Effects of attention on the speech reception threshold and pupil response of people with impaired and normal hearing

Auditory Challenges and Listening Effort in School-Age Children With Autism: Insights From Pupillary Dynamics During Speech-in-Noise Perception

PURPOSE

This study aimed to investigate challenges in speech-in-noise (SiN) processing faced by school-age children with autism spectrum conditions (ASCs) and their impact on listening effort.

METHOD

Participants, including 23 Mandarin-speaking children with ASCs and 19 age-matched neurotypical (NT) peers, underwent sentence recognition tests in both quiet and noisy conditions, with a speech-shaped steady-state noise masker presented at 0-dB signal-to-noise ratio in the noisy condition. Recognition accuracy rates and task-evoked pupil responses were compared to assess behavioral performance and listening effort during auditory tasks.

RESULTS

No main effect of group was found on accuracy rates. Instead, significant effects emerged for autistic trait scores, listening conditions, and their interaction, indicating that higher trait scores were associated with poorer performance in noise. Pupillometric data revealed significantly larger and earlier peak dilations, along with more varied pupillary dynamics in the ASC group relative to the NT group, especially under noisy conditions. Importantly, the ASC group's peak dilation in quiet mirrored that of the NT group in noise. However, the ASC group consistently exhibited reduced mean dilations than the NT group.

CONCLUSIONS

Pupillary responses suggest a different resource allocation pattern in ASCs: An initial sharper and larger dilation may signal an intense, narrowed resource allocation, likely linked to heightened arousal, engagement, and cognitive load, whereas a subsequent faster tail-off may indicate a greater decrease in resource availability and engagement, or a quicker release of arousal and cognitive load. The presence of noise further accentuates this pattern. This highlights the unique SiN processing challenges children with ASCs may face, underscoring the importance of a nuanced, individual-centric approach for interventions and support.

Changes in Listening Effort Through Pupil Response After Atresioplasty in Children With Congenital Aural Atresia

OBJECTIVES

This study aimed to determine whether the improvement of hearing by surgical treatment alleviates cognitive demands through pupil response in patients with unilateral congenital aural atresia (CAA).

DESIGN

A prospective study was performed on patients with unilateral CAA who were scheduled to undergo primary atresioplasty between November 2017 and May 2020. Pure-tone audiometry, auditory digit span test, Korean Speech Perception in Noise test, pupil measurement during speech tests, and questionnaires (Sound-Spatial-Qualities of Hearing Scale; subjective listening effort rating) were performed before and 6 months after surgery.

RESULTS

Of 30 consecutive patients who initially enrolled, only 18 patients (12 males and 6 females) were included in the analysis. When the improvement of the air-bone gap and interaural difference of air conduction within 30 dB was defined as a successful hearing outcome, successful hearing improvement was achieved in 50% of the 18 patients. In pupil measurement, the success group had a significantly smaller mean pupil dilation response than the nonsuccess group at 0 and -3 dB signal to noise ratio (SNR) (all p < 0.01). In addition, significant differences were identified between the two groups for peak dilation and peak latency at all noise levels (all p < 0.01). When analyzing the change in pupil response before and after surgery, the difference in relative mean pupil dilation in the success group was significantly greater than that in the nonsuccess group at -3 dB SNR ( p = 0.02). In addition, the success group showed a significantly greater change in peak latency than the nonsuccess group at the -3 dB SNR ( p < 0.01). The difference in peak dilation tended to be greater in the success group than in the nonsuccess group, but the difference was not statistically significant.

CONCLUSIONS

Patients with unilateral CAA who achieved surgically improved hearing had a smaller pupil dilation response than those who did not. These results suggest that successful hearing outcomes after surgery in patients with unilateral CAA may reduce the cognitive effort required to understand speech under difficult listening conditions.

Phonological discrimination and contrast detection in pupillometry

Introduction

The perception of phonemes is guided by both low-level acoustic cues and high-level linguistic context. However, differentiating between these two types of processing can be challenging. In this study, we explore the utility of pupillometry as a tool to investigate both low- and high-level processing of phonological stimuli, with a particular focus on its ability to capture novelty detection and cognitive processing during speech perception.

Methods

Pupillometric traces were recorded from a sample of 22 Danish-speaking adults, with self-reported normal hearing, while performing two phonological-contrast perception tasks: a nonword discrimination task, which included minimal-pair combinations specific to the Danish language, and a nonword detection task involving the detection of phonologically modified words within sentences. The study explored the perception of contrasts in both unprocessed speech and degraded speech input, processed with a vocoder.

Results

No difference in peak pupil dilation was observed when the contrast occurred between two isolated nonwords in the nonword discrimination task. For unprocessed speech, higher peak pupil dilations were measured when phonologically modified words were detected within a sentence compared to sentences without the nonwords. For vocoded speech, higher peak pupil dilation was observed for sentence stimuli, but not for the isolated nonwords, although performance decreased similarly for both tasks.

Conclusion

Our findings demonstrate the complexity of pupil dynamics in the presence of acoustic and phonological manipulation. Pupil responses seemed to reflect higher-level cognitive and lexical processing related to phonological perception rather than low-level perception of acoustic cues. However, the incorporation of multiple talkers in the stimuli, coupled with the relatively low task complexity, may have affected the pupil dilation.

The Influence of Hearing Loss on the Pupil Response to Degraded Speech

PURPOSE

Current evidence regarding the influence of hearing loss on the pupil response elicited by speech perception is inconsistent. This might be partially due to confounding effects of age. This study aimed to compare pupil responses in age-matched groups of normal-hearing (NH) and hard of hearing (HH) listeners during listening to speech.

METHOD

We tested the baseline pupil size and mean and peak pupil dilation response of 17 NH participants (Mage = 46 years; age range: 20-62 years) and 17 HH participants (Mage = 45 years; age range: 20-63 years) who were pairwise matched on age and educational level. Participants performed three speech perception tasks at a 50% intelligibility level: noise-vocoded speech and speech masked with either stationary noise or interfering speech. They also listened to speech presented in quiet.

RESULTS

Hearing loss was associated with poorer speech perception, except for noise-vocoded speech. In contrast to NH participants, performance of HH participants did not improve across trials for the interfering speech condition, and it decreased for speech in stationary noise. HH participants had a smaller mean pupil dilation in degraded speech conditions compared to NH participants, but not for speech in quiet. They also had a steeper decline in the baseline pupil size across trials. The baseline pupil size was smaller for noise-vocoded speech as compared to the other conditions. The normalized data showed an additional group effect on the baseline pupil response.

CONCLUSIONS

Hearing loss is associated with a smaller pupil response and steeper decline in baseline pupil size during the perception of degraded speech. This suggests difficulties of the HH participants to sustain their effort investment and performance across the test session.

Quantifying the Effect of Noise on Cognitive Processes: A Review of Psychophysiological Correlates of Workload

Noise is present in most work environments, including emissions from machines and devices, irrelevant speech from colleagues, and traffic noise. Although it is generally accepted that noise below the permissible exposure limits does not pose a considerable risk for auditory effects like hearing impairments. Yet, noise can have a direct adverse effect on cognitive performance (non-auditory effects like workload or stress). Under certain circumstances, the observable performance for a task carried out in silence compared to noisy surroundings may not differ. One possible explanation for this phenomenon needs further investigation: individuals may invest additional cognitive resources to overcome the distraction from irrelevant auditory stimulation. Recent developments in measurements of psychophysiological correlates and analysis methods of load-related parameters can shed light on this complex interaction. These objective measurements complement subjective self-report of perceived effort by quantifying unnoticed noise-related cognitive workload. In this review, literature databases were searched for peer-reviewed journal articles that deal with an at least partially irrelevant "auditory stimulation" during an ongoing "cognitive task" that is accompanied by "psychophysiological correlates" to quantify the "momentary workload." The spectrum of assessed types of "auditory stimulations" extended from speech stimuli (varying intelligibility), oddball sounds (repeating short tone sequences), and auditory stressors (white noise, task-irrelevant real-life sounds). The type of "auditory stimulation" was related (speech stimuli) or unrelated (oddball, auditory stressor) to the type of primary "cognitive task." The types of "cognitive tasks" include speech-related tasks, fundamental psychological assessment tasks, and real-world/simulated tasks. The "psychophysiological correlates" include pupillometry and eye-tracking, recordings of brain activity (hemodynamic, potentials), cardiovascular markers, skin conductance, endocrinological markers, and behavioral markers. The prevention of negative effects on health by unexpected stressful soundscapes during mental work starts with the continuous estimation of cognitive workload triggered by auditory noise. This review gives a comprehensive overview of methods that were tested for their sensitivity as markers of workload in various auditory settings during cognitive processing.

Cognitive and Physiological Measures of Listening Effort During Degraded Speech Perception: Relating Dual-Task and Pupillometry Paradigms

Purpose Listening effort is quickly becoming an important metric for assessing speech perception in less-than-ideal situations. However, the relationship between the construct of listening effort and the measures used to assess it remains unclear. We compared two measures of listening effort: a cognitive dual task and a physiological pupillometry task. We sought to investigate the relationship between these measures of effort and whether engaging effort impacts speech accuracy. Method In Experiment 1, 30 participants completed a dual task and a pupillometry task that were carefully matched in stimuli and design. The dual task consisted of a spoken word recognition task and a visual match-to-sample task. In the pupillometry task, pupil size was monitored while participants completed a spoken word recognition task. Both tasks presented words at three levels of listening difficulty (unmodified, eight-channel vocoding, and four-channel vocoding) and provided response feedback on every trial. We refined the pupillometry task in Experiment 2 (n = 31); crucially, participants no longer received response feedback. Finally, we ran a new group of subjects on both tasks in Experiment 3 (n = 30). Results In Experiment 1, accuracy in the visual task decreased with increased signal degradation in the dual task, but pupil size was sensitive to accuracy and not vocoding condition. After removing feedback in Experiment 2, changes in pupil size were predicted by listening condition, suggesting the task was now sensitive to engaged effort. Both tasks were sensitive to listening difficulty in Experiment 3, but there was no relationship between the tasks and neither task predicted speech accuracy. Conclusions Consistent with previous work, we found little evidence for a relationship between different measures of listening effort. We also found no evidence that effort predicts speech accuracy, suggesting that engaging more effort does not lead to improved speech recognition. Cognitive and physiological measures of listening effort are likely sensitive to different aspects of the construct of listening effort. Supplemental Material https://doi.org/10.23641/asha.16455900.

Listening Effort Is Not the Same as Speech Intelligibility Score

Listening effort is a valuable and important notion to measure because it is among the primary complaints of people with hearing loss. It is tempting and intuitive to accept speech intelligibility scores as a proxy for listening effort, but this link is likely oversimplified and lacks actionable explanatory power. This study was conducted to explain the mechanisms of listening effort that are not captured by intelligibility scores, using sentence-repetition tasks where specific kinds of mistakes were prospectively planned or analyzed retrospectively. Effort measured as changes in pupil size among 20 listeners with normal hearing and 19 listeners with cochlear implants. Experiment 1 demonstrates that mental correction of misperceived words increases effort even when responses are correct. Experiment 2 shows that for incorrect responses, listening effort is not a function of the proportion of words correct but is rather driven by the types of errors, position of errors within a sentence, and the need to resolve ambiguity, reflecting how easily the listener can make sense of a perception. A simple taxonomy of error types is provided that is both intuitive and consistent with data from these two experiments. The diversity of errors in these experiments implies that speech perception tasks can be designed prospectively to elicit the mistakes that are more closely linked with effort. Although mental corrective action and number of mistakes can scale together in many experiments, it is possible to dissociate them to advance toward a more explanatory (rather than correlational) account of listening effort.

Linguistic Complexity of Speech Recognition Test Sentences and Its Influence on Children's Verbal Repetition Accuracy

OBJECTIVES

Speech recognition (SR)-tests have been developed for children without considering the linguistic complexity of the sentences used. However, linguistic complexity is hypothesized to influence correct sentence repetition. The aim of this study is to identify lexical and grammatical parameters influencing verbal repetition accuracy of sentences derived from a Dutch SR-test when performed by 6-year-old typically developing children.

DESIGN

For this observational, cross-sectional study, 40 typically developing children aged 6 were recruited at four primary schools in the Netherlands. All children performed a sentence repetition task derived from an SR-test for adults. The sentence complexity was described beforehand with one lexical parameter, age of acquisition, and four grammatical parameters, specifically sentence length, prepositions, sentence structure, and verb inflection. A multiple logistic regression analysis was performed.

RESULTS

Sentences with a higher age of acquisition (odds ratio [OR] = 1.59) or greater sentence length (OR = 1.28) had a higher risk of repetition inaccuracy. Sentences including a spatial (OR = 1.25) or other preposition (OR = 1.25) were at increased risk for incorrect repetition, as were complex sentences (OR = 1.69) and sentences in the present perfect (OR = 1.44) or future tense (OR = 2.32).

CONCLUSIONS

The variation in verbal repetition accuracy in 6-year-old children is significantly influenced by both lexical and grammatical parameters. Linguistic complexity is an important factor to take into account when assessing speech intelligibility in children.

Pupil Dilation Is Sensitive to Semantic Ambiguity and Acoustic Degradation

Speech comprehension is challenged by background noise, acoustic interference, and linguistic factors, such as the presence of words with more than one meaning (homonyms and homophones). Previous work suggests that homophony in spoken language increases cognitive demand. Here, we measured pupil dilation—a physiological index of cognitive demand—while listeners heard high-ambiguity sentences, containing words with more than one meaning, or well-matched low-ambiguity sentences without ambiguous words. This semantic-ambiguity manipulation was crossed with an acoustic manipulation in two experiments. In Experiment 1, sentences were masked with 30-talker babble at 0 and +6 dB signal-to-noise ratio (SNR), and in Experiment 2, sentences were heard with or without a pink noise masker at –2 dB SNR. Speech comprehension was measured by asking listeners to judge the semantic relatedness of a visual probe word to the previous sentence. In both experiments, comprehension was lower for high- than for low-ambiguity sentences when SNRs were low. Pupils dilated more when sentences included ambiguous words, even when no noise was added (Experiment 2). Pupil also dilated more when SNRs were low. The effect of masking was larger than the effect of ambiguity for performance and pupil responses. This work demonstrates that the presence of homophones, a condition that is ubiquitous in natural language, increases cognitive demand and reduces intelligibility of speech heard with a noisy background.

Pupillometry Reveals That Context Benefit in Speech Perception Can Be Disrupted by Later-Occurring Sounds, Especially in Listeners With Cochlear Implants

Contextual cues can be used to improve speech recognition, especially for people with hearing impairment. However, previous work has suggested that when the auditory signal is degraded, context might be used more slowly than when the signal is clear. This potentially puts the hearing-impaired listener in a dilemma of continuing to process the last sentence when the next sentence has already begun. This study measured the time course of the benefit of context using pupillary responses to high- and low-context sentences that were followed by silence or various auditory distractors (babble noise, ignored digits, or attended digits). Participants were listeners with cochlear implants or normal hearing using a 12-channel noise vocoder. Context-related differences in pupil dilation were greater for normal hearing than for cochlear implant listeners, even when scaled for differences in pupil reactivity. The benefit of context was systematically reduced for both groups by the presence of the later-occurring sounds, including virtually complete negation when sentences were followed by another attended utterance. These results challenge how we interpret the benefit of context in experiments that present just one utterance at a time. If a listener uses context to “repair” part of a sentence, and later-occurring auditory stimuli interfere with that repair process, the benefit of context might not survive outside the idealized laboratory or clinical environment. Elevated listening effort in hearing-impaired listeners might therefore result not just from poor auditory encoding but also inefficient use of context and prolonged processing of misperceived utterances competing with perception of incoming speech.

The Pupil Dilation Response to Auditory Stimuli: Current State of Knowledge

The measurement of cognitive resource allocation during listening, or listening effort, provides valuable insight in the factors influencing auditory processing. In recent years, many studies inside and outside the field of hearing science have measured the pupil response evoked by auditory stimuli. The aim of the current review was to provide an exhaustive overview of these studies. The 146 studies included in this review originated from multiple domains, including hearing science and linguistics, but the review also covers research into motivation, memory, and emotion. The present review provides a unique overview of these studies and is organized according to the components of the Framework for Understanding Effortful Listening. A summary table presents the sample characteristics, an outline of the study design, stimuli, the pupil parameters analyzed, and the main findings of each study. The results indicate that the pupil response is sensitive to various task manipulations as well as interindividual differences. Many of the findings have been replicated. Frequent interactions between the independent factors affecting the pupil response have been reported, which indicates complex processes underlying cognitive resource allocation. This complexity should be taken into account in future studies that should focus more on interindividual differences, also including older participants. This review facilitates the careful design of new studies by indicating the factors that should be controlled for. In conclusion, measuring the pupil dilation response to auditory stimuli has been demonstrated to be sensitive method applicable to numerous research questions. The sensitivity of the measure calls for carefully designed stimuli.

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.