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Journal of Speech, Language, and Hearing Research : JSLHR logoLink to Journal of Speech, Language, and Hearing Research : JSLHR
. 2024 Aug 8;67(9):3217–3231. doi: 10.1044/2024_JSLHR-23-00465

Relations Among Multiple Dimensions of Self-Reported Listening Effort in Response to an Auditory Psychomotor Vigilance Task

Edward J Golob a,✉, Ricardo C Olayo a, Denver M Y Brown a, Jeffrey R Mock a
PMCID: PMC11427424  PMID: 39116317

Abstract

Purpose:

Listening effort is a broad construct, and there is no consensus on how to subdivide listening effort into dimensions. This project focuses on the subjective experience of effortful listening and tests if cognitive workload, mental fatigue, and mood are interrelated dimensions.

Method:

Two online studies tested young adults (n = 74 and n = 195) and measured subjective workload, fatigue (subscales of fatigue and energy), and mood (subscales of positive and negative mood) before and after a challenging listening task. In the listening effort task, participants responded to intermittent 1-kHz target tones in continuous white noise for approximately 12 min.

Results:

Correlations and principal component analysis showed that fatigue and mood were distinct but interrelated constructs that weakly correlated with workload. Effortful listening provoked increased fatigue and decreased energy and positive mood yet did not influence negative mood or workload.

Conclusions:

The findings suggest that self-reported listening effort has multiple dimensions that can have different responses to the same effortful listening episode. The results can help guide evidence-based development of clinical listening effort tests and may reveal mechanisms for how listening effort relates to quality of life in those with hearing impairment.

Supplemental Material:

https://doi.org/10.23641/asha.26418976

Humans have been accused of being “cognitive misers” because mental effort is often an aversive experience to be avoided (Taylor, 1981). The puzzle of understanding the functional significance of mental effort, and why it is often aversive, has occupied students in many disciplines such as psychology (Kool & Botvinick, 2014; Kurzban, 2016; Shenhav et al., 2017), Philosophy (Dewey, 1897), neuroscience (Sayalı & Badre, 2019), biology (Charnov, 1974), and business (Fishbach & Woolley, 2022). Listening effort is a type of mental effort that occurs under challenging listening conditions (Peelle, 2018; Pichora-Fuller et al., 2016). Some examples from everyday life include trying to have a conversation in a noisy restaurant, understanding the speech of someone with an unfamiliar accent or who is a “fast talker,” and hearing out individual parts of an orchestral piece. Having to apply excessive listening effort is a major problem for those with hearing impairment (Alhanbali et al., 2017; Arlinger et al., 2009; Hughes & Galvin, 2013; McGarrigle et al., 2021). Easing the burden of excessive listening effort would likely improve their quality of life, and is also a goal for improving hearing aid and cochlear implant usability (Alhanbali et al., 2017).

Many in the field recognize that the construct of listening effort is broad and needs to be characterized as having a multidimensional nature (Alhanbali et al., 2019; Francis & Love, 2020; McGarrigle et al., 2014; Moore & Picou, 2018; Peelle, 2018; Pichora-Fuller et al., 2016; Strand et al., 2020; Strauss & Francis, 2017). One complication is that listening effort studies use different categories of operational variables, including self-reports, behavioral performance, and biological measures. Self-reports typically use validated questionnaires, such as the NASA-Task Load Index (NASA-TLX; Hart & Staveland, 1988) or Speech, Spatial and Qualities of Hearing Scale (Gatehouse & Noble, 2004), that are given after effortful listening to probe the listener's memory of the listening experience. Behavioral measures focus on cognitive aspects of listening effort, such as how listening demand affects memory (McCoy et al., 2005; Rabbitt, 1968; Surprenant, 1999) or dual-task performance (Gagné et al., 2017; Tun et al., 2009). Psychophysiological methods have been used to identify biological correlates of listening effort and include electroencephalogram (EEG), functional magnetic resonance imaging, pupillometry, heart rate, skin conductance, and endocrine measures (Pichora-Fuller et al., 2016). Dissociations among self-report, behavioral, and physiological measures have been noted (Gosselin & Gagné, 2011; Larsby et al., 2005; Pichora-Fuller et al., 2016; Seeman & Sims, 2015; Zekveld et al., 2010), and further illustrate the complexities of identifying basic dimensions of listening effort.

According to one framework, there are three broad factors that relate to listening effort: the acoustic signal of interest (often speech), the environment, and the listener and individual differences among listeners (Peelle, 2018). Adjusting the level of a target signal is a simple way to influence effort (consider how much more effortful threshold testing is relative to detecting sounds at a comfortable volume). Time compressed speech, manipulating frequency differences for discrimination, unfamiliar accents, rapid stimulus rates, and complex syntax are other properties related to the acoustic signal that affect listening effort (Ayasse & Wingfield, 2018; Murphy et al., 2017; Yurgil & Golob, 2013). The second factor of environmental influences can be tested by presenting other sounds that compete with the signal of interest, as when listening for speech in noise (Killion et al., 2004; Smits et al., 2013). The third factor of individual differences is evident in a variety of ways, such as different levels of subjective effort on a given task even when performance is comparable. People with hearing impairment typically experience greater listening effort relative to controls (Ayasse & Wingfield, 2020; Krueger et al., 2017). Cognitive abilities also matter given their interplay with hearing (Arlinger et al., 2009; Rönnberg et al., 2008), such as observations that greater working memory capacity is associated with less effortful listening, particularly in older people and those with hearing impairment (Francis et al., 2021; Ohlenforst et al., 2017; Tun et al., 2009; Zekveld et al., 2010). Motivation is a fundamental variable for understanding individual differences in the initiation and persistence of listening in challenging situations (Herrmann & Johnsrude, 2020). Personality is related to intrinsic motivation (Cattell, 1957) and may also partially account for individual differences in listening effort. There is some evidence for relations between general “Big Five” personality factors and listening effort (Wöstmann et al., 2021; but cf. Strand et al., 2018). Sensory sensitivity is a more specific aspect of personality and has shown relations to listening effort (Strand et al., 2018; Wöstmann et al., 2021) and to how listening fatigue varies in adult aging (McGarrigle et al., 2021).

In summary, listening effort can be defined by the listener's conscious experience (self-reports), listener behavior, and neurobiological correlates of conscious experience and behavior. Three classes of factors influence listening effort: the target signal for listening, the environment such as concurrent sounds that may compete with the target, and attributes of the individual listener. Each of these topics is a major research area in its own right. This report focuses more narrowly on the listener's conscious experience and tests the idea that the conscious piece of the listening effort puzzle has at least three putative dimensions: mental workload, mental fatigue, and mood.

There is a rich literature on mental “workload,” reflecting the subjective intensity of attention (Kahneman, 1973) and how it arises in situations that tax attention and memory functions (Warm et al., 2008; Wickens, 2013; Young et al., 2015). A distinction can be made between objective “listening demand” arising from properties of the task and stimuli (acoustic signal of interest) and the subjective experience of workload (Richter, 2016). Workload results from the conjunction of listening demand and the capabilities of the individual performing a specific cognitive task. Human factors research has also identified multiple dimensions of workload, such as mental or temporal dimensions (e.g., via the NASA-TLX; Hart & Staveland, 1988), which can be differentially responsive to various types of listening demands (Alhanbali et al., 2021; Dimitrijevic et al., 2019; Strand et al., 2018). As with listening effort studies, there are a wide variety of ways to estimate workload with self-report, behavioral, and biological measures, and there is no agreed-upon gold standard (Young et al., 2015).

Workload is distinct from “mental fatigue.” Mental fatigue can exist when not performing a task (consider the mental fatigue that lingers after an intense writing session), and can increase over time even when listening is nearly effortless (Desmond & Hancock, 2020; Warm & Parasuraman, 2008). Conversely, a demanding task can generate little mental fatigue if it is freely chosen, comports with one's interests, and is conducive to experiencing a “flow state” (Csikszentmihalhi, 1997). Mental fatigue may be a normal and transient consequence of mental effort or a chronic state that accompanies some medical conditions (Chaudhuri & Behan, 2004; Hockey, 2013). The hypothesized connection between fatigue and workload is that experiencing workload over time can increase mental fatigue (Desmond & Hancock, 2020; Hockey, 2013). Cognitive models propose that mental fatigue acts as a signal that influences cognitive control of attention (or executive function) by representing the costs versus benefits of the current task relative to one's goals and other possible actions (i.e., opportunity costs; Hockey, 2011; Kurzban et al., 2013; Shenhav et al., 2017). According to these models, fatigue is a part of cognitive control and decision making.

Fatigue has long resisted a precise definition (Bartley & Chute, 1945), but its commonly recognized features include tiredness, weariness, lethargy, and difficulty concentrating (Hockey, 2013). Adding further complication, the term fatigue may refer to a consequence of physical exertion as well as mentation. Emerging work suggests that the relationship between physical and mental fatigue is bidirectional—mental fatigue influences athletic performance (Graham & Brown, 2021; McMorris et al., 2018) and exercise-induced fatigue affects cognition (McMorris & Hale, 2012). Conceptual differences in the nature of fatigue and its ability to exist without a concurrent task motivated prior researchers to treat fatigue as a separate factor from workload (Hornsby, 2013; Pichora-Fuller et al., 2016; Strauss & Francis, 2017). Fatigue is particularly relevant to people with hearing impairment, who have a greater risk of experiencing fatigue (Alhanbali et al., 2018; Hornsby, 2013; Kramer et al., 2006; Wang et al., 2018). Focus group discussions about the experience of fatigue in adults with hearing loss show that fatigue is multifaceted, including physical, mental, emotional, and social domains (Davis et al., 2021). The complex relations between listening demand and fatigue are further illustrated by the observation that self-reported daily fatigue in adults with hearing loss covaries with the personal impact of hearing loss, rather than the objective degree of hearing loss (Hornsby & Kipp, 2016). A similar role of perceived impairment is evident in a cross-sectional lifespan study of listening fatigue (McGarrigle et al., 2021).

Applying workload over time may also influence affect (emotion and mood; Francis & Love, 2020; Herrmann & Johnsrude, 2020). There are similarities between workload, fatigue, and affect. All are subjective experiences that can vary in intensity, and each conveys information that can be useful for guiding future behavior (Clore & Huntsinger, 2007). One distinction is that workload is a cognitive state only during task performance, while fatigue and mood are more general states. In addition, fatigue appears to have elements of both cognition and affect—fatigue is often an aversive experience but may also influence attention control. Convergent evidence from a wide variety of human and animal research suggests that affect has two basic dimensions (Cacioppo & Gardner, 1999; Davidson & Irwin, 1999; Depue et al., 1994; Gray, 1990; Schneirla, 1959). One dimension is aligned with positive affect and approach behaviors, and is frequently engaged throughout the day to accomplish desirable goals such as obtaining food. The other dimension entails negative affect and withdrawal behaviors, and is intermittently engaged when aversive stimuli and situations arise in order to promote self-preservation (Watson et al., 1999). There is some work on relations between mental effort and affect (Allen et al., 2013; Plass & Kalyuga, 2019), but it is just beginning to be explored in the context of listening effort (Francis & Love, 2020).

As summarized above, separate lines of research support the relevance of workload, fatigue, and mood to the experience of listening effort. This study posed two novel questions. The first question was: How do the three putative dimensions relate to each other? This question was addressed by using correlation and principal component analyses (PCAs) on self-reported workload, fatigue, and mood before performing an effortful listening task. The second question was: How does an effortful listening task influence baseline measures of workload, fatigue, and mood? The listening task was termed the auditory psychomotor vigilance task (auditory PVT). The auditory PVT can be viewed as a probe that may influence baseline workload, fatigue, and mood in specific ways. Two consecutive studies with a common set of methods and measures were performed. In the second study, some additional test and survey data were collected that were not included in Study 1.

Materials and Method

Participants

Participants accessed the study through an online portal for research course credit. A total of 98 participants were recruited in Study 1. The planned sample size in Study 2 was based on the range of effect sizes in Study 1 and was powered to detect an effect size of Pearson's r ≥ .20 (alpha = .05, beta = .80). For Study 2, the goal was to recruit > 220 participants and yielded a total of 235 participants. Participants were included if they were at least 18 years of age. Exclusion criteria included self-reported major hearing, neurological, or psychiatric disorders. Other exclusions based on participant behavior will be presented below after describing the procedures. Participants used a desktop or laptop computer to access the study via online presentation software (Millisecond), and the study took about 30–45 min to complete. Experiments in this report were performed in accordance with protocols approved by the institutional review board at our university.

Experimental Design and Procedure

After providing consent and demographic information, participants put on their earphones and adjusted their computer volume to a comfortable listening level. Participants then completed standardized instruments to assess their current states of fatigue (Visual Analog Scale–Fatigue [VAS-F]) and mood (Positive and Negative Affect Schedule [PANAS]).

Workload was then assessed by giving short (M = 20 s) samples of the auditory PVT at five different signal-to-noise ratios to define relations between different amounts of listening demand and subjective workload (described below). The auditory PVT was then continuously performed for about 12 min. After the auditory PVT, measures of fatigue and mood were repeated, and in Study 2, the workload measure was also given after the auditory PVT. Personality, motivation, and cognitive judgment measures were also collected but are not reported here.

The procedures of Study 1 were repeated in Study 2 to test for replicability, and more participants were recruited to increase statistical power.

Materials and Stimuli

Fatigue (VAS-F). Fatigue was measured with the VAS-F, which is a standardized self-report test (Lee et al., 1991), and has been used to study listening effort (Alhanbali et al., 2019). The VAS-F has 18 questions, which are divided into subscales of “energy” and “fatigue.” Each item has a pair of bipolar descriptors (e.g., not at all fatigued to extremely fatigued). In response to each question, participants moved a cursor along a horizontal line in between the two extreme descriptors (range: 0–100) and marked where they felt at the moment.

Mood (PANAS). Mood was measured using the standardized 10-item short form of the PANAS (Thompson, 2007), which was based on the work of Watson and Clark (1999). As implied by the name, the PANAS has subscales of positive and negative affect. The items are descriptors, such as “alert,” and range from anchors of very slightly to extremely. The VAS for the PANAS, which normally uses a five-item Likert scale, was validated by presenting the PANAS twice—once where answers were given with the visual analog response scale (line ranging from 0 to 100) and once with the Likert scale (counterbalanced order). The PANAS is considered to be a reliable instrument with many applications (Leue & Lange, 2011).

Auditory PVT to induce listening effort. Many different types of situations will engage listening effort, with a spectrum ranging from simple perception of nonverbal stimuli to complex language tasks (Rönnberg et al., 2013). The auditory PVT was designed to lie on the simpler end of the spectrum by having participants sustain their attention and press a button when they heard an intermittent pure-tone target embedded in continuous white noise. Similar tasks where attention is focused on an expected target frequency have been widely studied at the behavioral (Dai et al., 1991; Greenberg & Larkin, 1968) and neural (Polich, 2007) levels. The occasional pure tone is delivered within continuous white noise, which provides energetic masking (Fletcher, 1940). Listening demand was parametrically manipulated by varying the level of the white noise while keeping pure-tone targets at a fixed level. Thus, greater noise levels will reduce the signal-to-noise ratio.

The auditory PVT was based on the work of Dinges et al. (Basner et al., 2011; Dinges & Powell, 1985). Parameters from earlier work were used (Gabel et al., 2019; Jung et al., 2011), but with the addition of continuous background white noise to increase listening effort. This assumption was validated by the results (below), where workload was linearly related to signal-to-noise ratio. Participants pressed a button when they heard pure-tone targets (1,000 Hz, 200-ms duration) that were randomly presented at 2–10 s stimulus onset asynchrony intervals (approximately 120 total tones). Reaction time was defined relative to target onset, and participants were instructed to “press the spacebar as soon as you hear a target tone” in order to encourage rapid responding. Responses to targets can be considered “hits.” However, long reaction times relative to tone onset are ambiguous because they could either be a false alarm or a particularly slow response to the target. Study 1 followed the convention established by Dinges et al. of having a cutoff reaction time of > 1,000 ms to classify these ambiguous slow responses as “other,” which were examined separately from responses < 1,000 ms. The reaction times in Study 1 were somewhat longer than in other PVT paradigms. Consequently, in Study 2, the cutoff for reaction times considered to be responses to the tone was increased up to 2 s. This was a more generous assumption that slower responses reflect target detection but did not seem to affect the results because the auditory PVT findings from Studies 1 and 2 were very similar. Not responding before the cutoff time would be considered a “miss.”

Participants received brief training to familiarize themselves with the auditory PVT. Relations between listening demand and workload were defined by testing brief, 20-s trials containing three target tones at one of five levels of signal-to-noise ratio (−18, −15, −12, −9, −6 dB SPL; one trial per signal-to-noise ratio). Note that the absolute dB levels depended on each participant's computer settings. After listening to a signal-to-noise ratio sample participants were asked, “How difficult was it to hear the tone with that level of background noise?” They responded by placing a mark on a horizontal VAS that had a range of 0–100 and was anchored by descriptors of no effort (left side) and maximum effort (right side).

Exclusions based on participant behavior. Participants were asked to use a desktop or laptop computer. The software recognized those who used a smart phone and touch screen, which could affect reaction time and accuracy; if so, the participant was excluded. When asked about workload as a function of signal-to-noise ratio (see below), if participants always gave extreme answers of “0” (no workload) or “100” (maximum workload), they were excluded. The workload results were unchanged when rejected participants were included (see Supplemental Material S1). The rationale for excluding these few participants was that they were likely not answering the question as intended. Since the signal-to-noise ratio covered a large range (−6 to −18 dB SPL), even if workload was very low for a −6 dB signal-to-noise ratio, given what is known about masking effects, there should at least be some increase in workload when the signal-to-noise ratio is reduced by up to 12 dB.

The percentage of responses on the auditory PVT that are slower than a cutoff value (termed other responses in PVT work) needed to be within 3 SDs of the group mean, otherwise there were too few trials for reaction time analyses. Sampling calculations for Study 2 included the expectations that approximately 10% of participants would yield inadequate auditory PVT data (> 3 SD for % other responses) and that some would give workload responses of no workload or maximum workload at all signal-to-noise ratios. In Study 1, 24 participants were rejected (n = 14 use of phone, n = 7 always reported no workload, n = 3 inadequate auditory PVT data), leaving a total sample size for the analyses of 74 (ages 19.8 ± 3.1 years; 29 male, 44 female, one unknown). In Study 2, 40 participants were rejected (n = 7 cell phone, n = 21 always reported no workload or maximum workload, n = 12 inadequate auditory PVT data), giving a final sample size of 195 participants (ages 19.8 ± 2.4 years; 86 male, 109 female).

Data Analysis and Statistics

Analyses involving workload used reports at the -12-dB signal-to-noise ratio, which was the same signal-to-noise ratio in the auditory PVT. In order to determine if vigilance declined during the auditory PVT (Yang et al., 2018), auditory PVT reaction times were averaged over three periods that each lasted approximately 4 min (beginning, middle, end of the auditory PVT). If vigilance decreases over time, reaction times are expected to progressively increase. Reaction time slopes across the three time periods were calculated to measure performance declines over time and had mean linear r2 fits of .63 (Study 1) and .64 (Study 2). Reaction time variability tends to increase during vigilance tasks and is commonly measured by the coefficient of variation (Unsworth et al., 2021). The coefficient of variation normalizes reaction time variability within individuals by dividing the standard deviation across trials by mean reaction time. The coefficient of variation was measured for each of the three time periods of the auditory PVT (beginning, middle, end). Slopes of workload over the five signal-to-noise ratios were not calculated due to poor linear fits (mean r2 = .36 and .41).

The main analyses used Pearson correlations and repeated-measures analysis of variance (ANOVA; significance: p < .05), with Greenhouse–Geisser corrections for any violations of sphericity. Correlational analyses used linear (Pearson) modeling.

The validity of using the VAS responses for the PANAS was checked by giving the PANAS twice—once with VAS responses and once with the Likert scale judgments (in counterbalanced order). The correlations between the VAS and Likert responses were r = .798 for the positive subscale and r = .792 for the negative subscale. Given the strong similarity among the measures, we used the VAS measure for PANAS data in all of the analyses below.

PCA is an unsupervised learning model that can reduce a larger set of intercorrelated, higher dimensional measures onto common lower dimension components, if such a relationship exists (Abdi & Williams, 2010). PCA of the larger sample size from Study 2 data was performed to determine if mean workload, fatigue (subscales of fatigue and energy), and mood (subscales of positive and negative) measures taken before and after the auditory PVT could be reduced onto fewer putative dimensions of listening effort. Components were included for eigenvalues > 1, and Promax rotation was used to facilitate interpretation and account for small correlations among components (Abdi & Williams, 2010). PCA results can be misleading if dependent variables have different measurement scales, or if there are outlier scores (Lever et al., 2017). In each case, the components tend to reflect the measures with the largest values and outlier. This study used the same VAS (0–100) for all self-reported listening effort measures (workload, fatigue, mood) and had similar variation among subjects.

Results

Workload as a Function of Signal-to-Noise Ratio at Baseline

Task demand was compared to subjective workload by manipulating the signal-to-noise ratio of a 1,000 Hz pure-tone target versus white noise, from −6 dB (easier) to −18 dB (more difficult; see Figure 1A). Workload measures were based on listening to short (approximately 20 s) examples of the auditory PVT. In both studies, participants generally reported progressive increases in workload as the signal-to-noise ratio decreased. There was a wide range of individual differences in overall workload at all signal-to-noise ratios. For example, at −12 dB level used in the 12-min auditory PVT, the range was 0–90 (Study 1) and 0–100 (Study 2). As expected, in Study 1, a one-way ANOVA test across the five signal-to-noise ratios was significant, F(3.415, 249.260) = 7.713, p < .001, ηp2 = .096), and was well-fit by a linear contrast, t(292) = −5.515, p < .001. Workload in Study 2 was measured before and after the auditory PVT. A 2 (time: before vs. after auditory PVT) × 5 (signal-to-noise ratio) ANOVA test had a significant effect of signal-to-noise ratio, F(1.634, 575.464) = 416.950, p < .001, ηp2 = .682, and was well-fit by linear functions: before, t(776) = −34.911, p < .001; after, t(776) = −32.158, p < .001. The main effect of time and Time × Signal-to-Noise Ratio were nonsignificant (p ≥ .28, ηp2 < .007), indicating that auditory PVT workload judgments were comparable before versus after performing the auditory PVT.

Figure 1.

4 graphs. The first 2 graphs plot the workload VAS with respect to the signal to noise ratio in decibels for the 2 studies. A. Study 1. The curve passes through (negative 18, 50), (negative 15, 45), (negative 12, 43), (negative 9, 40), and (negative 6, 36). B. Study 2. The curve for the baseline passes through (negative 18, 50), (negative 15, 43), (negative 12, 40), (negative 9, 36), and (negative 6, 36). The curve for the posttest passes through (negative 18, 50), (negative 15, 48), (negative 12, 40), (negative 9, 39), and (negative 6, 38). The third and fourth graphs plot the normalized reaction time with respect to the time period for the 2 studies. C. Study 1. Beginning: 0.30. Middle: 0.43. End: 0.62. D. Study 2. Beginning: 0.3. Middle: 0.55. End: 0.63.

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Workload and auditory PVT data in Study 1 (left column) and Study 2 (right column). (A, B) Subjective workload as a function of listening demand. Findings showed progressive decreases in workload with increases in signal-to-noise ratio. (C, D) Reaction times on the auditory PVT progressively slowed with more time on task (beginning, middle, end tertiles). Error bars = SEM; Auditory PVT = auditory psychomotor vigilance task; VAS = Visual Analog Scale.

Performance on the Auditory PVT

Mean normalized reaction times in the beginning, middle, and end tertiles of the auditory PVT are shown in Figures 1C and 1D. Reaction times were normalized to better visualize the consistent increase in reaction times over time in the auditory PVT, without adding the extra variance of overall differences in how fast individuals respond. In Study 1, a one-way ANOVA across the three tertiles showed that reaction time slowed with more time-on-task, F(1.654, 120.754) = 4.834, p = .009, ηp2 = .062, with a slope of 9.4 ms per tertile. Similarly, Study 2 reaction times also linearly slowed with more time on task, F(1.806, 350.397) = 28.576, p < .001, ηp2 = .128, having a slope of 15.1 ms per tertile. The mean reaction times were 493.2 ms (SD = 131.1) and 501.9 ms (SD = 120.3) in Studies 1 and 2, respectively. Variability in reaction time was measured using the coefficient of variation, which had a significant increase over time in Study 2 task, F(1.972, 382.661) = 8.051, p < .001, ηp2 = .04, but not in Study 1, F(1.957, 142.885) = 0.554, p = .572, ηp2 = .008. The nonsignificant effect in Study 1 may relate to the smaller range of reaction times enforced by the 1-s cutoff. Accuracy in responding before the cutoff was high (hit rates: Study 1, 89.5% ± 14.3%; Study 2, 93.1% ± 10.4%), showing that participants could reliably detect the tones in noise at the −12 dB signal-to-noise ratio.

A one-way ANOVA of “% other” trials across the three tertiles (beginning, middle, end) was significant in Study 1, F(1.998, 145.859) = 3.565, p = .031, ηp2 = .047, and Study 2, F(1.889, 366.444) = 6.705, p < .001, ηp2 = .033. Increases in “% other” trials were due to the slowing of responses over time, which increased the number of trials that exceeded the 1,000 ms (Study 1) or 2,000 ms (Study 2) cutoff for classification as a “% other” trial. Collectively, these results show that objective performance on the auditory PVT declined over time in terms of response speed and possibly the ability to detect targets.

Question 1: Relations Among Workload, Fatigue, and Mood at Baseline

Correlations at baseline among workload, fatigue, and mood measures are shown in Table 1. There was a wide range of scores on each measure, ranging from 0 or single digits to about 90 out of a 0–100 range. The negative mood scale had a somewhat lower maximum of 75 on Study 1. Within the VAS-F and PANAS subscales, baseline correlations were large and statistically significant for the VAS-F and smaller and nonsignificant for the PANAS. Comparisons across the Fatigue (VAS-F) and Mood (PANAS) subscales revealed significant correlations between fatigue and both positive and negative mood, while energy was significantly related to positive mood. Energy was significantly related to negative mood in the higher powered Study 2, but not Study 1. For workload, none of the eight comparisons to the VAS-F and PANAS subscales were significant. Note that although 10 correlations were performed among baseline measures, the effect sizes and significance/nonsignificance were mostly very consistent between Studies 1 and 2. Taken together, the findings showed that fatigue and mood reports were distinct but interrelated and that neither was related to workload.

Table 1.

Correlations among candidate listening effort dimensions.

Instrument and subscale Study 1
 Study 2
Fatigue Energy Positive mood Negative mood Fatigue Energy Positive mood Negative mood
VAS–Fatigue
VAS–Energy −.516*** −.518***
PANAS–Positive mood −.468*** .573*** −.372*** .687***
PANAS–Negative mood .373*** −.169 −.284 .554*** −.229*** −.173
Workload .093 −.193 −.132 .258 .144 .056 −.070 .077
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Note. Significance was Bonferroni corrected in each study and defined as p < .005. Workload was measured at the −12 dB signal-to-noise ratio, which was used in the auditory psychomotor vigilance task. VAS = Visual Analog Scale; PANAS = Positive and Negative Affect Schedule.

***

p < .001.

Study 2 had workload, fatigue, and mood measures before and after the auditory PVT, which permitted a PCA to define underlying dimensions of the data. The PCA results showed that energy and positive mood loaded onto one component, negative mood loaded onto a second component, fatigue was an element in both of the first two components, and workload was indexed by a third component (see Table 2). The factor loadings suggest two components related to affective valence (positive, negative), a distinct workload component, and that fatigue is more related to affect than judgments of workload.

Table 2.

Study 2: Principal component analysis.

Instrument and subscale Principal components
 Uniqueness
1 2 3
Positive–Post 0.924 0.262
Positive–Baseline 0.877 0.283
Energy–Post 0.861 0.271
Energy–Baseline 0.757 0.364
Fatigue–Post −0.482 0.304
Negative–Baseline 0.971 0.176
Negative–Post 0.955 0.237
Fatigue–Baseline 0.622 0.328
Workload–Post 0.843 0.311
Workload–Baseline 0.836 0.336
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Note. Promax rotation was applied. The correlations among components were 1 versus 2 (r = −.430), 1 versus 3 (r = −.148), and 2 versus 3 (r = .279). Baseline = measured before performing the auditory psychomotor vigilance task; Post = measured after performing the auditory psychomotor vigilance task.

Question 2: Fatigue and Mood Before Versus After Performing the Auditory PVT

Measures of fatigue and mood before and after the auditory PVT are shown in Figure 2. The auditory PVT was fairly brief. Nonetheless, in Study 1, paired-sample t tests showed that relative to baseline, subjects reported greater fatigue, t(73) = 6.909, p < .001, d = 0.803; decreased energy, t(73) = −6.906, p < .001, d = 0.803; and less positive mood, t(73) = −7.834, p < .001, d = 0.911, afterwards (see Figure 2A). Negative mood, however, was about the same before and after the auditory PVT (p ≥ .64, d = 0.05). The findings were the same in Study 2 (see Figure 2B): Paired comparisons of baseline to posttest measures showed significant increases in fatigue, t(194) = −7.503, p < .001, d = 0.537; decreases in energy, t(402) = 8.747, p < .001, d = 0.626; and positive mood, t(402) = 9.213, p < .001, d = 0.660; and no significant difference in negative mood (p = .879, d = 0.011) and workload (p = .826, d = 0.016).

Figure 2.

To bar graphs depict the data for the self reported VAS Score for 2 studies during the baseline and posttest. A. Study 1. The data for the baseline are as follows. Fatigue: 48. Energy: 49. Positive: 49. Negative: 26. The data for posttest are as follows. Fatigue: 60. Energy: 35. Positive: 32. Negative: 25. B. Study 2. The data for the baseline are as follows. Fatigue: 42. Energy: 50. Positive: 50. Negative: 25. Workload: 40. The data for posttest are as follows. Fatigue: 50. Energy: 40. Positive: 40. Negative: 25. Workload: 43.

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Self-report measures before and after performing the auditory PVT in Study 1 (A) and Study 2 (B). Relative to baseline, in both studies the fatigue subscale increased, and energy and positive mood decreased after performing the auditory PVT. Negative mood was about the same before versus after the auditory PVT, as was workload in Study 2. Error bars = SEM; Auditory PVT = auditory psychomotor vigilance task; VAS = Visual Analog Scale.

General Discussion

Overview of Main Findings

This project drew inspiration from hearing science, human factors, and affective science in order to test the hypothesis that listening effort has multiple, interrelated dimensions that covary at baseline and in response to a listening effort challenge. In most instances, the results were consistent across the two studies. The auditory PVT was accompanied by substantial workload, and workload was linearly related to listening demand as defined by the signal-to-noise ratio. After performing the auditory PVT, there were large increases in fatigue and decreases in energy and positive mood, while negative mood and workload were comparable to baseline. Correlation and PCA methods showed that fatigue and mood were distinct but interrelated constructs, and both generally had weaker relations to workload than to each other.

Mapping Listening Demand Onto Workload

The auditory PVT required participants to attend to the perceptual features of the tone embedded in white noise, which is a classic endogenous selective attention task (Dai et al., 1991; Scharf, 1998). Strauss and Francis (2017) made a distinction between listening situations where effort arises from attending to external stimuli (e.g., signals in noise) and situations where effort is generated from internal processes such as trying to understand rapid speech in an unfamiliar accent. The auditory PVT in this study has participants maintain the goal of attending to the external feature of pitch and pressing a button in response to targets, and presumably did not engage higher level internal processes (e.g., working memory, executive function processes) in the same way that a speech task would. The distinction between listening for nonspeech and speech sounds is important because additional effort mechanisms may be deployed when listening to speech (Rönnberg et al., 2008). The present approach manipulated the signal-to-noise ratio, which likely created energetic, rather than informational, masking (Watson, 2005).

Relations Among Workload, Fatigue, and Mood

A previous study on listening effort dimensions examined a wide range of behavioral, self-report, clinical, and biological measures in participants over 55 years of age with pure-tone thresholds ranging from normal to severe hearing loss (Alhanbali et al., 2019). Alhanbali et al. found that a factor analysis model needed four dimensions to model nine multimodal measures, which suggested that listening effort is not a unitary construct. Other studies also found that some measures of listening effort tend to not have high correlations with each other (McGarrigle et al., 2014). Such challenges reflect general problems in psychology, such as how to relate psychophysiological and cognitive/behavioral measures (Cacioppo & Tassinary, 1990) or self-reports of conscious experience and behavior (Nisbett & Wilson, 1977). In contrast to prior work, the present study took a narrower approach by focusing on identifying dimensions of the listener's experience in young adults without self-reported hearing loss. The results of Studies 1 and 2 were very consistent in terms of relations and selective dissociations among workload, fatigue, and mood measures. Although, in principle, an effortful listening task could have parallel influences on largely independent aspects of conscious effort, the finding that relations among fatigue and mood exist at baseline and are then affected by effortful listening suggests a recruitment of interrelated dimensions. These findings can provide a platform for testing effort when listening to speech, defining relations of effort dimensions to cognition, and testing listening effort in other populations such as normal aging and those with hearing impairment.

For baseline correlations among VAS-F and PANAS subscales, relations among fatigue, energy, and positive mood had much larger effect sizes relative to those involving negative mood. Energy and positive mood were strongly correlated, which may seem problematic because fatigue and mood are considered to reflect different constructs. Looking at the surveys in detail, the questions about energy on the VAS-F subscale all related to positive affect (e.g., “energetic,” “active,” “vigorous,” “efficient,” “lively”), which probably accounts for the strong intercorrelation. When considering the PCA results, there is less of a concern, as the components cleaved along positive (approach) and negative (withdrawal) systems, with a third component for workload. Inclusion of fatigue within measures of affect is consistent with Hockey's contention that fatigue overlaps with affective states due to having elements of frustration and discomfort (Hockey, 2013). Correlations among components also had the expected inverse relation between the positive component and the negative and workload components, while the negative and workload components had a positive relationship. The present use of PCA differs from many other listening effort studies by focusing in-depth on self-report aspects of effort. Other studies have used PCA as a tool to define eye-tracking metrics to index effort (Książek et al., 2021), defining components of executive function in relation to effort (Brännström et al., 2018) and comparing sets of personality, noise sensitivity, and audiometric measures (Francis et al., 2021).

The present findings suggest that more work is needed to make cleaner distinctions between fatigue and mood. Although the Profile of Mood States takes more time to administer relative to the PANAS, it has a validated subscale of fatigue (Heuchert & McNair, 2012). A recent study on the Vanderbilt Fatigue Scale for Adults found that listening fatigue can be logically organized into four dimensions, but quantitative analyses showed only one latent construct (Hornsby et al., 2021). We note that the Vanderbilt Fatigue Scale examines daily life experiences and does not measure fatigue in the current moment, as was done in the present study. Nonetheless, perhaps the Fatigue subscale of the VAS loads well onto a general fatigue factor, while the Energy subscale less clearly reflects fatigue because it also has elements of positive mood.

Workload, fatigue, and mood on the auditory PVT were framed as aspects of mental effort that are evident in many situations, including challenging listening conditions. This was implied by selection of fatigue and mood measures that are used in many different types of research, such as sleep, vigilance, and psychiatric disorders. However, the premise remains to be tested, and future work may be able to distinguish general mental effort processes from those that are specific to listening situations.

Impact of Auditory PVT on Workload, Fatigue, and Mood

Both studies found large changes in fatigue, energy, and positive mood in the wake of performing the auditory PVT, while negative mood and workload remained stable. The approach of using a listening effort task to probe changes in baseline states is fairly novel. Most prior work focuses on the listening task itself and does not address how effortful listening affects baseline mental states such as fatigue and mood. Exceptions are works of Hornsby (2013), who observed that people using hearing aids experienced increased fatigue and difficulty in maintaining attention after performing listening effort tasks, and Alhanbali et al. (2021), who found that fatigue measured with the VAS-F (collapsed across subscales) increased after a speech-in-noise test. Combining data from both of our studies, 91% of participants had increases in fatigue (n = 250/276), 78% had decreases in energy (n = 216/276), and 77% had decreases in positive mood (n = 213/276). Changes in negative mood were near evenly split between increases and decreases (49% vs. 51%), as was workload in Study 2 (M = 54% vs. 46%). In addition to being consistent across participants, the magnitude of change in fatigue, energy, and positive mood was also moderate to large, with Cohen's d values ranging from 0.537 to 0.911 across studies and measures. These findings support the proposal that fatigue and affect change in response to experiencing listening effort (Francis & Love, 2020).

The auditory PVT provoked a dissociation between positive and negative mood. Relative to baseline, positive mood was reduced after the auditory PVT, but negative mood was stable. Affect is commonly mapped onto dimensions of arousal (low to high) and valence (positive to negative; Feldman-Barrett et al., 2007; Lang et al., 1997). The dissociation between positive and negative mood cannot be explained by a reduction in arousal. If arousal was reduced by performing the auditory PVT, then both positive and negative mood would have decreased. The dissociation also revealed that, at least in this study, there was not a simple trade-off between positive and negative affect—with less positivity accompanied by proportionally greater negativity. Instead, the results support theories that treat positive (approach) and negative (withdrawal) affect systems as having distinct functions (achieving goals that further benefit the organism vs. self-preservation) and time courses (relatively constant vs. infrequent usage; Watson et al., 1999). Another way to understand dissociations of positive and negative mood would be to explore previously identified neural correlates of positive and negative affect (Davidson & Irwin, 1999; Ledoux, 2000; Lindquist et al., 2012), but in the context of listening effort.

The questions about workload, fatigue, and mood asked about the participant's current mental and emotional states. They did not ask participants to remember what it was like when they were performing the auditory PVT. In contrast, self-report measures such as the NASA-TLX (Hart & Staveland, 1988) and the Borg CR-100 (Borg & Borg, 2002) ask participants to make retrospective judgments. These retrospective methods have face validity, provide insights into the listener's experience, and are sensible given the methodological challenges of asking participants to report their effort while also performing a task. The addition of effort reporting creates a dual task, while periodically cueing participants to report their effort acts as a task switch, which can partially refresh vigilance (Finkbeiner et al., 2016; Ralph et al., 2017). Retrospection, however, also has limitations because it relies on memories of effort during a listening task that can be biased and/or inaccurate. For example, retrospective affect judgments are driven by affect at its peak and at the end of the experience (“peak-end rule”; Redelmeier & Kahneman, 1996). Much of the actual experience does not contribute to the retrospective reports. In the current study, participants were asked about their workload in short blocks (approximately 20 s each), which should limit peak-end effects. More work would be needed to connect retrospective accounts of effort to in-the-moment measures of fatigue and mood. Online biological measures would also be valuable to unobtrusively index listening effort during task performance, as has been done using pupil dilation (Winn et al., 2018), skin conductance (Alhanbali et al., 2019), heart rate and variability (Slade et al., 2021), and EEG (Dimitrijevic et al., 2019) measures.

Clinical Relevance

Listening effort is an important aspect of hearing that can complement standard clinical testing (Francis & Love, 2020; Peelle, 2018; Pichora-Fuller et al., 2016), and there is interest in developing clinical instruments to measure listening effort. A validated metric of listening effort could also help with selection of hearing aid algorithms (Sarampalis et al., 2009). Contributions of the present study include defining multiple candidate dimensions of listening effort, which had highly replicable convergent validity and quantitative relations among dimensions at baseline testing. In response to a brief listening challenge, trajectories of change (fatigue, energy, positive mood) and stability (workload, negative mood) were defined. This profile may differ in clinical populations, who could have a lower threshold for experiencing higher workload and/or increases in negative mood after the auditory PVT. When viewed through the lens of approach–withdrawal theory, the relatively brief auditory PVT in participants with typical hearing may not be sufficiently aversive to trigger increases in negative mood. Future work could examine how greater listening demands relate to negative mood, as would be experienced by people with hearing impairment, smaller signal-to-noise ratios, or increased task duration. Each of these possibilities may lead to increased negative mood.

The changes in fatigue and mood after performing the auditory PVT were presumably transient, but the time to recover was not measured. Knowing more about the lingering consequences of effortful listening and recovery may help clinicians better understand how listening effort influences the quality of life in people with hearing loss (Davis et al., 2021; Pichora-Fuller et al., 2016). Those with hearing loss not only have a greater risk of experiencing fatigue in daily life, but they can also take longer to recover from a fatiguing listening task (Hornsby et al., 2016). The present study found that negative mood was about the same before and after the auditory PVT in young adults who reported no hearing impairment. To better understand drivers of quality of life in those with hearing loss, it is important to find boundary conditions for where challenging listening does or does not influence negative mood. The auditory PVT can be modified to have greater cognitive load by increasing the duration or by using speech targets, which may then affect negative mood, workload, and the time to recover.

Study Limitations

Online data collection has many limitations. One is that testing conditions cannot be as well controlled as in laboratory work, even though the results are often comparable (Casler et al., 2013). The auditory PVT presents stimuli that are well-above threshold, and participants only needed to listen at a comfortable level, but the specific sound level experienced by each listener is unknown because each participant used their own computer, sound card, earphones, and software settings, none of which were under experimental control. Thus, precise characterization of the stimuli was not feasible. The general relation between signal-to-noise ratio and workload, however, was very robust, agrees with prior laboratory research, and the main findings replicated well among Studies 1 and 2. This project tested college students in their early 20s with self-reported normal hearing, and thus was not objectively verified with audiometric testing. The question of generality to different populations, such as older people or those with hearing impairment, and different listening tasks, such as speech-in-noise or other manipulations of listening demand, remains to be addressed. Lastly, there was only one workload judgment per signal-to-noise ratio. This arrangement was sufficient to define Listening Demand × Overall Workload relations, but more trials at each signal-to-noise ratio would be needed for more precise measurements, such as defining the slope of workload versus signal-to-noise ratio.

Author Contributions

Edward J. Golob: Conceptualization, Formal Analysis, Funding acquisition, Methodology, Writing – original draft, Writing – review & editing. Ricardo C. Olayo: Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing. Denver M. Y. Brown: Writing – original draft, Writing – review & editing. Jeffrey R. Mock: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review & editing.

Data Availability Statement

The data sets generated and/or analyzed during the current study will be made available in the Open Science Framework repository.

Supplementary Material

Supplemental Material S1. Subjective workload vs. listening demand (signal/noise ratio) in Study 1 (A) and Study 2 (B) that included participants who rated the task as 0 or 100% effort at all signal/noise ratios. In Study 1, a repeated-measures ANOVA on listening demand (5) was significant, F(2.279, 221.030) = 127.750, p < .001, ηp2 = .568, and was well-fit by a linear contrast, t(388) = −22.584, p < .001. In Study 2, a 2 (time) × 5 (listening demand) ANOVA test also had a significant effect of listening demand, F(1.593, 372.702) = 426.879, p < .001, ηp2 = .646, and was well-fit by a linear contrast, t(936) = −41.263, p < .001. There was a nonsignificant trend toward greater workload after the auditory PVT, p = .074. Error bars = SEM.
JSLHR-67-3217-s001.jpg (169KB, jpg)

Acknowledgments

This study was supported by the National Institutes of Health Grant R01DC014736 (awarded to Edward J. Golob). The sponsors had no role in the design, implementation, and writing of this report.

Funding Statement

This study was supported by the National Institutes of Health Grant R01DC014736 (awarded to Edward J. Golob).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Material S1. Subjective workload vs. listening demand (signal/noise ratio) in Study 1 (A) and Study 2 (B) that included participants who rated the task as 0 or 100% effort at all signal/noise ratios. In Study 1, a repeated-measures ANOVA on listening demand (5) was significant, F(2.279, 221.030) = 127.750, p < .001, ηp2 = .568, and was well-fit by a linear contrast, t(388) = −22.584, p < .001. In Study 2, a 2 (time) × 5 (listening demand) ANOVA test also had a significant effect of listening demand, F(1.593, 372.702) = 426.879, p < .001, ηp2 = .646, and was well-fit by a linear contrast, t(936) = −41.263, p < .001. There was a nonsignificant trend toward greater workload after the auditory PVT, p = .074. Error bars = SEM.
JSLHR-67-3217-s001.jpg (169KB, jpg)

Data Availability Statement

The data sets generated and/or analyzed during the current study will be made available in the Open Science Framework repository.

Articles from Journal of Speech, Language, and Hearing Research : JSLHR are provided here courtesy of American Speech-Language-Hearing Association

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