Despite the evidence of a positive relationship between task demands and listening effort, the Framework for Understanding Effortful Listening (FUEL) highlights the important role of arousal on an individuals choice to engage in challenging listening tasks. Previous studies have interpreted physiological responses in conjunction with behavioral responses as markers of task engagement. The aim of the current study was to investigate the effect of potential changes in physiological arousal, indexed by the pupil baseline, on task engagement over the course of an auditory recall test. Furthermore, the aim was to investigate whether working memory (WM) capacity and the signal-to-noise ratio (SNR) at which the test was conducted had an effect on changes in arousal. Twenty-one adult hearing aid users with mild to moderately severe symmetrical sensorineural hearing loss were included. The pupil baseline was measured during the Sentence-final Word Identification and Recall (SWIR) test, which was administered in a background noise composed of sixteen talkers. The Reading Span (RS) test was used as a measure of WM capacity. The findings showed that the pupil baseline decreased over the course of the SWIR test. However, recall performance remained stable, indicating that the participants maintained the necessary engagement level required to perform the task. These findings were interpreted as a decline in arousal as a result of task habituation. There was no effect of WM capacity or individual SNR level on the change in pupil baseline over time. A significant interaction was found between WM capacity and SNR level on the overall mean pupil baseline. Individuals with higher WM capacity exhibited an overall larger mean pupil baseline at low SNR levels compared to individuals with poorer WM capacity. This may be related to the ability of individuals with higher WM capacity to perform better than individual with poorer WM capacity in challenging listening conditions.

The aim of the current study was to investigate whether task-evoked pupillary responses measured during encoding, individual working memory capacity and noise reduction in hearing aids were associated with the likelihood of subsequently recalling an item in an auditory free recall test combined with pupillometry. Participants with mild to moderately severe symmetrical sensorineural hearing loss (n = 21) were included. The Sentence-final Word Identification and Recall (SWIR) test was administered in a background noise composed of sixteen talkers with noise reduction in hearing aids activated and deactivated. The task-evoked peak pupil dilation (PPD) was measured. The Reading Span (RS) test was used as a measure of individual working memory capacity. Larger PPD at a single trial level was significantly associated with higher likelihood of subsequently recalling a word, presumably reflecting the intensity of attention devoted during encoding. There was no clear evidence of a significant relationship between working memory capacity and subsequent memory recall, which may be attributed to the SWIR test and RS test being administered in different modalities, as well as differences in task characteristics. Noise reduction did not have a significant effect on subsequent memory recall. This may be due to the background noise not having a detrimental effect on attentional processing at the favorable signal-to-noise ratio levels at which the test was conducted.

This thesis investigates how hearing aid users allocate working (WM) memory resources under various task demands when listening to and storing speech in memory for later recall. This was done by combining an auditory recall task, the Sentence-final Word Identification and Recall (SWIR) test, with pupillometry. Different pupillary responses were used to obtain insights into momentary WM resource allocation and overall WM resource allocation over time. The task demands were manipulated by varying hearing aid noise reduction settings, as well as by varying the task difficulty of the SWIR test and the task difficulty predictability.

The findings from the first two studies showed that recall performance in competing speech was better, and baseline pupillary responses were higher when noise reduction was activated compared to when it was not. This indicates that attenuating background noise frees up WM resources to be used for storing speech in memory rather than speech processing. While unpredictable task difficulty elicited higher baseline pupillary responses than predictable task difficulty, it did not have any effect on recall performance. This finding suggests that task difficulty predictability does not affect WM resource allocation. Instead, unpredictable task difficulty may lead to increased alertness in anticipation of the end of the SWIR test list. The findings of the third study showed that increased transient task-evoked pupillary responses, which reflect the momentary intensity of attention during encoding, were associated with a higher likelihood of subsequent recall. Moreover, higher WM capacity was also linked to higher likelihood of subsequent recall, presumably due to the ability to allocate more attentional resources during encoding. Lastly, the findings from the fourth study indicated that the combination of the SWIR test and pupillometry is suitable for capturing WM resource allocation. Although arousal decreased over time, recall performance remained stable, suggesting that participants did not reach the point of disengagement.

Overall, a novel learning from this thesis is that increased pupillary responses may be a marker of “successful effort” when additional WM resources are allocated to achieve a better recall performance in the SWIR test. Furthermore, this thesis gives insights into which factors affect WM resource allocation and how to reduce the amount of processing resources required to understand speech, which may contribute to optimizing auditory rehabilitation in the future.

Objectives: Communication requires cognitive processes which are not captured by traditional speech understanding tests. Under challenging listening situations, more working memory resources are needed to process speech, leaving fewer resources available for storage. The aim of the current study was to investigate the effect of task difficulty predictability, that is, knowing versus not knowing task difficulty in advance, and the effect of noise reduction on working memory resource allocation to processing and storage of speech heard in background noise. For this purpose, an "offline" behavioral measure, the Sentence-Final Word Identification and Recall (SWIR) test, and an "online" physiological measure, pupillometry, were combined. Moreover, the outcomes of the two measures were compared to investigate whether they reflect the same processes related to resource allocation. Design: Twenty-four experienced hearing aid users with moderate to moderately severe hearing loss participated in this study. The SWIR test and pupillometry were measured simultaneously with noise reduction in the test hearing aids activated and deactivated in a background noise composed of four-talker babble. The task of the SWIR test is to listen to lists of sentences, repeat the last word immediately after each sentence and recall the repeated words when the list is finished. The sentence baseline dilation, which is defined as the mean pupil dilation before each sentence, and task-evoked peak pupil dilation (PPD) were analyzed over the course of the lists. The task difficulty predictability was manipulated by including lists of three, five, and seven sentences. The test was conducted over two sessions, one during which the participants were informed about list length before each list (predictable task difficulty) and one during which they were not (unpredictable task difficulty). Results: The sentence baseline dilation was higher when task difficulty was unpredictable compared to predictable, except at the start of the list, where there was no difference. The PPD tended to be higher at the beginning of the list, this pattern being more prominent when task difficulty was unpredictable. Recall performance was better and sentence baseline dilation was higher when noise reduction was on, especially toward the end of longer lists. There was no effect of noise reduction on PPD. Conclusions: Task difficulty predictability did not have an effect on resource allocation, since recall performance was similar independently of whether task difficulty was predictable or unpredictable. The higher sentence baseline dilation when task difficulty was unpredictable likely reflected a difference in the recall strategy or higher degree of task engagement/alertness or arousal. Hence, pupillometry captured processes which the SWIR test does not capture. Noise reduction frees up resources to be used for storage of speech, which was reflected in the better recall performance and larger sentence baseline dilation toward the end of the list when noise reduction was on. Thus, both measures captured different temporal aspects of the same processes related to resource allocation with noise reduction on and off.

Objective:In the present study, we investigated whether varying the task difficulty of the Sentence-Final Word Identification and Recall (SWIR) Test has an effect on the benefit of noise reduction, as well as whether task difficulty predictability affects recall. The relationship between working memory and recall was examined. Design:Task difficulty was manipulated by varying the list length with noise reduction on and off in competing speech and speech-shaped noise. Half of the participants were informed about list length in advance. Working memory capacity was measured using the Reading Span. Study sample:Thirty-two experienced hearing aid users with moderate sensorineural hearing loss. Results:Task difficulty did not affect the noise reduction benefit and task difficulty predictability did not affect recall. Participants may have employed a different recall strategy when task difficulty was unpredictable and noise reduction off. Reading Span scores positively correlated with the SWIR test. Noise reduction improved recall in competing speech. Conclusions:The SWIR test with varying list length is suitable for detecting the benefit of noise reduction. The correlation with working memory suggests that the SWIR test could be modified to be adaptive to individual cognitive capacity. The results on noise and noise reduction replicate previous findings.