Evidence map›Paper›PMID 41467950›Full record

ArticleCognitive science2026

Replicate Me if You Can: Assessing Measurement Reliability of Individual Differences in Reading Across Measurement Occasions and Methods.

Patrick Haller, Cui Ding, Maja Stegenwallner-Schütz, David R Reich, Iva Koncic, Silvia Makowski, Lena A Jäger

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Article in Cognitive science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Patrick HallerDepartment of Computational Linguistics, University of Zurich.ORCID 0000-0002-8968-7587
Cui DingDepartment of Computational Linguistics, University of Zurich.ORCID 0009-0009-8808-9436
Maja Stegenwallner-SchützDepartment of Computer Science, University of Potsdam.ORCID 0000-0002-2103-2218
David R ReichDepartment of Computer Science, University of Potsdam.ORCID 0000-0002-3524-3788
Iva KoncicDepartment of Computational Linguistics, University of Zurich.
Silvia MakowskiDepartment of Computer Science, University of Potsdam.
Lena A JägerDepartment of Computational Linguistics, University of Zurich.ORCID 0000-0001-9018-9713

Funding

Bundesministerium für Bildung und Forschung 01lS20043Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 212276Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 220330
6 · The paper itself

Abstract

Psycholinguistic theories traditionally assume similar cognitive mechanisms across different speakers. However, more recently, researchers have begun to recognize the need to consider individual differences when explaining human cognition. An increasing number of studies have investigated how individual differences influence human sentence processing. Implicitly, these studies assume that individual-level effects can be replicated across experimental sessions and different assessment methods such as eye-tracking and self-paced reading. However, this assumption is challenged by the Reliability Paradox. Thus, a crucial first step for a principled investigation of individual differences in sentence processing is to establish their measurement reliability, that is, the correlation of individual-level effects across multiple measurement occasions and methods. In this work, we present the first naturalistic eye movement corpus of reading data with four experimental sessions from each participant (two eye-tracking sessions and two self-paced reading sessions). We deploy a two-task Bayesian hierarchical model to assess the measurement reliability of individual differences in a range of psycholinguistic phenomena that are well-established at the population level, namely, effects of word length, lexical frequency, surprisal, dependency length, and number of to-be-integrated dependents. While our results indicate high reliability across measurement occasions for the word length effect, it is only moderate for higher-level psycholinguistic predictors such as lexical frequency, dependency distance, and the number of to-be-integrated dependencies, and even low for surprisal. Moreover, even after accounting for spillover effects, we observe only low to moderate reliability at the individual level across methods (eye-tracking and self-paced reading) for most predictors, and poor reliability for predictors of syntactic integration. These findings underscore the importance of establishing measurement reliability before drawing inferences about individual differences in sentence processing.

Indexed as

Eye MovementsIndividualityPsycholinguisticsReadingAdultBayes TheoremEye-Tracking TechnologyFemaleHumansMaleReproducibility of ResultsYoung AdultEye‐trackingIndividual differencesMeasurement reliabilityNaturalistic reading corpusSelf‐paced readingSentence processingTwo‐task Bayesian modeling

Identifiers

PMID41467950
PMCPMC12752730

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.