Journal article
From real-time measures to real world differences: New [and old] statistical approaches to individual differences in real-time language processing
Brain research, Vol.1864, 149786
10/01/2025
DOI: 10.1016/j.brainres.2025.149786
PMID: 40544931
Abstract
In the last 30 years, the Visual World Paradigm has rapidly become a dominant experimental paradigm for understanding real-time language processing. Part of this derives from the rich visualizations of the millisecond-by-millisecond timecourse of language processing that it offers. While the field has converged on strong statistical approaches for this in experimental paradigms, a great deal of recent work has sought to apply the Visual World Paradigm in individual differences paradigms to examine factors such as development and aging, multilingualism and various types of communicative and cognitive disorders. However, these models are less useful when confronting issues that are at the core of individual differences work: of collinearity and co-morbidity among predictor variables. We review a new approach that may complement existing approaches by capitalizing on regression-based techniques that were developed to handle these issues. We argue that analysis should start by developing a quantitative "profile" of processing that can be captured in a small number of index variables that capture meaningful dimensions such as the degree of competition or the speed of settling on a target interpretation. These can then be used in more sophisticated regressions like hierarchical regression, commonality analysis and mediation analysis which can unpack shared and unique variance, and test multiple causal pathways. We illustrate this with tutorials based on our own work on hearing loss, development and language disorders that illustrate how these approaches can provide greater insight into individual differences.
Details
- Title: Subtitle
- From real-time measures to real world differences: New [and old] statistical approaches to individual differences in real-time language processing
- Creators
- Bob McMurray - University of IowaJacob Oleson - University of IowaEthan Kutlu - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Brain research, Vol.1864, 149786
- DOI
- 10.1016/j.brainres.2025.149786
- PMID
- 40544931
- NLM abbreviation
- Brain Res
- ISSN
- 0006-8993
- eISSN
- 1872-6240
- Publisher
- ELSEVIER
- Grant note
- NSF
The authors would like to thank members of the Growing Words Team (Jamie-Klein Packard, Alex Fell, Sneh Jhaveri, Abby Fergus, Eva Kelly, Nate Homan, Charlotte Jeppsen, Bruce Tomblin, Isaac Petersen and Ece Demir-Lira) and the Iowa Cochlear Implant Team (Sarah Colby, John Muegge, Francis Smith, Kristin Rooff, Marissa Huffman, Frankie Gillenwater and Camille Dunn) both for managing these large projects, and for valuable discussions about the appropriate analytic techniques. We'd like to thank, Thanassi Protopapas Jim Magnuson, Michael Spivey and Christoph Scheepers for very constructive comments on prior drafts of this manuscript. Preparation of this manuscript was supported by DC 008089 awarded to BM, DC 000242 awarded to BM and Bruce Gantz, and NSF grant 2104015 awarded to EK. Supplementary materials are available at https://osf.io/q8s4p/and include code for many of the examples described here.
- Language
- English
- Electronic publication date
- 06/20/2025
- Date published
- 10/01/2025
- Academic Unit
- Communication Sciences and Disorders; Linguistics; Psychological and Brain Sciences; Iowa Neuroscience Institute; Biostatistics; Center for Social Science Innovation; Otolaryngology
- Record Identifier
- 9984833521902771
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