Predictive language processing revealing usage-based variation

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)

Abstract

While theories on predictive processing posit that predictions are based on one’s prior experiences, experimental work has effectively ignored the fact that people differ from each other in their linguistic experiences and, consequently, in the predictions they generate. We examine usage-based variation by means of three groups of participants (recruiters, job-seekers, and people not (yet) looking for a job), two stimuli sets (word sequences characteristic of either job ads or news reports), and two experiments (a Completion task and a Voice Onset Time task). We show that differences in experiences with a particular register result in different expectations regarding word sequences characteristic of that register, thus pointing to differences in mental representations of language. Subsequently, we investigate to what extent different operationalizations of word predictability are accurate predictors of voice onset times. A measure of a participant’s own expectations proves to be a significant predictor of processing speed over and above word predictability measures based on amalgamated data. These findings point to actual individual differences and highlight the merits of going beyond amalgamated data. We thus demonstrate that is it feasible to empirically assess the variation implied in usage-based theories, and we advocate exploiting this opportunity.
Original languageEnglish
Pages (from-to)329-373
Number of pages45
JournalLanguage and Cognition
Volume10
Issue number2
Early online date4 Jun 2018
DOIs
Publication statusPublished - Jun 2018

Keywords

  • ADULT NATIVE SPEAKERS
  • ANTICIPATION
  • ASSOCIATIONS
  • EYE-MOVEMENTS
  • FREQUENCY
  • INDIVIDUAL-DIFFERENCES
  • MIXED MODELS
  • PREDICTABILITY
  • SENTENCE COMPREHENSION
  • WORD RECOGNITION
  • cloze probabilities
  • completion task
  • individual differences
  • surprisal
  • voice onset times

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