ArticleFrontiers in psychology2023
A structural equation mediation model captures the predictions amongst the parameters of the ease of language understanding model.
Article in Frontiers in psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Speech Perception in Noise: A Narrative Review of Cognitive Contribution and Hearing-Aid Signal Processing.Journal of Intelligence · 2026Review
- Authentic leadership and employee psychological safety: the mediating role of leader-member exchange and the moderating effect of cultural collectivism.BMC psychology · 2026Article
- Working memory capacity moderates the effect of hearing aid experience on phonological processing performance.Frontiers in neuroscience · 2025Article
- Listening to two-talker conversations in quiet settings: the role of listeners' cognitive processing capabilities for memory and listening effort.Scientific reports · 2024Article
- Impact of SNR, peripheral auditory sensitivity, and central cognitive profile on the psychometric relation between pupillary response and speech performance in CI users.Frontiers in neuroscience · 2023Article
- The cognitive hearing science perspective on perceiving, understanding, and remembering language: The ELU model.Frontiers in psychology · 2022Review
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3 authors.
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Abstract
Objective: The aim of the present study was to assess the validity of the Ease of Language Understanding (ELU) model through a statistical assessment of the relationships among its main parameters: processing speed, phonology, working memory (WM), and dB Speech Noise Ratio (SNR) for a given Speech Recognition Threshold (SRT) in a sample of hearing aid users from the n200 database. Methods: Hearing aid users were assessed on several hearing and cognitive tests. Latent Structural Equation Models (SEMs) were applied to investigate the relationship between the main parameters of the ELU model while controlling for age and PTA. Several competing models were assessed. Results: Analyses indicated that a mediating SEM was the best fit for the data. The results showed that (i) phonology independently predicted speech recognition threshold in both easy and adverse listening conditions and (ii) WM was not predictive of dB SNR for a given SRT in the easier listening conditions (iii) processing speed was predictive of dB SNR for a given SRT mediated Conclusion: The results were in line with the predictions of the ELU model: (i) phonology contributed to dB SNR for a given SRT in all listening conditions, (ii) WM is only invoked when listening conditions are adverse, (iii) better WM capacity aids the understanding of what has been said in adverse listening conditions, and finally (iv) the results highlight the importance and optimization of processing speed in conditions when listening conditions are adverse and WM is activated.
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