The effect of auditory training on listening effort in hearing-aid users: insights from a pupillometry study.

OBJECTIVE: The study investigated how auditory training affects effort exerted by hearing-impaired listeners in speech-in-noise task. DESIGN: Pupillometry was used to characterise listening effort during a hearing in noise test (HINT) before and after phoneme-in-noise identification training. Half of the study participants completed the training, while the other half formed an active control group. STUDY SAMPLE: Twenty 63-to-79 years old experienced hearing-aid users. RESULTS: Higher peak pupil dilations (PPDs) were obtained at the end of the study compared to the beginning in both groups of the participants. The analysis of pupil dilation in an extended time window revealed, however, that the magnitude of pupillary response increased more in the training than in the control group. The effect of training on effort was observed in pupil responses even when no improvement in HINT was found. CONCLUSION: The results demonstrate that using a listening effort metric adds additional insights into the effectiveness of auditory training compared to the situation when only speech-in-noise performance is considered. Trends observed in pupil responses suggested increased effort-both after the training and the placebo intervention-most likely reflecting the effect of the individual's motivation.

Predicting EEG Responses to Attended Speech via Deep Neural Networks for Speech.

Attending to the speech stream of interest in multi-talker environments can be a challenging task, particularly for listeners with hearing impairment. Research suggests that neural responses assessed with electroencephalography (EEG) are modulated by listener's auditory attention, revealing selective neural tracking (NT) of the attended speech. NT methods mostly rely on hand-engineered acoustic and linguistic speech features to predict the neural response. Only recently, deep neural network (DNN) models without specific linguistic information have been used to extract speech features for NT, demonstrating that speech features in hierarchical DNN layers can predict neural responses throughout the auditory pathway. In this study, we go one step further to investigate the suitability of similar DNN models for speech to predict neural responses to competing speech observed in EEG. We recorded EEG data using a 64-channel acquisition system from 17 listeners with normal hearing instructed to attend to one of two competing talkers. Our data revealed that EEG responses are significantly better predicted by DNN-extracted speech features than by hand-engineered acoustic features. Furthermore, analysis of hierarchical DNN layers showed that early layers yielded the highest predictions. Moreover, we found a significant increase in auditory attention classification accuracies with the use of DNN-extracted speech features over the use of hand-engineered acoustic features. These findings open a new avenue for development of new NT measures to evaluate and further advance hearing technology.

Previous Mental Load and Incentives Influence Anticipatory Arousal as Indexed by the Baseline Pupil Diameter in a Speech-in-Noise Task.

Listening effort and fatigue are common experiences when conversing in noisy environments. Much research has investigated listening effort in relation to listening demand using the speech-in-noise paradigm. Recent conceptualizations of listening effort postulate that mental fatigue should result in decreased arousal and a reluctance to invest further effort, particularly when the effort is not worthwhile. The aim of the study was to investigate the influence of fatigue on listening effort, in interaction with listening demands and motivation. To induce fatigue 30 adults with normal hearing completed a 40-minute long speech-in-noise task ("load sequence"). Pre- and post-load sequence listening effort was probed in easy and hard listening demands (individually adjusted signal-to-noise ratios); with high and low motivation (manipulated with monetary incentives). Subjective effort, estimated performance, and tendency to quit listening were collected using rating scales. Baseline pupil diameter and mean pupil dilation were recorded as indices of anticipatory arousal and objective effort. Self-reported effort and mean pupil dilation were overall larger during hard SNR as compared to easy SNR. Baseline pupil diameter declined from pre- to post-load sequence, suggesting an overall decrease in arousal. Monetary incentives had no influence on the baseline pupil diameter for the easy SNR condition, but for the hard SNR condition larger incentives led to larger baseline pupil diameter. These results suggest that anticipatory arousal may be influenced by fatigue and motivation effects. Models of listening effort should account for the independent influence of motivation and previous load on anticipatory arousal and effort in distinct parameters.

Daily sound exposure of hearing aids users during COVID-19 pandemic in Europe.

Introduction: This study aimed to investigate the daily sound exposure of hearing aid (HA) users during the COVID-19 pandemic, with a specific focus on the impact of different governance intervention levels. Methods: Modern HA technology was employed to measure and compare the sound exposure of HA users in three distinct periods: pre-pandemic, and two 14-day periods during the pandemic, corresponding to varying levels of governance interventions. The study sample comprised a total of 386 HA users in Europe during the pandemic, with daily sound exposure data collected as part of the main dataset. Results: The results revealed that, during the pandemic, the equivalent continuous sound pressure level (SPL) experienced by HA users decreased, while the signal-to-noise ratio (SNR) increased compared to the pre-pandemic period. Notably, this impact was found to be more pronounced (p < 0.05) when individuals were subjected to stronger governance intervention levels, characterized by lower SPL and higher SNR. Discussion: This study highlights the changes in daily sound exposure experienced by HA users during the COVID-19 pandemic, particularly influenced by the extent of governance interventions that restricted social activities. These findings emphasize the importance of considering the effects of pandemic-related governance measures on the sound environments of HA users and have implications for audiological interventions and support strategies during similar crises.

The Effects of Noise and Simulated Conductive Hearing Loss on Physiological Response Measures During Interactive Conversations.

PURPOSE: The purpose of this work was to study the effects of background noise and hearing attenuation associated with earplugs on three physiological measures, assumed to be markers of effort investment and arousal, during interactive communication. METHOD: Twelve pairs of older people (average age of 63.2 years) with age-adjusted normal hearing took part in a face-to-face communication to solve a Diapix task. Communication was held in different levels of babble noise (0, 60, and 70 dBA) and with two levels of hearing attenuation (0 and 25 dB) in quiet. The physiological measures obtained included pupil size, heart rate variability, and skin conductance. In addition, subjective ratings of perceived communication success, frustration, and effort were obtained. RESULTS: Ratings of perceived success, frustration, and effort confirmed that communication was more difficult in noise and with approximately 25-dB hearing attenuation and suggested that the implemented levels of noise and hearing attenuation resulted in comparable communication difficulties. Background noise at 70 dBA and hearing attenuation both led to an initial increase in pupil size (associated with effort), but only the effect of the background noise was sustained throughout the conversation. The 25-dB hearing attenuation led to a significant decrease of the high-frequency power of heart rate variability and a significant increase of skin conductance level, measured as the average z value of the electrodermal activity amplitude. CONCLUSION: This study demonstrated that several physiological measures appear to be viable indicators of changing communication conditions, with pupillometry and cardiovascular as well as electrodermal measures potentially being markers of communication difficulty.

Combining Multiple Psychophysiological Measures of Listening Effort: Challenges and Recommendations.

About one-third of all recently published studies on listening effort have used at least one physiological measure, providing evidence of the popularity of such measures in listening effort research. However, the specific measures employed, as well as the rationales used to justify their inclusion, vary greatly between studies, leading to a literature that is fragmented and difficult to integrate. A unified approach that assesses multiple psychophysiological measures justified by a single rationale would be preferable because it would advance our understanding of listening effort. However, such an approach comes with a number of challenges, including the need to develop a clear definition of listening effort that links to specific physiological measures, customized equipment that enables the simultaneous assessment of multiple measures, awareness of problems caused by the different timescales on which the measures operate, and statistical approaches that minimize the risk of type-I error inflation. This article discusses in detail the various obstacles for combining multiple physiological measures in listening effort research and provides recommendations on how to overcome them.

Time-specific Components of Pupil Responses Reveal Alternations in Effort Allocation Caused by Memory Task Demands During Speech Identification in Noise.

Daily communication may be effortful due to poor acoustic quality. In addition, memory demands can induce effort, especially for long or complex sentences. In the current study, we tested the impact of memory task demands and speech-to-noise ratio on the time-specific components of effort allocation during speech identification in noise. Thirty normally hearing adults (15 females, mean age 42.2 years) participated. In an established auditory memory test, listeners had to listen to a list of seven sentences in noise, and repeat the sentence-final word after presentation, and, if instructed, recall the repeated words. We tested the effects of speech-to-noise ratio (SNR; -4 dB, +1 dB) and recall (Recall; Yes, No), on the time-specific components of pupil responses, trial baseline pupil size, and their dynamics (change) along the list. We found three components in the pupil responses (early, middle, and late). While the additional memory task (recall versus no recall) lowered all components' values, SNR (-4 dB versus +1 dB SNR) increased the middle and late component values. Increasing memory demands (Recall) progressively increased trial baseline and steepened decrease of the late component's values. Trial baseline increased most steeply in the condition of +1 dB SNR with recall. The findings suggest that adding a recall to the auditory task alters effort allocation for listening. Listeners are dynamically re-allocating effort from listening to memorizing under changing memory and acoustic demands. The pupil baseline and the time-specific components of pupil responses provide a comprehensive picture of the interplay of SNR and recall on effort.

Baseline pupil size encodes task-related information and modulates the task-evoked response in a speech-in-noise task.

Pupillometry data are commonly reported relative to a baseline value recorded in a controlled pre-task condition. In this study, the influence of the experimental design and the preparatory processing related to task difficulty on the baseline pupil size was investigated during a speech intelligibility in noise paradigm. Furthermore, the relationship between the baseline pupil size and the temporal dynamics of the pupil response was assessed. The analysis revealed strong effects of block presentation order, within-block sentence order and task difficulty on the baseline values. An interaction between signal-to-noise ratio and block order was found, indicating that baseline values reflect listener expectations arising from the order in which the different blocks were presented. Furthermore, the baseline pupil size was found to affect the slope, delay and curvature of the pupillary response as well as the peak pupil dilation. This suggests that baseline correction might be sufficient when reporting pupillometry results in terms of mean pupil dilation only, but not when a more complex characterization of the temporal dynamics of the response is considered. By clarifying which factors affect baseline pupil size and how baseline values interact with the task-evoked response, the results from the present study can contribute to a better interpretation of the pupillary response as a marker of cognitive processing.

A Glimpse of Memory Through the Eyes: Pupillary Responses Measured During Encoding Reflect the Likelihood of Subsequent Memory Recall in an Auditory Free Recall Test.

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.

Hearing Aid Noise Reduction Lowers the Sustained Listening Effort During Continuous Speech in Noise-A Combined Pupillometry and EEG Study.

OBJECTIVES: The investigation of auditory cognitive processes recently moved from strictly controlled, trial-based paradigms toward the presentation of continuous speech. This also allows the investigation of listening effort on larger time scales (i.e., sustained listening effort). Here, we investigated the modulation of sustained listening effort by a noise reduction algorithm as applied in hearing aids in a listening scenario with noisy continuous speech. The investigated directional noise reduction algorithm mainly suppresses noise from the background. DESIGN: We recorded the pupil size and the EEG in 22 participants with hearing loss who listened to audio news clips in the presence of background multi-talker babble noise. We estimated how noise reduction (off, on) and signal-to-noise ratio (SNR; +3 dB, +8 dB) affect pupil size and the power in the parietal EEG alpha band (i.e., parietal alpha power) as well as the behavioral performance. RESULTS: Our results show that noise reduction reduces pupil size, while there was no significant effect of the SNR. It is important to note that we found interactions of SNR and noise reduction, which suggested that noise reduction reduces pupil size predominantly under the lower SNR. Parietal alpha power showed a similar yet nonsignificant pattern, with increased power under easier conditions. In line with the participants' reports that one of the two presented talkers was more intelligible, we found a reduced pupil size, increased parietal alpha power, and better performance when people listened to the more intelligible talker. CONCLUSIONS: We show that the modulation of sustained listening effort (e.g., by hearing aid noise reduction) as indicated by pupil size and parietal alpha power can be studied under more ecologically valid conditions. Mainly concluded from pupil size, we demonstrate that hearing aid noise reduction lowers sustained listening effort. Our study approximates to real-world listening scenarios and evaluates the benefit of the signal processing as can be found in a modern hearing aid.

The Effects of Task Difficulty Predictability and Noise Reduction on Recall Performance and Pupil Dilation Responses.

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.

Effect of Speech-to-Noise Ratio and Luminance on a Range of Current and Potential Pupil Response Measures to Assess Listening Effort.

In hearing research, pupillometry is an established method of studying listening effort. The focus of this study was to evaluate several pupil measures extracted from the Task-Evoked Pupil Responses (TEPRs) in speech-in-noise test. A range of analysis approaches was applied to extract these pupil measures, namely (a) pupil peak dilation (PPD); (b) mean pupil dilation (MPD); (c) index of pupillary activity; (d) growth curve analysis (GCA); and (e) principal component analysis (PCA). The effect of signal-to-noise ratio (SNR; Data Set A: -20 dB, -10 dB, +5 dB SNR) and luminance (Data Set B: 0.1 cd/m2, 360 cd/m2) on the TEPRs were investigated. Data Sets A and B were recorded during a speech-in-noise test and included TEPRs from 33 and 27 normal-hearing native Dutch speakers, respectively. The main results were as follows: (a) A significant effect of SNR was revealed for all pupil measures extracted in the time domain (PPD, MPD, GCA, PCA); (b) Two time series analysis approaches (GCA, PCA) provided modeled temporal profiles of TEPRs (GCA); and time windows spanning subtasks performed in a speech-in-noise test (PCA); and (c) All pupil measures revealed a significant effect of luminance. In conclusion, multiple pupil measures showed similar effects of SNR, suggesting that effort may be reflected in multiple aspects of TEPR. Moreover, a direct analysis of the pupil time course seems to provide a more holistic view of TEPRs, yet further research is needed to understand and interpret its measures. Further research is also required to find pupil measures less sensitive to changes in luminance.

How Do We Allocate Our Resources When Listening and Memorizing Speech in Noise? A Pupillometry Study.

OBJECTIVES: Actively following a conversation can be demanding and limited cognitive resources must be allocated to the processing of speech, retaining and encoding the perceived content, and preparing an answer. The aim of the present study was to disentangle the allocation of effort into the effort required for listening (listening effort) and the effort required for retention (memory effort) by means of pupil dilation. DESIGN: Twenty-five normal-hearing German speaking participants underwent a sentence final word identification and recall test, while pupillometry was conducted. The participants' task was to listen to a sentence in four-talker babble background noise and to repeat the final word afterward. At the end of a list of sentences, they were asked to recall as many of the final words as possible. Pupil dilation was recorded during different list lengths (three sentences versus six sentences) and varying memory load (recall versus no recall). Additionally, the effect of a noise reduction algorithm on performance, listening effort, and memory effort was evaluated. RESULTS: We analyzed pupil dilation both before each sentence (sentence baseline) as well as the dilation in response to each sentence relative to the sentence baseline (sentence dilation). The pupillometry data indicated a steeper increase of sentence baseline under recall compared to no recall, suggesting higher memory effort due to memory processing. This increase in sentence baseline was most prominent toward the end of the longer lists, that is, during the second half of six sentences. Without a recall task, sentence baseline declined over the course of the list. Noise reduction appeared to have a significant influence on effort allocation for listening, which was reflected in generally decreased sentence dilation. CONCLUSION: Our results showed that recording pupil dilation in a speech identification and recall task provides valuable insights beyond behavioral performance. It is a suitable tool to disentangle the allocation of effort to listening versus memorizing speech.

Neural Representation Enhanced for Speech and Reduced for Background Noise With a Hearing Aid Noise Reduction Scheme During a Selective Attention Task.

OBJECTIVES: Selectively attending to a target talker while ignoring multiple interferers (competing talkers and background noise) is more difficult for hearing-impaired (HI) individuals compared to normal-hearing (NH) listeners. Such tasks also become more difficult as background noise levels increase. To overcome these difficulties, hearing aids (HAs) offer noise reduction (NR) schemes. The objective of this study was to investigate the effect of NR processing (inactive, where the NR feature was switched off, vs. active, where the NR feature was switched on) on the neural representation of speech envelopes across two different background noise levels [+3 dB signal-to-noise ratio (SNR) and +8 dB SNR] by using a stimulus reconstruction (SR) method. DESIGN: To explore how NR processing supports the listeners' selective auditory attention, we recruited 22 HI participants fitted with HAs. To investigate the interplay between NR schemes, background noise, and neural representation of the speech envelopes, we used electroencephalography (EEG). The participants were instructed to listen to a target talker in front while ignoring a competing talker in front in the presence of multi-talker background babble noise. RESULTS: The results show that the neural representation of the attended speech envelope was enhanced by the active NR scheme for both background noise levels. The neural representation of the attended speech envelope at lower (+3 dB) SNR was shifted, approximately by 5 dB, toward the higher (+8 dB) SNR when the NR scheme was turned on. The neural representation of the ignored speech envelope was modulated by the NR scheme and was mostly enhanced in the conditions with more background noise. The neural representation of the background noise was modulated (i.e., reduced) by the NR scheme and was significantly reduced in the conditions with more background noise. The neural representation of the net sum of the ignored acoustic scene (ignored talker and background babble) was not modulated by the NR scheme but was significantly reduced in the conditions with a reduced level of background noise. Taken together, we showed that the active NR scheme enhanced the neural representation of both the attended and the ignored speakers and reduced the neural representation of background noise, while the net sum of the ignored acoustic scene was not enhanced. CONCLUSION: Altogether our results support the hypothesis that the NR schemes in HAs serve to enhance the neural representation of speech and reduce the neural representation of background noise during a selective attention task. We contend that these results provide a neural index that could be useful for assessing the effects of HAs on auditory and cognitive processing in HI populations.

Three New Outcome Measures That Tap Into Cognitive Processes Required for Real-Life Communication.

To increase the ecological validity of outcomes from laboratory evaluations of hearing and hearing devices, it is desirable to introduce more realistic outcome measures in the laboratory. This article presents and discusses three outcome measures that have been designed to go beyond traditional speech-in-noise measures to better reflect realistic everyday challenges. The outcome measures reviewed are: the Sentence-final Word Identification and Recall (SWIR) test that measures working memory performance while listening to speech in noise at ceiling performance; a neural tracking method that produces a quantitative measure of selective speech attention in noise; and pupillometry that measures changes in pupil dilation to assess listening effort while listening to speech in noise. According to evaluation data, the SWIR test provides a sensitive measure in situations where speech perception performance might be unaffected. Similarly, pupil dilation has also shown sensitivity in situations where traditional speech-in-noise measures are insensitive. Changes in working memory capacity and effort mobilization were found at positive signal-to-noise ratios (SNR), that is, at SNRs that might reflect everyday situations. Using stimulus reconstruction, it has been demonstrated that neural tracking is a robust method at determining to what degree a listener is attending to a specific talker in a typical cocktail party situation. Using both established and commercially available noise reduction schemes, data have further shown that all three measures are sensitive to variation in SNR. In summary, the new outcome measures seem suitable for testing hearing and hearing devices under more realistic and demanding everyday conditions than traditional speech-in-noise tests.

An exploratory Study of EEG Alpha Oscillation and Pupil Dilation in Hearing-Aid Users During Effortful listening to Continuous Speech.

Individuals with hearing loss allocate cognitive resources to comprehend noisy speech in everyday life scenarios. Such a scenario could be when they are exposed to ongoing speech and need to sustain their attention for a rather long period of time, which requires listening effort. Two well-established physiological methods that have been found to be sensitive to identify changes in listening effort are pupillometry and electroencephalography (EEG). However, these measurements have been used mainly for momentary, evoked or episodic effort. The aim of this study was to investigate how sustained effort manifests in pupillometry and EEG, using continuous speech with varying signal-to-noise ratio (SNR). Eight hearing-aid users participated in this exploratory study and performed a continuous speech-in-noise task. The speech material consisted of 30-second continuous streams that were presented from loudspeakers to the right and left side of the listener (±30° azimuth) in the presence of 4-talker background noise (+180° azimuth). The participants were instructed to attend either to the right or left speaker and ignore the other in a randomized order with two different SNR conditions: 0 dB and -5 dB (the difference between the target and the competing talker). The effects of SNR on listening effort were explored objectively using pupillometry and EEG. The results showed larger mean pupil dilation and decreased EEG alpha power in the parietal lobe during the more effortful condition. This study demonstrates that both measures are sensitive to changes in SNR during continuous speech.

Effect of Speech Rate on Neural Tracking of Speech.

Speech comprehension requires effort in demanding listening situations. Selective attention may be required for focusing on a specific talker in a multi-talker environment, may enhance effort by requiring additional cognitive resources, and is known to enhance the neural representation of the attended talker in the listener's neural response. The aim of the study was to investigate the relation of listening effort, as quantified by subjective effort ratings and pupil dilation, and neural speech tracking during sentence recognition. Task demands were varied using sentences with varying levels of linguistic complexity and using two different speech rates in a picture-matching paradigm with 20 normal-hearing listeners. The participants' task was to match the acoustically presented sentence with a picture presented before the acoustic stimulus. Afterwards they rated their perceived effort on a categorical effort scale. During each trial, pupil dilation (as an indicator of listening effort) and electroencephalogram (as an indicator of neural speech tracking) were recorded. Neither measure was significantly affected by linguistic complexity. However, speech rate showed a strong influence on subjectively rated effort, pupil dilation, and neural tracking. The neural tracking analysis revealed a shorter latency for faster sentences, which may reflect a neural adaptation to the rate of the input. No relation was found between neural tracking and listening effort, even though both measures were clearly influenced by speech rate. This is probably due to factors that influence both measures differently. Consequently, the amount of listening effort is not clearly represented in the neural tracking.

Benefit of Higher Maximum Force Output on Listening Effort in Bone-Anchored Hearing System Users: A Pupillometry Study.

OBJECTIVES: The aim of this study was to compare listening effort, as estimated via pupillary response, during a speech-in-noise test in bone-anchored hearing system (BAHS) users wearing three different sound processors. The three processors, Ponto Pro (PP), Ponto 3 (P3), and Ponto 3 SuperPower (P3SP), differ in terms of maximum force output (MFO) and MFO algorithm. The hypothesis was that listeners would allocate lower listening effort with the P3SP than with the PP, as a consequence of a higher MFO and, hence, fewer saturation artifacts in the signal. DESIGN: Pupil dilations were recorded in 21 BAHS users with a conductive or mixed hearing loss, during a speech-in-noise test performed at positive signal-to-noise ratios (SNRs), where the speech and noise levels were individually adjusted to lead to 95% correct intelligibility with the PP. The listeners had to listen to a sentence in noise, retain it for 3 seconds and then repeat it, while an eye-tracking camera recorded their pupil dilation. The three sound processors were tested in random order with a single-blinded experimental design. Two conditions were performed at the same SNR: Condition 1, where the speech level was designed to saturate the PP but not the P3SP, and condition 2, where the overall sound level was decreased relative to condition 1 to reduce saturation artifacts. RESULTS: The P3SP led to higher speech intelligibility than the PP in both conditions, while the performance with the P3 did not differ from the performance with the PP and the P3SP. Pupil dilations were analyzed in terms of both peak pupil dilation (PPD) and overall pupil dilation via growth curve analysis (GCA). In condition 1, a significantly lower PPD, indicating a decrease in listening effort, was obtained with the P3SP relative to the PP. The PPD obtained with the P3 did not differ from the PPD obtained with the other two sound processors. In condition 2, no difference in PPD was observed across the three processors. The GCA revealed that the overall pupil dilation was significantly lower, in both conditions, with both the P3SP and the P3 relative to the PP, and, in condition 1, also with the P3SP relative to the P3. CONCLUSIONS: The overall effort to process a moderate to loud speech signal was significantly reduced by using a sound processor with a higher MFO (P3SP and P3), as a consequence of fewer saturation artifacts. These findings suggest that sound processors with a higher MFO may help BAHS users in their everyday listening scenarios, in particular in noisy environments, by improving sound quality and, thus, decreasing the amount of cognitive resources utilized to process incoming speech sounds.

Measuring the Impact of Tinnitus on Aided Listening Effort Using Pupillary Response.

Tinnitus can have serious impact on a person's life and is a common auditory symptom that is especially comorbid with hearing loss. This study investigated processing effort required for speech recognition in a group of hearing-impaired people with tinnitus and a control group (CG) of hearing-impaired people without tinnitus by means of pupillary response. Furthermore, the relationship between the pupillary response, self-rating measures of tinnitus severity, and fatigue was examined. Participants performed a speech-in-noise task with a competing four-talker babble at two speech intelligibility levels (50% and 95%) with either an active or inactive noise-reduction scheme while the pupillary response was recorded. Tinnitus participants showed significantly smaller time-dependent pupil dilations and significantly higher fatigue ratings. No correlation was found for the tinnitus severity and pupillary response, but a significant correlation was found between the tinnitus severity and fatigue. As participants with tinnitus generally reported higher fatigue and showed smaller task-evoked pupil dilations, it was speculated that this may suggest an increased activity of the parasympathetic nervous system, which governs the bodily response during rest. The finding that tinnitus participants showed higher fatigue has clinical implications, highlighting the importance of taking steps to decrease the risk of developing long-term fatigue. Finally, the tinnitus participants showed reduced pupillary responses when noise reduction was activated, suggesting a reduced effort from hearing aid signal processing.

Best Practices and Advice for Using Pupillometry to Measure Listening Effort: An Introduction for Those Who Want to Get Started.

Within the field of hearing science, pupillometry is a widely used method for quantifying listening effort. Its use in research is growing exponentially, and many labs are (considering) applying pupillometry for the first time. Hence, there is a growing need for a methods paper on pupillometry covering topics spanning from experiment logistics and timing to data cleaning and what parameters to analyze. This article contains the basic information and considerations needed to plan, set up, and interpret a pupillometry experiment, as well as commentary about how to interpret the response. Included are practicalities like minimal system requirements for recording a pupil response and specifications for peripheral, equipment, experiment logistics and constraints, and different kinds of data processing. Additional details include participant inclusion and exclusion criteria and some methodological considerations that might not be necessary in other auditory experiments. We discuss what data should be recorded and how to monitor the data quality during recording in order to minimize artifacts. Data processing and analysis are considered as well. Finally, we share insights from the collective experience of the authors and discuss some of the challenges that still lie ahead.