Trends in syntactic parsing: anticipation, Bayesian estimation, and good-enough parsing

What eye and hand movements tell us about expectations towards argument order: An eye- and mouse-tracking study in German

Previous research on real-time sentence processing in German has shown that listeners use the morphological marking of accusative case on a sentence-initial noun phrase to not only interpret the current argument as the object and patient, but also to predict a plausible agent. So far, less is known about the use of case marking to predict the semantic role of upcoming arguments after the subject/agent has been encountered. In the present study, we examined the use of case marking for argument interpretation in transitive as well as ditransitive structures. We aimed to control for multiple factors that could have influenced processing in previous studies, including the animacy of arguments, world knowledge, and the perceptibility of the case cue. Our results from eye- and mouse-tracking indicate that the exploitation of the first case cue that enables the interpretation of the unfolding sentence is influenced by (i) the strength of argument order expectation and (ii) the perceptual salience of the case cue. PsycINFO code: 2720 Linguistics & Language & Speech.

I know how you'll say it: evidence of speaker-specific speech prediction

Most models of language comprehension assume that the linguistic system is able to pre-activate phonological information. However, the evidence for phonological prediction is mixed and controversial. In this study, we implement a paradigm that capitalizes on the fact that foreign speakers usually make phonological errors. We investigate whether speaker identity (native vs. foreign) is used to make specific phonological predictions. Fifty-two participants were recruited to read sentence frames followed by a last spoken word which was uttered by either a native or a foreign speaker. They were required to perform a lexical decision on the last spoken word, which could be either semantically predictable or not. Speaker identity (native vs. foreign) may or may not be cued by the face of the speaker. We observed that the face cue is effective in speeding up the lexical decision when the word is predictable, but it is not effective when the word is not predictable. This result shows that speech prediction takes into account the phonological variability between speakers, suggesting that it is possible to pre-activate in a detailed and specific way the phonological representation of a predictable word.

Age differences in the recruitment of syntactic analysis and semantic plausibility during sentence comprehension

Syntactic analysis and semantic plausibility provide important cues to build the meaningful representation of sentences. The purpose of this research is to explore the age-related differences in the use of syntactic analysis and semantic plausibility during sentence comprehension under different working memory load conditions. A sentence judgment task was implemented among a group of older and younger adults. Semantic plausibility (plausible, implausible) and syntactic consistency (consistent, inconsistent) were manipulated in the experimental stimuli, and working memory load (high, low) was varied by manipulating the presentation of the stimuli. The study revealed a stronger effect of semantic plausibility in older adults than in younger adults when working memory load was low. But no significant age difference in the effect of syntactic consistency was discovered. When working memory load was high, there was a stronger effect of semantic plausibility and a weaker effect of syntactic consistency in older adults than in younger adults, which suggests that older adults relied more on semantic plausibility and less on syntactic analysis than younger adults. The findings indicate that there is an age-related increase in the use of semantic plausibility, and a reduction in the use of syntactic analysis as working memory load increases.

Explainability of neural networks for child language: Agent-First strategy in comprehension of Korean active transitive construction

This study investigates how neural networks address the properties of children's linguistic knowledge, with a focus on the Agent-First strategy in comprehension of an active transitive construction in Korean. We develop various neural-network models and measure their classification performance on the test stimuli used in a behavioural experiment involving scrambling and omission of sentential components at varying degrees. Results show that, despite some compatibility of these models' performance with the children's response patterns, their performance does not fully approximate the children's utilisation of this strategy, demonstrating by-model and by-condition asymmetries. This study's findings suggest that neural networks can utilise information about formal co-occurrences to access the intended message to a certain degree, but the outcome of this process may be substantially different from how a child (as a developing processor) engages in comprehension. This implies some limits of neural networks on revealing the developmental trajectories of child language. RESEARCH HIGHLIGHTS: This study investigates how neural networks address properties of child language. We focus on the Agent-First strategy in comprehension of Korean active transitive. Results show by-model/condition asymmetries against children's response patterns. This implies some limits of neural networks on revealing properties of child language.

DAT-MT Accelerated Graph Fusion Dependency Parsing Model for Small Samples in Professional Fields

The rapid development of information technology has made the amount of information in massive texts far exceed human intuitive cognition, and dependency parsing can effectively deal with information overload. In the background of domain specialization, the migration and application of syntactic treebanks and the speed improvement in syntactic analysis models become the key to the efficiency of syntactic analysis. To realize domain migration of syntactic tree library and improve the speed of text parsing, this paper proposes a novel approach-the Double-Array Trie and Multi-threading (DAT-MT) accelerated graph fusion dependency parsing model. It effectively combines the specialized syntactic features from small-scale professional field corpus with the generalized syntactic features from large-scale news corpus, which improves the accuracy of syntactic relation recognition. Aiming at the problem of high space and time complexity brought by the graph fusion model, the DAT-MT method is proposed. It realizes the rapid mapping of massive Chinese character features to the model's prior parameters and the parallel processing of calculation, thereby improving the parsing speed. The experimental results show that the unlabeled attachment score (UAS) and the labeled attachment score (LAS) of the model are improved by 13.34% and 14.82% compared with the model with only the professional field corpus and improved by 3.14% and 3.40% compared with the model only with news corpus; both indicators are better than DDParser and LTP 4 methods based on deep learning. Additionally, the method in this paper achieves a speedup of about 3.7 times compared to the method with a red-black tree index and a single thread. Efficient and accurate syntactic analysis methods will benefit the real-time processing of massive texts in professional fields, such as multi-dimensional semantic correlation, professional feature extraction, and domain knowledge graph construction.

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.

Modeling Structure-Building in the Brain With CCG Parsing and Large Language Models

To model behavioral and neural correlates of language comprehension in naturalistic environments, researchers have turned to broad-coverage tools from natural-language processing and machine learning. Where syntactic structure is explicitly modeled, prior work has relied predominantly on context-free grammars (CFGs), yet such formalisms are not sufficiently expressive for human languages. Combinatory categorial grammars (CCGs) are sufficiently expressive directly compositional models of grammar with flexible constituency that affords incremental interpretation. In this work, we evaluate whether a more expressive CCG provides a better model than a CFG for human neural signals collected with functional magnetic resonance imaging (fMRI) while participants listen to an audiobook story. We further test between variants of CCG that differ in how they handle optional adjuncts. These evaluations are carried out against a baseline that includes estimates of next-word predictability from a transformer neural network language model. Such a comparison reveals unique contributions of CCG structure-building predominantly in the left posterior temporal lobe: CCG-derived measures offer a superior fit to neural signals compared to those derived from a CFG. These effects are spatially distinct from bilateral superior temporal effects that are unique to predictability. Neural effects for structure-building are thus separable from predictability during naturalistic listening, and those effects are best characterized by a grammar whose expressive power is motivated on independent linguistic grounds.

A noisy-channel approach to depth-charge illusions

The "depth-charge" sentence, No head injury is too trivial to be ignored, is often interpreted as "no matter how trivial head injuries are, we should not ignore them" while the literal meaning is the opposite - "we should ignore them". Four decades of research have failed to resolve the source of this entrenched semantic illusion. Here we adopt the noisy-channel framework for language comprehension to provide a potential explanation. We hypothesize that depth-charge sentences result from inferences whereby comprehenders derive the interpretation by weighing the plausibility of possible readings of the depth-charge sentences against the likelihood of plausible sentences being produced with errors. In four experiments, we find that (1) the more plausible the intended meaning of the depth-charge sentence is, the more likely the sentence is to be misinterpreted; and (2) the higher the likelihood of our hypothesized noise operations, the more likely depth-charge sentences are to be misinterpreted. These results suggest that misinterpretation is affected by both world knowledge and the distance between the depth-charge sentence and a plausible alternative, which is consistent with the noisy-channel framework.

Do we rely on good-enough processing in reading under auditory and visual noise?

Noise, as part of real-life communication flow, degrades the quality of linguistic input and affects language processing. According to predictions of the noisy-channel and good-enough processing models, noise should make comprehenders rely more on word-level semantics instead of actual syntactic relations. However, empirical evidence supporting this prediction is still lacking. For the first time, we investigated whether auditory (three-talker babble) and visual (short idioms appearing next to a target sentence on the screen) noise would trigger greater reliance on semantics and make readers of Russian sentences process the sentences superficially. Our findings suggest that, although Russian speakers generally relied on semantics in sentence comprehension, neither auditory nor visual noise increased this reliance. The only effect of noise on semantic processing was found in reading speed under auditory noise measured by first fixation duration: only without noise, the semantically implausible sentences were read slower than semantically plausible ones. These results do not support the predictions of the study based on the noisy-channel and good-enough processing models, which is discussed in light of the methodological differences among the studies of noise and their possible limitations.

Rational speech comprehension: Interaction between predictability, acoustic signal, and noise

Introduction

During speech comprehension, multiple sources of information are available to listeners, which are combined to guide the recognition process. Models of speech comprehension posit that when the acoustic speech signal is obscured, listeners rely more on information from other sources. However, these models take into account only word frequency information and local contexts (surrounding syllables), but not sentence-level information. To date, empirical studies investigating predictability effects in noise did not carefully control the tested speech sounds, while the literature investigating the effect of background noise on the recognition of speech sounds does not manipulate sentence predictability. Additionally, studies on the effect of background noise show conflicting results regarding which noise type affects speech comprehension most. We address this in the present experiment.

Methods

We investigate how listeners combine information from different sources when listening to sentences embedded in background noise. We manipulate top-down predictability, type of noise, and characteristics of the acoustic signal, thus creating conditions which differ in the extent to which a specific speech sound is masked in a way that is grounded in prior work on the confusability of speech sounds in noise. Participants complete an online word recognition experiment.

Results and discussion

The results show that participants rely more on the provided sentence context when the acoustic signal is harder to process. This is the case even when interactions of the background noise and speech sounds lead to small differences in intelligibility. Listeners probabilistically combine top-down predictions based on context with noisy bottom-up information from the acoustic signal, leading to a trade-off between the different types of information that is dependent on the combination of a specific type of background noise and speech sound.

Agrammatic output in non-fluent, including Broca's, aphasia as a rational behavior

Background

Speech of individuals with non-fluent, including Broca's, aphasia is often characterized as "agrammatic" because their output mostly consists of nouns and, to a lesser extent, verbs and lacks function words, like articles and prepositions, and correct morphological endings. Among the earliest accounts of agrammatic output in the early 1900s was the "economy of effort" idea whereby agrammatic output is construed as a way of coping with increases in the cost of language production. This idea resurfaced in the 1980s, but in general, the field of language research has largely focused on accounts of agrammatism that postulated core deficits in syntactic knowledge.

Aims

We here revisit the economy of effort hypothesis in light of increasing emphasis in cognitive science on rational and efficient behavior.

Main contribution

The critical idea is as follows: there is a cost per unit of linguistic output, and this cost is greater for patients with non-fluent aphasia. For a rational agent, this increase leads to shorter messages. Critically, the informative parts of the message should be preserved and the redundant ones (like the function words and inflectional markers) should be omitted. Although economy of effort is unlikely to provide a unifying account of agrammatic output in all patients-the relevant population is too heterogeneous and the empirical landscape too complex for any single-factor explanation-we argue that the idea of agrammatic output as a rational behavior was dismissed prematurely and appears to provide a plausible explanation for a large subset of the reported cases of expressive aphasia.

Conclusions

The rational account of expressive agrammatism should be evaluated more carefully and systematically. On the basic research side, pursuing this hypothesis may reveal how the human mind and brain optimize communicative efficiency in the presence of production difficulties. And on the applied side, this construal of expressive agrammatism emphasizes the strengths of some patients to flexibly adapt utterances in order to communicate in spite of grammatical difficulties; and focusing on these strengths may be more effective than trying to "fix" their grammar.

Schrödinger's tree-On syntax and neural language models

In the last half-decade, the field of natural language processing (NLP) has undergone two major transitions: the switch to neural networks as the primary modeling paradigm and the homogenization of the training regime (pre-train, then fine-tune). Amidst this process, language models have emerged as NLP's workhorse, displaying increasingly fluent generation capabilities and proving to be an indispensable means of knowledge transfer downstream. Due to the otherwise opaque, black-box nature of such models, researchers have employed aspects of linguistic theory in order to characterize their behavior. Questions central to syntax-the study of the hierarchical structure of language-have factored heavily into such work, shedding invaluable insights about models' inherent biases and their ability to make human-like generalizations. In this paper, we attempt to take stock of this growing body of literature. In doing so, we observe a lack of clarity across numerous dimensions, which influences the hypotheses that researchers form, as well as the conclusions they draw from their findings. To remedy this, we urge researchers to make careful considerations when investigating coding properties, selecting representations, and evaluating via downstream tasks. Furthermore, we outline the implications of the different types of research questions exhibited in studies on syntax, as well as the inherent pitfalls of aggregate metrics. Ultimately, we hope that our discussion adds nuance to the prospect of studying language models and paves the way for a less monolithic perspective on syntax in this context.

Domain-general conflict monitoring predicts neural and behavioral indices of linguistic error processing during reading comprehension

The ability to detect and respond to linguistic errors is critical for successful reading comprehension, but these skills can vary considerably across readers. In the current study, healthy adults (age 18-35) read short discourse scenarios for comprehension while monitoring for the presence of semantic anomalies. Using a factor analytic approach, we examined if performance in nonlinguistic conflict monitoring tasks (Stroop, AX-CPT) would predict individual differences in neural and behavioral measures of linguistic error processing. Consistent with this hypothesis, domain-general conflict monitoring predicted both readers' end-of-trial acceptability judgments and the amplitude of a late neural response (the P600) evoked by linguistic anomalies. The influence on the P600 was nonlinear, suggesting that online neural responses to linguistic errors are influenced by both the effectiveness and efficiency of domain-general conflict monitoring. These relationships were also highly specific and remained after controlling for variability in working memory capacity and verbal knowledge. Finally, we found that domain-general conflict monitoring also predicted individual variability in measures of reading comprehension, and that this relationship was partially mediated by behavioral measures of linguistic error detection. These findings inform our understanding of the role of domain-general executive functions in reading comprehension, with potential implications for the diagnosis and treatment of language impairments. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Remembering sentences is not all about memory: Convergent and discriminant validity of syntactic knowledge and its relationship with reading comprehension

Recent studies have found correlations between sentence-level tests and reading comprehension. However, the task demands of sentence-level tests are not well understood. The present study investigated syntactic knowledge as a construct by examining the convergent and discriminant validity of two sentence-level tasks, sentence comprehension and sentence repetition, designed to test syntactic knowledge and their relation with reading comprehension. Results from 86 Grade 6 students showed that the syntax tests were more highly correlated with each other than with tests of working memory and vocabulary. This suggests that the syntax measures tap into a set of skills that are at least partially separate from these other cognitive constructs. Furthermore, syntactic knowledge explained unique variance in reading comprehension beyond controls. The syntax tasks were working memory dependent, but working memory was not the primary reason why syntax tasks are correlated with reading comprehension.

Bilinguals on the garden-path: Individual differences in syntactic ambiguity resolution

Syntactic parsing plays a central role in the interpretation of sentences, but it is unclear to what extent non-native speakers can deploy native-like grammatical knowledge during online comprehension. The current eye-tracking study investigated how Chinese-English bilinguals and native English speakers respond to syntactic category and subcategorization information while reading sentences with OBJECT-SUBJECT ambiguities. We also obtained measures of English language experience, working memory capacity, and executive function to determine how these cognitive variables influence online parsing. During reading, monolinguals and bilinguals showed similar GARDEN-PATH EFFECTS related to syntactic reanalysis, but native English speakers responded more robustly to VERB SUBCATEGORIZATION cues. Readers with greater language experience and executive function showed increased sensitivity to verb subcategorization cues, but parsing was not influenced by working memory capacity. These results are consistent with exposure-based accounts of bilingual sentence processing, and they support a link between syntactic processing and domain-general cognitive control.

Why are the batteries in the microwave?: Use of semantic information under uncertainty in a search task

A major problem in human cognition is to understand how newly acquired information and long-standing beliefs about the environment combine to make decisions and plan behaviors. Over-dependence on long-standing beliefs may be a significant source of suboptimal decision-making in unusual circumstances. While the contribution of long-standing beliefs about the environment to search in real-world scenes is well-studied, less is known about how new evidence informs search decisions, and it is unclear whether the two sources of information are used together optimally to guide search. The present study expanded on the literature on semantic guidance in visual search by modeling a Bayesian ideal observer's use of long-standing semantic beliefs and recent experience in an active search task. The ability to adjust expectations to the task environment was simulated using the Bayesian ideal observer, and subjects' performance was compared to ideal observers that depended on prior knowledge and recent experience to varying degrees. Target locations were either congruent with scene semantics, incongruent with what would be expected from scene semantics, or random. Half of the subjects were able to learn to search for the target in incongruent locations over repeated experimental sessions when it was optimal to do so. These results suggest that searchers can learn to prioritize recent experience over knowledge of scenes in a near-optimal fashion when it is beneficial to do so, as long as the evidence from recent experience was learnable.

Listener's personality traits predict changes in pupil size during auditory language comprehension

Research suggests that listeners' comprehension of spoken language is concurrently affected by linguistic and non-linguistic factors, including individual difference factors. However, there is no systematic research on whether general personality traits affect language processing. We correlated 88 native English-speaking participants' Big-5 traits with their pupillary responses to spoken sentences that included grammatical errors, "He frequently have burgers for dinner"; semantic anomalies, "Dogs sometimes chase teas"; and statements incongruent with gender stereotyped expectations, such as "I sometimes buy my bras at Hudson's Bay", spoken by a male speaker. Generalized additive mixed models showed that the listener's Openness, Extraversion, Agreeableness, and Neuroticism traits modulated resource allocation to the three different types of unexpected stimuli. No personality trait affected changes in pupil size across the board: less open participants showed greater pupil dilation when processing sentences with grammatical errors; and more introverted listeners showed greater pupil dilation in response to both semantic anomalies and socio-cultural clashes. Our study is the first one demonstrating that personality traits systematically modulate listeners' online language processing. Our results suggest that individuals with different personality profiles exhibit different patterns of the allocation of cognitive resources during real-time language comprehension.

Delving Into the Working Mechanism of Prediction in Sentence Comprehension: An ERP Study

The present study aims to delineate the working mechanism of prediction in sentence comprehension, by disentangling the influence of the facilitated general memory retrieval from the coexistent influence of the predicted language-specific semantic and/or syntactic information for the first time. The results support that prediction might influence the downstream cognitive processing in two aspects: (1) the pre-activated information facilitates the retrieval of a matched input in memory and, (2) the pre-activated information interacts with higher-level semantic/syntactic processing. More importantly, the present findings suggest that these two types of influences seem to occur at different stages of sentence comprehension: the facilitated memory retrieval of the input modulates N400 amplitude and the latency of post-N400 late central-parietal positivity/P600, while the predicted semantic/syntactic information and/or their interactions modulate the amplitude of the late positivity. The present findings would be helpful for interpreting the underlying mechanism of observed effects in prediction studies.

Cortical Tracking of Speech: Toward Collaboration between the Fields of Signal and Sentence Processing

In recent years, a growing number of studies have used cortical tracking methods to investigate auditory language processing. Although most studies that employ cortical tracking stem from the field of auditory signal processing, this approach should also be of interest to psycholinguistics-particularly the subfield of sentence processing-given its potential to provide insight into dynamic language comprehension processes. However, there has been limited collaboration between these fields, which we suggest is partly because of differences in theoretical background and methodological constraints, some mutually exclusive. In this paper, we first review the theories and methodological constraints that have historically been prioritized in each field and provide concrete examples of how some of these constraints may be reconciled. We then elaborate on how further collaboration between the two fields could be mutually beneficial. Specifically, we argue that the use of cortical tracking methods may help resolve long-standing debates in the field of sentence processing that commonly used behavioral and neural measures (e.g., ERPs) have failed to adjudicate. Similarly, signal processing researchers who use cortical tracking may be able to reduce noise in the neural data and broaden the impact of their results by controlling for linguistic features of their stimuli and by using simple comprehension tasks. Overall, we argue that a balance between the methodological constraints of the two fields will lead to an overall improved understanding of language processing as well as greater clarity on what mechanisms cortical tracking of speech reflects. Increased collaboration will help resolve debates in both fields and will lead to new and exciting avenues for research.

Does comprehension (sometimes) go wrong for noncanonical sentences?

This article addresses the question of whether the human parsing mechanism (HPM) derives sentence meaning always from representations that are computed algorithmically or whether the HPM sometimes resorts to non-algorithmic strategies that may result in misinterpretations. Misinterpretation effects for noncanonical sentences, such as passives, constitute important evidence in favour of models allowing for nonveridical representations. However, it is unclear whether these effects reflect errors in the mapping of form to meaning, or difficulties specific to the procedure used to test comprehension. We report two experiments combining two different comprehension tasks to address these alternative possibilities. In Experiment 1, participants first judged the plausibility of canonical and noncanonical sentences and then named the agent or patient of the sentence. In Experiment 2, the order of the two tasks was reversed. Both tasks require the correct identification of agent or patient/theme, but differ regarding the complexity of operations required to complete the task successfully. In both experiments, participants made a substantial number of errors with agent/patient naming, even when they had correctly assessed sentence plausibility. We conclude that misinterpretation effects do not indicate parsing errors and therefore cannot serve as evidence for non-algorithmic processing. Our results support models of the HPM that assume algorithmic processing only.

The Domain-General Multiple Demand (MD) Network Does Not Support Core Aspects of Language Comprehension: A Large-Scale fMRI Investigation

Aside from the language-selective left-lateralized frontotemporal network, language comprehension sometimes recruits a domain-general bilateral frontoparietal network implicated in executive functions: the multiple demand (MD) network. However, the nature of the MD network's contributions to language comprehension remains debated. To illuminate the role of this network in language processing in humans, we conducted a large-scale fMRI investigation using data from 30 diverse word and sentence comprehension experiments (481 unique participants [female and male], 678 scanning sessions). In line with prior findings, the MD network was active during many language tasks. Moreover, similar to the language-selective network, which is robustly lateralized to the left hemisphere, these responses were stronger in the left-hemisphere MD regions. However, in contrast with the language-selective network, the MD network responded more strongly (1) to lists of unconnected words than to sentences, and (2) in paradigms with an explicit task compared with passive comprehension paradigms. Indeed, many passive comprehension tasks failed to elicit a response above the fixation baseline in the MD network, in contrast to strong responses in the language-selective network. Together, these results argue against a role for the MD network in core aspects of sentence comprehension, such as inhibiting irrelevant meanings or parses, keeping intermediate representations active in working memory, or predicting upcoming words or structures. These results align with recent evidence of relatively poor tracking of the linguistic signal by the MD regions during naturalistic comprehension, and instead suggest that the MD network's engagement during language processing reflects effort associated with extraneous task demands.SIGNIFICANCE STATEMENT Domain-general executive processes, such as working memory and cognitive control, have long been implicated in language comprehension, including in neuroimaging studies that have reported activation in domain-general multiple demand (MD) regions for linguistic manipulations. However, much prior evidence has come from paradigms where language interpretation is accompanied by extraneous tasks. Using a large fMRI dataset (30 experiments/481 participants/678 sessions), we demonstrate that MD regions are engaged during language comprehension in the presence of task demands, but not during passive reading/listening, conditions that strongly activate the frontotemporal language network. These results present a fundamental challenge to proposals whereby linguistic computations, such as inhibiting irrelevant meanings, keeping representations active in working memory, or predicting upcoming elements, draw on domain-general executive resources.

Implicit Gender Bias in Linguistic Descriptions for Expected Events: The Cases of the 2016 United States and 2017 United Kingdom Elections

Gender stereotypes influence subjective beliefs about the world, and this is reflected in our use of language. But do gender biases in language transparently reflect subjective beliefs? Or is the process of translating thought to language itself biased? During the 2016 United States (N = 24,863) and 2017 United Kingdom (N = 2,609) electoral campaigns, we compared participants' beliefs about the gender of the next head of government with their use and interpretation of pronouns referring to the next head of government. In the United States, even when the female candidate was expected to win, she pronouns were rarely produced and induced substantial comprehension disruption. In the United Kingdom, where the incumbent female candidate was heavily favored, she pronouns were preferred in production but yielded no comprehension advantage. These and other findings suggest that the language system itself is a source of implicit biases above and beyond previously known biases, such as those measured by the Implicit Association Test.

Composition is the Core Driver of the Language-selective Network

The frontotemporal language network responds robustly and selectively to sentences. But the features of linguistic input that drive this response and the computations that these language areas support remain debated. Two key features of sentences are typically confounded in natural linguistic input: words in sentences (a) are semantically and syntactically combinable into phrase- and clause-level meanings, and (b) occur in an order licensed by the language's grammar. Inspired by recent psycholinguistic work establishing that language processing is robust to word order violations, we hypothesized that the core linguistic computation is composition, and, thus, can take place even when the word order violates the grammatical constraints of the language. This hypothesis predicts that a linguistic string should elicit a sentence-level response in the language network provided that the words in that string can enter into dependency relationships as in typical sentences. We tested this prediction across two fMRI experiments (total N = 47) by introducing a varying number of local word swaps into naturalistic sentences, leading to progressively less syntactically well-formed strings. Critically, local dependency relationships were preserved because combinable words remained close to each other. As predicted, word order degradation did not decrease the magnitude of the blood oxygen level-dependent response in the language network, except when combinable words were so far apart that composition among nearby words was highly unlikely. This finding demonstrates that composition is robust to word order violations, and that the language regions respond as strongly as they do to naturalistic linguistic input, providing that composition can take place.

Neural Signal to Violations of Abstract Rules Using Speech-Like Stimuli

As the evidence of predictive processes playing a role in a wide variety of cognitive domains increases, the brain as a predictive machine becomes a central idea in neuroscience. In auditory processing, a considerable amount of progress has been made using variations of the Oddball design, but most of the existing work seems restricted to predictions based on physical features or conditional rules linking successive stimuli. To characterize the predictive capacity of the brain to abstract rules, we present here two experiments that use speech-like stimuli to overcome limitations and avoid common confounds. Pseudowords were presented in isolation, intermixed with infrequent deviants that contained unexpected phoneme sequences. As hypothesized, the occurrence of unexpected sequences of phonemes reliably elicited an early prediction error signal. These prediction error signals do not seemed to be modulated by attentional manipulations due to different task instructions, suggesting that the predictions are deployed even when the task at hand does not volitionally involve error detection. In contrast, the amount of syllables congruent with a standard pseudoword presented before the point of deviance exerted a strong modulation. Prediction error’s amplitude doubled when two congruent syllables were presented instead of one, despite keeping local transitional probabilities constant. This suggests that auditory predictions can be built integrating information beyond the immediate past. In sum, the results presented here further contribute to the understanding of the predictive capabilities of the human auditory system when facing complex stimuli and abstract rules.

Audiovisual speech segmentation in post-stroke aphasia: a pilot study

Background: Stroke may cause sentence comprehension disorders. Speech segmentation, i.e. the ability to detect word boundaries while listening to continuous speech, is an initial step allowing the successful identification of words and the accurate understanding of meaning within sentences. It has received little attention in people with post-stroke aphasia (PWA).Objectives: Our goal was to study speech segmentation in PWA and examine the potential benefit of seeing the speakers' articulatory gestures while segmenting sentences.Methods: Fourteen PWA and twelve healthy controls participated in this pilot study. Performance was measured with a word-monitoring task. In the auditory-only modality, participants were presented with auditory-only stimuli while in the audiovisual modality, visual speech cues (i.e. speaker's articulatory gestures) accompanied the auditory input. The proportion of correct responses was calculated for each participant and each modality. Visual enhancement was then calculated in order to estimate the potential benefit of seeing the speaker's articulatory gestures.Results: Both in auditory-only and audiovisual modalities, PWA performed significantly less well than controls, who had 100% correct performance in both modalities. The performance of PWA was correlated with their phonological ability. Six PWA used the visual cues. Group level analysis performed on PWA did not show any reliable difference between the auditory-only and audiovisual modalities (median of visual enhancement = 7% [Q1 - Q3: -5 - 39]).Conclusion: Our findings show that speech segmentation disorder may exist in PWA. This points to the importance of assessing and training speech segmentation after stroke. Further studies should investigate the characteristics of PWA who use visual speech cues during sentence processing.

The use of context in resolving syntactic ambiguity: Structural and semantic influences

Verb bias facilitates parsing of temporarily ambiguous sentences, but it is unclear when and how comprehenders use probabilistic knowledge about the combinatorial properties of verbs in context. In a self-paced reading experiment, participants read direct object/sentential complement sentences. Reading time in the critical region was investigated as a function of three forms of bias: structural bias (the frequency with which a verb appears in direct object/sentential complement sentences), lexical bias (the simple co-occurrence of verbs and other lexical items), and global bias (obtained from norming data about the use of verbs with specific noun phrases). For reading times at the critical word, structural bias was the only reliable predictor. However, global bias was superior to structural and lexical bias at the post-critical word and for offline acceptability ratings. The results suggest that structural information about verbs is available immediately, but that context-specific, semantic information becomes increasingly informative as processing proceeds.

Age differences in the effect of animacy on Mandarin sentence processing

Animate nouns are preferred for grammatical subjects, whereas inanimate nouns are preferred for grammatical objects. Animacy provides important semantic cues for sentence comprehension. However, how individuals’ ability to use this animacy cue changes with advancing age is still not clear. The current study investigated whether older adults and younger adults were differentially sensitive to this semantic constraint in processing Mandarin relative clauses, using a self-paced reading paradigm. The sentences used in the study contained subject relative clauses or object relative clauses and had animate or inanimate subjects. The results indicate that the animacy manipulation affected the younger adults more than the older adults in online processing. Younger adults had longer reading times for all segments in subject relative clauses than in object relative clauses when the subjects were inanimate, whereas there was no significant difference in reading times between subject and object relative clauses when the subjects were animate. In the older group, animacy was not found to influence the processing difficulty of subject relative clauses and object relative clauses. Compared with younger adults, older adults were less sensitive to animacy constraints in relative clause processing. The findings indicate that the use of animacy cues became less efficient in the ageing population. The results can be explained by the capacity constrained comprehension theory, according to which older adults have greater difficulty in integrating semantic information with syntactic processing due to the lack of sufficient cognitive resources.

The Influence of Globally Ungrammatical Local Syntactic Constraints on Real-Time Sentence Comprehension: Evidence From the Visual World Paradigm and Reading

We investigated the influence of globally ungrammatical local syntactic constraints on sentence comprehension, as well as the corresponding activation of global and local representations. In Experiment 1, participants viewed visual scenes with objects like a carousel and motorbike while hearing sentences with noun phrase (NP) or verb phrase (VP) modifiers like "The girl who likes the man (from London/very much) will ride the carousel." In both cases, "girl" and "ride" predicted carousel as the direct object; however, the locally coherent combination "the man from London will ride…" in NP cases alternatively predicted motorbike. During "ride," local constraints, although ruled out by the global constraints, influenced prediction as strongly as global constraints: While motorbike was fixated less than carousel in VP cases, it was fixated as much as carousel in NP cases. In Experiment 2, these local constraints likewise slowed reading times. We discuss implications for theories of sentence processing.

Remaining Puzzles about Morpheme Production in the Posterior Temporal Lobe

Using data from time-resolved cortical stimulation, intracranial neural recordings, and focal surgical resections, Lee et al. (2018) demonstrate that a small area within left posterior superior temporal gyrus (pSTG) supports the ability to produce functional morphemes but not other basic aspects of language production or comprehension. These findings are intriguing because they raise important questions about the functional architecture of language processing, including critically, the relationship between production and comprehension. Here, we highlight some of the puzzles that remain and that we hope will guide future empirical explorations of the cognitive and neural mechanisms that support our capacity for language.

Why reread? Evidence from garden-path and local coherence structures

Two eye-tracking experiments were conducted to compare the online reading and offline comprehension of main verb/reduced relative garden-path sentences and local coherence sentences. Rereading of early material in garden-path reduced relatives should be revisionary, aimed at reanalysing an earlier misparse; however, rereading of early material in a local coherence reduced relative need only be confirmatory, as the original parse of the earlier portion of these sentences is ultimately correct. Results of online and offline measures showed that local coherence structures elicited signals of reading disruption that arose earlier and lasted longer, and local coherence comprehension was also better than garden path comprehension. Few rereading measures in either sentence type were predicted by structural features of these sentences, nor was rereading related to comprehension accuracy, which was extremely low overall. Results are discussed with respect to selective reanalysis and good-enough processing.

Sleep Disrupts High-Level Speech Parsing Despite Significant Basic Auditory Processing

The extent to which the sleeping brain processes sensory information remains unclear. This is particularly true for continuous and complex stimuli such as speech, in which information is organized into hierarchically embedded structures. Recently, novel metrics for assessing the neural representation of continuous speech have been developed using noninvasive brain recordings that have thus far only been tested during wakefulness. Here we investigated, for the first time, the sleeping brain's capacity to process continuous speech at different hierarchical levels using a newly developed Concurrent Hierarchical Tracking (CHT) approach that allows monitoring the neural representation and processing-depth of continuous speech online. Speech sequences were compiled with syllables, words, phrases, and sentences occurring at fixed time intervals such that different linguistic levels correspond to distinct frequencies. This enabled us to distinguish their neural signatures in brain activity. We compared the neural tracking of intelligible versus unintelligible (scrambled and foreign) speech across states of wakefulness and sleep using high-density EEG in humans. We found that neural tracking of stimulus acoustics was comparable across wakefulness and sleep and similar across all conditions regardless of speech intelligibility. In contrast, neural tracking of higher-order linguistic constructs (words, phrases, and sentences) was only observed for intelligible speech during wakefulness and could not be detected at all during nonrapid eye movement or rapid eye movement sleep. These results suggest that, whereas low-level auditory processing is relatively preserved during sleep, higher-level hierarchical linguistic parsing is severely disrupted, thereby revealing the capacity and limits of language processing during sleep.SIGNIFICANCE STATEMENT Despite the persistence of some sensory processing during sleep, it is unclear whether high-level cognitive processes such as speech parsing are also preserved. We used a novel approach for studying the depth of speech processing across wakefulness and sleep while tracking neuronal activity with EEG. We found that responses to the auditory sound stream remained intact; however, the sleeping brain did not show signs of hierarchical parsing of the continuous stream of syllables into words, phrases, and sentences. The results suggest that sleep imposes a functional barrier between basic sensory processing and high-level cognitive processing. This paradigm also holds promise for studying residual cognitive abilities in a wide array of unresponsive states.

Resolving Conflicts Between Syntax and Plausibility in Sentence Comprehension

Comprehension of plausible and implausible object- and subject-relative clause sentences with and without prepositional phrases was examined. Undergraduates read each sentence then evaluated a statement as consistent or inconsistent with the sentence. Higher acceptance of consistent than inconsistent statements indicated reliance on syntactic analysis. Higher acceptance of plausible than implausible statements reflected reliance on semantic plausibility. There was greater reliance on semantic plausibility and lesser reliance on syntactic analysis for more complex object-relatives and sentences with prepositional phrases than for less complex subject-relatives and sentences without prepositional phrases. Comprehension accuracy and confidence were lower when syntactic analysis and semantic plausibility yielded conflicting interpretations. The conflict effect on comprehension was significant for complex sentences but not for less complex sentences. Working memory capacity predicted resolution of the syntax-plausibility conflict in more and less complex items only when sentences and statements were presented sequentially. Fluid intelligence predicted resolution of the conflict in more and less complex items under sequential and simultaneous presentation. Domain-general processes appear to be involved in resolving syntax-plausibility conflicts in sentence comprehension.

Measuring Metasyntactic Abilities: On a Classification of Metasyntactic Tasks

Researchers working on metasyntactic abilities (i.e., the metalinguistic ability associated with syntax) face the problem of defining and measuring them. Metasyntactic abilities is a multifaceted concept, which encompasses various types of behaviours, from being able to intentionally manipulate syntactic structures to being able to state syntactic rules, and the way in which it is defined and measured varies greatly from one study to another. The present paper proposes a theoretically informed classification of syntax related tasks. The first part presents previous research defining and distinguishing various types of syntactic and metasyntactic abilities and their interrelations. In the second part, commonly used tasks are described and analyzed in terms of the framework presented, with the aim of better pinpointing the type of ability measured by each task. Ultimately, with this analysis of commonly used tasks, we hope to offer criteria for discriminating between the various measures of metasyntactic abilities.

The relationship between priming and linguistic representations is mediated by processing constraints

Understanding the nature of linguistic representations undoubtedly will benefit from multiple types of evidence, including structural priming. Here, we argue that successfully gaining linguistic insights from structural priming requires us to better understand (1) the precise mappings between linguistic input and comprehenders' syntactic knowledge; and (2) the role of cognitive faculties such as memory and attention in structural priming.

The real-time prediction and inhibition of linguistic outcomes: Effects of language and literacy skill

Recent studies have found considerable individual variation in language comprehenders' predictive behaviors, as revealed by their anticipatory eye movements during language comprehension. The current study investigated the relationship between these predictive behaviors and the language and literacy skills of a diverse, community-based sample of young adults. We found that rapid automatized naming (RAN) was a key determinant of comprehenders' prediction ability (e.g., as reflected in predictive eye movements to a white cake on hearing "The boy will eat the white…"). Simultaneously, comprehension-based measures predicted participants' ability to inhibit eye movements to objects that shared features with predictable referents but were implausible completions (e.g., as reflected in eye movements to a white but inedible white car). These findings suggest that the excitatory and inhibitory mechanisms that support prediction during language processing are closely linked with specific cognitive abilities that support literacy. We show that a self-organizing cognitive architecture captures this pattern of results.

Do you what I say? People reconstruct the syntax of anomalous utterances

We frequently experience and successfully process anomalous utterances. Here we examine whether people do this by 'correcting' syntactic anomalies to yield well-formed representations. In two structural priming experiments, participants' syntactic choices in picture description were influenced as strongly by previously comprehended anomalous (missing-verb) prime sentences as by well-formed prime sentences. Our results suggest that comprehenders can reconstruct the constituent structure of anomalous utterances - even when such utterances lack a major structural component such as the verb. These results also imply that structural alignment in dialogue is unaffected if one interlocutor produces anomalous utterances.

Anticipating syntax during reading: Evidence from the boundary change paradigm

Previous evidence suggests that grammatical constraints have a rapid influence during language comprehension, particularly at the level of word categories (noun, verb, preposition). These findings are in conflict with a recent study from Angele, Laishley, Rayner, and Liversedge (2014), in which sentential fit had no early influence on word skipping rates during reading. In the present study, we used a gaze-contingent boundary change paradigm to manipulate the syntactic congruity of an upcoming noun or verb outside of participants' awareness. Across 3 experiments (total N = 148), we observed higher skipping rates for syntactically valid previews (The admiral would not confess . . .), when compared with violation previews (The admiral would not surgeon . . .). Readers were less likely to skip an ungrammatical continuation, even when that word was repeated within the same sentence (The admiral would not admiral . . .), suggesting that word-class constraints can take precedence over lexical repetition effects. To our knowledge, these results provide the first evidence for an influence of syntactic context during parafoveal word recognition. On the basis of the early time-course of this effect, we argue that readers can use grammatical constraints to generate syntactic expectations for upcoming words. (PsycINFO Database Record

When language comprehension goes wrong for the right reasons: Good-enough, underspecified, or shallow language processing

This paper contains an overview of language processing that can be described as "good enough", "underspecified", or "shallow". The central idea is that a nontrivial proportion of misunderstanding, misinterpretation, and miscommunication can be attributed not to random error, but instead to processing preferences of the human language processing system. In other words, the very architecture of the language processor favours certain types of processing errors because in a majority of instances, this "fast and frugal", less effortful processing is good enough to support communication. By way of historical background, connections are made between this relatively recent facet of psycholinguistic study, other recent language processing models, and related concepts in other areas of cognitive science. Finally, the nine papers included in this special issue are introduced as representative of novel explorations of good-enough, or underspecified, language processing.

What do we mean by prediction in language comprehension?

We consider several key aspects of prediction in language comprehension: its computational nature, the representational level(s) at which we predict, whether we use higher level representations to predictively pre-activate lower level representations, and whether we 'commit' in any way to our predictions, beyond pre-activation. We argue that the bulk of behavioral and neural evidence suggests that we predict probabilistically and at multiple levels and grains of representation. We also argue that we can, in principle, use higher level inferences to predictively pre-activate information at multiple lower representational levels. We also suggest that the degree and level of predictive pre-activation might be a function of the expected utility of prediction, which, in turn, may depend on comprehenders' goals and their estimates of the relative reliability of their prior knowledge and the bottom-up input. Finally, we argue that all these properties of language understanding can be naturally explained and productively explored within a multi-representational hierarchical actively generative architecture whose goal is to infer the message intended by the producer, and in which predictions play a crucial role in explaining the bottom-up input.

Neuronal bases of structural coherence in contemporary dance observation

The neuronal processes underlying dance observation have been the focus of an increasing number of brain imaging studies over the past decade. However, the existing literature mainly dealt with effects of motor and visual expertise, whereas the neural and cognitive mechanisms that underlie the interpretation of dance choreographies remained unexplored. Hence, much attention has been given to the action observation network (AON) whereas the role of other potentially relevant neuro-cognitive mechanisms such as mentalizing (theory of mind) or language (narrative comprehension) in dance understanding is yet to be elucidated. We report the results of an fMRI study where the structural coherence of short contemporary dance choreographies was manipulated parametrically using the same taped movement material. Our participants were all trained dancers. The whole-brain analysis argues that the interpretation of structurally coherent dance phrases involves a subpart (superior parietal) of the AON as well as mentalizing regions in the dorsomedial prefrontal cortex. An ROI analysis based on a similar study using linguistic materials (Pallier et al., 2011) suggests that structural processing in language and dance might share certain neural mechanisms.