In a recent white paper, the British Society of Audiology (BSA) Special Interest Group on Cognition in Hearing gave “the mental exertion required to attend to, and understand, an auditory message” as their working definition of listening effort based on dictionary entries (McGarrigle et al. 2014, pp. 434). According to the BSA group, agreement has not been reached about a standard definition of listening effort; however, they noted that a number of audiologists have used the term to refer to the attentional and cognitive resources required to understand speech (Hicks & Tharpe 2002; Anderson Gosselin & Gagné 2011; Fraser et al. 2011; Picou et al. 2011). The BSA group questioned if restricting the definition of listening effort to speech was overly narrow because listening to music or environmental sounds might also be effortful. They also pointed out that listening could become more effortful in adverse conditions for speech recognition but that listening could become less effortful if visual cues were available to the listener. The BSA group adopted the classification of adverse conditions for speech recognition used in the review by Mattys et al. (2012). This classification is similar to the well-known speech chain model (Denes & Pinson 1963; for an updated version including visual speech, see Humes & Bess 2013), whereby reductions in the quality of the speech signal being relayed could be attributed to factors related to the talker (e.g., talkers might have accents), the transmission (e.g., transmissions could be affected by noise, reverberation, or alterations of the signal by intervening technologies such as hearing aids), or the listener (e.g., listeners might have hearing loss).
The approach of our workshop group was to begin by adopting a more generic definition of mental effort as the deliberate allocation of mental resources to overcome obstacles in goal pursuit when carrying out a task. The generic definition of mental effort could be specified such that listening effort is simply effort involved in carrying out listening tasks. In agreement with the BSA group, our workshop consensus was that listening effort should extend beyond listening to speech to include intentional listening to any auditory source, including music and environmental sounds. Furthermore, for the purposes of our workshop, listening was considered in the broadest possible terms to extend from listening in artificial laboratory conditions to listening in the naturalistic conditions of everyday life. In contrast to listening in artificial laboratory conditions, listening in ecologically realistic conditions would likely entail binaural rather than only monaural listening, occur with multimodal rather than only auditory input, and involve the use of a wide range of contextual cues and linguistic and world knowledge.
Effort measured in the laboratory is likely to differ from effort experienced in the real world because of differences in the duration of tasks. Change in effort over time may be less apparent over the course of a relatively brief testing session in the laboratory than over the course of a day in a listener’s life. There could be cumulative effects of recurring episodes of effortful listening over days and years in a listener’s life. In addition, we recognized that the experience of effort in the moment might be modulated by the listener’s appraisal of future or long-term demands and the consequences of succeeding in the immediate task.
Consideration of the time course over which the person expends effort prompts consideration of “fatigue.” According to Hornsby et al. (2016, this issue, pp. 136S–144S), fatigue is a complex construct with a definition that varies depending on who uses the term (e.g., layperson, physiologist, cognitive psychologist, and physician) and the focus of their interest (e.g., physical fatigue in athletes, cognitive fatigue in individuals who have multiple sclerosis, and emotional fatigue in those who have depression). The article by Hornsby et al. (2016, this issue, pp. 136S–144S) reviews definitions and concepts from the broader fatigue literature and their relation to hearing (loss). Historically, fatigue has been defined as a mood state or subjective experience, and it has been measured in terms of fatigue-related performance decrements. Subjective fatigue is defined as a subjective experience or mood state that may manifest as feelings of weariness, tiredness, a lack of vigor or energy, or decreased motivation to continue doing a task. Transient or acute fatigue is due to the mental (and/or physical) demands of a given situation, whereas long-term fatigue is constant or recurrent and not necessarily due to specific transient events or situations. General fatigue is a general sense or feeling of being tired, worn out or sluggish, having low energy or motivation to complete at task; it may be caused by various underlying factors or mechanisms (e.g., sleep loss, medications, disease, or sustained physical or mental work). Mental fatigue and physical fatigue correspond to reduced ability, or desire, to perform mental or physical tasks. Mental fatigue is often associated with self-perceived or objectively measured difficulties with concentration, attention, clear thinking, or memory. Likewise, physical fatigue pertains to difficulties performing physical tasks, often as a result of sustained physical exertion or as a consequence of disease. Importantly, Hockey (2013) argues that the subjective fatigue experience serves an adaptive, goal-directed, function by forcing us to evaluate our current behaviors in terms of the effort required to achieve a reward from completion or continuation of a task. Should the effort-reward relationship be unfavorable, motivation to continue toward a given goal may be reduced. These general definitions and views of fatigue could be adapted such that listening fatigue is simply fatigue resulting from the continued application of effort during difficult listening tasks.
If fatigue is a lack of energy, then how is energy defined? From the perspective of physics, energy is the capacity to do work. With respect to humans, fatigue and energy are both mood states. In general, energy, vigor, and vitality are the same or similar, and being or feeling energetic has been described in various assessment tools as being or feeling active, vigorous, lively, or full of pep (Hornsby et al 2016, this issue, pp. 136S–144S). It is possible that fatigue is related to a decrease in the efficiency or availability of cognitive resources (Gergelyfi et al. 2015). Although fatigue is often negatively associated with energy, motivation to engage in a particular task may also be important, as is suggested by findings that people may experience fatigue for one task, but still have high energy for another task, and that the symptoms of fatigue may be reduced following a purely motivational intervention. This latter point also suggests that the relationship between fatigue and motivation could be bidirectional, such that fatigue may modulate motivation and vice versa. Thus, whether we consider effort or fatigue, it seems that we need to incorporate a motivational arousal dimension in our framework. Note that both energy level and motivation are included in the ICF (WHO 2001) comprehensive core set for hearing loss (Danermark et al. 2013; ICF Research Branch 2013): Energy level (b1300) refers to mental functions that produce vigour and stamina, and Motivation (b1301) refers to mental functions that produce the incentive to act, the conscious or unconscious driving force for action.
Theories, models, and frameworks can serve various purposes. According to Borg et al. (2008, pp. S131), “[A] model is defined as a set of related concepts that can quantitatively predict an outcome on the basis of certain premises. The framework is a series of defined concepts that are less precisely related and that are not formulated in a way that allows quantitative testing.” Our consensus was that, given the current state of knowledge in audiology about effortful listening, it was more reasonable to propose a conceptual framework, rather than a model, because we are not yet at a stage where we could quantitatively predict outcomes. Furthermore, as described earlier, there is an abundance of existing models pertaining to cognitive effort (see Wingfield 2016, this issue, pp. 35S–43S), and it seemed unnecessary to attempt to create yet another model for effortful listening that, for the most part, would incorporate the same core ideas that had already been promoted in prior models. In the interests of facilitating research and reducing confusion in the emerging audiology literature concerning listening effort, our consensus was that our FUEL should adapt and integrate several relevant conceptual dimensions based on multiple existing models. Our consensus was that the new framework could facilitate the future development of a model that could be used to quantify listening effort in audiology.
FRAMEWORK FOR UNDERSTANDING EFFORTFUL LISTENING
As mentioned earlier and described in more detail later, our FUEL is an adaptation of Kahneman’s (1973) model. Figure 1A is a reproduction of Kahneman’s original Capacity Model of Attention (1973; Figure 1.2, pp. 10). As depicted in Figure 1A, the core components of his capacity model are the tank of “available capacity” shown as fluctuating with “arousal” and the “allocation policy,” which governs how much of the available capacity will be supplied to which activities. According to Kahneman (1973, pp. 11), the allocation policy “is controlled by four factors: 1) Enduring dispositions which reflect the rules of involuntary attention (e.g., allocate capacity to any novel signal; to any object in sudden motion; to any conversation in which one’s name is mentioned); 2) Momentary intentions (e.g., listen to the voice on the right earphone; look for a redheaded man with a scar); 3) The evaluation of demands….; 4) Effects of arousal.” These four factors are shown as having arrows going to the allocation policy component shown in Figure 1A. Furthermore, he states that “The level of arousal is controlled by two sets of factors: 1) the demands imposed by the activities in which the organism engages, or prepares to engage; and 2) miscellaneous determinants, including the intensity of stimulation and the physiological effects of drugs or drive states” (pp. 17). These two factors are shown with arrows going to arousal. There are also two outputs. The main output labeled “responses” is shown at the bottom of the figure and represents the result of capacity having been allocated to one or many possible activities. There is also another output labeled “miscellaneous manifestations of arousal.”
Figure 1B is an adaptation of Figure 1A to show how our FUEL is an interpretation of Kahneman’s model in relation to listening effort. The original core component from Figure 1A is shown in green in Figure 1B as the available cognitive capacity varying with arousal. Also preserved are the core evaluation components shown in yellow: the evaluation of demands on capacity, the allocation policy, and the possible activities to which capacity is allocated. We did not alter the core components, but we note that the allocation policy (i.e., executive function) may also require the allocation of resources, especially in multitasking situations.
Figure 1B also includes some elaborations provided in Kahneman’s other figures. Specifically, the two bubbles colored yellow are adapted from Kahneman’s Figure 3.3 (1973, pp. 36) in which he introduces these components to show the effects of high and low arousal on attention and performance. We have added (dis)pleasure to these two bubbles. We have also changed his word “interfere” to “influence” because current thinking is that fatigue and (dis)pleasure can influence the evaluation of performance without being the results of performance. Some current models (e.g., Hockey 2013) suggest that the subjective (unpleasant) experience of fatigue may actually be a trigger that encourages the individual to evaluate the benefits of successful performance relative to the effort required to achieve, or maintain, that performance. Similarly, (dis)pleasure can predispose effort insofar as pleasure in anticipation of and during performing a task can be motivating (Matthen 2016, this issue, pp. 28S–34S). Importantly, the effects of arousal or motivation level on performance could offer an account for quitting even when the available capacity supply has not been exceeded by the demand for capacity.
Salmon-colored boxes in Figure 1B include direct inputs to the allocation policy or indirect inputs via the cognitive capacity component. We modified the labels of these components. Consistent with Kahneman’s explanation of the labels “enduring dispositions” and “momentary intentions,” we use the labels “automatic attention” and “intentional attention,” respectively, because these terms seem to be easier to relate to the study of listening effort; however, the examples for the two attention components given in Figure 1B are the same as those provided by Kahneman (1973). We relabeled his “miscellaneous determinants” as “input-related demands.” Our examples for input-related demands expand on Kahneman’s example of intense stimulation as a miscellaneous determinant of arousal (1973; Figure 2.2, pp. 18) and are tailored to stimulus, individual, and environmental factors pertinent to effortful listening. These input-related demands include those recognized as contributing to adverse listening conditions, namely factors affecting the quality of the source signal, signal transmission, and listener abilities (as discussed earlier; for a review, see Mattys et al. 2012). Here, however, they are expanded to align with the Speaker-Listener-Environment-Message model used in rehabilitative audiology that includes message-related linguistic and contextual factors (e.g., Erber 1988; Robertson et al. 1997).
Blue-colored boxes in Figure 1B indicate responses or outputs similar to Kahneman’s model. We have replaced the label “miscellaneous manifestations of arousal” with “automatic arousal responses,” but the examples are consistent with Kahneman’s (his Figure 2.2, pp. 18, 1973). Specifically, Kahneman indicates that the miscellaneous manifestations of arousal would include automatic responses such as pupillary dilation, increased skin conductance, and changes in heart responses. Finally, where the original Kahneman (1973) model simply indicates “responses,” we renamed the component of the model “attention-related responses.” We elaborated by adding examples of measures (cognitive-behavioral, brain, autonomic nervous system, and self-report) that could be used to index attention-related responses. These responses are candidates for measuring listening effort insofar as they support inferences regarding the allocation of capacity or the expending of effort.
Importantly, Kahneman (1973) recognized the need to understand effort in terms of cognition and motivational arousal. In keeping with that outlook, we developed a three-dimensional (3D) figure (Fig. 2) to depict how effort might be related to demands and also to motivation. Our Figure 2 is based on two figures from Kahneman’s book. First, our Figure 1 was influenced by Kahneman's Figure 2.1 (pp. 15) that plots effort as the capacity supplied as a function of the capacity demanded by a task. Second, his Figure 3.2 (pp. 34) also influenced our Figure 2. His Figure 3.2 shows how the quality of performance varies nonlinearly with arousal level (based on Yerkes & Dodson 1908), such that performance can be reduced for either very low or very high arousal levels but with the specific nature of the function depending on the complexity of the task. Furthermore, in our Figure 2, we have innovated by plotting motivation as a third axis to illustrate how the effort expended might vary (according to the allocation policy) with both the demands and the motivation dimensions. The demand dimension would depend on input-related demands (e.g., signal properties, hearing loss) and task demands based on automatic (e.g., default monitoring of the environment) and intentional attention factors (e.g., instructions). The motivation dimension would depend on how arousal or fatigue may influence the individual’s evaluation of the importance of success and the value of expending resources to meet demands on capacity. The axes for effort, demands, and motivation range from low to high; however, the units are unspecified. Notably, there is no agreement as to what would be an appropriate scale for any of the three dimensions, nor do we yet understand exactly how motivation and demands might interact to influence effort. In general, however, consistent with the views of Kahneman, it seems that there is potential for the measurement of effort to be compatible with a more traditional signal-detection approach. Although the shape of possible functions is unknown, for illustrative purposes, the motivation and demand dimensions are based on somewhat arbitrary sigmoidal functions using a four-parameter logistic model (Equation 1 with A = 0, B = 10, C = 0.5, and D = 1) consistent with typical psychometric functions.
A key advantage to depicting three dimensions is that some methods for assessing effort may be more sensitive to factors related to the nature of the sensory input or to task demands while other measures may be more sensitive to factors related to motivation and yet others may be influenced by an interaction of demands and motivation, including individual differences in auditory abilities and motivation. The 3D plot can serve to illustrate interindividual differences and intraindividual differences across conditions, as well as fluctuations in effort associated with variations in demands and motivation during the course of engaging in a complex task. For example, superimposed on the 3D plot is an illustration of how the effort expended by a person who is being studied might change over the time course of an activity as a function of both demand (e.g., task difficulty) and motivation (e.g., evaluation of success importance). For the case of a person attending a cocktail party, the following changes in effort due to changes in demands and motivation are plotted in the segments shown in the 3D plot: segment T0 to T1 shows little change in effort while demands are held constant at a low level, although there is increasing motivation as the person’s engagement in the task ramps up (this might happen if there is relatively little background noise but the topic of conversation becomes increasingly interesting); segments T1 to T2 to T3 show an increase in effort while motivation is held more or less constantly high but demands increase gradually (this might happen if the conversation continues to be highly interesting but the level of background noise increases as more people arrive at the party); segment T3 to T4 shows a sharp decrease in effort while demands remain more or less constantly high but motivation decreases rapidly (this might happen if the level of background noise remains steady but the highly interesting story finishes and the conversation becomes uninteresting). This final scenario is consistent with the development of fatigue. Specifically, our hypothetical individual could be viewed as initially being motivated to complete the demanding task and thus being willing to expend substantial effort to achieve that goal. However, over time, the effort-reward ratio becomes unacceptable, leading to the subjective experience of fatigue, a concomitant decrease in motivation to continue expending effort, and finally, a resultant drop in the effort expended on the task. At an extreme, it would be possible to use Figure 2 to illustrate a person “quitting” on one task and reallocating effort to another task. By explicitly portraying the possibility of independent and interactive contributions of various factors affecting demand and/or motivation, we hope that our FUEL will facilitate advances in our thinking about and our understanding of effortful listening. It may also guide research to discover what the underlying mechanisms are and how the connections between these mechanisms operate. Existing results may need to be reinterpreted in the light of our FUEL, and our FUEL may help in reconciling apparent discrepancies between studies. Our FUEL may also inspire the design and interpretation of future research and provide a useful support for counseling and the planning and evaluation of interventions to reduce effort by either altering factors pertaining to the demand and/or the motivation dimension.
METHODS AND MEASURES
The second of the three main goals of the Workshop was to consider the methods, techniques, and measures that have been used to study effortful listening. A very useful contribution of the white paper of the BSA group was the compilation of a list of purported measures of listening effort and, to a more limited extent, measures of fatigue. The measures they identified were organized into three categories according to the technique for administering the measures: behavioral measures, physiological measures, or self-report measures (see also Rudner et al. 2012). We considered the measures reviewed by the BSA group and also additional measures based on work covered in the articles of our workshop participants. It seems likely that, rather than inventing new measures, the future development of measures of listening effort and guidelines for their use in research or practice will involve clarifying which measure or combination of measures is most appropriate to use and for what purposes. Compared with measures of listening effort, however, it is less clear how measures of fatigue specific to listening could be developed.
Our consensus was that many measures could provide a measurable index of the construct of listening effort. However, in light of our proposed FUEL, a new way to categorize candidate measures is according to whether they have been used to primarily examine changes in listening effort as a function of variation in demands and/or as a function of motivation. It is potentially very useful to identify the measures that are most responsive to variations in the demand dimension versus the motivation dimension or the measures tap both dimensions. For example, behavioral cognitive measures (e.g., working memory span) have been used primarily to study the effects of manipulations in the demand dimension rather than the motivation dimension. Among physiological measures, some [e.g., preejection period (PEP)] have been used primarily to study the effects of manipulations in the motivation dimension rather than the demand dimension. Still other measures (e.g., pupil dilation) may capture changes in effort due to both demands and motivation. Furthermore, some self-report measures are based more on demands (e.g., the emphasis is on perception of task difficulty), while others are based more on motivation (e.g., the emphasis on success importance). With respect to our FUEL, some measures of effort might be mapped to responses (i.e., attention-related responses) that could depend on manipulations of demands (e.g., input-related demands) or motivation (e.g., the evaluation of demands in relation to performance). In addition, other measures may serve to assess inter- and intraindividual differences in available capacity (e.g., working memory), how it fluctuates with the amount of arousal (e.g., stress-related hormones), or how the allocation policy operates (e.g., executive functions).
In addition to delineating measures with respect to their mapping to the FUEL, our consensus was that future basic research will need to investigate the mechanisms underlying listening effort and that research to advance practice will need to consider the clinical purposes for investigating listening effort. We agreed that there were three broad purposes for using measures of listening effort in practice: (1) for assessment and the determination of candidacy for particular treatments or technologies; (2) to evaluate and compare outcomes of treatments or technologies; and (3) to screen for clinically significant cognitive impairment or dementia. Once one or more appropriate measures are identified and more is understood about how the measures relate to underlying mechanisms, work will still need to be done to validate and norm tests and to specify standard procedures for administering them and for interpreting them clinically.
The choice of the specific dependent measure of listening effort will depend on the purpose for which it is used. In the design of experiments, choices will also need to be made about how to implement variants of test protocols and conditions depending on the population, the intervention, and which comparisons will be made using the chosen dependent measure. These sorts of decisions may also apply to the implementation of chosen measures of listening effort in clinical protocols, following the Population, Intervention or interest, Comparison group or intervention, Outcome method advocated for evidence-based medicine (Sackett et al. 2000).
Population and Comparisons of Groups
In terms of population, the suitability of and norms for tests of listening effort and fatigue will need to be determined for different populations (e.g., children, healthy older adults, and adults with comorbidities such other sensory or cognitive impairments). Within groups, tests to detect interindividual differences may be of interest.
Between-group comparisons may be of interest: younger versus older adults; people with versus without hearing loss; people with less versus more hearing loss; people with sensory versus neural hearing loss; people who are healthy versus those who are depressed; cognitively normal versus cognitively impaired; native versus nonnative speakers, and so on. Longitudinal studies may also be valuable, especially given the ample evidence of plasticity and brain development in children and brain reorganization in aging adults. For example, brain imaging studies suggest that older adults may compensate for sensory or motor declines by activating more widespread brain regions (for reviews, see Li et al. 2005; Reuter-Lorenz & Cappell 2008; Park & Reuter-Lorenz 2009; Grady 2012); however, the potential for cognitive compensation for sensory or motor declines may be limited by cognitive declines (Seidler et al. 2010). Ideally, these changes should be followed longitudinally because age-related changes in cognition may be overestimated in cross-sectional studies in which cohort effects are not controlled (Rönnlund et al. 2005).
Interventions and Comparisons of Interventions
With reference to the FUEL, additional within-subject comparisons that warrant further research should explore how listening effort is affected by interventions (e.g., communication training or the use of hearing technologies) or experimental conditions that manipulate the demand dimension and/or the motivation dimension. For example, manipulations in the demand dimension could include comparisons such as steady state versus two-talker competition; normal versus speeded rate of speech; less versus more difference between the fundamental frequencies of the target and competing talker voices (male versus male or male versus female); with versus without spatial separation in multitalker scenes; with versus without visual cues; single-task versus dual-task conditions; with versus without supportive semantic or situational context; with familiar versus unfamiliar music; neutral versus emotional speech; with versus without hearing aid; with hearing aid A versus hearing aid B; and pre- vs. post musical training. Manipulations in the motivation dimension could include comparisons such as low versus high success importance conditions; conditions predisposing low versus high fatigue; conditions with versus without stereotype threat; before versus after self-efficacy training; pre- vs. post intervention to promote social support by a significant other of a person who is hard of hearing; pre- vs. post group interventions to develop strategies for goal pursuit/avoidance decisions; pre- vs. post intervention to optimize the pleasure of listening. Note that, for factors in the demand or motivation dimensions that affect listening effort, some manipulations may explore adverse affects that increase listening effort, while others may explore factors that increase listening ease or decrease listening effort. Ultimately, the factors that are modifiable in the direction of reducing listening effort (or even increasing listening pleasure) may foster new insights into existing successful interventions and/or the development of new interventions.
Kahneman (1973, pp. 185) comments that, “the observation of a close correspondence between behavioral and physiological measures provides strong support for an effort theory.” He goes on to say (pp. 188) that “The methodological moral is clear: effort or load should always be measured by at least two independent methods, so chosen that they are unlikely to cause structural interference in the same way…. either of these [behavioral] methods could be used in conjunction with physiological measures of effort and arousal….[or] a combination of a behavioral method with measurements of evoked cortical responses.” Our consensus was that research should be conducted to examine how well different measures of listening effort are correlated with each other and whether or not it would be advantageous to combine tests. Such research could influence clinical protocols insofar the evidence would support recommendations to the use of a single test or a battery of tests. Below is a list and description of candidate measures that have been and will likely continue to be used in research on listening effort. (Note that this list overlaps with but is not identical to the list published by the BSA group.) The general categories of the measures listed are cognitive-behavioral, physiological, and self-report measures.
Relevant cognitive domains that could be measured using behavioral tests to gauge listening effort include those that index working memory, attention, or speed of processing. These three domains are interrelated. Working memory capacity is limited and can be allocated to processing and storing information during the performance of complex activities such as language comprehension or listening while multitasking. More generally, attention is involved in the allocation of capacity to activities, including the selection and maintenance of information during the performance of one activity (selective attention) or multiple activities (divided attention). Furthermore, it is assumed that the speed of processing slows with increases in the amount of capacity demanded by a task. At a limit, if the available capacity is exceeded, then either processing must slow or else errors occur. Note that traditional audiologic measures of word recognition accuracy may indicate that capacity has been exceeded. In contrast, the appeal of measures of effort is that they could be used to assess how much capacity is allocated to listening as demands increase but before the limits of available capacity are exceeded (see Lunner et al 2016, this issue, pp. 145S–154S).
Tests of working memory based on tasks involving both the processing and the storage of information (e.g., the reading and listening spans in their several versions derived from Daneman & Carpenter 1980; see also Wingfield 2016, this issue, pp. 35S–43S) are more correlated to language comprehension than are other memory tests (e.g., digit span) based on tasks that involve only the storage of information (Daneman & Merikle 1996). In typical working memory span tests, the amount of capacity allocated to processing during a language processing task (i.e., listening or reading effort) is inferred by measuring the number of items that can be recalled from sets of varying size. Given the assumption that capacity if limited, if more capacity is allocated to listening (or reading), then less spare capacity will remain available for storing information. The listening (or reading) span is the maximum set size where the listener recalls all items in the set. Larger listening spans indicate that there was more spare capacity and that less capacity was used for processing information during listening (or reading). By manipulating input-related demands (e.g., the amount or type of background noise) and measuring listening span, it is possible to examine the effects of the manipulation on the allocation of capacity (e.g., Pichora-Fuller et al. 1995). Audiology researchers have used reading or listening span tests in experiments concerning speech-in-noise performance (for a review, see Besser et al. 2013). However, only recently has research begun to standardize a test of working memory span (the Word Auditory Recognition and Recall Measure; Smith, Pichora-Fuller, Wilson et al, in press) for clinical use by audiologists. Another test of working memory that has been used in research on listening is the N-back memory measure that manipulates working memory load (e.g., Rudner et al. 2015; Sommers & Phelps 2016, this issue, pp. 62S–68S). Other tests of listening working memory discussed at the workshop include the Sentence-final Word Identification and Recall test (Lunner et al 2016, this issue, pp. 145S–154S), the Cognitive Spare Capacity test and the Auditory Interference Span test (Rudner 2016, this issue, pp. 69S–76S), which have been developed specifically to measure spare capacity using Swedish speech materials. Spare capacity is important because it may provide an indication of how much information can be encoded into long-term memory and consolidated as knowledge in the process of learning.
As in Kahneman’s capacity model, in the FUEL, capacity can be allocated to one or more activities according to the allocation policy. Given that capacity is limited, the assumption is that as more capacity is allocated to one activity, less capacity will remain for another activity. The ability to divide attention between activities has most often been measured using the dual-task paradigm. Two tasks (a primary and a secondary one) are performed alone or simultaneously. Reduced performance on the secondary task when it is performed in the dual-task condition compared with when it is performed in a single-task condition is used to index the cost of dual-tasking or how much capacity is diverted from the secondary task and allocated to the primary task (see Edwards 2016, this issue, pp. 85S–91S). Insofar as dual-task cost is an index of how much capacity is allocated, it could be used to make inferences about listening effort. Note that the listening working memory span test can be considered as a special case of a dual-task test, with processing information during listening being the primary task and recall being the secondary task.
Speed of Processing
It is widely accepted in cognitive psychology that the amount of time spent to complete a task varies with the amount of capacity allocated to it. In general, processing speed is the fastest rate at which a cognitive operation can be performed with reasonable accuracy (Phillips 2016, this issue, pp. 44S–51S). This index of cognitive capacity allocation is used to gauge pervasive effects (Kail & Salthouse 1994), ranging from sensory to response stages of information processing (Kramer & Madden 2008). It follows a U-shaped trajectory over the lifespan, with differences in processing speed between younger and older adults being among the most widely replicated effects in the domain of cognitive aging (Salthouse 1996). Processing speed is measured as time to perform a given task (e.g., digit-symbol transcription or simple versus choice reaction time). It is not a “process pure” measure. Thus, it is advisable to use multiple measures of processing speed to allow findings to converge on a common underlying construct (Salthouse & Madden 2008). Reaction time is the most common behavioral measure of speed of processing. In the context of listening effort, this index might include measuring reaction time in the performance of a nonauditory task (e.g., simple versus choice reaction time to simple visual stimuli) and an auditory task, to examine domain-general variance associated with the underlying processing speed construct and domain-specific variance associated with auditory processing speed (Deary et al. 1989; Deary 1994; see Phillips 2016, this issue, pp. 44S–51S for a fuller discussion). When the accuracy of performance is at or near ceiling, hearing researchers have used reaction time measures to evaluate individual differences in speech-in-noise listening (e.g., Hällgren et al. 2001) and the effects of acoustic distortions and semantic context on listening (Goy et al. 2013).
Physiological measures that could be useful for measuring listening effort fall into two main categories: measures of brain activity and measures of the autonomic nervous system. The main techniques for measuring neural brain activity that may be useful for indexing listening effort are magnetic encephalography (MEG), evoked-response potentials (ERPs), alpha power in electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). In general, these techniques vary in the quality of information they yield regarding the timing and region-specific localization of brain activity, with ERP yielding the most precise timing information and fMRI yielding the most precise localization information. Measures of the autonomic nervous system may tap sympathetic or parasympathetic responses. In general, the “fight-or-flight” response of the sympathetic nervous system prepares the body for high-energy activity, whereas the parasympathetic nervous system has the complementary effect of relaxing the body and inhibiting or slowing many high-energy functions. Autonomic responses can be measured using pupil, cardiac, skin conductance, or hormonal responses. Some studies have also combined these techniques to investigate the associations among them.
MEG and ERP
MEG and ERP measurements have been used to study time-locked neural activity evoked by the presentation of and the response to stimuli (see Trembley & Backer 2016, this issue, pp. 155S–162S). For example, the amplitude of the time-locked auditory evoked P3a has been shown to be sensitive to the increased attentional demands of a task and the increased effort of listeners (Combs & Polich 2006; Bertoli & Bodmer 2014, 2016). The P3a is a positive-oriented scalp-recorded potential that has a maximum peak amplitude over frontal/central electrode sites with a peak latency falling in the range of 250 to 280 msec. It is associated with brain activity related to attention (especially orienting and involuntary shifts to changes in the environment) and the processing of stimulus novelty (Polich 2003). When the difficulty of speech-in-noise tests increases [e.g., signal to noise ratio (SNR) decreases], the amplitude of the novelty P3 and late positive potential changes; for this reason, ERPs such as these are considered to provide an indirect, physiological measure of listening effort; however, other explanations could also be given (see Trembley & Backer 2016, this issue, pp. 155S–162S).
Alpha Power in EEG
Changes in oscillatory power in EEG, including changes in alpha, theta, and other responses have been interpreted as reflecting increased demands on the storage and inhibition of information. For example, enhanced alpha oscillations (8–13 Hz of the continuous EEG signal) are documented as neural substrates of increased cognitive effort, in line with a functional, inhibitory role of alpha in controlling or gating local circuits of neural activity (e.g., Weisz et al. 2011). Recent research has shown that acoustic degradation (vocoding) of the signal increases alpha oscillations during listening, suggesting that enhanced alpha power is not only modulated by changing domain-general requirements such as the number of stored items, but that challenges arising from mild-to-severe sensory degradation also affect this system. Both manipulations cause an enhancement of oscillatory power in the same time–frequency range (Obleser et al. 2012). Notably, a recent study on alpha power modulation using a working memory paradigm in older hearing-impaired listeners showed that the degree of hearing loss predicted alpha power enhancement (Petersen et al. 2015).
Functional Magnetic Resonance Imaging
fMRI uses blood-oxygen-level-dependent contrast imaging to provide an estimate of brain activity based on the hemodynamic response to increased neuronal demand for oxygenated blood during a task. Notably, frontal brain regions in younger and older adults demonstrate an elevated hemodynamic response when listening tasks are challenging (Vaden et al. 2013, 2015). One interpretation of these kinds of blood-oxygen-level-dependent results related to task demands is that the elevated activity, particularly in the cingulate cortex, reflects a decision-making process about the expected value of working to optimize performance given the potential value realized from the task.
For many years, the pupil diameter has been considered to be an index of cognitive processing load (Kahneman 1973; Kramer et al 2016, this issue, pp. 126S–135S). There is ample evidence showing that the pupil diameter is sensitive to momentary, task-evoked load, and effort during mental tasks. However, different parameters in the pupillary response index different concepts or mechanisms. For example, peak pupil dilation indexes momentary load, whereas the resting pupil diameter before and after the presentation of the stimulus indexes an individual’s state of engagement. Pupil constriction, as evoked by light (pupil light reflex), indexes parasympathetic activity. Thus, the pupil response always combines the activity of both the sympathetic and the parasympathetic nervous systems. Pupil dilation has been correlated to changes in the acoustics of stimuli and to subjective loudness (Liao et al. 2015). With respect to motivation, a recent study in monkeys found that the firing rate of noradrenergic coeruleus neurons in the brain increased and was correlated with both pupil dilation and effort related to the energization of behavior (Varazzani et al. 2015). It is unknown how pupil responses such as the momentary peak pupil dilation relate to fatigue in the longer term or to stress as indexed by cortisol or other biomarkers of stress, but researchers began to explore these associations (e.g. Kramer et al. 2016, this issue, 126S–135S). Research on cognitive processing load during listening using pupillometry has shown that the pupil response during listening is sensitive to speech intelligibility (Zekveld et al. 2010), type of background noise (Koelewijn et al. 2012), syntactic complexity (Piquado et al. 2010), auditory stimulus characteristics (Kramer et al. 2013), degraded spectral resolution (Winn et al. 2015), cognitive abilities (Zekveld et al. 2011), and divided (versus focused) attention (Koelewijn et al. 2014).
Two cardiac measures that may be related to listening effort are heart-rate variability (HRV) and the PEP. HRV measures quantify the amount of variation in heart rate over time. HRV can be analyzed in both the time (e.g., standard deviations of interbeat intervals) and the frequency domains (e.g., spectral analysis of variations in interbeat intervals). Most HRV metrics reflect activity from both the sympathetic and the parasympathetic nervous systems; however, two measures, square root of the mean squared difference between normal beats and high-frequency HRV), reflect primarily parasympathetic activity. As reviewed by Mackersie and Calderon-Moultrie (2016, this issue, pp. 118S–125S), a reduction in HRV with increased listening task demand has been observed for several HRV measures, and thus may be useful as an index of listening effort.
The PEP refers to the time interval between the beginning of the excitation of the left heart ventricle and the opening of the aortic valve. It is a direct indicator of myocardial contraction force—the stronger the heart contracts, the shorter is the PEP. Given that myocardial contraction force is mainly determined by sympathetic activity, changes in PEP reflect changes in myocardial sympathetic activity. Researchers working on motivational intensity theory (e.g., Brehm & Self 1989; Wright 1996) have used this relationship between PEP and sympathetic activity to test the effort-related predictions (see Richter 2016, this issue, pp. 111S–117S). The use of PEP in research on listening effort could enable researchers to assess changes in myocardial sympathetic activity associated with listening effort. In combination with the assessment of high-frequency HRV as an indicator of parasympathetic activity, researchers may be able to examine the autonomic nervous system response that characterizes effortful listening.
Skin Conductance Responses
Skin conductance measures quantify the electrical activity on the skin surface. This activity is mediated by the sympathetic nervous system. Skin conductance measures have been used to infer automatic attention (orienting), effort, motivation, and emotional reactivity (Kahneman 1973; Andreassi 2007; Boucsein 2012). An increase in skin conductance with increasing listening task demands has also been observed for some speech repetition tasks, suggesting a potential role in the evaluation of listening effort (see Mackersie & Calderon-Moultrie 2016, this issue, pp. 118S–125S).
Endocrine biomarkers can be used to index the activity of the autonomic nervous system. Several stress hormones are involved in the regulation of the changes that occur in the body in response to stress. In particular, reactions to stress are associated with enhanced secretion of a number of hormones, including but not limited to, cortisol, chromogranin A, and α-amylase. Only a few studies have measured hormonal responses in studies of hearing loss; for example, one study reported preliminary evidence that the effects of noise on the performance of memory and attention tasks, subjective fatigue, and stress measured with cortisol and catecholamines differed between participants who had normal or impaired hearing (Jahncke & Halin 2012). As reviewed by Kramer et al. (2016, this issue, pp. 126S–135S), the relationship between biomarkers of stress and chronic stress resulting from hearing impairment or momentary stress evoked by speech testing is still controversial.
Self-Reported Listening Effort, Fatigue, or Stress
Certainly, people seeking help for hearing problems often provide spontaneous descriptions of their experiences of effortful listening or fatigue. Some researchers and clinicians have attempted to use self-report measures or subjective ratings to assess listeners’ self-perceived distress, effort, or fatigue. Visual analog scales (VASs) are often used to assess the self-reported momentary allocation of cognitive capacity to meet particular input-related demands of listening, either during or after a set of trials in the condition(s) of interest; for example, the listener may be asked to indicate on a VAS from 1 to 10 how effortful it was to listen to and repeat words in different SNR conditions. VASs may also be used to assess a listener’s motivation to complete a task (see Kramer et al 2016, this issue, pp. 126S–135S). Alternatively, single items addressing listening effort may be extracted from existing questionnaires, such as the Speech Spatial and Qualities of Hearing Scale (Gatehouse & Noble 2004; see also McGarrigle et al. 2014). Notably, perceived effort during task performance may be an indicator of listening effort, but such self-report measures may also be somewhat generic in nature and tap into some sort of more general chronic stress such as need for recovery (Nachtegaal et al. 2009) or fatigue (see Hornsby et al 2016, this issue, pp. 136S–144S).
One example of how self-report measures could be aligned with FUEL is a promising new self-report approach to determine a listener’s lowest acceptable performance level (Boothroyd & Schauer 2015), thereby gauging when a listener is likely to give up listening. To measure the lowest acceptable performance level, listeners were given a description of a common hypothetical scenario (conversing on an interesting topic with friends in a restaurant) in various SNR conditions corresponding to recently experienced speech-in-noise test conditions in which word recognition accuracy had been measured. For the hypothetical scenario in each SNR condition, listeners estimated their expected performance in terms of percent correct word recognition and then they indicated how long they would be able sustain attention and how long they would be willing to sustain attention to listening in the scenario. Listeners also rated how loud, annoying, and distracting the noise was and how much it interfered with speech understanding (following Mackersie et al. 2014; Lane & Mackersie 2015). Interestingly, using these self-report questions, it was possible to categorize listeners into two groups according to motivational factors, one group being more noise-focused and the other being more speech-focused. Importantly, although the two groups performed similarly on the listening test, they demonstrated different tendencies to quit listening with increasing input-related SNR demands, presumably because they differed in their motivation to listen in demanding situations. The noise-focused group was motivated to avoid noise, whereas the speech-focused group was motivated to listen to speech. Such a self-report measure could enable clinicians to consider input-related demands, as well as an individuals’ motivational focus in relation to their likelihood of sustaining the allocation of capacity (i.e., effort).
Concerning the third main area of the workshop, our consensus was that there is an imperative to translate knowledge about effortful listening into practice because it is a frequently reported and concerning issue for people who are hard of hearing and our current interventions do not adequately address it. Importantly, the need to address the issue of effortful listening compels us to draw on knowledge about auditory and cognitive processing and to augment it with knowledge about motivation and arousal so that we can better assess and ameliorate everyday listening experiences and functioning. Ultimately, such knowledge translation is necessary if we want to prevent avoiding or quitting as a short-term coping strategy and social withdrawal as a long-term health-compromising consequence of listening being too effortful to be sustained.
There is sufficient converging scientific evidence showing that the deployment of cognitive resources can be crucial for listening, especially when demands increase in challenging listening situations. There have been important advances in research, and numerous behavioral, physiological, and self-report measures have been used in experiments. In addition, the articles in this special issue provide many examples of research conducted with participants recruited from clinical populations and research conducted to evaluate the effects of different technologies on listening effort. Research has begun on fatigue in children and adults with hearing loss. Nevertheless, more knowledge is needed concerning the relationship between effortful listening and fatigue. In particular, there could be important clinical implications as new knowledge is discovered concerning the short- and long-term effects of effortful listening on fatigue and possible changes in the functioning of the autonomic nervous system due to chronic listening effort or fatigue. As of now, however, there are still no standardized measures of listening effort or fatigue that are ready for use in routine clinical practice.
Gathering evidence to show the relevance of measures of listening effort for practice and completing research to standardize tests, however, will not be sufficient to guarantee the adoption of measures of listening effort and fatigue in practice. This will only happen if the test protocols used in research can be modified to be feasible for audiologists to conduct within the time-constraints of busy clinics and using methods that are suited to a general population or special populations. Furthermore, for knowledge translation to succeed, audiologists will need (continuing) education to develop new competencies and become comfortable in administering and interpreting tests of listening effort. They will also need to develop expertise in using the results of such tests to inform the planning and evaluation of interventions, including matters related to hearing aid selection, fitting, acclimatization, adherence, and outcomes.
Questions regarding the appropriateness of cognitive screening for dementia by audiologists also call for the translation of knowledge about cognition into practice, but this type of cognitive screening testing differs in a number of ways from measuring listening effort. There is a solid literature demonstrating deficits in cognitive processes, including memory and language, in older adults who have dementia. Clearly, compared with listeners with normal cognition, those who have cognitive impairments will have even more difficulty allocating capacity to specific listening activities, especially in challenging situations. It may not be reasonable to test this population using measures of listening effort that are appropriate for people who have normal cognition. However, for numerous additional reasons, one being that people who are hard of hearing are at greater risk of developing dementia than peers with normal or near-normal hearing, dementia is a comorbidity that needs to be considered in planning rehabilitation for hearing loss, at least by audiologists working with older adults (Pichora-Fuller et al. 2013). There are widely used standardized screening tests for dementia, but performance on these tests can be negatively affected by sensory impairments, and more research is needed to adapt test protocols for people who have sensory impairments (Dupuis et al. 2015; Phillips 2016, this issue, pp. 44S–51S). Again, for successful adoption of these tests in practice, audiologists will need (continuing) education so that they develop new competencies and become comfortable in administering and interpreting cognitive screening tests. Even if audiologists have access to the results of cognitive tests conducted by neuropsychologists, geriatricians, or family physicians, and do not administer such tests themselves, they will still need to develop expertise in using the results of such tests to inform their practice with older adults, especially given the aging of the population.
There are many potential ways in which a better understanding of effortful listening could revolutionize practice. However, we are still in the early stages of exploring how to combine and adapt elements of existing theories and models to facilitate a better understanding of effortful listening and the mechanisms underpinning it. We hope that our proposed FUEL can be used to guide future research and to expedite the translation of existing and new scientific knowledge about effortful listening into practical applications that could be implemented in audiology clinics and hearing technology industries. Below is a summary of key priorities for research and practice.
Priorities for Research
A large number of research priorities were identified at the workshop. These are organized below roughly according to the Population, Intervention or interest, Comparison intervention or group, Outcome method advocated for evidence-based medicine (Sackett et al. 2000).
Populations and Comparisons of Groups
Future research on listening effort and fatigue may apply to the general population, including people with normal hearing or with specific degrees or types of hearing loss; however, a lifespan perspective will be needed to discover if and how effortful listening changes as the auditory system develops in children and adolescents or declines in adults. Studies will need to use longitudinal designs, rather than only cross-sectional designs, to determine the short- and long-term associations between listening effort and adjustment to hearing loss. Over time, how do changes in hearing abilities alter the effects of input-related demands and motivation on listening effort? Conversely, over time, how does effortful listening or fatigue affect everyday functioning in terms of participation in social activities, stress, and coping associated with hearing loss or readiness to seek help or take action to manage hearing problems? Is effortful listening associated with psychological, social, or health factors?
Interests—Mechanisms Underpinning Listening Effort and Fatigue
To continue to develop the FUEL, research will be needed to map out the functions underlying the demands and motivation dimensions illustrated in Figure 2. What can patterns of brain activation in response to manipulations of input-related demands and/or motivation (arousal, success importance or adaptive control) reveal about the mechanisms underpinning listening effort or fatigue? Brain imaging, electrophysiological, and neurophysiological (e.g., neurotransmitters) studies will be needed to elucidate the cortical regions and processes involved in effortful listening, how they vary according to demands and motivation, and how they may change over time. Research could also explore and develop applications of Motivational Intensity Theory to particular challenging auditory tasks. Research will need to consider what confounding factors (e.g., cognitive reserve, personality) should be controlled or factored into an individual differences approach to the study of listening effort.
The FUEL should be used for research to identify potential modifiable moderators of listening effort in terms of demands and motivation with the aim of using these research findings to guide the design of interventions that could reduce listening effort. New interventions might be based on research showing how the allocation policy can be altered by training or counseling. New approaches to rehabilitation might be structured based on research regarding the relative importance of automatic and intentional attention compared with input-related demands on capacity, including demands related to source (e.g., accent or emotion of the talker), transmission (e.g., background noise or device), or listener (e.g., hearing loss) factors. New motivational interventions might exploit research on the use of behavioral and neuroeconomics approaches to provide quantitative metrics for explaining when, why, and how much people experience effort and which factors could potentially be modified? A patient-centered approach could incorporate research findings demonstrating the potential for modifying motivation by using strategies to promote task (dis)engagement or boost self-efficacy or listening pleasure. Similarly, new interventions could be developed in response to findings showing that the expected value (success importance) of listening affects the perception or onset of fatigue.
Comparisons of Short Versus Long-term Effects of Treated Versus Untreated Listening Effort
Research is needed to determine whether transient or short-term listening effort and/or fatigue can progress to become chronic debilitating conditions (stress, cognitive impairment, fatigue) and whether interventions could counteract such deleterious long-term effects.
There are a large number of potential measures of listening effort and fatigue, but few have been sufficiently operationalized and none have been standardized for clinical use by audiologists. The FUEL could be used in research to determine which of the potential measures are the best, either alone or in combination, for gauging listening effort for different purposes and in different populations. Research to assess the strength of the correlations among measures will be needed to guide decisions about the possible advantages of using a test battery. The ecological validity of potential measures should also be studied to determine how well they predict the everyday experiences of listeners in realistic communication situations, including their likelihood of quitting listening tasks in specific conditions such as conversational interactions. Research using ecological monitoring methods and mobile technology in the real world could be used to validate laboratory-based or clinic-based measures of effortful listening. Research may also clarify if there is a cognitive or listening analog of a physical fatigue measure.
Priorities for Clinical Practice
Many of the priorities for research should lead to the development of new clinical practices. Priorities for practice involve both deepening our understanding regarding what underlies successful aspects of existing practice and developing new practices.
Development of Clinically Feasible and Relevant Measures
Many potential measures of listening effort have been used in the laboratory, but none have been adapted for clinical use. As described in the section on Knowledge Translation, research and education will be needed before viable measures of listening effort could be endorsed for use in the clinic. Research will be needed to determine test properties and the sensitivity and specificity of clinically feasible versions of tests to assess individuals and the outcomes of interventions. An important prerequisite for changing clinical practice will be to establish the purposes for and advantages of using such measures.
Guidelines for Use of Measures in Assessing Candidacy for Interventions
Guidelines will need to be developed concerning the appropriate use of new measures of listening effort. For example, some guidelines might cover how audiologists could use these measures to determine which device features or training regiments to recommend or to predict who would benefit most, report more problems listening, or be more likely to quit listening in what sorts of situations. These measures might influence counseling individuals about appraising success importance, setting goals for managing how listening effort is spent, or how to derive more pleasure from listening or minimize input-related demands (e.g., by selection/modification of communication environments to reduce adversity). Such counseling about listening effort could complement considerations of other emerging topics in rehabilitative assessment such as factors that predispose help-seeking, readiness to take action to manage hearing problems, the benefits of improving self-efficacy, the advantages of social support, and ways to overcome stigma or even stress and risk of dementia (Pichora-Fuller 2016, this issue, pp. 92S–100S). Guidelines would also be needed regarding the appropriate use of cognitive screening for clinically significant cognitive impairment or dementia in rehabilitative audiology, including during hearing aid fitting or training for the person who is hard of hearing or their significant other or caregiver.
If it becomes feasible to measure listening effort in the clinic, existing interventions could be reframed or new interventions developed to reduce listening effort or fatigue (and/or increase ease or pleasure) and to train individuals in strategies to control or regulate the allocation of effort. Based on the research described earlier, interventions could be developed to modify the time course over which listening effort or fatigue affects new hearing aid wearers (e.g., they might be trained to increase endurance, immunizing them from abandoning device use) or to sustain social participation in experienced users and prevent social withdrawal or reduce the risk of dementia.
Evaluating Outcomes of Interventions
Of course, measures of listening effort will need to be validated for use in evaluating the effectiveness of interventions. New outcome measures to evaluate change in listening effort would be extremely useful for evaluating the effectiveness of existing interventions (e.g., do hearing aids reduce/increase effort), to evaluate the comparative effects of different treatments or treatment combinations, and to determine if or how the effects of treatments depend on individual differences related to listening effort.
Other Practice-Related Issues
The development of measures of listening effort suitable for use by audiologists will raise other practice-related issues, including (1) revising the audiology curriculum to educate audiologist about listening effort, how to measure it, and how to use test results, (2) establishing or updating information-sharing about measures of listening effort with interprofessional team members (e.g., psychologists, geriatricians), and (3) delivering public education to increase awareness of new research findings and new rehabilitative options based on new knowledge about listening effort.
Our consensus resulted in a proposed FUEL. Our FUEL interprets core concepts from Kahneman’s seminal Capacity Model of Attention in relation to studies of listening effort and fatigue. The 3D plot in Figure 2 based on our FUEL provides a way to visualize how the demands and motivation dimensions could independently or interactively modulate effort. Although the scales for the dimensions remain unknown, by visualizing the combined effects of demands and motivation on effort, the 3D figure offers a tool that may inspire a new era of research on listening effort and fatigue that will yield knowledge that can be translated into practice. Areas of practice that could benefit from measures of effort include assessing candidacy for particular technical and/or therapeutic interventions and the evaluation of outcomes. Another important area of practice is cognitive screening for dementia; this area involves the assessment of cognitive ability but is distinct from the measurement of listening effort or fatigue.
We thank the Oticon Foundation for their financial support and staff at the Eriksholm Research Centre for facilitating the workshop. We also acknowledge the excellent feedback we received in the months following the workshop from many colleagues who were not workshop participants. Alexander Francis shared information from the special sessions on Listening Effort that he had organized at the 169th Meeting of the Acoustical Society of America held in May 2015. Erv Hafter and Frederick Gallun provided helpful comments about the applicability of a signal-detection approach to listening effort.
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