The influence of informational masking on speech perception and pupil response in adults with hearing impairment Thomas Koelewijn,a) Adriana A. Zekveld,b) Joost M. Festen, and Sophia E. Kramer Department of Otolaryngology-Head and Neck Surgery, section Audiology and EMGO Institute for Health and Care Research, VU University Medical Center, Boelelaan 1118, 1081 HZ Amsterdam, The Netherlands

(Received 6 June 2013; revised 30 December 2013; accepted 8 January 2014) A recent pupillometry study on adults with normal hearing indicates that the pupil response during speech perception (cognitive processing load) is strongly affected by the type of speech masker. The current study extends these results by recording the pupil response in 32 participants with hearing impairment (mean age 59 yr) while they were listening to sentences masked by fluctuating noise or a single-talker. Efforts were made to improve audibility of all sounds by means of spectral shaping. Additionally, participants performed tests measuring verbal working memory capacity, inhibition of interfering information in working memory, and linguistic closure. The results showed worse speech reception thresholds for speech masked by single-talker speech compared to fluctuating noise. In line with previous results for participants with normal hearing, the pupil response was larger when listening to speech masked by a single-talker compared to fluctuating noise. Regression analysis revealed that larger working memory capacity and better inhibition of interfering information related to better speech reception thresholds, but these variables did not account for inter-individual differences in the pupil response. In conclusion, people with hearing impairment show more cognitive load during speech processing when there is interfering speech compared to C 2014 Acoustical Society of America. [http://dx.doi.org/10.1121/1.4863198] fluctuating noise. V PACS number(s): 43.71.Ky, 43.66.Dc [EB]

I. INTRODUCTION

Research shows that speech perception is not predicted by the pure-tone audiogram alone (e.g., Pichora-Fuller et al., 1995; George et al., 2007). In addition to auditory thresholds, speech perception is based on linguistic and working memory (WM) related cognitive abilities (R€onnberg et al., 2008; Kramer et al., 2009). These abilities are thought to partly compensate for sensorineural hearing loss and this might lead to an additional increase in cognitive processing load (Pichora-Fuller et al., 1995). Pupillometry is a method to quantify cognitive processing load (Kahneman and Beatty, 1966; Beatty, 1982). In general, a larger task-evoked pupil response reflects higher cognitive load. This can be caused by a number of factors such as allocation of attention or the use of WM related resources (Kahneman, 1973). Interestingly, the pupil dilation response has also been measured during language processing (Hy€on€a et al., 1995; Kramer et al., 1997, Brown et al., 1999). Kramer et al. (1997) showed that changes in signal-to-noise ratio (SNR) of speech in noise affect the pupil dilation response. Two studies by Zekveld et al. (2010, 2011) replicated these results by showing an effect of speech intelligibility level on the pupil response in participants with normal hearing and participants with hearing impairment. Lower intelligibility levels result

a)

Author to whom correspondence should be addressed. Electronic mail: [email protected] b) Also at: The Linnaeus Centre HEAD, Department of Behavioral Sciences and Learning, Swedish, Link€ oping University, S-581 83 Link€ oping, Sweden. 1596

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in larger pupil responses, which can be interpreted as higher cognitive load (Just et al., 2003). In a recent study (Koelewijn et al., 2012a) we showed that young and older adults with normal hearing acuity have a larger pupil response when processing speech masked by a single-talker masker as compared to speech masked by fluctuating noise. The pupil response seems to vary in response to informational masking caused by the speech information of the single-talker masker. The amplitude variations and the frequency spectrum for the fluctuating noise that consisted of two-band vocoded speech were made to largely overlap, resulting in similar amounts of energetic masking. Interfering speech has additional features like semantic content and voice characteristics (e.g., Brungart et al., 2001). These features allow us to disentangle different streams of information (Parsons, 1976; Bronkhorst, 2000), but can also create contextual overlap resulting in informational masking (Kidd et al., 2008). The larger pupil response shown in our previous studies (Koelewijn et al., 2012a; Koelewijn et al., 2012b) most probably reflected additional processes needed to ignore this irrelevant speech information. So far, it is unknown whether informational masking affects the pupil response of people with hearing impairment. In a follow-up study (Koelewijn et al., 2012b) of adults with normal hearing we found that better inhibition of interfering information in WM, larger WM capacity (WMC), and better linguistic closure related to lower (better) speech reception thresholds (SRT). The same factors were also related to a larger pupil response but this relation was only significant for speech masked by a single-talker, not for speech masked by fluctuating noise. The SRT results are in line with the notion that people with larger WMC have a

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benefit when it comes to processing speech in noise (e.g., Rudner et al., 2011; Desjardins and Doherty, 2013; Ng et al., 2013). For people with hearing impairment, the reading span (Rspan) score (a measure of WMC) was associated with sentence (Rudner et al., 2011) and word (Picou et al., 2013) recognition in stationary noise (for review, see Akeroyd, 2008; Besser et al., 2013). Ng et al. (2013) showed that WMC also relates to the amount of benefit hearing aid users have from noise reduction when performing a word identification and recall task. Recent studies assessed the relationships between objectively measured listening effort, cognitive capacity and hearing loss (Picou et al., 2011; Desjardins and Doherty, 2013; Picou et al., 2013). Desjardins and Doherty (2013) examined the effect of age, hearing loss, and masker type on listening effort as assessed by means of a dual-task paradigm (DTP). DTPs are assumed to reflect listening effort. The assumption is that additional load in the primary task, in this case speech reception, affects performance in a secondary task, which was a visuomotor tracking task. In the older participants no effect of speech (two-talker or six-talker) and non-speech (speech-shaped noise) maskers on listening effort was shown, regardless of hearing impairment. These results suggest no effect of informational masking on listening effort. However, Desjardins and Doherty (2013) did show increased effort for the six-talker babble compared to the speech-shaped noise masker in the young normal hearing adults, which is in line with Koelewijn et al. (2012a). Increased listening effort was significantly associated with smaller WMC and slower processing speed in both participants with normal hearing and participants with hearing impairment. Picou et al. (2011) investigated the effect of WMC and lip-reading ability on listening effort during speech processing, with or without additional visual cues. The outcome of a paired-associates recall task was used as a measure of listening effort, supposing that increased effort results in fewer resources available for rehearsal and recall of words. In this task a sequence of word pairs presented in quiet or noise had to be recalled. Participants with normal hearing, better lip-reading ability, and larger WMC had better word recall during speech processing in noise. In addition they showed that word recall decreased with increasing noise levels. A follow-up study among hearing aid users (Picou et al., 2013) revealed a similar increase of listening effort with increasing noise levels. Interestingly, it was reduced with hearing aid use. Picou et al. (2011) also observed a negative correlation between verbal working memory and listening effort. These studies (Picou et al., 2011; Desjardins and Doherty, 2013; Picou et al., 2013) indicate that lower listening effort is associated with larger WMC and vice versa, which is in contrast to Koelewijn et al. (2012b) where larger pupil responses were associated with larger WMC. In addition to WMC, other specific linguistic abilities also play a role in speech perception in adverse listening conditions (Zekveld et al., 2007a; Kramer et al., 2009; Zekveld et al., 2011). In a recent study, Zekveld et al. (2011) assessed the relation between pupil response during listening to speech in noise and linguistic closure as measured with the text reception threshold (TRT) task. TRT is the visual J. Acoust. Soc. Am., Vol. 135, No. 3, March 2014

analog of the SRT task. Linguistic closure refers to the ability to integrate fragmentary linguistic information into a recognizable sentence. The study revealed that participants with hearing impairment had poorer SRTs than participants with normal hearing. In addition, the pupil response during speech processing was larger with better TRT performance in both participants with normal hearing and participants with hearing impairment. This suggests that engagement of cognitive resources and fluent language processing are related. Although WMC capacity and TRT performance relate to speech reception in different kinds of masker types, the relation between WMC or TRT and the pupil response in individuals with hearing impairment has never been investigated for maskers containing speech information. The current study investigated how different masker types affected the pupil response during speech processing among adults with hearing loss. Similar to our previous study (Koelewijn et al., 2012b) speech intelligibility level was manipulated, and the target speech was masked by either fluctuating noise or a single-talker masker. This allowed us to investigate the effect of informational masking on the pupil response. During the SRT task, the pupil response was recorded in each trial. Spectral shaping was performed on all stimuli in order to study the effect of hearing impairment on the pupil response during speech perception in noise with adapted speech and masker levels. This is important when investigating how hearing impairment affects cognitive processing of speech in noise (Amos and Humes, 2007; Humes, 2007). We included several measures of cognitive capacity to investigate their relation with SRT and the pupil response in individuals with hearing impairment. For measuring verbal WMC, the Reading span (Rspan), and Listening span (Lspan) tasks were used (Besser et al., 2013). Additionally, a Dutch version of the size-comparison span (SICspan) task (Sorqvist et al., 2010; Sorqvist and Ronnberg, 2012) was included. The SICspan measures WMC and the ability to inhibit irrelevant linguistic information while storing information in WM. Finally, the current study also included the TRT task (Zekveld et al., 2007b). We anticipated that participants with hearing impairment would show relatively poor SRTs (Festen and Plomp, 1990; Bernstein and Grant, 2009). In addition, we expected the pupil response of participants with hearing impairment to show effects of intelligibility level (Kramer et al., 1997; Zekveld et al., 2011). Also, we hypothesized that more cognitive load would be required for the single-talker masker compared to the fluctuating noise conditions because of informational masking, and that this would translate as a larger pupil response. This would be in line with our previous results for listeners with normal hearing (Koelewijn et al., 2012a; Koelewijn et al., 2012b), but it would contradict the results of Desjardins and Doherty (2013) who did not find and effect of informational masking (two-talker masker vs speech-shaped noise) on listening effort as assessed with visuomotor tracking task (DTP) in people with hearing impairment. Finally, this study was conducted to assess whether inter-individual differences in the SRT and the pupil response are partially explained by WMC, the ability to inhibit irrelevant linguistic information, and/or TRT Koelewijn et al.: Cognitive load in adults with hearing loss

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scores. In line with our previous results (Koelewijn et al., 2012b) we anticipated that hearing impaired participants with larger WMC and better cognitive linguistic closure abilities would show a larger task evoked pupil response than listeners with smaller cognitive capacities. We anticipated that this would especially be the case for listening to speech masked by an interfering speaker compared to speech masked by a merely energetic fluctuating masker. II. METHODS A. Participants

Thirty-two adults with hearing impairment (aged between 31 and 76 yr, mean age 59 yr, 13 males), recruited at the VU University Medical Centre participated in the study. The sample size was based on an a priori analysis of the statistical power of a linear multiple regression (Erdfelder et al., 1996) assuming a moderate effect size (f 2 ¼ 0.25) of the relation between the SRT and WMC tests (Koelewijn et al., 2012b) at a significance level of 0.05 and a power of 0.80. This sample size provided sufficient power for all analysis of variance (ANOVA) analyses. Participants had pure tone hearing thresholds, averaged over both ears and over octave frequencies 1–4 kHz., from 33.3 to 60.0 dB hearing level (HL) [mean ¼ 45.0 dB HL, standard deviation (SD) ¼ 7.7 dB]. The average thresholds of the better ear ranged from 28 to 60 dB HL, and the average thresholds of the poorer ears ranged from 35 to 63 dB HL. Mean audiograms for the better and poorer ears are shown in Fig. 1. The asymmetry between the ears was on average 6.4 dB (SD ¼ 7.0 dB). As a criterion for sensorineural hearing loss, the air-bone gap should be less than 10 dB at octave frequencies between 0.5 and 2 kHz. Of 32 participants, 26 met this criterion, five had mixed hearing loss, and for one participant conductive hearing loss could not be fully excluded based on the available data. Participants had no history of neurological diseases, reported normal or corrected-to-normal vision, and were screened for good near-vision acuity (Bailey and Lovie, 1980). They were native Dutch speakers, able to read words with a minimal font size of 16 pt at a 60 cm distance, and they provided written informed consent in accordance

FIG. 1. Mean audiograms (dB HL) (ISO 389, 1991) for the better and poorer ears over participants. Error bars show the standard deviations among participants for each frequency. 1598

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with the Ethics Committee of the VU University Medical Center. B. Experimental tests 1. SRT

The SRT (Plomp and Mimpen, 1979) was measured by presenting speech in fluctuating noise or speech masked with a single-talker masker (Festen and Plomp, 1990; Koelewijn et al., 2012a). The SRT was estimated using an adaptive procedure that converged on the SNR associated with perceiving either 50% or 84 of the sentences entirely correct (Levitt, 1971). Manipulation of both masker type and intelligibility level resulted in a total of four conditions that were presented in a blocked fashion. Each condition contained 39 everyday Dutch sentences obtained from an open set (Versfeld et al., 2000) and the order of the conditions was counterbalanced over participants. The target sentences were spoken by a female voice. For the single-talker masker, concatenated sentences from another set were used that were spoken by a male voice. The fluctuating noise was created by multiplying the noise signal by the envelope of the speech of the single-talker masker for two separate frequency bands below and above 1 kHz (Festen and Plomp, 1990). Both masker types had a long-term average frequency spectrum identical to the spectrum of the target speech signal (Versfeld et al., 2000). Note that this implies identical average magnitude spectra of both masker types. In order to derive the average spectrum of the female voice, all sentences were first concatenated, after which the average spectrum of the entire file was calculated. Second, the average spectrum of the masker files was subtracted from the spectrum of the female voice. Third, from this difference spectrum an impulse response was calculated. Finally, each mask was convolved with this impulse response. All sound manipulations were performed using Matlab. The SNR was adaptively manipulated by means of a staircase procedure. To obtain the 50% and 84% intelligibility levels, a 1-up-1-down procedure (Plomp and Mimpen, 1979) and a 4-up-1-down procedure (Levitt, 1971) were used, respectively. After a correct response, the masker level for the following sentence increased by 2 dB, and after an incorrect response, the masker level decreased by 2 dB. In the 4-up-1-down procedure, participants had to make four consecutive correct answers before the masker level increased. For each block, the SNR of the first trial started below threshold (i.e., 10 dB SNR). The first sentence of each block was repeated, while the masker level was decreased with steps of 4 dB, until the participant repeated the entire sentence correctly. There were 3-s masker fringes before and after presentation of the target sentence. The length of each trial co-varied with the length of the presented sentence, which had a mean duration of 1.8 s (range 1.4–2.7 s). At the end of the trial a 1000 Hz tone was presented for 1 s (answerprompt) after which participants were allowed to respond. Before the experiment, participants were familiarized to the task at an intermediate sentence intelligibility level of 71% (2-up-1-down procedure) by listening and responding to 13 practice sentences for both masker types. Koelewijn et al.: Cognitive load in adults with hearing loss

2. TRT

The TRT task (Zekveld et al., 2007b) is a visual analog of the SRT task. In this task sentences were visually presented on a computer screen in a red font (font size 26) on a white background partially masked by black vertical bars. These bars were evenly distributed across the screen and the width of the bars was varied depending on the required percentage of unmasked text. Sentences were presented on a screen in a word-by-word fashion with word-onset timings similar to the corresponding recorded SRT sentences. After the onset of the last word the full sentence remained on the screen for 500 ms (Besser et al., 2012). An adaptive 1-up-1down procedure was applied, targeting the percentage of unmasked text required to read 50% of the sentences without any error. Four lists of 13 sentences were presented. The first list was for practice purposes and data for this list were excluded from the analysis. The TRT score was defined by the average percentage of unmasked text in the three remaining tests with the first four sentences of each list excluded. Lower thresholds indicated better performance. 3. Rspan and Lspan

Rspan and Lspan tests were used to assess verbal WMC in the visual and auditory domain respectively (Besser et al., 2013). Each test consisted of 54 sentences that were presented in sets ranging from three up to six sentences. Half of the sentences were semantically incorrect. First, participants performed a semantic judgment task after the presentation of each individual sentence. Second, after an entire set had been presented, participants had to report either the initial or final noun of each sentence in the correct order. Participants did not know beforehand whether they were to recall the initial or final nouns. In the Rspan test, each sentence was visually presented (Andersson et al., 2001; Besser et al., 2012). In order to improve audibility the Lspan sentences received spectral shaping as specified below and presented to both ears through headphones. Subjects responded verbally in both tests. Prior to each test participants practiced on three sentence sets. The span size corresponds to the total number of correctly recalled target words irrespective of their order of presentation, with a maximum score of 54. Higher scores indicate better performance. 4. SICspan

The “size-comparison span” (SICspan) task (Sorqvist et al., 2010; Sorqvist and Ronnberg, 2012), is a visual task that concurrently measures WMC and the ability to inhibit irrelevant linguistic information. The participants were asked to make relative size judgments between two items (e.g., is LAKE bigger than SEA?) by pressing “J” key for yes and “N” for no on a QWERTY keyboard. Each question was followed by a single to-be-remembered word, which was semantically related to the object items in the sentence (e.g., RIVER). Sentences and words were presented on screen in black (font size 36) on a light gray background. Ten sets were presented containing two up to six size comparison questions. Each question was followed by a to-be-remembered word. J. Acoust. Soc. Am., Vol. 135, No. 3, March 2014

After completion of a set, participants were asked to verbally recall the to-be-remembered words in order of presentation. The size comparison items and to-be-remembered words within each set were from the same semantic category. Therefore, in order for the participants to perform the task, the size-judgment items from the questions had to be inhibited while recalling the to-be-remembered words. Between sets, the semantic categories differed. The SICspan score used in this study was the total number of correctly remembered items independent of order, which leads to a maximum score of 40. The higher the score the better the performance on the SICspan task. C. Apparatus

All participants were tested in a sound treated room. During the SRT task participants had to fixate their gaze at a dot (diameter 0.47 ) located at eye level on a white wall at 3.5-m distance. During the SRT test, the pupil diameter of the left eye was measured at a 50 Hz-sampling rate by an infrared eye-tracker (SMI, 2D Video-Oculography, version 4). Light intensity was adjusted by an overhead light source such that the pupil diameter was around the middle of its dilation range at the start of the experiment. For both the SRT and the Lspan task, audio in the form of stereo wave files (44.1 Hz, 16 bit) was presented binaurally by an external soundcard (Creative SoundBlaster, 24 bit) through headphones (Sennheisser, HD 280, 64 X). During the TRT, SICspan, RSpan, and Lspan tasks, participants were seated in front of a computer screen (Dell, 17 in.) at 60 cm viewing distance. All tests were presented on a PC running Windows XP (Dell, Optiplex GX745, 2.66 GHz 2Core). D. Spectral shaping

Participants did not wear hearing aids during the experiment. Instead, sound files presented in the SRT and Lspan task were amplified and spectrally shaped, for each participant and each ear individually, based on their pure tone thresholds, in 1/3-octave steps within the range of 0.315–6.3 kHz (Amos and Humes, 2007; Humes, 2007). The mean hearing thresholds in dB sound pressure level (SPL) are presented in Fig. 1. The speech spectrum of the SRT task sentences, at 1/3-octave band levels with and without frequency shaping are shown in Fig. 2. Note that the speech spectrum of the Lspan sentences was the same as that for the SRT task sentences. Before shaping, the speech was adjusted to 55 dB SPL. The 1/3-octave speech levels lower than 20 dB above HL were amplified to this level. The level within each 1/3-octave band never exceeded 95 dB (SPL) to avoid saturation of the headphones. For each 1/3-octave, speech levels were contained below the middle of the participants’ dynamic range, for which the upper range was based on the participants’ uncomfortable loudness level in the audiogram with a maximum of 110 dB (SPL). Finally, during the SRT practice blocks, participants were allowed to attenuate the overall sound level up to 4 dB if they considered that a more comfortable hearing level. This setting was then used during the rest of the experiment and incorporated in the shaped speech levels shown in Fig. 2. This procedure assured Koelewijn et al.: Cognitive load in adults with hearing loss

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exclusion criterion was an amplitude difference larger than 2 SDs as calculated for each individual x- or y-trace between the start of the baseline and the response prompt. All remaining traces were baseline-corrected by subtracting the mean pupil size within the 1-s period prior to speech onset. For each subject, all traces were averaged separately per condition. After that, the peak pupil dilation (PPD) was calculated. PPD was defined as the highest value (in mm) within a time window of 4.4 s after speech onset, which resembled the interval between speech onset and the response prompt. F. Statistical analysis FIG. 2. Mean hearing thresholds in dB SPL over both ears and all participants (squares). Also shown are the speech levels at one-third octave bands before spectral shaping at a level of 55 dB SPL (triangles) and the mean speech levels after amplification and spectral shaping (circles).

comfortable speech levels. As a check we always asked participants about the sound quality after the SRT practice blocks. None of them indicated any clipping or distortion. As a final objective measure for audibility we calculated the speech intelligibility index (SII) for the spectrally shaped sound files in quiet. The average SII in quiet for the better ear was 0.75 (range 0.64–0.82, SD ¼ 0.04) and the average SII in quiet for the poorer ear was 0.73 (range 0.61–0.81, SD ¼ 0.05). In order to manipulate the SNR, the overall gain of the masker level was changed in respect to the speech level that was fixed over conditions. E. Procedure

The test session started with either the Rspan or the Lspan task (order was balanced over subjects). Additionally, participants performed two conditions of the SRT task (order was balanced over subjects). This was followed by a 10-min break after which participants performed the SICspan task followed by the two remaining blocks of the SRT task. After a second 10-min break participants performed the remaining Lspan or Rspan task. The session was ended by performing the TRT task and in total took 2.5 to 3 h. During the SRT task, the pupil diameter was recorded during each trial and pupil x- and y-traces were recorded for detecting horizontal and vertical eye-movements, respectively. Traces were separately stored for each trial as epochs starting with the onset of the masker and ending when the response prompt was presented. Pupil traces and SRT data of the first four sentences were omitted from further analysis. For all remaining traces, pupil diameter values more than three SDs smaller than the mean and all “zero” values were coded as blinks. Traces containing more than 15% blinks were excluded and remaining traces were deblinked by means of linear interpolation. On both the x- and y-traces a spike detection algorithm was used to detect eye movements. This algorithm uses a 100 ms time window that slides with 20 ms steps in which maximum amplitude differences are calculated between all time points within the window for each step. The 1600

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A repeated measures ANOVA testing the effects of intelligibility (50% and 84%) and masker type (fluctuating noise and single-talker masker) was performed on the SRTs and PPD values. The skewness of these dependent variables stayed within two times their standard deviations indicating that the assumption of a normal distribution of these variables was not violated. Statistically significant (p < 0.05) interactions were further analyzed by means of Bonferroni corrected two-tailed paired samples t-tests. Second, Pearson correlation coefficients were calculated to test the relations between age, PTA (Pure tone audiometry), Rspan, Lspan, SICspan, TRT, SRT, and PPD. Two-tailed t-tests were applied without using Bonferroni corrections. This allowed direct comparison with the results of participants with normal hearing who performed similar tests in our previous study (Koelewijn et al., 2012b). Note that performing multiple tests raises the chance of type-I errors. Therefore, the results of the correlation analyses should be interpreted cautiously. Linear regression analyses were performed to examine the associations between variance in SRT or PPD (dependent variables) and WMC (Rspan, Lspan), WMC and inhibition (SICspan), and TRT as independent factors. For each dependent variable, regression analyses were performed separately for each masker type and both intelligibility levels. Note that separate association models were run for each of the independent cognitive measures (rather than stepwise regression analyses) to avert co-linearity and to allow examination of the relation between the individual predictors and the dependent variables. Also, regression analyses with PTA as an independent measure, and SRT and PPD as dependent measures were performed to examine how these measures were associated with the degree of hearing loss. Finally, we used regression models to examine the associations between informational masking as reflected by the difference between fluctuating noise and single-talker conditions (dependent variables) and WMC, TRT, and PTA as independent variables. We calculated difference scores for both the SRT (DSRT) and PPD (DPPD) by subtracting the outcome for fluctuating noise averaged over both intelligibility conditions from the outcome for the single-talker averaged over both intelligibility conditions. For each regression model, we examined whether age and PTA were significant mediators of the relationship between the dependent and independent variables, except for the models in which PTA was an independent variable. A variable was considered as a relevant covariate when the regression coefficient changed Koelewijn et al.: Cognitive load in adults with hearing loss

by at least 10% after adding the potential covariate to the analysis. Additionally, the potential confounder had to be significantly associated with both the independent (cognitive abilities and TRT) and the dependent (SRT or pupil response) variables. All statistical analyses were performed using SPSS version 17. III. RESULTS A. Behavioral results for speech perception

The average SRT scores and PPD values for each condition are reported in Table I. An ANOVA on the SRTs showed a main effect of Intelligibility (F1,31 ¼ 326.62, p < 0.001) with lower SRTs50% (mean SNR ¼ 3.4 dB) than SRTs84% (mean SNR ¼ 2.8). Additionally, a main effect of masker type was observed (F1,31 ¼ 24.37, p < 0.001) with higher thresholds for the single-talker masker (mean SNR ¼ 0.7) than for fluctuating noise (mean SNR ¼ 1.3). Also, an interaction between intelligibility level and masker type was observed (F1,31 ¼ 6.29, p ¼ 0.018). Post hoc analysis in the form of two Bonferroni corrected two-tailed paired samples t-tests for the two masker conditions at 84% intelligibility showed a 2.8 dB higher (worse) threshold for the single-talker masker compared to fluctuating noise conditions (p < 0.001). At 50% intelligibility a smaller difference of 1.2 dB (p < 0.02) was observed, which explains the observed interaction. B. Pupil data for speech perception

Pupil traces containing a large number of blinks (in total 1.6% of the traces) and/or large eye movements (in total 12.7% of the traces) were excluded from further analysis. PPD was calculated over the remaining traces for each condition. The mean traces for the four conditions are plotted in Fig. 3. An ANOVA on the PPD revealed a main effect of intelligibility level (F1,31 ¼ 31.43, p < 0.001), with a larger PPD in the 50% intelligibility conditions (mean ¼ 0.21 mm) compared to the 84% intelligibility conditions (mean ¼ 0.15 mm). Additionally, there was an effect of masker type (F1,31 ¼ 13.51, p < 0.01) with a larger average PPD for the single-talker masker conditions (mean ¼ 0.20 mm) compared to the fluctuating noise conditions (mean ¼ 0.16 mm). No interaction between intelligibility level and masker type was observed (F1,31 ¼ 0.40, p ¼ 0.53).

TABLE I. The average SRT and PPD values for both levels of intelligibility and for both masker types. Intelligibility

Fluctuating

SRT 50% 84% PPD 50% 84%

4.0 (3.2) 1.4 (4.4)

Single-talker

SNR (SD), dB 2.8 (4.0) 4.2 (4.5)

PPD (SD), mm 0.19 (0.08) 0.23 (0.10) 0.14 (0.09) 0.17 (0.08)

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FIG. 3. Pupil responses per condition averaged over participants. The onset of the sentences is at 0 s. The baseline is indicated as the average pupil diameter over one second preceding the start of the sentence. The area between the second and third dotted lines indicates the time window used for calculating the mean pupil dilation.

C. Descriptive statistics cognitive tests and correlation analyses

Total scores for the Rspan (mean ¼ 15.3, SD ¼ 4.6), Lspan (mean ¼ 22.2, SD ¼ 5.5) and SICspan tasks (mean¼ 23.3, SD ¼ 6.1) were calculated for each participant. Additionally, the individual TRTs were calculated (mean¼ 60.5, SD ¼ 3.1). Pearson correlation coefficients between age, PTA, each of the span tasks (Rspan, Lspan, and SICspan), TRT, SRT, and PPD were computed. These correlations showed a significant association between age and SICspan (R ¼ 0.356, p ¼ 0.045) indicating that SICspan scores dropped with increasing age (Table II). There were no significant associations between PTA and any of the cognitive tasks, and no relations between the SRTs and any of the cognitive tasks. Pearson correlations among each of the WM span tests (Rspan, Lspan, SICspan) ranged between 0.55 and TABLE II. Two-tailed Pearson correlations (* ¼ p < 0.05, ** ¼ p < 0.01) between age, PTA, Rspan, Lspan, SICspan, TRT, both SRT and PPD with fluctuating noise at 50% (SRTF50) and 84% (SRTF84) intelligibility, and both SRT and PPD with a single-talker masker at 50% (SRTST50) and 84% (SRTST84) intelligibility. Lower TRTs and SRTs indicate better performance.

Age PTA Rspan Lspan SICspan TRT SRTF50 SRTF84 SRTST50 SRTST84 PPDF50 PPDF48 PPDST50 PPDST84

Age

PTA

Rspan

Lspan

SICspan

TRT

X 0.11 0.27 0.32 0.36* 0.22 0.06 0.01 0.24 0.12 0.07 0.012 0.14 0.06

X 0.05 0.20 0.04 0.31 0.64** 0.58** 0.64** 0.68** 0.16 0.19 .09 0.05

X 0.61** 0.55** 0.28 0.13 0.02 0.19 0.28 0.02 0.29 0.03 0.07

X 0.58** 0.30 0.22 0.16 0.25 0.11 0.15 0.24 0.20 0.12

X 0.49** 0.26 0.06 0.37* 0.35* 0.11 0.10 0.01 0.04

X 0.23 0.19 0.20 0.39* 0.14 0.14 0.02 0.03

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0.61 and were statistically significant. The TRT correlated significantly with the SICspan (R ¼ 0.49, p < 0.01) indicating that better text reception was associated with better performance on the SICspan. PTA correlated significantly with all SRTs. The SRTs in both single-talker masker conditions correlated significantly with SICspan, which was further investigated by means of regression analyses as described in the next paragraph. D. Relation between cognitive abilities, SRT, and PPD

To examine whether the SRTs and PPDs during speech perception were associated with WMC (Rspan, Lspan), WMC and inhibition (SICspan), and linguistic closure (TRT), regression analyses were performed for the SRTs and PPDs separately. The slope (B), the amount of variance explained (R2) and the p values for the independent factors explaining the performance in SRT50% and SRT84% are shown in Table III. Table IV shows the results for the PPD in the 50% and 84% intelligibility conditions. PTA appeared to be a confounder in one of the analyses, and was therefore included in that model. Age did not confound any of the equations. The SRT regression analyses (Table III) showed no significant associations between speech reception and cognitive abilities for the fluctuating noise condition at either 50% (SRTF50) or 84% (SRTF84) intelligibility. For the single-talker masker at 50% (SRTST50) and 84% (SRTST84) intelligibility, significant associations were found with SICspan (R2 ¼ 0.14 and R2 ¼ 0.12, respectively). In both models, higher (better) SICspan scores were related to lower (better) SRTs. The outcomes of the regression analyses with PPD as the dependent measure (Table IV) showed no associations between PPD and TABLE III. Regression models with SRTST50, SRTF50, SRTST84, and SRTF84 as dependent variables, and PTA, TRT, and the cognitive capacity measures as independent variables. Shown are the unstandardized regression coefficients (B) and the variance fitted (R2) for all associations. The p-values of significant associations (p < 0.05) are presented in bold. The models including Rspan, Lspan, SICspan, and TRT as the independent variables were adjusted for PTA (*) and age (**) to examine if they were significant confounder. Age did not appear to be a significant confound in any of the analyses.

Fluctuating PTA Rspan Lspan SICspan TRT

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TABLE IV. Regression models with PPDs in the four conditions as dependent variables, and PTA, TRT, and the cognitive capacity measures as independent variables. Shown are the unstandardized regression coefficients (B) and the explained variance (R2) for all associations. The p-values of significant associations (p < 0.05) are presented in bold. PTA and age did not appear to be a significant confound in any of the analyses.

2

P

Fluctuating

p

B

R

0.27

0.41

0.000

0.33

0.34

0.000

PTA

0.09 0.13 0.14 0.24

0.02 0.05 0.07 0.05

0.488 0.220 0.148 0.211

0.02 0.12 0.04 0.02

0.02 0.03 0.00 0.04

0.896 0.379 0.756 0.292

Rspan Lspan SICspan TRT

SRTST50

SRTST84 p

B

R2

p

0.34

0.41

0.000

0.40

0.46

0.000

PTA

0.16 0.18 0.25 0.26

0.04 0.06 0.14 0.04

0.306 0.170 0.036 0.275

0.21 0.10 0.26 0.30*

0.05 0.01 0.12 0.16

0.232 0.537 0.049 0.144

Rspan Lspan SICspan TRT

p

B

R2

P

0.03

0.372

0.00

0.04

0.298

0.00 0.02 0.01 0.02

0.930 0.408 0.561 0.462

0.08 0.06 0.01 0.02

0.108 0.186 0.585 0.455

B

R

0.00 0.00 0.00 0.00 0.00

2

0.01 0.00 0.00 0.00

PPDST50

Single-talker

R2

J. Acoust. Soc. Am., Vol. 135, No. 3, March 2014

PPDF84

PPDF50

R

B

Rspan Lspan SICspan TRT

This study assessed the effects of different masker types on speech reception and cognitive load in adults with hearing impairment. Note that we used the same tests and methodology as in our previous study, which included participants with normal hearing (Koelewijn et al., 2012b). This was done in order to be able to compare the results between these two studies. A graphic representation of the findings in both studies is presented in Fig. 5. As shown in Fig. 5, we observed relatively high SRTs as compared to the SRTs of adults with normal hearing (Koelewijn et al., 2012a; Koelewijn et al., 2012b). The effect of hearing impairment on SRTs was previously shown by Festen and Plomp (1990) and Bernstein and Grant (2009). In the study of Festen and Plomp (1990), the auditory stimuli were not spectrally shaped, and the authors argued that the relatively high SRTs for participants with hearing impairment could be partially explained by audibility effects. Although in the current study speech was spectrally shaped, audibility can never be optimally restored

B

Single-talker

PTA

IV. DISCUSSION

SRTF84

SRTF50 2

cognitive abilities in any of the SRT conditions. Additionally, we performed regression analyses with similar independent variables as before, but now with DSRT and DPPD (D ¼ single-talker masker  fluctuating noise) as dependent variables (Table V). The variance in DSRT was significantly associated with SICspan (R2 ¼ 0.19, p ¼ 0.013). The results in Fig. 4 show a positive DSRT for people with a low SICspan score, which indicates that their performance was worse for the single-talker masker conditions than for fluctuating noise conditions. For individuals with a high SICspan scores DSRT was near zero indicating that informational masking did not affect their performance. No association between DPPD and any of the independent variables was observed.

PPDST84

R2

p

0.00

0.01

0.622

0.00 0.00 0.00 0.00

0.00 0.04 0.00 0.00

0.878 0.282 0.949 0.932

B

R2

p

0.00

0.00

0.772

0.00 0.00 0.00 0.00

0.00 0.02 0.00 0.00

0.715 0.509 0.842 0.873

B

Koelewijn et al.: Cognitive load in adults with hearing loss

TABLE V. Associations (p < 0.05) between the dependent variables DSRT and DPPD [D ¼ (ST50 þ ST84/2)  (F50 þ F84/2)], and the cognitive capacity measures and PTA. Shown are the unstandardized regression coefficients (B) and the variance (R2). PTA and age did not appear to be significant confounders in any of the analyses. DSRT

PTA Rspan Lspan SICspan TRT

FIG. 4. SICspan performance as function of DSRT [(ST50 þ ST84/2) – (F50 þ F84/2)]. A positive DSRT reflects worse performance for the single-talker masker compared to fluctuating noise conditions.

specifically when participants suffer a severe high frequency loss. Still, speech levels were presented above threshold for all participants for the range of 0.3–6.3 kHz, which covers most of the speech spectrum. Restoring audibility might have affected the small group of participants with mixed hearing loss differently than the majority of participants with pure sensorineural loss. However, for none of the analyses, the results were affected when excluding the data of the participants with mixed hearing loss. Additionally, suprathreshold deficits associated with sensorineural hearing loss could also explain the higher SRTs. This was supported by the significant contribution of PTA in the regression model explaining the SRTs (see Table III). These results are in line with those of George et al. (2007) who also observed that the audiogram explained a significant part of the variance in

DPPD

B

R2

p

B

R2

P

0.07

0.05

0.221

0.00

0.02

0.402

0.13 0.01 0.16 0.16

0.07 0.02 0.19 0.05

0.141 0.925 0.013 0.226

0.00 0.00 0.00 0.00

0.10 0.00 0.00 0.03

0.086 0.799 0.851 0.347

speech perception, even when audibility effects were accounted for. In addition, the current results show higher SRTs for the single-talker masker conditions compared to fluctuating noise. This difference is most probably caused by informational masking (Kidd et al., 2008). Our results seem to indicate that listeners with hearing impairment are more strongly affected by informational masking than normally hearing adults, because in our previous study among normal hearing participants, no informational masking effect was shown on performance (Koelewijn et al., 2012b). The PPDs showed an effect of intelligibility level and masker type. This finding is in agreement with Zekveld et al. (2011) who also observed an intelligibility effect on the PPD in participants with hearing impairment. It is interesting to note that the effect of masker type on the PPD, which we found in our previous study among normal hearing listeners (Koelewijn et al., 2012a; Koelewijn et al., 2012b), was replicated in the current study among hearing impaired listeners. It demonstrates that the different types of background maskers affect the pupil response similarly in participants

FIG. 5. Average PPD as function of the average SRT for both masker and intelligibility conditions for both participants with hearing impairment from the current study and participants with normal hearing from Koelewijn et al. (2012b). The ovals denote the standard deviations of both the SRTs and PPDs.

J. Acoust. Soc. Am., Vol. 135, No. 3, March 2014

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with normal and impaired hearing. This finding is in line with the conclusions of Picou et al. (2011, 2013). In those studies, word recall was similar in listeners with normal and impaired hearing, a result that was interpreted as reflecting comparable listening effort. Importantly, lower intelligibility results at lower SNRs and larger PPDs, while higher masker complexity results in higher SNRs and larger PPDs. In other words, the pupil response does not solely reflect speech intelligibility but also informational masking. Hence, “cognitive processing load” as reflected by the pupil response is not the same as “performance” as shown by the SRT. As shown in Fig. 5, the overall magnitude of the PPD in the four conditions in the group with hearing impairment (current study) seems slightly smaller than the PPD of the listeners with normal hearing (Koelewijn et al., 2012b). Although no significant correlations between age and PPDs where shown (see Table II), the participants with hearing impairment were on average eight years older than the group of individuals with normal hearing. Although there is evidence that that pupil size decreases linearly with age (Winn et al., 1994), we failed to find a significant age-related difference in PPD when comparing groups of young and middleaged adults (Koelewijn et al., 2012a). Alternatively, smaller pupil responses could reflect actual age-related decline in brain activity as shown in prefrontal areas related to memory functions (Grady et al., 2006). These issues need to be further investigated in future research. Independent of this possible age-effect on the PPD, the PPDs were similarly affected by intelligibility level and masker type as previously observed for young and middle-aged adults with normal hearing (Koelewijn et al., 2012a; Koelewijn et al., 2012b). We anticipated that participants with hearing impairment with larger WMC and better linguistic closure abilities would have lower (better) SRTs and a larger pupil response while listening to speech masked by an interfering speaker as compared to an energetic fluctuating masker. Regression analyses indeed revealed that participants with higher (better) SICspan scores had lower SRTs. This is in line with the idea that larger WMC results in better speech reception as was also shown by (e.g., Rudner et al., 2011; Zekveld et al., 2011; Desjardins and Doherty, 2013; Ng et al., 2013). However, this result only applied to speech perception in the single-talker masker conditions. As shown in Fig. 4, participants with a high SICspan score got comparable SRTs in both masker conditions, while individuals with a relatively low SICspan score got poorer SRTs in the single-talker masker compared to fluctuating noise. This is highly interesting as it indicates that people who score better on the SICspan task experience less hindrance from interfering speech relative to fluctuating noise than people with lower SICspan scores. SICspan reflects WMC measured while participants inhibit irrelevant linguistic information (Sorqvist et al., 2010). In this study, Rspan and Lspan scores did not relate to this SRT difference, suggesting that the ability to inhibit irrelevant linguistic information, as reflected in the SICspan, is a more important predictor of SRTs in interfering speech than WMC itself. It should be noted that, overall, the Lspan scores were relatively high compared to the Rspan scores. This finding is in line with previous results for 1604

J. Acoust. Soc. Am., Vol. 135, No. 3, March 2014

participants with normal hearing (Koelewijn et al., 2012b; Besser et al., 2013) and illustrates that hearing loss does not affect storage of auditory information in working memory, at least not when there are no competing maskers and when presentation levels are adjusted for hearing impairment. Measured cognitive abilities were not associated with subject variance in PPDs in hearing impaired adults. This is in contrast to recent studies among listeners with hearing impairment in which listening effort was partly explained by differences in cognitive ability (Picou et al., 2011; Desjardins and Doherty, 2013; Picou et al., 2013). Still, these studies used other measures to quantify listening effort. In our previous study, among participants with normal hearing (Koelewijn et al., 2012b), the PPD was partly explained by SICspan and TRT scores. This difference between participants with normal and impaired hearing cannot be explained by a difference in cognitive performance per se, because the mean and range of the performances on the cognitive tests were similar for both groups of participants. What did differ was the variance in PPD between participants in the current and previous study (SD between 0.08 and 0.10 mm and SD between 0.12 and 0.16 mm, respectively). The lack of associations could therefore be related to the relatively small variance in PPD in the current sample. The underlying factors that explain the relatively small variance in PPD’s in the hearingimpaired group needs further investigation. According to findings observed by Grady et al. (2006) we suggest that an overall diminished brain activity may relate to decrease in variance. Yet another explanation for the absence of significant contributions of the cognitive measures in the regression models explaining PPDs could be that cognitive processes other than WMC, such as attentional processes, are responsible for observed PPDs in the hearing impaired listeners. This was also suggested by (Best et al., 2010; Meister et al., 2013) who investigated the influence of attention on speech intelligibility. Best et al. (2010) showed the benefit of only focusing on one instead of two sentences in a speech in noise task. Additionally, Meister et al. (2013) indicated that speech recognition was related to WM skills when selective attention in contrast to divided attention was required. Future studies should examine to what extent selective attention exploited during listening to speech in different types of noise influences both the SRT and PPD. Finally, our previous (Zekveld et al., 2010, 2011; Koelewijn et al., 2012a; Koelewijn et al., 2012b) and current results indicate that pupillometry is of value in hearing science. It shows effects that are complementary to those reflected by traditional measures like the SRT. Pupillometry can become a useful method for diagnostic assessment of the level of cognitive load during speech processing in individuals with or without hearing impairment. A great advantage of this method is that it can be used in conjunction with a standard SRT task. Still, more experimental research is needed to explain inter-individual differences observed in the pupil response in both individuals with hearing impairment and individuals with normal hearing. It is worth investigating to what extent this method can be applied in clinical settings to evaluate the benefit of hearing devices. To conclude, the results suggests that individuals with hearing impairment seem less able to understand speech in Koelewijn et al.: Cognitive load in adults with hearing loss

the presence of fluctuating noise or an interfering speech masker than people with normal hearing (Koelewijn et al., 2012a; Koelewijn et al., 2012b). The PPD is sensitive to different intelligibility levels (Kramer et al., 1997; Zekveld et al., 2010, 2011) and different types of background maskers (Koelewijn et al., 2012a; Koelewijn et al., 2012b) in both individuals with normal hearing and individuals with hearing impairment. In contrast to normally hearing listeners, interindividual differences in WMC, inhibitory control, and TRT were not associated with the PPD in listeners with hearing impairment. Future research will investigate what cognitive processes other than those measured in the current study are associated with the pupil response during the processing of masked speech. ACKNOWLEDGMENTS

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