Impact of Word Properties on List Learning: An Explanatory Item Analysis

• Published in: Neuropsychology 2023-03, Vol.37 (3), p.268-283
• Main Authors: Goette, William F., Schaffert, Jeff, Carlew, Anne, Rossetti, Heidi, Lacritz, Laura H., De Boeck, Paul, Cullum, C. Munro
• Format: Article
• Language: English
• Subjects: Age Differences; Aged; Bayes Theorem; Female; Human; Humans; Language; Learning; Male; Measurement; Memory; Phonemes; Semantics; Verbal Learning - physiology; Word Frequency
• ISSN: 0894-4105; 1931-1559
• DOI: 10.1037/neu0000810

Objective: A variety of factors affect list learning performance and relatively few studies have examined the impact of word selection on these tests. This study examines the effect of both language and memory processing of individual words on list learning. Method: Item-response data from 1,219 participants, Mage = 74.41 (SD = 7.13), Medu = 13.30 (SD = 2.72), in the Harmonized Cognitive Assessment Protocol were used. A Bayesian generalized (non)linear multilevel modeling framework was used to specify the measurement and explanatory item-response theory models. Explanatory effects on items due to learning over trials, serial position of words, and six word properties obtained through the English Lexicon Project were modeled. Results: A two parameter logistic (2PL) model with trial-specific learning effects produced the best measurement fit. Evidence of the serial position effect on word learning was observed. Robust positive effects on word learning were observed for body-object integration while robust negative effects were observed for word frequency, concreteness, and semantic diversity. A weak negative effect of average age of acquisition and a weak positive effect for the number of phonemes in the word were also observed. Conclusions: Results demonstrate that list learning performance depends on factors beyond the repetition of words. Identification of item factors that predict learning could extend to a range of test development problems including translation, form equating, item revision, and item bias. In data harmonization efforts, these methods can also be used to help link tests via shared item features and testing of whether these features are equally explanatory across samples. Key Points Question: What aspects of learning and language affect how list learning tests measure memory? Findings: Words on list learning tests are easier or harder to encode because of prior English-language exposure and associations with the words, but once a word is learned and incorporated into ones' lexicon, it is easier to recall it later within the context of a memory test. Importance: The methods and approach described can be extended to a wide variety of tests to integrate cognitive science to deepen the understanding of common neuropsychological tests and why some groups may perform differently on them. Next Steps: The methods need to be applied to other tests and applied directly to the question of whether certain item properties can explain things like item