic information processing capabilities of the individual.

Setting the frame: the human brain activates a basic low-frequency network for language processing.

Low-frequency fluctuations (LFFs) are a major source of variation in fMRI data. This has been established in numerous experiments-particularly in the resting state. Here we investigate LFFs in a task-dependent setting. We hypothesized that LFFs may contain information about cognitive networks that are specific to the overall task domain without being time locked to stimulus onsets. We analyzed data of 6 fMRI experiments, 4 of which belonged to the language domain. After regressing out specifics of the experimental design and low-pass filtering (<0.1 Hz), we found that the 4 language experiments produced a correlational pattern that was not present in the 2 nonlanguage studies. Specifically, a region in the posterior part of the left superior temporal sulcus/gyrus was consistently correlated with both the left Brodmann's area 44 and the left frontal operculum in all 4 language studies, whereas this correlation was not found in the 2 other experiments. This finding indicates the existence of a basic network that acts as a general framework for language processing. In contrast to networks obtained by a conventional conjunction analysis of activation maps, this network is independent of experimental specifics such as stimulus onsets and exists in the low-frequency range.