Abstract

While it is widely acknowledged that both predictive expectations and retrodictive integration influence language processing, the individual differences that affect these two processes and the best metrics for observing them have yet to be fully described. The present study aims to contribute to the debate by investigating the extent to which experienced-based variables modulate the processing of word pairs (bigrams). Specifically, we investigate how age and reading experience correlate with lexical anticipation and integration, and how this effect can be captured by the metrics of forward and backward transition probability (TP). Participants read more and less strongly associated bigrams, paired to control for known lexical covariates such as bigram frequency and meaning (i.e., absolute control, total control, absolute silence, total silence) in a self-paced reading (SPR) task. They additionally completed assessments of exposure to print text (Author Recognition Test, Shipley vocabulary assessment, Words that Go Together task) and provided their age. Results show that both older age and lesser reading experience individually correlate with stronger TP effects. Moreover, TP effects differ across the spillover region (the two words following the noun in the bigram).

Highlights

  • Comprehending language is one of the most complicated tasks that humans perform on a regular basis, yet we do it with astounding proficiency and ease

  • We fit a four-way interaction between age, reading experience, position, and forward transition probability (FTP)/backward transition probability (BTP), as we hypothesized that the effect of experience may change over the critical region as a factor of these two types of experience

  • The effect of transition probability varied by position: Higher FTP correlated with faster response times in the spillover region but slower response times on the noun (β 0.0059 (SE: 0.0014), p < 0.001, Figure 8)

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Summary

Introduction

Comprehending language is one of the most complicated tasks that humans perform on a regular basis, yet we do it with astounding proficiency and ease. Probabilistic models of language acquisition and processing are situated within the broader paradigm of probabilistic cognition, which assumes that humans learn about and construct a mental representation of the world based on distributional information from the environment and apply this statistical knowledge in interacting with and predicting future states of the world. These abilities for statistical learning and processing are claimed to be key ingredients to cognition in domains as different as motor control, visual perception, causal learning, inferential reasoning and language (Chater and Oaksford, 2008; Perfors et al, 2011; Tenenbaum et al, 2011; Griffiths et al, 2012; Lupyan and Clark, 2015; Ordin et al, 2020).

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