Is Having Hearing Loss Fundamentally Different? Multigroup Structural Equation Modeling of the Effect of Cognitive Functioning on Speech Identification

Marsja, Erik; Stenbäck, Victoria; Moradi, Shahram; Danielsson, Henrik; Rönnberg, Jerker

doi:10.1097/aud.0000000000001196

Cited by 8 publications

(7 citation statements)

References 71 publications

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“…The previously mentioned study by Marsja et al (2022) suggests impressively similar (if not identical) cognitive predictions from one hearing-impaired group compared to a normal-hearing group on a matrix sentence latent construct. This finding suggests a powerful generalization of the case that Cognitive Hearing Science—and the ELU model—applies to anyone, regardless of hearing status.…”

Section: Generalizationsmentioning

confidence: 70%

“…Indeed, Gordon-Salant and Cole (2016) showed the same results to hold across age groups, with RST as part of the most prominent predictors. In the same vein, with large samples, Marsja et al (2022) used the n200 database ( Rönnberg et al, 2016 ) to study potential differences in cognitive involvement due to hearing loss. Marsja et al (2022) used a multi-group structural equation model (SEM) approach where the purpose was to assess whether the contribution of a “Cognition” latent variable (based on RST, a visuospatial WM test and a semantic WM word-pair test, and Raven’s matrices) was equally related to a SPIN criterion (Hagerman matrix sentences, Hagerman, 1982 ) for hearing-impaired hearing-aid users compared to normal-hearing participants.…”

Section: Recent Findingsmentioning

confidence: 99%

“…In the same vein, with large samples, Marsja et al (2022) used the n200 database ( Rönnberg et al, 2016 ) to study potential differences in cognitive involvement due to hearing loss. Marsja et al (2022) used a multi-group structural equation model (SEM) approach where the purpose was to assess whether the contribution of a “Cognition” latent variable (based on RST, a visuospatial WM test and a semantic WM word-pair test, and Raven’s matrices) was equally related to a SPIN criterion (Hagerman matrix sentences, Hagerman, 1982 ) for hearing-impaired hearing-aid users compared to normal-hearing participants. The results, based on 200 participants per group, show that the Cognition variable accounted for identical beta weights (-0.32) in both groups of equal average age (60 years), when the groups were compared on an outcome latent construct based on Hagerman matrix sentences.…”

Section: Recent Findingsmentioning

confidence: 99%

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The cognitive hearing science perspective on perceiving, understanding, and remembering language: The ELU model

et al. 2022

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The review gives an introductory description of the successive development of data patterns based on comparisons between hearing-impaired and normal hearing participants’ speech understanding skills, later prompting the formulation of the Ease of Language Understanding (ELU) model. The model builds on the interaction between an input buffer (RAMBPHO, Rapid Automatic Multimodal Binding of PHOnology) and three memory systems: working memory (WM), semantic long-term memory (SLTM), and episodic long-term memory (ELTM). RAMBPHO input may either match or mismatch multimodal SLTM representations. Given a match, lexical access is accomplished rapidly and implicitly within approximately 100–400 ms. Given a mismatch, the prediction is that WM is engaged explicitly to repair the meaning of the input – in interaction with SLTM and ELTM – taking seconds rather than milliseconds. The multimodal and multilevel nature of representations held in WM and LTM are at the center of the review, being integral parts of the prediction and postdiction components of language understanding. Finally, some hypotheses based on a selective use-disuse of memory systems mechanism are described in relation to mild cognitive impairment and dementia. Alternative speech perception and WM models are evaluated, and recent developments and generalisations, ELU model tests, and boundaries are discussed.

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Section: Generalizationsmentioning

confidence: 70%

Section: Recent Findingsmentioning

confidence: 99%

Section: Recent Findingsmentioning

confidence: 99%

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The cognitive hearing science perspective on perceiving, understanding, and remembering language: The ELU model

et al. 2022

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“…Loughrey et al [ 82 ] used a meta-analysis of 36 studies and over 20,000 patients to show that age-related hearing loss is a significant predictor of a range of types of cognitive decline. However, even among older adults with normal hearing, cognitive performance predicts poor speech understanding; Marsja et al [ 9 ] used structural equation modeling on data from 399 older listeners (199 with and 200 without hearing loss) which indicated that cognitive performance was a strong predictor of speech understanding.…”

Section: Discussionmentioning

confidence: 99%

“…Previous experiments conducted in our laboratories and elsewhere have identified a set of psychophysical tasks of auditory processing (so-called “suprathreshold” auditory processing tests) that appear to predict performance on speech tasks with either noise or speech as the interference [ 2 , 3 , 4 , 5 , 6 , 7 ]. However, with a few notable exceptions—Rönnberg et al and Marsja et al [ 8 , 9 ]—most of these investigations have either explored no more than one or two auditory processing tasks at a time, or have lacked the statistical power to allow firm conclusions to be drawn (for an example, see Neher et al [ 7 ]) making it difficult to determine which independent predictors contribute most to speech-in-competition performance. To address the limitations of traditional laboratory-based psychophysical assessments of auditory processing ability, we developed a new assessment platform—Portable Automated Rapid Testing (PART)—that allows direct testing of a diverse range of auditory processing abilities and facilitates the collection of large data sets with consistent procedures.…”

Section: Introductionmentioning

confidence: 99%

Relating Suprathreshold Auditory Processing Abilities to Speech Understanding in Competition

et al. 2022

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(1) Background: Difficulty hearing in noise is exacerbated in older adults. Older adults are more likely to have audiometric hearing loss, although some individuals with normal pure-tone audiograms also have difficulty perceiving speech in noise. Additional variables also likely account for speech understanding in noise. It has been suggested that one important class of variables is the ability to process auditory information once it has been detected. Here, we tested a set of these “suprathreshold” auditory processing abilities and related them to performance on a two-part test of speech understanding in competition with and without spatial separation of the target and masking speech. Testing was administered in the Portable Automated Rapid Testing (PART) application developed by our team; PART facilitates psychoacoustic assessments of auditory processing. (2) Methods: Forty-one individuals (average age 51 years), completed assessments of sensitivity to temporal fine structure (TFS) and spectrotemporal modulation (STM) detection via an iPad running the PART application. Statistical models were used to evaluate the strength of associations between performance on the auditory processing tasks and speech understanding in competition. Age and pure-tone-average (PTA) were also included as potential predictors. (3) Results: The model providing the best fit also included age and a measure of diotic frequency modulation (FM) detection but none of the other potential predictors. However, even the best fitting models accounted for 31% or less of the variance, supporting work suggesting that other variables (e.g., cognitive processing abilities) also contribute significantly to speech understanding in noise. (4) Conclusions: The results of the current study do not provide strong support for previous suggestions that suprathreshold processing abilities alone can be used to explain difficulties in speech understanding in competition among older adults. This discrepancy could be due to the speech tests used, the listeners tested, or the suprathreshold tests chosen. Future work with larger numbers of participants is warranted, including a range of cognitive tests and additional assessments of suprathreshold auditory processing abilities.

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Informational Masking and Listening Effort in Speech Recognition in Noise: The Role of Working Memory Capacity and Inhibitory Control in Older Adults With and Without Hearing Impairment

Stenbäck

Marsja

Hällgren

et al. 2022

J Speech Lang Hear Res

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Purpose: The study aimed to assess the relationship between (a) speech recognition in noise, mask type, working memory capacity (WMC), and inhibitory control and (b) self-rated listening effort, speech material, and mask type, in older adults with and without hearing impairment. It was of special interest to assess the relationship between WMC, inhibitory control, and speech recognition in noise when informational maskers masked target speech. Method: A mixed design was used. A group ( N = 24) of older ( M age = 69.7 years) individuals with hearing impairment and a group of age normal–hearing adults ( M age = 59.3 years, SD = 6.5) participated in the study. The participants were presented with auditory tests in a sound-attenuated room and with cognitive tests in a quiet office. The participants were asked to rate listening effort after being presented with energetic and informational background maskers in two different speech materials used in this study (i.e., Hearing In Noise Test and Hagerman test). Linear mixed-effects models were set up to assess the effect of the two different speech materials, energetic and informational maskers, hearing ability, WMC, inhibitory control, and self-rated listening effort. Results: Results showed that WMC and inhibitory control were of importance for speech recognition in noise, even when controlling for pure-tone average 4 hearing thresholds and age, when the maskers were informational. Concerning listening effort, on the other hand, the results suggest that hearing ability, but not cognitive abilities, is important for self-rated listening effort in speech recognition in noise. Conclusions: Speech-in-noise recognition is more dependent on WMC for older adults in informational maskers than in energetic maskers. Hearing ability is a stronger predictor than cognition for self-rated listening effort. Supplemental Material: https://doi.org/10.23641/asha.21357648

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Is Having Hearing Loss Fundamentally Different? Multigroup Structural Equation Modeling of the Effect of Cognitive Functioning on Speech Identification

Cited by 8 publications

References 71 publications

The cognitive hearing science perspective on perceiving, understanding, and remembering language: The ELU model

The cognitive hearing science perspective on perceiving, understanding, and remembering language: The ELU model

Relating Suprathreshold Auditory Processing Abilities to Speech Understanding in Competition

Informational Masking and Listening Effort in Speech Recognition in Noise: The Role of Working Memory Capacity and Inhibitory Control in Older Adults With and Without Hearing Impairment

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