Measures of speech understanding are vital to the characterization of hearing loss and the evaluation of treatment outcome, but they do not capture the cognitive load inherent in spoken communication nor the effort needed to attain a given level of performance in everyday listening conditions. A variety of measures have been proposed in an attempt to fill these gaps in assessment, including cognitive measures, self-report measures, and physiological measures. In this article, we adopt the definitions of cognitive load and effort presented in the consensus paper from the Eriksholm Workshop on Hearing Impairment and Cognitive Energy (Pichora-Fuller et al. 2016, this issue, pp. 5S–27S). Cognitive load is defined as “the extent to which the demands imposed by the task at a given moment consume the resources available to maintain successful task execution” (pp. 11S–14S); effort is defined as the “deliberate allocation of resources to overcome obstacles in goal pursuit when carrying out a task” (pp. 11S–14S).
Over the past decade, numerous studies have established the feasibility of incorporating cognitive measures into spoken language processing tasks (e.g., McCoy et al. 2005; Akeroyd 2008; Rönnberg et al. 2008; Stewart & Wingfield 2009; Ng et al. 2013; McGarrigle et al. 2014) (see also Humes & Young 2016, this issue, pp. 52S–61S; Lunner et al. 2016, this issue, pp. 145S–154S; Rudner 2016, this issue, pp. 69S–76S; Sommer & Phelps 2016, this issue, pp. 62S–68S). These behavioral cognitive measures, which include measures of working memory span, processing speed, and comprehension accuracy, have sometimes been used to make inferences about the amount of listening effort exerted during task completion.
Physiological measures can also be used to index changes in cognitive and/or emotional states. An increase in cognitive load, or the introduction of emotional stressors, can induce a complex series of neural, endocrine, and immune responses. These responses include changes in the patterns and amount of brain activity (e.g., alpha EEG activity, late positive potential, P300 event-related potentials e.g., Howells et al. 2010; Bertoli & Bodmer 2014; Petersen et al. 2015; Wisniewski et al. 2015) and changes in the autonomic nervous system. Specific changes in the autonomic nervous system induced by cognitive and emotional challenges include arousal of the sympathetic nervous system (i.e., the fight or flight response) and a decrease in parasympathetic nervous system activity, which is responsible for rest and recovery. Autonomic nervous activity can be assessed using cardiovascular measures (e.g., cardiac and blood pressure measures), electrodermal activity (e.g., skin conductance), and pupillometry (Kramer et al. 1997, 2013, 2016, this issue, pp. 126S–135S; Zekveld et al. 2010, 2011).
This article focuses on measures of electrodermal activity and heart rate variability (HRV). We describe results from the existing literature and report new findings from a study designed to examine the effects of talker speaking rate on skin conductance and HRV. Finally, we discuss two factors, stress and sound-tolerance problems, which may influence cognitive load during auditory tasks and contribute to psychophysiological reactivity.
For the purposes of this article, the term “task demand” refers to “the cognitive and perceptual resources needed to complete a task” as defined in the Eriksholm Workshop Consensus paper (Pichora-Fuller et al. 2016, this issue, pp. 5S–27S). We assume that an increase in task demand leads to an increase in effort. We also note, however, that effort may be modulated by other factors such as success importance and extreme task difficulty (see Richter 2016, this issue, pp. 111S–117S). As discussed by Richter, if a task is too difficult, listeners may disengage from the task to conserve energy. Therefore, the relationship between task demand and effort may break down if a task becomes too difficult or if success importance is low.
HRV and Skin Conductance
Heart Rate Variability
HRV is the natural fluctuation in interbeat intervals that occurs over time. Figure 1 (bottom panel) illustrates heart rate fluctuations at rest over a 10-min period, during which heart rate variations between 76 and 121 beats per minute were recorded. This variability is considered to be a positive health marker and is thought to be an index of adaptability to environmental challenges (Thayer et al. 2009, 2012). Reduced HRV at rest has been linked to negative health outcomes, including cardiovascular disease and all-cause mortality (Tsuji et al. 1994; Villareal et al. 2002).
The time intervals between the successive R peaks of the electrocardiographic (ECG) recording (“R-R” or “interbeat” intervals) are analyzed using either time-domain or spectral analyses. Figure 1 (top panel) shows three cycles of an ECG recording illustrating the R peaks (large positive peaks in each cycle) and the R-R intervals. Generally, time-domain analyses evaluate the variability of interbeat intervals (e.g., standard deviation). Most time-domain measures reflect a combination of sympathetic and parasympathetic activity; however, the root mean of the squared successive differences (RMSSD) is believed to reflect primarily parasympathetic activity (Friedman et al. 2002). RMSSD is highly correlated with the spectral measure of parasympathetic activity (described later). The standard deviation of the normal R-R interval (SDNN) time-domain measure reflects the total HRV power, and therefore cannot be used to quantify independent contributions from the sympathetic and parasympathetic systems. Although typically used for long-term recordings (i.e., 24 hr), SDNN is sometimes used for short-term recordings; however, because SDNN increases with the recording duration, durations of short-term recordings must be standardized across conditions (Malik 1996). Spectral analyses of the time-varying waveform compute the power present in discrete frequency bands. The high-frequency band (HF-HRV) (0.15 to 0.40 Hz) reflects parasympathetic nervous system activity, whereas the low-frequency band (LF-HRV) (0.04 to 0.149 Hz) reflects both parasympathetic and sympathetic activity (Malik 1996).
A number of studies have examined HRV as a dependent measure. An increase in task difficulty often results in a decrease in HRV measured using parasympathetic components (HF-HRV and RMSSD), and the 0.1-Hz component (typically corresponding to the LF-HRV band). This pattern has been demonstrated for a variety of cognitive tasks involving attention (Wood et al. 2002; Duschek et al. 2009), response inhibition (Byrd et al. 2015), and memory (Aasman et al. 1987; Redondo & Del Valle-Inclán 1992; Hansen et al. 2003; Fairclough & Roberts 2011; Mukherjee et al. 2011; Byrd et al. 2015).
Resting (no task) parasympathetic activity indexed by HRV (i.e., RMSSD and HF-HRV) has also been used in a number of studies as an independent variable. Using this approach, individual differences in parasympathetic indexes of HRV have been linked to both executive function (Thayer et al. 2009) and emotion regulation (Appelhans & Luecken 2006). For example, Hansen et al. (2003) measured performance of adults with low and high resting HRV (RMSSD) using both executive and nonexecutive tasks. Those with low resting HRV had lower performance on executive tasks (poorer accuracy and longer reaction times) than those with high HRV. There were no significant differences between the groups for nonexecutive tasks, however. Persons with low resting parasympathetic activity, as indexed by RMSSD, have also been shown to have higher levels of the stress hormone cortisol during and immediately after a series of cognitive tasks (Johnsen et al. 2012), suggesting that persons with low resting parasympathetic activity may have an increased vulnerability to acute stress.
Electrodermal measures, such as skin conductance, are mediated by the sympathetic nervous system. Skin conductance is measured with surface electrodes attached to the palm or fingers and reflects moisture on the surface of the skin. During challenging situations, sympathetic nervous system arousal leads to an increase in skin conductance.
Task-induced increases in skin conductance have been reported for a variety of cognitive tasks, involving mental arithmetic, memory, vigilance, visual tracking, and Stroop interference (Aslan et al. 1981; Reimer & Mehler 2011; Hartley et al. 2012; Mehler et al. 2012; Reinhardt et al. 2012; Choi et al. 2013; Kindermann & Werner 2014). Some investigators, however, have not found significant task-related changes in skin conductance (Chang & Huang 2012; Benikos et al. 2013).
Reactivity During Auditory Tasks
Few studies have evaluated HRV or electrodermal activity during auditory tasks and generally, such studies were not aimed at determining the effects of task difficulty (O’Gorman & Lloyd 1988; Wilson & Sasse 2001; Aue et al. 2011). Recently, an accelerating interest in objective and potentially portable measures of listening effort within the hearing-science and audiology communities has led to increased research in this area (Mackersie & Cones 2011; Dorman et al. 2012; Francis et al. 2013; Seeman & Sims 2015).
One approach used by auditory researchers is to determine if changes in speech understanding scores with increasing task difficulty are accompanied by changes in psychophysiological activity. Recent studies using normal-hearing listeners tested under different signal to noise ratios (SNRs) showed that a reduction in sentence repetition accuracy scores, resulting from a decrease in SNR, was accompanied by a decrease in a time-domain HRV measure (SDNN) that reflects both sympathetic and parasympathetic nervous system activity (Dorman et al. 2012; Seeman & Sims 2015). Seeman and Sims reported significant changes in SDNN for SNRs separated by 10 dB or more, corresponding to mean changes in sentence repetition scores of 19 to 78 percentage points. In the same study, they replicated the findings of Mackersie et al. (2015), showing that skin conductance was not sensitive to changes in SNR. Based on these findings, HRV appears to be a better measure than skin conductance for examining the effect of changes in SNR on autonomic nervous system reactivity during sentence repetition tasks.
A second approach is to determine if psychophysiological measures are more sensitive than are word repetition accuracy measures by testing under conditions in which performance has been equalized. This approach was used in two studies in which a dichotic digits test was administered to normal-hearing listeners (Mackersie & Cones 2011; Seeman & Sims 2015). In these studies, there were three levels of task demand: low (one digit presented to one ear), medium (one dichotic pair of digits presented simultaneously to both ears), and high (two dichotic pairs of different digits presented in sequence to both ears). Mackersie and Cones reported that although digit repetition was similar for three levels of task demand, skin conductance and electromyographic activity increased significantly with higher task demand, consistent with an increase in listening effort when task demand was higher. Later, Seeman and Sims replicated the earlier skin conductance findings of Mackersie’s group and reported that the SDNN time-domain HRV measure was also sensitive to changes in task demand under equal performance conditions.
There is consistent evidence from behavioral studies that spoken language communication may require more cognitive processing resources for persons with hearing loss than for persons with normal hearing, even when speech can be clearly understood (Rabbitt 1991; McCoy et al. 2005; Tun et al. 2009; Rudner & Lunner 2014). Recently, skin conductance and HF-HRV of listeners with and without hearing loss were evaluated to determine if hearing-loss effects were also evident in skin conductance and HF-HRV activity recorded during listening tasks (Mackersie et al. 2015). An adaptive procedure was used to determine, for each listener, the SNR threshold corresponding to sentence repetition accuracy scores of approximately 80%. Psychometric functions were then obtained across a range of SNRs referenced to listeners’ adaptive thresholds and resulted in similar performance functions for the two groups. Despite similar sentence repetition scores for the two groups, skin conductance noise reactivity of those with hearing loss (relative to quiet) was significantly higher than reactivity of those with normal hearing across the range of SNRs. Also, the HF-HRV of participants with hearing loss was significantly lower than that of those with normal hearing, but only at the two lowest SNRs. Although there were significant differences in skin conductance reactivity between the two groups, there were no significant changes across SNR. The group differences in skin conductance were not present in the quiet or baseline conditions, implicating reactivity to noise as a factor. The finding that persons with hearing loss show greater changes in sympathetic arousal and parasympathetic withdrawal during sentence repetition tasks complements the behavioral studies, which showed that people who are hard of hearing have slower processing time and poorer memory for spoken language even when speech understanding is similar to that of persons with normal hearing (Rabbitt 1991; McCoy et al. 2005; Tun et al. 2010).
The studies reviewed earlier suggest that a physiological stress response occurs with increased auditory task demand under some listening conditions. Moreover, physiological reactivity is generally greater for listeners with hearing loss. Based on studies conducted in noise, it appears that HRV measures, including both SDNN (sympathetic + parasympathetic) and HF-HRV (parasympathetic), are more sensitive to variations in SNR than are skin conductance measures. The effects of other forms of acoustic degradation relevant to people who are hard of hearing have not yet been reported, however. In the next section, we present results from a new study examining the effects of talker speaking rate, another factor known to negatively impact speech understanding.
Effect of Talker Speaking Rate on HRV and Skin Conductance
The goal of the study was to determine the effects of speaking rate on skin conductance and HF-HRV under conditions in which word repetition accuracy scores were equated.
Participants were 26 young adults (9 males and 17 females) ranging in age from 20 to 35 years (mean: 25.3). Participants had pure-tone thresholds of 20 dB HL or lower for octave frequencies between 250 and 8000 Hz.
Materials and Instrumentation
Narrative materials recorded by a male talker from the AudioCASPER program (Boothroyd 2010) were time-compressed by 25% using the pitch-synchronous-overlap-add module from PRAAT software (Boersma & Weenink 1996). AudioCASPER was originally designed as self-paced auditory training program but has been adapted for use in word repetition testing. The narrative used in this study was presented in short (5 to 10 word) segments (e.g., “I took a deep breath.”; “‘Everyone will be happy to see you’, she said.”). The presentation was paused after each segment to allow listeners to repeat the words they heard in each segment. The percentage of words repeated correctly was scored for 100 to 104 word sections of the narrative; each section stopped at the end of a sentence.
ECG and skin conductance recordings were obtained with the Nexus-10 system. ECG activity was recorded from three silver-silver chloride (Ag/AgCl) electrodes attached to the chest. Skin conductance was recorded using two Ag/AgCl electrodes attached to the palmar surface of one hand. The sampling rates were 1000 and 32 Hz for the ECG and skin conductance recordings, respectively. Skin conductance recordings were visually inspected for artifacts, and segments containing artifacts were excluded from the record. Mean skin conductance levels were calculated for each condition using the BioTrace software. Kubios HRV analysis software (v2.1) was used to process and analyze the ECG data (Tarvainen et al. 2014). Interbeat intervals were extracted from the ECG recordings and were visually inspected for artifacts and premature beats. Segments containing artifacts were excluded, and missing interbeat intervals were interpolated from adjacent interval values. Power in the high-frequency band (HF-HRV: 0.15 to 0.4 Hz) was calculated using fast Fourier analysis. Respiration was recorded at a sampling rate of 32 Hz using a respiration belt worn at the level of the diaphragm. Respiration rate was extracted using the BioTrace software and was used as a covariate in the HF-HRV analysis to evaluate the possible influence of respiration rate on HF-HRV.
Speech was presented at 65 dB SPL in the presence of four-talker babble and was delivered to a loudspeaker via the speech channels of an audiometer. Performance was controlled by determining the SNR needed to equalize mean word repetition accuracy (to approximately 80%) for each condition. The SNR corresponding to 80% was estimated from performance functions obtained for SNRs ranging from −3 to +6 dB. On average, a 3-dB increase of SNR for the fast rate was needed to match performance to that obtained for the normal rate. Repetition accuracy of the words in the narrative was then measured at the fast and normal rates under these equal performance conditions (+3 dB SNR fast; 0 dB SNR normal) while monitoring skin conductance and ECG activity. Each speaking rate was tested twice (total of 200 to 208 words) in alternating order; the starting condition (fast versus slow) was counterbalanced across the participants (e.g., fast [+3 dB SNR] test 1, normal [0 dB SNR] test 1, Break, fast [+3 dB SNR] test 2, normal [0 dB SNR] test 2). Psychophysiological activity was recorded during a 10-min baseline period before beginning the word repetition task and during each condition. Two-minute recovery periods separated each word repetition test.
Mean recognition scores were 79.0% and 80.7% for the fast and the normal rate sentences, respectively; there was no significant difference between these scores (t(25) = 0.86, p = 0.40, Es = 0.17). HF-HRV was log-transformed to correct the positive skew in the data. Figure 2 shows the mean skin conductance and log-transformed HF-HRV power for the baseline, fast speaking rate condition, and normal speaking rate condition. Relative to the baseline, skin conductance increased and HF-HRV decreased during the word repetition test. Skin conductance reactivity (i.e., the increase in skin conductance relative to baseline) was greater for the fast speaking rate (7.96 μS, SD = 6.23) than for the normal speaking rate (6.39, SD = 6.01; (t(25) = 2.92, p = 0.008, Es = 0.60). Also, HF-HRV reactivity (i.e., the reduction in HF-HRV relative to baseline) was significantly greater for the fast rate (−0.73, SD = 0.80) than for the normal rate (−0.36, SD = 0.85; t(25) = 3.30, p = 0.003, Es = 0.63). The HF-HRV analysis was repeated using respiration rate as a covariate to evaluate its influence on HF-HRV. The analysis of covariance confirmed that the differences between HF-HRV reactivity for the fast and slow speaking rates remained significant after controlling for the effect of respiration rate (F(1,23) = 9.18, p = 0.006, ηp2 = 0.29). These findings, indicating greater sympathetic nervous system arousal and parasympathetic withdrawal when the speaking rate was fast, suggest that greater effort was needed to sustain performance in the fast rate condition.
The findings noted above occurred in the context of equal performance of approximately 80%. Therefore, it is unlikely that listeners disengaged during the normal rate condition as might be expected if one task was too difficult. The more favorable SNR at the fast rate that was needed to equalize performance, however, may have led to a less sympathetic nervous system arousal and parasympathetic withdrawal than would have been observed if the SNRs for the fast and slow rates had been equal.
It is important to note that although skin conductance and HF-HRV are known to reflect sympathetic and parasympathetic activity, respectively, they assess autonomic activity at different organs. Given that sympathetic outflow to organs is not uniform (Esler et al. 1985), it cannot be assumed that sympathetic activity measured using skin conductance indicates changes in sympathetic myocardial activity. This fact does not diminish the value of using skin conductance as an indicator of sympathetic nervous system arousal; however, the relative changes in skin conductance and HF-HRV in response to experimental manipulations cannot be interpreted as an indication of cardiac autonomic balance.
The results from the studies described earlier suggest that both skin conductance and HRV measures are sensitive to task demand; however, the relative sensitivity of the measures appears to vary with the type of task manipulations and the populations tested. For manipulations involving changes in SNR, both time-domain HRV measures and HF-HRV appear to be more sensitive than skin conductance. Relative to quiet, however, skin conductance reactivity to even low levels of noise is substantial among listeners with hearing loss, but does not change as the SNR (and repetition scores) decreases. In contrast, the sensitivity of skin conductance and HF-HRV are similar for changes in speaking rate. It is possible that skin conductance is more sensitive to tasks that required faster processing speed (e.g., fast speaking rate) or place greater demand on selective attention (e.g., dichotic task). More work is needed to clarify the factors that influence the relative sensitivity of these measures.
Caveats: Effort, Stress, Motivation?
The speech understanding studies just described provide clear evidence that increasing task demand is associated with psychophysiological changes even when there is no change in performance. It is tempting to conclude that these psychophysiological changes can provide direct indices of the listening effort required to sustain performance. It is possible, however, that they also reflect other factors, such as modulation of motivation, or stress in response to such things as the test environment, the nature of the task, self-perceived performance failure, and some properties of the sound stimuli. Although laboratory speech recognition tasks are less likely to elicit stress than are real-life interactions, participants do report moderate levels of stress during speech recognition tasks, as indexed by the NASA-TLX stress/frustration subscale (Mackersie & Cones 2011; Bologna et al. 2013; Mackersie et al. 2015). Older adults may be particularly vulnerable to unintended laboratory-induced stress. For example, Sindi et al. (2013) examined cortisol levels during a memory task under laboratory conditions designed to favor either younger adults or older adults. The conditions that favored younger adults involved younger research assistants, an unfamiliar testing location, and afternoon sessions. When laboratory conditions favored young adults, the cortisol levels of the older adults were higher than cortisol levels obtained at home. In contrast, when laboratory conditions favored older adults, the cortisol levels of older adults were similar to those obtained at home. These findings suggest that physiological reactivity during listening tasks may reflect the combined effects of effort and stress, even in a laboratory setting.
In the real world, stress involving negative emotional responses can occur when hearing difficulties result in communication breakdown during a social interaction. A substantial number of persons with hearing loss fear others’ reactions to their hearing loss (Erdman & Demorest 1998; Kricos et al. 2007) and many find themselves in listening environments that they are unable to control. Social evaluative threat (fear of negative evaluation by others) and a low sense of control are two of the most potent stressors (Dickerson & Kemeny 2004).
Sound aversion, defined as a negative emotional reaction to sound (Pichora-Fuller et al. 2016, this issue, pp. 5S–27S), is another possible contributor to stress (see Rylander 2004, for review). Sound aversion in response to noise or other sound may occur as a result of loudness discomfort (e.g., loudness recruitment), annoying sound quality, negative associations, distraction, or communication interference. Sound can even interfere with performance on nonauditory tasks (Wright et al. 2014). As reviewed by Wright et al. (2014), the effects of noise are greater for more complex tasks. In addition, individuals who are noise sensitive (based on self-report) are more vulnerable to negative effects of noise on information processing than are those with lower noise sensitivity (Pawlaczyk-Luszczyńiska et al. 2005; Sandrock et al. 2009).
There is evidence that persons who are hard of hearing are more disturbed by sounds in the environment than those with normal hearing. New hearing aid users are especially vulnerable to sound-tolerance problems, referring to amplified sound as intrusive, distracting, and overwhelming, according to a recent qualitative study (Dawes et al. 2014). Although these initial reactions improve with time, even experienced hearing aid users report greater disturbance by real-life sounds (day care and traffic noise) than do normal-hearing listeners (Hua et al. 2014). Greater disturbance by sound may also result in more physiological stress. A recent report documented higher levels of salivary cortisol in persons with hearing loss than in those with normal hearing when exposed to moderate-level noise (60 dBA Leq) while completing nonauditory cognitive tasks (Jahncke & Halin 2012), although cortisol levels were similar for the two groups in a low-noise environment (30 dBA Leq). Also, participants with hearing loss had poorer performance on reading comprehension/recall and (nonauditory) word memory tasks than did their normal-hearing counterparts when tested in the presence of moderate-level noise.
To the extent that coping with stress requires cognitive resources, it may leave fewer resources for the effort required to process stimuli that are diminished by background sound, hearing loss, or both. Stress has been viewed as a form of internal “noise” that competes for resources needed for cognitive operations (Mandler 1979). There is evidence that both acute and chronic stress impair working memory and aspects of executive function that are relevant to spoken language processing (Lupien et al. 2007; Qin et al. 2009; Schoofs et al. 2009; Sindi et al. 2013). Within the Framework for Understanding Effortful Listening “FUEL” (Pichora-Fuller et al. 2016, this issue, pp. 5S–27S), listener stress resulting from sound aversion or social evaluative threat may influence his/her evaluation of demands on capacity, allocation policy, and the attention-related responses (see Pichora-Fuller 2016, this issue, pp. 5S–27S, Figure 1b). Thus, it is possible that a stress-related decrease in the availability of cognitive resources can increase the effort needed to maintain successful communication interactions. This increased effort may, in turn, lead to additional stress that is further amplified.
The few studies that have evaluated the effects of auditory task difficulty on skin conductance and HF-HRV reactivity suggest that these measures could be useful in the evaluation of effort and/or stress. Although skin conductance appears to be sensitive to variations in task demand when speech understanding is high, it appears to be less sensitive than HF-HRV to variations in task demand when listening conditions become more challenging (Mackersie et al. 2015). Further work involving participants who are hard of hearing is needed to evaluate the relative sensitivity of these and other measures (e.g., pupillometry, pre-ejection period, alpha EEG, behavioral measures) under a wide variety of conditions and tasks and to examine factors underlying individual differences in reactivity. The time course of changes after the fitting of hearing amplification and other rehabilitative interventions would also be of interest to determine if physiological stress responses decrease over time with hearing aid use.
An advantage of laboratory studies is the ability to closely control the acoustic characteristics of the stimuli and the test environment. There is a need, however, to supplement audiological assessments by incorporating more realistic communication tasks and social contexts that mimic real-life communication. It may be challenging, however, to reproduce in a laboratory the social evaluative threat and pressures to maintain communication competency inherent in real-life interactions. Nevertheless, with the growing availability of portable skin conductance and HRV monitors, ambulatory recording of psychophysiological reactivity during real-life interactions is possible, but this method of assessment has inherent challenges (Wilhelm et al. 2006). Ambulatory recording may be supplemented with subjective assessment of communication-related effort and stress in the field using ecological momentary assessments that can be easily deployed using a mobile device, such as a smart phone (Galvez et al. 2012). From a clinical standpoint, information about changes in the physiological stress responses after intervention may help guide decisions regarding further audiological management. For example, individuals with persistently high physiological stress indicators after audiological intervention may benefit from other forms of interventions (e.g., self-efficacy and/or stress-reduction training) to attenuate these responses.
Most psychophysiological studies emphasize the physiological reactivity in response to task manipulation. Information about physiological recovery and identification of prolonged activation of the stress responses are considered by some to be a critical, but often overlooked aspect of the stress response (Linden et al. 1997; Brosschot et al. 2005, 2006). Prolonged stress activation may contribute to the higher prevalence of self-reported chronic stress and burnout among persons with hearing loss (Kramer et al. 2006; Hasson et al. 2011). Recovery data from laboratory task- or stress-induced physiological changes may, therefore, provide information about resilience to cognitive and emotional stress and fatigue that would provide rehabilitative audiologists with new insights into the impact of hearing loss on their patients.
In summary, there is clear evidence of sympathetic nervous system arousal and parasympathetic withdrawal in response to increased listening task demand, even when performance is unchanged. Much work needs to be done, however, to understand how psychophysiological responses recorded during listening tasks are influenced by the acoustic characteristics of stimuli, the task demand, motivation, and by the characteristics and emotional responses of the individual.
The authors have no conflicts of interest to disclose.
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