Purpose: Although the existing literature has explored the link between cognitive functioning and speech recognition in noise, the specific role of fluid intelligence still needs to be studied. Given the established association between working memory capacity (WMC) and fluid intelligence and the predictive power of WMC for speech recognition in noise, we aimed to elucidate the mediating role of fluid intelligence.

Method: We used data from the n200 study, a longitudinal investigation into aging, hearing ability, and cognitive functioning. We analyzed two age-matched samples: participants with hearing aids and a group with normal hearing. WMC was assessed using the Reading Span task, and fluid intelligence was measured with Raven's Progressive Matrices. Speech recognition in noise was evaluated using Hagerman sentences presented to target 80% speech-reception thresholds in four-talker babble. Data were analyzed using mediation analysis to examine fluid intelligence as a mediator between WMC and speech recognition in noise.

Results: We found a partial mediating effect of fluid intelligence on the relationship between WMC and speech recognition in noise, and that hearing status did not moderate this effect. In other words, WMC and fluid intelligence were related, and fluid intelligence partially explained the influence of WMC on speech recognition in noise.

Conclusions: This study shows the importance of fluid intelligence in speech recognition in noise, regardless of hearing status. Future research should use other advanced statistical techniques and explore various speech recognition tests and background maskers to deepen our understanding of the interplay between WMC and fluid intelligence in speech recognition.

Facial mimicry, the tendency to imitate facial expressions of other individuals, has been shown to play a critical role in the processing of emotion expressions. At the same time, there is evidence suggesting that its role might change when the cognitive demands of the situation increase. In such situations, understanding another person is dependent on working memory. However, whether facial mimicry influences working memory representations for facial emotion expressions is not fully understood. In the present study, we experimentally interfered with facial mimicry by using established behavioral procedures, and investigated how this interference influenced working memory recall for facial emotion expressions. Healthy, young adults (N = 36) performed an emotion expression n-back paradigm with two levels of working memory load, low (1-back) and high (2-back), and three levels of mimicry interference: high, low, and no interference. Results showed that, after controlling for block order and individual differences in the perceived valence and arousal of the stimuli, the high level of mimicry interference impaired accuracy when working memory load was low (1-back) but, unexpectedly, not when load was high (2-back). Working memory load had a detrimental effect on performance in all three mimicry conditions. We conclude that facial mimicry might support working memory for emotion expressions when task load is low, but that the supporting effect possibly is reduced when the task becomes more cognitively challenging.

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 via WM in the more adverse conditions.

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.

Objective

Using data from the n200-study, we aimed to investigate the relationship between behavioural (the Swedish HINT and Hagerman speech-in-noise tests) and self-report (Speech, Spatial and Qualities of Hearing Questionnaire (SSQ)) measures of listening under adverse conditions.

Design

The Swedish HINT was masked with a speech-shaped noise (SSN), the Hagerman was masked with a SSN and a four-talker babble, and the subscales from the SSQ were used as a self-report measure. The HINT and Hagerman were administered through an experimental hearing aid.

Study sample

This study included 191 hearing aid users with hearing loss (mean PTA4 = 37.6, SD = 10.8) and 195 normally hearing adults (mean PTA4 = 10.0, SD = 6.0).ResultsThe present study found correlations between behavioural measures of speech-in-noise and self-report scores of the SSQ in normally hearing individuals, but not in hearing aid users.

Conclusion

The present study may help identify relationships between clinically used behavioural measures, and a self-report measure of speech recognition. The results from the present study suggest that use of a self-report measure as a complement to behavioural speech in noise tests might help to further our understanding of how self-report, and behavioural results can be generalised to everyday functioning.

Objectives: Previous research suggests that there is a robust relationship between cognitive functioning and speech-in-noise performance for older adults with age-related hearing loss. For normal-hearing adults, on the other hand, the research is not entirely clear. Therefore, the current study aimed to examine the relationship between cognitive functioning, aging, and speech-in-noise, in a group of older normal-hearing persons and older persons with hearing loss who wear hearing aids.

Design: We analyzed data from 199 older normal-hearing individuals (mean age = 61.2) and 200 older individuals with hearing loss (mean age = 60.9) using multigroup structural equation modeling. Four cognitively related tasks were used to create a cognitive functioning construct: the reading span task, a visuospatial working memory task, the semantic word-pairs task, and Raven’s progressive matrices. Speech-in-noise, on the other hand, was measured using Hagerman sentences. The Hagerman sentences were presented via an experimental hearing aid to both normal hearing and hearing-impaired groups. Furthermore, the sentences were presented with one of the two background noise conditions: the Hagerman original speech-shaped noise or four-talker babble. Each noise condition was also presented with three different hearing processing settings: linear processing, fast compression, and noise reduction.

Results: Cognitive functioning was significantly related to speech-in-noise identification. Moreover, aging had a significant effect on both speech-in-noise and cognitive functioning. With regression weights constrained to be equal for the two groups, the final model had the best fit to the data. Importantly, the results showed that the relationship between cognitive functioning and speech-in-noise was not different for the two groups. Furthermore, the same pattern was evident for aging: the effects of aging on cognitive functioning and aging on speech-in-noise were not different between groups.

Conclusion: Our findings revealed similar cognitive functioning and aging effects on speech-in-noise performance in older normal-hearing and aided hearing-impaired listeners. In conclusion, the findings support the Ease of Language Understanding model as cognitive processes play a critical role in speech-in-noise independent from the hearing status of elderly individuals.

To conceptualize the communicative role of working memory (WM), the Ease-of-Language Understanding (ELU) model was proposed (e.g., Rönnberg, 2003; Rönnberg et al., 2008, 2013, 2019, 2020). The model states that ease of language understanding is determined by the speed and accuracy with which the signal is matched to existing multimodal language representations. When matching is fast and complete, language understanding is effortless; this process may be facilitated by predictions based on the contents of WM. However, when the contents of the language signal mismatches with existing representations, WM is triggered to access knowledge in semantic long-term memory (SLTM) and personal experience from episodic long-term memory (ELTM) – promoting inference-making and postdictions in WM. The interplay between WM and LTM is fundamental to language understanding; its efficiency becomes apparent in adverse conditions and its breakdown may explain cognitive decline and dementia. Empirical support, limitations, and future studies will be discussed.

Purpose

The purpose of this study was to conceptualize the subtle balancing act between language input and prediction (cognitive priming of future input) to achieve understanding of communicated content. When understanding fails, reconstructive postdiction is initiated. Three memory systems play important roles: working memory (WM), episodic long-term memory (ELTM), and semantic long-term memory (SLTM). The axiom of the Ease of Language Understanding (ELU) model is that explicit WM resources are invoked by a mismatch between language input—in the form of rapid automatic multimodal binding of phonology—and multimodal phonological and lexical representations in SLTM. However, if there is a match between rapid automatic multimodal binding of phonology output and SLTM/ELTM representations, language processing continues rapidly and implicitly.

Method and Results

In our first ELU approach, we focused on experimental manipulations of signal processing in hearing aids and background noise to cause a mismatch with LTM representations; both resulted in increased dependence on WM. Our second—and main approach relevant for this review article—focuses on the relative effects of age-related hearing loss on the three memory systems. According to the ELU, WM is predicted to be frequently occupied with reconstruction of what was actually heard, resulting in a relative disuse of phonological/lexical representations in the ELTM and SLTM systems. The prediction and results do not depend on test modality per se but rather on the particular memory system. This will be further discussed.

Conclusions

Related to the literature on ELTM decline as precursors of dementia and the fact that the risk for Alzheimer's disease increases substantially over time due to hearing loss, there is a possibility that lowered ELTM due to hearing loss and disuse may be part of the causal chain linking hearing loss and dementia. Future ELU research will focus on this possibility.