Communicative predictions can overrule linguistic priors

Tracking dynamic social impressions from multidimensional voice representation

Recent research by Lavan et al. explores how individuals form complex impressions from voices. Using electroencephalography and behavioral measures, the study identifies distinct time courses for discerning traits, with early acoustic processing preceding higher-order perception. These findings shed light on the temporal dynamics of voice-based person perception and its neural underpinnings.

Adaptation at the Syntax-Semantics Interface: Evidence From a Vernacular Structure

Expanding on psycholinguistic research on linguistic adaptation, the phenomenon whereby speakers change how they comprehend or produce structures as a result of cumulative exposure to less frequent or unfamiliar linguistic structures, this study asked whether speakers can learn semantic and syntactic properties of the American English vernacular negative auxiliary inversion (NAI) structure (e.g., didn't everybody eat, meaning "not everybody ate") during the course of an experiment. Formal theoretical analyses of NAI informed the design of a task in which American English-speaking participants unfamiliar with this structure were exposed to NAI sentences in either semantically ambiguous or unambiguous contexts. Participants rapidly adapted to the interpretive properties of NAI, selecting responses similar to what would be expected of a native speaker after only limited exposure to semantically ambiguous input. On a separate ratings task, participants displayed knowledge of syntactic restrictions on NAI subject type, despite having no previous exposure. We discuss the results in the context of other experimental studies of adaptation and suggest the implementation of top-down strategies via analogy to other familiar structure types as possible explanations for the behaviors observed in this study. The study illustrates the value of integrating insights from formal theoretical research and psycholinguistic methods in research on adaptation and highlights the need for more interdisciplinary and cross-disciplinary work in both experimental and naturalistic contexts to understand this phenomenon.

Cognitive and sensory expectations independently shape musical expectancy and pleasure

Expectation is crucial for our enjoyment of music, yet the underlying generative mechanisms remain unclear. While sensory models derive predictions based on local acoustic information in the auditory signal, cognitive models assume abstract knowledge of music structure acquired over the long term. To evaluate these two contrasting mechanisms, we compared simulations from four computational models of musical expectancy against subjective expectancy and pleasantness ratings of over 1000 chords sampled from 739 US Billboard pop songs. Bayesian model comparison revealed that listeners' expectancy and pleasantness ratings were predicted by the independent, non-overlapping, contributions of cognitive and sensory expectations. Furthermore, cognitive expectations explained over twice the variance in listeners' perceived surprise compared to sensory expectations, suggesting a larger relative importance of long-term representations of music structure over short-term sensory-acoustic information in musical expectancy. Our results thus emphasize the distinct, albeit complementary, roles of cognitive and sensory expectations in shaping musical pleasure, and suggest that this expectancy-driven mechanism depends on musical information represented at different levels of abstraction along the neural hierarchy. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

Updating constructions: additive effects of prior and current experience during sentence production

While much earlier work has indicated that prior verb bias from lifelong language experience influences language processing, recent findings highlight the fact that verb biases induced during lab-based exposure sessions also influence processing. We investigated the nature of updating, i.e., how prior and current experience might interact in guiding subsequent sentence production. Participants underwent a short training session where we manipulated the bias of known English dative verbs. The prior bias of each verb for the double-object (DO) versus the prepositional-object (PO) dative was estimated using a corpus. Current verb bias was counterbalanced and controlled experimentally. Bayesian mixed-effects logistic models of participants' responses (DO or PO) during subsequent free-choice production showed that both the prior and current verb biases affected speakers' construction choice. These effects were additive and not interactive, contrary to the prediction from error-based learning models. Semantic similarity to other verbs and their experimentally manipulated biases influenced sentence production, consistent with item-based analogy and exemplar theory. These results shed light on the potential mechanisms underlying language updating and the adaptation of sentence production to ongoing experience.

Online neurostimulation of Broca's area does not interfere with syntactic predictions: A combined TMS-EEG approach to basic linguistic combination

Categorical predictions have been proposed as the key mechanism supporting the fast pace of syntactic composition in language. Accordingly, grammar-based expectations are formed-e.g., the determiner "a" triggers the prediction for a noun-and facilitate the analysis of incoming syntactic information, which is then checked against a single or few other word categories. Previous functional neuroimaging studies point towards Broca's area in the left inferior frontal gyrus (IFG) as one fundamental cortical region involved in categorical prediction during incremental language processing. Causal evidence for this hypothesis is however still missing. In this study, we combined Electroencephalography (EEG) and Transcranial Magnetic Stimulation (TMS) to test whether Broca's area is functionally relevant in predictive mechanisms for language. We transiently perturbed Broca's area during the first word in a two-word construction, while simultaneously measuring the Event-Related Potential (ERP) correlates of syntactic composition. We reasoned that if Broca's area is involved in predictive mechanisms for syntax, disruptive TMS during the first word would mitigate the difference in the ERP responses for predicted and unpredicted categories in basic two-word constructions. Contrary to this hypothesis, perturbation of Broca's area at the predictive stage did not affect the ERP correlates of basic composition. The correlation strength between the electrical field induced by TMS and the ERP responses further confirmed this pattern. We discuss the present results considering an alternative account of the role of Broca's area in syntactic composition, namely the bottom-up integration of words into constituents, and of compensatory mechanisms within the language predictive network.

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.

Who speaks next? Adaptations to speaker identity in processing spoken sentences

When listening to a speaker, we need to adapt to her individual speaking characteristics, such as error proneness, accent, etc. The present study investigated two aspects of adaptation to speaker identity during processing spoken sentences in multi-speaker situations: the effect of speaker sequence across sentences and the effect of learning speaker-specific error probability. Spoken sentences were presented, cued, and accompanied by one of three portraits that were labeled as the speakers' faces. In Block 1 speaker-specific probabilities of syntax errors were 10%, 50%, or 90%; in Block 2 they were uniformly 50%. In both blocks, speech errors elicited P600 effects in the scalp recorded ERP. We found a speaker sequence effect only in Block 1: the P600 to target words was larger after speaker switches than after speaker repetitions, independent of sentence correctness. In Block 1, listeners showed higher accuracy in judging sentence correctness spoken by speakers with lower error proportions. No speaker-specific differences in target word P600 and accuracy were found in Block 2. When speakers differ in error proneness, listeners seem to flexibly adapt their speech processing for the upcoming sentence through attention reorientation and resource reallocation if the speaker is about to change, and through proactive maintenance of neural resources if the speaker remains the same.

The time course of speaker-specific language processing

Listeners are sensitive to a speaker's individual language use and generate expectations for particular speakers. It is unclear, however, how such expectations affect online language processing. In the present EEG study, we presented thirty-two participants with auditory sentence stimuli of two speakers. Speakers differed in their use of two particular syntactic structures, easy subject-initial SOV structures and more difficult object-initial OSV structures. One speaker, the SOV-Speaker, had a high proportion of SOV sentences (75%) and a low proportion of OSV sentences (25%), and vice-versa for the OSV-Speaker. Participants were exposed to the speakers' individual language use in a training session followed by a test session on the consecutive day. ERP-results show that early stages of sentence processing are driven by syntactic processing only and are unaffected by speaker-specific expectations. In a late stage, however, an interaction between speaker and syntax information was observed. For the SOV-Speaker condition, the classical P600-effect reflected the effort of processing difficult and unexpected sentence structures. For the OSV-Speaker condition, both structures elicited different responses on frontal electrodes, possibly indexing effort to switch from a local speaker model to a global model of language use. Overall, the study identifies distinct neural mechanisms related to speaker-specific expectations.

Rational and Flexible Adaptation of Sentence Production to Ongoing Language Experience

Whether sentences are formulated primarily using lexically based or non-lexically based information has been much debated. In this perspective article, I review evidence for rational flexibility in the sentence production architecture. Sentences can be constructed flexibly via lexically dependent or independent routes, and rationally depending on the statistical properties of the input and the validity of lexical vs. abstract cues for predicting sentence structure. Different neural pathways appear to be recruited for individuals with different executive function abilities and for verbs with different statistical properties, suggesting that alternative routes are available for producing the same structure. Together, extant evidence indicates that the human brain adapts to ongoing language experience during adulthood, and that the nature of the adjustment may depend rationally on the statistical contingencies of the current context.

That's Cool. Computational Sociolinguistic Methods for Investigating Individual Lexico-grammatical Variation

The present study deals with variation in the use of lexico-grammatical patterns and emphasizes the need to embrace individual variation. Targeting the pattern that’s adj (as in that’s right, that’s nice or that’s okay) as a case study, we use a tailor-made Python script to systematically retrieve grammatical and semantic information about all instances of this construction in BNC2014 as well as sociolinguistic information enabling us to study social and individual lexico-grammatical variation among speakers who have used this pattern. The dataset amounts to 4,394 tokens produced by 445 speakers using 159 adjective types in 931 conversations. Using detailed descriptive statistics and mixed-effects regression models, we show that while the choice of some adjectives is partly determined by social variables, situational and especially individual variation is rampant overall. Adopting a cognitive-linguistic perspective and relying on the notion of entrenchment, we interpret these findings as reflecting individual speakers' routines. We argue that computational sociolinguistics is in an ideal position to contribute to the data-driven investigation of individual lexico-grammatical variation and encourage computational sociolinguists to grab this opportunity. For the routines of individual speakers ultimately both underlie and compromise systematic social variation and trigger and steer well-known types of language change including grammaticalization, pragmaticalization and change by invited inference.

Neural Mechanisms Underlying the Dynamic Updating of Native Language

Language users encounter different sentence structures from different people in different contexts. Although syntactic variability and adults' ability to adapt to it are both widely acknowledged, the relevant mechanisms and neural substrates are unknown. We hypothesized that syntactic updating might rely on cognitive control, which can help detect and resolve mismatch between prior linguistic expectations and new language experiences that countervail those expectations and thereby assist in accurately encoding new input. Using functional neuroimaging (fMRI), we investigated updating in garden-path sentence comprehension to test the prediction that regions within the left inferior frontal cortex might be relevant neural substrates, and additionally, explored the role of regions within the multiple demand network. Participants read ambiguous and unambiguous main-verb and relative-clause sentences. Ambiguous relative-clause sentences led to a garden-path effect in the left pars opercularis within the lateral frontal cortex and the left anterior insula/frontal operculum within the multiple demand network. This effect decreased upon repeated exposure to relative-clause sentences, consistent with updating. The two regions showed several contrastive patterns, including different activation relative to baseline, correlation with performance in a cognitive control task (the Stroop task), and verb-specificity versus generality in adaptation. Together, these results offer new insight into how the brain updates native language. They demonstrate the involvement of left frontal brain regions in helping the language system adjust to new experiences, with different areas playing distinct functional roles.

Distinct Neural Networks Relate to Common and Speaker-Specific Language Priors

Effective natural communication requires listeners to incorporate not only very general linguistic principles which evolved during a lifetime but also other information like the specific individual language use of a particular interlocutor. Traditionally, research has focused on the general linguistic rules, and brain science has shown a left hemispheric fronto-temporal brain network related to this processing. The present fMRI research explores speaker-specific individual language use because it is unknown whether this processing is supported by similar or distinct neural structures. Twenty-eight participants listened to sentences of persons who used more easy or difficult language. This was done by manipulating the proportion of easy SOV vs. complex OSV sentences for each speaker. Furthermore, ambiguous probe sentences were included to test top-down influences of speaker information in the absence of syntactic structure information. We observed distinct neural processing for syntactic complexity and speaker-specific language use. Syntactic complexity correlated with left frontal and posterior temporal regions. Speaker-specific processing correlated with bilateral (right-dominant) fronto-parietal brain regions. Finally, the top-down influence of speaker information was found in frontal and striatal brain regions, suggesting a mechanism for controlled syntactic processing. These findings show distinct neural networks related to general language principles as well as speaker-specific individual language use.

Three- to Four-Year-Old Children Rapidly Adapt Their Predictions and Use Them to Learn Novel Word Meanings

Adults create and update predictions about what speakers will say next. This study asks whether prediction can drive language acquisition, by testing whether 3- to 4-year-old children (n = 45) adapt to recent information when learning novel words. The study used a syntactic context which can precede both nouns and verbs to manipulate children's predictions about what syntactic category will follow. Children for whom the syntactic context predicted verbs were more likely to infer that a novel word appearing in this context referred to an action, than children for whom it predicted nouns. This suggests that children make rapid changes to their predictions, and use this information to learn novel information, supporting the role of prediction in language acquisition.