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RESEARCH ARTICLE

Age-Related Differences in Auditory Cortex
Activity During Spoken Word Recognition

Chad S. Rogers1

Kristin J. Van Engen3

, Michael S. Jones2

, Sarah McConkey2
, Mitchell S. Sommers3, and Jonathan E. Peelle2

, Brent Spehar2

a n o p e n a c c e s s

j o u r n a l

1Department of Psychology, Union College, Schenectady, NY, USA
2Department of Otolaryngology, Washington University in St. Louis, St. Louis, MO, USA
3Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA

Keywords: speech perception, cognitive aging, speech production

ABSTRACT

Understanding spoken words requires the rapid matching of a complex acoustic stimulus
with stored lexical representations. The degree to which brain networks supporting spoken
word recognition are affected by adult aging remains poorly understood. In the current study
we used fMRI to measure the brain responses to spoken words in two conditions: an attentive
listening condition, in which no response was required, and a repetition task. Listeners were
29 young adults (aged 19–30 years) and 32 older adults (aged 65–81 years) without self-reported
hearing difficulty. We found largely similar patterns of activity during word perception for
both young and older adults, centered on the bilateral superior temporal gyrus. As expected,
the repetition condition resulted in significantly more activity in areas related to motor planning
and execution (including the premotor cortex and supplemental motor area) compared to
the attentive listening condition. Importantly, however, older adults showed significantly
less activity in probabilistically defined auditory cortex than young adults when listening
to individual words in both the attentive listening and repetition tasks. Age differences in
auditory cortex activity were seen selectively for words (no age differences were present for
1-channel vocoded speech, used as a control condition), and could not be easily explained
by accuracy on the task, movement in the scanner, or hearing sensitivity (available on a subset
of participants). These findings indicate largely similar patterns of brain activity for young and
older adults when listening to words in quiet, but suggest less recruitment of auditory cortex
by the older adults.

INTRODUCTION
Understanding spoken words requires mapping complex acoustic signals to a listener’s stored
lexical representations. Evidence from neuropsychology and cognitive neuroscience provides
increasingly converging evidence about the roles of the bilateral temporal cortex (particularly
the superior temporal gyrus and the middle temporal gyrus) in processing speech acoustics
and recognizing single words (Binder et al., 2000; Hickok & Poeppel, 2007; Peelle, Johnsrude,
& Davis, 2010). However, the degree to which the networks supporting spoken word recognition
change over our lifetime remains unclear. The goals of the current study were to test whether
young and older adults relied on different brain networks during successful spoken word recog-
nition, and whether any age differences were related to the specific task.

Citation: Rogers, C. S., Jones, M. S.,
McConkey, S., Spehar, B., Van Engen,
K. J., Sommers, M. S., & Peelle, J. E.
(2020). Age-related differences in
auditory cortex activity during spoken
word recognition. Neurobiology of
Language, 1(4), 452–473. https://doi.
org/10.1162/nol_a_00021

DOI:
https://doi.org/10.1162/nol_a_00021

Supporting Information:
https://doi.org/10.1162/nol_a_00021

Received: 5 March 2020
Accepted: 11 August 2020

Competing Interests: The authors have
declared that no competing interests
exist.

Corresponding Author:
Chad S. Rogers
rogersc@union.edu

Handling Editor:
Ingrid Johnsrude

Copyright: © 2020 Massachusetts
Institute of Technology. Published
under a Creative Commons Attribution
4.0 International (CC BY 4.0) license.

The MIT Press

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Spoken word recognition in older adults

Important themes when considering older adults’ language processing include the degree to
which linguistic processing is preserved, and whether older adults may adopt different strategies
when understanding language compared to young adults (Peelle, 2019; Wingfield & Stine-
Morrow, 2000). Particularly important for spoken word recognition is that adult aging frequently
brings changes to both hearing sensitivity (Peelle & Wingfield, 2016) and cognitive ability (Park
et al., 2002). Thus, it is not surprising that older adults’ spoken word perception differs from that
of young adults, particularly in the presence of background noise (Humes, 1996). Older adults
tend to take longer to recognize words (Lash, Rogers, Zoller, & Wingfield, 2013; Wingfield,
Aberdeen, & Stine, 1991), make more recognition errors than young adults, and show increased
sensitivity to factors such as the number of phonological neighbors (competitors) associated with
a given target word (Sommers & Danielson, 1999). An open question centers on the brain
networks on which older adults rely during spoken word recognition. Of particular interest is
whether additional regions may be recruited to support successful recognition, compared to
those engaged by young adults.

A number of studies have investigated neural activity during older adults’ speech processing in
noise or other acoustic degradation, using an assortment of tasks and testing participants with
different levels of hearing (Bilodeau-Mercure, Lortie, Sato, Guitton, & Tremblay, 2015; Hwang,
Li, Wu, Chen, & Liu, 2007; Manan, Yusoff, Franz, & Mukari, 2017; Manan, Franz, Yusoff, &
Mukari, 2015; Wong et al., 2009). Harris, Dubno, Keren, Ahlstrom, and Eckert (2009), for exam-
ple, examined spoken word recognition in young and older adults. They varied the intelligibility
of the target items using low-pass filtering of the acoustic signal. During scanning, participants
were asked to repeat back the word they heard. The authors found increased activity in regions
associated with word processing, including the auditory cortex and the premotor cortex, when
words were more intelligible; these intelligibility-related changes did not statistically differ
between young and older adults. Older adults did show more activation in the anterior cingulate
cortex and the supplemental motor area than the young adults did, suggesting a possible increase
in top-down executive control.

Age differences in speech understanding have also been studied in the context of sentence
comprehension. One common finding is that during successful sentence processing, older adults
show additional activity compared to young adults (e.g., in contralateral homologs to regions
seen in young adults, or in regions beyond the network activated by young adults; Peelle,
Troiani, Wingfield, & Grossman, 2010; Tyler et al., 2010). These findings have been interpreted
in a compensation framework in which older adults are less efficient using a core speech network
and need to recruit additional regions to support successful comprehension (Wingfield &
Grossman, 2006). However, at least some of this additional activity has been shown to be related
to the tasks performed by participants in the scanner, which frequently contain metalinguistic
decisions not required during everyday conversation (Davis, Zhuang, Wright, & Tyler, 2014).
Thus, it may be that core language computations are well-preserved in aging (Campbell et al.,
2016; Shafto & Tyler, 2014).

The role of executive attention in older adults’ spoken word recognition has also been of sig-
nificant interest. Listening to speech that is acoustically degraded can result in perception errors,
after which listeners must re-engage attention systems to support successful listening. The cingulo-
opercular network, an executive attention network (Neta et al., 2015; Power & Petersen, 2013),
shows increased activity following perception errors (similar to error-related activity in other
domains). Crucially, when listening to spoken words in background noise, increased cingulo-
opercular activity following one trial is associated with recognition success on the following trial
(Vaden et al., 2016; Vaden et al., 2013), consistent with a role in maintaining task-related attention
(Eckert et al., 2009).

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Spoken word recognition in older adults

An important challenge when considering the performance of listeners with hearing loss is
that words may not be equally intelligible to all listeners. A common measure of accuracy in
spoken word recognition is to ask listeners to repeat each word after hearing it; however, this type
of task requires motor responses, which may obscure activations related to speech perception
and increase participant motion in the scanner (Gracco, Tremblay, & Pike, 2005). In addition,
differences in the brain regions coordinating speech production in older adults (Bilodeau-
Mercure & Tremblay, 2016; Tremblay, Sato, & Deschamps, 2017) may interfere with clear
measurements of activity during perception and recognition. The degree to which motor effects
resulting from word repetition may obscure activity related to speech perception is unclear. In
sentence processing tasks, task effects can be significant (Davis et al., 2014), and if not accounted
for may obscure what are actually consistent patterns of language-related activity across the life-
span (Campbell et al., 2016).

In the current study we investigated spoken word processing in young and older adult listeners
in the absence of background noise. We compared paradigms requiring words to be repeated
with “attentive listening” (no motor response required). Our interest is, first, whether age differ-
ences exist in the brain networks supporting spoken word recognition, and second, whether these
differences are affected by the choice of task. Thus, our primary analyses will focus on activity
seen for words (greater than noise) in the experimental conditions.

The influence of psycholinguistic factors on spoken word recognition has long been appre-
ciated. In a secondary set of analyses, we will investigate whether word frequency or phono-
logical neighborhood density modulate activity during spoken word recognition. Although
behavioral and electrophysiological studies suggest that high frequency words are processed
more quickly than low frequency words, the degree to which this might be captured in fMRI is
unclear. Similarly, although neighborhood density effects are widely reported in behavioral
studies (with words from dense neighborhoods typically being more difficult to process), the
degree to which lexical competition effects may differ with age is unclear.

MATERIALS AND METHODS

Stimuli, data, and analysis scripts are available from https://osf.io/vmzag/.

Participants

We recruited two groups of participants (young and older adults) for this study. The young
adults were 29 self-reported healthy, right-handed adults, aged 19–30 years (M = 23.8, SD =
2.9, 19 female), and were recruited via the Washington University in St. Louis Department of
Psychological and Brain Sciences Subject Pool. Older adult participants were 32 self-reported
healthy, right-handed adults, aged 65–81 years (M = 71.0, SD = 5.0, 17 female). All participants
self-reported themselves to be native speakers of American English with no history of neurolog-
ical difficulty, and with normal hearing (and no history of a diagnosed hearing problem).
Participants were compensated for their participation, and all provided informed consent com-
mensurate with practices approved by the Washington University in St. Louis Institutional
Review Board.

Audiograms were collected on a subset of eight young and nine older participants using
pure-tone audiometry (Figure 1a). We summarized hearing ability using a better-ear pure tone
average (PTA) at 1, 2, and 4 kHz. PTAs in participants’ better hearing ears ranged from −3.33
to 8.33 dB HL in young adults (M = 2.92, SD = 4.15), and 8.33 to 23.3 dB HL in older adults
(M = 23.3, SD = 9.17).

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Spoken word recognition in older adults

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Figure 1.
Experiment overview. (a) Audiograms for the subset of participants on whom hearing was available for left and right ears. Individual
participants are shown in thin lines, group means in thick lines. (b) Frequency of occurrence and phonological neighborhood density for the
240 experimental items. (c) Task design for attentive listening and word repetition tasks. (d) Behavioral accuracy for the repetition condition for
young and older adults. HAL = Hyperspace Analogue to Language, EPI = echo planar imaging.

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Materials

Stimuli for this study were 375 monosyllabic consonant-vowel-consonant words. The auditory
stimuli were recorded at 48 kHz using a 16-bit digital-to-analog converter with an Audio
Technica 2035 microphone in a quiet room. Words were spoken by a female speaker with
a standard American dialect. Root-mean-square amplitude of the stimuli was equated.

Out of the full set of words, 75 words were vocoded using a single channel with white noise as
a carrier signal (Shannon, Zeng, Kamath, Wygonski, & Ekelid, 1995) using jp_vocode.m from
http://github.com/jpeelle/jp_matlab. These stimuli were used for an unintelligible baseline
“noise” condition. The remaining 300 words were divided into five lists of 60 words, using
MATCH software (Van Casteren & Davis, 2007), and were balanced for word frequency (as
measured by the log of the Hyperspace Analogue to Language dataset), orthographic length,
concreteness (Brysbaert, Warriner, & Kuperman, 2014), and familiarity (Balota et al., 2007).
The distribution of word frequency and phonological neighborhood density are shown in
Figure 1b.

One of these lists was combined with 15 of the noise vocoded words and used for word
repetition task practice outside of the scanner. The remaining four lists of 60 words served as

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Spoken word recognition in older adults

the critical items inside the scanner, with half of the lists used for attentive listening (120 total
words) and the other half for word repetition (120 total words). Word lists were counterba-
lanced such that each word was presented in both “listen” and “repeat” conditions across
participants.

Procedure

Prior to scanning, participants were taken to a quiet room. (The room was not sound isolated and
low frequency noise from the building heating, ventilation, and air conditioning system was typi-
cally present.) During that time participants provided informed consent, completed demographic
questionnaires, and a subset had their hearing tested using a calibrated Maico MA40 portable
audiometer (Maico Diagnostics, Inc., Eden Prairie MN) by an audiologist-trained researcher.

Participants were then instructed for the two tasks they would perform in the scanner:
attentive listening and word repetition. During attentive listening, participants were asked to
stay alert, still, and keep their eyes focused on a fixation cross while listening to a sequence of
auditory sounds, including words, silence, and noise (single-channel noise vocoded words).
During word repetition, participants were asked to do the same as in attentive listening, with
the addition of repeating the word they just heard aloud. Participants were instructed to repeat
the words following the volume acquisition after each word (Figure 1c). Participants were told
to give their best guess if they could not understand a word. Participants practiced a simulation
of the word repetition task until the experimenter was confident that the participant understood
the pacing and the nature of the task. Sound levels were adjusted to achieve audible presen-
tations at the beginning of the study and thereafter not adjusted.

Functional MRI scanning took place over the course of four scanning blocks, where partic-
ipants alternated between blocks of attentive listening and word repetition (Figure 1c). The
order of blocks was counterbalanced such that participants were equally likely to begin with
a word repetition or an attentive listening block. During word repetition, participants’ spoken
responses were recorded using an in-bore Fibersound optical microphone. These responses
were scored for accuracy offline by a research assistant (Figure 1d).

MRI Data Acquisition and Processing

The MRI data collected in this study are available from https://openneuro.org/datasets/ds002382
(Poldrack et al., 2013). MRI data were acquired using a Siemens Prisma scanner (Siemens
Medical Systems) at 3 T equipped with a 32-channel head coil. Scan sequences began with a
T1-weighted structural volume using an MPRAGE sequence (repetition time [TR] = 2.4 s, echo
time [TE] = 2.2 ms, flip angle = 8°, 300 × 320 matrix, voxel size = 0.8 mm isotropic). Blood
oxygenation level-dependent fMRI images were acquired using a multiband echo planar imag-
ing sequence (Feinberg et al., 2010; TR = 3.07 s, TA = 0.770 s, TE = 37 ms, flip angle = 37°, voxel
size = 2 mm isotropic, multiband factor = 8). (The flip angle was suboptimal due to an error
setting up the sequences; although discovered partway through the study, we left it unchanged
to maintain consistent data quality. With a TR of ~3 s we would expect a better signal-to-noise ratio
with a flip angle of 90°.) We used a sparse imaging design in which there was a 2.3 s delay between
scanning acquisitions and the TR was longer than the acquisition time to allow for minimal scanning
noise during stimulus presentation and audio recording of participant responses (Edmister,
Talavage, Ledden, & Weisskoff, 1999; Hall et al., 1999).

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Spoken word recognition in older adults

Analysis of the MRI data was performed using Automatic Analysis version 5.4.0 (Cusack et al.,
2015; RRID:SCR_003560), which scripted a combination of SPM12 ( Wellcome Trust Centre for
Neuroimaging) version 7487 (RRID:SCR_007037) and FMRIB Software Library (FSL; FMRIB
Analysis Group; Jenkinson, Beckmann, Behrens, Woolrich, & Smith, 2012) version 6.0.1
(RRID:SCR_002823).

Data were realigned using rigid-body image registration, and functional data were coregis-
tered with the bias-corrected T1-weighted structural image. Spatial and functional images were
normalized to MNI space using a unified segmentation approach (Ashburner & Friston, 2005),
and resampled to 2 mm. Finally, the functional data were smoothed using an 8 mm full width at
half maximum Gaussian kernel.

For the attentive listening condition, we did not have measures of accuracy, so we analyzed
all trials. For the repetition condition, we analyzed only trials associated with correct responses.
For both tasks, we modeled the noise condition in addition to words. Finally, we included three
parametric modulators for word events: word frequency, phonological neighborhood density,
and their interaction. To avoid order effects (Mumford, Poline, & Poldrack, 2015), these were not
orthogonalized.

Motion effects were of particular importance given that participants were speaking during the
repetition condition. To mitigate the effects of motion, we used a thresholding approach in
which high motion frames were individually modeled for each subject using a delta function
in the general linear model (see, e.g., Siegel et al., 2014). Motion was quantified using framewise
displacement (FD), calculated from the six motion parameters estimated during realignment,
assuming the head is a sphere having a radius of 50 mm (Power, Barnes, Snyder, Schlaggar,
& Petersen, 2012). We then chose an FD threshold (0.561) that we used for all participants.
Our rationale was that some participants move more, and thus produce worse data; we therefore
wanted to use a single threshold for all participants, resulting in more data exclusion from high-
motion participants. This threshold resulted in 2.2–19.4% (M = 6.21, SD = 4.45) data exclusion
for the young adults and 2.8–58.4% (M = 22.6, SD = 15.3) data exclusion for the older adults. For
each frame exceeding this threshold, we added a column to that participant’s design matrix con-
sisting of a delta function at the time point in question, which effectively excludes the variance of
that frame from the model.

Contrast images from single subject analyses were analyzed at the second level using permu-
tation testing (FSL randomise; 5,000 permutations; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL), with
a cluster-forming threshold of p < 0.001 (uncorrected) and results corrected for multiple com- parisons based on cluster extent ( p < 0.05). Images (contrast images and unthresholded t maps) are available from https://identifiers.org/neurovault.collection:6735 (Gorgolewski et al., 2015). Anatomical localization was performed using converging evidence from author experience (Devlin & Poldrack, 2007) viewing statistical maps overlaid in MRIcroGL (Rorden & Brett, 2000), supplemented by atlas labels (Tzourio-Mazoyer et al., 2002). For region of interest (ROI) analysis of primary auditory cortex, we used probabilistic maps based on postmortem human histological staining (Morosan et al., 2001), available in the SPM Anatomy toolbox (Eickhoff et al., 2005; RRID:SCR_013273). We created a binary mask for re- gions Te1.0 and Te1.1 and then extracted parameter estimates for noise and word contrasts for the attentive listening and repetition conditions from each participant’s first-level analyses by averaging over all voxels in each ROI (left auditory, right auditory). Outputs from analysis stages used for quality control are available from https://osf.io/vmzag/ in the aa_report folder. Neurobiology of Language 457 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u n o / l / l a r t i c e - p d f / / / / 1 4 4 5 2 1 8 6 7 7 6 9 n o _ a _ 0 0 0 2 1 p d / . l f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Spoken word recognition in older adults RESULTS Behavioral Data We analyzed the accuracy data using a linear mixed effects analysis, implemented using the lme4 and lmerTest packages in R version 3.6.2 (Bates, Mächler, Bolker, & Walker, 2015; Kuznetsova, Brockhoff, & Christensen, 2017; RRID:SCR_001905). Because trial-level accuracy data was binary, we used logistic regression. We first tested for age differences using a model that included age group as a fixed factor and subject as a random factor: m0 <- glmer(accuracy ~ age_group + (1 | subject), data = df, family = "binomial"> older adults

Region
Left superior temporal gyrus

Left Heschl’s gyrus

Size (μl)
8,472

Right superior temporal gyrus

3,400

Right superior temporal gyrus

t score
6.29

4.18

4.08

4.84

3.37

x
−62

−40

−42

Coordinates
y
−16

−30

−24

−16

z
8

DISCUSSION

We used fMRI to examine neural activity during spoken word recognition in quiet for young
and older adult listeners. In both ROI and whole-brain analyses, we found converging evi-
dence for reduced activity in the auditory cortex for the older adults. The age differences in
auditory cortex activation were present in both the attentive listening task and the word rep-
etition task: Although the repetition task resulted in more widespread activation overall, pat-
terns of age-related differences in the auditory cortex were comparable.

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Figure 4. Whole-brain activity for the repetition condition (correct responses only). Top: Unthresholded parameter estimates. Middle:
Unthresholded t maps. Bottom: Thresholded t maps ( p < 0.05, cluster corrected). White ovals highlight left and right auditory cortex. Neurobiology of Language 463 Spoken word recognition in older adults Table 6. Peak activations for repetition condition greater than noise, young adults Region Left superior temporal gyrus Size (μl) 581,432 t score 14 Left postcentral gyrus Left postcentral gyrus Right postcentral gyrus Left postcentral gyrus Left putamen Right superior temporal gyrus Supplemental motor area Right superior temporal gyrus Right superior temporal gyrus Right putamen Right precentral gyrus Left paracentral lobule Left Heschl’s gyrus Left inferior frontal gyrus Left inferior temporal gyrus Left insula Left inferior parietal cortex Right insula Dorsal anterior cingulate 13.9 13.8 13.7 13.4 12.9 12.5 12.4 12.3 11.4 10.6 10.6 10.5 9.89 9.58 9.13 8.84 8.41 8.19 8.11 x −60 −42 −48 44 −52 −24 56 0 52 66 28 20 −18 −36 −52 −44 −32 −38 34 −8 Coordinates y −14 −16 −14 −12 −8 0 −10 0 −16 −20 0 −28 −30 −30 10 −56 22 −36 20 12 z 4 38 40 36 30 4 4 58 6 2 −4 60 60 14 22 −10 4 42 6 38 There are a number of possible explanations for older adults’ reduced activity during spo- ken word recognition. One possibility is that age differences in intelligibility might play a role. Intelligible speech is associated with increased activity in a broad network of frontal and tem- poral regions (Davis & Johnsrude, 2003; Kuchinsky et al., 2012), and in prior studies of older adults, intelligibility has correlated with auditory cortex activity (Harris et al., 2009). We re- stricted our analyses to correct responses in the repetition condition, and found no statistical support for a relationship between intelligibility and auditory cortex activation (although nu- merically, participants with better accuracy showed more activity than participants with worse accuracy). The fact that young and older adults showed comparable activity in the auditory cortex during noise trials, with age differences emerging for word recognition trials, is significant. Group differences in activation could be driven not only by neural processing, but also by such factors as neurovascular coupling, goodness-of-fit of a canonical hemodynamic re- sponse, or movement within the scanner—in other words, artifacts that might differentially Neurobiology of Language 464 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u n o / l / l a r t i c e - p d f / / / / 1 4 4 5 2 1 8 6 7 7 6 9 n o _ a _ 0 0 0 2 1 p d / . l f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Spoken word recognition in older adults Table 7. Peak activations for repetition condition greater than noise, older adults Region Left postcentral gyrus Size (μl) 278,528 t score 10.1 9.49 9.35 9.26 9.19 8.14 8.01 7.84 7.68 7.51 7.39 6.87 6.76 6.72 6.48 6.44 6.44 6.32 6.25 5.97 4.13 3.98 3.6 3.56 3.41 3.35 3.33 3.3 3.04 3,784 Supplemental motor area Right postcentral gyrus Left postcentral gyrus Right superior temporal gyrus Right postcentral gyrus Left superior temporal gyrus Left superior temporal gyrus Left superior parietal cortex Left inferior frontal gyrus Left superior temporal gyrus Left precentral gyrus Right insula Right putamen Left insula Left postcentral Left caudate Right insula Left inferior parietal cortex Fornix Left thalamus Superior cerebellar pedunculus Left superior cerebellar pedunculus Right superior cerebellar pedunculus Right thalamus Right thalamus Left superior cerebellar pedunculus Right thalamus Left superior cerebellar pedunculus Neurobiology of Language x −44 −2 42 −56 64 56 −60 −44 −26 −44 −62 20 32 18 −30 −18 −16 36 −46 6 −12 0 −4 2 12 16 −6 14 −10 Coordinates y −14 4 −12 −4 −18 −4 −14 −22 −66 8 −28 −28 26 16 24 −30 14 18 −32 0 −18 −24 −28 −14 −20 −18 −34 −18 −34 z 34 56 36 24 0 28 2 10 52 26 4 60 0 0 4 58 8 8 40 6 0 2 −14 −8 2 −2 −2 8 −20 465 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u n o / l / l a r t i c e - p d f / / / / 1 4 4 5 2 1 8 6 7 7 6 9 n o _ a _ 0 0 0 2 1 p d . / l f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Spoken word recognition in older adults Table 8. Peak activations for repetition condition greater than noise, young > older adults

Size (μl)
5,224

t score
5.22

Region
Right Heschl’s gyrus

Right Heschl’s gyrus

Right superior temporal sulcus

Right superior temporal gyrus

Left Heschl’s gyrus

4,600

Left superior temporal gyrus

Left Heschl’s gyrus

Left superior temporal gyrus

Left postcentral gyrus

4,248

Left postcentral gyrus

4.99

4.18

4.08

3.79

3.62

4.92

4.17

3.78

3.66

3.47

3.29

3.19

5.26

5.21

5.12

3.88

x
48

−36

−64

−62

−60

−46

−62

−52

−50

−46

−42

−54

Coordinates
y
−20

−26

−30

−26

−8

−34

−30

−18

−32

−18

−24

−38

−16

−14

−16

−18

−8

z
10

−4

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impact model parameter estimates in young and older adults but are not of theoretical interest
in this context. Although impossible to completely rule out, the selective age differences for
speech (but not noise) are consistent with a condition-specific—and thus we argue, neural—
interpretation.

Recent evidence suggests age-related changes in temporal sensitivity in auditory regions
can be detected with fMRI (Erb, Schmitt, & Obleser, 2020). Although our current stimuli do
not allow us to explore specific acoustic features, one possibility is that the age-related differ-
ences in auditory activity we observed reflect well-known changes in auditory cortical pro-
cessing that occur in normal aging (Peelle & Wingfield, 2016). Given the increased
acoustic complexity of the words relative to noise, acoustic processing differences might drive
overall response differences. Such changes may also reflect decreased stimulation as a result of
hearing loss; we had insufficient data to rule out this possibility. It is important to note that we
cannot completely rule out audibility effects. Even though we limited our responses to correct
identification trials, specific acoustic features may still have been less audible for the older
adults. It remains an open question whether varying the presentation level of the stimuli would
change the age effects we observed.

Age differences in auditory processing are not the only explanation for our results. The au-
ditory cortex is positioned in a hierarchy of speech processing regions that include both

Neurobiology of Language

466

Spoken word recognition in older adults

Figure 5. Whole-brain activity for the repetition condition > attentive listening. Top: Unthresholded parameter estimates. Middle:
Unthresholded t maps. Bottom: Thresholded t maps ( p < 0.05, cluster corrected). White ovals highlight left and right auditory cortex. There were no significant differences between young and older adults in the repetition > listening contrast.

ascending and descending projections (Davis & Johnsrude, 2007; Peelle, Johnsrude, et al.,
2010). The auditory cortex not only is sensitive to changes in acoustic information, but also
reflects top-down effects of expectation and prediction (Signoret, Johnsrude, Classon, &
Rudner, 2018; Sohoglu, Peelle, Carlyon, & Davis, 2012; Wild et al., 2012). Thus, the observed
age differences in the auditory cortex may reflect differential top-down modulation of auditory
activity in young and older adult listeners.

Indeed, prior to conducting this study, we expected to observe increased activity (e.g., in
the prefrontal cortex) for older adults relative to young adults, reflecting top-down compensa-
tion for reduced auditory sensitivity. Such activity would be consistent with increased cogni-
tive demand during speech perception in listeners with hearing loss or other acoustic
challenges (Peelle, 2018; Pichora-Fuller et al., 2016). Although we were somewhat surprised
not to see this, in retrospect, perhaps it would be expected. The stimuli in the current study
were presented in quiet, and thus may not have challenged perception sufficiently to robustly
engage frontal brain networks. We conclude that during perception of acoustically clear
words, older adults do not seem to require additional resources from the frontal cortex;
whether this changes with increasing speech demands (either acoustic or linguistic) remains
an open question.

We did not observe significant effects of either word frequency or phonological neighbor-
hood density on activity during spoken word recognition. These results stand in contrast to prior
studies showing frequency effects in visual word perception in fMRI (Hauk, Davis, &
Pulvermüller, 2008; Kronbichler et al., 2004), and word frequency effects in electrophysiolog-
ical responses (Embick, Hackl, Schaeffer, Kelepir, & Marantz, 2001). Prior fMRI studies of lexical

Neurobiology of Language

467

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Spoken word recognition in older adults

Table 9.

Peak activations for word recognition in the repetition condition greater than listening condition, young adults

Size (μl)
311,408

Region
Left postcentral gyrus

Right postcentral gyrus

Left putamen

Supplemental motor area

Right postcentral gyrus

Left Heschl’s gyrus

Right superior temporal gyrus

Left postcentral gyrus

Right Heschl’s gyrus

Right putamen

Left insula

Left inferior frontal gyrus

Right insula

Left superior temporal sulcus

Anterior cingulate

Right superior temporal gyrus

Left inferior parietal cortex

Right calcarine sulcus

Right cerebellum

t score
13.8

13.8

10.4

9.97

9.81

9.36

9.11

9.06

8.96

8.91

7.16

7.13

7.11

6.23

5.61

5.6

5.47

5.31

x
−44

−24

−36

−18

−32

−46

−52

−8

−38

Coordinates
y
−14

−12

−2

−28

−32

−20

−30

−26

−42

−24

−36

−66

−60

z
36

−4

−22

competition (including phonological neighborhood density) have been mixed, with some
studies finding effects (Zhuang, Randall, Stamatakis, Marslen-Wilson, & Tyler, 2011) and others
not (Binder et al., 2003). It could be that a wider range of frequency or density or a greater num-
ber of stimuli would be needed to identify such effects.

Finally, we found largely comparable age differences in the attentive listening and repetition
conditions in the auditory cortex. The similarity of the results suggests that using a repetition task
may be a reasonable choice in studies of spoken word recognition: Although repetition tasks
necessarily engage regions related to articulation and hearing one’s own voice, in our data these
were not differentially affected by age. An advantage of using a repetition task, of course, is that trial-
by-trial accuracy measures can be obtained, which are frequently useful. It is worth noting that our
finding of comparable activity in young and older adults for attentive listening and repetition tasks
may not generalize to other stimuli or tasks (Campbell et al., 2016; Davis et al., 2014).

A significant limitation of our current study is that we only collected hearing sensitivity data
on a minority of our participants. Thus, although we saw a trend toward poorer hearing being
associated with reduced auditory cortex activation, it is challenging to draw any firm conclu-
sions regarding the relationship between hearing sensitivity and brain activity. Prior studies
using sentence-level materials have found relationships between hearing sensitivity and brain

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Table 10. Peak activations for word recognition in the repetition condition greater than listening condition, older adults

Region
Left postcentral gyrus

Size (μl)
238,712

t score
10.21

Right postcentral gyrus

Supplemental motor area

Right postcentral gyrus

Left Heschl’s gyrus

Left superior parietal cortex

Left postcentral gyrus

Right precentral gyrus

Left precentral gyrus

Right putamen

Right caudate

Right insula

Left inferior parietal cortex

Anterior cingulate

Left postcentral gyrus

Left insula

Right superior parietal cortex

Left insula

Left caudate

Left precentral gyrus

Right superior temporal gyrus

4,632

Right Heschl’s gyrus

Right superior temporal gyrus

Right posterior insula

9.04

7.56

7.43

6.45

6.43

6.03

6.02

5.88

5.84

5.73

5.69

5.49

5.44

5.38

5.29

5.28

5.16

5.13

4.12

4.11

4.1

4.09

3.14

x
−44

−2

−42

−28

−18

−46

−44

−6

−26

−46

−32

−14

−32

Coordinates
y
−14

−12

−4

−24

−64

−30

−28

−32

−40

−68

−2

−22

−24

−16

−24

z
34

−2

activity in both young (Lee et al., 2018) and older (Peelle, Troiani, Grossman, & Wingfield,
2011) adults. Future investigations with a larger sample of participants with hearing data will
be needed to further explore the effects of hearing in spoken word recognition.

From a broader perspective, the link between spoken word recognition and everyday commu-
nication is not always straightforward. Much of our everyday communication occurs in the context
of semantically meaningful, coherent sentences, frequently with the added availability of visual

Neurobiology of Language

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Spoken word recognition in older adults

speech and gesture cues. Given potential age differences in reliance on many of these cues—
including older adults’ seemingly greater reliance on semantic context (Rogers, 2016; Rogers,
Jacoby, & Sommers, 2012; Wingfield & Lindfield, 1995)—it seems likely that our findings using
isolated spoken words cannot be extrapolated to richer naturalistic settings.

In summary, we observed largely overlapping brain regions supporting spoken word recogni-
tion in young and older adults in the absence of background noise. Older adults showed less
activity than young adults in the auditory cortex when listening to words, but not noise. These
patterns of age difference were present regardless of the task (attentive listening vs. repetition).

ACKNOWLEDGMENTS

Research reported here was funded by grant R01 DC014281 from the US National Institutes of
Health. The multiband echo planar imaging sequence was provided by the University of
Minnesota Center for Magnetic Resonance Research. We are grateful to Linda Hood for assis-
tance with data collection, and to Henry Greenstein, Ben Muller, Olivia Murray, Connor
Perkins, and Tracy Zhang for help with data scoring.

FUNDING INFORMATION

Jonathan E. Peelle, National Institute on Deafness and Other Communication Disorders (http://
dx.doi.org/10.13039/100000055), Award ID: R01 DC014281.

AUTHOR CONTRIBUTIONS

Chad S. Rogers: Conceptualization: Equal; Data curation: Equal; Investigation: Equal; Project
administration: Equal; Supervision: Supporting; Validation: Equal; Writing–Review & Editing:
Equal. Michael S. Jones: Formal analysis: Lead; Methodology: Equal; Software: Lead; Validation:
Lead; Writing–Review & Editing: Equal. Sarah McConkey: Investigation: Equal; Project administra-
tion: Equal; Writing–Review & Editing: Equal. Brent Spehar: Conceptualization: Equal;
Investigation: Supporting; Resources: Supporting; Writing–Review & Editing: Equal. Kristin J. Van
Engen: Conceptualization: Equal; Funding acquisition: Supporting; Project administration: Equal;
Writing–Review & Editing: Equal. Mitchell S. Sommers: Conceptualization: Equal; Funding acqui-
sition: Supporting; Project administration: Supporting; Writing–Review & Editing: Equal. Jonathan
E. Peelle: Conceptualization: Equal; Data curation: Equal; Formal analysis: Equal; Funding acqui-
sition: Lead; Project administration: Equal; Supervision: Lead; Visualization: Lead; Writing–
Original Draft: Lead; Writing–Review & Editing: Equal.

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