Most hearing aid fittings today are almost solely based on the patient’s audiogram. Although the loss of gain in the cochlea is important, for a more optimal fitting, more individual parameters of the patient’s cochlear loss together with the patient's cognitive abilities to process the auditory signal are required (Stenfelt & Rönnberg, 2009; Edwards, 2007). Moreover, the evaluation of the fitting is often based on a speech in noise task and the aim is to improve the individual patient’s signal to noise ratio (SNR) thresholds. As a consequence, hearing aid fitting may be seen as a process aimed to improve the patient’s SNR threshold rather than to improve communication ability. However, subsequent to a hearing aid fitting, there can be great differences in SNR improvement between patients that have identical hearing impairment in terms of threshold data (the audiogram). The reasons are certainly complex but one contributing factor may be the individual differences in cognitive capacity and associated listening effort. Another way to think about amplified hearing is to ease a subject’s listening effort (Sarampalis, et al., 2009). When the speech signal is degraded by noise or by a hearing impairment, more high-order cognitive or top-down processes are required to perceive and understand the signal, and listening is therefore more effortful. It is assumed that a hearing aid would ease the listening effort for a hearing impaired person. However, it is not clear how to measure the listening effort. We here present a test that will tap into the different cognitive aspects of listening effort, the Auditory Inference Span Test (AIST). The AIST is a dual task hearing in noise test, that combines auditory and memory processing and is well suited as a clinical test for listening effort
Listening in noise is often perceived to be effortful. This is partly because cognitive resources are engaged in separating the target signal from background noise, leaving fewer resources for storage and processing of the content of the message in working memory. The Auditory Inference Span Test (AIST) is designed to assess listening effort by measuring the ability to maintain and process heard information. The aim of this study was to use AIST to investigate the effect of background noise types and signal-to-noise ratio (SNR) on listening effort, as a function of working memory capacity (WMC) and updating ability (UA). The AIST was administered in three types of background noise: steady-state speech-shaped noise, amplitude modulated speech-shaped noise, and unintelligible speech. Three SNRs targeting 90% speech intelligibility or better were used in each of the three noise types, giving nine different conditions. The reading span test assessed WMC, while UA was assessed with the letter memory test. Twenty young adults with normal hearing participated in the study. Results showed that AIST performance was not influenced by noise type at the same intelligibility level, but became worse with worse SNR when background noise was speech-like. Performance on AIST also decreased with increasing memory load level. Correlations between AIST performance and the cognitive measurements suggested that WMC is of more importance for listening when SNRs are worse, while UA is of more importance for listening in easier SNRs. The results indicated that in young adults with normal hearing, the effort involved in listening in noise at high intelligibility levels is independent of the noise type. However, when noise is speech-like and intelligibility decreases, listening effort increases, probably due to extra demands on cognitive resources added by the informational masking created by the speech fragments and vocal sounds in the background noise.
This paper is an introduction to the emotional qualities of sound and music, and we suggest that the visual and the aural modalities should be combined in the design of visualizations involving emotional expressions. We therefore propose that visualization design should incorporate sonic interaction design drawing on musicology, cognitive neuroscience of music, and psychology of music, and identify what we see as key research challenges for such an approach.
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