When we read or listen to language, we are faced with the challenge of inferring intended messages from noisy input. This challenge is exacerbated by considerable variability between and within speakers. Focusing on syntactic processing (parsing), we test the hypothesis that language comprehenders rapidly adapt to the syntactic statistics of novel linguistic environments (e.g., speakers or genres). Two self-paced reading experiments investigate changes in readers’ syntactic expectations based on repeated exposure to sentences with temporary syntactic ambiguities (so-called “garden path sentences”). These sentences typically lead to a clear expectation violation signature when the temporary ambiguity is resolved to an a priori less expected structure (e.g., based on the statistics of the lexical context). We find that comprehenders rapidly adapt their syntactic expectations to converge towards the local statistics of novel environments. Specifically, repeated exposure to a priori unexpected structures can reduce, and even completely undo, their processing disadvantage (Experiment 1). The opposite is also observed: a priori expected structures become less expected (even eliciting garden paths) in environments where they are hardly ever observed (Experiment 2). Our findings suggest that, when changes in syntactic statistics are to be expected (e.g., when entering a novel environment), comprehenders can rapidly adapt their expectations, thereby overcoming the processing disadvantage that mistaken expectations would otherwise cause. Our findings take a step towards unifying insights from research in expectation-based models of language processing, syntactic priming, and statistical learning.

Suicide is among the 10 most common causes of death, as assessed by the World Health Organization. For every death by suicide, an estimated 138 people’s lives are meaningfully affected, and almost any other statistic around suicide deaths is equally alarming. The pervasiveness of social media—and the near-ubiquity of mobile devices used to access social media networks—offers new types of data for understanding the behavior of those who (attempt to) take their own lives and suggests new possibilities for preventive intervention. We demonstrate the feasibility of using social media data to detect those at risk for suicide. Specifically, we use natural language processing and machine learning (specifically deep learning) techniques to detect quantifiable signals around suicide attempts, and describe designs for an automated system for estimating suicide risk, usable by those without specialized mental health training (eg, a primary care doctor). We also discuss the ethical use of such technology and examine privacy implications. Currently, this technology is only used for intervention for individuals who have “opted in” for the analysis and intervention, but the technology enables scalable screening for suicide risk, potentially identifying many people who are at risk preventively and prior to any engagement with a health care system. This raises a significant cultural question about the trade-off between privacy and prevention—we have potentially life-saving technology that is currently reaching only a fraction of the possible people at risk because of respect for their privacy. Is the current trade-off between privacy and prevention the right one?

This study provides evidence for implicit learning in syntactic comprehension. By reanalyzing data from a syntactic priming experiment (Thothathiri & Snedeker, 2008), we find that the error signal associated with a syntactic prime influences comprehenders' subsequent syntactic expectations. This follows directly from error-based implicit learning accounts of syntactic priming, but it is unexpected under accounts that consider syntactic priming a consequence of temporary increases in base-level activation. More generally, the results raise questions about the principles underlying the maintenance of implicit statistical knowledge relevant to language processing, and about possible functional motivations for syntactic priming.

Recently processed syntactic information is likely to play a fundamental role in online sentence comprehension. For example, there is now a good deal of evidence that the processing of a syntactic structure (the target) is facilitated if the same structure was processed on the immediately preceding trial (the prime), a phenomenon known as structural priming. However, compared with structural priming in production, structural priming in comprehension remains relatively understudied. We investigate an aspect of structural priming in comprehension that is comparatively well understood in production but has received little attention in comprehension: the cumulative effect of structural primes on subsequently processed sentences. We further ask whether this effect is modulated by lexical overlap between preceding primes and the target. In 3 self-paced reading experiments, we find that structural priming effects in comprehension are cumulative and of similar magnitude both with and without lexical overlap. We discuss the relevance of our results to questions about the relationship between recent experience and online language processing. (PsycINFO Database Record

We present a framework of second and additional language (L2/Ln) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/Ln learning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/Ln acquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/Ln learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa.

Depression is a global mental health condition that affects all cultures. Despite this, the way depression is expressed varies by culture. Uptake of machine learning technology for diagnosing mental health conditions means that increasingly more depression classifiers are created from online language data. Yet, culture is rarely considered as a factor affecting online language in this literature. This study explores cultural differences in online language data of users with depression. Written language data from 1,593 users with self-reported depression from the online peer support community 7 Cups of Tea was analyzed using the Linguistic Inquiry and Word Count (LIWC), topic modeling, data visualization, and other techniques. We compared the language of users identifying as White, Black or African American, Hispanic or Latino, and Asian or Pacific Islander. Exploratory analyses revealed crosscultural differences in depression expression in online language data, particularly in relation to emotion expression, cognition, and functioning. The results have important implications for avoiding depression misclassification from machine-driven assessments when used in a clinical setting, and for avoiding inadvertent cultural biases in this line of research more broadly.

Accounts of individual differences in online language processing ability often focus on the explanatory utility of verbal working memory, as measured by reading span tasks. Although variability in reading span task performance likely reflects individual differences in multiple underlying traits, skills, and processes, accumulating evidence suggests that reading span scores also reflect variability in the linguistic experiences of an individual. Here, through an individual differences approach, we first demonstrate that reading span scores correlate significantly with measures of the amount of experience an individual has had with written language (gauged by measures that provide "proxy estimates" of print exposure). We then explore the relationship between reading span scores and online language processing ability. Individuals with higher reading spans demonstrated greater sensitivity to violations of statistical regularities found in natural language-as evinced by higher reading times (RTs) on the disambiguating region of garden-path sentences-relative to their lower span counterparts. This result held after statistically controlling for individual differences in a non-linguistic operation span task. Taken together, these results suggest that accounts of individual differences in sentence processing can benefit from a stronger focus on experiential factors, especially when considered in relation to variability in perceptual and learning abilities that influence the amount of benefit gleaned from such experience.

People with Williams syndrome (WS) have been consistently described as showing heightened sociability, gregariousness, and interest in people, in conjunction with an uneven cognitive profile and mild to moderate intellectual or learning disability. To explore the mechanisms underlying this unusual social–behavioral phenotype, we investigated whether individuals with WS show an atypical appraisal style and autonomic responsiveness to emotionally laden images with social or nonsocial content. Adolescents and adults with WS were compared to chronological age-matched and nonverbal mental age-matched groups in their responses to positive and negative images with or without social content, using measures of self-selected viewing time (SSVT), autonomic arousal reflected in pupil dilation measures, and likeability ratings. The participants with WS looked significantly longer at the social images compared to images without social content and had reduced arousal to the negative social images compared to the control groups. In contrast to the comparison groups, the explicit ratings of likeability in the WS group did not correlate with their SSVT; instead, they reflected an appraisal style of more extreme ratings. This distinctive pattern of viewing interest, likeability ratings, and autonomic arousal to images with social content in the WS group suggests that their heightened social drive may be related to atypical functioning of reward-related brain systems reflected in SSVT and autonomic reactivity measures, but not in explicit ratings.