Relationship between behavioral and physiological spectral-ripple discrimination

Abstract

Previous studies have found a significant correlation between spectral-ripple discrimination and speech and music perception in cochlear implant (CI) users. This relationship could be of use to clinicians and scientists who are interested in using spectral-ripple stimuli in the assessment and habilitation of CI users. However, previous psychoacoustic tasks used to assess spectral discrimination are not suitable for all populations, and it would be beneficial to develop methods that could be used to test all age ranges, including pediatric implant users. Additionally, it is important to understand how ripple stimuli are processed in the central auditory system and how their neural representation contributes to behavioral performance. For this reason, we developed a single-interval, yes/no paradigm that could potentially be used both behaviorally and electrophysiologically to estimate spectral-ripple threshold. In experiment 1, behavioral thresholds obtained using the single-interval method were compared to thresholds obtained using a previously established three-alternative forced-choice method. A significant correlation was found (r = 0.84, p = 0.0002) in 14 adult CI users. The spectral-ripple threshold obtained using the new method also correlated with speech perception in quiet and noise. In experiment 2, the effect of the number of vocoder-processing channels on the behavioral and physiological threshold in normal-hearing listeners was determined. Behavioral thresholds, using the new single-interval method, as well as cortical P1-N1-P2 responses changed as a function of the number of channels. Better behavioral and physiological performance (i.e., better discrimination ability at higher ripple densities) was observed as more channels added. In experiment 3, the relationship between behavioral and physiological data was examined. Amplitudes of the P1-N1-P2 "change" responses were significantly correlated with d' values from the single-interval behavioral procedure. Results suggest that the single-interval procedure with spectral-ripple phase inversion in ongoing stimuli is a valid approach for measuring behavioral or physiological spectral resolution.

Figures

FIG. 1

Stimuli waveforms (upper panels), spectrograms (middle panels), and zoom-in of time–domain waveforms from 1.98 to 2.02 s (lower panels) for a standard-inverted stimulus (left side) and a standard-standard stimulus (right side). The ripple density was 1 ripple/octave for both stimuli.

FIG. 2

The sound processor outputs for spectral-ripple density of 1 ripple/octave (left panel) and 6 ripples/octave (right panel) are shown. The upper panel plots show electrodograms for the standard-mixed ripple stimuli, which represent the biphasic pulses (in μA) computed by HiResolution strategy of Advanced Bionics devices. The lower panel plots show average outputs (in μA) over the duration of the standard phase (first 2 s, filled circles) and the inverted phase (last 2 s, open circles) ripple stimuli for 16 electrodes. Electrode 16 represents the highest frequency channel.

FIG. 3

Psychometric functions for single-interval spectral-ripple discrimination for 14 CI subjects. Each panel represents data for an individual subject. Data points used for linear regression fits are shown as filled circles. Linear fits are shown as solid lines. The second number shown in upper right corner in each panel shows threshold. The third number shown in upper right corner shows psychometric-function slope (in units of d′/ripple). Error bars show 80% confidence intervals. Open circles represent points other than those used to estimate the slope (e.g., upper and lower asymptotic points).

FIG. 4

Relationship between spectral-ripple thresholds determined using the three-interval procedure and those derived from the single-interval procedure in 14 CI subjects. Linear regression is represented by the solid line.

FIG. 5

Relationship between single-interval spectral-ripple thresholds and speech reception thresholds in noise (left panel) and CNC scores (right panel). Linear regressions are represented by the dashed line for two-talker babble and the solid line for steady-state noise in the left panel.

FIG. 6

Behavioral and physiological thresholds from experiment 2 as a function of number of vocoder processing channels. Error bars show standard error across subjects.

FIG. 7

Individual data from experiment 2. A shows physiologic data for subject N01. Left time–domain cortical auditory evoked potential waveforms are plotted from electrode Cz. Each row represents a different vocoder channel condition (e.g., 24 channel, bottom row). Within each panel, responses to standard-inverted ripple stimuli are shown. The corresponding ripple density is displayed next to each waveform (e.g., 0.25, top trace in each row). For comparison, the behavioral vocoder spectral-ripple discrimination thresholds are plotted as theta symbols. Right for each cortical auditory evoked potential waveform, results of the Rayleigh test applied to coefficients of a time-frequency analysis of the cortical auditory evoked potential waveforms are plotted. P values less than 0.001 from the Rayleigh test are indicated by the shaded areas. Statistically significant “onset” responses are present prior to 0.5 s, while the “change” responses are represented by low p values in the 2.1–2.4 s time range. B shows time–domain cortical auditory evoked potential waveforms for two subjects, N02 and N03.

FIG. 7

Individual data from experiment 2. A shows physiologic data for subject N01. Left time–domain cortical auditory evoked potential waveforms are plotted from electrode Cz. Each row represents a different vocoder channel condition (e.g., 24 channel, bottom row). Within each panel, responses to standard-inverted ripple stimuli are shown. The corresponding ripple density is displayed next to each waveform (e.g., 0.25, top trace in each row). For comparison, the behavioral vocoder spectral-ripple discrimination thresholds are plotted as theta symbols. Right for each cortical auditory evoked potential waveform, results of the Rayleigh test applied to coefficients of a time-frequency analysis of the cortical auditory evoked potential waveforms are plotted. P values less than 0.001 from the Rayleigh test are indicated by the shaded areas. Statistically significant “onset” responses are present prior to 0.5 s, while the “change” responses are represented by low p values in the 2.1–2.4 s time range. B shows time–domain cortical auditory evoked potential waveforms for two subjects, N02 and N03.

FIG. 8

Average spectral-ripple thresholds for four different vocoder conditions in NH listeners (left 3-AFC test, right one-interval test). For comparison, the mean spectral-ripple threshold for the 14 CI subjects is indicated as filled triangles. Error bars represent 95% confidence intervals. Data points are slightly horizontally displaced for clarity.

FIG. 9

Time–domain cortical auditory evoked potential waveforms are shown. Each column shows individual normal-hearing subject’s data at different ripple densities (e.g., 0.25 ripples/octave). Stimuli were processed with the eight-channel noise vocoder simulation. The corresponding ripple density is displayed next to each waveform. Amplitude of “change” response decreases as ripple density increases.

FIG. 10

Relationships between behavioral and physiological measures are illustrated in scatterplots of normalized amplitude of N1-P2 “change” responses (filled symbols plotted in μV on the left y-axis) and behavioral discrimination performance (open symbols plotted in d′ on the right y-axis) as a function of ripple density. Data for individual subject N01 (circles), N02 (triangles), and N03 (squares) are represented by different symbols.

FIG. 11

The brain–behavior relationship is represented in scatterplots of the normalized amplitude of N1-P2 “change” responses and behavioral d′ data. Data for individual subject N01 (circles), N02 (triangles), and N03 (squares) are represented by different symbols. Significant correlations were found for each subject; linear fits between the two measures are shown as lines for each subject.