Listeners.

A total of 48 listeners with BiCIs participated in the present study (age 18–85 years; mean age 55 years). Their demographic information is listed in Table 1. Of those listeners, 44 were implanted during adulthood. All listeners had at least 11 months of listening experience with their CIs. Data from eight younger adult listeners with NH who were presented with the same stimuli were included as a point of comparison. All testing procedures were approved by the Health Sciences Institutional Review Board at the University of Wisconsin-Madison (submission number 2015-1438-CR005). Before participation began, listeners provided written informed consent.

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Table 1. Demographic information for listeners with BiCIs.

https://doi.org/10.1371/journal.pone.0263516.t001

Stimuli and testing apparatus.

The stimuli and testing apparatus employed in this study were described previously by Jones and colleagues [27]. Stimuli were trains of four pink noise bursts of 170 ms duration separated by a 50-ms inter-stimulus interval. They were presented at an average level of 50 decibels sound pressure level, A-weighted [dB(A)]. This level was chosen to prevent the activation of automatic gain control of Cochlear devices. On each presentation, the level was roved by ±4 dB to discourage the use of monaural loudness cues. Similarly, ±10 dB spectral rove [53] was applied to discourage the use of monaural spectral cues due to room modes, or consistent differences in the spectral makeup of a signal that vary depending upon the reflections associated with different wall geometries that may affect localization [35, 54]. Signal processing and presentation were completed on a desktop computer using custom-written MATLAB software.

Stimuli were presented from 19 identical loudspeakers (Center/Surround IV; Cambridge SoundWorks) mounted in a semicircular, half-arc approximately 1.2 m from listeners’ heads. Speakers were spaced in 10-degree increments spanning ±90 degrees azimuth. The height of the seat for listeners was adjusted to zero degrees elevation with respect to loudspeakers. Loudspeakers were covered with a dark, acoustically transparent curtain. Stimuli were presented via a Tucker-Davis Technologies System3 with RP2.1, HB7, and PA5 units (digital processor, amplifier, and attenuator, respectively) with a 48-kHz sampling rate. All testing was completed in a single-walled, sound-attenuating booth (Industrial Acoustics Company, Inc.) with dimensions of 2.90 × 2.74 × 2.44 m. Sound-attenuating foam was attached to walls to reduce reflections in the booth.

Task.

All testing was completed using listeners’ personal devices with everyday settings. Prior to testing, a pink noise burst was presented from a loudspeaker directly in front of the listener’s head. If the perceived location of the speaker was biased toward one side, the volume on the sound processor was adjusted until the listener perceived a centered image. Unfortunately, this information was not recorded during testing and we are therefore unable to report the listeners requiring loudness adjustments. Familiarization was completed prior to testing to familiarize listeners with the task by demonstrating how to use the graphical user interface and explaining the task to listeners. Listeners were instructed to face forward and keep their heads as still as possible before beginning each block of testing.

On each trial, stimuli were presented from one of the 19 loudspeakers. The task of the listener was to identify the location of the target loudspeaker. Listeners initiated each stimulus presentation using a graphical user interface implemented in MATLAB on a touchscreen located directly in front of them. Following stimulus presentation, listeners could respond on a visual representation of the semi-circular arc as to the perceived location of each sound. The response location appeared on the graphical user interface and could be adjusted until the listener was satisfied with their decision. The locations of loudspeakers were not shown in the visual representation and responses were restricted to ±90 degrees azimuth. Listeners were not able to repeat stimulus presentations. Once a listener made their decision, they would end the trial and a new trial would be initiated. No feedback was provided after each trial. Testing was completed in three blocks consisting of 95 trials, or five repetitions per loudspeaker. This resulted in a total of 285 trials total, or 15 repetitions per loudspeaker, and took approximately one hour to collect. Listeners with NH were only presented with 10 repetitions per loudspeaker.

Analyses.

To address the goals of the present study, the data were analyzed with four different measures of localization performance. The first measure, RMS error, is by far the most common metric of localization performance in the NH and BiCI literature (e.g., [3, 12, 14, 26, 29, 32, 39, 55]). As mentioned in the Introduction, it characterizes average error and is insensitive to some differences in the pattern of localization responses. The second measure, LSI, was proposed as a statistical measure of sensitivity by assessing the pattern of localization responses [22]. The third measure, parameters of a sigmoidal curve fit to the target vs. response angles, characterizes the pattern of mean response angles, inspired by Jones and colleagues [27]. The final measure is a novel analysis approach developed for the purpose of this study. Listeners are sorted into meaningfully different categories based upon their pattern of localization responses when compared against bootstrapped simulations from each category. A brief description is provided in the Results section, and additional details are provided in the supplementary code and appendix that accompany this paper.

The patient-dependent factors considered in the present study are shown in Fig 2. Onset of deafness was determined by the answers to the questions: “Did you ever experience hearing? If so, for how long? When did you first start wearing a hearing aid in each ear?” Duration of bilateral impairment was computed as the number of years between age at onset of deafness and age at second implantation. The duration of acoustic exposure was calculated as the number of years between birth and age at second implantation. The inter-implantation delay was calculated as the number of years between age at first and second implantation. Years with bilateral and at least one CI were computed as the number of years from first or second implantation to age at testing, respectively. Together these factors capture information relevant to a listener’s early and later auditory deprivation and experience. For additional details, please see [4]. To determine which factors to include in regression models associated with the different measures of performance, we used the step function in the lmerTest package for mixed-effects models and stats package for fixed-effects models in R. This performs stepwise model comparison to determine the model with the smallest Akaike information criterion (i.e., a measure of predictive power similar to adjusted R² in regression), removing factors that provide the least prediction and balancing the number of parameters with the increased model fit. It is compatible with fixed- and mixed-effects models. As seen in Fig 2, some of these factors are highly related to one another. We chose to exclude factors with a high effect size correlation (> .75) from the same model selection process. Thus, only years of bilateral hearing impairment or age at testing were included within the same stepwise model comparison.

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Fig 2. Relationship between patient-dependent factors.

Each row and column correspond to a different patient-dependent factor that is given along the diagonal with a histogram showing its values. Values in each cell of the upper-triangle correspond to Pearson correlation coefficients. Plots in each cell of the lower-triangle show the relationship between two factors.

https://doi.org/10.1371/journal.pone.0263516.g002

Root-mean-square error.

The relationship between patient-dependent factors and RMS error was assessed over all target angles and is shown in Fig 4A. These factors were chosen based upon the results of the stepwise model comparison test. The RMS error tended to increase with increasing age at testing, but there was considerable variability in this effect. Data in Fig 4A were fitted with a multiple linear regression with dependent variable RMS error and fixed-effects of age of onset of deafness (categories of ≤5 and >5 years) and age at testing (continuous). The multiple regression significantly predicted RMS error (F(2,45) = 3.682, p < .05), providing a relatively poor fit to the data with an adjusted R² = 0.10. Age at onset of deafness had a significant effect (t(45) = -2.616, p < .05), with an average of 11.132 degrees less RMS error for listeners with a later age at onset of deafness. There was no significant effect of age at testing (t(45) = 1.856, p = .070).

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Fig 4. Relationship between RMS error over all target angles, the LSI, and patient-dependent factors.

Individuals with an onset of deafness ≤5 or >5 years are shown with green ▽ and magenta ⬠ symbols, respectively. (A) The x-axis represents age at testing in years. The y-axis represents the RMS error, where higher values indicate poorer performance. The adjusted R² was taken from a regression including factors of onset of deafness group and age at testing. The sub-panel offset to the right shows data from listeners with NH. (B) The x-axis represents the age at testing in years. The y-axis represents the LSI, where lower values indicate poorer performance. The adjusted R² was taken from a regression including factors of onset of deafness group and age at testing. The sub-panel offset to the right shows data from NH listeners. (C) The x- and y-axes represent the LSI and RMS error, respectively. The R² corresponds to the coefficient of determination taken from a correlation between overall RMS error and the LSI.

https://doi.org/10.1371/journal.pone.0263516.g004

An alternative analysis approach would be to evaluate the RMS error for each target angle. This would account for, to some degree, differences in the amount of error across target angles. As discussed in previous reports [3, 13, 27] and shown in Fig 3, the amount of error tends to increase at lateral target angles for listeners with BiCIs. One previous approach to address this problem has been to only take the RMS error over a limited range of target locations (e.g., ±60 degrees [13]). However, this approach ignores valuable data and removes important features from the pattern of localization responses. Thus, mixed-effects regression was implemented in the lme4 package [59] in version 3.5.1 of R [60], and included the following fixed-effects: square of target angle, age at onset of deafness (categories of ≤5 and >5 years), and their interaction. This model was chosen based on the results of a stepwise model comparison test. The square of target angle was included in the analysis to capture the fact that the amount of error grew at a faster rate between lateral target angles than more medial angles. Intercept was treated as a random effect and allowed to vary randomly with listener. Including intercept as a random effect accounts for differences in average performance across individuals. To avoid violating assumptions in the regression (linear relationship between independent variables with dependent variable, constant variance, and normality of residuals), the RMS error was transformed by taking the natural logarithm. The log-transformed RMS error had a minimum of 1.457, median of 3.227, mean of 3.253, and maximum of 4.965. Degrees of freedom were calculated using the Satterwhaite approximation [61].

The results of the stepwise model comparison regression analysis are given in Table 2. There were significant effects of age at onset of deafness and squared target angle. The point estimates for both effects were in the anticipated direction. That is, an earlier onset of deafness and further lateral target angle were both associated with increased RMS error.

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Table 2. Fixed-effects coefficients from mixed-effects model.

https://doi.org/10.1371/journal.pone.0263516.t002

There was a significant age of onset of deafness × squared target angle interaction. Surprisingly, this interaction had a positive coefficient. This suggests that the amount of error at further lateral target angles was smaller, proportionally, for earlier onsets of deafness. Squared target angle varied between 0 and 8100. Thus, at the lateral-most target angles, log(RMS error) increased by 0.239 for the earlier onset of deafness group and 0.591 for the later onset of deafness group. Thus, for listeners with a later onset of deafness, the average error associated with increasingly lateral target angles would have been more than double that of listeners with an earlier onset of deafness. This suggests that listeners with an earlier onset of deafness had greater average error and proportionally fewer errors near the center of the head compared to listeners with a later onset of deafness. The resulting error at the most lateral target angles was similar between the earlier and later onset of deafness groups, suggesting that the difference between groups was driven primarily by errors for medial target angles. The implications of this interaction are considered further in the Discussion.

Localization sensitivity analysis.

The LSI was formulated by Zheng and colleagues [22] with the intention of providing a statistic that revealed information about the pattern of localization responses and listeners’ localization acuity. The LSI is a summary statistic that is similar in spirit to generating a confusion matrix and averaging the confusions for all target and response angles. The rows in a confusion matrix correspond to the dependent variable (i.e., response angle) and the columns correspond to the independent variable (i.e., target angle). The value of each element in the matrix is determined by the number of occurrences (cases where the response angle equaled a particular target angle). Because the dependent variable of the localization task used in the present study (i.e., response angle) is continuously distributed, it is not possible to present the data in a confusion matrix. Instead, the distribution of responses was compared for each pair of target angles, resulting in a matrix of values that indicated their degree of similarity. The LSI was calculated by computing the Kruskall-Wallis (i.e., rank order-based) statistic between the responses at two different target angles, calculating the p-value of said statistic, converting the p-value to the corresponding positive z-score, and averaging z-scores across all pairs of target angles. Thus, the LSI is akin to the mean d’ [62] across all pairs of target angles. For reference, calculation of d’ is completed by taking the difference between means of two different distributions assumed to be standard normal. In this case, the distributions being compared are response angles for two different target angle presentations (i.e., for all pair-wise combinations). Since d’ is in z-score units, the LSI has a similar interpretation to the average of many d’ values. When the LSI is near zero, this indicates poor performance (overlapping response angles for differing target angles). When the LSI is large, this indicates optimal performance (disparate response angles for differing target angles). For additional details, see the paper in which the LSI was formulated [22].

The relationship between the LSI and patient-dependent factors is shown in Fig 4B. These factors were chosen based upon the results of the stepwise model comparison test. The LSI tended to decrease with increasing age at testing, though this effect was quite variable across participants. Data in Fig 4B were fitted with a multiple linear regression with dependent variable LSI and fixed-effects of age of onset of deafness (categories of ≤5 and >5 years) and age at testing (continuous). As is visually apparent in Fig 4B, the multiple regression was significantly predictive of the LSI (F(2,45) = 6.022, p < .01) and provided a relatively poor fit to the data yielding an adjusted R² = .18. Age at onset of deafness had a significant effect (t(45) = 3.393, p < .01), with an average of 0.861 greater LSI for listeners with a later age at onset of deafness. There was also a significant effect of age at testing (t(45) = -2.225, p < .05), with each increasing year resulting in approximately 0.015 units less localization sensitivity. For the oldest listener, this resulted in 1.289 units less localization sensitivity. Because age at testing and years of acoustic hearing were so strongly correlated, their relationship will be discussed in more detail in the Discussion.

The results showed that the LSI had an inverse relationship with RMS error across all target angles (Fig 4C). A Pearson correlation revealed a strong correlation between the LSI and RMS error (R² = .86). This is consistent with the original paper including LSI’s formulation for localization with children who use BiCIs [22]. Fig 4C suggests that for many values of the RMS error (x-axis), especially until roughly 40 degrees of RMS error where there is greater residual error in the correlation, the LSI (y-axis) provides the same information for localization performance. This is intuitive, since for low values of the RMS error, the LSI would be high in general. When the RMS error is high, however, the types of errors made by participants may differ, suggesting that it might be more useful to determine the types of errors made by listeners. This suggests that LSI is providing new and important information that is not provided by overall RMS error at the largest values of overall RMS error, which provides greater insight into its utility assessing sound source localization development in children. The issue with the LSI is that it is only indicative of a greater amount of confusions. Therefore, the LSI does not give a meaningful indication as to the pattern of localization responses. Moreover, there are examples where the LSI is similar between listeners but the pattern of localization responses is quite different. One example of such a case is presented in Fig 1. Listener IAG (whose LSI is 1.26), biased toward medial angles, readily confuses the loudspeaker at zero degrees with ±30 degrees, as does listener IAZ (whose LSI is 1.33), who does not exhibit this bias. However, as is obvious from Fig 1, the localization response patterns of listeners IAG and IAZ are extremely different from one another. Thus, the LSI does not appear to be useful for the current experimental goals but may prove useful for other applications. This is considered further in the Discussion.

Four-parameter logistic functions.

It may also be of interest to determine how a patient with BiCIs performs on a localization task by describing the shape of the function. One approach used by Jones and colleagues [27] was to fit the error across absolute target angle (generalized between the left and right) with orthogonal polynomials. This approach collapses errors across both sides of the head. The issue with collapsing errors across both sides of the head is that listeners with BiCIs can exhibit different kinds of errors on either side of the head depending on the listener, CI configurations (e.g., unilateral vs. bilateral), and stimulus [3, 13, 31, 40, 42]. Instead, the data in the present study were fit with an S-shaped function that models the maximum and minimum response angles as well as the slope. These parameters describe important features of localization performance and give some indication of where a listener struggles to identify sound sources. Clearly there is a relationship between the maximum, minimum, and slope of the logistic function, but they do not always co-vary. For example, ideal performance would result in a high maximum and low minimum, with a 1:1 slope between target and response angle. However, a person who is insensitive to small changes in location and is able to accurately judge left from right may still identify a sound far to the left or right, but transition between these extremes as soon as a sound crosses the center of the head. This strategy has been noted in children with BiCIs [22]. Separately, the maximum and minimum response angles also provide indices of bias toward one side of the head.

In the present study, we chose to fit data using the four-parameter logistic function shown in Eq 1: (1) where y_ij represents the ith response for the jth target angle, α represents the right-most response angle, β represents the left-most response angle, S(x) represents the sigmoidal function S(x) = 1/(1+exp(-x)), μ represents the center of the curve along the target angles, x_ij represents the ith repetition at the jth target angle, σ represents the slope of the curve, and ε_ij represents one instance (for the ith repetition at the jth target angle) of random errors that are independently and identically distributed.

In general, this modeling problem requires at least three, and typically four, parameters. Four-parameter logistic functions have practically interpretable parameters corresponding to the upper- and lower-asymptote, midpoint along the x-axis, and slope (see Eq 1). Parameter estimates were generated by fitting functions to localization data iteratively with non-linear least squares estimation using the Curve Fitting Toolbox of MATLAB and the trust-region-reflective algorithm [63] to update parameter estimates and minimize residual error. Estimates of β and α were bounded between [–90, 90] and were initialized at -90 and 90, respectively. Estimates of μ were bounded between [–90, 90] and were initialized at 0. Estimates of σ were bounded between [–100, 100] and were initialized at -50, corresponding to a nearly 1:1 relationship between target and response angles. The relationship between parameter estimates and patient-dependent factors is illustrated in Fig 5.

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Fig 5. Relationship between logistic parameters and patient-dependent factors.

Individuals with an onset of deafness ≤5 or >5 years are shown in green ☆ and magenta ◊ symbols, respectively. The x-axis represents age at testing in years. The adjusted R² was taken from a regression including factors of onset of deafness group and age at testing. The sub-panel offset to the right shows data from NH listeners. An illustration of the localization function is shown to the right of the sub-panels for aid of interpretation. (A) The y-axis represents the range of response angles (α–β from Eq 1) of the localization function, where lower values indicate smaller ranges of responses and poorer performance. (B) The y-axis represents the slope (σ from Eq 1) of the localization function.

https://doi.org/10.1371/journal.pone.0263516.g005

We chose to evaluate two important features of the localization function: the range over which responses fell (α–β in Eq 1; Fig 5A) and the slope (σ in Eq 1; Fig 5B). Values nearer to 180 in the range of responses for the localization function are indicative of good performance, showing that listeners perceived far-lateral locations when targets were presented there. This is similar to studies of the perceived intracranial locations of sounds when interaural cues are manipulated and presented via headphones or direct stimulation that evaluate range of responses [64–67]. Since many of the errors in localization performance occur at far lateral target angles, the range of responses, along with the slope, suggests the range of target angles for which listeners are sensitive to changes in location. From Fig 5, it is visually apparent that there is little or no relationship between the patient-dependent factors explored for the listeners in this study and the features of the localization function. We chose to include the same predictors as those used for overall RMS and LSI for the sake of consistency. Additional stepwise model selections revealed that no regression models provided significant predictions of the data. While listeners with BiCIs varied somewhat in the parameters associated with the logistic function, listeners with NH showed remarkably similar parameter estimates. The range over which responses fell was highly correlated with RMS error (Pearson r = -0.732) and LSI (Pearson r = 0.661). In contrast, slope was not correlated with RMS error (Pearson r = -0.081) or LSI (Pearson r = 0.109).

Unsupervised machine learning.

While fitting the data using a logistic curve has the advantage that the function can be described using combinations of four parameters, this method of analysis ignores the variability across target loudspeaker locations. Further, as described by Zheng and colleagues [22] and Grieco-Calub and Litovsky [10], often times it is more intuitive to interpret localization performance in terms of “categories.” A similar approach has been used for distinguishing psychometric functions from different groups of listeners in pitch perception experiments [68]. That is, there are meaningful characteristics by which listeners with BiCIs differ from one another. The issue is that there is not a standard analysis technique to address this problem. The solution proposed by Grieco-Calub and Litovsky [10] relies on comparison of specific features of collected data against a control group. The solution proposed by Reiss and colleagues [68] relies on significant changes in parameters of functions depending upon which ear(s) are stimulated within the same individuals, which does not apply directly to the present study, but could be employed in, for example, a longitudinal study. Both studies share the approach of comparing data from a particular condition or group against some baseline.

To address this problem, we developed a new, more adaptable method of analysis inspired by those used by Zheng and colleagues [22] and Grieco-Calub and Litovsky [10], in which listeners were sorted into predefined categories according to their similarity to data simulated by bootstrapping from these categories. While we defined the categories in the present study, the method is flexible such that parameters of simulated data could be adapted by future researchers and refined by the field. Simulated sampling of these categories was completed using bootstrapping, and data from listeners were then compared against bootstrapped samples. An illustration of the different categories of responses is shown in Fig 6.

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Fig 6. Categories of localization performance.

Plotted as in Fig 3, except that each panel corresponds to a different category of localization performance as defined by experimenters. Each row and column represent a different distribution of mean and standard deviation (SD) of response angles, respectively.

https://doi.org/10.1371/journal.pone.0263516.g006

The choice of parameters and corresponding categories provided in the present study were guided by Zheng et al. [22]. They described five broad categories of localization functions, ranging from NH-like means and small standard deviations to a nearly random distribution of responses across target angles (i.e., flat means and large standard deviations). Different patterns of means and standard deviations are intended to reflect everyday-relevant changes in localization performance important for device design, changes across time, and, as studied here, differences between groups of patients. We consider the pattern across means to reflect the bias (or lack thereof) in the listeners’ responses, and the pattern across standard deviation to reflect reliability (variability) of their spatial perception across presentation intervals.

Response angle means and SDs at each target angle from each listener with BiCIs were compared against 50 simulated subjects of each category from Fig 6. Each of these simulated subjects had 15 repetitions per target angle, i.e., the same as listeners in the experiment, and were classified into one of twenty “clusters” using the partitioning around medoids (PAM) unsupervised machine learning algorithm [69]. This resulted in a total of 1000 simulated subjects (50 simulated subjects from 20 categories) compared against one listener with BiCIs. The category of the simulated localization data could be extracted following clustering. Accordingly, the category of the listener with BiCIs was taken as the mode of the simulated categories within the same cluster. For example, say that listener IAG was sorted into a cluster containing the following: 36 Biased Right Means, Larger Right SDs, 8 Compressed Means, Larger Right SDs, and 7 Biased Left Means, Larger Left SDs from Fig 6. Then the category of listener IAG would be taken as Biased Right Means, Larger Left SDs because this was the mode of the cluster into which listener IAG was categorized. This process was repeated 50 times with newly simulated data for each listener so that the reliability of clustering could be assessed.

The method described above uses bootstrapping (many simulations from a series of localization patterns described in Fig 6) to sort listeners into categories. Bootstrapping is essential to our approach since each localization function from the data observed from a listener with BiCIs is only a sample from their underlying localization category. That is, data are sampled from some population of localization responses that would be produced by a particular listener in one particular experimental setup. These bootstrap simulations are generated iteratively, and the process is repeated 50 times to ensure reliability of the category assignment for each listener with BiCIs. Thus, the method returns a single result as well as the reliability with which a listener was sorted into a particular category. Reliability of clustering for the same three listeners from Fig 1 is shown in Fig 7. In these examples, each panel corresponds to the same categories of performance provided in Fig 6. The numbers and colors correspond to the frequency with which each listener was sorted into each category. For listener ICT, the PAM algorithm most consistently sorted their localization responses with the Ideal Means, Ideal SDs category, and sometimes into the Ideal Means, Larger Lateral SDs category. For listener IAG, the algorithm was less consistent overall. For listener IAZ, however, the algorithm provides unanimous support for the Flat Means, Ideal SDs category. Where both RMS error and the LSI failed to distinguish between these listeners, this new method of analysis using bootstrap simulations and unsupervised machine learning succeeded. Further, the output of the algorithm can be used to determine the confidence of a particular category assignment.

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Fig 7. Examples of frequency of assignment to specific categories defined in Fig 6.

(A), (B), and (C) correspond to listeners ICT, IAG, and IAZ, respectively. The x-axis corresponds to the mean category, the y-axis corresponds to the SD category, and the z-axis corresponds to the frequency of assignment. Since this process was repeated 50 times, there were a total of 50 assignments in each subplot.

https://doi.org/10.1371/journal.pone.0263516.g007

The ultimate category assignment was determined by taking the mode of results like those presented Fig 7 for every listener. A histogram of category assignments is plotted in Fig 8 across all 48 listeners in the study, separated into two separate panels corresponding to the earlier and later onset of deafness groups. We chose to include age at onset of deafness as the predictor because it was the most robust predictor with the other measures provided in the present manuscript. This new method of analysis revealed that the later onset of deafness group (Fig 8B) was more likely to be sorted into the ideal SDs categories than the earlier onset of deafness group. A chi-square test for difference in counts indicated that there were significantly more listeners sorted into the ideal SDs categories for the later (12 ideal SDs, 20 other; a rate of 3:5) compared to earlier (1 ideal SDs, 15 other; a rate of 1:15) onset of deafness groups (χ²(1) = 5.275, p < .05). This result is consistent with the hypothesis that an earlier onset of deafness leads to poorer localization performance; specifically, less consistent perceptions of sound location from one interval to the next. A power analysis assuming an equal sample size to that obtained in the present study based on 10,000 simulations in R and an alpha level of .05 revealed power of .70. In other words, based on the result obtained, one would expect to commit a Type II error (failure to reject the null hypothesis) approximately 30% of the time.

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Fig 8. Histogram indicating category assignments by onset of deafness group.

The x-axis corresponds to the mean category, the y-axis corresponds to the SD category, and the z-axis corresponds to the frequency of assignment (with a total of 16 listeners in panel A and 32 listeners in panel B). (A) and (B) correspond to the ≤5 and >5 years onset of deafness groups, respectively. The range of the z-axis was scaled based upon the number of listeners in each group, with the maximum equal to half of the group size.

https://doi.org/10.1371/journal.pone.0263516.g008

Additionally, it seemed that there might be a difference between the assignment to the ideal means category between the listeners with earlier and later onset of deafness, with earlier (12 ideal means, 4 other; a rate of 3:1) compared to later (18 ideal means, 14 other; a rate of 9:7) onset of deafness patients showing more listeners in the ideal means categories, contrary to our initial hypothesis. However, a chi-square test for difference in counts indicated that there was not sufficient statistical evidence to conclude that the number of listeners in the ideal means categories differed between onset of deafness groups (χ²(1) = 1.600, p = .206). No continuity corrections were completed with either chi-square test. For comparison, data from listeners with NH were also tested. All iterations for each of the eight participants were sorted into the ideal means, ideal SDs category.

Referring back to Fig 3, the greater amounts of variability at each target angle for the earlier (1 ideal SDs, 15 other; a rate of 1:15) compared to the later (12 ideal SDs, 20 other; a rate of 3:5) onset of deafness group which significantly distinguished them from the later onset of deafness group can easily be seen, though it was not obvious initially. Thus, this new method of analysis was sensitive to differences in localization functions and showed a new trend in the data. It should also be noted that there was considerable heterogeneity in the mean response angles in both groups. While the difference between groups was not significant in the present study, these differences may be explained by other patient-dependent factors that were not explored.

Review of binaural processing.

After a sound is transmitted to the auditory nerve (via electrical stimulation for listeners with CIs or depolarization by hair cells for listeners with acoustic hearing), it first arrives at the cochlear nucleus. Bushy cells in the cochlear nucleus refine the temporal precision by integrating many auditory nerve inputs [70] and receiving inhibition from neighboring cells [71, 72]. This refined temporal precision is important for accurate encoding of ITDs, resulting in humans’ sensitivity on the order of microseconds, far below the refractory period for most neurons in the central nervous system. The medial superior olive (MSO) is suspected to code for fine-structure ITDs [73, 74]. It receives excitatory input from spherical bushy cells on both sides of the brainstem [75, 76] and acts as a coincidence detector, exhibiting selective ITD-tuning. The MSO also receives inhibitory inputs from the lateral and medial nuclei of the trapezoid body [77], which plays a role in ITD tuning [78]. The lateral superior olive (LSO) is suspected to code for envelope ITDs [79, 80] and ILDs [81, 82]. It receives excitatory input from the ipsilateral cochlear nucleus and inhibitory inputs from the contralateral medial nucleus of the trapezoid body [83], which in turn receives its inputs from globular bushy cells [84]. There is ongoing debate as to whether the ipsilateral input to the LSO comes from spherical bushy cells [75] or T-stellate cells [85], both of which are found in the cochlear nucleus. In all three of these nuclei (the cochlear nucleus, MSO, and LSO), precisely timed excitation and inhibition are essential for the accurate encoding of binaural cues. Moreover, all three nuclei have demonstrated the presence of specialized inhibitory (hyperpolarization) channels that allow for their extraordinarily precise temporal properties [86]. Thus, changes in the strength of inputs (excitatory or inhibitory) and anatomical organization of these nuclei could be detrimental for binaural processing.

Deafness during development.

Hearing loss during development leads to an imbalance in the ratio of excitation and inhibition in the cochlear nucleus, MSO, and LSO. The “central gain hypothesis” posits that hearing loss leads to an overall increase in excitation to compensate for the loss of input to the auditory system [87]. This conceptual model appears to provide a better explanation of physiological responses of the auditory cortex, but there is still compelling evidence in the brainstem and midbrain to suggest a shift in the balance of inhibition and excitation that accompanies hearing loss (e.g., [18, 51, 88]). Crucially, a shift in the balance of excitation and inhibition would affect the ability to encode binaural cues in the mature auditory system since the cochlear nucleus, MSO, and LSO rely on inhibition to maintain their exquisite temporal precision.

The results from the present study showed that localization responses were less reliable for listeners with earlier onset of deafness, driven primarily by medial target angles and greater amounts of variability. This is consistent with the notion that binaural encoding is less reliable in listeners with an earlier onset of deafness, but is inconsistent with the idea that responses are generally more biased. In fact, there was a non-significant trend in the unsupervised machine learning analyses to suggest that more listeners with an earlier onset of deafness tended to exhibit a higher proportion of ideal response angle means. This suggests that the primary limitation to localization performance is poorer binaural encoding, or that if the problem is with decoding, it relates to a listeners confidence or reliability in responses. Proportionally greater errors at lateral target angles for listeners with a later onset of deafness may suggest challenges using the greatest magnitude ITD or ILD provided by clinical processors in listeners with histories of acoustic hearing. It may be that the way ITDs or ILDs are represented by clinical processors is inconsistent with the spatial cues represented by acoustic hearing.

Results from the present study are generally consistent with many physiological studies in animals implying the presence of a critical period for the organization of the auditory brainstem. An imbalance of excitation and inhibition during early stages of development can cause malformation of anatomical structures in the brainstem [18, 51]. A developmental window during which the underlying cellular components that contribute to this anatomical reorganization are downregulated would make the binaural system susceptible to disorganization during early hearing loss [89]. Cells in the cochlear nucleus that project to binaural cells undergo morphological changes during development and are affected by hearing loss [90]. The post-synaptic density of the endbulb of Held synapse (i.e., specialized synapse in the cochlear nucleus) is preserved with early CI stimulation, but other morphological changes occur with deafness in cats despite stimulation [91]. In the MSO, Mongolian gerbils that were reared with omnidirectional noise show poorer tuning to ITDs compared to gerbils that were reared normally [92]. In the inferior colliculus, which receives direct projections from the MSO and LSO, rabbits reared with early hearing loss that receive BiCIs after maturity exhibit poorer tuning to ITDs and greater variability in cellular responses than animals that were reared normally or received BiCIs shortly after birth [88, 93]. When animals are raised normally and not deafened prior to CI stimulation, acoustical and CI stimulation can yield similar cellular responses [94], suggesting that deafness and device limitations are the primary contributors to little binaural sensitivity. Histological analysis demonstrated that early- and late-deafened animals showed similar deterioration of the auditory periphery [88], suggesting that reorganization in the brainstem and peripheral degradations contribute to changes in sensitivity to binaural cues. Behaviorally in humans, limited exposure to binaural hearing has also been associated with little or no sensitivity to ITDs [56, 57] and poorer localization outcomes [7].

In contrast, three studies in rats have shown a similar sensitivity in tuning between animals with NH and those who were early-deafened and received CI stimulation [95–97]. Supportive of their assertion that tuning was similar, many neurons recorded from the inferior colliculus in these rats were significantly tuned to ITDs, in contrast to previous studies with rabbits [88]. The studies in rats also used smaller ITDs, within the range a rat would be exposed during real life, unlike the experiments with rabbits. Recent experiments in rabbits support the idea that consistent BiCI stimulation can restore ITD sensitivity [98]. It should be noted that because few animals participated in the study by Rosskothen-Kuhl and colleagues, the lack of effect (i.e., no significant difference in ITD tuning between NH and late-implanted groups) in one study could have been driven by the variability in rats with NH [95].

Differences between the studies with animals and humans on the effects of early auditory deprivation could result from a lack of exposure to consistent ITDs. In humans with BiCIs, CI processors are left uncoordinated. Additionally, CI processing discards temporal fine-structure [43], which would remove the presence of fine-structure ITDs altogether, whereas this information was provided to rats in the studies by Rosskothen-Kuhl and colleagues. A recent study with the largest sample size of adult listeners with BiCIs to date (many of the same listeners that participated in the present study) showed conflicting evidence of a relationship between ITD sensitivity and age at onset of deafness when listeners were separated into groups who became deaf before or after 18 years of age [4]. This recent study makes it clear that earlier onsets of deafness are not the most robust predictor of sensitivity to ITDs, and further underscore the need for more research on this topic.

The majority of published studies imply a functionally relevant reorganization of the brainstem that occurs when the auditory system does not receive coherent bilateral stimulation during development. Results from the present study are consistent with this body of literature and demonstrate patterns that are generally consistent with previous localization experiments in adults and children with BiCIs [7, 10, 13, 14]. However, it is clear from the recently contrasting results that more work is needed to fully understand the role of early, bilateral stimulation on the ability to accurately encode spatial cues in the brainstem and midbrain. Specifically of interest is whether the binaural system can overcome the limitations of early auditory deprivation with coherent binaural stimulation later in life. The majority of listeners in the present study received BiCIs in adulthood, which makes them vastly different from new patients that are congenitally deaf or become deaf early in life and have earlier access to CIs. It is entirely possible that some of the differences observed between the earlier and later onset of deafness groups could be driven by a latent variable that has more to do with the etiology of disease that led to earlier or later deafness rather than early auditory experience.

Deafness during adulthood.

When deafness occurs later in life, it is thought that the auditory brainstem and midbrain will have typically developed due to normal input that leads to the activity-dependent reorganization discussed in the previous section. Thus, poor localization performance should be due to other factors, or deterioration of those typically developed circuits. We therefore expected both listeners with longer periods of auditory deprivation to have poorer localization outcomes. Instead, factors related to auditory deprivation (e.g., inter-implantation delay, years of bilateral impairment) were not the most robust predictors of performance. One explanation for this weak effect is provided by Thakkar et al. [4], showing that losses in ITD sensitivity due to a longer period of bilateral deafness may be ameliorated by experience with CIs. This result also explains why experience with CIs by itself is not often associated with localization performance.

Another recent study provided evidence that deterioration of the worse ear is more important than the difference or asymmetry in function between ears. Ihlefeld et al. [99] showed that the ear with poorer temporal sensitivity could predict the amount of ITD sensitivity in adults with BiCIs. A related study in listeners with NH showed that, for comparisons of temporal information of simultaneously presented sounds within- or across-ears, poorer fidelity of temporal information in only one of the two places-of-stimulation degrades performance [100]. Together with the results of Reeder et al. [14], these studies suggest that the more important variable is not the delay between ears, but the longer length of deafness, likely leading to greater deterioration of the auditory periphery due to a lack of stimulation.

Aging.

Another important factor that could lead to differences in performance across listeners is age at testing. Aging results in several changes to the auditory system. The loss of hair cells accelerates age-related increases in the death of auditory nerve fibers in humans [101]. Decreases in amplitude for all waves and increases in latency for early waves of the auditory brainstem response occur with increasing age in humans [102], suggesting fewer or less consistent inputs. Interestingly, the temporal resolution of individual auditory nerve fibers seems to remain intact during aging in the absence of hearing loss [103]. Thus, changes in the auditory brainstem response may be related to the loss of auditory nerve fibers associated with aging. A decrease in inhibition in the cochlear nucleus of mice also occurs with aging, resulting in poorer temporal processing [104]. The medial nucleus of the trapezoid body, which supplies inhibition to the LSO and MSO, has fewer living cells in older rats [105]. These results suggest that aging affects the inputs to the nuclei that perform binaural computations and imply that functional deficits may occur. This could explain some of the overlap in localization performance between the listeners with earlier (younger) and later (older) onsets of deafness in the present study.

The present study demonstrated some evidence of poorer localization performance for older listeners with BiCIs. However, aging was also not the most robust predictor of localization performance. Age at testing was almost perfectly confounded with years of acoustic exposure (i.e., age at second implantation). Thus, it could be that the lack of robustness in the effect of aging relates to the confound between these variables, where a longer period of acoustic exposure improves performance. This same finding was demonstrated in the same group of listeners who participated in the present study by Thakkar and colleagues when evaluating ILD sensitivity. Because the model predictions were so similar, this provides additional evidence that sound source localization in listeners with BiCIs is driven by ILDs (e.g., [33, 41]).

Older listeners with NH exhibit reduced sensitivity to ITDs [106, 107], smaller binaural masking level differences [106, 108, 109], and greater overall RMS error in localization [15] compared to younger listeners with NH. Studies measuring the perceived intracranial location of a sound based on binaural cues delivered via headphones or direct stimulation showed smaller ranges where listeners perceived a sound containing ITDs and ILDs for middle-aged [67] and older [64, 110] listeners compared to younger listeners with NH. Crucially, when large enough ITDs were provided to older listeners with NH or BiCIs in these studies, ITDs were lateralized to similar extents [64, 67]. This final result could help explain the medial localization responses to lateral targets (e.g., compressed means shape in Fig 6) observed in many listeners with BiCIs in previous studies [3, 13, 27, 40] and the present study. It may be that older listeners will respond with more lateral angles at lateral target angles if consistent, large ITDs are applied to stimuli.