Publications

Abstract

Individual differences in using language are prevalent in our daily lives. Language skills are often assessed in vocational (predominantly written language) and diagnostic contexts. Not much is known, however, about individual differences in spoken language skills. The lack of research is in part due to the lack of suitable test instruments. We introduce the Individual Differences in Language Skills (IDLaS-NL) test battery, a set of 31 behavioural tests that can be used to capture variability in language and relevant general cognitive skills in adult speakers of Dutch. The battery was designed to measure word and sentence production and comprehension skills, linguistic knowledge, nonverbal processing speed, working memory, and nonverbal reasoning. The present article outlines the structure of the battery, describes the materials and procedure of each test, and evaluates the battery’s factor structure based on the results of a sample of 748 Dutch adults, aged between 18 and 30 years, most of them students. The analyses demonstrate that the battery has good construct validity and can be reliably administered both in the lab and via the internet. We therefore recommend the battery as a valuable new tool to assess individual differences in language knowledge and skills; this future work may include linking language skills to other aspects of human cognition and life outcomes.

Abstract

In reply to Magnuson, Crinnion, Luthra, Gaston, and Grubb (2023), we challenge their conclusion that on-line activation feedback improves word recognition. This type of feedback is instantiated in the TRACE model (McClelland & Elman, 1986) as top-down spread of activation from lexical to phoneme nodes. We give two main reasons why Magnuson et al.'s demonstration that activation feedback speeds up word recognition in TRACE is not informative about whether activation feedback helps humans recognize words. First, the same speed-up could be achieved by changing other parameters in TRACE. Second, more fundamentally, there is room for improvement in TRACE's performance only because the model, unlike Bayesian models, is suboptimal. We also challenge Magnuson et al.'s claim that the available empirical data support activation feedback. The data they base this claim on are open to alternative explanations and there are data against activation feedback that they do not discuss. We argue, therefore, that there are no computational or empirical grounds to conclude that activation feedback benefits human spoken-word recognition and indeed no theoretical grounds why activation feedback would exist. Other types of feedback, for example feedback to support perceptual learning, likely do exist, precisely because they can help listeners recognize words.