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. 2025 Mar 1;36(2):95–106. doi: 10.3766/jaaa.230043

Different Attention Domains and Speech-in-Noise Performance: A Preliminary Study

Payton Guinn *, Ishara Ramkissoon †,‡, Mark Hedrick §, Dania Rishiq #,✉
PMCID: PMC12445273  PMID: 40246522

Abstract

Purpose:

The primary aim of this preliminary study was to explore the relationship between five attention domains, cognitive flexibility, and speech-in-noise (SIN) performance in both auditory-only (AO) and audiovisual (AV) modalities.

Methods:

Ten younger and 10 middle-aged adult participants who had standard pure-tone averages no greater than 15 dB HL completed the following three behavioral measures. The Multimodal Lexical Sentence Test for Adults (Kirk et al, 2012) was used to evaluate speech-in-noise performance in AO and AV modalities. Two lists of 12 sentences were presented at a fixed 0-dB signal-to-noise ratio for each of the AO and AV conditions. The Attention Processing Training test (Sohlberg and Mateer, 2005) was administered to each participant, presented bilaterally at 60 dB HL via insert earphones to assess five domains of attention: sustained attention (I), complex sustained attention (II), selective attention (III), divided attention (IV), and alternating attention (V). The Comprehensive Trail-Making Test, Second Edition (Reynolds, 2019) was administered to assess participants’ inhibitory control and cognitive flexibility, which are heavily influenced by attention.

Results:

Correlation and regression analyses of these sample data indicated a significant link between alternating attention and SIN performance in the auditory modality in the younger adults. This link was not observed in middle-aged adults, nor for audiovisual SIN performance.

Conclusions:

In this study sample, younger individuals with better alternating attention abilities were able to better use contextual information to understand speech in noisy situations without visual context support. The younger adults capitalized on their alternating attention capacities to improve their auditory-only SIN performance, whereas the middle-aged adults did not demonstrate this ability despite similar (sometimes better) alternating attention scores. Alternating attention was not used in the AV modality in either group, possibly due to the simultaneous demand of visual and auditory inputs.

Keywords: speech-in-noise, attention domains, cognition, cognitive flexibility, auditory modality, audiovisual modality

INTRODUCTION

Cognition is a broad concept that encompasses several mental capacities such as reasoning, working memory, attention, executive function, and processing speed (Schoof and Rosen, 2014; Rönnberg et al, 2019). The auditory system and its function are highly integrated with cognition, an integration that is dynamic and underpinned with complex neural interactions (Anderson et al, 2013). It is believed that various cognitive functions are critical for everyday communication, especially in challenging listening conditions (e.g., Shen et al, 2016; Souza, 2018). Cognition is also linked to speech-in-noise (SIN) performance (e.g., Schvartz et al, 2008; Anderson et al, 2013; Billings et al, 2019) as well as to benefits from certain hearing aid processing features (e.g., Souza and Sirow, 2014; Souza, 2018).

The role of cognition is particularly important for understanding speech in noisy environments and when listening to competing acoustic stimuli (Akeroyd, 2008; Ferguson and Henshaw, 2015; Heinrich et al, 2015). Understanding SIN engages various cognitive functions such as attention and working memory. For example, selective attention is often recruited to extract relevant speech cues and to suppress irrelevant background noise, whereas working memory is recruited to store relevant information for later lexical retrieval—a necessary function for speech recognition.

Attention

Attention, the specific cognitive interest of this current study, is a fragment of the holistic concept of cognition. William James (1890) described attention as a concentration of consciousness and a mental process that steers the focus on one out of multiple objects or thoughts (Bayles and Kaszniak, 1987). It also involves a complex system that includes “paying attention, concentrating, focusing, dealing with distraction, and effectively allocating attentional resources” (Sohlberg and Mateer, 2005).

Attention is a crucial aspect of a person’s overall cognitive function and daily learning activities because it supports other cognitive systems (Posner and Petersen, 1990). Memory, including working memory, short-term and long-term memories, and other cognitive abilities rely on the availability of attentional allocations (Russell and D’Hollosy, 1992). Attentional abilities need to be focused for the effective recall of information (memory), especially in the face of interfering extraneous stimuli (Oberauer, 2019). It is also believed that attention has an interdependent relationship with memory processes and executive function (Sohlberg and Mateer, 2001). Furthermore, memory deficits were found to be associated with reduced attentional allocations (Ponsford and Kinsella, 1988). That is, poor attention abilities often underlie difficulties with memory and new learning (Sohlberg and Mateer, 2005).

Attention Domains

Although the general concept of attention is agreed upon, there are numerous models that describe the different domains of attention. A hierarchical model of attention was proposed by Sohlberg and Mateer (1989), wherein the functionality of each domain is dependent on those below it. This model divides attention into the following five domains: focused, sustained, selective, alternating, and divided attention (Sohlberg and Mateer, 1987, 1989). These five domains are the focus of this current study.

Sohlberg and Mateer (1989, 2001, 2005) defined the first domain, focused attention, as the basic ability of an individual to respond to a specific, internal or external, stimulus from a single sensory input. The response can be elicited from auditory, visual, or tactile stimuli. An example of focused attention would be turning your head toward an auditory signal (Sohlberg and Mateer, 1989, 2001, 2005).

The second domain, sustained attention, refers to an individual’s ability to maintain a continuous response to a consistent and unvaried stimulus (Sohlberg and Mateer, 1989, 2001, 2005). Sustained attention can be divided into two subcategories: vigilance and working memory. Vigilance refers to the ability to maintain attention over time during a continuous activity. If a patient’s vigilance abilities are impaired, he or she may only focus on a task for a brief period of time.

Selective attention, the third domain, is the capacity to maintain focus on a stimulus with the presence of a simultaneous distracting or competing stimulus (Sohlberg and Mateer, 1989, 2001, 2005). This ability is important while listening to speech in a noisy environment, which requires selective attention to focus on the speech signal and ignoring the competing background noise (Shinn-Cunningham and Best, 2008). Attention deficits affecting this domain will cause an individual to be easily distracted by competing signals (Sohlberg and Mateer, 2001).

Alternating attention, the fourth domain, refers to an individual’s ability to shift his or her focus of attention between tasks with different cognitive demands. This requires the individual to control which information is selectively processed (Sohlberg and Mateer, 1989, 2001, 2005). An example of this domain would be shifting focus between answering the phone and typing on the computer. Those with alternating attention deficits will have difficulties shifting their focus of attention between two different stimuli (Sohlberg and Mateer, 2001).

The fifth and final domain is divided attention, which is defined as the ability of the individual to respond simultaneously to multiple tasks (Sohlberg and Mateer, 1989, 2001, 2005). Divided attention involves either rapid, continuous alternating of attention or relying on the unconscious automatic processing for at least one of the tasks. An individual who is cooking and holding a conversation would be using the divided attention domain (Sohlberg and Mateer, 2001).

Cognition and SIN Performance

Numerous studies reported a link between cognition and SIN performance. For example, Billings et al (2019) reported that executive function is associated with better speech understanding in older adults. Anderson et al (2013) also investigated this notion further and found that cognitive abilities, specifically working memory and attention, help to determine how well an older adult understands SIN. Schvartz et al (2008) also found that the speed of processing and attention play a critical role in predicting SIN performance.

Cognition plays a significant role in understanding SIN in older listeners and those with a hearing impairment and in compensating for the deteriorating sensory function in these populations. Older adults and individuals with hearing loss may draw on cognitive compensatory mechanisms to fill in the gaps in the auditory message. That is, if the auditory signal is misrepresented as a result of aging and/or hearing loss, cognitive processing fills in the missing details in the auditory message. Furthermore, age-related changes in working memory and attention have been linked to poor performance on speech recognition testing (Hulme and Tordoff, 1989; Rouleau and Belleville, 1996; Wingfield and Ducharme, 1999; Billings et al, 2019).

Notwithstanding the links between cognition and SIN perception described above, the extent of the contribution from working memory and attention remains inconclusive. Whereas Anderson et al (2013) emphasized the strong cognitive influence of auditory working memory (as opposed to auditory attention) on SIN performance, results from Oberfeld and Klöckner-Nowotny (2016) reported no significant association between working memory and speech understanding and highlighted poor selective attention as the underlying reason behind the difficulties observed in complex listening environments.

The review of literature reveals numerous studies have explored how attention, as an overall concept, affects SIN performance (e.g., Schvartz et al, 2008; Wong et al, 2009; Anderson et al, 2013; Billings et al, 2019). Some of these investigations specifically focused on selective attention and its role in understanding SIN (Shinn-Cunningham and Best, 2008; Forte et al, 2017). However, the literature is relatively limited on examining how each individual attention domain may affect SIN performance. This current study will, therefore, examine the relationship between SIN performance and five attention domains (Sohlberg and Mateer, 2005) to highlight specific contributions, if any, per attention domain. This study will also add to the existing body of research that supports the association between cognition and speech understanding in noise.

Objectives

The primary objective of this preliminary study was to explore the relationship between five attention domains and SIN performance, in both auditory-only (AO) and audiovisual (AV) modalities. Specifically, the research questions of this study were the following: (1) Which attention domain(s) may uniquely relate to SIN performance? (2) Does the contribution of certain attention domains to SIN vary across presentation modalities, AO vs. AV? (3) Does age affect the contribution of certain attention domains to SIN performance?

METHODS

Participants

This preliminary study included 10 younger adults (YA) and 10 middle-aged adults (MA) who were recruited by word of mouth and advertisement in the local community. The YA group consisted of all females aged 22 to 25 years (average = 23.3 years ± 1.1) and the MA group consisted of eight females and two males aged 46 to 56 years (average 50.4 years ± 3.2).

Comprehensive audiometric evaluations, history, and otoscopic examinations were carried out in accordance with the American Speech-Language-Hearing Association (ASHA) best practice guidelines. Figure 1 presents the average audiometric data for the tested (right) ear for each of the participant groups. The mean standard pure-tone average (PTA) for the right ear for the YA participants was 5.7 dB HL and was 13.3 dB HL for the MA participants.

Figure 1.

Figure 1.

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Mean audiometric thresholds (in dB HL) for the right (test) ear of younger and middle-aged adults with standard error bars.

Furthermore, participants completed a case history questionnaire, providing information about any possible medical diagnoses, including attention-deficit/hyperactivity disorder (ADHD). One YA participant and one MA participant reported that they had been diagnosed with ADHD and were medicated at the time of data collection. To ensure adequate cognitive function, all participants were administered the Mini Mental State Examination (MMSE; Folstein et al, 1975), and they all scored within a satisfactory range of the MMSE. In addition, all participants had adequate visual acuity or adequate corrected visual acuity (Snellen acuity of 20/20), which was determined by a conventional Snellen acuity chart testing.

Procedures

The Attention Processing Training (APT) test (Sohlberg and Mateer, 2005) was administered through the Otometrics Madsen Astera 2 audiometer at 60 dB HL, bilaterally via ER-3A insert earphones. Each participant was administered all five subtests of the APT because each subtest is designed to measure a specific attention domain (I–V). The subtests were sustained attention (I), complex sustained attention (II), selective attention (III), divided attention (IV), and alternating attention (V). Each individual subtest had a unique task consisting of recognizing various targets and the participant used a clicker to indicate when the target was recognized. Before every subtest, participants were read the test instructions for identifying the targets and a practice trial was completed. The practice trial was repeated, if necessary, until the participant demonstrated that he or she understood the task (Sohlberg and Mateer, 2005). Subtest I sampled sustained attention ability through a target detection task, and the participant indicated each time the number “2” was heard in a random set of digits. Subtest II assessed complex sustained attention with a mental control and decision-making format, and participants indicated each time a digit was presented that was one less than the previous digit (i.e., the participant listened for a descending pattern). Subtest III, a measure of selective attention, was similar to subtest II; however, it included the addition of a competing background speech signal. The task required the participant to disregard the competing speech and focus solely on the digits to identify the descending pattern. Subtest IV evaluated divided attention abilities through combined visual and auditory tasks. The participant listened for the number “2” and indicated each time it was heard, while simultaneously reading digits arranged in random order on a sheet of paper and crossing out each even number. Subtest V measured alternating attention, and the participant alternated between listening for ascending and descending digit sequences.

The Multimodal Lexical Sentence Test for Adults (MLST-A; Kirk et al, 2012) was administered to each participant to evaluate SIN performance. The test comprised 12 equivalent lists of 24 sentences each; the sentences were seven to nine words each and included three key words, for a total of 72 key words per list. The sentences were controlled for lexical characteristics of frequency (i.e., how often words occur in a language) and neighborhood density (i.e., the number of phonologically similar words in the lexicon) (Bell and Wilson, 2001; Kirk et al, 2012; Rishiq et al, 2016). The recordings of test sentences included five male and five female talkers (Bell and Wilson, 2001; Kirk et al, 2012). The MLST-A test material was presented to each participant’s right ear at 60 dB HL via ER-3A insert earphones, at a fixed signal-to-noise ratio (SNR; 0 dB). The background noise, a speech-shaped noise, was presented binaurally at 60 dB HL to achieve the 0 dB SNR. Pilot testing conducted earlier had determined the requisite SNR presentation necessary to reduce floor and ceiling effects. The sentences were presented in both AO and AV modalities. Video recordings for the AV condition were presented on a 19-inch computer monitor, positioned in front, 1 m away from the participant. The sentence lists and test conditions were randomized for each participant.

The Comprehensive Trail-Making Test Second Edition (CTMT2) was administered to each participant to assess attention and cognitive flexibility (Reynolds, 2019). The CTMT2 consisted of five “visual search and sequencing tasks,” which are called trails. One’s ability at trail-making depends on attention, set-shifting/cognitive flexibility, concentration, and resistance to distraction. The tasks involve connecting the visual target, which are numbers in digits and word format, as well as letters, in a specific order as quickly as possible. The participants used a pencil or pen to create the trails by connecting the targets. The trails were completed in the order as described in the test manual. A timer was used to record how long it took each participant to complete each trail. Each participant completed a practice trail before completing the five trails. Errors in the trail included marking a number or letter out of sequence. During the test, errors were corrected by the test administrator, therefore increasing the time it took to complete the trail.

All of the aforementioned behavioral tests (APT, MLST-A, and CTMT2) were administered in a true random order to the research participants. Table 1 describes and summarizes all the behavioral tests used in this study.

Table 1.

Description of the Behavioral Tests Used in This Study

Test (Subtest) Construct Description
APT–sustained attention Sustained attention ability through target detection (Sohlberg and Mateer, 1987) Participants indicated each time the number “2” was heard in a random set of digits.
APT–complex sustained attention Complex sustained attention with a mental control and decision-making format (Sohlberg and Mateer, 1987) Participants indicated each time a digit was presented that was one less than the previous digit (i.e., a descending pattern).
APT–selective attention Selective attention with a mental control and decision-making format in the presence of a competing signal (Sohlberg and Mateer, 1987) Participants disregarded the competing speech and focused solely on the digits to identify the descending pattern.
APT–divided attention Divided attention ability through combined visual and auditory tasks (Sohlberg and Mateer, 1987) Participants listened for the number “2” and indicated each time it was heard, while simultaneously reading digits arranged in random order on a sheet of paper and crossing out each even number.
APT–alternating attention Alternating attention ability (Sohlberg and Mateer, 1987) Participants alternated between listening for ascending and descending digit sequences.
MLST-AO Speech-in-noise performance (Kirk et al, 2012) Participants listened and repeated back sentences presented in background noise.
MLST-AV Speech-in-noise performance (Kirk et al, 2012) Participants listened and repeated back sentences presented in background noise while watching the speaker on a monitor.
CTMT2 Attention, set-shifting/cognitive flexibility, concentration, resistance to distraction (Reynolds, 2019) Participants used a pencil or pen to create the trails by connecting the targets as quickly as possible.
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Note: AO = auditory-only, APT = Attention Processing Training, AV = audiovisual, CTMT2 = Comprehensive Trail-Making Test Second Edition, MLST-A = Multimodal Lexical Sentence Test for Adults.

RESULTS

The current study sought to examine the contribution of five attention domains to SIN performance in two participant groups, YA compared to MA, for speech presented in AO and AV conditions. Table 2 displays the descriptive statistics for the auditory, cognitive (attention), and SIN measures for the two participant groups. To examine the association between measures, a statistical correlation matrix was constructed using Pearson coefficients. The results revealed a significant relation between SIN (in AO modality) and the APT alternating attention task (r = 0.419, p = 0.033). No other attention measures were significantly linked to SIN performance. Significant correlations could also be seen between individual APT attention tasks (Table 3), which shows that the number of participants recruited in this study provided adequate statistical power to identify relationships between APT attention tasks that should exist.

Table 2.

Means and (Standard Deviations) for All Measures for Younger Adults (YA) and Older Adults (OA)

Measure YA Mean (SD) OA Mean (SD)
APT–sustained attention 29.90 (0.316) 30.00 (0.00)
APT–complex sustained attention 22.40 (5.542) 24.00 (5.354)
APT–selective attention 24.00 (5.925) 26.60 (4.142)
APT–alternating attention 18.50 (4.859) 19.40 (2.716)
APT–divided attention 29.00 (1.155) 27.80 (2.150)
CTMT2–Inhibitory Control Index (ICI) 51.00 (9.238) 58.30 (4.270)
CTMT2–Set-Shifting Index (SSI) 50.40 (10.679) 58.80 (6.052)
CTMT2–Total Composite Index (TCI) 50.80 (10.229) 59.20 (4.590)
MLST-A (AO) percentage score 35.00% (9.08) 41.70% (9.70%)
MLST-A (AV) percentage score 75.50% (8.10%) 77.70% (12.94%)
PTA for RE (dB) 5.67 (1.79) 13.33 (7.89)
PTA for LE (dB) 5.00 (1.57) 10.50 (6.58)
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Note: APT = Attention Processing Training, CTMT2 = Comprehensive Trail-Making Test Second Edition, MLST-A = Multimodal Lexical Sentence Test for Adults, RE = right ear, LE = left ear, PTA = pure-tone average threshold.

Table 3.

Correlations Between the Individual Attention Processing Training (APT) Tasks

Sustained Attention Complex Sustained Attention Selective Attention Divided Attention Alternating Attention
Sustained attention 1 0.009
(p = 0.971)		 0.014
(p = 0.954)		 −0.211
(p = 0.373)		 0.241
(p = 0.306)
Complex sustained attention 0.009
(p = 0.971)		 1 0.835**
(p < 0.001)		 0.090
(p = 0.706)		 0.699**
(p = <0.001)
Selective attention 0.014
(p = 0.954)		 0.835**
(p = <0.001)		 1 0.032
(p = 0.894)		 0.729**
(p = <0.001)
Divided attention −0.211
(p = 0.373)		 0.090
(p = 0.706)		 0.032
(p = 0.894)		 1 0.270
(p = 0.250)
Alternating attention 0.241
(p = 0.306)		 0.699**
(p = <0.001)		 0.729**
(p = <0.001)		 0.270
(p = 0.250)		 1
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**

p < 0.001.

Multiple regression analyses were conducted to investigate whether different attention domains were predictive of SIN performance for both modalities. The analyses indicated that alternating attention significantly predicted SIN performance in the AO modality (R2 = 0.643, β = 1.015, p < 0.001). This result is reflected in Figure 2, which shows the spread of alternating attention scores in relation to SIN in the AO mode for YA and MA. To investigate aging effects on this relation between alternating attention and SIN performance (in the AO mode), regression analyses were performed independently for the two age groups. Upon separating the two groups, regression remained significant for the YA only (R2 = 0.483, p = 0.026) but not for the MA (R2 = 0.001, p = 0.947), as seen in Figures 3 and 4. Regression analyses revealed no significant results for the other four attention domains.

Figure 2.

Figure 2.

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Scatter plot showing the Multimodal Lexical Sentence Test for Adults (MLST-A) scores in the auditory-only modality as a function of the alternating attention scores of the Attentional Processing Training (APT) test (R2 = 0.643, *p < 0.001).

Figure 3.

Figure 3.

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Scatter plot showing the Multimodal Lexical Sentence Test for Adults (MLST-A) scores in the auditory-only modality as a function of the alternating attention scores of the Attentional Processing Training (APT) test for younger adults (R2 = 0.483, *p = 0.026).

Figure 4.

Figure 4.

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Scatter plot showing the Multimodal Lexical Sentence Test for Adults (MLST-A) scores in the auditory-only modality as a function of the alternating attention scores of the Attentional Processing Training (APT) test for middle-aged adults (R2 = 0.001, p = 0.947).

DISCUSSION

The goal of this preliminary study was to evaluate the relationship between the various attention domains and SIN performance in both AO and AV modalities in younger and middle-aged listeners. To do so, the participants completed two attention behavioral measures (the APT and the CTMT2) and a SIN measure (MLST-A) in the AO and AV modalities.

The analyses of this study data revealed a significant link between alternating attention and SIN performance in the AO modality, which was particularly observed in the YA group.

Given the reported links between cognition and speech recognition in noise (e.g., Schvartz et al, 2008; Anderson et al, 2013; Billings et al, 2019), we anticipated that several of the attention metrics would correlate with SIN performance. The preliminary statistical analysis of our sample data revealed that alternating attention, among all the attention measures used in this study, was the only domain that positively correlated with SIN performance. This attention domain also predicted SIN performance in our sample. That is, participants with better alternating attention performance scored higher on SIN testing. This influence of alternating attention on SIN was specifically observed for the AO modality but not for the AV SIN performance. The current result supports the role of alternating attention in mediating speech recognition in noise performance, at least in the AO mode. This is an interesting finding, given that the specific role of alternating attention in understanding SIN is often neglected in literature compared to other cognitive functions such as selective attention and working memory. Before further discussion of this finding, we would like to remind the reader of the definitions and some of the literature on alternating attention.

Alternating Attention

Alternating attention is described as an individual’s ability to shift his or her focus of attention between tasks with different cognitive demands, requiring the individual to control which information is selectively processed. It involves the disengagement and the re-engagement of the focus of attention toward different tasks, due to the inability to process all available information at the same time (Lezak et al, 2015). Alternating attention has also been defined as the speed of shifting attentional focus between tasks (Parasuraman, 1998); individuals with better alternating attention tend to exhibit the ability to shift the focus of their attention between tasks in a rapid manner.

The cognitive abilities encompassed in alternating attention include mental tracking, inhibitory control, and shifting attention (Sánchez-Cubillo et al, 2009). An example of alternating attention would be reading a recipe and preparing the food. The individual’s focus is switching between two tasks (e.g., reading and preparing food) that require different cognitive demands (Bhasin, 2018). That is, one of the tasks is completely ignored (reading) while the focus is solely on the other task (preparing food).

Furthermore, alternating attention is believed to depend on the ‘‘orienting” neural network (Pozuelos et al, 2014), which consists of the ability to prioritize sensory input and to shift attention through space in order to attend to various sensory events (Posner et al, 2014). This network is typically triggered by specific spatial cues as well as cues in other sensory modalities (Geva et al, 2013). It is also responsible for distributing the focus of attention between features of a single object or several objects and in one or several modalities (Geva et al, 2013).

Additionally, previous research indicated that alternating attention was involved in the preparation of saccadic eye movements (e.g., Hoffman and Subramaniam, 1995) and in reading comprehension in children (e.g., Francis et al, 2008). Further, alternating attention skills were linked to the use of supporting context (using contextual information), as opposed to conflicting context (Park, 2015).

Alternating Attention and SIN Performance

The current results suggest that alternating attention abilities could contribute to understanding speech in challenging listening environments (i.e., listening to sentences in noise with the absence of visual cues). Taking some of the previously mentioned literature into consideration, this finding suggests that individuals with better alternating attention abilities who were able to rapidly shift their attentional focus may have easily scanned the different portions of the auditory message and were able to decode the message more accurately without mistakes. Furthermore, this finding also suggests that individuals with better alternating attention can better use contextual information to understand speech in noisy situations without visual context support.

AO vs. AV SIN Performance

What is quite interesting is the finding that pertains to AV SIN performance. None of the cognitive tasks employed in this study seem to contribute to AV SIN performance. It is possible that AV processing does not rely heavily on attention capacities as much as AO circuits do. Furthermore, it is possible that the current result of alternating attention’s not significantly contributing to performance in the AV modality is due to alternating attention’s not being used as much when there is simultaneous demand of visual and auditory inputs in speech perception tasks.

More complex attention tasks (e.g., dual task paradigms) and other cognitive measures (e.g., working memory tests) could be employed to visualize possible differences in AV vs. AO SIN performances. Cognitive involvement in AV SIN performance versus AO performance remains to be explored in future studies.

Age Effects

Links between cognitive decline and poorer SIN performance in older adults are often reported in the literature. For example, age-related changes in working memory and attention have been linked to poorer performance on speech recognition testing in various studies (e.g., Hulme and Tordoff, 1989; Rouleau and Belleville, 1996; Wingfield and Ducharme, 1999; Billings et al, 2019). Similarly, we anticipated that age would affect the relationship between various attention domains and SIN performance. As predicted, the results of the current study support past literature. Our preliminary findings suggest that age seems to influence and modulate the relationship between alternating attention and SIN, but not in the way we anticipated. YA and MA did not show significant group differences in cognitive or SIN performances per se. We saw differences in SIN performance with cognitive gains, particularly in alternating attention, that was only observed in YA. Put differently, improvements in AO SIN performance were seen with better alternating attention scores only in the YA group but not in the middle-aged listeners. This could potentially indicate that YA in this sample were capitalizing on their alternating attention capacities to improve their SIN performance. MA did not demonstrate this ability despite similar (sometimes better) alternating attention scores. Perhaps the MA could not tap into their readily available cognitive resources as YA could do when faced with adverse listening environments (e.g., listening in noise, in the absence of visual cues). Another possibility is that the alternating attention cognitive reserve in MA is not adequate to raise their SIN scores. Perhaps they require greater amounts of attentional resources to do so—compared to YA, who can raise and enhance their SIN performance with minimal amounts of cognitive reserve.

The findings of this study resonate with the Framework for Understanding Effortful Listening model (FUEL; Pichora-Fuller et al, 2016), which describes the role of attention in understanding speech in challenging listening situations. The FUEL is an adaptation of Kahneman’s (1973) Capacity Model of Attention that suggests individuals have a limited capacity of attentional resources and that the decision to allocate attentional resources to a specific task (i.e., listening in noise) depends on the demands of the task and the motivation of the individual. Regarding the tasks of listening, the FUEL suggests that as the quality of the speech signal becomes degraded the demand of attentional resources increases, which in turn increases the degree of listening effort (Pichora-Fuller et al, 2016). Therefore, age-related declines in overall capacity and/or in the ability to control attentional resources could contribute to MA’s difficulties in understanding speech in the presence of noise (Phillips, 2016).

In light of the FUEL model, our findings suggest that poor SIN performance is not only limited by cognitive resources, but it may also be restricted by the ability to control and access these resources. That is, the way MA access their cognitive resources could also be impaired. Consequently, training MA on how to access their resources could potentially help them in challenging listening situations. For example, cognitive-auditory training programs could focus on improving MA’s access and control of existing resources, and not only focus on improving their cognitive reserve. Current findings highlight the importance of considering alternating attention abilities in MA when managing their speech understanding struggles, especially in difficult listening situations.

Clinical Relevance and Applications

According to the ASHA, it is within the audiologists’ scope of practice to screen for possible cognitive dysfunction (American Speech-Language-Hearing Association, 2018). As more research and knowledge continue to emerge regarding the role of cognitive abilities in the communication process and in the intervention of patients with hearing loss, the clinical scope of practice in audiology could potentially expand.

The influence of cognitive abilities, specifically working memory, on hearing aid fittings speaks to the importance of using cognitive tests in audiology practice. There is great individual variability in how patients respond to hearing-aid processing features. Previous reports have found that this variability can be attributed, in part, to the patient’s cognitive functions, specifically working memory. Several studies (e.g., Arehart et al, 2013; Souza, 2018) showed that working memory is linked to the listener’s response to amplification. For example, a study by Souza and Sirow (2014) found that patients with good working memory demonstrated better speech understanding performance for fast-acting compression parameters compared to patients with poorer working memory. An implication for these findings would suggest that audiologists may need to fit hearing aids differently for patients with poor working memory than for patients with better working memory capacity. Likewise, similar applications could be applied with other cognitive capacities such as attention and its various domains. Audiologists could potentially target a patient’s attention abilities based on specific domains to better meet the patient’s needs. For example, targeting various attention domains, especially selective and alternating attention, in cognitive-auditory (hybrid) training programs may help in improving the patient’s speech understanding in noise. Auditory training interventions that emphasize training across a range of attention domains could potentially be more successful in supporting communication under challenging listening conditions.

Behavioral assessments, such as subtests of the APT, and questionnaires, such as the Everyday Life Attention Scale (Groen et al, 2019), may also be used to better understand which specific attention domain to target for each patient. Consequently, the preliminary findings of this study may encourage the use and the further understanding of attention-specific tests in audiological assessments alongside other global cognitive screeners (e.g., the Mini-Cog Test and the Montreal Cognitive Screening Assessment).

Other possible applications of the current study findings relate to incorporating alternating attention factors in digital signal processing models. Specifically, device manufacturers could explore how to incorporate alternating attention ability when developing signal processing strategies for hearing prosthetics such as hearing aids, cochlear implants, and assistive listening devices.

Study Limitations and Future Direction

The limitations of this current study need to be acknowledged. The study sample is relatively small, which may have restricted our interpretation of the results. Therefore, this study can be seen as a preliminary investigation highlighting the association between alternating attention and SIN performance. Results, though significant, need to be replicated in future studies using a larger sample size. A larger sample would likely increase statistical power and strengthen the generalizability of the findings.

Future research may also investigate the relationship between the various attention domains and SIN performance in individuals with hearing loss (e.g., with different degrees, types, and configurations). Subsequent studies could also evaluate the effects of attention domains on SIN performance in adults wearing hearing aids, similar to the studies (e.g., Arehart et al, 2013; Souza and Sirow, 2014; Souza, 2018) that investigated the effects of working memory on the patient’s response to different hearing aid features (e.g., fast-acting compression). Finally, this research paradigm could be extended to patients with learning disabilities, who may manifest attention deficits and SIN struggles as well.

CONCLUSIONS

Three behavioral assessments were conducted in YA versus MA who have PTAs no greater than 15 dB HL to evaluate the relationship between attention (five domains), cognitive flexibility, and SIN performance in both AO and AV modalities. Preliminary significant findings revealed a link between one attention domain, alternating attention, and SIN performance in the auditory modality for YA only. However, this link was not observed for AV SIN performance in either group of participants. The findings of this study suggest that YA could be capitalizing on their alternating attention capacities to better use contextual information to understand speech in noisy situations without visual context support, thus improving their SIN in the AO modality. Implications for audiological practice, including expansion of clinical assessments and designing auditory training programs, were discussed.

Abbreviations:

ADHD

attention-deficit/hyperactivity disorder

AO

auditory-only

APT

Attention Processing Training

ASHA

American Speech-Language-Hearing Association

AV

audiovisual

CTMT2

Comprehensive Trail-Making Test Second Edition

FUEL

Framework for Understanding Effortful Listening

MA

middle-aged adults

MLST-A

Multimodal Lexical Sentence Test for Adults

MMSE

Mini Mental State Examination

PTA

pure-tone average

SIN

speech-in-noise

SNR

signal-to-noise ratio

YA

younger adults

Footnotes

Any mention of a product, service, or procedure in the Journal of the American Academy of Audiology does not constitute an endorsement of the product, service, or procedure by the American Academy of Audiology.

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