Individual differences in cue weighting in sentence comprehension: An evaluation using Approximate Bayesian Computation

Abstract

Cue-based retrieval theories of sentence processing assume that syntactic dependencies are resolved through a content-addressable search process. An important recent claim is that in certain dependency types, the retrieval cues are weighted such that one cue dominates. This cue-weighting proposal aims to explain the observed average behavior, but here we show that there is systematic individual-level variation in cue weighting. Using the Lewis and Vasishth cue-based retrieval model, we estimated individual-level parameters for processing speed and cue weighting using 13 published datasets; hierarchical Approximate Bayesian Computation (ABC) was used to estimate the parameters. The modeling reveals a nuanced picture of cue weighting: we find support for the idea that some participants weight cues differentially, but not all participants do. Only fast readers tend to have the higher weighting for structural cues, suggesting that reading proficiency might be associated with cue weighting. A broader achievement of the work is to demonstrate how individual differences can be investigated in computational models of sentence processing without compromising the complexity of the model.

Conscious rereading is confirmatory: Evidence from bidirectional self-paced reading

“…It is thus possible that the use of selective rereading strategy is at least partially due to perceived ungrammaticality, as opposed to attempts at reanalyzing the structure, as originally claimed by Frazier and Rayner (1982). More generally, the presence of individual differences in reading strategies, and possibly in the application of grammar rules, highlights the need for an increased focus away from average behavior and onto the behavior of individual subjects (Yadav et al, 2021). Selective rereading at the text level has been found to correlate with reading proficiency (Minguela, Solé, & Pieschl, 2015;Zabrucky & Commander, 1993), but it is unclear if the connection extends to the sentence level across different types of manipulations.…”

“…In this context, recall that the results of previous studies on the effect of similarity-based interference in reflexive dependencies are inconsistent (Dillon, Mishler, Sloggett, & Phillips, 2013;Jäger et al, 2017Jäger et al, , 2020Parker & Phillips, 2017). If readers indeed differ widely in their response to this type of interference, this may explain why the effects vary across studies, as certain strategies may be represented in different participant samples to different extents (Yadav, Paape, Smith, Dillon, & Vasishth, 2021). We will now discuss the results' broader implications.…”

Conscious rereading is confirmatory: Evidence from bidirectional self-paced reading

“…In this context, recall that the results of previous studies on the effect of similarity-based interference in reflexive dependencies are inconsistent (Jäger et al, 2017;Parker and Phillips, 2017;Jäger et al, 2020;Dillon et al, 2013). If readers indeed differ widely in their response to this type of interference, this may explain why the effects vary across studies, as certain strategies may be represented in different participant samples to different extents (Yadav et al, 2021). We will now discuss the results' broader implications.…”

Number feature distortion modulates cue-based retrieval in reading

“…Systematic modeling of individual differences can reveal important insights about the underlying process (Yadav, Paape, Smith, Dillon, & Vasishth, 2022). For example, one can model individual differences in number agreement effects as variation in the percolation rate parameter of the feature percolation-plus-retrieval model.…”