Working memory is important for online language processing during conversation. We use it to maintain relevant information, to inhibit or ignore irrelevant information, and to attend to conversation selectively. Working memory helps us to keep track of and actively participate in conversation, including taking turns and following the gist. This paper examines the Ease of Language Understanding model (i.e., the ELU model, Rönnberg, 2003; Rönnberg et al., 2008) in light of new behavioral and neural findings concerning the role of working memory capacity (WMC) in uni-modal and bimodal language processing. The new ELU model is a meaning prediction system that depends on phonological and semantic interactions in rapid implicit and slower explicit processing mechanisms that both depend on WMC albeit in different ways. It is based on findings that address the relationship between WMC and (a) early attention processes in listening to speech, (b) signal processing in hearing aids and its effects on short-term memory, (c) inhibition of speech maskers and its effect on episodic long-term memory, (d) the effects of hearing impairment on episodic and semantic long-term memory, and finally, (e) listening effort. New predictions and clinical implications are outlined. Comparisons with other WMC and speech perception models are made.

In response to recommendations to redefine statistical significance to p ≤ .005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.

The credibility of scientific claims depends upon the transparency of the research products upon which they are based (e.g., study protocols, data, materials, and analysis scripts). As psychology navigates a period of unprecedented introspection, user-friendly tools and services that support open science have flourished. However, the plethora of decisions and choices involved can be bewildering. Here we provide a practical guide to help researchers navigate the process of preparing and sharing the products of their research (e.g., choosing a repository, preparing their research products for sharing, structuring folders, etc.). Being an open scientist means adopting a few straightforward research management practices, which lead to less error prone, reproducible research workflows. Further, this adoption can be piecemealeach incremental step towards complete transparency adds positive value. Transparent research practices not only improve the efficiency of individual researchers, they enhance the credibility of the knowledge generated by the scientific community.

The overall relationships between hearing loss and memory systems were predicted by the ease of language understanding model (J. Rönnberg, 2003), but the exact mechanisms of episodic memory decline in hearing aid users (i.e., mismatch/disuse, attentional resources, or information degradation) remain open for further experiments. The hearing aid industry should strive to design signal processing algorithms that are cognition friendly.

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Objective: The aims of the current n200 study were to assess the structural relations between three classes of test variables (i.e. HEARING, COGNITION and aided speech-in-noise OUTCOMES) and to describe the theoretical implications of these relations for the Ease of Language Understanding (ELU) model. Study sample: Participants were 200 hard-of-hearing hearing-aid users, with a mean age of 60.8 years. Forty-three percent were females and the mean hearing threshold in the better ear was 37.4 dB HL. Design: LEVEL1 factor analyses extracted one factor per test and/or cognitive function based on a priori conceptualizations. The more abstract LEVEL 2 factor analyses were performed separately for the three classes of test variables. Results: The HEARING test variables resulted in two LEVEL 2 factors, which we labelled SENSITIVITY and TEMPORAL FINE STRUCTURE; the COGNITIVE variables in one COGNITION factor only, and OUTCOMES in two factors, NO CONTEXT and CONTEXT. COGNITION predicted the NO CONTEXT factor to a stronger extent than the CONTEXT outcome factor. TEMPORAL FINE STRUCTURE and SENSITIVITY were associated with COGNITION and all three contributed significantly and independently to especially the NO CONTEXT outcome scores (R2 = 0.40). Conclusions: All LEVEL 2 factors are important theoretically as well as for clinical assessment.

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The credibility of scientific claims depends upon the transparency of the research products upon which they are based (e.g., study protocols, data, materials, and analysis scripts). As psychology navigates a period of unprecedented introspection, user-friendly tools and services that support open science have flourished. There has never been a better time to embrace transparent research practices. However, the plethora of decisions and choices involved can be bewildering. Here we provide a practical guide to help researchers navigate the process of preparing and sharing the products of their research. Being an open scientist means adopting a few straightforward research management practices, which lead to less error prone, reproducible research workflows. Further, this adoption can be piecemeal – each incremental step towards complete transparency adds positive value. Transparent research practices not only improve the efficiency of individual researchers, they enhance the credibility of the knowledge generated by the scientific community.