The brain normally compares the timing and intensity of the sounds that reach each ear to work out a sound’s origin. Hearing loss in one ear disrupts these between-ear comparisons, which causes listeners to make errors in this process. With time, however, the brain adapts to this hearing loss and once again learns to localize sounds accurately.

Previous research has shown that young ferrets can adapt to hearing loss in one ear in two distinct ways. The ferrets either learn to remap the altered between-ear comparisons, caused by losing hearing in one ear, onto their new locations. Alternatively, the ferrets learn to locate sounds using only their good ear. Each strategy is suited to localizing different types of sound, but it was not known how this adaptive flexibility unfolds over time, whether it persists throughout the lifespan, or whether it is shared by other species.

Now, Keating et al. show that, with some coaching, adult humans also adapt to temporary loss of hearing in one ear using the same two strategies. In the experiments, adult humans were trained to localize different kinds of sounds while wearing an earplug in one ear. These sounds were presented from 12 loudspeakers arranged in a horizontal circle around the person being tested. The experiments showed that short periods of behavioral training enable adult humans to adapt to a hearing loss in one ear and recover their ability to localize sounds.

Just like the ferrets, adult humans learned to correctly associate altered between-ear comparisons with their new locations and to rely more on the cues from the unplugged ear to locate sound. Which of these adaptive strategies the participants used depended on the frequencies present in the sounds. The cells in the ear and brain that detect and make sense of sound typically respond best to a limited range of frequencies, and so this suggests that each strategy relies on a distinct set of cells. Keating et al. confirmed in ferrets that different brain cells are indeed used to bring about adaptation to hearing loss in one ear using each strategy. These insights may aid the development of new therapies to treat hearing loss.

A major challenge faced by the brain is to maintain stable and accurate representations of the world despite changes in sensory input. This is important because the statistical structure of sensory experience varies across different environments (Mlynarski and Jost, 2014; Qian et al., 2012; Seydell et al., 2010), but also because long-term changes in sensory input result from a range of sensory impairments (Feldman and Brecht, 2005; Keating and King, 2015; Sengpiel, 2014). Adaptation to altered inputs has been demonstrated in different sensory systems, particularly during development, and serves to shape neural circuits to the specific inputs experienced by the individual (Margolis et al., 2014; Mendonca, 2014; Schreiner and Polley, 2014; Sur et al., 2013). However, many ecologically important aspects of neural processing require the integration of multiple sensory cues, either within or across different sensory modalities (Seilheimer et al., 2014; Seydell et al., 2010). A specific change in sensory input may therefore have a considerable impact on some cues whilst leaving others intact. In such cases, adaptation can be achieved in two distinct ways, as demonstrated by recent studies of sound localization following monaural deprivation during infancy (Keating et al., 2013; 2015).

Monaural deprivation alters the binaural spatial cues that normally determine the perceived location of a sound in the horizontal plane (Figure 1A) (Kumpik et al., 2010; Lupo et al., 2011). Adaptation can therefore be achieved by learning the altered relationships between particular cue values and spatial locations (Gold and Knudsen, 2000; Keating et al., 2015; Knudsen et al., 1984), a process referred to as cue remapping. However, at least in mammals, monaural spectral cues are also available to judge sound source location in both the horizontal and vertical planes (Carlile et al., 2005). These spectral cues arise from the acoustic properties of the head and external ears, which filter sounds in a direction-dependent way (Figure 1B). Monaural deprivation has no effect on the spectral cues available to the non-deprived ear. This means it is possible to adapt by learning to rely more on these unchanged spectral cues, whilst learning to ignore the altered binaural cues (Kacelnik et al., 2006; Keating et al., 2013; Kumpik et al., 2010), a form of adaptation referred to as cue reweighting.

Figure 1 with 2 supplements see all

Download asset Open asset

Developmental studies of sound localization plasticity following monaural deprivation have found evidence for both cue remapping (Gold and Knudsen, 2000; Keating et al., 2015; Knudsen et al., 1984) and cue reweighting (Keating et al., 2013), but it is not known whether these adaptive processes can occur simultaneously. Indeed, until recently, it was thought that monaural deprivation might induce different adaptive processes in different species (Keating and King, 2013; Shamma, 2015). However, whilst we now know that ferrets use both cue remapping and reweighting to adapt to monaural deprivation experienced during development (Keating et al., 2013; 2015), it is not known whether the same neural populations are involved in each case.

It is also not known whether the ability to use both adaptive processes is restricted to specific species or developmental epochs. Although the mature auditory system can adapt to monaural deprivation using cue reweighting (Kumpik et al., 2010), conflicting evidence for cue remapping has been obtained in adult humans fitted with an earplug in one ear for several days (Florentine, 1976; McPartland et al., 1997). To the extent that adaptive changes in binaural cue sensitivity are possible in adulthood, as suggested by other sensory manipulations (Trapeau and Schonwiesner, 2015), these may occur at the expense of cue reweighting. It is therefore unclear whether the same adult individuals can adapt to a unilateral hearing loss using multiple adaptive processes. Although numerous studies have shown that spatial hearing is more plastic early in life (Keating and King, 2013; Knudsen et al., 1984; Popescu and Polley, 2010), behavioral training can facilitate accommodation to altered cues in adulthood (Carlile, 2014; Carlile et al., 2014; Kacelnik et al., 2006; Shinn-Cunningham et al., 1998). Here, we show that adult humans are equally capable of using both adaptive processes, provided they are given appropriate training. Moreover, our results suggest that cue remapping and reweighting are neurophysiologically distinct, which we confirmed by recording from auditory cortical neurons in ferrets reared with an intermittent hearing loss in one ear.

Adult humans were trained to localize sounds from 12 loudspeakers in the horizontal plane (Figure 1—figure supplement 1A) whilst wearing an earplug in one ear (~5600 trials split into 7 sessions completed in < 3 weeks). In order to directly measure the efficacy of training, earplugs were worn only during training sessions. This contrasts with previous work in which adult humans received minimal training, but were required to wear earplugs for extended periods of everyday life (Florentine, 1976; Kumpik et al., 2010; McPartland et al., 1997). On ~50% of trials, subjects were required to localize flat-spectrum broadband noise (0.5–20 kHz), which provide all of the available auditory spatial cues (Blauert, 1997). With these cue-rich stimuli, trials were repeated following incorrect responses (“correction trials”) and subjects were given performance feedback. Across training sessions, sound localization performance (% correct) gradually improved (Figure 1C–F; slope values >0; bootstrap test, p<0.01; Cohen’s d = 1.43), indicating that relatively short periods of training are sufficient to drive adaptation.

To determine the relative contributions of cue remapping and reweighting to these changes in localization accuracy, we measured the extent of adaptation for two additional stimulus types that restrict the availability of specific cues. For these cue-restricted stimuli, which were randomly interleaved with cue-rich stimuli, correction trials were not used and no feedback was given. The first of these additional stimulus types comprised broadband noise with a random spectral profile that varied across trials (Figure 1—figure supplement 1B). These stimuli disrupt spectral localization cues because it is unclear whether specific spectral features are produced by the filtering effects of the head and ears or are instead properties of the sound itself (Figure 1B) (Keating et al., 2013). Consequently, if subjects adapt to asymmetric hearing loss by giving greater weight to the spectral cues provided by the non-deprived ear, we would expect to see less improvement in sound localization performance for random-spectrum sounds than for flat-spectrum sounds. This is precisely what we found (Figure 1E,F; random-spectrum slope values < flat-spectrum slope values; bootstrap test, p<0.01; Cohen’s d = 1.18; see also Figure 1—figure supplement 2), indicating that adaptation involves learning to rely more on spectral cues.

However, if adaptation were solely dependent on this type of cue reweighting, we would expect no improvement in sound localization for narrowband sounds, such as pure tones. This is because spectral cues require a comparison of sound energy at different frequencies, which is not possible for these sounds (Figure 1B) (Carlile et al., 2005). Improved localization of pure tones would therefore indicate adaptive processing of binaural cues. Because interaural time differences (ITDs) and interaural level differences (ILDs) are respectively the primary cues for localizing low- (<1.5 kHz) and high-frequency (≥1.5 kHz) tones (Blauert, 1997), we tested each of these stimuli separately. To detect changes in binaural sensitivity, and facilitate comparison with previous work (Keating et al., 2015; Kumpik et al., 2010), stimulus and response locations in the front and rear hemifields were collapsed. This produces a measure of performance that is insensitive to front-back errors, which reflect failures in spectral, rather than binaural, processing. We observed improvements in subjects’ ability to localize both low- and high-frequency pure tones over time, demonstrated by a decline in error magnitude (Figure 2E,F; Δ error <0; bootstrap test, p<0.01). The initial bias toward the side of the open ear was also reduced (Figure 2G,H; Δ bias <0; bootstrap test, p<0.01; low-frequency, Cohen’s d = 0.7; high-frequency, Cohen’s d = 0.96). Adaptation therefore involves a shift in the mapping of altered binaural cues onto spatial location. Together, these results show that subjects adapted to monaural deprivation using a combination of both cue remapping and cue reweighting.

Figure 2

Download asset Open asset

We next considered the relationship between these two adaptive processes. Although cue remapping and cue reweighting share a similar time-course (significant correlation between the amount of remapping and reweighting across sessions; Figure 3A, r = 0.81, P = 0.028), the overall amount of cue remapping exhibited by each subject was independent of the amount of cue reweighting (Figure 3B, r = 0.03, P = 0.90). This inter-subject variability was not attributable to differences in the effectiveness of earplugs used (Figure 3—figure supplement 1). Instead, we found that these two adaptive processes are affected by the frequency composition of the stimulus in different ways (Figure 3C, interaction between sound frequency and adaptation type, p = 0.005, permutation test). As expected, cue reweighting was greater for frequencies where spectral cues are most prominent in humans (≥4 kHz, Figure 3C, p<0.05, post-hoc test; Figure 3—figure supplement 2) (Blauert, 1997; Hofman and Van Opstal, 2002), whereas equal amounts of cue remapping were observed for tones above and below 4 kHz (Figure 3C, p>0.05, post-hoc test).

Figure 3 with 2 supplements see all

Download asset Open asset

This indicates that these adaptive processes are relatively independent of one another and suggests that they may depend on distinct neural substrates. This motivated us to revisit neurophysiological measures of cue reweighting and remapping in ferrets reared with an intermittent hearing loss in one ear (Figure 3D) (Keating et al., 2013; Keating et al., 2015). In common with our human behavioral data, we found no correlation between the degree of cue reweighting and remapping in cortical neurons recorded from ferrets raised with one ear plugged (Figure 3E, r = 0.08, p = 0.073). The type of plasticity observed also depended on the frequency preference of the neurons (Figure 3F, interaction between unit characteristic frequency and adaptation process, p = 0.012, permutation test). Greater cue reweighting was found in neurons tuned to frequencies where spectral cues are most prominent in ferrets (>8 kHz, Figure 3F, p<0.05, post-hoc test; frequency tuning bandwidth at 10 dB above threshold (µ ± SD) = 0.97 ± 0.51 octaves) (Carlile and King, 1994; Keating et al., 2013), whereas equal amounts of cue remapping occurred in neurons tuned to low and high frequencies (Figure 3F, p>0.05, post-hoc test). Thus, different neurons can exhibit cue remapping and reweighting in a relatively independent manner.

We have shown that adult humans can adapt to asymmetric hearing loss by both learning to rely more on the unchanged spectral localization cues available and by remapping the altered binaural cues onto appropriate spatial locations. Recent work has shown that both adaptive processes occur in response to monaural deprivation during development (Keating et al., 2013; 2015). Our results suggest that this flexibility is likely to be a general feature of neural processing that also occurs in adulthood. Moreover, we show that these two forms of adaptation emerge together and that remapping of binaural spatial cues occurs at low as well as high frequencies, indicating plasticity in the processing of both ITDs and ILDs.

Although adaptive changes in sound localization have previously been observed when human subjects wear an earplug for prolonged periods of everyday life (Kumpik et al., 2010), we found here that much shorter periods of training are sufficient to induce adaptation to an episodic hearing loss. Our results also demonstrate that subjects adapt using a combination of cue remapping and cue reweighting. In contrast, previous work has shown that cue remapping did not occur when subjects wore an earplug most of the time for several days, and were therefore able to interact with their natural environments under these hearing conditions, but received relatively little training (Kumpik et al., 2010). This suggests that the nature of adaptation may depend on the behavioral or environmental context in which it occurs. Consequently, it should be possible to devise training protocols that would help subjects to adapt to altered auditory inputs in ways that do not ordinarily occur, or occur more slowly, during the course of everyday life.

When both adaptive processes occur together, observed either behaviourally in adult humans or neurophysiologically in monaurally-deprived ferrets, there was no obvious relationship between the amount of cue remapping and reweighting. This is at least in part because the spatial cues involved differ in their frequency dependence. Whereas equal amounts of binaural cue remapping occurred at different frequencies, spanning the range where both ITDs and ILDs are available, reweighting of spectral cues was restricted to those frequencies where these cues are most prominent. This suggests that the neural substrates for cue remapping and reweighting are at least partially distinct, with separate populations of cortical neurons displaying different types of spatial plasticity depending on their frequency preferences and sensitivity to different spatial cues.

It is not known, however, whether remapping and reweighting occur at different stages of the processing hierarchy. Although experience-dependent plasticity in the processing of binaural cues has been observed at multiple levels of the auditory pathway (Keating et al., 2015; Popescu and Polley, 2010; Seidl and Grothe, 2005), the changes induced by unilateral hearing loss during development are more extensive in the cortex than in the midbrain (Popescu and Polley, 2010). Much less is known about the neural processing of spectral localization cues and how this might be affected by experience (Carlile et al., 2005; Keating et al., 2013). However, reweighting of these cues is likely to reflect a change in the way they are integrated with other cues, which is thought to occur in the inferior colliculus (Chase and Young, 2005). This is consistent with the finding that adaptive changes in sound localization behavior in monaurally deprived adult ferrets rely on descending projections from the cortex to the inferior colliculus (Bajo et al., 2010). It is likely therefore that adaptive plasticity emerges via dynamic interactions between different stages of processing (Keating and King, 2015).

Although we found evidence for both cue reweighting and cue remapping in our human behavioral and ferret neurophysiological data, the nature of the episodic hearing loss in each case was very different. Whereas ferrets had one ear occluded for ~80% of the time over the course of several months of development (Keating et al., 2013; 2015), adult human subjects wore an earplug for only ~7 hr in total (1 hr every 1–3 days). It is not known whether comparable physiological changes to those observed in the ferrets are responsible for the rapid shifts in localization strategy in adult human listeners following these brief periods of acute hearing loss. Nevertheless, the close similarity in the results obtained in each species has important implications for the generality of our findings.

Our results emphasize the flexibility of neural systems when changes in sensory input affect ethologically important aspects of sensory processing, such as sound localization. They also reveal individual differences in the adaptive strategy adopted (Figure 3B). Further work is needed to understand the causes of these differences and to determine whether knowing how different individuals adapt to hearing loss could help tailor rehabilitation strategies. Our results also highlight the importance of training in promoting multiple adaptive processes, and this is likely to be relevant to other aspects of sensory processing (Feldman and Brecht, 2005; Keating and King, 2015; Sengpiel, 2014), particularly in situations where changes in sensory input affect some cues but not others.

All procedures involving human listeners conformed to ethical standards approved by the Central University Research Ethics Committee (CUREC) at the University of Oxford. All work involving animals was approved by the local ethical review committee and performed under licenses granted by the UK Home Office under the Animals (Scientific Procedures) Act of 1986. 11 audiologically normal human subjects (2 male, 9 female; aged 18–30) took part in the behavioral study. Sample size was determined on the basis of previous work, in which effect sizes of 2 – 4.6 were observed in human subjects who adapted to an earplug in one ear (Kumpik et al., 2010). To achieve a desired power of 0.8 with an alpha level of 0.001, 6–10 subjects were therefore required. All subjects provided written informed consent and were paid for their participation. Neurophysiological data were obtained from 13 ferrets (6 male, 7 female), seven of which were reared with an intermittent unilateral hearing loss, the details of which have been described previously (Keating et al., 2013). Briefly, earplugs were first introduced to the left ear of ferrets between postnatal day 25 and 29, shortly after the age of hearing onset in these animals. From then on, an earplug was worn ~80% of the time within any 15-day period, with normal hearing experienced otherwise. To achieve this, earplugs were monitored routinely and replaced or removed as necessary. All remaining ferrets were reared under normal hearing conditions. Expected effect sizes were less clear for neurophysiological changes so sample sizes were chosen based on previous studies in our lab (Dahmen et al., 2010).

For both human and animal subjects, hearing loss was induced by inserting an earplug into one ear (EAR Classic), which attenuated (low-pass filter, attenuation of 20–40 dB in humans, and 15–45 dB in ferrets) and delayed (150 µs in humans and 110 µs in ferrets) acoustical input (Keating et al., 2013; Kumpik et al., 2010). For 10 of the 11 human subjects tested, we measured hearing thresholds at 1–8 kHz in octave steps and assessed the impact on those thresholds of wearing an earplug in the trained ear (Figure 3—figure supplement 1). This yielded very similar results to those reported previously in humans (Kumpik et al., 2010).

1. 1. Bajo VM
   2. Nodal FR
   3. Moore DR
   4. King AJ

   (2010) The descending corticocollicular pathway mediates learning-induced auditory plasticity

   Nature Neuroscience 13:253–260.

   https://doi.org/10.1038/nn.2466

   • Google Scholar
2. 1. Bartlett EL
   2. Sadagopan S
   3. Wang X

   (2011) Fine frequency tuning in monkey auditory cortex and thalamus

   Journal of Neurophysiology 106:849–859.

   https://doi.org/10.1152/jn.00559.2010

   • Google Scholar
3. 1. Bizley JK
   2. Nodal FR
   3. Nelken I
   4. King AJ

   (2005) Functional organization of ferret auditory cortex

   Cerebral Cortex 15:1637–1653.

   https://doi.org/10.1093/cercor/bhi042

   • Google Scholar
4. Book

   1. Blauert J

   (1997)

   Spatial Hearing: The Psychophysics of Human Sound Localization

   London: MIT Press.

   • Google Scholar
5. 1. Carlile S
   2. King AJ

   (1994)

   Monaural and binaural spectrum level cues in the ferret: acoustics and the neural representation of auditory space

   Journal of Neurophysiology 71:785–801.

   • Google Scholar
6. 1. Carlile S
   2. Martin R
   3. McAnally K

   (2005) Spectral information in sound localization

   International Review of Neurobiology 70:399–434.

   https://doi.org/10.1016/S0074-7742(05)70012-X

   • Google Scholar
7. 1. Carlile S

   (2014) The plastic ear and perceptual relearning in auditory spatial perception

   Frontiers in Neuroscience 8:237.

   https://doi.org/10.3389/fnins.2014.00237

   • Google Scholar
8. 1. Carlile S
   2. Balachandar K
   3. Kelly H

   (2014) Accommodating to new ears: the effects of sensory and sensory-motor feedback

   The Journal Socof the Acoustical Society of America 135:2002–2011.

   https://doi.org/10.1121/1.4868369

   • Google Scholar
9. 1. Chase SM
   2. Young ED

   (2005) Limited segregation of different types of sound localization information among classes of units in the inferior colliculus

   Journal of Neuroscience 25:7575–7585.

   https://doi.org/10.1523/JNEUROSCI.0915-05.2005

   • Google Scholar
10. 1. Dahmen JC
    2. Keating P
    3. Nodal FR
    4. Schulz AL
    5. King AJ

    (2010) Adaptation to stimulus statistics in the perception and neural representation of auditory space

    Neuron 66:937–948.

    https://doi.org/10.1016/j.neuron.2010.05.018

    • Google Scholar
11. 1. Feldman DE
    2. Brecht M

    (2005) Map plasticity in somatosensory cortex

    Science 310:810–815.

    https://doi.org/10.1126/science.1115807

    • Google Scholar
12. 1. Florentine M

    (1976)

    Relation between lateralization and loudness in asymmetrical hearing losses

    Journal of the American Audiology Society 1:243–251.

    • Google Scholar
13. 1. Gold JI
    2. Knudsen EI

    (2000)

    Abnormal auditory experience induces frequency-specific adjustments in unit tuning for binaural localization cues in the optic tectum of juvenile owls

    Journal of Neuroscience 20:862–877.

    • Google Scholar
14. 1. Hofman PM
    2. Van Opstal AJ

    (2002) Bayesian reconstruction of sound localization cues from responses to random spectra

    Biological Cybernetics 86:305–316.

    https://doi.org/10.1007/s00422-001-0294-x

    • Google Scholar
15. 1. Kacelnik O
    2. Nodal FR
    3. Parsons CH
    4. King AJ

    (2006) Training-induced plasticity of auditory localization in adult mammals

    PLoS Biology 4:627–638.

    https://doi.org/10.1371/journal.pbio.0040071

    • Google Scholar
16. 1. Keating P
    2. Dahmen JC
    3. King AJ

    (2013) Context-specific reweighting of auditory spatial cues following altered experience during development

    Current Biology  23:1291–1299.

    https://doi.org/10.1016/j.cub.2013.05.045

    • Google Scholar
17. 1. Keating P
    2. King AJ

    (2013) Developmental plasticity of spatial hearing following asymmetric hearing loss: context-dependent cue integration and its clinical implications

    Frontiers in Systems Neuroscience 7:123.

    https://doi.org/10.3389/fnsys.2013.00123

    • Google Scholar
18. 1. Keating P
    2. Dahmen JC
    3. King AJ

    (2015) Complementary adaptive processes contribute to the developmental plasticity of spatial hearing

    Nature Neuroscience 18:185–187.

    https://doi.org/10.1038/nn.3914

    • Google Scholar
19. 1. Keating P
    2. King AJ

    (2015) Sound localization in a changing world

    Current Opinion in Neurobiology 35:35–43.

    https://doi.org/10.1016/j.conb.2015.06.005

    • Google Scholar
20. 1. Knudsen EI
    2. Esterly SD
    3. Knudsen PF

    (1984)

    Monaural occlusion alters sound localization during a sensitive period in the barn owl

    Journal of Neuroscience  4:1001–1011.

    • Google Scholar
21. 1. Kumpik DP
    2. Kacelnik O
    3. King AJ

    (2010) Adaptive reweighting of auditory localization cues in response to chronic unilateral earplugging in humans

    Journal of Neuroscience 30:4883–4894.

    https://doi.org/10.1523/JNEUROSCI.5488-09.2010

    • Google Scholar
22. 1. Lupo JE
    2. Koka K
    3. Thornton JL
    4. Tollin DJ

    (2011) The effects of experimentally induced conductive hearing loss on spectral and temporal aspects of sound transmission through the ear

    Hearing Research 272:30–41.

    https://doi.org/10.1016/j.heares.2010.11.003

    • Google Scholar
23. Book

    1. Manly BFJ

    (2007)

    Randomization, Bootstrap, and Monte Carlo Methods in Biology (3rd ed)

    London: Chapman & Hall.

    • Google Scholar
24. 1. Margolis DJ
    2. Lütcke H
    3. Helmchen F

    (2014) Microcircuit dynamics of map plasticity in barrel cortex

    Current Opinion in Neurobiology 24:76–81.

    https://doi.org/10.1016/j.conb.2013.08.019

    • Google Scholar
25. 1. McPartland JL
    2. Culling JF
    3. Moore DR

    (1997) Changes in lateralization and loudness judgements during one week of unilateral ear plugging

    Hearing Research 113:165–172.

    https://doi.org/10.1016/S0378-5955(97)00142-1

    • Google Scholar
26. 1. Mendonça C

    (2014) A review on auditory space adaptations to altered head-related cues

    Frontiers in Neuroscience 8:219.

    https://doi.org/10.3389/fnins.2014.00219

    • Google Scholar
27. 1. Młynarski W
    2. Jost J

    (2014)

    Statistics of natural binaural sounds

    PLoS ONE 9:e108968.

    • Google Scholar
28. 1. Popescu MV
    2. Polley DB

    (2010) Monaural deprivation disrupts development of binaural selectivity in auditory midbrain and cortex

    Neuron 65:718–731.

    https://doi.org/10.1016/j.neuron.2010.02.019

    • Google Scholar
29. 1. Qian T
    2. Jaeger TF
    3. Aslin RN

    (2012) Learning to represent a multi-context environment: more than detecting changes

    Frontiers in Psychology 3:228.

    https://doi.org/10.3389/fpsyg.2012.00228

    • Google Scholar
30. 1. Schreiner CE
    2. Polley DB

    (2014) Auditory map plasticity: diversity in causes and consequences

    Current Opinion in Neurobiology 24:143–156.

    https://doi.org/10.1016/j.conb.2013.11.009

    • Google Scholar
31. 1. Seidl AH
    2. Grothe B

    (2005) Development of sound localization mechanisms in the mongolian gerbil is shaped by early acoustic experience

    Journal of Neurophysiology 94:1028–1036.

    https://doi.org/10.1152/jn.01143.2004

    • Google Scholar
32. 1. Seilheimer RL
    2. Rosenberg A
    3. Angelaki DE

    (2014) Models and processes of multisensory cue combination

    Current Opinion in Neurobiology 25:38–46.

    https://doi.org/10.1016/j.conb.2013.11.008

    • Google Scholar
33. 1. Sengpiel F

    (2014) Plasticity of the visual cortex and treatment of amblyopia

    Current Biology  24:R936–940.

    https://doi.org/10.1016/j.cub.2014.05.063

    • Google Scholar
34. 1. Seydell A
    2. Knill DC
    3. Trommershäuser J

    (2010) Adapting internal statistical models for interpreting visual cues to depth

    Journal of Vision 10:1–27.

    https://doi.org/10.1167/10.4.1

    • Google Scholar
35. 1. Shamma SA

    (2015) A convergent tale of two species

    Nature Neuroscience 18:168–169.

    https://doi.org/10.1038/nn.3928

    • Google Scholar
36. 1. Shinn-Cunningham BG
    2. Durlach NI
    3. Held RM

    (1998) Adapting to supernormal auditory localization cues. I. Bias and resolution

    The Journal of the Acoustical Society of America 103:3656–3666.

    https://doi.org/10.1121/1.423088

    • Google Scholar
37. 1. Sur M
    2. Nagakura I
    3. Chen N
    4. Sugihara H

    (2013) Mechanisms of plasticity in the developing and adult visual cortex

    Progress in Brain Research 207:243–254.

    https://doi.org/10.1016/B978-0-444-63327-9.00002-3

    • Google Scholar
38. 1. Trapeau R
    2. Schönwiesner M

    (2015) Adaptation to shifted interaural time differences changes encoding of sound location in human auditory cortex

    NeuroImage 118:26–38.

    https://doi.org/10.1016/j.neuroimage.2015.06.006

    • Google Scholar

Author details

1. Peter Keating

   Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom

   Present address

   Ear Institute, University College London, London, United Kingdom

   Contribution

   PK, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   peter.keating@dpag.ox.ac.uk

   Competing interests

   No competing interests declared.

   "This ORCID iD identifies the author of this article:" 0000-0002-0670-9075

2. Onayomi Rosenior-Patten

   Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom

   Contribution

   OR-P, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

3. Johannes C Dahmen

   Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom

   Contribution

   JCD, Acquisition of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

4. Olivia Bell

   Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom

   Contribution

   OB, Acquisition of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

5. Andrew J King

   Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom

   Contribution

   AJK, Conception and design, Drafting or revising the article

   Competing interests

   AJK: Reviewing editor, eLife.

   "This ORCID iD identifies the author of this article:" 0000-0001-5180-7179

• 1,685

  views

• 407

  downloads

• 40

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.