Content area
Our goal is to present the relationships between working memory (WM) and auditory processing abilities in school-age children. We begin with an overview of auditory processing, the conceptualization of auditory processing disorder, and the assessment of auditory processing abilities in children. Next, we describe a model of WM and a model of auditory processing followed by their comparison. Evidence for the relationships between WM and auditory processing abilities in school-age children follows. Specifically, we present evidence for the association (or lack thereof) between WM/attention and auditory processing test performance. In conclusion, we describe a new framework for understanding auditory processing abilities in children based on integrated evidence from cognitive science, hearing science, and language science. We also discuss clinical implications in children that could inform future research.
Purpose: Our goal is to present the relationships between working memory (WM) and auditory processing abilities in school-age children.
Review and Discussion: We begin with an overview of auditory processing, the conceptualization of auditory processing disorder, and the assessment of auditory processing abilities in children. Next, we describe a model of WM and a model of auditory processing followed by their comparison. Evidence for the relationships between WM and auditory processing abilities in school-age children follows. Specifically, we present evidence for the association (or lack thereof) between WM/attention and auditory processing test performance.
Clinical Implications: In conclusion, we describe a new framework for understanding auditory processing abilities in children based on integrated evidence from cognitive science, hearing science, and language science. We also discuss clinical implications in children that could inform future research.
Auditory processing is defined as the decoding of auditory stimuli along the auditory pathway in the central nervous system (CNS; Abrams & Kraus, 2015). Behavioral performance typically ascribed to auditory processing includes sound localization, sound lateralization, auditory discrimination, auditory pattern recognition, temporal processing, and speech perception in competing or degraded listening conditions (American Speech-Language-Hearing Association [ASHA], 2005). Clinical assessment of these abilities, as defined here, is within the scope of practice of an audiologist. Adequate auditory processing abilities are integral to listening in a variety of functional situations and are therefore associated with receptive and expressive language (both spoken and written) and overall learning abilities (ASHA, 2005). Acoustic input received by the peripheral auditory system is encoded and conveyed through the central auditory pathway to the cortex. For spoken language stimuli, phonemic processing is fundamental, and this involves auditory areas in the temporal lobe among other brain regions. Sound reception and acoustic or phonemic analysis are therefore included as components of auditory processing (Richard, 2013). Phonemic processing is part of linguistic processing, which is a complex cognitive function that additionally involves assigning meaning to stimuli, comprehending syntax and discourse. Based on current knowledge about the development, organization, and functioning of the CNS, it is evident that auditory processing engages bottom-up (ascending central auditory system), top-down (descending/efferent system and cortical centers), and association and commissural neural pathways (Moore, 2012; Schmithorst, Farah, & Keith, 2013). A list of auditory processes and their descriptions are presented in Table 1.
(Central) Auditory Processing Disorder
The concept of central auditory processing disorder (APD) has its origins in descriptions of children who had listening difficulties despite normal peripheral hearing thresholds (Myklebust, 1954). APD came to be characterized as a distinct clinical entity based on tests of auditory processing in individuals with known neurological lesions of the central auditory pathway (Kimura, 1961). Several tests have since been developed to assess the functioning of the central auditory pathway when significant listening difficulties are reported even in the absence of identified neurological abnormalities (Dawes & Bishop, 2009; Jerger, 1998). In the following sections, we restrict our reference of auditory processing and APD to the developmental context of school-age children's listening abilities.
As per the position statement of the working group on APD established by ASHA (2005), APD is a diagnostic entity that requires demonstration of "a deficit in the neural processing of auditory stimuli that is not due to higher-order language, cognitive, or related factors." It is also stated that, "the deficit in neural processing of auditory stimuli may coexist with, but is not the result of dysfunction in other modalities. APD can also lead to or be associated with difficulties in learning (e.g., spelling, reading), speech, language, attention, social, and related functions" (ASHA, 2005). In the American Academy of Audiology (AAA, 2010) clinical practice guidelines, APD is defined as a disorder that may result from a variety of deficits in the functioning of the central auditory pathway that may be caused by neurological diseases or neurotoxic substances. AAA (2010) also specifies that age-related changes, communication or developmental disorders, and peripheral hearing loss can also affect the functioning of the auditory pathway. In addition to their auditory processing deficits, individuals diagnosed with APD often report difficulties in learning, language, and reading abilities. The AAA (2010) definition does not specify exclusion of other diagnoses for the use of the term APD, whereas ASHA recognizes the use of APD as a diagnostic term only if it is established that other conditions such as language or cognitive impairment are not causing the auditory processing deficits. The AAA definition considers auditory processing deficits as being associated with a range of different conditions (e.g., individuals with hearing loss and language or reading difficulties) and not strictly always as a distinct entity. The AAA (2010) definition, therefore, is broad and includes developmental APD as a subgroup. AAA (2010) guidelines recommend that APD testing be done only for children who are developmentally 7 years or older. This is because of the time course of neural maturation and high variability in performance on behavioral APD tests seen in younger children.
The British Society of Audiology addressed differences between their interpretation of the concept of APD and the AAA (2010) definition in a "white paper" (Moore, Rosen, Bamiou, Campbell, & Sirimanna, 2013). In this article, the authors focused on developmental APD (i.e., APD not linked to peripheral hearing loss or known neurological lesions). The authors highlighted that poor performance on nonspeech psychoacoustic measures did not correlate consistently or strongly with the listening difficulties faced by children diagnosed with APD. Instead, speech-based measures such as listening in noise and cognitive measures were stronger indicators of children's reported functional listening difficulties. The authors concluded that APD might be indicative of a broader neurodevelopmental disorder rather than a unique clinical entity because it often co-occurred with language and learning disorders (Moore et al., 2013).
Assessment of APD
In addition to electrophysiological measures, ASHA (2005) specifies five auditory areas in behavioral assessment as a guide for assessing and diagnosing APD. These areas are (a) auditory discrimination of differences in frequency, intensity, or temporal parameters; (b) temporal processing and patterning (e.g., sequencing, patterning, gap detection, backward/forward masking); (c) dichotic listening (speech); (d) monaural low-redundancy speech perception (e.g., degraded speech, speech in noise, competing speech); and (e) binaural interaction (e.g., masking level difference, localization, lateralization). No currently recommended APD test battery contains a test from all of the above areas (Dawes & Bishop, 2009). There is no widely accepted consensus on which deficits are necessary for diagnosis, which measures have high sensitivity and specificity, or which skill(s) corresponds to weakness in a specific functional auditory-language ability or learning outcome. Many tests for auditory processing are criticized for having poor psychometrics (Dawes & Bishop, 2009). Assessment for APD has included parent/teacher questionnaires, behavioral screening tests, test batteries (Barry, Tomlin, Moore, & Dillon, 2015; Bellis, 2002; Bellis & Ferre, 1999; Domitz & Schow, 2000; Ferre, 2002; Geffner & Goldman, 2010, 2009; Jerger& Musiek, 2000; Katz, 1992, 2001; Keith, 2000, 2009; Medwetsky, 2011; Musiek, 1983; Musiek & Chermak, 1994; Richard & Ferre, 2006), and electrophysiological measures (Jerger & Musiek, 2000; Kraus & Hornickel, 2013a). A multidisciplinary team approach including the parent, educator, audiologist, speech-language pathologist, and psychologist is recommended for assessment and intervention. Surveys of audiologists indicate that the majority of audiologists do not administer any one recommended auditory processing test battery (Emanuel, Ficca, & Korczak, 2011). Instead, clinicians administer tests based on their experience and based on the child's history. Emanuel et al. (2011) reported that only 54% of audiologists refer for an evaluation by a speech-language pathologist, 37% by an educational specialist, and 36% by a psychologist. Although national guidelines recommended it, audiologists are not mandated to consult other professionals when diagnosing APD (AAA, 2010; ASHA, 2005).
As a response to the need for a unified framework, Moore and colleagues in the United Kingdom-based multicenter study of auditory processing (IHR Multicentre Study of Auditory Processing [IMAP]; Moore, Ferguson, Edmondson-Jones, Ratib, & Riley, 2010) created a research test battery that included cognitive and language measures. IMAP included experimental auditory tasks (backward and simultaneous masking, frequency discrimination, vowelconsonant-vowel speech in noise), sustained attention auditory and visual tasks, and standardized cognitive tests (forward and backward digit span, nonword repetition, nonverbal IQ, reading). Language performance was not measured. However, rating scales such as the Children's Communication Checklist-2 (CCC-2; Bishop, 2003) and Children's Auditory Processing Performance Scale (CHAPPS; Smoski, Brunt, & Tannahill, 1998) were used as indicators of language abilities. Results indicated that cognitive factors were the best predictors of children's listening difficulties in realistic listening environments.
Traditional APD test batteries require failure (i.e., performance < 2 SDs from the mean) in more than one type of behavioral APD test or significant failure in one area (i.e., performance < 3 SDs). Given performance heterogeneity and problems with interpreting test scores using the traditional approach, a hierarchical adaptive approach to testing has been proposed (Cameron, Glyde, Dillon, King, & Gillies, 2015; Dillon, Cameron, Glyde, Wilson, & Tomlin, 2012). In this approach, an initial assessment indicates if and what further assessments are needed. The goal is to keep the test battery short to avoid fatigue and only administer additional tests when scores fall below some cutoff point in a given test. For example, after administering questionnaires and hearing screening, the Listening in Spatialized Noise-Sentences Test (LiSN-S; Cameron & Dillon, 2007) is recommended. If children perform poorly on the LiSN-S, a verbal memory test is given. The recommendation is to administer the dichotic digits test last and only if verbal memory scores are good, because poor verbal memory is known to affect the dichotic digits test. In summary, a hierarchical testing model includes master assessments that first determine the nature of the listening difficulty as manifested in daily listening situations (e.g., competing signals, rapid speech, reverberation, spatial/ pitch cues, demands on auditory working memory [WM]). This is followed by detailed tests that are selected based on failures in the master assessment (Dillon et al., 2012). Selecting tests for hierarchical APD assessment is based on the availability of deficit-specific treatment and can include cognitive and language measures, although which specific measures is not yet delineated.
Clinicians and researchers continue to lack a consensual theoretical and clinical framework for conceptualizing APD because professionals from different disciplines characterize it differently (Jerger, 1998). Under the Individuals With Disabilities Education Act, APD is not a qualifying diagnosis for special education services in U.S. public schools. It also does not fit under existing categories of disability given the diversity of its symptoms. Policy makers are perplexed about the nature of APD and how to best serve children with the diagnosis. These controversies have left clinicians who administer APD tests without a standard protocol. A multidisciplinary team evaluation is ideal but often not implemented. The result is weak diagnostic criteria and poor educational support for the children. Children diagnosed with APD often have comorbid deficits in attention, language, and memory (Chermak, Hall, & Musiek, 1999; Ferguson, Hall, Riley, & Moore, 2011; Riccio, Hynd, Cohen, Hall, & Molt, 1994). Auditory processing is part of a complex cognitive system (Banai & Kraus, 2014). Given the heterogeneous cognitive profiles of children suspected to have APD, it is suggested that auditory processing deficits are best characterized as a subcomponent that may be affected rather than categorizing the deficits as a unique clinical disorder (Banai & Kraus, 2014; Pennington & Bishop, 2009). This approach emphasizes functional outcomes related to listening difficulties, in addition to identifying the source(s) of difficulty, which are typically multifactorial during development (Moore, 2015; Pennington, 2006). Unlike conditions such as autism or intellectual disability, the developmental course of APD is not well defined. Thus, current limitations in the assessment of APD primarily relate to (a) an incomplete understanding of the underlying source(s) of a child's behavioral auditory symptoms and (b) the potential influence of nonauditory or higher-order cognitive influences on auditory processing measurement.
In this article, our aim is to describe the links between WM and auditory processing abilities in school-age children. The first rationale for this stems from similarities observed between WM and APD models. We first describe a prominent model of WM and compare it to a model of APD. Next, based on developmental studies, we present the literature on the influence (or lack thereof) of WM mechanisms on auditory processing test performance. An influence of cognitive processes (e.g., attention, WM) on auditory processing measurement challenges the notion of assessing auditory processing as a bottom-up linear mechanism and categorizing the deficits as a unique disorder. Alternatively, identification of auditory processes that are least influenced by cognitive factors may support their inclusion in clinical protocols used by audiologists, especially if supported by large-scale studies that have been replicated. We then elaborate on a novel framework for conceptualizing APD, which is motivated by integrated evidence from across disciplines on children's auditory processing abilities, developmental models of memory and attention, and the association of auditory processing abilities to cognitive and language performance. The rationale for this also lies in cumulative evidence on the comorbidity between APD and language, attention, and learning disorders. An integrated framework has the potential to advance our understanding of developmental listening difficulties in children and improve assessment and treatment procedures. Importantly, misdiagnoses can be reduced and sources of deficits can be better identified. We end by discussing clinical implications that arise from studying the above links.
A Model of WM
Miller, Galanter, and Pribram introduced the term WM in 1960 in their publication "Plans and the Structure of Behavior." They defined WM as the ability to access quickly one of many plans stored in memory toward its execution. Atkinson and Shiffrin (1968) used the term WM to refer to the short-term memory (STM) component of their model of memory. In addition, their model included sensory memory and a long-term memory (LTM) store. According to the Atkinson and Shiffrin model, STM/WM contained sensory information, which was attended to and brought into conscious awareness. Baddeley and Hitch in 1974 provided a more elaborate and functional description of WM through a multicomponent model (Baddeley, 2000; see Figure 1). They corroborated evidence for and proposed verbal and visuospatial STM as components of a larger WM system. The system also involved a central executive, which was a supervisory attentional system. Norman and Shallice in 1980 defined the distinction between automatic processing versus controlled processing, with the latter needing greater recruitment of the supervisory attentional system. The central executive in the Baddeley and Hitch model is responsible for the control of information in WM. According to this model, WM capacity is essential for actively maintaining serial order of information and is particularly crucial for units that are difficult to encode semantically. Alternatively, LTM systems are associated with efficient functions such as linguistic proficiency in a primary or well-practiced spoken language or ease of language comprehension (Baddeley, 2012). This is because information in LTM is organized based on meaning and associative learning.
Baddeley and colleagues proposed WM as an active memory system that individuals use to hold and manipulate information in conscious awareness. Central executive processes such as sustained attention, attention allocation, inhibition, updating, and shifting provide the attention focus and control needed for encoding input, planning action, problem solving, and recalling information from LTM (Baddeley, 2012). Domain-specific components are dedicated to process verbal information (i.e., phonological loop/phonological STM) and visuospatial information (i.e., visuospatial sketchpad/visuospatial STM), respectively. Subsequently, an episodic buffer component was proposed. The buffer provides for integration of multiple sources of information (e.g., audiovisual integration), including information from LTM (Baddeley, 2003). Since the multicomponent model of WM was proposed, several models of WM have evolved (Baddeley, 2012; Barrouillet, Gavens, Vergauwe, Gaillard, & Camos, 2009; Cowan et al., 2005; Unsworth & Engle, 2007). Discussion of the various models is beyond the scope of this article. Whereas each model differs in structural and functional details, most converge on evidence that the WM system has both domain-general and domain-specific components, which interact during cognitive processing (Baddeley, 2012; Barrouillet et al., 2009; Camos & Barrouillet, 2014; Jarrold & Towse, 2006). Importantly, many studies suggest significant improvements in children's WM capacity between 7 and 12 years of age (Camos & Barrouillet, 2011; Gathercole, 1999; Magimairaj & Montgomery, 2012). In this article, we use the WM model by Baddeley and colleagues to guide our review of the association between WM and auditory processing in children. We chose this model because there is a cumulative body of literature that supports it and because most studies related to WM assessment or intervention in children with language and related learning impairments are based on this model (Gathercole, 1999).
A Model of Auditory Processing
One prominent model of auditory processing is the Buffalo model (Katz, 1992). In this model of auditory processing, four categories are defined based on audiological APD assessment results. These are decoding, tolerancefading memory, integration, and organization problems (see Figure 2). This categorization resulted from performance patterns on three tests: a phonemic synthesis test, a speechin-noise test, and the dichotic Staggered Spondaic Word (SSW)1 Test (Katz, 1962, 1963, 1968, 1973), which is central to the battery. In addition, children's behavioral symptoms and academic profiles are considered. Decoding relates to identification, manipulation, and recall of phonemes. Tolerance-fading memory involves verbal (auditory) STM recall in the presence of noise. Integration relates to interhemispheric information transfer (indexed by binaural integration or separation tasks) and integration of auditory-visual information. The fourth category reflects organization, including planning and sequencing. For each category, corresponding neuroanatomical sites and systems in the CNS are proposed (Tillery, 2015). Also described are functional behaviors associated with each category, for example, phonemic processing (for decoding), verbal STM recall in noise (for tolerance-fading memory), integrating audiovisual information during reading and spelling (for integration), and performance on executive function tasks (for organization).2
Comparison Between Models of Auditory Processing and WM
When examining a prominent model of auditory processing, we observed parallels to the multicomponent model of Baddeley and colleagues. For example, the Buffalo model parallels Baddeley's multicomponent WM model in that both include the WM components of verbal STM (auditory decoding, tolerance-fading memory), the episodic buffer (integrating audiovisual and LTM information), and the central executive (organization). The WM model proposed by Baddeley and colleagues has been highly influential and is supported by a cumulative body of literature from clinical as well as neurotypical populations and across behavioral and neuroimaging studies in both children and adults. Categories in the Buffalo model of auditory processing may be interpreted to reflect deficits in one or more components of the WM system as defined by Baddeley (2003). Auditory decoding, audiovisual integration, and organization abilities can also be associated with language processing at various levels of complexity. For example, poor phonological processing abilities, such as those seen in some children with specific language impairment or dyslexia, could potentially lead to auditory processing profiles that correspond to APD models (Dawes & Bishop, 2009). Similarly, auditory memory and auditory sequencing of spoken material that are often challenging for children diagnosed with APD are integral functions attributed to the phonological loop component of WM. Whether processing of nonlinguistic auditory sounds also uses the same subcomponent of the WM system has not been well explored (Baddeley, 2012). Nevertheless, reported listening problems in children are typically manifested as difficulties in listening to speech in daily environments and especially when the target speech signal is degraded due to factors such as noise and reverberation. In addition, deficits in attention and executive functioning that often coexist in children diagnosed with APD are key functions attributed to the central executive component of WM. Importantly, another challenge to traditional APD models is their description of neural substrates. As seen in Figure 2, all neural substrates described in the Buffalo model categories are at the cortical level (Tillery, 2015). These same areas underlie attention, WM, LTM, and language functioning (including reading), thus posing a challenge for the diagnostic categorization as being unique to APD.
In summary, WM components, such as the phonological loop, episodic buffer, and central executive, correspond to key components defined in prominent auditory processing models (Bellis, 2002; Ferre, 2002; Katz, 1992; Medwetsky, 2011). The comorbidity of auditory processing, attention, language, and reading disorders supports this notion (Sharma, Purdy, & Kelly, 2009). Remarkably similar cognitive behavioral profiles have been reported in children diagnosed with APD and children diagnosed as having specific language impairment (Ferguson et al., 2011). Medwetsky (2011) proposed a spoken language processing model to broaden the concept of auditory processing to include auditory, language, and cognitive mechanisms. Measures of verbal STM, WM, and attention (all of which are key components of Baddeley's multicomponent model) were included in this model in addition to auditory processing measures. However, the Medwetsky model itself has not been tested systematically. Other studies have reported that cognitive factors such as "WM and executive attention" and "processing speed and alerting attention" underlie listening difficulties in children suspected to have APD, in addition to a "general auditory factor" (Ahmmed et al., 2014). These studies will be elaborated in the next section. According to current evidence, both domain-general attention and domain-specific (auditory-verbal) components of WM show relationships to children's auditory processing abilities.
Relationships Between WM and Auditory Processing Abilities in School-Age Children Age-Related Links
In assessing auditory processing abilities, it is recommended that behavioral testing be conducted for children who are developmentally 7 years or older (AAA, 2010; ASHA, 2005). This is because of the difficulty in test interpretation in younger children. The age of 7 years is also mentioned in studies of WM as the developmental period that marks the beginning of significant improvements in WM functioning (Camos & Barrouillet, 2011; Cowan, 2017; Gaillard, Barrouillet, Jarrold, & Camos, 2011). For example, a significant developmental shift is noted in children's verbal rehearsal and attention allocation ability at 7 years with continued improvement through 11 years (Camos & Barrouillet, 2011). Similarly, variability on tests of auditory processing and electrophysiological measures, such as the middle latency response, reduces from 7 years onward (Weihing, Schochat, & Musiek, 2012). Processing of degraded speech improves in children markedly between 5 and 12 years of age (Eggermont, 2014). This developmental overlap between auditory processing abilities and WM abilities is important to reconcile.
Relationships Between Attention, WM, and Auditory Processing Test Performance
There is substantial clinical and research evidence that supports the comorbidity between attention and auditory processing. Attention, an integral component of WM, is suggested to be primarily responsible for the strong association between WM and complex cognitive task performance (Barrouillet et al., 2009; Cowan et al., 2005; Engle, 2002; Lépine, Barrouillet, & Camos, 2005). Both caregivers and professionals describe children suspected to have APD or diagnosed with APD as having behavioral deficits such as inattention, distractibility, and poor organization, in addition to their listening difficulties. The direction of causality of whether APD causes attention deficits or an attention deficit leads to auditory processing deficits remains elusive. Some auditory processing task performances (e.g., dichotic SSW, phonemic synthesis, and speech in noise) have proven to be not influenced by medications used to treat attention disorders such as attention deficit disorder (ADD) or attention-deficit/hyperactivity disorder (ADHD; Tillery, Katz, & Keller, 2000). This finding implied that variation in attention did not influence these behavioral measures. Other studies have shown some auditory processing abilities (e.g., frequency discrimination) to be more susceptible than others (e.g., frequency modulation detection)3 to attention variation (Sutcliffe, Bishop, Houghton, & Taylor, 2006). Overall, the influence of attention potentially varies depending on the nature and demands of an auditory processing task.
Researchers have suggested that behavioral symptoms of APD primarily reflect cognitive processing abilities such as attention (Ahmmed et al., 2014; Moore, 2012; Moore et al., 2010, 2013; Tomlin, Dillon, Sharma, & Rance, 2015). For example, in a large population-based study (N = 1,469) by Moore et al. (2010), children with poor auditory processing also scored low on cognitive and communication measures, albeit the strength of the correlation between auditory perception and cognitive scores (attention, STM, WM, nonverbal IQ) was low. Cognitive performance and attention variations that led to response variability in auditory processing best predicted listening abilities, including speech perception in noise (SPIN). Auditory processing measures used by Moore et al. (2010) were experimental (i.e., not standardized). They were backward and simultaneous masking, frequency discrimination, and vowel-consonant-vowel words in noise. Ahmmed et al. (2014) used a factor analytic approach to identify auditory and nonauditory factors related to children's listening difficulties (N = 110). They administered a widely used clinical APD test battery (SCAN-C comprising of dichotic speech, speech-in-noise, and auditory closure tests; Keith, 2009), along with a research test battery that included backward and simultaneous masking, frequency discrimination, motor speed tasks with auditory/ visual stimuli, attention, WM, nonverbal intelligence, and reading, to 6- to 11-year-old children. All children were previously referred for APD assessment due to reported listening difficulties.
Results revealed (a) a "general auditory processing" factor, (b) a "WM and executive attention" factor, and (c) a "processing speed and alerting attention" factor. Despite a separable auditory processing factor, the authors found that cognitive factors best predicted functional listening outcomes. Next, we present studies specific to two auditory processing tests (dichotic listening and SPIN) and their relationship with WM and attention. These tests are hallmark APD measures that have been consistently used.
Dichotic Listening
The dichotic listening paradigm is one measure that is consistently included in all auditory processing test batteries as a measure of interhemispheric communication (corpus callosum) and brainstem or cortical integrity (depending on the type of stimuli used and the required response). Originally, this paradigm was used to demonstrate auditory deficits resulting from well-defined unilateral temporal lobe lesions (Kimura, 1961). The paradigm involves presenting auditory stimuli to both ears simultaneously and requiring the participant to report from both ears (assessing binaural integration) or only from one randomly selected ear while ignoring the other ear (dichotic competing words/competing sentences assessing binaural separation). Importantly, the dichotic listening paradigm is also included in cognitive test batteries as a measure of auditory attention. For example, the Woodcock-Johnson III Tests of Cognitive Abilities (Woodcock, McGrew, & Mather, 2001) includes a Dichotic Listening subtest as a measure of selective auditory attention where participants are required to selectively ignore one ear and attend to the other. Furthermore, using behavioral and neuroimaging procedures, studies have now established that attention, which is an integral component of WM, plays a robust role during dichotic listening tasks (Hugdahl et al., 2009; Jones, Moore, & Amitay, 2015; Schmithorst et al., 2013).
Tomlin et al. (2015) reported significant associations between cognitive measures (i.e., STM, WM, attention, nonverbal IQ), functional outcomes, and a subset of auditory processing tests that included dichotic digits. Dichotic digits showed the highest correlation (moderate effect size) with auditory WM and attention, in accordance with results reported by Sharma et al. (2009). Similarly, Ahmmed et al. (2014) reported significant positive correlations between dichotic tests (competing words and sentences) and STM. Gyldenkæ rne, Dillon, Sharma, and Purdy (2014) investigated the links between attention and auditory processing in 101 children with listening difficulties and a control sample of 18 children who had no reported listening difficulties (age range was 7-12 years). Positive correlations (small effect size) were reported between dichotic digits performance and both auditory and visual sustained attention ability. However, their data also indicated that poor APD test performance occurred in some children in the presence of normal-range sustained attention scores. The authors concluded that, although APD test performance can be influenced by attention, there was a possibility that deficits in attention and poor auditory processing test performance were affected by a third underlying factor that was common to both. Given the small effect size of the correlations, it was suggested that poor auditory performance was not completely explained by attention deficits, thereby suggesting the need for differential treatment when indicated. As an attempt to isolate the influence of cognitive factors on dichotic listening testing, Cameron et al. (Cameron, Glyde, Dillon, & Whitfield, 2016; Cameron, Glyde, Dillon, Whitfield, & Seymour, 2016) developed the Dichotic Digits Difference Test, which is based on difference scores obtained from Diotic (same stimuli presented to both ears simultaneously) and Dichotic subtests (different stimuli to each ear presented simultaneously). Because both tasks are assumed to share the same response demands and the diotic condition serves as a control condition, any difference in performance was attributed to binaural integration abilities. Results suggested that factors other than dichotic listening ability, such as attention and memory, still accounted for significant variance in children's performance.
SPIN
Several developmental studies have explored the association between children's WM capacity and SPIN using different types of speech stimuli (Cameron & Dillon, 2008; Eisenberg, Shannon, Martinez, Wygonski, & Boothroyd, 2000; Magimairaj, Nagaraj, & Benafield, 2018; McCreery, Spratford, Kirby, & Brennan, 2017; Osman & Sullivan, 2014; Sullivan, Osman, & Schafer, 2015; Tomlin et al., 2015). Some of these studies were motivated in part by studies in older adults with hearing impairment that suggested an association between SPIN and WM capacity (Akeroyd, 2008; Holt & Lotto, 2008; McCoy et al., 2005; Rönnberg et al., 2013; Rönnberg, Rudner, Foo, & Lunner, 2008). McCreery et al. (2017) examined the association between WM capacity, language, and SPIN in 96 typically developing schoolage children between 5 and 12 years of age. WM capacity was indexed using subtests of the Automated Working Memory Assessment (Alloway, 2007). Speech perception was measured in steady-state speech-shaped noise. Monosyllabic words, syntactically correct but semantically anomalous sentences (e.g., The jaws giggle at the frosty tractor) and sentences with anomalous semantics and syntax (e.g., Ghost four smart tooth) were used as speech stimuli in an immediate recall paradigm. Standardized receptive vocabulary and syntactic knowledge measures were also administered. Across all three stimulus categories, WM capacity significantly predicted SPIN. In addition, better vocabulary was associated with better SPIN for both sentence types (but not for monosyllabic words).
According to Sullivan and colleagues, listening comprehension in noise places greater demands on children's WM because cognitive resources are shared between the primary task of listening and the secondary requirement of filtering out noise. Osman and Sullivan (2014) evaluated the influence of noise on auditory WM performance in 20 typically developing 8- to 10-year-old children. Children's WM performance was significantly poorer in noise relative to quiet, and this was attributed to increased recruitment of cognitive resources in noise. To the same sample of children, a listening comprehension task was administered in quiet and noise (Sullivan et al., 2015). Comprehension involved listening to two- to seven-sentence-long passages and answering questions about the main idea, vocabulary, reasoning, details, and comprehension. Stronger correlations between listening comprehension and WM in noise relative to quiet were interpreted as reflecting increased WM demands in noise.
Cameron and Dillon (2008) developed the LiSN-S to assess children suspected to have APD. LiSN-S is a speech repetition task in which children's speech perception ability is measured in the presence of competing speech noise. However, the spatial location of the target speech is fixed directly in front while changing the perceived spatial location of competing speech noise to either come from the front (0° azimuth) or from the sides (±90° azimuth). Along with the spatial location, the vocal characteristic (pitch) of the competing speech noise was also varied to be the same or different from the target speaker. This resulted in two conditions, one in which pitch and the spatial location of the target and competing speech matched ("low cue" condition) and the other called "high cue" condition, where both the pitch and the spatial locations were different. Importantly, LiSN-S was shown to be distinct from other auditory processes (Cameron & Dillon, 2008) and also appeared to be less influenced by high-order cognitive abilities such as WM and language (Tomlin et al., 2015). Whereas no positive correlation was observed for LiSN-S in the high-cue condition with STM, WM, or attention, in the LiSN-S low-cue condition, weak correlations (small effect size) were reported with STM and auditory WM (r = . 18-.27), but no significant correlations with attention (Tomlin et al., 2015). In addition, any difference in scores between the high- and low-cue conditions is indicative of auditory processing deficits because both subtests have the same task requirements. The test design minimizes the influence of attention, language knowledge, and auditory memory. However, even if cognitive and language factors played a role, they would affect both conditions similarly and therefore allow differentiation of an auditory processing deficit (Dillon et al., 2012).
Given the need for more large-scale studies to study the association between WM and SPIN in children, we examined the relation between children's SPIN, language abilities, and WM capacity in 83 school-age children who represented a continuum of individual differences in cognitive abilities (Magimairaj et al., 2018). Children's SPIN was assessed using the Bamford-Kowal-Bench Speech-inNoise Test (BKB-SIN; Etymotic Research, 2005) and used as the dependent variable. BKB-SIN was selected because it is widely used clinically and is standardized for schoolage children. Multiple standardized measures of language and WM measures were used to predict SPIN. As expected, significant positive correlations were observed between attention, memory, and language measures (controlling for age). However, none of the predictors correlated significantly with BKB-SIN performance. On principal component analysis, distinct factors were revealed for WM, language, SPIN, and nonverbal IQ, respectively. The loading of BKB-SIN as a unique factor was robust (with minimal secondary loading from sentence recall and STM). These findings did not support an association between WM capacity and SPIN in children as measured using the BKB-SIN. Findings corroborated the utility ofthe BKBSIN in audiological assessment given its dissociation from cognitive-linguistic factors.
Differences between Magimairaj et al. (2018) and studies that have shown an association between SPIN and WM (i.e., McCreery et al., 2017; Sullivan et al., 2015) may be due to the nature of the speech stimuli used. In Magimairaj et al., meaningful sentences were used with three to four keywords to be scored. Listening to meaningful spoken stimuli represents every communication and hence is ecologically valid. McCreery et al. (2017) used sentences with four keywords. However, their sentences were void of meaning and ungrammatical or had correct grammar without meaning. Repeating unrelated words is comparable to the word span task, which is typically used as a measure of phonological STM. Grammatically correct but meaningless sentences potentially tap on syntactic knowledge in addition to STM. The association observed between WM and SPIN in McCreery et al. (2017) may have been mediated by STM. Sullivan et al. (2015) used a listening comprehension task in noise in contrast to a sentence repetition in noise paradigm as used in Magimairaj et al. However, an absence ofpartial correlations (controlling for age) and the small sample size in Sullivan et al. (2015) may also explain the differences between study results. Overall, existing evidence suggests either weak or no associations between cognitive-linguistic factors and SPIN tasks that use meaningful words/sentences in a repetition paradigm (Caldwell & Nittrouer, 2013; Cameron & Dillon, 2008; Eisenberg et al., 2000; Magimairaj et al., 2018; Moore et al., 2010; Nittrouer et al., 2013; Tomlin et al., 2015). These results in children align with studies that report no significant association between WM capacity and speech identification and comprehension in noise in older adults with hearing impairment (Nagaraj, 2017; Sheft, Shafiro, Wang, Barnes, & Shah, 2015; Smith & Pichora-Fuller, 2015). Furthermore, meta-analysis data on young adults with normal hearing suggest that WM capacity was not reliably associated with individuals' speech identification in noise (Füllgrabe & Rosen, 2016). These data suggest that WM predicts less than 2% variance in speech identification in noise (Füllgrabe & Rosen, 2016). More studies in children are warranted to replicate the proposed role of WM capacity in SPIN using stimuli of varying length and linguistic complexity.
Evidence for Dissociations Between Other Auditory Processing and Cognitive Measures
Studies have reported data on auditory processing tasks that appear distinct from cognitive measures and explain unique variance in listening ability. For example, Sharma et al. (2009) observed no significant correlation between attention/memory and gap detection or masking level difference in a sample of 68 children suspected to have APD or diagnosed with APD. Riccio, Cohen, Garrison, and Smith (2005) found no significant correlation between subtests of the screening test for APDs (SCAN; Keith, 1986) and the SSW Test (Katz, 1962), with visual attention skills, in a sample of 36 children who had a diagnosis of APD or ADHD or both. However, this result was not replicated. Gyldenkæ rne et al. (2014) reported a significant positive relationship between sustained visual attention ability and auditory processing test performance. All these studies also reported that not all children diagnosed with APD had auditory attention and memory deficits and vice versa. In a study by Rosen, Cohen, and Vanniasegaram (2010), weak or nonexistent correlations were reported between auditory processing (i.e., tone discrimination and consonant cluster minimal pairs discrimination) and language and nonverbal IQ in children diagnosed with APD as well as in the control group.
Evidence Supporting That WM Modulates Auditory Processing Abilities and Their Development
Cognitive factors such as WM shape neural processing of auditory stimuli (Kraus & Hornickel, 2013b; Kraus, Strait, & Parbery-Clark, 2012; Strait, Parbery-Clark, Hittner, & Kraus, 2012). Kraus and colleagues have demonstrated changes in auditory brainstem function resulting from long-term language and music experience as well as from short-term auditory and musical training. Importantly, auditory attention and WM are identified among contributing factors that influence the neuroplasticity of subcortical areas (e.g., Hornickel, Chandrasekaran, Zecker, & Kraus, 2011; Kraus & Chandrasekaran, 2010; Kraus & Hornickel, 2013b; Kraus & White-Schwoch, 2015; Strait et al., 2012). The scope of interactions between cognitive, sensory, and reward circuits is supported by the larger number of descending projection fibers from the cortex in comparison to ascending projection fibers (Kraus & Hornickel, 2013b). Studies also suggest that development of auditory selective attention mediates children's listening in noise ability. For example, Jones et al. (2015) demonstrated that children's listening in unpredictable noise became adultlike by 9-11 years of age and selective attention ability was a major factor that accounted for the children's auditory judgments.
Clinical Implications
An Integrated Framework for Conceptualizing Auditory Processing
From our review of the relationships between WM and auditory processing abilities in children, what is known about the integrated functioning of the nervous system (Moore, 2012; Schmithorst et al., 2013), and evidence of multifactorial risk factors in neurodevelopmental disorders (Moore, 2015; Pennington, 2006), it is clear that we need a novel framework for understanding auditory processing abilities in children. The framework must include cognitive and linguistic factors as potential sources of deficits along with auditory factors. We provide a schematic for such a framework (see Figure 3). The basis for this framework is the cumulative evidence across the disciplines of hearing, language, and cognitive science. This includes studies in typically developing children and those suspected to have APD, the literature on children with specific language impairment, and models of developmental attention and memory. In addition, in-depth review of WM models and methodological approaches to the assessment of WM were considered toward this conceptualization. Interestingly, traditional APD models have shown remarkable overlap with WM models. Advances in the WM literature need to be effectively integrated to studies in children suspected to have APD. For example, one recommendation is to use multiple tasks to measure WM capacity (Conway et al., 2005). Another is to use time-controlled WM tasks (Barrouillet et al., 2009) because cognitive performance is measured as a function of cognitive load, which is governed by the time allowed to perform a task (Barrouillet & Camos, 2010). Such methodological details have not been incorporated in studies of APD. Similarly, control of cognitive or language load of auditory tasks has been a challenge for traditional APD tests.
As indicated in our framework (see Figure 3), cognitive, linguistic, and auditory factors may play a role individually or jointly in any combination to cause listening difficulties in children. This is because these systems are linked and are not compartmentalized. For example, WM allows us to bring information to conscious awareness during controlled cognitive tasks, including auditory processing tasks. Processing and retaining serial order of auditory stimuli is a critical function of WM. Similarly, WM plays a role in various aspects of language processing as is evidenced by interactions between phonological STM, LTM, the episodic buffer, and the central executive. Auditory processing and language processing are often described to be on a continuum but are in fact highly integrated and can be reciprocal in their influence. The heterogeneous profiles of children with listening difficulties reflect combinations of deficits across these factors. Based on such a framework, one approach may be to develop and standardize a front-end unified assessment, which includes only the most sensitive areas that are known to characterize difficulties in children with reported listening problems (i.e., taking into account overlapping conditions such as APD, specific language impairment, ADD/ADHD, and learning disability) across areas of language, attention, memory, and auditory processes. The uniqueness of the measures would be important. Doing so may be an efficient way to ensure that no major area goes unrecognized up front. Based on failures or profiles noted, comprehensive discipline-specific assessments may follow. Another approach may be to develop combination tasks that combine two sensitive areas, thereby increasing the chances of early detection of a potential problem. Most important, building a quick broad initial profile has significant clinical application in helping clinicians with "where to start" when they are tasked with evaluating a child with listening difficulties. Such an assessment must also adhere to established standards to ensure high specificity and sensitivity. For example, Standards for Psychological and Educational Testing (American Educational Research Association, American Psychological Association, National Council on Measurement in Education, & Joint Committee on Standards for Educational and Psychological Testing, 2014) may be used as a guide for establishing task validity and reliability. Incorporating tasks that are engaging for children and available on one integrated platform would be ideal. A measure for use as an initial tool must be made available for use across professional disciplines so that key areas are profiled efficiently and parents get a better view of their child's difficulties.
Moore et al. (2010) in the IMAP project and Dillon et al. (2012) have also proposed the assessment of cognition and language along with auditory processes, and both these lines of research have made significant contributions toward better understanding children with listening difficulties. However, more work is needed. The most sensitive language and cognitive tasks and the best ways to measure them in children suspected to have APD are not yet well established. For example, in the IMAP battery, children's language was not measured directly, but a parent questionnaire was used as an indicator of language abilities (CCC-2; Bishop, 2003). IMAP also included the CHAPPS listening behavior checklist, which is reported to have poor validity as a screening measure (Wilson et al., 2011). One important auditory component not included in the IMAP is binaural integration. The IMAP comprises a battery of tests from various sources and includes both novel and already available standardized tasks. The IMAP auditory processing tests are language free, and inclusion of cognitive measures in the protocol was an important addition. The tasks have been designed to be engaging for children and control for fatigue. However, the reliability of some of the tasks is reported to be inadequate (Ahmmed et al., 2014).
Dillon and colleagues have developed differential testing procedures such as the use of difference scores (difference between tests that differ in only one factor) to help differentiate and isolate factors that contribute to performance. For example, the LiSN-S test is based on difference scores and allows assessment of children's spatial processing ability. In addition, the test targets one of the primary reported complaints, which is difficulty in listening in noisy environments. A similar innovative paradigm was reported in a study of children with specific language impairment. Leonard, Deevy, Fey, and Bredin-Oja (2013) developed a picture-pointing sentence comprehension task to isolate cognitive capacity limitations from languagebased deficits in children's sentence comprehension. Performance difference in two sentence conditions that are identical in linguistic structure but differ only in cognitive demand due to difference in the nature of picture foils helped isolate cognitive capacity limitations. More such paradigms are needed for better differentiation of sources of deficits. Decision tree and hierarchical approaches are not new to clinical assessment. However, their application to children with significant listening difficulties is just beginning (Dillon et al., 2012). Continued work is needed in this regard.
Instead of developing diagnostic APD categories/ labels (which has not proven to be the most effective in getting services for children with listening difficulties), the goal of assessment must be to delineate specific deficit areas in a given child that have the potential for remediation. Hierarchical assessments can be time and cost-effective because they can lead to determination of target areas that may benefit from intervention (Dillon et al., 2012). To avoid confusion to parents, the use of APD as a categorical diagnostic term in children with developmental listening difficulties should be restricted unless cross-disciplinary evaluations are complete. Finally, our framework reflects the importance of a multidisciplinary team approach in not only developing front-end or hierarchical assessments but also in designing effective interventions. Importantly, management of functional listening and learning difficulties must be joint efforts of speech language pathologists and audiologists, along with parents and relevant professionals. Integrated treatment approaches need to undergo systematic testing to ensure they address the key areas that are difficult for children. This is also important to demonstrate that any novel treatments are distinct from conventional language intervention approaches in terms of benefit. In summary, proposing such a framework is important because it is clear that there are children with significant auditory symptoms that affect their ability to listen, learn, and function as well as their peers. These children slip through the cracks due to the heterogeneity of their symptoms.
Other Implications
Based on our review of the relationships between WM and auditory processing, we note several other clinical implications. First, given the overlap in developmental trajectories of auditory processing abilities and WM abilities, age at evaluation must be taken into account during assessment and intervention for auditory processing difficulties. APD testing is not recommended for children developmentally younger than 7 years (AAA, 2010). Clinicians must adhere to this recommendation given the substantial evidence that supports this. Doing so may save time and resources for parents and children and avoid misdiagnosis. However, any significant listening difficulties noted in children at any age (including at younger ages) must call for a speech-language evaluation, a peripheral hearing assessment, and cognitive assessment as indicated. This is because identification of breakdowns in any level of language or cognitive processing triggers the consideration of language enrichment opportunities for the children. Enriching overall language knowledge and processing abilities (e.g., phonological awareness, vocabulary) early has the potential to improve children's functional communication abilities or compensate for deficits. Enhancing children's language knowledge and processing may be important to listening in complex auditory environments. Studies in adults have shown that listening in degraded conditions is crucially constrained by an individuals' lexical knowledge (Benard, Mensink, & Baskent, 2014; Nagaraj & Magimairaj, 2017). Systematic studies in children are needed to model the contribution of specific linguistic factors to listening in complex environments.
Second, comorbidity of auditory processing deficits or WM deficits with other developmental disorders (e.g., learning disability, ADD/ADHD, specific language impairment) is often a rule and not an exception because of the multifactorial nature of risk factors for developmental disorders (Pennington, 2006) and due to the organization and integrated functioning of the CNS (Kraus & Hornickel, 2013a). In other words, a deficit in one area of processing (e.g., WM or auditory processing) does not necessarily mandate a stand-alone diagnostic category for that area (Kamhi, 2011). Clinicians must therefore effectively collaborate with other professionals and flexibly incorporate techniques that accommodate the child's deficits. To assess auditory processing, understanding stimulus characteristics, their neural representation, and their psychological correlates is important. To this end, cognitive systems such as WM are integral to auditory processing abilities and their development. Most important, as an alternative to diagnostic categorization, a decision tree approach can better enable clinicians to reach specific goals (First, 2013). Dillon et al. (2012) proposed a hierarchical approach to APD assessment with master assessments followed by detailed auditory assessments as indicated. Cameron et al. (2015) evaluated the hierarchical assessment model and followed it up with deficit-specific treatment for spatial processing deficits or memory deficits or improved auditory access (LiSN and Learn or Memory Booster or FM listening system). Significant improvements were reported for all training options. Clinicians and researchers must move toward a decision tree approach that integrates cognitive-linguistic factors effectively in addition to auditory processing assessment. One possibility is to use a broad but efficient cross-disciplinary assessment up front that incorporates attention, memory, language, and audition so that no area is missed. Such a tool must be made available to multiple professionals who can then refer children for disciplinespecific comprehensive evaluations. More research is needed to add to our knowledge about those areas of auditory processing that are clearly distinct or least influenced by other cognitive measures. Also, auditory assessments must be designed to minimize language or cognitive influences as much as possible, and use of redundant measures across disciplines must be minimized.
Third, improving auditory access using assistive listening devices (e.g., FM systems) has been shown to be effective (Cameron et al., 2015). There are also studies that support targeted auditory training (using nonspeech stimuli or speech stimuli) for children with language and listening difficulties (e.g., Kraus et al., 1995; Merzenich et al., 1996; Tallal, 2004; Tallal & Gaab, 2006; Tallal et al., 1996). However, most of these studies lack high levels of evidence. For example, it has been demonstrated that auditory training programs such as FastForword are not significantly different from traditional language interventions (Fey et al., 2011; Gillam et al., 2008; Kamhi, 2011). Another challenge to decontextualized auditory or computerized cognitive training programs has been a lack of consistent far-transfer effects (Halliday, Taylor, Millward, & Moore, 2012; Murphy, Moore, & Schochat, 2015; Shipstead, Hicks, & Engle, 2012). Given the prominence of children's difficulty listening in complex auditory environments and emerging evidence suggesting a distinction of SPIN and spatialized listening from other auditory and cognitive factors (e.g., Cameron & Dillon, 2008; Magimairaj et al., 2018), listening training in spatialized noise appears to hold promise for intervention. This needs to be systematically replicated across independent studies.
Finally, auditory-language stimulation and enrichment must be emphasized during development (Kraus & Chandrasekaran, 2010; Kraus & Hornickel, 2013b). This concept is not new. However, the current research evidence on neuroplasticity makes a compelling case to promote the enhancement of children's auditory experiences beginning early in the critical period (Zhao & Kuhl, 2016). Emphasizing prevention should be at the forefront, not just assessment and remediation. In addition, integrating auditory enrichment with functional and contextualized activities (e.g., musical experience, play, language experience, and social interaction) and optimizing attention and memory load may allow the children to receive the most benefits.
Acknowledgments
A Hearing Health Foundation's Emerging Research Grant to the authors and a Medical Research Endowment Grant from the University of Arkansas for Medical Sciences to the second author funded the authors' studies cited in this article.
JSSW is a dichotic listening task used to assess binaural integration. In this task, two overlapping spondee words (two-syllable words such as lap-top, air-plane) are presented to the ears simultaneously. The second syllable of the first spondee overlaps with the first syllable of the second spondee. The listener is asked to repeat both the spondee words.
2Similarly, in the Bellis-Ferre model of auditory processing (Bellis, 2002; Ferre, 2002), auditory decoding, prosody, and integration are recognized. Auditory decoding corresponds to phonemic analysis. The prosodic category relates to perception of tonal patterns and includes binaural integration and separation abilities. Integration includes interhemispheric skills (e.g., multimodal task performance), including binaural integration and separation. Secondary profiles include auditory association and output organization.
3Frequency modulation detection is the ability to detect the fluctuation in frequency or pitch in a sound.
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