David Landsberger – David.Landsberger@nyumc.org

New York University School of Medicine
Department of Otolaryngology – EAR-Lab
462 First Ave STE NBV 5E5
New York, NY 10016, USA
www.ear-lab.org

Popular version of 1aPP1 Electrode length, placement, and frequency allocation distort place coding for bilateral, bimodal, and single-sided deafened cochlear implant users
Presented Monday morning, May 7, 2018, 8:05-8:25 AM, Nicollet D2
175th ASA Meeting, Minneapolis, Minnesota.

Imagine listening to the world with two ears that are tuned differently from each other. A key pressed on a piano would be perceived as different notes in the left and right ear. A person talking would sound like two different people simultaneously saying the same thing, one to each ear. This is in fact the experience for many people listening with two ears where one of the two ears has a cochlear implant.

The cochlea in a normal hearing ear is arranged “tonotopically.”  That is, high frequencies are represented in the bottom (base) of the cochlea and low frequencies are represented at the top (apex) of the cochlea. The regions between the base and apex of the cochlea represent different frequencies and are ordered along the cochlea from low (in the apical region) to high (in the basal region) along the cochlea.

Cochlear implants take advantage of the tonotopic property using an array of electrodes inside the cochlea. Stimulation from an electrode placed deeper into the cochlea provides a lower pitch than an electrode placed closer to the base of the cochlea.  Cochlear implant signal processing therefore provides information about low frequencies on apical electrodes and high frequencies on basal electrodes.

However, there is a mismatch between the frequency represented by a given electrode and the frequency expected by a normal ear at the same location. For example, the deepest electrode might represent 150-200 Hz but be placed in a location that expects approximately 1000 Hz. One factor effecting this relationship is the placement of the electrodes in the cochlea.  This depends on electrode length, surgical placement, and size of the individual’s cochlea.  Another factor is the “frequency allocation” which is the mapping of which frequency ranges are represented by each electrode [1]. The result is that the world is presented pitch shifted (and warped) by a cochlear implant relative to what would be expected by a normal ear.

This distortion may or may not be an issue for traditional cochlear implant users who are bilaterally deaf and listen to the world via a single unilateral implant. For these users, although pitch may be transposed, the transposition is consistent and therefore may be easier to perceptually manage. However, it has become more common for cochlear implant users to listen to the world with two ears (i.e. a cochlear implant in each ear, or a cochlear implant in one ear with acoustic hearing in the other). In this situation, each ear will be differently transposed. This may result in a single auditory object being perceived as two independent auditory objects and may provide contralateral spectral interference. The bilateral listener with a cochlear implant will likely listen to the world with conflicting information provided to each ear.

In the following presentation, we will quantify the magnitudes of these distortions across ears. We will discuss limitations (and potential modifications) to electrode design frequency allocations to minimize this problem for cochlear implant users listening with two ears.

Audio Demos:

(Figure 1) audio files “chickenleg.wav” and “ring.wav”

“Two audio demonstartions of listening to sounds that are differently tuned in each ear. In each sample, a sound is presented normally to one ear and pitch shifted to the other ear.  The first sample consists of speech while the second sample consists of music. These samples simulate only a pitch shift and not hearing loss or the sound quality of a cochlear implant. Note: demos should be played back over headphones.”

[1] D.M. Landsberger, M. Svrakic, J.T. Roland and M. Svirsky, “The Relationship Between Insertion Angles, Default Frequency Allocations, and Spiral Ganglion Place Pitch in Cochlear Implants,” Ear Hear, vol. 36, pp. e207-13., 2015.

Joshua Bernstein – joshua.g.bernstein.civ@mail.mil

Kenneth Jensen – kjensen@hjf.org

Walter Reed National Military Medical Center
4954 N. Palmer Rd.
Bethesda, MD 20889

Jack Noble – jack.noble@vanderbilt.edu
Vanderbilt University
2301 Vanderbilt Pl.
Nashville, TN 37235

Olga Stakhovskaya – ostakhov@umd.edu
Matthew Goupell – goupell@umd.edu
University of Maryland – College Park
7251 Preinkert Drive
College Park, MD 20742

Popular version of 1aPP2, “Measuring spectral asymmetry for cochlear-implant listeners with single-sided deafness”
Presented Monday morning, May 7, 2018
175th ASA Meeting, Minneapolis, MN

Having two ears provides tremendous benefits in our busy world: helping people to communicate in noisy environments, to tell where sounds are coming from, and to feel a general sense of three-dimensionality. People who go deaf in one ear (single-sided deafness) are therefore at a considerable disadvantage compared to people with access to sound in both ears.

Recently, cochlear implants have been explored as a way to restore some hearing to the deaf ear for people with single-sided deafness. A cochlear implant bypasses the normal inner-ear function, relaying sound information directly to the auditory nerve and brain via small electrical bursts.  While traditionally prescribed to people with two deaf ears, recent studies show that cochlear implants can restore some aspects of spatial hearing to people with single-sided deafness [1, 2].

The benefits that a cochlear implant provides to a person with single-sided deafness might not be as large as they could be because the device was never designed for this population. We know that for a given sound frequency, the cochlear implant stimulates the incorrect place in the cochlea (the snail-shaped hearing organ in the inner ear). Figure 1A shows the snail-shaped cochlea straightened into line. A normal-hearing ear processes the full frequency range (20-20,000 Hz) of from the one end of the cochlea to the other. However, cochlear implants deliver frequencies to the wrong cochlear locations because the device cannot be placed to allow access to the full range of frequency-specific nerve cells. Frequency mismatch could make it difficult for people with single-sided deafness to combine the sounds across the two ears, causing them to “hear double” instead of somewhat, although imperfectly, in stereo.

Figure 1.  A schematic of an unrolled cochlea showing how frequency mismatch arises because the cochlear implant electrode array (blue) cannot be inserted all the way to the end of the cochlea. (A) Programming a cochlear implant in a standard way leads to a frequency mismatch between the cochlear-implant (green) and normal-hearing ears (red). (B) Adjusting the cochlear implant frequency allocation could reduce or eliminate this mismatch. 

Legend: Sound examples, best experienced over headphones.

Our research aims to reprogram cochlear implants to frequency-align the two ears for people with single-sided deafness (Figure 1B) by measuring where in the cochlea individual electrical contacts (electrodes) are stimulating. We compared three methods: computed-tomography (CT) scans (like an x-ray) to visualize electrode locations within the cochlea; having the listener compare the relative pitches of the sounds presented to the two ears; and having the listener judge small (~1ms) differences in the arrival time of sounds at the two ears [3]. The timing judgments – the only of the measurements that required listeners to use their two ears together – gave similar estimates of electrode location to the CT scans. In contrast, pitch measurements gave different estimates, suggesting that the brain rewired itself to accommodate pitch differences, but did not rewire itself for spatial hearing. Device programming based on either the timing or CT measurements shows the most promise to improve the ability to use the ears in concert with one another. Our next step will be to make these programming changes to see if they improve stereo hearing.

[The views expressed in this abstract are those of the authors and do not reflect the official policy of the Department of Army/Navy/Air Force, Department of Defense, or U.S. Government.]

[1] Bernstein, J., Schuchman, G., and Rivera, A, “Head shadow and binaural squelch for unilaterally deaf cochlear implantees,” Otology and Neurotology, vol. 38, pp. e195-e202, 2017.

[2] Vermeire, K., and Van de Heyning, P. “Binaural hearing after cochlear implantation in subjects with unilateral sensorineural deafness and tinnitus,” Audiology and Neurootology, vol. 14, pp. 163–171, 2009.

[3] Bernstein, J., Stakhovskaya, O., Schuchman, G., Jensen, K., and Goupell, M, “Interaural time-difference discrimination as a measure of place of simulation for cochlear-implant users with single-sided deafness,” Trends in Hearing, Vol. 19, p. 2331216515617143, 2018.

Danielle Sacchinelli  -dms0043@auburn.edu
Aurora J. Weaver – ajw0055@auburn.edu
Martha W. Wilson – paxtomw@auburn.edu
Anne Rankin Cannon- arc0073@auburn.edu
Auburn University
1199 Haley Center
Auburn, AL 36849

Popular version of 3aPPa3, “Does the right ear advantage persist in mature auditory systems when cognitive demand for processing increases?”
Presented Wednesday morning, December 6, 2017, 8:00-12:00 AM, Studios Foyer
174th ASA Meeting, New Orleans
Click here to read the abstract

A dichotic listening task presents two different sound sequences simultaneously to both ears. Performance on these tasks measures selective auditory attention for each ear, either binaural separation or binaural integration (see Figure 1 for examples). Based on the anatomical model of auditory processing, the right ear has a slight advantage, compared with the left ear, on dichotic listening tasks. This is due to left brain hemispheric dominance for language, which receives direct auditory input from the right ear (i.e., strong contralateral auditory pathway; Kimura, 1967).

Clinical tests of auditory function quantify this right ear advantage for dichotic listening tasks to assess maturity of the auditory system, in addition to other clinical implications. Accurate performance on dichotic tests relies on both sensory organization and memory. As a child matures, the right ear advantage decreases until it is no longer clinically significant. However, clinically available dichotic-digits tests use only 1, 2, (e.g., Dichotic digits test; Musiek, 1983; Musiek, et al., 1991) or 3 (i.e., Dichotic Digits–MAPA; Schow, Seikel, Brockett, & Whitaker, 2007) digit sets in each ear for testing. See Figure 1 for maximum task demands of clinical tests for binaural integration, instructions “B”, using free recall protocol (Guenette, 2006).

Daily listening often requires an adult to process competing information that extends six items of sensory input. This study investigated the impact of increasing cognitive demands on ear performance asymmetries (i.e., right versus left) in mature auditory systems. Forty-two participants (i.e., 19-28 year-olds) performed dichotic binaural separation tasks (adapted from the Dspan Task; Nagaraj, 2017), for 2, 3, 4, 5, 6, 7, 8, and 9-digit lists. Listeners recalled the sequence presented to one ear while ignoring the sequence presented to the opposite ear (i.e., binaural separation; directed ear protocol). See Figure 1 for an example of the experimental binaural separation tasks (i.e., digit length = 3 used for condition 2) and instructions “A” for directed ear recall.

Results in Figure 2 show a significant effect for directed ear performance as task demands increase (i.e., digit list length). The overall evaluation of the list length (Figure 2) does not reveal the impact of working memory capacity limits (i.e., maximum items that can be recalled for an ongoing task) for each participant. Therefore, a digit span was measured to estimate each participant’s simple working memory capacity. Planned comparisons for ear performance relative to a participant’s digit span (i.e., below = n-2, at span = n, and above span = n+2 digit lists, where n = digit span) evaluated the role of cognitive demand on ear asymmetries.

Planned t-test comparisons revealed a significant performance asymmetry above span (i.e., n+2). No significant differences were identified for performance relative to, or below, an individual’s simple memory capacity. This indicates the persistence of the right ear advantage in mature auditory systems when listening demands exceeded an individual’s auditory memory capacity.

Overall, the study found the right ear continues to show better performance on dichotic listening tasks, even in mature adults. This persistent right ear advantage occurred when the number of digits in the sequence exceeded the participants’ digit span capacity. We believe such demands are a realistic aspect of every day listening, as individuals attempt to retain sensory information in demanding listening environments. These results may help us modify existing clinical tests, or develop a new task, to more precisely reveal performance asymmetries based on an individual’s auditory working memory capacity.

Figure 1. Displays an example of dichotic digit stimuli presentation, with both “A” binaural separation tasks (i.e., directed ear) and “B” binaural integration (i.e., free recall) instructions.

Figure 2. Displays ear performance on the binaural separation task across all participants. Note: the orange box highlights the maximum demands of commercially available dichotic-digits tests; participant performance reflects a lack of asymmetry under these cognitive demands.

Figure 3. Displays participant ear performance on the binaural separation task relative to digit span.

1. Kimura, D. (1967). Functional asymmetry of the brain in dichotic listening. Cortex, 3(2), 163- 176.
2. Musiek, F., (1983). Assessment of central auditory dysfunction: The dichotic digit test revisited. Ear and Hearing, 4(2), 79-83.
3. Musiek, F., Gollegly, K., Kibbe, K., & Verkest-Lenz, S. (1991). Proposed screening test for central auditory disorders: Follow-up on the dichotic digits test. The AmericanJournal of Otology, 12:2, 109-113.
4. Schow, R., Seikel, A., Brockett, J., Whitaker, M., (2007). Multiple Auditory Processing Assessment (MAPA); Test Manual 1.0 version. AUDITEC, St. Louis, MO. PDF available from http://www2.isu.edu/csed/audiology/mapa/MAPA_Manual.pdf
5. Guenette, L.A. (2006). How to administer the Dichotic Digit Test. The Hearing Journal, 59 (2), 50.
6. Nagaraj, N. K. (2017). Working Memory and Speech Comprehension in Older Adults with Hearing Impairment. Journal of Speech Language and Hearing Research, 60(10), 2949-2964. doi: 10.1044/2017_JSLHR-H-17-0022.

Akio Honda- honda@yamanash-eiwa.ac.jp
Yamanashi-Eiwa College
888 Yokone-machi, Kofu
Yamanashi, Japan 400-8555

Popular version of paper 4aPP4, “Effects of listener’s whole-body rotation and sound duration on sound localization accuracy”
Presented Thursday morning, December 7, 2017, 9:45-10:00 AM, Studio 4
174^th ASA Meeting, New Orleans

Sound localization is an important ability to make daily life safe and rich. When trying to localize a sound, our head/body movement is known to facilitate sound localization, which creates dynamic changes to the information input to each ear [1–4]. However, earlier reports have described that sound localization accuracy deteriorates during a listener’s head rotation [5–7]. Moreover, the facilitative effects of a listener’s movement differ depending on the sound features [3–4]. Therefore, the interaction between a listener’s movement and sound features remains unclear. For this study, we used a digitally controlled spinning chair to assess the effects of a listener’s whole-body rotation and sound duration on horizontal sound localization accuracy.

In this experiment, listeners were 12 adults with normal audition. Stimuli were 1/3-octave band noise bursts (center frequency = 1 kHz, SPL = 65 dB) of 50, 200, and 1000 ms duration. Each stimulus was presented from a loudspeaker in a circular array (1.2 m radius) with loudspeaker separation of 2.5 deg (total 25 loudspeakers). Listeners were unable to see the loudspeakers because an acoustically transparent curtain was placed between the listener and the circular loudspeaker array while maintaining brighter conditions inside the curtain than outside. We assigned numbers for the azimuth angle at 1.25 degree intervals: the number zero was 31.25 deg to the left; the number 25 was in front of the listener; and the number 50 was 31.25 deg to the right. These numbers were presented on the curtain to facilitate responses. Listeners sitting on the spinning chair set at the circle center were asked to report the number corresponding to the position of the presented stimulus (see Fig. 1).

In the chair-still condition, listeners faced forward with the head aligned frontward (0 deg). Then the stimulus was presented from one loudspeaker of the circular array. In the chair-rotation condition, listeners faced forward with the head 15 deg left or 15 deg right. Then, the chair rotated for 30 deg clockwise or counterclockwise respectively when the listener first faced 15 deg left or right. During the rotation, when listeners faced forward with the head front at 0 deg, the stimulus was presented from one of the loudspeakers in the circular array.

We analyzed the angular errors in the horizontal planes. The angular errors were calculated as the difference between the perceptually localized position and the physical target position. Figure 2 depicts the mean horizontal sound localization performance.

Our results demonstrated superior sound localization accuracy of the chair-rotation condition to that of a chair-still condition. Moreover, a significant effect of sound duration was observed; the accuracy for 200 ms stimuli seems worst among the durations used. However, the interaction of the test condition and the sound duration was not significant.

These findings suggest that the sound localization performance might be improved if listeners are able to obtain dynamic auditory information from their movement.  Furthermore, the duration difference of target sound was not crucially important for their sound localization accuracy. Of course, other explanations are possible. For instance, listeners might be better able to localize the sound using shorter sound (less than 50 ms), although a halfway longer duration such as 200 ms would not provide effective dynamic information to facilitate sound localization. Irrespective of the interpretation, our results provide valuable suggestions for future studies undertaken to elucidate the interaction between a listener’s movement and sound duration.

Fig. 1 Outline of the loudspeaker array system.

Fig. 2 Results of angular error in the horizontal planes

References:

• Wallach, “On sound localization,” J. Acoust. Soc. Am., 10, 270–274 (1939).
• Honda, H. Shibata, S. Hidaka, J. Gyoba, Y. Iwaya, and Y. Suzuki, “Effects of Head Movement and Proprioceptive Feedback in Training of Sound Localization,” i-Perception, 4, 253–264 (2013).
• Iwaya, Y. Suzuki and D. Kimura, “Effects of head movement on front-back error in sound localization,” Acoust. Sci. Technol., 24, 322–324 (2003).
• Perrett and W. Noble, “The contribution of head motion cues to localization of low-pass noise,” Percept. Psychophys., 59, 1018–1026 (1997).
• Cooper, S. Carlile and D. Alais, “Distortions of auditory space during rapid head turns,” Exp. Brain. Res., 191, 209–219 (2008).
• Leung, D. Alais and S. Carlile, “Compression of auditory space during rapid head turns,” Proc. Natl. Acad. Sci. U.S.A., 105, 6492–6497 (2008).
• Honda, K. Ohba, Y. Iwaya, and Y. Suzuki, “Detection of sound image movement during horizontal head rotation,” i-Perception, 7, 2041669516669614 (2016).

Aurora J. Weaver – ajw0055@auburn.edu
Molly Murdock- mem0092@auburn.edu
Auburn University
1199 Haley Center
Auburn, AL 36849

Jeffrey J. DiGiovanni – digiovan@ohio.edu
Ohio University
W151a Grover Center
Athens, Ohio

Dennis T. Ries – Dennis.Ries@ucdenver.edu
University of Colordo Anshutz Medical Campus
Building 500, Mailstop F546
13001 East 17^th Place, Room E4326C
Aurora, CO 80045

Popular version of paper 3aPPb7
Presented Wednesday morning, December 6, 2017
174th ASA Meeting, New Orleans

Infants are inherently sensitive to the relational properties of music (e.g., musical intervals, melody).¹ Knowledge of complex structural properties of music (e.g., key, scale), however, are learned to varying degrees through early school age.^1-3 Acquisition of some features does not require specialized instruction, but extensive musical training further enhances the ability to learn musical structures.⁴ Related to this project, formal musical instruction is linked to improvement in listening tasks (other than music) that stress attention in adult participants.^5,6,7   

Musical training influences sound processing in the brain through learning-based processes while also enhancing lower-level acoustic processing within the brainstem⁸. Behavioral and physiological evidence suggest there is a critical period for pitch processing refinement within these systems between the ages of 7-to-11 years.^9-13 The purpose of this project was to determine the contributions of musical training and age to refinement of pitch processing beyond this critical period.

Individuals with extensive and active instrumental musical training were matched in age with individuals with limited instrumental musical training. This comparison served as a baseline to evaluate the extent of presumed physiologic changes within the brain/brainstem relative to the amount and duration of musical training.^14,15 We hypothesized that the processing mechanisms for active musicians become increasingly more efficient over time, due to training. Therefore, this group can focus more mental resources on the retention of sound information during pitch perception tasks of varying difficulty. Sixty-six participants, in three different age groups (i.e., 10-12 year olds; 13-15 year olds, and adults), completed two experiments.

The first experiment included a measure of non-verbal auditory working memory (pitch pattern span [PPS]).¹⁶ The second experiment used a pitch matching task, which closely modeled the procedure implemented by Ross and colleagues.^17-19 Figure 1 displays the individual PPS scores for each instrumental training group as a function of age in years.

Figure 1. Individual PPS scores (y-axis) for each instrumental training group as a function of age in years (x- axis). The participant scores in the active group are represented by filled in circles, and the participants with limited instrumental training are open circles.

The second experimental task, a pitch matching production task, eliminated the typical need to understand musical terminology (e.g. naming musical notes). This method provided a direct comparison of musicians and non-musicians, when they could only rely on their listening skills to remember a target, and to match the pitch to an ongoing tonal sequence.^17-19 We wanted to evaluate pitch matching accuracy (via constant error) and consistency (via standard deviation) in individuals with limited and active instrumental musical training. Figure 2 illustrates the timing pattern and describes the task procedure. Each participant completed thirty pitch matches.

Figure 2. Schematic representation of timing pattern of the pure-tones showing the target and examples of the first three comparison tones that might have followed. Once the pitch target had been presented, an adjustable dial appeared on a touch screen and the presentation of the first comparison stimulus occurred 0.2 seconds later. Note the frequency of the 1st comparison tone was placed randomly 4-6 semitones above or below the target tone (not represent in this figure). The values of subsequent tones were controlled by the participant through movement of the onscreen dial. Presentation of comparison tones continued, at the same time interval, until a participant had adjusted the pitch of the ongoing comparison tones using the GUI dial to match the pitch target

Figure 3 depicts distribution of responses across age groups and instrumental training groups (see figure legend). Statistical analyses (i.e., Manova and Linear Regression) revealed that duration of instrumental musical training and age uniquely contribute to enhanced memory for pitch, indicated by greater PPS scores and smaller standard deviations of the pitch matches. Unexpectedly, based on the task procedures where participates are equally likely to match a pitch above or below the target, the youngest children (ages 10-12) demonstrated significantly sharper pitch matches (i.e., positive constant error) across pitch matches than the older participants (13 and older; see Figure 3 dashed lines). That is, across music groups, the youngest participants on average tended to produce sharper pitch matches than the presented target pitch.

Figure 3. Displays the proportion of response matches produced as a function of the deviation in half-steps (smallest musical distance between notes, e.g., progressively going up the white and black keys on a piano) across age groups in rows (ages 10-12 years, top; ages 13-15 years, middle; ages 18-35 years, bottom) and instrumental training groups by column (Limited, left; Active, right). The dashed line depicts the overall accuracy (i.e., constant error) across pitch matches produced by each participant subgroup.

Matching individuals in age groups, with and without active musical training, allowed the comparison of the unique contributions of age and duration of music training on pitch memory. Consistent with our hypothesis, individuals with active and longer durations of musical training produced greater PPS scores and performance on pitch matching was less degraded (i.e., produced smaller standards deviations across pitch matches) than age-matched groups. Most individuals can distinguish pitch changes in half note steps, although they may have considerable difficulty establishing a reliable relationship between a frequency and its note value.^20,21,23,24 There are individuals, however, with absolute pitch, who have the capacity to name a musical note without the use of a reference tone.²⁴ While no participant in either music group (Active or Limited) reported absolute pitch, two participants in the active music group matched all thirty pitch matches within 1 semitone; that is, within one half step (HS) of the target. This may indicate that the two listeners were using memory of the categorical notes to facilitate pitch matches (e.g., using their memory of the note A4, could help when matching a target pitch close to 440 Hz in the task). Consist with previous application of this method,^17,18,19 the pitch matching production task did identify participants who possess similar categorical memory for tonal pitch when musical notes and terminology were removed from the production method.

References

1. Schellenberg, E. G., & Trehub, S. E. (1996). Natural musical intervals: Evidence from infant listeners. Psychological Science, 7(5), 272-277.
2. Fujioka, T., Ross, B., Kakigi, R., Pantev, C., & Trainor, L. (2006). One year of musical training affects development of auditory cortical evoked fields in young children. Brain, 129(10), 2593-2608.
3. Trehub, S. E., Bull, D., & Thorpe, L. A. (1984). Infants’ perception of melodies: The role of melodic contour. Child Development, 55(3), 821-830. doi:10.1111/1467-8624.ep12424362
4. Morrongiello, B. A., & Roes, C. L. (1990). Developmental changes in children’s perception of musical sequences: Effects of musical training. Developmental Psychology, 26(5), 814-820.
5. Strait, D., Kraus, N., Parbery-Clark, A., & Ashley, R. (2010). Musical experience shapes top-down auditory mechanisms: evidence from masking and auditory attention performance. Hearing Research, 261, 22-29.
6. Williamson, V. J., Baddeley, A. D., & Hitch, G. J. (2010). Musicians’ and nonmusicians’ memory for verbal and musical sequences: Comparing phonological similarity and pitch proximity. Memory and Cognition, 38(2), 163-175. doi: 10.3758/MC.38.2.163.
7. Schön, D., Magne, C. & Besson, M. The music of speech: Music training facilitates pitch processing in both music and language. Psychophysiology 41, 341–349 (2004).
8. Kraus, N., Skoe, E., Parbery-Clark, A. & Ashley, R. Experience-induced Malleability in Neural Encoding of Pitch, Timbre, and Timing. N. Y. Acad. Sci. 1169, 543–557 (2009).
9. Banai, K., Sabin, A.T., Wright, B.A. (2011). Separable developmental trajectories for the abilities to detect auditory amplitude and frequency modulation. Hearing Research, 280, 219-227.
10. Dawes, P., & Bishop, D.V., 2008. Maturation of visual and auditory temporal processing in school-aged children. J. Speech. Lang. Hear. Res. 51, 1002-1015.
11. Moore, D., Cowan, J., Riley, A., Edmondson-Jones, A., & Ferguson, M. (2011). Development of auditory processing in 6- to 11-yr-old children. Ear and Hearing, 32, 269-285.
12. Morrongiello, B. A., & Roes, C. L. (1990). Developmental changes in children’s perception of musical sequences: Effects of musical training. Developmental Psychology, 26, 814-820.
13. Sutcliffe, P., & Bishop, D. (2005). Psychophysical design influences frequency discrimination performance in young children. Journal of Experimental Child. Psychology, 91, 249-270
14. Habib, M., & Besson, M. (2009). What do musical training and musical experience teach us about brain plasticity? Music Perception, 26, 279-285.
15. Zatorre, R. J. (2003). Music and the brain. Annals of the New York Academy of    Sciences, 999, 4-14
16. Weaver, A.J., DiGiovanni, J.J & Ries, D.T. (2015). The Influence of Musical Training and Maturation on Pitch Perception and Memory. Poster AAS, Scottsdale, AZ
17. Ross, D. A., & Marks, L. E. (2009). Absolute pitch in children prior to the beginning of musical training. Annals of the New York Academy of Sciences, 1169, 199-204. doi:10.1111/j.1749-6632.2009.04847.x
18. Ross, D. A., Olson, I. R., & Gore, J. (2003). Absolute pitch does not depend on early musical training. Annals of the New York Academy of Sciences, 999(1), 522-526.
19. Ross, D. A., Olson, I. R., Marks, L., & Gore, J. (2004). A nonmusical paradigm for identifying absolute pitch possessors. Journal of the Acoustical Society of America, 116, 1793-1799. Ross, Olsen, and Gore’s procedure (2003)
20. Levitin, D. (2006). This is your brain on music: The science of human obsession. New York, NY: Dutton.
21. Moore, B. C. J. (2003). An introduction to the psychology of hearing. London, UK: Academic Press.
22. Hyde KL, Peretz I, Zatorre RJ. Evidence for the role of the right auditory cortex in fine pitch resolution. Neuropsychologia 2008;46:632–639. [PubMed: 17959204]
23. McDermott, J. H., & Oxenham, A. J. (2008). Music perception, pitch, and the auditory system. Current Opinion in Neurobiology, 18(4), 452–463. http://doi.org/10.1016/j.conb.2008.09.005
24. Dooley, K., & Deutsch, D. (2010). Absolute pitch correlates with high performance on musical dictation. Journal of the Acoustic Society of America, 128(2), 890-893. doi:10.1121/1.3458848