Listening difficulties in children: Bottom-up and top-down contributions

Journal of Communication Disorders 45 (2012) 411–418

Contents lists available at SciVerse ScienceDirect

Journal of Communication Disorders

Listening difficulties in children: Bottom-up and top-down contributions David R. Moore * MRC Institute of Hearing Research, Nottingham NG7 2RD, UK

A R T I C L E I N F O

A B S T R A C T

Available online 20 June 2012

The brain mechanisms of hearing include large regions of the anterior temporal, prefrontal, and inferior parietal cortex, and an extensive network of descending connections between the cortex and sub-cortical components of what is presently known as the auditory system. One important function of these additional (‘top-down’) mechanisms for hearing is to modulate the ascending, sensory (‘bottom-up’) auditory information from the ear. In children, normal, immature hearing during the first decade of life is more strongly influenced by top-down mechanisms than in adulthood. In some children, impaired top-down function presents a significant challenge to their auditory perception, often associated with a range of language and learning difficulties and sometimes called auditory processing disorder. Learning outcomes: Readers will be able to (a) discuss the difference between and integration of auditory information in the ascending, descending, and cortical auditory centres, (b) state alternate interpretations of normal maturation of human hearing in typical children, (c) explain how sensory and cognitive contributions to auditory temporal and spectral processing may be teased apart, (d) discuss how listening difficulties may be assessed in children, and (e) critically assess whether APD is really an auditory problem or may be symptomatic of a broader neurodevelopmental disorder. ß 2012 Elsevier Inc. All rights reserved.

Keywords: Hearing Immaturity Spectral Temporal Efferent APD

1. Introduction Neuronal responses to sound in humans, both in sub-cortical and primary cortical auditory system, develop early in childhood (Moore, 2002) and achieve adult-like threshold and latency by about the end of the fourth year of life (Barnet, 1975; Ponton, Eggermont, Coupland, & Winkelaar, 1992). By contrast, behavioural thresholds for some, apparently simple, auditory tasks (e.g. tone frequency discrimination) remain immature in the ‘average’ child until after their 10th birthday, or even beyond (Hartley, Wright, Hogan, & Moore, 2000; Moore, Cowan, Riley, Edmondson-Jones, & Ferguson, 2011). The reasons for this mismatch, and their implications for the development of normal and abnormal communication in children, form the basis of this paper. For an earlier, complementary review, see Werner (2007). 2. The real auditory brain Different parts of the brain develop at different rates, and it is well recognized that some parts of the brain do not mature until well after the age of ten. For example, development of the prefrontal cortex is not complete until the mid-20s (U.S. Dept Health Human Services, 2012) – a fact often cited in explanation of some more extreme aspects of adolescent male behaviour. One possible reason for the delayed development of behavioural responses to sound is that hearing involves

* Tel.: +44 115 922 3431; fax: +44 115 951 8503. E-mail address: [email protected]. 0021-9924/$ – see front matter ß 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jcomdis.2012.06.006

412

D.R. Moore / Journal of Communication Disorders 45 (2012) 411–418

Fig. 1. Participation in hearing of primate cortical areas beyond the classic ‘central auditory system’. (A) Cortical activation in monkeys produced by visual (pink) and auditory (blue) stimulation. The auditory stimuli were a wide array of simple and complex sounds including tones, noise, music and vocalizations. Visual stimuli were moving, high contrast geometric patterns. Some areas (mauve) were activated by both visual and auditory stimulation. Adapted from Poremba et al. (2003). (B) Projections of neurons from auditory cortex to other auditory-related neocortical areas. From Hackett (2011).

neural processing beyond the recognized auditory system. It has long been known that neurons receiving multi-modal sensory input in ‘association’ areas of cortex respond to simple sounds (Wester, Irvine, & Thompson, 1974). Large parts of the prefrontal and inferior parietal cortex of rhesus monkeys are activated by sounds, as is most of the superior temporal gyrus (Fig. 1A; Poremba, Saunders, Sokoloff, Crane, & Cook, 2003). fMRI studies of humans, including children (Patel, Cahill, Ret, Schmithorst, & Choo, 2007), show widespread activation of frontal, temporal, and parietal cortical areas. Some, including inferior prefrontal cortex and the posterior temporoparietal areas, have been implicated in decision making and other aspects of responding to sounds (Holland, Vannest, Mecoli, Jacola, & Tillema, 2007; Sharp, Awad, Warren, Wise, & Vigliocco, 2010). Given the direct stimulation of these cortical areas by sound, and their identified role in hearing, it is unclear why they are not considered part of the auditory system, especially when functionally similar regions of cortex responding to visual stimuli are recognized parts of the visual system. All the ‘non-auditory’ cortical areas described above have reciprocal connections with the recognized auditory cortex (Fig. 1B; Hackett, 2011), and thus form the origins of the ‘top-down’, descending, ‘efferent’ auditory system (He & Yu, 2010; Schofield, 2010). Neurons in primary auditory cortex (A1) project to all levels of the ‘bottom-up’, ascending, ‘afferent’ system; the thalamus, midbrain, brainstem, and the ear (cochlea and middle ear muscles). Recent research has begun to reveal some of the functions of these descending pathways including the modulation of binaural processing (Nakamoto, Jones, & Palmer, 2008) and auditory learning (Bajo, Nodal, Moore, & King, 2010; de Boer & Thornton, 2008; Irving, Moore, Liberman, & Sumner, 2011). Efferent pathways are active early in life (Morlet, Goforth, Hood, Ferber, & Duclaux, 1999; Roux, Wersinger, McIntosh, Fuchs, & Glowatzki, 2011) and it thus seems likely that they are necessarily involved in the maturation and maintenance of hearing. 3. Measuring hearing in children A variety of special tactics is used in the clinic to obtain reliable measures of hearing in children. Tactics include ‘automated’ response measures (e.g. head turns; Hutchings, Meyer, & Moore, 1992), cartoon-character computer graphics, and play-like tests (Moore, Ferguson, Halliday, & Riley, 2008). However, even where these tactics are employed, they often suggest insensitive audiograms (Werner, Folsom, & Mancl, 1994). Some of this apparent insensitivity is due to the use of sound field audiometry with young children and some is due to lack of measurement control. But some is due to inconsistent and impaired perception in young (<10 years old) children. Under more controlled listening conditions, in the laboratory, children are generally found to have higher (i.e. less sensitive) response thresholds than adults for most, if not all, aspects of auditory perception. They also tend to have greater response variability, both within and between individuals (Moore et al., 2008; Moore, Cowan, et al., 2011). To explain these findings, it is useful to consider further the mechanisms of auditory encoding in the brain. Auditory perception (AP) involves the conversion of acoustic signals into neural signals in the ear, the routing and elaboration of the neural signals within the ascending auditory system and, as outlined above, their integration with those from other brain systems in the cerebral cortex, resulting in consciousness and ‘action’. Cortical signals are sent back down the descending auditory system and exert a modulating influence on processing within the ascending systems of both the brain and the ear. The extent to which bottom-up influences from the ear and top-down influences from the cortex influence AP depends on the nature of the auditory stimulus and task, and on the nature and extent of activity in other systems (e.g. vision, attention, memory, emotion). All these processes are, in turn, dependent on the individual and are influenced by underlying biology, experience, and age. When children are expected to respond to sound, whether in everyday life or in the clinic or lab, there is thus a great deal more influencing their hearing than the physical properties of the sound arriving at their ears and the activation of the

D.R. Moore / Journal of Communication Disorders 45 (2012) 411–418

413

Fig. 2. Methods and results of ‘three interval, forced-choice’ auditory frequency discrimination testing in children. (A) A child is seated comfortably and wearing headphones while playing a series of trials in a computer game involving selection of the ‘odd-one-out’ (see inset in (B); further details of stimuli in Fig. 3 legend). Sounds in each interval are paired with visual movement of the corresponding cartoon character. (B) Performance of a typical child (left) and an inattentive child (right) on 2 or 3 sequences of trials (‘tracks’) presented to a ‘1 up, 3 down’ staircase rule. Typical children perform this task in a qualitative similar fashion to adults, with responses ‘tracking’ a threshold level of difficulty, in this case around 1% Hz for a 1 kHz signal. In contrast, some children become inattentive and show more or less variable threshold responses over the relatively short (20–30 trials) tracks. From Moore et al. (2008).

recognized auditory system. For example, when a child participating in our hearing tests is asked to press one of three buttons to indicate the ‘odd-one-out’ among three successively presented sounds (Fig. 2A; Moore et al., 2008; Moore, Cowan, et al., 2011; Moore, Ferguson, Edmondson-Jones, Ratib, & Riley, 2010), we are asking her or him to perform a number of functions. These include attending closely to the sound and to the accompanying visual display on a computer screen, associating each sound with a particular button, storing that association within memory, and playing out the association after the end of the third sound by making an appropriate motor response (action) – pressing the correct button. They must then quickly refocus attention as they listen for the presentation of the next set of sounds. Listening in a noisy school classroom is a much greater challenge, with poor signal to noise, lots of distraction, and the need to follow complex instructions, to name but a few potential sources of inconsistent and incorrect actions. 4. Listening difficulties In general, children perform auditory functions more unreliably than adults (Fig. 2B). As above, while physiological responses to simple sounds in the central auditory system are largely mature by 4 years of age, behavioural responses in a wide variety of AP tasks remain immature in most children until around 8–9 years, with some variability between tasks (Moore, Cowan, et al., 2011). It is important to note that this is not true of all children. Some attain adult-like response threshold and reliability much earlier than most others, for example in pure tone frequency discrimination (Halliday, Taylor, Edmonson-Jones, & Moore, 2008). If we assume that the biological basis of ascending auditory system function is very similar across individuals of a given age, as animal studies suggest (see Moore, Cowan, et al., 2011), the fact that AP thresholds mature early in some children suggests that ascending auditory function matures early in all normally hearing children, as indicated by the physiology. The later development of hearing in most children is thus likely to be due to the maturation of top-down processes controlling AP (Werner, 2007). While the hearing sensitivity of most children is consistent, at least after they ‘grow out’ of middle ear disease (by about 5–6 years; Roberts, Hunter, Gravel, Rosenfeld, & Berman, 2004), some children appear to perceive target sounds particularly poorly in challenging conditions. As above, the challenge can include noisy or distracting environments, or understanding instructions or questions. However, when tested audiometrically, their hearing is normal. It has become common practice in some countries to use the designation ‘auditory processing disorder’ (APD) for these children. Although their difficulties are complex and heterogeneous (Cacace & McFarland, 2009), and will not be discussed in detail here, professional audiology societies (ASHA, AAA) have issued guidance documents on APD. These generally state (e.g. ASHA, 2005) that APD is (i) a problem of the central auditory system, (ii) dissociated from multi-modal cognitive and language problems, and (iii) evidenced by poor performance on basic AP tasks, including auditory discrimination, temporal aspects of audition, and auditory performance with degraded or competing sounds. In short, APD is seen as due to impaired bottom-up function of the auditory system. To test the specific hypotheses that (i) immature hearing in typical older (>4 year old) children is attributable to immature top-down processing, and (ii) that poor listening (‘APD’) is associated with impaired AP, we designed a large population study of 6–11-year-old mainstream school children (the ‘IMAP’ study; n = 1621–1469 with normal tone sensitivity; Moore et al., 2010). In that study we compared the ability of individuals and groups of age-, sex- and

D.R. Moore / Journal of Communication Disorders 45 (2012) 411–418

414

A

Backward Masking (BM)

B

Simultaneous Masking (SM)

SM

BM50 Time

SMN

Freq

BM0

Temporal Resolution (TR) = BM0 – BM50

Noise

20 ms tone

Frequency Resolution (FR) = SM - SMN

Fig. 3. Tests used to separate sensory and cognitive contributions to auditory perception. Shaded boxes represent noise stimuli and short, horizontal lines represent tones presented in trials, each of three successive intervals. Two individual tests each of backward (BM0, BM50) and simultaneous (SM, SMN) masking were used to derive measures of temporal (TR) and frequency (FR) resolution, as described in the text. Further details of this ‘IMAP’ test procedure, including video links of children taking the tests and author interviews, may be found in Barry, Ferguson, and Moore (2010) and Moore et al. (2010).

economically stratified children to perform tests of AP, including carefully matched pairs of masking tests (Fig. 3). The rationale of this design was that performance on single (‘individual’) tests of AP, such as auditory backward masking (BM), a measure of auditory temporal processing (Hill, Hartley, Glasberg, Moore, & Moore, 2004; Wright et al., 1997), reflects a variety of sensory and other factors, as described above. So, for example, the 3-alternative, 3-interval test of BM (Fig. 3) required the child initially to indicate, via a button press (Fig. 2A), which of 3 successive noise bursts, each indicated visually by a cartoon on the computer screen, was preceded by a short (20 ms) tone burst (‘‘the one that sounded a little bit different’’). The child thus had to understand the (simple) instructions, focus on the audiovisual task, listen carefully to the increasingly difficult (adaptive) sound test, remember the order of the sounds, select and press the correct button, etc. It can easily be seen that such a test of BM is far more than just a hearing test. In fact, the various test demands somewhat resemble the pattern of difficulties reported by the caregivers of children diagnosed with APD. In contrast, when the thresholds for two variants of individual BM tests, BM0 and BM50 (Fig. 3A), are subtracted, the result is a relatively pure, ‘derived’ measure of sensory temporal resolution (TR). In the original, BM0 version of the test, the terminal envelope of the tone is exactly abutted with the beginning of the noise burst – there is a 0 ms gap between the tone and noise. On the other hand, in the BM50 version, there is a 50 ms gap between tone and noise. At a given signal to noise level, the tone is more audible in the BM50 version because the masking provided by the noise, being temporally removed from the tone, is reduced (Hill et al., 2004). Each version of the test is, conventionally, considered a test of auditory temporal processing but, as above, both the BM0 and BM50 tests involve a large number of skills in addition to the sensory detection of the tone. However, if we assume that the non-sensory aspects of the test are identical in the two versions, by subtracting the threshold scores of each test (the sound level at which the tone is just masked), we can derive a relatively pure measure of sensory function. The assumption that the two versions of the test involve similar ‘task demands’ is supported by the anecdotal observation that it is very difficult for a normally hearing adult listener to distinguish between them. The ASHA and AAA guidelines would seem to predict that poor performance would be seen by a child with APD on both individual and derived versions of the BM test, since they have a fundamental temporal processing problem. If, however, the child had only cognitive difficulties, without a sensory problem, poor performance would be seen only on the individual tests. Using identical reasoning, two individual variants of a simultaneous masking task, without (SM) and with (SMN) a spectral notch in the masker (Fig. 3B), provide an estimate of sensory frequency resolution (FR; also called ‘frequency selectivity’; Patterson, 1976). When we compared threshold performance on these tests, and on an individual test of pure tone frequency discrimination (FD; Fig. 2B, inset), with a variety of standard measures of cognitive and language skills, we found weak, but highly significant correlations between the individual AP and cognitive test scores (Table 1; Moore et al., 2010). Correlations between the derived AP and cognitive test scores were, in contrast, mostly non-significant. These results and other results from the IMAP study, together with preliminary data from children with mild hearing loss (Moore, Zobay, & Ferguson, 2011) seemed to confirm that the subtraction method was able to differentiate between sensory and cognitive contributions to AP task performance. Poor AP is associated with impaired cognition and language, but only if the AP test includes cognitive and language components. When a purely sensory measure, presumably reflecting only bottom-up processing in the central auditory system, is used the relationship is lost. It may be noted that the many reports in the communication disorders literature showing a relationship between AP and language/literacy problems among children with specific language impairment (SLI; Miller, 2011; Tallal, Miller, Bedi, Byma, & Wang, 1996) and dyslexia (Amitay, Ben-Yehudah, Banai, & Ahissar, 2002) have all used what are called here individual tests of AP. This suggests that, rather than measuring impaired sensory processing and, in particular, impaired temporal processing, they were actually measuring the ability of the children described in their reports to perform the cognitive aspects of the AP tasks used (also called the ‘procedural’ aspects of the task; Ortiz & Wright, 2009).

D.R. Moore / Journal of Communication Disorders 45 (2012) 411–418

415

Table 1 Correlation between AP, cognitive, and VCV test scores for 6–12-year-old normally hearing children participating in the IMAP study (n = 1469). NVIQ: nonverbal intelligence (Wechsler); NW-Rep: non-word repetition (from the NEPSY battery); words and nonwords: number of words read from a list (the TOWRE); VCV: vowel–consonant–vowel speech in noise test; CCC2-GCC: the general communication composite score from Children’s Communication Checklist (2nd edition). BM0

r NVIQ Digit span NW-Rep Words Nonwords VCV CCC2-GCC

BM50

0.19 0.14 0.14 0.19 0.17 0.14 0.06

SM

0.20 0.14 0.17 0.16 0.16 0.14 0.082

SMN

0.15 0.11 0.11 0.16 0.15 0.062 0.05

0.22 0.13 0.11 0.16 0.13 0.11 0.091

TR

FR

0.02 <0.01 0.01 0.05 0.04 0.03 0.01

FD

0.10 0.05 0.03 0.04 0.03 0.081 0.101

VCV

0.31 0.21 0.20 0.25 0.25 0.11 0.19

CCC2-GCC

0.12 0.10 0.29 0.15 0.14 – 0.091

0.17 0.12 0.16 0.26 0.27 0.091 –

Adapted and extended from Moore et al. (2010). Bold: p < 0.001, other correlations p > 0.05. 1 p < 0.01. 2 p < 0.05

Since only a small minority of children are thought to have APD, their results might be masked in a correlation analysis by the vast majority who do not have APD (Chermak & Musiek, 1997; Hind, Haines-Bazrafshan, Benton, Brassington, & Towle, 2011), explaining the relatively weak correlations in Table 1, even for the individual AP tests. To examine this possibility, we asked whether poor AP performance was predictive of poor cognitive performance. The sample of children with normal hearing was divided into typical (upper 95% of thresholds) and poorer (lower 5%) AP performers (Moore et al., 2010). If the poorer AP performers are representative of a minority that includes children with APD, as the ASHA and AAA guidelines suggest, we might expect to find poor performance of that group on all the cognitive tests. Moreover, both individual and derived AP measures (Fig. 3) should show that relationship. In fact, we did find such a relationship but, again, it was found only for the individual tests, not for the derived tests (Fig. 4). Thus, children who had difficulty mastering the demands of the AP tests were, perhaps unsurprisingly, those who had relatively poor NVIQ, working memory, language, and literacy. In contrast, those who had poor sensory performance on the AP tests had typical cognitive function. 5. Auditory processing disorder Why are children referred for APD assessment? The main reasons given appear to be, first, noisy or distracting environments and, second, understanding instructions or questions. The analysis of AP data above addresses the second of these reasons. Children who perform poorly on individual AP tests tend to have poor cognitive and language skills. What is the relation, however, between listening in everyday, noisy and distracting environments and AP thresholds? Fig. 5A shows the results of how poorer and typical performers on AP tests performed on a speech-in-noise test involving a nonsense syllable speech target (vowel–consonant–vowel; e.g. ‘aha’), and a single speaker envelope masker (ICRA5; Moore et al., 2010). Note that, in this VCV speech-in-noise test, both the target and the masker were speech-based, but lacked meaning. The results closely resembled those found in the cognitive tests (Fig. 4). Poorer performers on the individual AP tests had significantly higher thresholds (i.e. higher age-standardized signal-to-noise ratios) than typical performers but, again, poorer performers on the sensory TR test did not. In this instance, however, poorer sensory FR was modestly but significantly

Cognitive test score

A

Intelligence

52

AP Test BM0 SM FD TR FR

Typical Poorer (top 95%) (bottom 5%)

C

Language

D

(NW-rep)

12

110

11

105

10

100

9

95

Literacy (TOWRE–words)

8

7 40

Memory (Digit span)

9

48

44

B

(NVIQ)

(mean ± 95% c.i.) Typical

8 Poorer

90 Typical

Poorer

Typical

Poorer

Auditory processing

Fig. 4. Mean (95% CI) standard score on four cognitive tests for children who were in the top 95% (‘typical’) or bottom 5% (‘poorer’) of performance (threshold) on each individual (open symbols) or derived (filled symbols) test of auditory perception (AP). For clarity, results for BM50 and SMN individual tests are not presented, but they were similar to the results presented for BM0 and SM, respectively. Poorer performers on individual AP tests (see Fig. 3) also had relatively poorer scores on the cognitive tests, whereas poorer performers on the derived tests performed normally on the cognitive tests. From Moore et al. (2010).

D.R. Moore / Journal of Communication Disorders 45 (2012) 411–418

416

A

B

Speech-in-noise

-0.2

85

0

80

Communication

75 0.2 0.4

AP Test BM0

70

SM

65

FD

0.6

VCV

60

TR

0.8

FR

Typical

55 Poorer

Typical

Poorer

Auditory Processing Fig. 5. Mean standardized score on (A) vowel–consonant–vowel perception, and (B) children’s communication checklist (CCC-2; Bishop, 2003) ‘general communication composite’ score for children who were in the upper 95% (‘typical’) or lower 5% (‘poorer’) of performance (threshold) on each AP test. These (A) speech-in-noise and (B) communication indices showed the same overall relation to AP as the cognitive tests (Fig. 4), except that children with poor frequency resolution (FR) and VCV thresholds also had reduced CCC-2 performance. Modified from Moore et al. (2010).

associated with poorer VCV (see also Table 1). Speech perception in speech-modulated noise was thus related to the cognitive demands of the AP task and to spectral, but not to temporal, sensory acuity. Although counter-intuitive, these findings appear to support previous reports that sensory acuity may be relatively weakly related to speech perception (e.g. Smits, Kapteyn, & Houtgast, 2004). If children with ‘APD’ have speech-in-noise problems, these results suggest they are more likely to have a general cognitive problem than a specific auditory processing problem. Sensory processing appears to bear little relationship either to listening in noise or to understanding instructions, as measured by laboratory tests. However, those tests may not be reliable indicators of performance in real life situations. An alternative form of measurement is the questionnaire. Users of the self-report SSQ (Gatehouse & Noble, 2004) or the parental-report Children’s Communication Checklist (CCC-2; Bishop, 2003) may know that these meticulously crafted questionnaires can be more informative clinical tools than objective tests (see Bishop & McDonald, 2009). Unfortunately, there are no well-validated and standardized questionnaires of listening skills. Specifically, the widely used CHAPPS questionnaire (Smoski, Brunt, & Tannahill, 1992) has a very biased response spectrum (Moore et al., 2010). However, the CCC-2 is sensitive to the clinical presentation of APD in as much as children referred for APD have poorer scores on the General Communication Composite (GCC) score of the CCC-2 than do typical children. In fact, they do as poorly as children diagnosed with specific language impairment (Ferguson, Hall, Riley, & Moore, 2011). In the IMAP study we found (Fig. 5B) that children scoring poorly on all the individual AP tests again scored poorly on the GCC. For this comparison, however, while poorer performers on the derived measure of temporal resolution (TR) received typical parental reports of communication, those with poor frequency resolution (FR) were rated as having poor communication skills. For the whole sample (n = 1469), there was a significant correlation (p  0.001) between the GCC and both a composite AP score and FR. These results on FR are the only ones of which I am aware indicating a link between true sensory processing and listening problems. They need to be followed up with further research. The success of the CCC-2 (GCC) questionnaire in building bridges between the clinic, the home, and the research lab (see also Ramirez-Inscoe & Moore, 2011) has led to a suggestion (BSA, 2011) that a specific, listening-based questionnaire could fulfil at least two needs in the assessment of children with suspected listening difficulties (aka APD). First, it could serve as a screening test for the onward referral of children. In those for whom the listening difficulty was identified as the dominant symptom, referral to an audiology service would then be set on a rational footing. Conversely, those found to have more speech- or language-related difficulties could be referred to a speech-language pathologist. Second, it could serve as a standard against which other, possibly more objective diagnostic methods could be compared. For example, the link hypothesised above between FR and listening problems could be tested directly against such a standard. Biological markers (e.g. neuroimaging, Schmithorst, Holland, & Plante, 2011; complex ABR, Skoe & Kraus, 2010) may also be more appropriately compared with the clinical presentation of listening difficulties than with other existing APD test material. 6. Conclusions Auditory perception necessarily involves the integration in the brain of bottom-up, auditory ‘sensory’ information with top-down, multimodal ‘cognitive’ information. Bottom-up development of sensory function occurs very early; in humans mostly before the time of birth. However, top-down processes contributing to AP may continue to mature into adulthood. In

D.R. Moore / Journal of Communication Disorders 45 (2012) 411–418

417

scientific tests, AP appears mature by 10–12 years of age. During maturation AP is also variable, both within and between children. Immature cognitive utilization of available sensory information typically leads to episodes of impaired AP. This can happen over a time scale of minutes to days but, for most children aged 6–12 years, performance (threshold) on AP tests is generally stable across that time scale. For some children, however, episodes of impaired AP are sufficiently frequent to produce concern and referral. During referral, poor cognitive skills necessary for hearing can easily be confused with impaired sensory function. Recent data suggest that this confusion may lie at the heart of the notion, and diagnosis, of auditory processing disorder (APD). It has been found, for example, that when evaluation is carefully controlled to separate the sensory and cognitive contributions to auditory temporal and spectral processing, poor test performance among children aged 6–12 years is caused almost exclusively by the cognitive contribution. This is also generally the case for the broader evaluation of communication skills provided by a parental questionnaire, but we have some evidence for a relation between impaired spectral resolution and parental reports of poor listening. I suggest that well-validated questionnaires should form an important component of the evaluation of APD, both from a clinical and from a research perspective. Acknowledgments The research from my laboratory described in this paper was generously supported by the intramural programme of the Medical Research Council [grant number U135070147]. It was greatly facilitated by the contribution of many colleagues, most notably Mel Ferguson, Alison Riley, Victor Chilekwa, Tim Folkard, Mark Edmondson-Jones, and Oliver Zobay.

Appendix A. Continuing education 1. Hearing necessarily involves auditory processing in: a. The ascending central auditory system. b. the descending, efferent system. c. cortical centres in the frontal and parietal cortex. d. All of the above. 2. Is the normal maturation of human hearing after the age of 4 years primarily due to: a. development of the cochlea and auditory nerve. b. increased temporal synchrony of neurons in the primary auditory cortex. c. improved integrative processing in the cerebral cortex beyond the classical auditory system. d. increased efferent innervation. 3. Are poor listening skills in children with APD primarily due to: a. impaired sensory processing. b. impaired cognitive function. c. impaired temporal processing. d. inoculation with the MMR vaccine. 4. A well-validated questionnaire on listening skills: a. may be useful in the clinic but has no place in scientific research. b. could serve as a useful and scientifically valid screening tool for APD. c. is not as useful as one quickly made up to study a specific clinical sample. d. cannot replace an individual clinical history of listening skills. 5. When testing hearing in a child the following processes contribute to the result: a. The successful conduction and transduction of sound by the ear. b. Neural activity and coding in the central auditory system. c. The emotional state of the child. d. All of the above.

References American Speech-Language-Hearing Association (ASHA). (2005). Working Group on Auditory Processing Disorders. (Central) auditory processinghttp://www.asha.org/docs/html/TR2005-00043.html. Amitay, S., Ben-Yehudah, G., Banai, K., & Ahissar, M. (2002). Disabled readers suffer from visual and auditory impairments but not from a specific magnocellular deficit. Brain, 125(10), 2272–2285. Bajo, V. M., Nodal, F. R., Moore, D. R., & King, A. J. (2010). The descending corticocollicular pathway mediates learning-induced auditory plasticity. Nature Neuroscience, 13, 253–260. Barnet, A. B. (1975). Frequency-specific maturation of the eighth nerve and brain-stem auditory pathway: Evidence from derived auditory brain-stem responses (ABRs). Electroencephalography and Clinical Neurophysiology, 39(1), 29–41. Barry, J. G., Ferguson, M. A., & Moore, D. R. (2010). Making sense of listening: The IMAP test battery. Journal of Visualized Experiments, 44, 2139. Bishop, D. V., & McDonald, D. (2009). Identifying language impairment in children: Combining language test scores with parental report. International Journal of Language and Communication Disorders, 44(5), 600–615. Bishop, D. V. M. (2003). The children’s communication checklist, version 2 (CCC-2). London: The Psychological Corporation. British Society of Audiology (BSA). (2011). APD position statementhttp://www.thebsa.org.uk/images/stories/docs/BSA_APD_PositionPaper_31March11_FINAL.pdf.

418

D.R. Moore / Journal of Communication Disorders 45 (2012) 411–418

Cacace, A. T., & McFarland, D. J. (Eds.). (2009). Controversies in central auditory processing disorder. San Diego, CA: Plural Publishing. Chermak, G. D., & Musiek, F. E. (1997). Central auditory processing disorders: New perspectives. San Diego, CA: Singular. de Boer, J., & Thornton, A. R. D. (2008). Neural correlates of perceptual learning in the auditory brainstem: efferent activity predicts and reflects improvement at a speech-in-noise discrimination task. Journal of Neuroscience, 28(19), 4929–4937. Ferguson, M. A., Hall, R. L., Riley, A., & Moore, D. R. (2011). Communication, listening, speech and cognition in children with auditory processing disorder (APD) or specific language impairment (SLI). Journal of Speech, Language and Hearing Research, 54, 211–227. Gatehouse, S., & Noble, W. (2004). The Speech, Spatial and Qualities of Hearing Scale (SSQ). International Journal of Audiology, 43, 85–99. Hackett, T. A. (2011). Information flow in the auditory cortical network. Hearing Research, 271(1–2), 133–146. Halliday, L. F., Taylor, J. L., Edmonson-Jones, A. M., & Moore, D. R. (2008). Frequency discrimination learning in children. Journal of the Acoustical Society of America, 123, 4393–4402. Hartley, D. E. H., Wright, B. A., Hogan, S. C., & Moore, D. R. (2000). Age related improvements in auditory backward and simultaneous masking in 6 to 10 year old children. Journal of Speech, Language and Hearing Research, 43, 1402–1415. He, J., & Yu, Y. (2010). Role of descending control in the auditory pathway. In A. Rees & A. R. Palmer (Eds.), The oxford handbook of auditory science: The auditory brain (pp. 247–268). New York: Oxford University Press. Hill, P. R., Hartley, D. E., Glasberg, B. R., Moore, B. C., & Moore, D. R. (2004). Auditory processing efficiency and temporal resolution in children and adults. Journal of Speech, Language and Hearing Research, 47, 1022–1029. Hind, S. E., Haines-Bazrafshan, R., Benton, C. L., Brassington, W., & Towle, B. (2011). Prevalence of clinical referrals having hearing thresholds within normal limits. International Journal of Audiology, 50, 708–716. Holland, S. K., Vannest, J., Mecoli, M., Jacola, L. M., & Tillema, J.-M. (2007). Functional MRI of language lateralization during development in children. International Journal of Audiology, 46, 533–551. Hutchings, M. E., Meyer, S. E., & Moore, D. R. (1992). Binaural masking level differences in infants with and without a history of otitis media with effusion. Hearing Research, 63, 71–78. Irving, S., Moore, D. R., Liberman, M. C., & Sumner, C. J. (2011). Olivocochlear efferent control in sound localization and experience-dependent learning. Journal of Neuroscience, 31, 2493–2501. Miller, C. A. (2011). Auditory processing theories of language disorders: Past, present, and future. Language, Speech, and Hearing Services in Schools, 42(3), 309–319. Moore, D. R. (2002). Auditory development and the role of experience. British Medical Bulletin, 63, 171–182. Moore, D. R., Cowan, J. A., Riley, A., Edmondson-Jones, A. M., & Ferguson, M. A. (2011). Development of auditory processing in 6- to 11-yr-old children. Ear and Hearing, 3, 269–285. Moore, D. R., Ferguson, M. A., Edmondson-Jones, A. M., Ratib, S., & Riley, A. (2010). The nature of auditory processing disorder in children. Pediatrics, 126, e382– e390. Moore, D. R., Ferguson, M. A., Halliday, L. F., & Riley, A. (2008). Frequency discrimination in children: Perception, learning and attention. Hearing Research, 238, 147–154. Moore, D. R., Zobay, O., & Ferguson, M. A. (2011). Auditory and cognitive performance of 6–11 year old children with mild hearing loss. ARO MidWinter meeting (abstract 222). Morlet, T., Goforth, L., Hood, L. J., Ferber, C., & Duclaux, R. (1999). Development of human cochlear active mechanism asymmetry: Involvement of the medial olivocochlear system? Hearing Research, 134, 153–162. Nakamoto, K. T., Jones, S. J., & Palmer, A. R. (2008). Descending projections from auditory cortex modulate sensitivity in the midbrain to cues for spatial position. Journal of Neurophysiology, 99, 2347–2356. Ortiz, J. A., & Wright, B. A. (2009). Contributions of procedure and stimulus learning to early, rapid perceptual improvements. Journal of Experimental Psychololgy: Human Perception & Performance, 35(1), 188–194. Patel, A. M., Cahill, L. D., Ret, J., Schmithorst, V., & Choo, D. (2007). Functional magnetic resonance imaging of hearing-impaired children under sedation before cochlear implantation. Archives of Otolaryngology-Head & Neck Surgery, 133(7), 677–683. Patterson, R. D. (1976). Auditory filter shapes derived with noise stimuli. Journal of the Acoustical Society of America, 59, 640–654. Ponton, C. W., Eggermont, J. J., Coupland, S. G., & Winkelaar, R. J. (1992). Frequency-specific maturation of the eighth nerve and brain-stem auditory pathway: Evidence from derived auditory brain-stem responses (ABRs). Journal of the Acoustical Society of America, 91(3), 1576–1586. Poremba, A., Saunders, R. C., Sokoloff, L., Crane, A., & Cook, M. (2003). Functional mapping of the primate auditory system. Science, 299, 568–572. Ramirez-Inscoe, J., & Moore, D. R. (2011). Processes that influence communicative impairments in deaf children using cochlear implants. Ear and Hearing, 32, 690–698. Roberts, J., Hunter, L., Gravel, J., Rosenfeld, R., & Berman, S. (2004). Otitis media, hearing loss, and language learning: Controversies and current research. Journal of Developmental and Behavioral Pediatrics, 25(2), 110–122. Roux, I., Wersinger, E., McIntosh, J. M., Fuchs, P. A., & Glowatzki, E. (2011). Onset of cholinergic efferent synaptic function in sensory hair cells of the rat cochlea. Journal of Neuroscience, 31(42), 15092–15101. Schmithorst, V. J., Holland, S. K., & Plante, E. (2011). Diffusion tensor imaging reveals white matter microstructure correlations with auditory processing ability. Ear and Hearing, 32(2), 156–167. Schofield, B. R. (2010). Structural organization of the descending auditory pathway. In A. Rees & A. R. Palmer (Eds.), The oxford handbook of auditory science: The auditory brain (pp. 43–64). New York: Oxford UP. Sharp, D. J., Awad, M., Warren, J. E., Wise, R. J., & Vigliocco, G. (2010). The neural response to changing semantic and perceptual complexity during language processing. Human Brain Mapping, 31(3), 365–377. Skoe, E., & Kraus, N. (2010). Auditory brain stem response to complex sounds: A tutorial. Ear and Hearing, 31(3), 302–324. Smits, C., Kapteyn, T. S., & Houtgast, T. (2004). Development and validation of an automatic speech-in-noise screening test by telephone. International Journal of Audiology, 43, 15–28. Smoski, W. J., Brunt, M. A., & Tannahill, J. C. (1992). Listening characteristics of children with central auditory processing disorders. Language, Speech and Hearing Services in Schools, 23, 145–152. Tallal, P., Miller, S. L., Bedi, G., Byma, G., & Wang, X. (1996). Language comprehension in language-learning impaired children improved with acoustically modified speech. Science, 271, 81–84. U.S. Dept Health Human Services. (2012). http://www.hhs.gov/opa/familylife/tech_assistance/etraining/adolescent_brain/Development/prefrontal_cortex/ index.html#3. Werner, L. A. (2007). Issues in human auditory development. Journal of Communication Disorders, 40(4), 275–283. Werner, L. A., Folsom, R. C., & Mancl, L. R. (1994). The relationship between auditory brainstem response latencies and behavioral thresholds in normal hearing infants and adults. Hearing Research, 77(1–2), 88–98. Wester, K. G., Irvine, D. R., & Thompson, R. F. (1974). Acoustic tuning of single cells in middle suprasylvian cortex of cat. Brain Research, 76(3), 493–502. Wright, B. A., Lombardino, L. J., King, W. M., Puranik, C. S., Leonard, C. M., & Merzenich, M. M. (1997). Deficits in auditory temporal and spectral resolution in language-impaired children. Nature, 387, 176–178.