Dependency distance: A new perspective on syntactic patterns in natural languages

Language transfer in L2 academic writings: a dependency grammar approach

Dependency distance (DD) is an important factor in language processing and can affect the ease with which a sentence is understood. Previous studies have investigated the role of DD in L2 writing, but little is known about how the native language influences DD in L2 academic writing. This study is probably the first one that investigates, though a large dataset of over 400 million words, whether the native language of L2 writers influences the DD in their academic writings. Using a dataset of over 2.2 million abstracts of articles downloaded from Scopus in the fields of Arts & Humanities and Social Sciences, the study analyzes the DD patterns, parsed by the latest version of the syntactic parser Stanford Corenlp 4.5.5, in the academic writing of L2 learners from different language backgrounds. It is found that native languages influence the DD of English L2 academic writings. When the mean dependency distance (MDD) of native languages is much longer than that of native English, the MDD of their English L2 academic writings will be much longer than that of English native academic writings. The findings of this study will deepen our insights into the influence of native language transfer on L2 academic writing, potentially shaping pedagogical strategies in L2 academic writing education.

Age-related differences in the interplay of fluency and complexity in Chinese-speaking seniors' oral narratives

BACKGROUND

Previous studies on language production in normal ageing have primarily focused on distinct dimensions of older adults' spoken language performance, such as fluency and complexity. However, little attention has been paid to the complex, interconnected relations between these dimensions. Additionally, older adults have been treated as a homogeneous group, with little consideration for the differential characteristics of language performance across different stages of ageing.

AIMS

This study aims to investigate how increasing age impacts Chinese seniors' oral language performance, focusing on fluency (articulation rate and dysfluency rate), complexity (lexical and syntactic) and the potential interactions between these dimensions.

METHODS & PROCEDURES

Spontaneous oral narratives were collected from 60 normally ageing individuals, who were categorised into three groups: young-old (60-69 years old), middle-old (70-79 years old) and old-old (≥80 years old). Four measures for assessing language performance, namely, articulation rate, dysfluency rate, lexical complexity and syntactic complexity, were derived from the oral narratives. Dynamic systems techniques, including moving correlations, locally estimated scatterplot smoothing and Monte Carlo simulations, were employed for data analysis.

OUTCOMES & RESULTS

This study revealed two major findings. First, across different age groups, the seniors' oral narratives significantly differed in the aspect of articulation rate and syntactic complexity. Specifically, both the young-old and the middle-old groups exhibited significantly higher articulation rates than the old-old group; the middle-old group also demonstrated significantly higher syntactic complexity compared to the old-old group. Second, the distinct subsystems (i.e., articulation rate, dysfluency rate, lexical and syntactic complexity) of seniors' oral narratives demonstrated varying interactions across different stages of ageing. While these subsystems tended to coordinate with each other in young-old individuals, they exhibited a greater tendency to compete in middle-old and old-old individuals.

CONCLUSIONS & IMPLICATIONS

The findings reveal that subsystems of older adults' oral narratives display varying interactions with the increase of age, indicating that focusing solely on one dimension of language performance may result in inaccurate or misleading conclusions. Therefore, a multi-index comprehensive assessment should be employed for the enhancement of clinical evaluations of language performance in older adults. Additionally, it is vital to consider the interactional patterns (i.e., support or competition) between language subsystems when assessing language performance in normal ageing.

WHAT THIS PAPER ADDS

What is already known on the subject Previous research on language production in normal ageing has primarily focused on distinct dimensions of older adults' spoken language performance, such as fluency, vocabulary richness and grammatical complexity, overlooking the intricate interconnections between these dimensions. However, investigating these connections is significant for a thorough and in-depth understanding of language production in late adulthood, which operates as a system comprising interconnected components. Furthermore, existing studies have predominantly treated older adults as a homogeneous group, comparing their language use to that of young people. This approach lacks justification, given the substantial variations in language use among seniors at different stages of ageing. What this paper adds to existing knowledge The present findings illuminate the dynamic nature of language production in normal ageing. Not only do the distinct dimensions or subsystems of older adults' oral narratives (e.g., articulation rate, dysfluency rate, syntactic complexity) change with age, but more important, the ways in which these subsystems interact with each other also evolve with age, resulting in changing states of spoken language production in the ageing process. More specifically, our study provides evidence that the interactions among subsystems of older adults' oral narratives predominately transition from supportive relationships in earlier stages to competitive ones at later stages of ageing. What are the potential or actual clinical implications of this work? The present study suggests the use of multiple linguistic indices related to various aspects of language performance to assess the spoken language production of older adults. This is highlighted by the observation that various dimensions of older adults' oral narratives continuously interact with one another across different stages of ageing, and hence focusing solely on one dimension of language performance tends to lead to inaccurate or misleading results. Additionally, the patterns of interaction (i.e., support or competition) between different dimensions of language performance may reflect older adults' capacity to coordinate and control attentional resources and therefore should be taken into consideration in clinical evaluations of language performance in older adults.

Automated Measures of Syntactic Complexity in Natural Speech Production: Older and Younger Adults as a Case Study

PURPOSE

Multiple methods have been suggested for quantifying syntactic complexity in speech. We compared eight automated syntactic complexity metrics to determine which best captured verified syntactic differences between old and young adults.

METHOD

We used natural speech samples produced in a picture description task by younger (n = 76, ages 18-22 years) and older (n = 36, ages 53-89 years) healthy participants, manually transcribed and segmented into sentences. We manually verified that older participants produced fewer complex structures. We developed a metric of syntactic complexity using automatically extracted syntactic structures as features in a multidimensional metric. We compared our metric to seven other metrics: Yngve score, Frazier score, Frazier-Roark score, developmental level, syntactic frequency, mean dependency distance, and sentence length. We examined the success of each metric in identifying the age group using logistic regression models. We repeated the analysis with automatic transcription and segmentation using an automatic speech recognition (ASR) system.

RESULTS

Our multidimensional metric was successful in predicting age group (area under the curve [AUC] = 0.87), and it performed better than the other metrics. High AUCs were also achieved by the Yngve score (0.84) and sentence length (0.84). However, in a fully automated pipeline with ASR, the performance of these two metrics dropped (to 0.73 and 0.46, respectively), while the performance of the multidimensional metric remained relatively high (0.81).

CONCLUSIONS

Syntactic complexity in spontaneous speech can be quantified by directly assessing syntactic structures and considering them in a multivariable manner. It can be derived automatically, saving considerable time and effort compared to manually analyzing large-scale corpora, while maintaining high face validity and robustness.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.24964179.

Information-theoretic principles in incremental language production

I apply a recently emerging perspective on the complexity of action selection, the rate-distortion theory of control, to provide a computational-level model of errors and difficulties in human language production, which is grounded in information theory and control theory. Language production is cast as the sequential selection of actions to achieve a communicative goal subject to a capacity constraint on cognitive control. In a series of calculations, simulations, corpus analyses, and comparisons to experimental data, I show that the model directly predicts some of the major known qualitative and quantitative phenomena in language production, including semantic interference and predictability effects in word choice; accessibility-based ("easy-first") production preferences in word order alternations; and the existence and distribution of disfluencies including filled pauses, corrections, and false starts. I connect the rate-distortion view to existing models of human language production, to probabilistic models of semantics and pragmatics, and to proposals for controlled language generation in the machine learning and reinforcement learning literature.

Task-based explanation for genre effects: Evidence from a dependency treebank

In task-based second language (L2) writing research, genre effects on linguistic features are usually explained by either task complexity hypothesis or differences in communicative demands. The basic distinction between the two explanations is determined by whether cognitive factors are at work. To date, the actual causes for L2 learners' different linguistic features in different genres are still unclear. Aiming at providing empirical evidence for explaining the mechanism of genre effects, this investigation uses dependency-grammar-based measures to examine the role of cognitive factors in L2 argumentative, narrative, and descriptive writings. A total of 540 compositions from three different proficiency groups of English as a foreign language learners were collected, and their mean dependency distances and their distributions of dependency distance were calculated. It was found that in all proficiency groups of compositions, dependency distance distributions of five types showed significant differences between genres. Since dependency distance reflects cognitive load, those five dependency types were able to show that cognitive factors are at play in the writing process. Among the five types, the phrasal dependency relation types could reveal genre effects regardless of learners' language proficiency, and clausal dependency relation types might pinpoint learners' threshold of perceiving task complexity. The findings suggest that genre effects on linguistic features in L2 writings may result from different cognitive demand imposed by writing tasks with different genres, and genre effect may exhibit variation among different proficiency groups.

Head and dependent marking and dependency length in possessive noun phrases: a typological study of morphological and syntactic complexity

The interaction of morphosyntactic features has been of great interest in research on linguistic complexity. In this paper we approach such interactions in possessive noun phrases. First, we study the interaction of head marking and dependent marking in this domain with typological feature data and with multilingual corpus data. The data suggest that there is a clear inverse relationship between head and dependent marking in possessive noun phrases in terms of complexity. The result points to evidence on complexity trade-offs and to productive integration of typological and corpus-based approaches. Second, we explore whether zero versus overt morphological marking as a measure of morphological complexity affects dependency length as a measure of syntactic complexity. Data from multilingual corpora suggest that there is no cross-linguistic trend between these measures in possessive noun phrases.

Native language differences in the structural connectome of the human brain

Is the neuroanatomy of the language structural connectome modulated by the life-long experience of speaking a specific language? The current study compared the brain white matter connections of the language and speech production network in a large cohort of 94 native speakers of two very different languages: an Indo-European morphosyntactically complex language (German) and a Semitic root-based language (Arabic). Using high-resolution diffusion-weighted MRI and tractography-based network statistics of the language connectome, we demonstrated that German native speakers exhibited stronger connectivity in an intra-hemispheric frontal to parietal/temporal dorsal language network, known to be associated with complex syntax processing. In comparison, Arabic native speakers showed stronger connectivity in the connections between semantic language regions, including the left temporo-parietal network, and stronger inter-hemispheric connections via the posterior corpus callosum connecting bilateral superior temporal and inferior parietal regions. The current study suggests that the structural language connectome develops and is modulated by environmental factors such as the characteristic processing demands of the native language.

An investigation of the diachronic trend of dependency distance minimization in magazines and news

The principle of minimization of dependency distance (DD) can reduce the working memory burden of language speakers, that is, reduce the cognitive burden during the communication process. This investigation demonstrated the dependency distance (based on the dependency grammar) minimization principle from a diachronic perspective in two text types of magazines and news with inspection indicators of mean dependency distance (MDD) and normalized dependency distance (NDD). This research revealed a fluctuation tendency around a certain axis concerning diachronic dependency distance variation. This research also indicated that news text balances language complexity and communication efficiency better than magazine text.

Foreign language anxiety and dependency distance in English–Chinese interpretation classrooms

Foreign language anxiety (FLA) has been identified as a crucial affective factor in language learning. Similar to the situation in language classes, university students in interpretation classes are required to perform in a foreign language when their language skills are inadequate. Investigations are needed to determine the specific impact of FLA on interpretation learning. This study investigated the effects of the specific interpretation classroom FLA (ICFLA) on interpretation learning and dependency distance (DD) as an indicator of learners’ cognitive load. The participants were 49 undergraduate and graduate students enrolled in English–Chinese interpretation classes at a university in Hong Kong. The results showed a significant negative correlation between ICFLA levels and consecutive interpretation achievement scores. ICFLA was also negatively correlated with DD in consecutive interpretations. Four factors underlying ICFLA were identified. The findings of this study would provide useful insights for researchers and educators to understand the nature and effect of FLA in different settings.

Effects of Syntactic Distance and Word Order on Language Processing: An Investigation Based on a Psycholinguistic Treebank of English

We conducted a broad-coverage investigation of the effects of syntactic distance and word order on language processing against a dependency-annotated reading time corpus of English. A combined method of quantitative syntax and psycholinguistic analyses was adopted to yield converging evidence. It was found that (i) head-initial structures allow greater structural complexity, i.e., larger head-dependent distance, than head-final structures in both language comprehension and production; (ii) within the capacity limit of working memory, syntactic distance is a positive predictor of reading time for a word with a preceding head, whereas a negative predictor of reading time for a word with a following head; and (iii) at the sentence level, syntactic distance is a significant predictor of sentence reading time. These results suggest that (i) different word orders may enjoy different processing mechanisms in terms of cognitive difficulty and processes, which can be explained by an incremental language parser; and (ii) in addition to distance, word order should also be considered as a factor affecting language processing, which is an important extension to distance-based language processing models. Taken as a whole, our study paves the way for corpus-based integration of quantitative linguistic and psycholinguistic methods into understanding language processing and its underlying cognitive mechanisms.

A Reappraisal of Dependency Length Minimization as a Linguistic Universal

Dependency length minimization is widely regarded as a cross-linguistic universal reflecting syntactic complexity in natural languages. A typical way to operationalize dependency length in corpus-based studies has been to count the number of words between syntactically related words. However, such a formulation ignores the syntactic nature of the linguistic material that intervenes a dependency. In this work, we investigate if the number of syntactic heads (rather than the number of words) that intervene a dependency better captures the syntactic complexity across languages. We demonstrate that the dependency length minimization constraint in terms of the number of words could arise as a consequence of constraints on the intervening heads and the tree properties such as node arity. The current study highlights the importance of syntactic heads as central regions of structure building during processing. The results show that when syntactically related words are nonadjacent, increased structure building in the intervening region is avoided.

Crosslinguistic word order variation reflects evolutionary pressures of dependency and information locality

Languages vary considerably in syntactic structure. About 40% of the world's languages have subject-verb-object order, and about 40% have subject-object-verb order. Extensive work has sought to explain this word order variation across languages. However, the existing approaches are not able to explain coherently the frequency distribution and evolution of word order in individual languages. We propose that variation in word order reflects different ways of balancing competing pressures of dependency locality and information locality, whereby languages favor placing elements together when they are syntactically related or contextually informative about each other. Using data from 80 languages in 17 language families and phylogenetic modeling, we demonstrate that languages evolve to balance these pressures, such that word order change is accompanied by change in the frequency distribution of the syntactic structures that speakers communicate to maintain overall efficiency. Variability in word order thus reflects different ways in which languages resolve these evolutionary pressures. We identify relevant characteristics that result from this joint optimization, particularly the frequency with which subjects and objects are expressed together for the same verb. Our findings suggest that syntactic structure and usage across languages coadapt to support efficient communication under limited cognitive resources.

The Radical Unacceptability Hypothesis: Accounting for Unacceptability without Universal Constraints

The Radical Unacceptability Hypothesis (RUH) has been proposed as a way of explaining the unacceptability of extraction from islands and frozen structures. This hypothesis explicitly assumes a distinction between unacceptability due to violations of local well-formedness conditions—conditions on constituency, constituent order, and morphological form—and unacceptability due to extra-grammatical factors. We explore the RUH with respect to classical islands, and extend it to a broader range of phenomena, including freezing, A′ chain interactions, zero-relative clauses, topic islands, weak crossover, extraction from subjects and parasitic gaps, and sensitivity to information structure. The picture that emerges is consistent with the RUH, and suggests more generally that the unacceptability of extraction from otherwise well-formed configurations reflects non-syntactic factors, not principles of grammar.

Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting

Atmospheric water harvesting by strong adsorbents is a feasible method of solving the shortage of water resources, especially for arid regions. In this study, a machine learning (ML)-assisted high-throughput computational screening is employed to calculate the capture of H2O from N2 and O2 for 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) and 137,953 hypothetical MOFs (hMOFs). Through the univariate analysis of MOF structure-performance relationships, Qst is shown to be a key descriptor. Moreover, three ML algorithms (random forest, gradient boosted regression trees, and neighbor component analysis (NCA)) are applied to hunt for the complicated interrelation between six descriptors and performance. After the optimizing strategy of grid search and five-fold cross-validation is performed, three ML can effectively build the predictive model for CoRE-MOFs, and the accuracy R2 of NCA can reach 0.97. In addition, based on the relative importance of the descriptors by ML, it can be quantitatively concluded that the Qst is dominant in governing the capture of H2O. Besides, the NCA model trained by 6013 CoRE-MOFs can predict the selectivity of hMOFs with a R2 of 0.86, which is more universal than other models. Finally, 10 CoRE-MOFs and 10 hMOFs with high performance are identified. The computational screening and prediction of ML could provide guidance and inspiration for the development of materials for water harvesting in the atmosphere.

Morpheme Ordering Across Languages Reflects Optimization for Processing Efficiency

The ordering of morphemes in a word displays well-documented regularities across languages. Previous work has explained these in terms of notions such as semantic scope, relevance, and productivity. Here, we test a recently formulated processing theory of the ordering of linguistic units, the efficient tradeoff hypothesis (Hahn et al., 2021). The claim of the theory is that morpheme ordering can partly be explained by the optimization of a tradeoff between memory and surprisal. This claim has received initial empirical support from two languages. In this work, we test this idea more extensively using data from four additional agglutinative languages with significant amounts of morphology, and by considering nouns in addition to verbs. We find that the efficient tradeoff hypothesis predicts ordering in most cases with high accuracy, and accounts for cross-linguistic regularities in noun and verb inflection. Our work adds to a growing body of work suggesting that many ordering properties of language arise from a pressure for efficient language processing.

Optimality of syntactic dependency distances

It is often stated that human languages, as other biological systems, are shaped by cost-cutting pressures but, to what extent? Attempts to quantify the degree of optimality of languages by means of an optimality score have been scarce and focused mostly on English. Here we recast the problem of the optimality of the word order of a sentence as an optimization problem on a spatial network where the vertices are words, arcs indicate syntactic dependencies, and the space is defined by the linear order of the words in the sentence. We introduce a score to quantify the cognitive pressure to reduce the distance between linked words in a sentence. The analysis of sentences from 93 languages representing 19 linguistic families reveals that half of languages are optimized to a 70% or more. The score indicates that distances are not significantly reduced in a few languages and confirms two theoretical predictions: that longer sentences are more optimized and that distances are more likely to be longer than expected by chance in short sentences. We present a hierarchical ranking of languages by their degree of optimization. The score has implications for various fields of language research (dependency linguistics, typology, historical linguistics, clinical linguistics, and cognitive science). Finally, the principles behind the design of the score have implications for network science.

Investigating genre distinctions through discourse distance and discourse network

The notion of genre has been widely explored using quantitative methods from both lexical and syntactical perspectives. However, discourse structure has rarely been used to examine genre. Mostly concerned with the interrelation of discourse units, discourse structure can play a crucial role in genre analysis. Nevertheless, few quantitative studies have explored genre distinctions from a discourse structure perspective. Here, we use two English discourse corpora (RST-DT and GUM) to investigate discourse structure from a novel viewpoint. The RST-DT is divided into four small subcorpora distinguished according to genre, and another corpus (GUM) containing seven genres are used for cross-verification. An RST (rhetorical structure theory) tree is converted into dependency representations by taking information from RST annotations to calculate the discourse distance through a process similar to that used to calculate syntactic dependency distance. Moreover, the data on dependency representations deriving from the two corpora are readily convertible into network data. Afterwards, we examine different genres in the two corpora by combining discourse distance and discourse network. The two methods are mutually complementary in comprehensively revealing the distinctiveness of various genres. Accordingly, we propose an effective quantitative method for assessing genre differences using discourse distance and discourse network. This quantitative study can help us better understand the nature of genre.

Differentiating Interpreting Types: Connecting Complex Networks to Cognitive Complexity

Prominent interpreting models have illustrated different processing mechanisms of simultaneous interpreting and consecutive interpreting. Although great efforts have been made, a macroscopic examination into interpreting outputs is sparse. Since complex network is a powerful and feasible tool to capture the holistic features of language, the present study adopts this novel approach to investigate different properties of syntactic dependency networks based on simultaneous interpreting and consecutive interpreting outputs. Our results show that consecutive interpreting networks demonstrate higher degrees, higher clustering coefficients, and a more important role of function words among the central vertices than simultaneous interpreting networks. These findings suggest a better connectivity, better transitivity, and a lower degree of vocabulary richness in consecutive interpreting outputs. Our research provides an integrative framework for the understanding of underlying mechanisms in diverse interpreting types.

Multilayer networks: An untapped tool for understanding bilingual neurocognition

Cross-linguistic similarity is a term so broad and multi-faceted that it is not easily defined. The degree of overlap between languages is known to affect lexical competition during online processing and production, and its relevance for second language acquisition has also been established. Nevertheless, determining what makes two languages similar (or not) increases in complexity when multiple levels of the language hierarchy (e.g., phonology, syntax) are considered. How can we feasibly account for the patterns of convergence and divergence at each level of representation, as well as the interactions between them? The growing field of network science brings new methodologies to bear on this longstanding question. Below, we summarize current network science approaches to modeling language structure and discuss implications for understanding various linguistic processes. Critically, we stress the particular value of multilayer techniques, unique and powerful in their ability to simultaneously accommodate an array of node-to-node (or word-to-word) relationships.

Principle of Least Effort and Sentence Length in Public Speaking

The analysis of sentence lengths in the inaugural speeches of US presidents and the annual speeches of UK party leaders is carried out. Transcripts of the speeches are used, rather than the oral production. It is discovered that the average sentence length in these speeches decreases linearly with time, with the slope of 0.13 ± 0.03 words/year. It is shown that among the analyzed distributions (log-normal, folded and half normal, Weibull, generalized Pareto, Rayleigh) the Weibull is the best distribution for describing sentence length. These two results can be considered a consequence of the principle of least effort. The connection of this principle with the well-known principles of maximum and minimum entropy production is discussed.

Prenominal adjective order is such a fat big deal because adjectives are ordered by likely need

When multiple adjectives precede a noun in English, they are often ordered in a way that is implicitly understood by all fluent speakers of the language. Adjective order might be described as a big fat deal, but to describe it as a fat big deal betrays a lack of knowledge of English. Sweet (A New English Grammar: Part II, 1898/1955) proposed two related semantic principles to explain the phenomenon: definiteness of denotation (adjectives that denote a property that is most independent of the modified noun must be placed furthest from that noun) and closeness of adjective/noun in meaning (adjectives that denote properties essential to or inherent in the modified noun are placed closer to the noun). These observational descriptions of the phenomenon have received experimental support (Martin, Journal of Verbal Learning and Verbal Behavior, 8(6), 697-704, 1969). However, the issue of why Sweet's rules are true has not yet been solved. I propose, operationalize, test, and find strong support for a simple theory: that prenominal adjective order reflects likely need, the a priori probability that a particular adjective will be needed.

Word Order Typology Interacts With Linguistic Complexity: A Cross-Linguistic Corpus Study

Much previous work has suggested that word order preferences across languages can be explained by the dependency distance minimization constraint (Ferrer-i Cancho, 2008, 2015; Hawkins, 1994). Consistent with this claim, corpus studies have shown that the average distance between a head (e.g., verb) and its dependent (e.g., noun) tends to be short cross-linguistically (Ferrer-i Cancho, 2014; Futrell, Mahowald, & Gibson, 2015; Liu, Xu, & Liang, 2017). This implies that on average languages avoid inefficient or complex structures for simpler structures. But a number of studies in psycholinguistics (Konieczny, 2000; Levy & Keller, 2013; Vasishth, Suckow, Lewis, & Kern, 2010) show that the comprehension system can adapt to the typological properties of a language, for example, verb-final order, leading to more complex structures, for example, having longer linear distance between a head and its dependent. In this paper, we conduct a corpus study for a group of 38 languages, which were either Subject-Verb-Object (SVO) or Subject-Object-Verb (SOV), in order to investigate the role of word order typology in determining syntactic complexity. We present results aggregated across all dependency types, as well as for specific verbal (objects, indirect objects, and adjuncts) and nonverbal (nominal, adjectival, and adverbial) dependencies. The results suggest that dependency distance in a language is determined by the default word order of a language, and crucially, the direction of a dependency (whether the head precedes the dependent or follows it; e.g., whether the noun precedes the verb or follows it). Particularly we show that in SOV languages (e.g., Hindi, Korean) as well as SVO languages (e.g., English, Spanish), longer linear distance (measured as number of words) between head and dependent arises in structures when they mirror the default word order of the language. In addition to showing results on linear distance, we also investigate the influence of word order typology on hierarchical distance (HD; measured as number of heads between head and dependent). The results for HD are similar to that of linear distance. At the same time, in comparison to linear distance, the influence of adaptability on HD seems less strong. In particular, the results show that most languages tend to avoid greater structural depth. Together, these results show evidence for "limited adaptability" to the default word order preferences in a language. Our results support a large body of work in the processing literature that highlights the importance of linguistic exposure and its interaction with working memory constraints in determining sentence complexity. Our results also point to the possible role of other factors such as the morphological richness of a language and a multifactor account of sentence complexity remains a promising area for future investigation.

The role of iconicity and simultaneity for efficient communication: The case of Italian Sign Language (LIS)

A fundamental assumption about language is that, regardless of language modality, it faces the linearization problem, i.e., an event that occurs simultaneously in the world has to be split in language to be organized on a temporal scale. However, the visual modality of signed languages allows its users not only to express meaning in a linear manner but also to use iconicity and multiple articulators together to encode information simultaneously. Accordingly, in cases when it is necessary to encode informatively rich events, signers can take advantage of simultaneous encoding in order to represent information about different referents and their actions simultaneously. This in turn would lead to more iconic and direct representation. Up to now, there has been no experimental study focusing on simultaneous encoding of information in signed languages and its possible advantage for efficient communication. In the present study, we assessed how many information units can be encoded simultaneously in Italian Sign Language (LIS) and whether the amount of simultaneously encoded information varies based on the amount of information that is required to be expressed. Twenty-three deaf adults participated in a director-matcher game in which they described 30 images of events that varied in amount of information they contained. Results revealed that as the information that had to be encoded increased, signers also increased use of multiple articulators to encode different information (i.e., kinematic simultaneity) and density of simultaneously encoded information in their production. Present findings show how the fundamental properties of signed languages, i.e., iconicity and simultaneity, are used for the purpose of efficient information encoding in Italian Sign Language (LIS).

Lossy-Context Surprisal: An Information-Theoretic Model of Memory Effects in Sentence Processing

A key component of research on human sentence processing is to characterize the processing difficulty associated with the comprehension of words in context. Models that explain and predict this difficulty can be broadly divided into two kinds, expectation-based and memory-based. In this work, we present a new model of incremental sentence processing difficulty that unifies and extends key features of both kinds of models. Our model, lossy-context surprisal, holds that the processing difficulty at a word in context is proportional to the surprisal of the word given a lossy memory representation of the context-that is, a memory representation that does not contain complete information about previous words. We show that this model provides an intuitive explanation for an outstanding puzzle involving interactions of memory and expectations: language-dependent structural forgetting, where the effects of memory on sentence processing appear to be moderated by language statistics. Furthermore, we demonstrate that dependency locality effects, a signature prediction of memory-based theories, can be derived from lossy-context surprisal as a special case of a novel, more general principle called information locality.

Universals of word order reflect optimization of grammars for efficient communication

The universal properties of human languages have been the subject of intense study across the language sciences. We report computational and corpus evidence for the hypothesis that a prominent subset of these universal properties-those related to word order-result from a process of optimization for efficient communication among humans, trading off the need to reduce complexity with the need to reduce ambiguity. We formalize these two pressures with information-theoretic and neural-network models of complexity and ambiguity and simulate grammars with optimized word-order parameters on large-scale data from 51 languages. Evolution of grammars toward efficiency results in word-order patterns that predict a large subset of the major word-order correlations across languages.

Atypical Repetition in Daily Conversation on Different Days for Detecting Alzheimer Disease: Evaluation of Phone-Call Data From Regular Monitoring Service

BACKGROUND

Identifying signs of Alzheimer disease (AD) through longitudinal and passive monitoring techniques has become increasingly important. Previous studies have succeeded in quantifying language dysfunctions and identifying AD from speech data collected during neuropsychological tests. However, whether and how we can quantify language dysfunction in daily conversation remains unexplored.

OBJECTIVE

The objective of this study was to explore the linguistic features that can be used for differentiating AD patients from daily conversations.

METHODS

We analyzed daily conversational data of seniors with and without AD obtained from longitudinal follow-up in a regular monitoring service (from n=15 individuals including 2 AD patients at an average follow-up period of 16.1 months; 1032 conversational data items obtained during phone calls and approximately 221 person-hours). In addition to the standard linguistic features used in previous studies on connected speech data during neuropsychological tests, we extracted novel features related to atypical repetition of words and topics reported by previous observational and descriptive studies as one of the prominent characteristics in everyday conversations of AD patients.

RESULTS

When we compared the discriminative power for AD, we found that atypical repetition in two conversations on different days outperformed other linguistic features used in previous studies on speech data during neuropsychological tests. It was also a better indicator than atypical repetition in single conversations as well as that in two conversations separated by a specific number of conversations.

CONCLUSIONS

Our results show how linguistic features related to atypical repetition across days could be used for detecting AD from daily conversations in a passive manner by taking advantage of longitudinal data.

How Efficiency Shapes Human Language

Cognitive science applies diverse tools and perspectives to study human language. Recently, an exciting body of work has examined linguistic phenomena through the lens of efficiency in usage: what otherwise puzzling features of language find explanation in formal accounts of how language might be optimized for communication and learning? Here, we review studies that deploy formal tools from probability and information theory to understand how and why language works the way that it does, focusing on phenomena ranging from the lexicon through syntax. These studies show how a pervasive pressure for efficiency guides the forms of natural language and indicate that a rich future for language research lies in connecting linguistics to cognitive psychology and mathematical theories of communication and inference.

Eye activity tracks task-relevant structures during speech and auditory sequence perception

The sensory and motor systems jointly contribute to complex behaviors, but whether motor systems are involved in high-order perceptual tasks such as speech and auditory comprehension remain debated. Here, we show that ocular muscle activity is synchronized to mentally constructed sentences during speech listening, in the absence of any sentence-related visual or prosodic cue. Ocular tracking of sentences is observed in the vertical electrooculogram (EOG), whether the eyes are open or closed, and in eye blinks measured by eyetracking. Critically, the phase of sentence-tracking ocular activity is strongly modulated by temporal attention, i.e., which word in a sentence is attended. Ocular activity also tracks high-level structures in non-linguistic auditory and visual sequences, and captures rapid fluctuations in temporal attention. Ocular tracking of non-visual rhythms possibly reflects global neural entrainment to task-relevant temporal structures across sensory and motor areas, which could serve to implement temporal attention and coordinate cortical networks.

Predicting Fluency With Language Proficiency, Working Memory, and Directionality in Simultaneous Interpreting

Simultaneous interpreting (SI) is a complex bilingual verbal activity that poses great challenges for working memory (WM) and language proficiency. Fluency is one of the crucial indicators in evaluating SI quality, the violation of which is characterized by disfluency indicators such as interruptions, hesitations, repetitions, corrections, and blanks. To uncover factors underlying fluency in SI, 22 interpreting students performed a battery of tasks to test their language proficiency and WM. Two SI tasks, both from Chinese to English and from English to Chinese, were also conducted, and fluency was evaluated according to the five indicators. Two factors (language proficiency and WM) and the five objectively measured disfluency indicators were then used as input for a regression analysis in both directions to model factors underlying fluency in SI performance. The results reveal that, with fluency measured as a whole, WM and directionality yield a significant effect on fluency, and that WM is the only variable that predicts fluency in both directions, accounting for 50 and 51% of the variation in the occurrence of disfluencies in Chinese-English and English-Chinese interpreting, respectively. The findings clarify for the first time the role of language proficiency, WM, and directionality upon fluency in SI, indicating the critical role of WM capability as compared with language skills in fluent production. The research also supports the position that, for interpreting students, interpreting performance tends to be more fluent in the non-native to native language direction.

Scarcity of crossing dependencies: A direct outcome of a specific constraint?

The structure of a sentence can be represented as a network where vertices are words and edges indicate syntactic dependencies. Interestingly, crossing syntactic dependencies have been observed to be infrequent in human languages. This leads to the question of whether the scarcity of crossings in languages arises from an independent and specific constraint on crossings. We provide statistical evidence suggesting that this is not the case, as the proportion of dependency crossings of sentences from a wide range of languages can be accurately estimated by a simple predictor based on a null hypothesis on the local probability that two dependencies cross given their lengths. The relative error of this predictor never exceeds 5% on average, whereas the error of a baseline predictor assuming a random ordering of the words of a sentence is at least six times greater. Our results suggest that the low frequency of crossings in natural languages is neither originated by hidden knowledge of language nor by the undesirability of crossings per se, but as a mere side effect of the principle of dependency length minimization.

Dependency Distance Differences across Interpreting Types: Implications for Cognitive Demand

Interpreting is generally recognized as a particularly demanding language processing task for the cognitive system. Dependency distance, the linear distance between two syntactically related words in a sentence, is an index of sentence complexity and is also able to reflect the cognitive constraints during various tasks. In the current research, we examine the difference in dependency distance among three interpreting types, namely, simultaneous interpreting, consecutive interpreting and read-out translated speech based on a treebank comprising these types of interpreting output texts with dependency annotation. Results show that different interpreting renditions yield different dependency distances, and consecutive interpreting texts entail the smallest dependency distance other than those of simultaneous interpreting and read-out translated speech, suggesting that consecutive interpreting bears heavier cognitive demands than simultaneous interpreting. The current research suggests for the first time that interpreting is an extremely demanding cognitive task that can further mediate the dependency distance of output sentences. Such findings may be due to the minimization of dependency distance under cognitive constraints.

DDM at Work: Reply to comments on "Dependency distance: A new perspective on syntactic patterns in natural languages"

We provide responses to the commentaries in this volume to evaluate, clarify and extend some of the arguments in Dependency distance: A new perspective on syntactic patterns in natural languages. Evidences show that DDM (dependency distance minimization) is an important linguistic universal, biologically or cognitively motivated, in shaping the language system. As a general tendency, DDM works quite well in theoretical argumentations as well as practical applications. However, this does not mean that DDM is the only linguistic universal that works: it is highly possible that other factors, which might be biologically, physically, socially or culturally motivated, work as well to jointly mold languages.

Generalizing dependency distance: Comment on "Dependency distance: A new perspective on syntactic patterns in natural languages" by Haitao Liu et al

With the support of the comprehensive review in Liu et al. [14], we consider dependency distance minimization to be firmly established as a quantitative property of syntactic trees. In this comment, we consider future empirical and theoretical directions for this concept, including a recent information-theoretic reinterpretation of dependency locality effects as proposed by Futrell and Levy [4].