Alex B. Fine

Rapid Expectation Adaptation during Syntactic Comprehension.

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.

A multimeasure approach to investigating affective appraisal of social information in Williams syndrome

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.

Biases in Predicting the Human Language Model

We consider the prediction of three human behavioral measures ‐ lexical decision, word naming, and picture naming ‐ through the lens of domain bias in language modeling. Contrasting the predictive ability of statistics derived from 6 different corpora, we find intuitive results showing that, e.g., a British corpus overpredicts the speed with which an American will react to the words ward and duke, and that the Google n-grams overpredicts familiarity with technology terms. This study aims to provoke increased consideration of the human language model by NLP practitioners: biases are not limited to differences between corpora (i.e. “train” vs. “test”); they can exist as well between corpora and the intended user of the resultant technology.