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. Author manuscript; available in PMC: 2017 Jan 1.
Published in final edited form as: Exp Aging Res. 2016 Jan-Feb;42(1):64–85. doi: 10.1080/0361073X.2016.1108712

Task-related vigilance during speech recognition in noise for older adults with hearing loss

Stefanie E Kuchinsky 1,*, Kenneth I Vaden Jr 2, Jayne B Ahlstrom 2, Stephanie L Cute 2, Larry E Humes 3, Judy R Dubno 2, Mark A Eckert 2,*
PMCID: PMC4702493  NIHMSID: NIHMS746967  PMID: 26683041

Abstract

Background/Study Context

Vigilance refers to the ability to sustain and adapt attentional focus in response to changing task demands. For older adults with hearing loss, vigilant listening may be particularly effortful and variable across individuals. This study examined the extent to which neural responses to sudden, unexpected changes in task structure (e.g., from rest to speech recognition epochs) were related to pupillometry measures of listening effort.

Methods

Individual differences in the task-evoked pupil response during word recognition were used to predict functional MRI estimates of neural responses to salient transitions between quiet rest, noisy rest, and word recognition in unintelligible, fluctuating background noise. Participants included 29 older adults (M = 70.2 years old) with hearing loss (pure tone average across all frequencies = 36.1 dB HL, SD = 6.7).

Results

Individuals with a greater average pupil response exhibited a more vigilant pattern of responding on a standardized continuous performance test (response time variability across varying inter-stimulus intervals r(27) = .38, p = .04). Across participants there was widespread neural engagement of attention and sensory-related cortices in response to transitions between blocks of rest and word recognition conditions. Individuals who exhibited larger task-evoked pupil dilation also showed even greater activity in the right primary auditory cortex in response to changes in task structure.

Conclusion

Pupillometric estimates of speech recognition effort predicted variation in activity within cortical regions that were responsive to salient changes in the environment for older adults with hearing loss. The current study suggests that maintaining vigilant attention may come at the cost of increased listening effort.

Listening to speech in background noise is particularly challenging for older adults with hearing loss (Larsby, Hällgren, Lyxell, & Arlinger, 2005; Plomp, 1994). The widespread cortical regions that older adults engage compared to younger adults during speech recognition (Eckert et al., 2008; Kuchinsky et al., 2012; Peelle, Troiani, Wingfield, & Grossman, 2010; Vaden, Kuchinsky, Ahlstrom, Dubno, & Eckert, 2015; Wingfield & Grossman, 2006; Wong et al., 2009) are consistent with behavioral and physiological evidence demonstrating increased listening-related effort with aging and hearing loss (McCoy et al., 2005; Pichora-Fuller, Schneider, & Daneman, 1995; Zekveld, Kramer, & Festen, 2011). Sustaining attention in the face of changes in listening demands may be especially effortful for older adults, who tend to exhibit poorer adaptability to sudden shifts in experimental conditions (Ridderinkhof, Span, & van der Molen, 2002). As a result, abrupt and unexpected changes in the listening environment, such as when a conversational partner begins speaking or with the onset of a novel background noise, may be experienced as highly salient events. Task-related vigilance in response to such transitions entails maintaining current and adapting to changing task goals (Dosenbach et al., 2006).

Functional imaging studies of auditory perception have shown that major task transitions elicit brain activity within auditory cortex (Huang, Belliveau, Tengshe, & Ahveninen, 2012; Seifritz et al., 2002) and domain-general attention networks, particularly the right-lateralized ventral attention network (VAN) (Fox, Snyder, Barch, Gusnard, & Raichle, 2005; Vaden et al., 2013). The VAN includes the right anterior insula and temporoparietal junction. This system has been shown to support rapid orienting to novel or unexpected stimuli (Corbetta & Shulman, 2002) and vigilant responding (see Corbetta & Shulman, 2011 for a summary).

Several age-related neural changes may alter the extent to which individuals are sensitive to changes in task structure. First, older adults exhibit substantial inter-subject variability in auditory cortex (Baum & Beauchamp, 2014) and attention-related neural responses (Vaden et al., 2015) to speech stimuli. This variability may be exacerbated by the age-related recruitment of increasingly distributed neural activity to support declining task performance (Cabeza, Anderson, Locantore, & McIntosh, 2002; Eckert et al., 2008; Schneider-Garces et al., 2010).

Secondly, there may be considerable individual differences in the level of effort exerted during speech recognition amongst older adults. For example, individuals with smaller verbal or cognitive capacities or with poorer hearing thresholds exhibit smaller pupillary responses to speech stimuli, indicative of a reduced ability to engage in the effortful processing required in challenging listening conditions (Kuchinsky et al., 2014; Zekveld et al., 2011). Task-evoked pupil dilation is an autonomic response that has been used to index cognitive effort in both younger and older adults (Kuchinsky et al., 2013; Piquado, Isaacowitz, & Wingfield, 2010; Zekveld, Kramer, & Festen, 2010; Zekveld et al., 2011).

Neurophysiological evidence may provide a basis for linking task-related listening effort and neural indices of vigilance. Noradrenergic activity driven by the firing of neurons in the locus coeruleus (LC) brainstem nucleus modulates sensory- and attention-related cortical engagement (Aston-Jones & Cohen, 2005; Rajkowski, Kubiak, & Aston-Jones, 1993) and vigilant patterns of orienting (Hermans et al., 2011). LC activity serves to support transitioning between task-engaged and -disengaged states of attention, which may be indexed via pupillometry: larger task-evoked pupil dilation has been associated with greater engagement in an auditory oddball discrimination task (Gilzenrat, Nieuwenhuis, Jepma, & Cohen, 2010). However age-related declines in the number of LC neurons (Manaye, McIntire, Mann, & German, 1995) are thought to reduce task engagement for older adults (Jennings, Stiller, & Brock, 1988). Interventions may mitigate this decline; speech-perception training has been observed to result in an increased average pupil response for older adults with hearing loss (Kuchinsky et al., 2014). Together, these findings suggest that older adults who exert greater task-related effort may be more vigilant in responding to unexpected changes in task structure.

The current study tested the hypothesis that individual differences in task-related listening effort relate to behavioral and neural indices of vigilance in older adults with hearing loss. We first examined whether individuals who exerted greater listening effort (mean task-evoked pupil response) exhibited greater vigilance on a behavioral measure of reaction time (RT) variability that was obtained from a standardized continuous performance test (CPT). In particular, previous research has demonstrated that RTs in response to changing inter-stimulus intervals (ISIs) on the CPT are more variable with increasing distractibility and with attention deficit disorders (Advokat, Martino, Hill, & Gouvier, 2007; Epstein et al., 2003).

Secondly, we predicted that individuals who exhibited greater listening effort (larger pupil responses) would engage auditory and attention-related neural activity to a greater degree in response to unexpected transitions in experimental epochs (i.e., quiet rest, rest with background noise, and speech-in-noise recognition). We discuss the implications of this work and the need for future research to elucidate the conditions in which vigilant listening supports speech recognition performance, particularly for older adults with hearing loss.

Method

The methodology for the pupillometry task is described in previous studies (Kuchinsky et al., 2013, 2014). The pupillometry data of the 29 participants presented here were analyzed in detail in Kuchinsky et al. (2014). The functional imaging data have not been published previously. Pupillometry and functional imaging data were collected in separate sessions.

Participants

Twenty-nine older adults aged 60 to 88 years old (M = 70.2 years old, SD = 8.5; 12 female) with mild to moderate sloping sensorineural hearing loss participated in the present study. Participants were monolingual native English speakers, had normal or corrected-to-normal vision, and were right-handed (Edinburgh handedness score M = 95.8, SD = 8.1, on a scale from −100/left-handed to +100/right-handed; Oldfield, 1971). All participants reported no history of neurological or psychological disorders or learning disabilities, were not taking psychoactive medications and had no contraindications for MRI scanning. Informed consent was obtained from participants in accordance with the Medical University of South Carolina’s Institutional Review Board and the Declaration of Helsinki.

Audiometric assessment

Pure tone thresholds at standard frequency intervals (between .25 and 8.0 kHz) were measured with a Madsen OB922 audiometer and TDH-39 headphones that were calibrated to American National Standards Institute standards (American National Standards Institute, 2010). Participants had mild-to-moderate, sloping high-frequency sensorineural hearing loss (Figure 1), but did not use hearing aids. Asymmetric thresholds were limited to a 15 dB difference between ears at each frequency. Experimental stimuli were presented to the right ear for most participants (although both ER-3A earphones were inserted), unless using the left ear was required to ensure audibility with spectral shaping (N = 4; described below). Monaural presentation facilitated spectral shaping, which was based on the hearing thresholds of a particular ear. Mean pure tone average (PTA) across all frequencies (.25–8.0 kHz) for the tested ear was 36.1 dB HL (SD = 6.7). PTA was not significantly correlated with age in this sample, r(27) = .18, p = .35. (The same pattern of study results was obtained when averaging pure-tone thresholds at .5, 1.0, and 2.0 kHz.)

Figure 1.

Figure 1

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Mean (standard error bars) pure tone thresholds for the tested ear across the 29 older-adult study participants.

Materials

Following previous approaches for spectral shaping, stimulus levels at each one-third octave band frequency were generally 20 dB higher than individual thresholds, ensuring audibility through at least 3.0 kHz (Burk & Humes, 2008; Humes, Burk, Strauser, & Kinney, 2009). Background stimuli consisted of an unintelligible, two-talker (male and female speakers) International Collegium for Rehabilitative Audiology (ICRA, CD Track 6) vocoded noise (Dreschler, Verschuure, Ludvigsen, & Westermann, 2001). The long-term spectra for the ICRA noise were matched to that of the two-talker speech from which it was created (see Humes et al., 2009, pp. 8–9).

For the fMRI task, 120 monosyllabic, consonant-vowel-consonant word stimuli (e.g., “like”) were selected from the 600 most frequent words in the English language, which were used in previous speech-perception training studies (Humes et al., 2009). Word recordings were spoken by a young adult male talker, who was a native speaker of North American English, and had an average duration of 435 ms (SD = 65). The sets of words across all conditions were balanced across the following phonological and lexical properties (all p > .10): number of letters, log word frequency (Hyperspace Analogue to Language, HAL, norms; Lund & Burgess, 1996), phonological and orthographic neighborhood density, mean frequency of phonological and orthographic neighbors, all retrieved from the English Lexicon Project database (Balota et al., 2007), and biphoneme probability (weighted and unweighted by word frequency) retrieved from the IPhOD database (Vaden, Halpin, & Hickok, 2009).

A baseline signal-to-noise ratio (SNR) for each individual was determined using an initial open-set word-in-noise recognition task. Participants listened to and repeated 200 of the 600 most frequent words of English presented in ICRA noise. A default baseline SNR of −2 dB was selected based on previous open-set testing with these items in a group of older adults with hearing loss (Humes et al., 2009). This SNR was adjusted only for individuals for whom open-set word recognition scores were less than 30%. Thus, all participants experienced a moderate level of task difficulty (word identification, M = 39.3%, SD = 8.6%). A baseline −1 dB SNR was used for six participants and 0 dB SNR was used for one participant.

During the fMRI experiment, each word stimulus occurred in one of three SNRs: baseline, easier (2 dB SNR better than baseline), and harder (2 dB SNR worse than baseline). The order of SNR presentation was balanced within participants such that each SNR block, which consisted of four words, appeared before and after each other block approximately the same number of times within the experiment for each participant. Each participant received the same order of events, facilitating the comparison of vigilance-related effects across individuals.

Procedure

Initial sessions involved obtaining informed consent, audiometric testing, individualized SNR determination through open-set word recognition, and cognitive testing. Cognitive testing included Conners’ CPT, which is a standardized go/no-go task in which participants view a series of letters presented at 1, 2, or 4 second ISIs. Participants press the spacebar on a computer keyboard in response to every letter except X. High standard error of RTs with changing ISIs is indicative of difficulty adapting to varying task demands (Conners, 2004).

Pupillometry Session

The pupillometry task was an orthographic version of the visual world paradigm, in which participants identified 192 words in ICRA noise from four options on a computer screen, one by one. Stimuli were selected from among the recordings of the 600 most frequent words of English and presented at the SNRs listed above for the fMRI experiment. The pupillometry data from these 29 participants have been analyzed in Kuchinsky et al. (2014), which examined group differences in listening effort related to speech-perception training. The current study focused on individual differences in the overall shape of the pupil response with respect to word stimuli across all trials (before training).

MRI Session

The same individuals participated in a neuroimaging session on a different day (within M = 8.3 days, SD = 8.4), during which they performed a similar words-in-noise recognition task while functional imaging data were collected in the MRI scanner (26 minutes). E-Prime software (Psychology Software Tools, Inc., Sharpsburg, PA) controlled trial presentation and recorded behavioral responses. Each trial was triggered by the onset of a whole-brain image acquisition (Figure 2A). The experimental run comprised three conditions: quiet rest in which no speech or noise stimuli were presented, background-noise only presentation, and word-recognition in noise trials. Auditory stimuli were presented via piezoelectric insert earphones (Model S14, Sensimetrics Corporation, Malden, MA) with Etymotic ER-3A foam tips, providing at least 30 dB external noise attenuation (for more details see Sensimetrics Corportation, 2015). Presentation levels of speech and noise stimuli through the earphones were calibrated with a precision sound level meter (Larson Davis 800B) with the corresponding amplifier voltage output levels verified prior to each participant’s MRI session. Speech and noise were spectrally shaped, as described previously; presentation levels were selected to be similar to those used for the speech recognition tasks outside the scanner. A sparse-sampling imaging sequence was used to collect a whole-brain volume once every 8.6 seconds. This allowed for speech stimuli to be presented in the absence of volume acquisition noise (see Figure 2A). As shown in Figure 2B, the sequence of conditions was: 10 trials of rest, 15 trials of noise-only, 60 word-recognition trials, 10 trials of rest, 60 word-recognition trials, 15 trials of noise-only, and 10 trials of rest. The seven major task transitions between each epoch are indicated by arrows.

Figure 2.

Figure 2

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Overview of fMRI task. A) Word-in-noise trial procedure using a sparse-sampling imaging protocol (TR = 8.6 sec). B) Experimental run: white = quiet, hashed = speech-shaped noise only, increasingly dark shaded bars = words in noise at easier, baseline, and harder SNRs. Major task transitions occurred at seven time points indicated by arrows.

A central fixation cross was presented on a projection screen for all word-recognition trials. On these trials, participants were asked to report whether they understood the word they heard. One second following the word onset, a color change in the cross cued participants to respond that they (1) understood (i.e., reported word recognition), (2) were unsure of, or (3) did not understand the word. Button presses were recorded with an ergonomic fiber optic response box (Psychology Software Tools, Inc., Sharpsburg, PA). This task ensured that participants attended to the speech stimuli and avoided head motion artifact from oral responses.

Image acquisition

Structural and functional data were collected on a Siemens 3T TIM TRIO scanner with a 32-channel head coil. T1-weighted MPRAGE images were acquired in 160 slices [256×256 matrix, TR = 8.13 ms, TE = 3.7 ms, flip angle = 8°, slice thickness = 1.0 mm, gap = 0]. Using a sparse-sampling imaging protocol, 180 whole-brain single-shot echo-planar images were acquired during the task [36 slices, 64×64 matrix, TR = 8.6 s, TE = 35 ms, TA = 1647 ms, slice thickness = 3.0 mm, gap = 0, sequential order, GRAPPA parallel imaging, acceleration factor = 2]. Each functional image had 3.0 mm isomorphic voxels. Three volumes were collected at the beginning of the functional run to allow for the magnetization to stabilize and were excluded from analyses.

Analyses

Pupillometry

Pupillometry analyses are summarized here and described in our previous studies (Kuchinsky et al., 2013, 2014). The data were preprocessed (blink removal, linear interpolation, smoothing, within-trial standardization, and finally subtraction of the mean dilation in the immediately preceding baseline epoch in which only the background noise was presented for one second prior to word onset. Trials were excluded for which reaction times were more than 2.5 standard deviations greater than an individual’s mean (2.97% of trials) or for which 50% or more of the data had to be interpolated (11.0% of trials).

To obtain an estimate of overall listening-related effort, pupil size data from all other word recognition trials were analyzed (similar results were obtained when only examining trials for correctly identified words). Data from 0 to 2000 ms following word onset was analyzed with growth curve analysis (GCA). GCA is a mixed-effects modeling approach that characterizes the shape of the pupil response across time through the fitting of orthogonalized polynomial terms. The intercept captures the mean amplitude of the task-evoked pupil response relative to the noise-only baseline that immediately preceded word onset (Figure 3). Higher order, orthogonal polynomials independently capture linear (slope) and non-linear (quadratic, cubic) changes in the pupil response across time. A third-order polynomial function (intercept through cubic terms) provided a good fit of the shape of the pupil response across time (Figure 3; also see Kuchinsky et al., 2014). Individual differences in the mean pupil response size were obtained by extracting the random effect term that described the relative size of the intercept for each participant (yielding one value per individual) (Mirman, Dixon, & Magnuson, 2008).

Figure 3.

Figure 3

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Average pupil response following word onset from the pupillometry testing session. Standardized pupil size across time is plotted as a gray line. A third-order polynomial was fit to capture the nonlinear shape of the data (dashed black line). The average intercept of this model is shown via the black horizontal line.

Study-Specific Template

A study-specific structural template was used to ensure that all images were coregistered to a common coordinate space. Unified segmentation and diffeomorphic anatomical registration (DARTEL) of the individual structural images were performed in Statistical Parametric Mapping software, version 8 (SPM8; www.fil.ion.ucl.ac.uk/spm; Ashburner & Friston, 2005; Ashburner, 2007). The DARTEL procedure warps native-space gray matter images to a common coordinate space, providing good alignment across participants (Eckert, Keren, Roberts, Calhoun, & Harris, 2010). Realigned functional data were coregistered to each individual’s T1-weighted image using a mutual information algorithm. The DARTEL normalization parameters for each participant’s segmented gray matter image were then applied to their anatomically aligned functional data.

Functional Preprocessing

Each participant’s functional data were preprocessed using SPM8 to realign, unwarp, and co-register each functional image to the individual’s native-space structural scan prior to spatial normalization. An 8 mm Gaussian spatial smoothing kernel was used to ensure the data were normally distributed for statistical testing. The Linear Model of the Global Signal method (Macey, Macey, Kumar, & Harper, 2004) was used to residualize the global mean signal from the preprocessed images.

General Linear Model

A subject-level analysis was performed to estimate differences in activity across the experimental conditions for each individual. The model contained the following parameters: one representing the first trial following majors transitions between rest, noise-only, and word-recognition trial types, one representing the onset and duration of speech-shaped noise (excluding transitions), one specifying the onset and duration for words with a parameter indicating SNR condition (excluding transitions), and six nuisance regressors. Two of the six nuisance regressors coded for brain volumes that contained voxel or volume intensities that exceeded 2.5 standard deviations from the mean time series intensity (Vaden, Muftuler, & Hickok, 2010). The other four nuisance regressors summarized absolute and trial-to-trial 3D translational and rotational head motion based on the six realignment parameters generated in SPM (http://www.nitrc.org/projects/pythagoras; Kuchinsky et al., 2012; Wilke, 2011). The implicit baseline consisted of quiet rest epochs (excluding transition trials).

The models described above were convolved with the canonical hemodynamic response function and analyzed with SPM8 batch processing tools. Contrasts were derived in the subject-level analysis to examine differences in neural activity between conditions of interest. Group analyses were then performed to examine the consistency of contrast effects across participants. A vector of individual differences in the mean pupil response was included as a covariate at the group-level. Analyses were limited to voxels that fell within the whole-brain mask. The peak voxel threshold was each set at p < 0.001 uncorrected, with p < .05 family-wise error corrected (FWE) cluster extent threshold to correct for multiple comparisons (Friston, Worsley, Frackowiak, Mazziotta, & Evans, 1994) for all fMRI analyses.

Results

The neuroimaging speech recognition task was challenging, with participants on average reporting (via button press) that they understood 30% (SD = 18%) of the words. Behavioral responses were consistent across individuals: neither age nor hearing loss (PTA) were significantly related to reported recognition or log RT in any condition, all |r(27)| ≤ .31, p ≥ .10.

Response to Task Transitions

The response to task transitions was robust across a number of brain regions, despite comprising only seven trials (Figure 2B). The results are depicted in Figure 4 with peak coordinates listed in Table 1. Such task changes elicited increased activity in a variety of attention-related regions corresponding to distinct networks that included: 1) the dorsal attention network (DAN), which supports response selection and cognitive control (bilateral inferior parietal lobules and lateral prefrontal cortex); 2) the VAN, which supports rapid orienting to salient and unpredicted stimuli (right anterior insula, right temporoparietal junction); and 3) the cingulo-opercular network (CON), which is engaged to support task performance in challenging and error-prone conditions (bilateral anterior insula, anterior cingulate cortex; see Petersen & Posner, 2012, and Corbetta & Shulman, 2002, for reviews), as well as bilateral superior temporal gyrus/sulcus, extending into primary auditory cortex (Heschl’s gyrus, HG).

Figure 4.

Figure 4

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Lateral to medial gray-matter slices of the study-specific brain template depict the effect of task-set transitions (red) and individual differences in responsivity to such transitions with average pupil dilation (green; overlap in yellow). Larger mean pupil dilation was related to right HG activity during salient transitions between task blocks. Peak threshold p < .001, cluster size extent threshold p < .05 FWE

Table 1. Peak MNI Coordinates.

Peak coordinates of neural responses to task transitions and individual differences. Peak threshold p < .001, uncorrected, cluster extent threshold p < .05 FWE (at least 62 voxels). Italicized regions represent sub-peaks within the same cluster.

Contrast Cluster Peak t Voxels x y z
Task-set transitions
Precuneus 10.95 3288 −6 −66 45
  R superior temporal sulcus 10.13 52 −39 13
  R inferior parietal lobule 8.25 30 −67 47
R middle frontal sulcus 8.77 1371 37 48 10
  R inferior frontal gyrus 8.76 37 14 26
  R dorsolateral prefrontal cortex 8.57 40 36 26
L insula 6.73 90 −36 9 7
5.53 −30 15 2
R MCC 6.59 459 4 5 49
  L mid-cingulate cortex 6.44 −7 10 37
  R anterior cingulate cortex 6.22 3 27 34
Calcarine cortex 6.40 262 −3 −75 9
4.94 6 −91 10
4.29 −15 −66 13
L IFS 5.69 432 −50 20 21
  Frontal pole 5.50 −33 57 9
  L Precentral gyrus 5.48 −45 3 39
Larger pupil dilation with transitions
R Heschl’s gyrus 6.01 69 55 −18 10
4.61 42 −23 10
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Individual Differences

Individuals with a larger task-evoked pupil response (intercept term) exhibited less variability (standard error) on RTs to changes in ISIs in the CPT task, r(27) = .38, p = .04. This suggests that greater listening effort among these older adults was related to a more vigilant pattern of behavioral responding. Age and hearing loss were not correlated with mean pupil size: age, r(27) = −.15, p = .43; PTA, r(27) = .11, p = .57.

Individual variation in the mean amplitude of the task-evoked pupil response was entered as a subject-level covariate into GLM analyses of the functional imaging data. This analysis tested the hypothesis that variability in the response to task transitions is characterized by the overall size of the pupil response (Figure 4, Table 1). Individuals with larger pupil responses increasingly recruited regions beyond those engaged in response to salient changes. In particular, larger pupil dilation was associated with increased activity in right HG in response to transitions between task epochs (Figure 4, Table 1). There was no relationship between right HG activity and age, hearing loss, test ear, reported word recognition, or mean or variance (standard error) in log RT (for all five correlations: |r(27)| < .32, p > .09).

Discussion

The results demonstrated that older adults with hearing loss engaged robust neural activity in response to salient task transitions during listening (i.e., between quiet rest, rest in noise, and word recognition) throughout attention- and auditory-related regions. Individuals who exerted greater listening effort (in a pupillometry study) exhibited a more vigilant pattern of behavioral responses on a continuous performance test and more extensive transition-related activity in right primary auditory cortex.

Neural responses to major transitions during listening included auditory cortex as well as regions within the ventral, dorsal, and cingulo-opercular attention systems. These executive function networks have been shown to be engaged during challenging task conditions to detect and resolve competing information or task goals (Corbetta & Shulman, 2002; Petersen & Posner, 2012). Specifically, previous studies have observed VAN activity in response to detecting salient stimuli (Fox et al., 2005; Sridharan, Levitin, & Menon, 2008; Vaden et al., 2013), the DAN to support orientating (Dosenbach et al., 2006; Shulman et al., 2009), the CON to monitor and optimize task performance (Dosenbach et al., 2006; Vaden et al., 2013), and auditory cortex to novelty-triggered shifts in attention (Huang et al., 2012). As the transitions in the current study involved both a change in auditory input (quiet vs. noise vs. words-in-noise) and task goals (rest vs. word recognition), these events were likely to be perceived as highly salient. Indeed, results of previous research have demonstrated that the VAN supports orienting (Corbetta, Patel, & Shulman, 2008) to stimuli that are visually or auditorily ‘attention-grabbing’ (Kim, 2014; Nardo, Santangelo, & Macaluso, 2011), particularly when the stimuli are relevant to the task goals (Scalf, Ahn, Beck, & Lleras, 2014). Such interactions within the VAN (Kim, 2014) suggest that transition effects may be especially robust when salient stimuli signal changes in behavioral demands. Future investigations of vigilant listening may extend the results of the current study by examining the relative contribution of bottom-up and top-down salience to transition effects.

The pattern of results may have been also particularly widespread because older adults tend to engage distributed attention- and sensory-related activity (Cabeza et al., 2002; Eckert et al., 2008; Schneider-Garces et al., 2010; Wingfield & Grossman, 2006; Wong et al., 2009). Whether or not this highly distributed cortical engagement results from an age-related increase in compensatory functioning or reduction in the specificity of cortical function (dedifferentiation; see Wingfield & Grossman, 2006 for a review) remains to be evaluated.

There was a remarkable increase in the responsiveness of auditory cortex to transitions in the structure of the word recognition fMRI task. While auditory cortex has been shown to be responsive to sudden or unexpected auditory events (Huang et al., 2012), the current results indicate that the magnitude of response is impacted by individual differences in listening effort. Individuals with a larger pupil response exhibited more vigilant patterns of behavioral responding on a standardized test of continuous performance and recruited more extensive portions of the right primary auditory cortex. The absence of a significant relationship between right HG activity and speech recognition performance suggests that this upregulation may not necessarily be beneficial, but rather symptomatic of age-related dedifferentation within auditory cortex. Alternatively, the use of a self-reported measure of speech recognition and overall low reported recognition accuracy in this study potentially hindered our ability to observe such a correlation. In addition, given that age and hearing loss were not strongly related to listening effort, future studies designed to examine interactions between age, hearing loss, and effort on the responsiveness of auditory cortex to salient events may benefit from including a larger and more heterogeneous subject sample than in the current study.

Considering the observed widespread activity exhibited during transitions between task epochs, the impact of individual differences on vigilance was relatively focal. This pattern could be due to limitations of the current study sample and design. Given the previously noted negative relationship between aging and task-engagement (Jennings et al., 1988), the range of vigilant listening ability in our generally healthy older adult sample may be lower and more restricted than for younger adults or individuals with attention deficit disorders. In that context, it may seem counter-intuitive that we observed widespread cortical responses to salient task transitions in this older-adult sample compared to the more circumscribed VAN responses that are observed in younger adults using a similar modeling approach (Vaden et al., 2013). Loss of inhibition through reduced local GABAergic control (Endo, Yanagawa, Obata, & Isa, 2005) and/or reduced systems level control (Geerligs, Renken, Saliasi, Maurits, & Lorist, 2014) because of major fiber tract declines (Gong et al., 2009) may have contributed to a change in the pattern of salience responses in this sample of older adults. Additionally, prior exposure to the relatively unfamiliar spectrally shaped speech and the challenging SNRs in baseline and pupillometry sessions may have led participants to be aware of the demands of this difficult task and thus particularly task-engaged overall. Indeed, word recognition in the current fMRI task was reported to be very challenging (30% reported recognition overall, 39% in the easiest SNR, 23% in the hardest SNR). Likely as a result of the overall difficulty and the small (2 dB) SNR differences between conditions, no significant changes in neural activity were observed with SNR (not shown).

The infrequency and thus uncertainty of transitions in the current study potentially also maximized the salience of these trials and the size of the response. For example, older adults show larger task-switching costs in performance with increasing task uncertainty compared to younger adults (Kray, Li, & Lindenberger, 2002). This uncertainty may also be important to consider in event-related designs that involve abrupt changes in task goals on each trial. Providing external cues (Kray et al., 2002) or minimizing similarity across tasks (Wheatley, Scialfa, Boot, Kramer, & Alexander, 2012) may at least partially mitigate age-related variation in task-switching. Future studies that vary the frequency or order of task transitions within or across individuals may provide tests of these hypotheses.

Another design consideration of this study is that pupillometry and neuroimaging data were collected in sessions scheduled on different days. The delay between these measurements could have limited correlations between pupillary measures of within-subject variance in arousal state and word recognition performance within the imaging task. The strength of the relation between brain activity and pupillometry collected on separate days indicates that the results reflect stable trait or baseline differences in vigilance across older adults.

At least one previous simultaneous pupillometry and neuroimaging study has examined the neural responses associated with effort during speech recognition (Zekveld, Heslenfeld, Johnsrude, Versfeld, & Kramer, 2014). Despite the methodological differences, the results from both Zekveld et al.’s (2014) study with normal-hearing younger adults and the present study demonstrated a relationship between larger pupil size and increased auditory cortex engagement. This parallel pattern of results indicates that variation in listening effort may impact the extent to which individuals engage in auditory processing during challenging listening conditions.

Models of LC function describe optimal performance as occurring with a high degree of task-engagement (Aston-Jones & Cohen, 2005). However, the current study (in line with previous work) suggests that maintaining vigilant attention in the face of changing and challenging task demands comes at the cost of greater listening effort. Indeed, the negative consequences of vigilant listening on fatigue and quality of life for older adults and children with hearing loss are of increasing interest to researchers (Bess & Hornsby, 2014; Hornsby, 2013; McGarrigle et al., 2014). Understanding the impact of individual differences in listening effort may be key to ensuring that these individuals optimally benefit from interventions designed to enhance communication.

Acknowledgments

We thank the study participants for their contribution. This work was supported in part by a Hearing Health Foundation Centurion Clinical Research Award, the National Institute on Deafness and other Communication Disorders (P50 DC00422), Indiana University, and was conducted in a Medical University of South Carolina facility constructed with support from Research Facilities Improvement Program (C06 RR14516) from the National Center for Research Resources, NIH. This project was also supported by the South Carolina Clinical and Translational (SCTR) Institute, with an academic home at the Medical University of South Carolina, NIH/NCRR (UL1 RR029882).

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