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ORIGINAL RESEARCH
published: 15 April 2020
doi: 10.3389/fnins.2020.00362
Edited by:
Jonathan B. Fritz,
New York University, United States
Reviewed by:
Paul Hinckley Delano,
University of Chile, Chile
Jaakko Kauramäki,
University of Helsinki, Finland
John J. Guinan,
Massachusetts Eye & Ear Infirmary
and Harvard Medical School,
United States
*Correspondence:
Lars Riecke
l.riecke@maastrichtuniversity.nl
Specialty section:
This article was submitted to
Auditory Cognitive Neuroscience,
a section of the journal
Frontiers in Neuroscience
Received: 06 November 2019
Accepted: 24 March 2020
Published: 15 April 2020
Citation:
Riecke L, Marianu I-A and
De Martino F (2020) Effect of Auditory
Predictability on the Human
Peripheral Auditory System.
Front. Neurosci. 14:362.
doi: 10.3389/fnins.2020.00362
Effect of Auditory Predictability on
the Human Peripheral Auditory
System
Lars Riecke1
*, Irina-Andreea Marianu1 and Federico De Martino1,2
1 Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht,
Netherlands, 2 Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
Auditory perception is facilitated by prior knowledge about the statistics of the acoustic
environment. Predictions about upcoming auditory stimuli are processed at various
stages along the human auditory pathway, including the cortex and midbrain. Whether
such auditory predictions are processed also at hierarchically lower stages—in the
peripheral auditory system—is unclear. To address this question, we assessed outer
hair cell (OHC) activity in response to isochronous tone sequences and varied the
predictability and behavioral relevance of the individual tones (by manipulating toneto-tone probabilities and the human participants’ task, respectively). We found that
predictability alters the amplitude of distortion-product otoacoustic emissions (DPOAEs,
a measure of OHC activity) in a manner that depends on the behavioral relevance of the
tones. Simultaneously recorded cortical responses showed a significant effect of both
predictability and behavioral relevance of the tones, indicating that their experimental
manipulations were effective in central auditory processing stages. Our results provide
evidence for a top-down effect on the processing of auditory predictability in the human
peripheral auditory system, in line with previous studies showing peripheral effects of
auditory attention.
Keywords: auditory attention, auditory efferent, prediction, expectancy, cochlea, electroencephalography,
otoacoustic emission
INTRODUCTION
Many socially relevant sounds in our natural environment arise from acoustic signals that have
characteristic, regular spectral-temporal structures. The melody and rhythm of music, for instance,
arise from specific spectral and temporal relations among the individual notes. Such a regular
structure renders the constituent acoustic elements more predictable (in both time and spectral
content), and human listeners can exploit this predictability to process and perceive the acoustic
input more effectively. For example, prior knowledge of the pitch of an upcoming tone has been
shown to facilitate perceptual detection of this tone in noise (Hafter et al., 1993). Prior pitch cues,
if valid, also improve listeners’ judgments of the pitch, duration, and intensity of tones presented
in melodic contexts (Dowling et al., 1987) or isolation (Mondor and Bregman, 1994; Ward and
Mori, 1996). How the brain implements auditory predictions has been investigated extensively over
the last decade. The common view (e.g., Clark, 2013) is that the brain aims to match ‘bottom-up’
acoustic input with ‘top-down’ auditory predictions at multiple levels of the auditory processing
hierarchy by generating and dynamically updating the neural activity patterns that upcoming
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Riecke et al. Peripheral Processing of Predictable Sounds
acoustic inputs are expected to evoke. This predictive coding
theory (Rao and Ballard, 1999; Friston, 2005) has been supported
by a large number of human studies showing evidence for
predictive processing in the auditory cortex (reviews: Bendixen
et al., 2012; Heilbron and Chait, 2018) and hierarchically lower,
subcortical processing stages, including the medial geniculate
body and the inferior colliculus (Slabu et al., 2012; Cacciaglia
et al., 2015) (reviews: Winkler et al., 2009; Grimm and Escera,
2012; Chandrasekaran et al., 2014). It is still unclear whether
predictive processing occurs also at the lowest stage of auditory
processing, in the peripheral auditory system.
The peripheral auditory system receives top-down feedback
from the central auditory system via the medial olivocochlear
(MOC) efferent system (Huffman and Henson, 1990; LopezPoveda, 2018). The MOC system is a network of neurons
located in the medial part of the superior olivary complex in
the brainstem that receives ascending input from the cochlear
nucleus and descending input via corticofugal projections.
Efferent MOC fibers project to outer hair cells (OHCs) in
the cochlea and activation of these fibers alters OHC activity,
effectively reducing cochlear gain (reviews: Guinan, 2006, 2018).
Efferent MOC-fiber activity can be modulated in a ‘topdown’ manner: electric microstimulation or deactivation of the
auditory cortex alters OHC activity as measured with cochlear
microphonics or otoacoustic emissions (OAEs) (Perrot et al.,
2006; Dragicevic et al., 2015; Terreros and Delano, 2015; Jager and
Kossl, 2016). Similarly, changes in arousal or endogenous (interor intramodal) attention may lead to OHC-activity changes as
measured with OAEs (Puel et al., 1988; Froehlich et al., 1990,
1993; Giard et al., 1994; Ferber-Viart et al., 1995; Maison et al.,
2001; de Boer and Thornton, 2007; Harkrider and Bowers, 2009;
Smith et al., 2012; Srinivasan et al., 2012, 2014; Walsh et al., 2014,
2015; Wittekindt et al., 2014; Smith and Cone, 2015), although
the existence and direction of these top-down attention effects
are still debated (Picton et al., 1971; Avan and Bonfils, 1992;
Michie et al., 1996; Beim et al., 2018, 2019; Francis et al., 2018;
Lopez-Poveda, 2018).
Given that the efferent MOC system may reflect the state of
endogenous attention, it might be able to reflect also the presence
of auditory predictions generated in the central auditory system.
Indeed, sectioning the efferent MOC fibers in humans impairs the
aforementioned facilitating effect of pitch cues on tone-in-noise
detection (Scharf et al., 1997). Animal electrophysiology findings
further show that auditory-nerve fibers adapt to the statistics of
acoustic input (Joris and Yin, 1992; Wen et al., 2009). These
findings suggest that the peripheral auditory system may play a
role in auditory predictions.
In the present study, we tested the hypothesis that auditory
predictions are processed in the human auditory peripheral
system. We presented 22 human listeners with isochronous
complex-tone sequences and assessed tone-evoked OHC activity
by measuring distortion-product OAEs (DPOAEs). To induce
variations in auditory prediction we varied the predictability
of the individual tones within a sequence (by manipulating
tone-to-tone probabilities and keeping their acoustic properties
constant) while concomitantly manipulating their behavioral
relevance (by changing the listeners’ task). To check for the
effect of our manipulation at central auditory processing stages,
we simultaneously measured cortical tone-evoked activity (using
electroencephalography, EEG) and behavioral auditory-detection
performance. According to our hypothesis, auditory predictions
should alter both cortical and peripheral tone-evoked activity. We
predicted that increases in auditory predictability would lead to
significant changes in both DPOAE and EEG that are strongest
when the tones are behaviorally relevant.
MATERIALS AND METHODS
Participants
Twenty-two healthy volunteers (ages: 19–28 years, 15 females)
participated in the study. They had normal hearing (see
section “Procedure”) and normal or corrected-to-normal vision.
Participants gave their written informed consent before taking
part and were compensated for their participation. The
experimental procedure was approved by the local research
ethics committee (Ethical Review Committee Psychology and
Neuroscience, Maastricht University).
Stimuli and Tasks
Auditory Stimuli
Figure 1A illustrates an exemplary auditory stimulus. Auditory
stimuli were isochronous sequences of five complex tones.
Each tone lasted 340 ms and was preceded by a silent gap
of 20 ms (pre-tone interval). Each tone was composed of two
synchronous sinusoids—so-called primaries—with frequencies
f 1 and f 2 = 1.22 × f 1, resulting in a cubic distortion product,
DP = 2 × f 1 − f 2. The intensity of the higher primary was defined
as the test level. The intensity of the lower primary was always set
15 dB lower. These settings were chosen to facilitate elicitation of
DPOAEs (Probst et al., 1991). Exact values for DP and test level
were set individually for each participant (see section “Auditory
Stimulus Fine-Tuning”). On half of the trials, a pseudorandomly
chosen tone of the sequence contained a frequency glide during
its final 40-ms portion, defining the auditory target. The glide
was implemented by linearly changing the frequency of each
primary by 20%.
Visual Stimuli
Figure 1B illustrates an exemplary visual stimulus. Visual stimuli
were displays of the letters ‘L’ and ‘T’ on a PC screen. The letters
were arranged pseudorandomly in a 4 × 4 matrix spanning a
visual angle of approximately 5◦
. Each instance of the letter ‘L’ was
colored in blue or red. Each instance of the letter ‘T’ was colored
in blue, except for a single pseudorandomly chosen instance that
was colored in red on half of the trials to define the visual target.
Tasks
On half of the trials, participants performed a behavioral task in
the auditory sensory modality, for which they were instructed
to attend to the auditory stimuli, ignore the visual stimuli, and
detect the auditory target. On the other half, they performed the
task in the visual modality, for which they received analogous
instructions. Participants were informed that the probability of
the target to occur was 50% for each task. They were further
instructed to keep still and postpone any movement to a rest
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FIGURE 1 | Stimuli, tasks, experimental design, and procedure. Panel (A) illustrates an exemplary auditory stimulus in a schematic spectrogram. The auditory stimuli
comprised an isochronous sequence of five complex tones that were designed to evoke five consecutive OAEs at different DPs. The circle highlights an auditory
target, which was a brief frequency glide at the end of one of tones. Panel (B) illustrates an exemplary visual stimulus. The circle highlights the visual target, which
was the red letter ‘T’. Panel (C) illustrates the 2 × 2 experimental design. The predictability of the tones was varied by changing their frequency either
pseudorandomly (low tone predictability) or monotonically (high tone predictability) across the auditory stimulus. The behavioral relevance of the tones was varied by
having participants perform an auditory or visual target-detection task that required them to focus their attention on the tones (high tone relevance) or ignore them
(low tone relevance), respectively. The yellow squares outline the task-relevant stimuli. Panel (D) sketches the overall procedure (top) and the task/stimulus protocol
in the main experiment (bottom). Colored rectangles represent blocks of 50 trials from a given experimental condition (see C). Blue and red hue represents low and
high tone-relevance condition (R), respectively. Light and saturated color represents low and high tone-predictability condition (P), respectively. Each condition was
presented twice and the order of blocks was counterbalanced across participants. The direction of the tone sequence [ascending (see A) or descending] was fixed
within high-predictability blocks and counterbalanced across the two presentations of these blocks.
interval to avoid artifacts in the physiological recordings during
stimulation intervals.
Trials contained a stimulation interval of 1.8 s followed by
a response and rest interval of in total 7.1 s. The stimulation
interval involved the synchronous presentation of an auditory
and visual stimulus. Participants reported immediately after
the stimulation whether they had perceived the designated
target (‘yes’ response) or not (‘no’ response) by pressing a
corresponding key with the index or middle finger of their
right hand. After each response, they received visual feedback
regarding response correctness and then relaxed until the next
trial. Trials were preceded by silent gaps of 60 ms (pre-trial
interval) during which the OAE recording started up (see section
“OAE Recording”).
Experimental Design
Figure 1C illustrates the experimental design. The study used a
2 × 2 within-subjects design. The independent variables were
the predictability of the tones and the behavioral relevance of the
tones. The manipulation of tone predictability was implemented
by sequencing the five tones in an either highly predictable
or unpredictable manner: Tone frequency was changed either
monotonically (ascending or descending) or pseudorandomly
across the tone sequence in each auditory stimulus, resulting
in a local (tone-to-tone) probability of either 100% (highpredictability condition) or on average 45.7% (low-predictability
condition; probability per position: 20, 25, 33, 50, and 100%).
The increase in probability across positions within pseudorandom sequences resulted from permuting the five tones
without repetition, which was done to distribute tone frequencies
(and the associated DPOAE levels) equally across conditions.
The manipulation of the behavioral relevance of the tones was
implemented by rendering the auditory stimuli either relevant or
irrelevant for the participants’ behavioral goal. Participants were
instructed to perform either the auditory task (high tone-relevance
condition) or the visual task (low tone-relevance condition), which
required them to focus their attention on the tones or ignore
them, respectively (see section “Tasks”).
To further facilitate global (sequence-to-sequence) predictions
in the high-predictability condition and avoid exhaustive task
switching, trials belonging to a given condition were presented
in blocks; see Figure 1D (bottom). The order of blocks
was counterbalanced across participants to reduce potential
carryover effects. Furthermore, the following variables were
counterbalanced across trials within each block: the frequency
and sequential position of the auditory target tone, the location
of the visual target, and the sequential position of each tone
(only for low-predictability blocks). The high- and low-relevance
conditions were matched for stimulation.
Procedure
Figure 1D (top) summarizes the session procedure, which was
conducted in a sound-attenuated, electrically shielded chamber
isolated from the experimenter. Participants were first screened
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for potential hearing loss, defined as a pure-tone hearing
threshold above 25 dB HL at 0.75, 1, 1.5, 2, 3, or 4 kHz
in the test ear.
Auditory Stimulus Fine-Tuning
Acoustically induced MOC effects tend to be more observable
for higher signal-to-noise ratio (SNR) OAE measurements
(Goodman et al., 2013) and for OAEs elicited at lower test
levels (Ryan and Kemp, 1996; Veuillet et al., 1996), probably
because OHCs apply less cochlear gain to lower-level acoustic
input (Robles and Ruggero, 2001). To increase the likelihood of
observing an auditory prediction effect, test levels in our study
were chosen to be relatively low while being sufficiently high to
reliably elicit DPOAEs of similar SNR across participants.
To this end, a minimum test level and a set of most effective
tones were defined individually for each participant as follows.
First, DPOAEs were measured for eight different tones at a
relatively high test level of 50 dB SPL. The frequencies of these
tones were chosen to ensure that DPs (390, 780, 1,560, 1,950,
2,730, 3,120, 3,510, or 3,900 Hz) fell into distinct, resolvable
frequency bins in the peripheral auditory system (Glasberg and
Moore, 1990) and our OAE data analysis (see section “OAE Data
Analysis”). Each DPOAE measurement involved 50 repetitions
of a given tone presented at the same rate as the tones within
the experimental stimuli. The tones that were found to evoke
the five highest DPOAE SNRs (DPOAE level relative to the noise
floor, see section “OAE Data Analysis”) were selected for the main
experiment. The frequency distribution of the DPs that were
selected for the main experiment is shown in Figure 2A.
Second, for the least effective selected tone (the one yielding
the lowest DPOAE SNR), a DPOAE threshold was measured,
defined as the minimum test level required for eliciting a
DPOAE SNR of minimally 6 dB in half of the epochs. The
threshold was measured with an adaptive staircase procedure
and a one-down one-up tracking rule as follows: the test level
was initially set to 35 dB SPL and then adaptively changed
until eight reversals in the direction of change (from increasing
to decreasing level or vice versa) occurred, after which the
procedure terminated. The change size was gradually reduced
from an initial 12 dB, to 6 dB (after the second reversal) and
then to 3 dB (after the fourth reversal). The DPOAE threshold
was computed as the average test level at the last four reversals.
Third, to ensure that all tones reliably elicited DPOAEs at
a fixed low test level, DPOAEs were measured for the five
selected tones as above, now using as test level the obtained
DPOAE threshold. When a tone was observed to fail the criterion
(DPOAE SNR ≥ 3 dB), the test level was increased by 3 dB and
the measurement was repeated until the criterion was met. The
test level resulting from this procedure was used for all tones in
the main experiment and its average value was 30.7 ± 6.9 dB SPL
(mean ± SD across participants, range: 18.5–48.5 dB SPL).
Experiment
Participants were familiarized with the stimuli and the auditory
and visual targets, and they practiced the tasks before the
experiment. Each experimental block contained 50 trials
(corresponding to 250 tones), resulting in a block duration of
7.7 min. For each task condition (high or low tone-relevance),
the low-predictability condition was presented in two blocks and
each high-predictability condition (ascending or descending) was
presented in a single block. This resulted in the presentation of
eight blocks in total (Figure 1D bottom), corresponding to an
overall number of 400 trials (corresponding to 2,000 tones) and
an overall experiment duration of 61.3 min excluding breaks.
Consecutive blocks were separated by short breaks terminated
by the participant. Before and during each block, the current
tone-frequency order (ascending, descending, or random) and
the current task (auditory or visual) were visually indicated to the
participant. After the experiment, participants rated the difficulty
of each task on a five-point scale.
OAE Recording
The two primaries were presented to the participant’s right
ear via two speakers mounted in a calibrated in-ear probe.
OAEs were simultaneously recorded with a microphone in
the probe. Auditory stimulation and recordings were sampled
at 22.05 kHz and controlled using the Interacoustics Titan
and Research Platform software (Interacoustics, Middelfart,
Denmark) running on MATLAB (MathWorks, Natick, MA,
United States). The Titan device requires the acquisition of
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previous attention studies using acoustic MOC-reflex elicitors
(e.g., Beim et al., 2018). Figure 4B shows the DPOAE level
for each condition. Task-relevant and task-irrelevant tones
(auditory vs. visual task) elicited DPOAEs of overall similar
level. When the tones were task-relevant, DPOAEs elicited
by highly predictable tones were on average 0.54 ± 0.24 dB
stronger than those elicited by less predictable tones. For
task-irrelevant tones, a difference in the opposite direction
(−0.42 ± 0.20 dB) was observed. Statistical analysis confirmed
these observations, revealing no significant main effect of
tone predictability (F1,21 = 0.12, P = 0.73) or tone relevance
(F1,21 = 0.32, P = 0.58), and a significant interaction tone
predictability × tone relevance (F1,21 = 11.50, P = 0.0028).
Post hoc tests revealed a significant positive effect of tone
predictability in the high relevance condition (t21 = 2.21,
P = 0.038), and an opposite non-significant effect in the
low relevance condition (t21 = −2.08, P = 0.051). Thus,
these results provide evidence that the effect of auditory
predictability on tone-evoked DPOAEs depends on the
behavioral relevance of the tones.
The predictability-related difference in the high relevance
condition, which we refer to as 1DPOAElevel, was slightly larger
for tones presented at early positions within the tone sequence
vs. tones presented at later positions within the same sequence
(first vs. second half of the tone sequence, balanced for tone
frequencies; average difference: 0.17 dB; Figure 4C).
To exclude that the OAE results could be explained by
potential residual participant motion (Francis et al., 2018), the
FIGURE 3 | Behavioral results. Panel (A) illustrates participants’ overall performance as assessed with d
0
for each target-detection task. Panel (B) illustrates
performance on the auditory task for each tone-predictability condition, and the probability value associated with the effect of predictability. Panel (C) illustrates the
predictability-related change in auditory performance (1Auditoryperformance = high minus low-predictability condition) during the first and second half of the tone
sequence. All data show summary statistics (mean ± SEM) across participants.
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FIGURE 4 | OAE results. Panel (A) illustrates the overall sound-pressure level spectrum of participants’ OAE recordings, averaged across tone frequencies and tone
positions, and aligned with respect to the distortion product (DP) frequency. The DPOAE level was computed as the magnitude of the DP-frequency bin. The noise
floor (delineated by the dashed lines) was computed as the average level in a 20-Hz wide band centered on, and excluding, the DP-frequency bin. The DPOAE SNR
was computed by subtracting the noise floor from the DPOAE level and was observed to be on average 13.4 ± 4.8 dB (mean ± SD across participants). Panel (B)
illustrates the DPOAE level pooled across tone frequencies and tone positions for each condition, and probability values associated with the effect of predictability
(for each tone-relevance condition) and its modulation by tone relevance. Panel (C) illustrates the predictability-related change in DPOAE levels (1DPOAElevel = high
minus low-predictability condition) elicited during the first and second half of the tone sequence in the auditory task. Panels (D,E) are analogous to panel (B), but
show, respectively, the level of the noise floor and the level of physiological ear-canal noise (instead of the DPOAE level). All data show summary statistics
(mean ± SEM) across participants.
same statistical analyses as above were applied to the level
of the noise floor and a measure of physiological ear-canal
noise (the broadband sound level recorded during the pretone interval; see section “OAE Data Analysis”). Results are
shown, respectively, in Figures 4D,E, revealing no significant
interaction tone predictability × tone relevance (noise floor:
F1,21 = 0.038, P = 0.85; physiological ear-canal noise: F1,21 = 0.13,
P = 0.73) and no significant effect of tone predictability in
either the high relevance condition (noise floor: t21 = −0.60,
P = 0.55; physiological ear-canal noise: t21 = −1.73, P = 0.10)
or low relevance condition (noise floor: t21 = −0.46, P = 0.65;
physiological ear-canal noise: t21 = −1.17, P = 0.26). These
statistical results indicate that the noise level did not differ
significantly among the experimental conditions of interest.
However, note that the non-significant results for the ear-canal
noise may partially relate to the much shorter observation
window (pre-tone interval < tone interval). EEG Results Figure 5 shows the EEG results. Figure 5A shows participants’ AEP pooled across tone frequencies and positions, irrespective of the presence of the auditory target; the upper plot shows these data pooled across conditions and the lower plot shows them separately for each condition, revealing differences between conditions especially during the interval of the N1 component. Figure 5B shows the extracted N1 peak amplitude for each condition. Poorly predictable tones elicited overall stronger N1 amplitudes than highly predictable tones. Moreover, taskrelevant tones elicited overall stronger N1 amplitudes than task-irrelevant tones. Statistical analysis of N1 peak amplitude confirmed these observations, revealing significant main effects of tone predictability (F1,21 = 12.19, P = 0.0022) and tone relevance (F1,21 = 19.16, P = 0.00026), and no significant interaction tone predictability × tone relevance (F1,21 = 1.11, P = 0.30). Post hoc tests showed a significant effect of tone predictability in the low tone-relevance condition (t21 = −3.58, P = 0.00087) and a corresponding non-significant effect in the high tone-relevance condition (t21 = −1.70, P = 0.052). The trend in the high tone-relevance condition, which we refer to as 1N1amplitude, was slightly larger for tones presented at early positions within the tone sequence vs. tones presented at later positions within the same sequence (first vs. second half of the tone sequence, balanced for tone frequencies; average difference: 0.14 µV; Figure 5C). Analogous analyses of P1 and P2 revealed no significant result (all P > 0.05),
except for a significant main effect of tone relevance on P1
(F1,21 = 5.86, P = 0.025). Thus, the EEG results indicate that
the predictability and behavioral relevance of tones modulate the
amplitudes of N1 and possibly longer-latency AEP components
(see section “Discussion”).
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FIGURE 5 | EEG results. Panel (A) illustrates participants’ average AEP evoked by single tones at frontal-central electrodes. The upper plot shows data pooled
across tone frequencies, tone positions, and conditions, irrespective of the presence of the auditory target (mean ± SEM across participants, represented by the
black waveform and shaded area). The horizontal bar represents the interval of a single tone and the dashed vertical lines delineate time windows from which peak
amplitudes of P1, N1, and P2 were extracted. The lower plot shows the same data as the upper plot, but stratified for conditions (mean across participants). The
different conditions are represented by different hue and brightness, which are labeled in (B). Panel (B) illustrates N1 peak amplitude for each condition
(mean ± SEM across participants), and probability values associated with the effect of predictability (for each tone-relevance condition) and its modulation by tone
relevance. Panel (C) illustrates the predictability-related change in N1 amplitudes (1N1amplitude = high minus low-predictability condition) elicited during the first and
second half of the tone sequence in the auditory task.
Combined OAE-EEG Results
Figure 6 shows results from the correlation analysis testing
for coupling between predictability effects on OAE and EEG
(1OAE and 1EEG). The same measures as above were analyzed
(DPOAE level and N1 peak amplitude). Initial analyses yielded
no significant correlation between predictability effects on
OAE, EEG, or behavior (all P > 0.05). Restricting the
analysis to those participants who benefited from predictability
as expected (better auditory performance for high vs. lowpredictability condition; N = 14, of which two were outliers
and rejected) revealed a significant positive correlation between
1DPOAElevel and 1N1amplitude (τ = 0.39, P = 0.043). No
significant correlation was observed in the low relevance
FIGURE 6 | Combined OAE-EEG results. The scatterplot shows results from
a correlation analysis testing for coupling between predictability effects on
OAE and EEG (1DPOAElevel × 1N1amplitude) during the auditory task. The
analysis focused on data of participants whose auditory performance showed
a qualitative benefit from predictability. Filled circles represent those
participants; open circles represent remaining participants and two outliers.
Correlation coefficient τ and P-value describe, respectively, the strength and
statistical significance of the linear relationship (regression line, plotted in gray).
condition (τ = 0.23, P = 0.14). These results suggest that
predictability effects on DPOAEs and N1 amplitude might be
functionally coupled.
DISCUSSION
The goal of our study was to test whether auditory predictions
are processed in the human auditory peripheral system. To
this end, we assessed whether OHCs are sensitive to the
predictability of acoustic input by measuring DPOAE-level
changes between a statistically structured sequence and a
pseudorandom sequence composed of the same tones. Each tone
had an equal probability of occurrence (20%), but the toneto-tone (transitional) probabilities differed: only in the highly
predictable sequence, all transitional probabilities were 100% and
thus allowed listeners to generate strong auditory predictions. We
observed that physically identical tones with different transitional
probabilities can elicit different DPOAEs dependent on whether
these tones are attended. Based on this result, we conclude
that auditory predictability may influence OHC activity under
auditory attention.
Predictability-Induced Changes in
Auditory Peripheral Responses
We observed a significantly larger predictability-induced
DPOAE-level change in the high vs. low-relevance condition.
A possible explanation for this effect is that the auditory,
but not visual, task required participants to pay attention to
the acoustic input and thereby encouraged them to actively
generate and exploit predictions. The effect of our auditory
task on predictability-induced DPOAE changes fits with
previous OAE results showing effects of auditory attention
on peripheral auditory processing, although these results are
still debated (see section “Introduction”). Moreover, it is in
line with studies showing a top-down attentional modulation
of the effect of auditory predictability on cortical responses
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Riecke et al. Peripheral Processing of Predictable Sounds
(reviews: Schroger et al., 2015; Heilbron and Chait, 2018).
Probably only under auditory attention, predictive brain signals
in our study (which we observed in cortex; see next section) were
fed in a ‘top-down’ manner and gated to the peripheral auditory
system. Alternatively, visual attention may have prevented these
predictive signals from reaching the peripheral auditory system.
We further observed a positive effect of predictability
on DPOAE levels in the high-relevance condition, which
provides support for our hypothesis that auditory predictability
influences peripheral auditory processing. Whether the observed
effect reflects a potential cochlear amplification of actively
anticipated input or an attenuation of this input cannot be
disambiguated based on our DPOAE data. Efferent MOC
signals may alter the amplitudes and phases of the two
DPOAE components (primaries) by different amounts, which
can result in either positive or negative DPOAE-level changes
depending on the specific primary frequencies (Guinan, 2006)—
an ambiguity that might explain why some previous DPOAE
studies observed opposite effects of auditory attention (Smith
et al., 2012; Srinivasan et al., 2012, 2014; Wittekindt et al., 2014).
Future studies may disambiguate the direction of peripheral
predictability effects by measuring, e.g., stimulus-frequency
OAEs (SFOAEs), which require only a single test frequency but
otherwise more sophisticated methods (Guinan, 2006; LopezPoveda, 2018). In sum, our DPOAE results suggest that OHCs
may be sensitive to auditory predictability when the listener is
paying top-down attention to the acoustic input.
Predictability-Induced Changes in
Cortical Responses
We observed that highly predictable tones evoke significantly
smaller N1 amplitudes than poorly predictable tones, which is
in line with previous EEG results showing a suppressive effect
of auditory predictability on N1 (Schafer and Marcus, 1973;
Martikainen et al., 2005; Baess et al., 2011). Given that listeners
in our study could generate accurate predictions only in the
high-predictability condition, the suppressive effect on N1 may
reflect a cortical signal of fulfilled predictions (a ‘match’ signal),
specifically the so-called repetition positivity (RP) (Bendixen
et al., 2012). The auditory RP is an attenuation of auditory-evoked
responses over the frontocentral scalp in a latency range from 50
to 250 ms. It increases with the number of stimulus repetitions
(Bendixen et al., 2008) and the predictability of these stimuli
(Costa-Faidella et al., 2011; Todorovic et al., 2011) even when
attention is diverted away from them (Haenschel et al., 2005). The
early portion (40–60 ms) of the RP is mostly affected by stimulus
repetitions, whereas its later portion (100–200 ms) is more
sensitive to predictability (Todorovic and de Lange, 2012). Thus,
the suppressive effect on N1 in our study may reflect auditory
predictions rather than sensory adaptation (Heilbron and Chait,
2018). However, the observed suppression may also reflect
frequency-specific adaptation, which was probably stronger in
the high vs. low-predictability condition (due to overall smaller
tone-to-tone frequency changes). While consecutive DPOAEs
could not interact within single auditory filters in the peripheral
auditory system (see section “Auditory Stimulus Fine-Tuning”),
they might have done so within broader filters in the auditory
cortex (Bartlett and Wang, 2005). It should be noted that the
observed cortical suppression may have affected also much later
AEPs. Owing to the fast tone-presentation rate, these later AEPs
and consecutive N1 responses overlapped, making it difficult to
disentangle them (see section “EEG Data Analysis”).
The observed suppressive effect presumably extends to the
auditory brainstem, as suggested by corresponding predictability
effects observed in an EEG study that measured auditory
brainstem responses to auditory stimuli similar to ours (patterned
vs. pseudorandom complex tone sequences) (Skoe et al., 2013).
This would imply that our high-predictability condition induced
an internal predictive model in the cortex (and possibly
subcortex) that may have sent predictive signals ‘top-down’ to
hierarchically lower auditory processing stages, especially when
the listener paid attention to the acoustic input.
We did not observe cortical signals of violated predictions
(‘mismatch’ signals), such as the mismatch negativity (Naatanen
et al., 2001), probably because the high-predictability condition
contained no regularity violation and the low-predictability
condition did not allow generating highly accurate predictions.
We further observed that behaviorally relevant tones evoked
significantly larger N1 amplitudes than irrelevant tones, which is
in line with previous EEG results showing an enhancing effect
of auditory attention on N1 (e.g., Picton and Hillyard, 1974;
Naatanen, 1982).
In sum, while our EEG results cannot disentangle effects
on N1 and much later AEP components, they confirm that
our experimental manipulations of predictability and behavioral
relevance were effective in the cortex.
Coupling Between Predictability Effects
on Cortical and Peripheral Responses?
Our results suggest that predictability effects on DPOAE and
N1 might be positively correlated. More specifically, listeners
who showed larger predictability effects on DPOAE tended to
show also larger predictability effects on N1 amplitude. Moreover,
predictability-induced changes in DPOAE, N1, and auditory
performance were qualitatively (non-significantly) stronger for
tones presented early vs. late during a given tone sequence,
which might reflect reduced effectiveness of our predictability
manipulation toward the end of auditory stimuli (due to the
blocked design and the fact that tone-to-tone probabilities
in the low-predictability condition approximated those in the
high-predictability condition at later tone positions; see section
“Experimental Design”). Together, these observations might
suggest that prediction effects on different auditory processing
stages are functionally coupled, which would imply that the
effects propagate through the auditory system. However, it should
be noted that the interpretation of our correlation results needs
to be treated with caution because these results are based on
only a subset of participants (those who appeared to benefit
from predictions).
Previous studies indeed found similar prediction effects in
multiple stages of the central auditory processing hierarchy
(see section “Introduction”). Whether these stages locally
generate auditory predictions or inherit them ‘top-down’ from
higher stages is still unclear (Escera and Malmierca, 2014;
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Riecke et al. Peripheral Processing of Predictable Sounds
Malmierca et al., 2019), although auditory prediction effects seem
to become stronger toward higher hierarchical stages (Gill et al.,
2008; Rummell et al., 2016). It is further possible that functional
coupling of cortical and peripheral top-down modulations is
mediated by slow (<10 as="" br="" brain="" hz="" periodic="" signals="" suggested=""> by recent DPOAE/EEG-oscillation findings on endogenous
intermodal (audiovisual) attention (Dragicevic et al., 2019).
No Significant Effect of Predictability on
Behavioral Responses
We observed better auditory performance in the high vs. lowpredictability condition, but this difference was only small
and not statistically significant. Thus, apparently not all our
participants made effective use of predictions, despite the fact that
our auditory stimuli and task were designed to encourage the use
of such predictions. A possible explanation for our null result is
that the pitch-specific predictions that could be inferred from our
highly predictable stimuli did not provide the most potent cue for
detecting the auditory target (which was a sudden pitch change
within a tone). Moreover, perceptual benefits from predictions
may be more observable under conditions of sensory uncertainty
or ambiguity (de Lange et al., 2018). Our auditory targets were not
necessarily ambiguous or near participants’ perceptual detection
threshold, implying that predictions derived from our stimuli
were not optimally effective for these particular stimuli and our
auditory task. In sum, our behavioral null result shows that our
participants’ auditory perception did not reliably benefit from
predictions. Future studies may achieve stronger predictability
effects by using more ambiguous auditory stimuli and improved
tasks that allow for stricter control of listeners’ use of predictions.
Critical Considerations
The level of our auditory stimuli (on average ∼30 dB SPL)
was probably sufficiently low to prevent elicitation of middleear muscle reflexes, but whether it was sufficiently high to elicit
ipsilateral MOC reflexes, which seem to require levels of at least
∼30 dB SPL (Guinan et al., 2003), remains unclear. Even if not all
tones in our study elicited MOC reflexes, it remains possible that
corticofugal top-down signals modulated MOC efferent activity
in the absence of an acoustic elicitor, as suggested by studies
showing attention effects on OAEs without an acoustic MOCreflex elicitor (Puel et al., 1988; Giard et al., 1994) (but see Picton
et al., 1971). Future studies should measure OAEs at higher SNR
and use contralateral acoustic noise to control MOC reflexes
more effectively to increase the strength of top-down modulation
of the MOC reflex.
The auditory targets in our auditory task (which were defined
by the pitch of the two primaries) differed from the investigated
DPOAE frequencies, similar to some previous OAE attention
studies (Srinivasan et al., 2012, 2014; Wittekindt et al., 2014).
It is unlikely that this difference largely reduced effect sizes in
our study because acoustically evoked (Norman and Thornton,
1993; Lisowska et al., 2002) (review: Guinan, 2018) and attentionmodulated (Srinivasan et al., 2012, 2014; Wittekindt et al., 2014;
Beim et al., 2018) MOC reflexes show only little frequency tuning
in humans.
It should be noted that our interpretation is constrained
by our auditory stimulus design to predictions of pitch or
spectral structure. Moreover, the high-predictability blocks
involved exposure to fixed patterns over relatively long durations,
which likely facilitated long-term learning of acoustic structure.
Whether our results generalize to predictions of other features,
such as timing, or to short-term (echoic) memory of acoustic
structure remains to be investigated.
CONCLUSION
Our study shows that intramodal top-down attention may
modulate the processing of predictable spectral input in the
peripheral auditory system, thereby adding support to current
debate on the sensitivity of the human auditory peripheral system
to top-down modulation (e.g., Beim et al., 2018; Lopez-Poveda,
2018). Our observations further indicate that predictability
may affect auditory processing in the peripheral auditory
system. Future studies may observe stronger predictability effects
on peripheral auditory processing by measuring higher-SNR
SFOAEs in the presence of an acoustic contralateral MOC-reflex
elicitor and using more optimal (ambiguous) auditory stimuli.
DATA AVAILABILITY STATEMENT
The datasets generated for this study are available on request to
the corresponding author.
ETHICS STATEMENT
The studies involving human participants were reviewed
and approved by Ethical Review Committee Psychology and
Neuroscience, Maastricht University. The patients/participants
provided their written informed consent to participate in
this study.
AUTHOR CONTRIBUTIONS
LR, FD, and I-AM conceived the study. I-AM acquired the data.
LR and I-AM performed the statistical analysis. LR wrote the first
draft of the manuscript. All authors contributed to manuscript
revision, read and approved the submitted version.
FUNDING
This work was funded by the Netherlands Organisation for
Scientific Research (NWO; VIDI grant 864-13-012 to FDM).
ACKNOWLEDGMENTS
The authors thank M. Sleijpen, A. Lipinski, R. Brinkmann, J.
Gädtke, P. Künzel, J. Schreurs, and J. van der Meij for help with
the data acquisition.
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REFERENCES
Avan, P., and Bonfils, P. (1992). Analysis of possible interactions of an attentional
task with cochlear micromechanics. Hear. Res. 57, 269–275. doi: 10.1016/0378-
5955(92)90156-h
Baess, P., Horvath, J., Jacobsen, T., and Schroger, E. (2011). Selective suppression
of self-initiated sounds in an auditory stream: an ERP study. Psychophysiology
48, 1276–1283. doi: 10.1111/j.1469-8986.2011.01196.x
Bartlett, E. L., and Wang, X. (2005). Long-lasting modulation by stimulus context
in primate auditory cortex. J. Neurophysiol. 94, 83–104. doi: 10.1152/jn.01124.
2004
Beim, J. A., Oxenham, A. J., and Wojtczak, M. (2018). Examining replicability of an
otoacoustic measure of cochlear function during selective attention. J. Acoust.
Soc. Am. 144:2882. doi: 10.1121/1.5079311
Beim, J. A., Oxenham, A. J., and Wojtczak, M. (2019). No effects of attention
or visual perceptual load on cochlear function, as measured with stimulusfrequency otoacoustic emissions. J. Acoust. Soc. Am. 146:1475. doi: 10.1121/1.
5123391
Bendixen, A., Prinz, W., Horvath, J., Trujillo-Barreto, N. J., and Schroger, E. (2008).
Rapid extraction of auditory feature contingencies. Neuroimage 41, 1111–1119.
doi: 10.1016/j.neuroimage.2008.03.040
Bendixen, A., SanMiguel, I., and Schroger, E. (2012). Early electrophysiological
indicators for predictive processing in audition: a review. Int. J. Psychophysiol.
83, 120–131. doi: 10.1016/j.ijpsycho.2011.08.003
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate - a
practical and powerful approach to multiple testing. J. R. Stat. Soc. B Methodol.
57, 289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x
Brown, G. S., and White, K. G. (2005). The optimal correction for estimating
extreme discriminability. Behav. Res. Methods 37, 436–449. doi: 10.3758/
bf03192712
Cacciaglia, R., Escera, C., Slabu, L., Grimm, S., Sanjuan, A., Ventura-Campos,
N., et al. (2015). Involvement of the human midbrain and thalamus in
auditory deviance detection. Neuropsychologia 68, 51–58. doi: 10.1016/j.
neuropsychologia.2015.01.001
Chandrasekaran, B., Skoe, E., and Kraus, N. (2014). An integrative model of
subcortical auditory plasticity. Brain Topogr. 27, 539–552. doi: 10.1007/s10548-
013-0323-9
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the
future of cognitive science. Behav. Brain Sci. 36, 181–204. doi: 10.1017/
S0140525X12000477
Costa-Faidella, J., Grimm, S., Slabu, L., Diaz-Santaella, F., and Escera, C. (2011).
Multiple time scales of adaptation in the auditory system as revealed by
human evoked potentials. Psychophysiology 48, 774–783. doi: 10.1111/j.1469-
8986.2010.01144.x
de Boer, J., and Thornton, A. R. (2007). Effect of subject task on contralateral
suppression of click evoked otoacoustic emissions. Hear. Res. 233, 117–123.
doi: 10.1016/j.heares.2007.08.002
de Lange, F. P., Heilbron, M., and Kok, P. (2018). How do expectations shape
perception? Trends Cogn. Sci. 22, 764–779. doi: 10.1016/j.tics.2018.06.002
Delorme, A., and Makeig, S. (2004). EEGLAB: an open source toolbox for analysis
of single-trial EEG dynamics including independent component analysis.
J. Neurosci. Methods 134, 9–21. doi: 10.1016/j.jneumeth.2003.10.009
Dowling, W. J., Lung, K. M., and Herrbold, S. (1987). Aiming attention in pitch
and time in the perception of interleaved melodies. Percept. Psychophys. 41,
642–656. doi: 10.3758/bf03210496
Dragicevic, C. D., Aedo, C., Leon, A., Bowen, M., Jara, N., Terreros, G., et al. (2015).
The olivocochlear reflex strength and cochlear sensitivity are independently
modulated by auditory cortex microstimulation. J. Assoc. Res. Otolaryngol. 16,
223–240. doi: 10.1007/s10162-015-0509-9
Dragicevic, C. D., Marcenaro, B., Navarrete, M., Robles, L., and Delano, P. H.
(2019). Oscillatory infrasonic modulation of the cochlear amplifier by selective
attention. PLoS One 14:e0208939. doi: 10.1371/journal.pone.0208939
Escera, C., and Malmierca, M. S. (2014). The auditory novelty system: an attempt
to integrate human and animal research. Psychophysiology 51, 111–123. doi:
10.1111/psyp.12156
Ferber-Viart, C., Duclaux, R., Collet, L., and Guyonnard, F. (1995). Influence of
auditory stimulation and visual attention on otoacoustic emissions. Physiol.
Behav. 57, 1075–1079. doi: 10.1016/0031-9384(95)00012-8
Francis, N. A., Zhao, W., and Guinan, J. J. Jr. (2018). Auditory attention
reduced ear-canal noise in humans by reducing subject motion, not by medial
olivocochlear efferent inhibition: implications for measuring otoacoustic
emissions during a behavioral task. Front. Syst. Neurosci. 12:42. doi: 10.3389/
fnsys.2018.00042
Friston, K. (2005). A theory of cortical responses. Philos. Trans. R. Soc. Lond. B Biol.
Sci. 360, 815–836. doi: 10.1098/rstb.2005.1622
Froehlich, P., Collet, L., Chanal, J. M., and Morgon, A. (1990). Variability of the
influence of a visual task on the active micromechanical properties of the
cochlea. Brain Res. 508, 286–288. doi: 10.1016/0006-8993(90)90408-4
Froehlich, P., Collet, L., and Morgon, A. (1993). Transiently evoked otoacoustic
emission amplitudes change with changes of directed attention. Physiol. Behav.
53, 679–682. doi: 10.1016/0031-9384(93)90173-d
Giard, M. H., Collet, L., Bouchet, P., and Pernier, J. (1994). Auditory selective
attention in the human cochlea. Brain Res. 633, 353–356. doi: 10.1016/0006-
8993(94)91561-x
Gill, P., Woolley, S. M., Fremouw, T., and Theunissen, F. E. (2008). What’s that
sound? Auditory area CLM encodes stimulus surprise, not intensity or intensity
changes. J. Neurophysiol. 99, 2809–2820. doi: 10.1152/jn.01270.2007
Glasberg, B. R., and Moore, B. C. (1990). Derivation of auditory filter shapes
from notched-noise data. Hear. Res. 47, 103–138. doi: 10.1016/0378-5955(90)
90170-t
Goodman, S. S., Mertes, I. B., Lewis, J. D., and Weissbeck, D. K. (2013). Medial
olivocochlear-induced transient-evoked otoacoustic emission amplitude shifts
in individual subjects. J. Assoc. Res. Otolaryngol. 14, 829–842. doi: 10.1007/
s10162-013-0409-9
Grimm, S., and Escera, C. (2012). Auditory deviance detection revisited: evidence
for a hierarchical novelty system. Int. J. Psychophysiol. 85, 88–92. doi: 10.1016/j.
ijpsycho.2011.05.012
Guinan, J. J. Jr. (2006). Olivocochlear efferents: anatomy, physiology, function,
and the measurement of efferent effects in humans. Ear Hear. 27, 589–607.
doi: 10.1097/01.aud.0000240507.83072.e7
Guinan, J. J. Jr. (2018). Olivocochlear efferents: their action, effects, measurement
and uses, and the impact of the new conception of cochlear mechanical
responses. Hear. Res. 362, 38–47. doi: 10.1016/j.heares.2017.12.012
Guinan, J. J. Jr., Backus, B. C., Lilaonitkul, W., and Aharonson, V. (2003).
Medial olivocochlear efferent reflex in humans: otoacoustic emission (OAE)
measurement issues and the advantages of stimulus frequency OAEs. J. Assoc.
Res. Otolaryngol. 4, 521–540. doi: 10.1007/s10162-002-3037-3
Haenschel, C., Vernon, D. J., Dwivedi, P., Gruzelier, J. H., and Baldeweg, T. (2005).
Event-related brain potential correlates of human auditory sensory memorytrace formation. J. Neurosci. 25, 10494–10501. doi: 10.1523/JNEUROSCI.1227-
05.2005
Hafter, E. R., Schlauch, R. S., and Tang, J. (1993). Attending to auditory filters
that were not stimulated directly. J. Acoust. Soc. Am. 94(2 Pt 1), 743–747.
doi: 10.1121/1.408203
Harkrider, A. W., and Bowers, C. D. (2009). Evidence for a cortically mediated
release from inhibition in the human cochlea. J. Am. Acad. Audiol. 20, 208–215.
doi: 10.3766/jaaa.20.3.7
Heilbron, M., and Chait, M. (2018). Great expectations: is there evidence for
predictive coding in auditory cortex? Neuroscience 389, 54–73. doi: 10.1016/j.
neuroscience.2017.07.061
Huffman, R. F., and Henson, O. W. Jr. (1990). The descending auditory pathway
and acousticomotor systems: connections with the inferior colliculus. Brain Res.
Brain Res. Rev. 15, 295–323. doi: 10.1016/0165-0173(90)90005-9
Jaeger, M., Bleichner, M. G., Bauer, A. R., Mirkovic, B., and Debener, S. (2018).
Did you listen to the beat? Auditory steady-state responses in the human
electroencephalogram at 4 and 7 hz modulation rates reflect selective attention.
Brain Topogr. 31, 811–826. doi: 10.1007/s10548-018-0637-8
Jager, K., and Kossl, M. (2016). Corticofugal modulation of DPOAEs in gerbils.
Hear. Res. 332, 61–72. doi: 10.1016/j.heares.2015.11.008
Joris, P. X., and Yin, T. C. (1992). Responses to amplitude-modulated tones in the
auditory nerve of the cat. J. Acoust. Soc. Am. 91, 215–232. doi: 10.1121/1.402757
Lisowska, G., Smurzynski, J., Morawski, K., Namyslowski, G., and Probst, R.
(2002). Influence of contralateral stimulation by two-tone complexes, narrowband and broad-band noise signals on the 2f1-f2 distortion product otoacoustic
emission levels in humans. Acta Otolaryngol. 122, 613–619. doi: 10.1080/
000164802320396286
Frontiers in Neuroscience | www.frontiersin.org 11 April 2020 | Volume 14 | Article 362
fnins-14-00362 April 10, 2020 Time: 17:59 # 12
Riecke et al. Peripheral Processing of Predictable Sounds
Lopez-Poveda, E. A. (2018). Olivocochlear efferents in animals and humans: from
anatomy to clinical relevance. Front. Neurol. 9:197. doi: 10.3389/fneur.2018.
00197
Macmillan, N. A., and Creelman, C. D. (1991). Detection Theory: A User’s Guide.
Cambridge, MA: Cambridge UP.
Maison, S., Micheyl, C., and Collet, L. (2001). Influence of focused auditory
attention on cochlear activity in humans. Psychophysiology 38, 35–40. doi:
10.1111/1469-8986.3810035
Malmierca, M. S., Nino-Aguillon, B. E., Nieto-Diego, J., Porteros, A., PerezGonzalez, D., and Escera, C. (2019). Pattern-sensitive neurons reveal encoding
of complex auditory regularities in the rat inferior colliculus. Neuroimage 184,
889–900. doi: 10.1016/j.neuroimage.2018.10.012
Martikainen, M. H., Kaneko, K., and Hari, R. (2005). Suppressed responses to selftriggered sounds in the human auditory cortex. Cereb. Cortex 15, 299–302.
doi: 10.1093/cercor/bhh131
Michie, P. T., LePage, E. L., Solowij, N., Haller, M., and Terry, L. (1996). Evoked
otoacoustic emissions and auditory selective attention. Hear. Res. 98, 54–67.
doi: 10.1016/0378-5955(96)00059-7
Mondor, T. A., and Bregman, A. S. (1994). Allocating attention to frequency
regions. Percept. Psychophys. 56, 268–276. doi: 10.3758/bf03209761
Naatanen, R. (1982). Processing negativity: an evoked-potential reflection of
selective attention. Psychol. Bull. 92, 605–640. doi: 10.1037/0033-2909.92.3.605
Naatanen, R., Tervaniemi, M., Sussman, E., Paavilainen, P., and Winkler, I. (2001).
"Primitive intelligence" in the auditory cortex. Trends Neurosci. 24, 283–288.
doi: 10.1016/s0166-2236(00)01790-2
Norman, M., and Thornton, A. R. (1993). Frequency analysis of the contralateral
suppression of evoked otoacoustic emissions by narrow-band noise. Br. J.
Audiol. 27, 281–289. doi: 10.3109/03005369309076705
Perrot, X., Ryvlin, P., Isnard, J., Guenot, M., Catenoix, H., Fischer, C., et al. (2006).
Evidence for corticofugal modulation of peripheral auditory activity in humans.
Cereb. Cortex 16, 941–948. doi: 10.1093/cercor/bhj035
Picton, T. W., and Hillyard, S. A. (1974). Human auditory evoked potentials. II.
Effects of attention. Electroencephalogr. Clin. Neurophysiol. 36, 191–199. doi:
10.1016/0013-4694(74)90156-4
Picton, T. W., Hillyard, S. A., Galambos, R., and Schiff, M. (1971). Human auditory
attention: a central or peripheral process? Science 173, 351–353. doi: 10.1126/
science.173.3994.351
Probst, R., Lonsbury-Martin, B. L., and Martin, G. K. (1991). A review of
otoacoustic emissions. J. Acoust. Soc. Am. 89, 2027–2067. doi: 10.1121/1.400897
Puel, J. L., Bonfils, P., and Pujol, R. (1988). Selective attention modifies the active
micromechanical properties of the cochlea. Brain Res. 447, 380–383. doi: 10.
1016/0006-8993(88)91144-4
Rao, R. P., and Ballard, D. H. (1999). Predictive coding in the visual cortex:
a functional interpretation of some extra-classical receptive-field effects. Nat.
Neurosci. 2, 79–87. doi: 10.1038/4580
Robles, L., and Ruggero, M. A. (2001). Mechanics of the mammalian cochlea.
Physiol. Rev. 81, 1305–1352. doi: 10.1152/physrev.2001.81.3.1305
Rummell, B. P., Klee, J. L., and Sigurdsson, T. (2016). Attenuation of responses
to self-generated sounds in auditory cortical neurons. J. Neurosci. 36, 12010–
12026. doi: 10.1523/JNEUROSCI.1564-16.2016
Ryan, S., and Kemp, D. T. (1996). The influence of evoking stimulus level on the
neural suppression of transient evoked otoacoustic emissions. Hear. Res. 94,
140–147. doi: 10.1016/0378-5955(96)00021-4
Schafer, E. W., and Marcus, M. M. (1973). Self-stimulation alters human sensory
brain responses. Science 181, 175–177. doi: 10.1126/science.181.4095.175
Scharf, B., Magnan, J., and Chays, A. (1997). On the role of the olivocochlear
bundle in hearing: 16 case studies. Hear. Res. 103, 101–122. doi: 10.1016/s0378-
5955(96)00168-2
Schroger, E., Marzecova, A., and SanMiguel, I. (2015). Attention and prediction in
human audition: a lesson from cognitive psychophysiology. Eur. J. Neurosci. 41,
641–664. doi: 10.1111/ejn.12816
Skoe, E., Krizman, J., Spitzer, E., and Kraus, N. (2013). The auditory brainstem
is a barometer of rapid auditory learning. Neuroscience 243, 104–114. doi:
10.1016/j.neuroscience.2013.03.009
Slabu, L., Grimm, S., and Escera, C. (2012). Novelty detection in the human
auditory brainstem. J. Neurosci. 32, 1447–1452. doi: 10.1523/JNEUROSCI.
2557-11.2012
Smith, D. W., Aouad, R. K., and Keil, A. (2012). Cognitive task demands modulate
the sensitivity of the human cochlea. Front. Psychol. 3:30. doi: 10.3389/fpsyg.
2012.00030
Smith, S. B., and Cone, B. (2015). The medial olivocochlear reflex in children
during active listening. Int. J. Audiol. 54, 518–523. doi: 10.3109/14992027.2015.
1008105
Srinivasan, S., Keil, A., Stratis, K., Osborne, A. F., Cerwonka, C., Wong, J., et al.
(2014). Interaural attention modulates outer hair cell function. Eur. J. Neurosci.
40, 3785–3792. doi: 10.1111/ejn.12746
Srinivasan, S., Keil, A., Stratis, K., Woodruff Carr, K. L., and Smith, D. W. (2012).
Effects of cross-modal selective attention on the sensory periphery: cochlear
sensitivity is altered by selective attention. Neuroscience 223, 325–332. doi:
10.1016/j.neuroscience.2012.07.062
Terreros, G., and Delano, P. H. (2015). Corticofugal modulation of peripheral
auditory responses. Front. Syst. Neurosci. 9:134. doi: 10.3389/fnsys.2015.00134
Todorovic, A., and de Lange, F. P. (2012). Repetition suppression and expectation
suppression are dissociable in time in early auditory evoked fields. J. Neurosci.
32, 13389–13395. doi: 10.1523/JNEUROSCI.2227-12.2012
Todorovic, A., van Ede, F., Maris, E., and de Lange, F. P. (2011). Prior expectation
mediates neural adaptation to repeated sounds in the auditory cortex: an MEG
study. J. Neurosci. 31, 9118–9123. doi: 10.1523/JNEUROSCI.1425-11.2011
Vaughan, H. G. Jr., and Ritter, W. (1970). The sources of auditory evoked responses
recorded from the human scalp. Electroencephalogr. Clin. Neurophysiol. 28,
360–367. doi: 10.1016/0013-4694(70)90228-2
Veuillet, E., Duverdy-Bertholon, F., and Collet, L. (1996). Effect of contralateral
acoustic stimulation on the growth of click-evoked otoacoustic emissions in
humans. Hear. Res. 93, 128–135. doi: 10.1016/0378-5955(95)00212-x
Walsh, K. P., Pasanen, E. G., and McFadden, D. (2014). Selective attention reduces
physiological noise in the external ear canals of humans. I: auditory attention.
Hear. Res. 312, 143–159. doi: 10.1016/j.heares.2014.03.012
Walsh, K. P., Pasanen, E. G., and McFadden, D. (2015). Changes in otoacoustic
emissions during selective auditory and visual attention. J. Acoust. Soc. Am. 137,
2737–2757. doi: 10.1121/1.4919350
Ward, L. M., and Mori, S. (1996). Attention cueing aids auditory intensity
resolution. J. Acoust. Soc. Am. 100, 1722–1727. doi: 10.1121/1.416069
Wen, B., Wang, G. I., Dean, I., and Delgutte, B. (2009). Dynamic range adaptation
to sound level statistics in the auditory nerve. J. Neurosci. 29, 13797–13808.
doi: 10.1523/JNEUROSCI.5610-08.2009
Winkler, I., Denham, S. L., and Nelken, I. (2009). Modeling the auditory scene:
predictive regularity representations and perceptual objects. Trends Cogn. Sci.
13, 532–540. doi: 10.1016/j.tics.2009.09.003
Wittekindt, A., Kaiser, J., and Abel, C. (2014). Attentional modulation of the
inner ear: a combined otoacoustic emission and EEG study. J. Neurosci. 34,
9995–10002. doi: 10.1523/JNEUROSCI.4861-13.2014
Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
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Frontiers in Neuroscience | www.frontiersin.org 12 April 2020 | Volume 14 | Article 362
ORIGINAL RESEARCH
published: 15 April 2020
doi: 10.3389/fnins.2020.00362
Edited by:
Jonathan B. Fritz,
New York University, United States
Reviewed by:
Paul Hinckley Delano,
University of Chile, Chile
Jaakko Kauramäki,
University of Helsinki, Finland
John J. Guinan,
Massachusetts Eye & Ear Infirmary
and Harvard Medical School,
United States
*Correspondence:
Lars Riecke
l.riecke@maastrichtuniversity.nl
Specialty section:
This article was submitted to
Auditory Cognitive Neuroscience,
a section of the journal
Frontiers in Neuroscience
Received: 06 November 2019
Accepted: 24 March 2020
Published: 15 April 2020
Citation:
Riecke L, Marianu I-A and
De Martino F (2020) Effect of Auditory
Predictability on the Human
Peripheral Auditory System.
Front. Neurosci. 14:362.
doi: 10.3389/fnins.2020.00362
Effect of Auditory Predictability on
the Human Peripheral Auditory
System
Lars Riecke1
*, Irina-Andreea Marianu1 and Federico De Martino1,2
1 Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht,
Netherlands, 2 Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
Auditory perception is facilitated by prior knowledge about the statistics of the acoustic
environment. Predictions about upcoming auditory stimuli are processed at various
stages along the human auditory pathway, including the cortex and midbrain. Whether
such auditory predictions are processed also at hierarchically lower stages—in the
peripheral auditory system—is unclear. To address this question, we assessed outer
hair cell (OHC) activity in response to isochronous tone sequences and varied the
predictability and behavioral relevance of the individual tones (by manipulating toneto-tone probabilities and the human participants’ task, respectively). We found that
predictability alters the amplitude of distortion-product otoacoustic emissions (DPOAEs,
a measure of OHC activity) in a manner that depends on the behavioral relevance of the
tones. Simultaneously recorded cortical responses showed a significant effect of both
predictability and behavioral relevance of the tones, indicating that their experimental
manipulations were effective in central auditory processing stages. Our results provide
evidence for a top-down effect on the processing of auditory predictability in the human
peripheral auditory system, in line with previous studies showing peripheral effects of
auditory attention.
Keywords: auditory attention, auditory efferent, prediction, expectancy, cochlea, electroencephalography,
otoacoustic emission
INTRODUCTION
Many socially relevant sounds in our natural environment arise from acoustic signals that have
characteristic, regular spectral-temporal structures. The melody and rhythm of music, for instance,
arise from specific spectral and temporal relations among the individual notes. Such a regular
structure renders the constituent acoustic elements more predictable (in both time and spectral
content), and human listeners can exploit this predictability to process and perceive the acoustic
input more effectively. For example, prior knowledge of the pitch of an upcoming tone has been
shown to facilitate perceptual detection of this tone in noise (Hafter et al., 1993). Prior pitch cues,
if valid, also improve listeners’ judgments of the pitch, duration, and intensity of tones presented
in melodic contexts (Dowling et al., 1987) or isolation (Mondor and Bregman, 1994; Ward and
Mori, 1996). How the brain implements auditory predictions has been investigated extensively over
the last decade. The common view (e.g., Clark, 2013) is that the brain aims to match ‘bottom-up’
acoustic input with ‘top-down’ auditory predictions at multiple levels of the auditory processing
hierarchy by generating and dynamically updating the neural activity patterns that upcoming
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Riecke et al. Peripheral Processing of Predictable Sounds
acoustic inputs are expected to evoke. This predictive coding
theory (Rao and Ballard, 1999; Friston, 2005) has been supported
by a large number of human studies showing evidence for
predictive processing in the auditory cortex (reviews: Bendixen
et al., 2012; Heilbron and Chait, 2018) and hierarchically lower,
subcortical processing stages, including the medial geniculate
body and the inferior colliculus (Slabu et al., 2012; Cacciaglia
et al., 2015) (reviews: Winkler et al., 2009; Grimm and Escera,
2012; Chandrasekaran et al., 2014). It is still unclear whether
predictive processing occurs also at the lowest stage of auditory
processing, in the peripheral auditory system.
The peripheral auditory system receives top-down feedback
from the central auditory system via the medial olivocochlear
(MOC) efferent system (Huffman and Henson, 1990; LopezPoveda, 2018). The MOC system is a network of neurons
located in the medial part of the superior olivary complex in
the brainstem that receives ascending input from the cochlear
nucleus and descending input via corticofugal projections.
Efferent MOC fibers project to outer hair cells (OHCs) in
the cochlea and activation of these fibers alters OHC activity,
effectively reducing cochlear gain (reviews: Guinan, 2006, 2018).
Efferent MOC-fiber activity can be modulated in a ‘topdown’ manner: electric microstimulation or deactivation of the
auditory cortex alters OHC activity as measured with cochlear
microphonics or otoacoustic emissions (OAEs) (Perrot et al.,
2006; Dragicevic et al., 2015; Terreros and Delano, 2015; Jager and
Kossl, 2016). Similarly, changes in arousal or endogenous (interor intramodal) attention may lead to OHC-activity changes as
measured with OAEs (Puel et al., 1988; Froehlich et al., 1990,
1993; Giard et al., 1994; Ferber-Viart et al., 1995; Maison et al.,
2001; de Boer and Thornton, 2007; Harkrider and Bowers, 2009;
Smith et al., 2012; Srinivasan et al., 2012, 2014; Walsh et al., 2014,
2015; Wittekindt et al., 2014; Smith and Cone, 2015), although
the existence and direction of these top-down attention effects
are still debated (Picton et al., 1971; Avan and Bonfils, 1992;
Michie et al., 1996; Beim et al., 2018, 2019; Francis et al., 2018;
Lopez-Poveda, 2018).
Given that the efferent MOC system may reflect the state of
endogenous attention, it might be able to reflect also the presence
of auditory predictions generated in the central auditory system.
Indeed, sectioning the efferent MOC fibers in humans impairs the
aforementioned facilitating effect of pitch cues on tone-in-noise
detection (Scharf et al., 1997). Animal electrophysiology findings
further show that auditory-nerve fibers adapt to the statistics of
acoustic input (Joris and Yin, 1992; Wen et al., 2009). These
findings suggest that the peripheral auditory system may play a
role in auditory predictions.
In the present study, we tested the hypothesis that auditory
predictions are processed in the human auditory peripheral
system. We presented 22 human listeners with isochronous
complex-tone sequences and assessed tone-evoked OHC activity
by measuring distortion-product OAEs (DPOAEs). To induce
variations in auditory prediction we varied the predictability
of the individual tones within a sequence (by manipulating
tone-to-tone probabilities and keeping their acoustic properties
constant) while concomitantly manipulating their behavioral
relevance (by changing the listeners’ task). To check for the
effect of our manipulation at central auditory processing stages,
we simultaneously measured cortical tone-evoked activity (using
electroencephalography, EEG) and behavioral auditory-detection
performance. According to our hypothesis, auditory predictions
should alter both cortical and peripheral tone-evoked activity. We
predicted that increases in auditory predictability would lead to
significant changes in both DPOAE and EEG that are strongest
when the tones are behaviorally relevant.
MATERIALS AND METHODS
Participants
Twenty-two healthy volunteers (ages: 19–28 years, 15 females)
participated in the study. They had normal hearing (see
section “Procedure”) and normal or corrected-to-normal vision.
Participants gave their written informed consent before taking
part and were compensated for their participation. The
experimental procedure was approved by the local research
ethics committee (Ethical Review Committee Psychology and
Neuroscience, Maastricht University).
Stimuli and Tasks
Auditory Stimuli
Figure 1A illustrates an exemplary auditory stimulus. Auditory
stimuli were isochronous sequences of five complex tones.
Each tone lasted 340 ms and was preceded by a silent gap
of 20 ms (pre-tone interval). Each tone was composed of two
synchronous sinusoids—so-called primaries—with frequencies
f 1 and f 2 = 1.22 × f 1, resulting in a cubic distortion product,
DP = 2 × f 1 − f 2. The intensity of the higher primary was defined
as the test level. The intensity of the lower primary was always set
15 dB lower. These settings were chosen to facilitate elicitation of
DPOAEs (Probst et al., 1991). Exact values for DP and test level
were set individually for each participant (see section “Auditory
Stimulus Fine-Tuning”). On half of the trials, a pseudorandomly
chosen tone of the sequence contained a frequency glide during
its final 40-ms portion, defining the auditory target. The glide
was implemented by linearly changing the frequency of each
primary by 20%.
Visual Stimuli
Figure 1B illustrates an exemplary visual stimulus. Visual stimuli
were displays of the letters ‘L’ and ‘T’ on a PC screen. The letters
were arranged pseudorandomly in a 4 × 4 matrix spanning a
visual angle of approximately 5◦
. Each instance of the letter ‘L’ was
colored in blue or red. Each instance of the letter ‘T’ was colored
in blue, except for a single pseudorandomly chosen instance that
was colored in red on half of the trials to define the visual target.
Tasks
On half of the trials, participants performed a behavioral task in
the auditory sensory modality, for which they were instructed
to attend to the auditory stimuli, ignore the visual stimuli, and
detect the auditory target. On the other half, they performed the
task in the visual modality, for which they received analogous
instructions. Participants were informed that the probability of
the target to occur was 50% for each task. They were further
instructed to keep still and postpone any movement to a rest
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FIGURE 1 | Stimuli, tasks, experimental design, and procedure. Panel (A) illustrates an exemplary auditory stimulus in a schematic spectrogram. The auditory stimuli
comprised an isochronous sequence of five complex tones that were designed to evoke five consecutive OAEs at different DPs. The circle highlights an auditory
target, which was a brief frequency glide at the end of one of tones. Panel (B) illustrates an exemplary visual stimulus. The circle highlights the visual target, which
was the red letter ‘T’. Panel (C) illustrates the 2 × 2 experimental design. The predictability of the tones was varied by changing their frequency either
pseudorandomly (low tone predictability) or monotonically (high tone predictability) across the auditory stimulus. The behavioral relevance of the tones was varied by
having participants perform an auditory or visual target-detection task that required them to focus their attention on the tones (high tone relevance) or ignore them
(low tone relevance), respectively. The yellow squares outline the task-relevant stimuli. Panel (D) sketches the overall procedure (top) and the task/stimulus protocol
in the main experiment (bottom). Colored rectangles represent blocks of 50 trials from a given experimental condition (see C). Blue and red hue represents low and
high tone-relevance condition (R), respectively. Light and saturated color represents low and high tone-predictability condition (P), respectively. Each condition was
presented twice and the order of blocks was counterbalanced across participants. The direction of the tone sequence [ascending (see A) or descending] was fixed
within high-predictability blocks and counterbalanced across the two presentations of these blocks.
interval to avoid artifacts in the physiological recordings during
stimulation intervals.
Trials contained a stimulation interval of 1.8 s followed by
a response and rest interval of in total 7.1 s. The stimulation
interval involved the synchronous presentation of an auditory
and visual stimulus. Participants reported immediately after
the stimulation whether they had perceived the designated
target (‘yes’ response) or not (‘no’ response) by pressing a
corresponding key with the index or middle finger of their
right hand. After each response, they received visual feedback
regarding response correctness and then relaxed until the next
trial. Trials were preceded by silent gaps of 60 ms (pre-trial
interval) during which the OAE recording started up (see section
“OAE Recording”).
Experimental Design
Figure 1C illustrates the experimental design. The study used a
2 × 2 within-subjects design. The independent variables were
the predictability of the tones and the behavioral relevance of the
tones. The manipulation of tone predictability was implemented
by sequencing the five tones in an either highly predictable
or unpredictable manner: Tone frequency was changed either
monotonically (ascending or descending) or pseudorandomly
across the tone sequence in each auditory stimulus, resulting
in a local (tone-to-tone) probability of either 100% (highpredictability condition) or on average 45.7% (low-predictability
condition; probability per position: 20, 25, 33, 50, and 100%).
The increase in probability across positions within pseudorandom sequences resulted from permuting the five tones
without repetition, which was done to distribute tone frequencies
(and the associated DPOAE levels) equally across conditions.
The manipulation of the behavioral relevance of the tones was
implemented by rendering the auditory stimuli either relevant or
irrelevant for the participants’ behavioral goal. Participants were
instructed to perform either the auditory task (high tone-relevance
condition) or the visual task (low tone-relevance condition), which
required them to focus their attention on the tones or ignore
them, respectively (see section “Tasks”).
To further facilitate global (sequence-to-sequence) predictions
in the high-predictability condition and avoid exhaustive task
switching, trials belonging to a given condition were presented
in blocks; see Figure 1D (bottom). The order of blocks
was counterbalanced across participants to reduce potential
carryover effects. Furthermore, the following variables were
counterbalanced across trials within each block: the frequency
and sequential position of the auditory target tone, the location
of the visual target, and the sequential position of each tone
(only for low-predictability blocks). The high- and low-relevance
conditions were matched for stimulation.
Procedure
Figure 1D (top) summarizes the session procedure, which was
conducted in a sound-attenuated, electrically shielded chamber
isolated from the experimenter. Participants were first screened
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for potential hearing loss, defined as a pure-tone hearing
threshold above 25 dB HL at 0.75, 1, 1.5, 2, 3, or 4 kHz
in the test ear.
Auditory Stimulus Fine-Tuning
Acoustically induced MOC effects tend to be more observable
for higher signal-to-noise ratio (SNR) OAE measurements
(Goodman et al., 2013) and for OAEs elicited at lower test
levels (Ryan and Kemp, 1996; Veuillet et al., 1996), probably
because OHCs apply less cochlear gain to lower-level acoustic
input (Robles and Ruggero, 2001). To increase the likelihood of
observing an auditory prediction effect, test levels in our study
were chosen to be relatively low while being sufficiently high to
reliably elicit DPOAEs of similar SNR across participants.
To this end, a minimum test level and a set of most effective
tones were defined individually for each participant as follows.
First, DPOAEs were measured for eight different tones at a
relatively high test level of 50 dB SPL. The frequencies of these
tones were chosen to ensure that DPs (390, 780, 1,560, 1,950,
2,730, 3,120, 3,510, or 3,900 Hz) fell into distinct, resolvable
frequency bins in the peripheral auditory system (Glasberg and
Moore, 1990) and our OAE data analysis (see section “OAE Data
Analysis”). Each DPOAE measurement involved 50 repetitions
of a given tone presented at the same rate as the tones within
the experimental stimuli. The tones that were found to evoke
the five highest DPOAE SNRs (DPOAE level relative to the noise
floor, see section “OAE Data Analysis”) were selected for the main
experiment. The frequency distribution of the DPs that were
selected for the main experiment is shown in Figure 2A.
Second, for the least effective selected tone (the one yielding
the lowest DPOAE SNR), a DPOAE threshold was measured,
defined as the minimum test level required for eliciting a
DPOAE SNR of minimally 6 dB in half of the epochs. The
threshold was measured with an adaptive staircase procedure
and a one-down one-up tracking rule as follows: the test level
was initially set to 35 dB SPL and then adaptively changed
until eight reversals in the direction of change (from increasing
to decreasing level or vice versa) occurred, after which the
procedure terminated. The change size was gradually reduced
from an initial 12 dB, to 6 dB (after the second reversal) and
then to 3 dB (after the fourth reversal). The DPOAE threshold
was computed as the average test level at the last four reversals.
Third, to ensure that all tones reliably elicited DPOAEs at
a fixed low test level, DPOAEs were measured for the five
selected tones as above, now using as test level the obtained
DPOAE threshold. When a tone was observed to fail the criterion
(DPOAE SNR ≥ 3 dB), the test level was increased by 3 dB and
the measurement was repeated until the criterion was met. The
test level resulting from this procedure was used for all tones in
the main experiment and its average value was 30.7 ± 6.9 dB SPL
(mean ± SD across participants, range: 18.5–48.5 dB SPL).
Experiment
Participants were familiarized with the stimuli and the auditory
and visual targets, and they practiced the tasks before the
experiment. Each experimental block contained 50 trials
(corresponding to 250 tones), resulting in a block duration of
7.7 min. For each task condition (high or low tone-relevance),
the low-predictability condition was presented in two blocks and
each high-predictability condition (ascending or descending) was
presented in a single block. This resulted in the presentation of
eight blocks in total (Figure 1D bottom), corresponding to an
overall number of 400 trials (corresponding to 2,000 tones) and
an overall experiment duration of 61.3 min excluding breaks.
Consecutive blocks were separated by short breaks terminated
by the participant. Before and during each block, the current
tone-frequency order (ascending, descending, or random) and
the current task (auditory or visual) were visually indicated to the
participant. After the experiment, participants rated the difficulty
of each task on a five-point scale.
OAE Recording
The two primaries were presented to the participant’s right
ear via two speakers mounted in a calibrated in-ear probe.
OAEs were simultaneously recorded with a microphone in
the probe. Auditory stimulation and recordings were sampled
at 22.05 kHz and controlled using the Interacoustics Titan
and Research Platform software (Interacoustics, Middelfart,
Denmark) running on MATLAB (MathWorks, Natick, MA,
United States). The Titan device requires the acquisition of
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previous attention studies using acoustic MOC-reflex elicitors
(e.g., Beim et al., 2018). Figure 4B shows the DPOAE level
for each condition. Task-relevant and task-irrelevant tones
(auditory vs. visual task) elicited DPOAEs of overall similar
level. When the tones were task-relevant, DPOAEs elicited
by highly predictable tones were on average 0.54 ± 0.24 dB
stronger than those elicited by less predictable tones. For
task-irrelevant tones, a difference in the opposite direction
(−0.42 ± 0.20 dB) was observed. Statistical analysis confirmed
these observations, revealing no significant main effect of
tone predictability (F1,21 = 0.12, P = 0.73) or tone relevance
(F1,21 = 0.32, P = 0.58), and a significant interaction tone
predictability × tone relevance (F1,21 = 11.50, P = 0.0028).
Post hoc tests revealed a significant positive effect of tone
predictability in the high relevance condition (t21 = 2.21,
P = 0.038), and an opposite non-significant effect in the
low relevance condition (t21 = −2.08, P = 0.051). Thus,
these results provide evidence that the effect of auditory
predictability on tone-evoked DPOAEs depends on the
behavioral relevance of the tones.
The predictability-related difference in the high relevance
condition, which we refer to as 1DPOAElevel, was slightly larger
for tones presented at early positions within the tone sequence
vs. tones presented at later positions within the same sequence
(first vs. second half of the tone sequence, balanced for tone
frequencies; average difference: 0.17 dB; Figure 4C).
To exclude that the OAE results could be explained by
potential residual participant motion (Francis et al., 2018), the
FIGURE 3 | Behavioral results. Panel (A) illustrates participants’ overall performance as assessed with d
0
for each target-detection task. Panel (B) illustrates
performance on the auditory task for each tone-predictability condition, and the probability value associated with the effect of predictability. Panel (C) illustrates the
predictability-related change in auditory performance (1Auditoryperformance = high minus low-predictability condition) during the first and second half of the tone
sequence. All data show summary statistics (mean ± SEM) across participants.
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FIGURE 4 | OAE results. Panel (A) illustrates the overall sound-pressure level spectrum of participants’ OAE recordings, averaged across tone frequencies and tone
positions, and aligned with respect to the distortion product (DP) frequency. The DPOAE level was computed as the magnitude of the DP-frequency bin. The noise
floor (delineated by the dashed lines) was computed as the average level in a 20-Hz wide band centered on, and excluding, the DP-frequency bin. The DPOAE SNR
was computed by subtracting the noise floor from the DPOAE level and was observed to be on average 13.4 ± 4.8 dB (mean ± SD across participants). Panel (B)
illustrates the DPOAE level pooled across tone frequencies and tone positions for each condition, and probability values associated with the effect of predictability
(for each tone-relevance condition) and its modulation by tone relevance. Panel (C) illustrates the predictability-related change in DPOAE levels (1DPOAElevel = high
minus low-predictability condition) elicited during the first and second half of the tone sequence in the auditory task. Panels (D,E) are analogous to panel (B), but
show, respectively, the level of the noise floor and the level of physiological ear-canal noise (instead of the DPOAE level). All data show summary statistics
(mean ± SEM) across participants.
same statistical analyses as above were applied to the level
of the noise floor and a measure of physiological ear-canal
noise (the broadband sound level recorded during the pretone interval; see section “OAE Data Analysis”). Results are
shown, respectively, in Figures 4D,E, revealing no significant
interaction tone predictability × tone relevance (noise floor:
F1,21 = 0.038, P = 0.85; physiological ear-canal noise: F1,21 = 0.13,
P = 0.73) and no significant effect of tone predictability in
either the high relevance condition (noise floor: t21 = −0.60,
P = 0.55; physiological ear-canal noise: t21 = −1.73, P = 0.10)
or low relevance condition (noise floor: t21 = −0.46, P = 0.65;
physiological ear-canal noise: t21 = −1.17, P = 0.26). These
statistical results indicate that the noise level did not differ
significantly among the experimental conditions of interest.
However, note that the non-significant results for the ear-canal
noise may partially relate to the much shorter observation
window (pre-tone interval < tone interval). EEG Results Figure 5 shows the EEG results. Figure 5A shows participants’ AEP pooled across tone frequencies and positions, irrespective of the presence of the auditory target; the upper plot shows these data pooled across conditions and the lower plot shows them separately for each condition, revealing differences between conditions especially during the interval of the N1 component. Figure 5B shows the extracted N1 peak amplitude for each condition. Poorly predictable tones elicited overall stronger N1 amplitudes than highly predictable tones. Moreover, taskrelevant tones elicited overall stronger N1 amplitudes than task-irrelevant tones. Statistical analysis of N1 peak amplitude confirmed these observations, revealing significant main effects of tone predictability (F1,21 = 12.19, P = 0.0022) and tone relevance (F1,21 = 19.16, P = 0.00026), and no significant interaction tone predictability × tone relevance (F1,21 = 1.11, P = 0.30). Post hoc tests showed a significant effect of tone predictability in the low tone-relevance condition (t21 = −3.58, P = 0.00087) and a corresponding non-significant effect in the high tone-relevance condition (t21 = −1.70, P = 0.052). The trend in the high tone-relevance condition, which we refer to as 1N1amplitude, was slightly larger for tones presented at early positions within the tone sequence vs. tones presented at later positions within the same sequence (first vs. second half of the tone sequence, balanced for tone frequencies; average difference: 0.14 µV; Figure 5C). Analogous analyses of P1 and P2 revealed no significant result (all P > 0.05),
except for a significant main effect of tone relevance on P1
(F1,21 = 5.86, P = 0.025). Thus, the EEG results indicate that
the predictability and behavioral relevance of tones modulate the
amplitudes of N1 and possibly longer-latency AEP components
(see section “Discussion”).
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FIGURE 5 | EEG results. Panel (A) illustrates participants’ average AEP evoked by single tones at frontal-central electrodes. The upper plot shows data pooled
across tone frequencies, tone positions, and conditions, irrespective of the presence of the auditory target (mean ± SEM across participants, represented by the
black waveform and shaded area). The horizontal bar represents the interval of a single tone and the dashed vertical lines delineate time windows from which peak
amplitudes of P1, N1, and P2 were extracted. The lower plot shows the same data as the upper plot, but stratified for conditions (mean across participants). The
different conditions are represented by different hue and brightness, which are labeled in (B). Panel (B) illustrates N1 peak amplitude for each condition
(mean ± SEM across participants), and probability values associated with the effect of predictability (for each tone-relevance condition) and its modulation by tone
relevance. Panel (C) illustrates the predictability-related change in N1 amplitudes (1N1amplitude = high minus low-predictability condition) elicited during the first and
second half of the tone sequence in the auditory task.
Combined OAE-EEG Results
Figure 6 shows results from the correlation analysis testing
for coupling between predictability effects on OAE and EEG
(1OAE and 1EEG). The same measures as above were analyzed
(DPOAE level and N1 peak amplitude). Initial analyses yielded
no significant correlation between predictability effects on
OAE, EEG, or behavior (all P > 0.05). Restricting the
analysis to those participants who benefited from predictability
as expected (better auditory performance for high vs. lowpredictability condition; N = 14, of which two were outliers
and rejected) revealed a significant positive correlation between
1DPOAElevel and 1N1amplitude (τ = 0.39, P = 0.043). No
significant correlation was observed in the low relevance
FIGURE 6 | Combined OAE-EEG results. The scatterplot shows results from
a correlation analysis testing for coupling between predictability effects on
OAE and EEG (1DPOAElevel × 1N1amplitude) during the auditory task. The
analysis focused on data of participants whose auditory performance showed
a qualitative benefit from predictability. Filled circles represent those
participants; open circles represent remaining participants and two outliers.
Correlation coefficient τ and P-value describe, respectively, the strength and
statistical significance of the linear relationship (regression line, plotted in gray).
condition (τ = 0.23, P = 0.14). These results suggest that
predictability effects on DPOAEs and N1 amplitude might be
functionally coupled.
DISCUSSION
The goal of our study was to test whether auditory predictions
are processed in the human auditory peripheral system. To
this end, we assessed whether OHCs are sensitive to the
predictability of acoustic input by measuring DPOAE-level
changes between a statistically structured sequence and a
pseudorandom sequence composed of the same tones. Each tone
had an equal probability of occurrence (20%), but the toneto-tone (transitional) probabilities differed: only in the highly
predictable sequence, all transitional probabilities were 100% and
thus allowed listeners to generate strong auditory predictions. We
observed that physically identical tones with different transitional
probabilities can elicit different DPOAEs dependent on whether
these tones are attended. Based on this result, we conclude
that auditory predictability may influence OHC activity under
auditory attention.
Predictability-Induced Changes in
Auditory Peripheral Responses
We observed a significantly larger predictability-induced
DPOAE-level change in the high vs. low-relevance condition.
A possible explanation for this effect is that the auditory,
but not visual, task required participants to pay attention to
the acoustic input and thereby encouraged them to actively
generate and exploit predictions. The effect of our auditory
task on predictability-induced DPOAE changes fits with
previous OAE results showing effects of auditory attention
on peripheral auditory processing, although these results are
still debated (see section “Introduction”). Moreover, it is in
line with studies showing a top-down attentional modulation
of the effect of auditory predictability on cortical responses
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(reviews: Schroger et al., 2015; Heilbron and Chait, 2018).
Probably only under auditory attention, predictive brain signals
in our study (which we observed in cortex; see next section) were
fed in a ‘top-down’ manner and gated to the peripheral auditory
system. Alternatively, visual attention may have prevented these
predictive signals from reaching the peripheral auditory system.
We further observed a positive effect of predictability
on DPOAE levels in the high-relevance condition, which
provides support for our hypothesis that auditory predictability
influences peripheral auditory processing. Whether the observed
effect reflects a potential cochlear amplification of actively
anticipated input or an attenuation of this input cannot be
disambiguated based on our DPOAE data. Efferent MOC
signals may alter the amplitudes and phases of the two
DPOAE components (primaries) by different amounts, which
can result in either positive or negative DPOAE-level changes
depending on the specific primary frequencies (Guinan, 2006)—
an ambiguity that might explain why some previous DPOAE
studies observed opposite effects of auditory attention (Smith
et al., 2012; Srinivasan et al., 2012, 2014; Wittekindt et al., 2014).
Future studies may disambiguate the direction of peripheral
predictability effects by measuring, e.g., stimulus-frequency
OAEs (SFOAEs), which require only a single test frequency but
otherwise more sophisticated methods (Guinan, 2006; LopezPoveda, 2018). In sum, our DPOAE results suggest that OHCs
may be sensitive to auditory predictability when the listener is
paying top-down attention to the acoustic input.
Predictability-Induced Changes in
Cortical Responses
We observed that highly predictable tones evoke significantly
smaller N1 amplitudes than poorly predictable tones, which is
in line with previous EEG results showing a suppressive effect
of auditory predictability on N1 (Schafer and Marcus, 1973;
Martikainen et al., 2005; Baess et al., 2011). Given that listeners
in our study could generate accurate predictions only in the
high-predictability condition, the suppressive effect on N1 may
reflect a cortical signal of fulfilled predictions (a ‘match’ signal),
specifically the so-called repetition positivity (RP) (Bendixen
et al., 2012). The auditory RP is an attenuation of auditory-evoked
responses over the frontocentral scalp in a latency range from 50
to 250 ms. It increases with the number of stimulus repetitions
(Bendixen et al., 2008) and the predictability of these stimuli
(Costa-Faidella et al., 2011; Todorovic et al., 2011) even when
attention is diverted away from them (Haenschel et al., 2005). The
early portion (40–60 ms) of the RP is mostly affected by stimulus
repetitions, whereas its later portion (100–200 ms) is more
sensitive to predictability (Todorovic and de Lange, 2012). Thus,
the suppressive effect on N1 in our study may reflect auditory
predictions rather than sensory adaptation (Heilbron and Chait,
2018). However, the observed suppression may also reflect
frequency-specific adaptation, which was probably stronger in
the high vs. low-predictability condition (due to overall smaller
tone-to-tone frequency changes). While consecutive DPOAEs
could not interact within single auditory filters in the peripheral
auditory system (see section “Auditory Stimulus Fine-Tuning”),
they might have done so within broader filters in the auditory
cortex (Bartlett and Wang, 2005). It should be noted that the
observed cortical suppression may have affected also much later
AEPs. Owing to the fast tone-presentation rate, these later AEPs
and consecutive N1 responses overlapped, making it difficult to
disentangle them (see section “EEG Data Analysis”).
The observed suppressive effect presumably extends to the
auditory brainstem, as suggested by corresponding predictability
effects observed in an EEG study that measured auditory
brainstem responses to auditory stimuli similar to ours (patterned
vs. pseudorandom complex tone sequences) (Skoe et al., 2013).
This would imply that our high-predictability condition induced
an internal predictive model in the cortex (and possibly
subcortex) that may have sent predictive signals ‘top-down’ to
hierarchically lower auditory processing stages, especially when
the listener paid attention to the acoustic input.
We did not observe cortical signals of violated predictions
(‘mismatch’ signals), such as the mismatch negativity (Naatanen
et al., 2001), probably because the high-predictability condition
contained no regularity violation and the low-predictability
condition did not allow generating highly accurate predictions.
We further observed that behaviorally relevant tones evoked
significantly larger N1 amplitudes than irrelevant tones, which is
in line with previous EEG results showing an enhancing effect
of auditory attention on N1 (e.g., Picton and Hillyard, 1974;
Naatanen, 1982).
In sum, while our EEG results cannot disentangle effects
on N1 and much later AEP components, they confirm that
our experimental manipulations of predictability and behavioral
relevance were effective in the cortex.
Coupling Between Predictability Effects
on Cortical and Peripheral Responses?
Our results suggest that predictability effects on DPOAE and
N1 might be positively correlated. More specifically, listeners
who showed larger predictability effects on DPOAE tended to
show also larger predictability effects on N1 amplitude. Moreover,
predictability-induced changes in DPOAE, N1, and auditory
performance were qualitatively (non-significantly) stronger for
tones presented early vs. late during a given tone sequence,
which might reflect reduced effectiveness of our predictability
manipulation toward the end of auditory stimuli (due to the
blocked design and the fact that tone-to-tone probabilities
in the low-predictability condition approximated those in the
high-predictability condition at later tone positions; see section
“Experimental Design”). Together, these observations might
suggest that prediction effects on different auditory processing
stages are functionally coupled, which would imply that the
effects propagate through the auditory system. However, it should
be noted that the interpretation of our correlation results needs
to be treated with caution because these results are based on
only a subset of participants (those who appeared to benefit
from predictions).
Previous studies indeed found similar prediction effects in
multiple stages of the central auditory processing hierarchy
(see section “Introduction”). Whether these stages locally
generate auditory predictions or inherit them ‘top-down’ from
higher stages is still unclear (Escera and Malmierca, 2014;
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Riecke et al. Peripheral Processing of Predictable Sounds
Malmierca et al., 2019), although auditory prediction effects seem
to become stronger toward higher hierarchical stages (Gill et al.,
2008; Rummell et al., 2016). It is further possible that functional
coupling of cortical and peripheral top-down modulations is
mediated by slow (<10 as="" br="" brain="" hz="" periodic="" signals="" suggested=""> by recent DPOAE/EEG-oscillation findings on endogenous
intermodal (audiovisual) attention (Dragicevic et al., 2019).
No Significant Effect of Predictability on
Behavioral Responses
We observed better auditory performance in the high vs. lowpredictability condition, but this difference was only small
and not statistically significant. Thus, apparently not all our
participants made effective use of predictions, despite the fact that
our auditory stimuli and task were designed to encourage the use
of such predictions. A possible explanation for our null result is
that the pitch-specific predictions that could be inferred from our
highly predictable stimuli did not provide the most potent cue for
detecting the auditory target (which was a sudden pitch change
within a tone). Moreover, perceptual benefits from predictions
may be more observable under conditions of sensory uncertainty
or ambiguity (de Lange et al., 2018). Our auditory targets were not
necessarily ambiguous or near participants’ perceptual detection
threshold, implying that predictions derived from our stimuli
were not optimally effective for these particular stimuli and our
auditory task. In sum, our behavioral null result shows that our
participants’ auditory perception did not reliably benefit from
predictions. Future studies may achieve stronger predictability
effects by using more ambiguous auditory stimuli and improved
tasks that allow for stricter control of listeners’ use of predictions.
Critical Considerations
The level of our auditory stimuli (on average ∼30 dB SPL)
was probably sufficiently low to prevent elicitation of middleear muscle reflexes, but whether it was sufficiently high to elicit
ipsilateral MOC reflexes, which seem to require levels of at least
∼30 dB SPL (Guinan et al., 2003), remains unclear. Even if not all
tones in our study elicited MOC reflexes, it remains possible that
corticofugal top-down signals modulated MOC efferent activity
in the absence of an acoustic elicitor, as suggested by studies
showing attention effects on OAEs without an acoustic MOCreflex elicitor (Puel et al., 1988; Giard et al., 1994) (but see Picton
et al., 1971). Future studies should measure OAEs at higher SNR
and use contralateral acoustic noise to control MOC reflexes
more effectively to increase the strength of top-down modulation
of the MOC reflex.
The auditory targets in our auditory task (which were defined
by the pitch of the two primaries) differed from the investigated
DPOAE frequencies, similar to some previous OAE attention
studies (Srinivasan et al., 2012, 2014; Wittekindt et al., 2014).
It is unlikely that this difference largely reduced effect sizes in
our study because acoustically evoked (Norman and Thornton,
1993; Lisowska et al., 2002) (review: Guinan, 2018) and attentionmodulated (Srinivasan et al., 2012, 2014; Wittekindt et al., 2014;
Beim et al., 2018) MOC reflexes show only little frequency tuning
in humans.
It should be noted that our interpretation is constrained
by our auditory stimulus design to predictions of pitch or
spectral structure. Moreover, the high-predictability blocks
involved exposure to fixed patterns over relatively long durations,
which likely facilitated long-term learning of acoustic structure.
Whether our results generalize to predictions of other features,
such as timing, or to short-term (echoic) memory of acoustic
structure remains to be investigated.
CONCLUSION
Our study shows that intramodal top-down attention may
modulate the processing of predictable spectral input in the
peripheral auditory system, thereby adding support to current
debate on the sensitivity of the human auditory peripheral system
to top-down modulation (e.g., Beim et al., 2018; Lopez-Poveda,
2018). Our observations further indicate that predictability
may affect auditory processing in the peripheral auditory
system. Future studies may observe stronger predictability effects
on peripheral auditory processing by measuring higher-SNR
SFOAEs in the presence of an acoustic contralateral MOC-reflex
elicitor and using more optimal (ambiguous) auditory stimuli.
DATA AVAILABILITY STATEMENT
The datasets generated for this study are available on request to
the corresponding author.
ETHICS STATEMENT
The studies involving human participants were reviewed
and approved by Ethical Review Committee Psychology and
Neuroscience, Maastricht University. The patients/participants
provided their written informed consent to participate in
this study.
AUTHOR CONTRIBUTIONS
LR, FD, and I-AM conceived the study. I-AM acquired the data.
LR and I-AM performed the statistical analysis. LR wrote the first
draft of the manuscript. All authors contributed to manuscript
revision, read and approved the submitted version.
FUNDING
This work was funded by the Netherlands Organisation for
Scientific Research (NWO; VIDI grant 864-13-012 to FDM).
ACKNOWLEDGMENTS
The authors thank M. Sleijpen, A. Lipinski, R. Brinkmann, J.
Gädtke, P. Künzel, J. Schreurs, and J. van der Meij for help with
the data acquisition.
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REFERENCES
Avan, P., and Bonfils, P. (1992). Analysis of possible interactions of an attentional
task with cochlear micromechanics. Hear. Res. 57, 269–275. doi: 10.1016/0378-
5955(92)90156-h
Baess, P., Horvath, J., Jacobsen, T., and Schroger, E. (2011). Selective suppression
of self-initiated sounds in an auditory stream: an ERP study. Psychophysiology
48, 1276–1283. doi: 10.1111/j.1469-8986.2011.01196.x
Bartlett, E. L., and Wang, X. (2005). Long-lasting modulation by stimulus context
in primate auditory cortex. J. Neurophysiol. 94, 83–104. doi: 10.1152/jn.01124.
2004
Beim, J. A., Oxenham, A. J., and Wojtczak, M. (2018). Examining replicability of an
otoacoustic measure of cochlear function during selective attention. J. Acoust.
Soc. Am. 144:2882. doi: 10.1121/1.5079311
Beim, J. A., Oxenham, A. J., and Wojtczak, M. (2019). No effects of attention
or visual perceptual load on cochlear function, as measured with stimulusfrequency otoacoustic emissions. J. Acoust. Soc. Am. 146:1475. doi: 10.1121/1.
5123391
Bendixen, A., Prinz, W., Horvath, J., Trujillo-Barreto, N. J., and Schroger, E. (2008).
Rapid extraction of auditory feature contingencies. Neuroimage 41, 1111–1119.
doi: 10.1016/j.neuroimage.2008.03.040
Bendixen, A., SanMiguel, I., and Schroger, E. (2012). Early electrophysiological
indicators for predictive processing in audition: a review. Int. J. Psychophysiol.
83, 120–131. doi: 10.1016/j.ijpsycho.2011.08.003
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate - a
practical and powerful approach to multiple testing. J. R. Stat. Soc. B Methodol.
57, 289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x
Brown, G. S., and White, K. G. (2005). The optimal correction for estimating
extreme discriminability. Behav. Res. Methods 37, 436–449. doi: 10.3758/
bf03192712
Cacciaglia, R., Escera, C., Slabu, L., Grimm, S., Sanjuan, A., Ventura-Campos,
N., et al. (2015). Involvement of the human midbrain and thalamus in
auditory deviance detection. Neuropsychologia 68, 51–58. doi: 10.1016/j.
neuropsychologia.2015.01.001
Chandrasekaran, B., Skoe, E., and Kraus, N. (2014). An integrative model of
subcortical auditory plasticity. Brain Topogr. 27, 539–552. doi: 10.1007/s10548-
013-0323-9
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the
future of cognitive science. Behav. Brain Sci. 36, 181–204. doi: 10.1017/
S0140525X12000477
Costa-Faidella, J., Grimm, S., Slabu, L., Diaz-Santaella, F., and Escera, C. (2011).
Multiple time scales of adaptation in the auditory system as revealed by
human evoked potentials. Psychophysiology 48, 774–783. doi: 10.1111/j.1469-
8986.2010.01144.x
de Boer, J., and Thornton, A. R. (2007). Effect of subject task on contralateral
suppression of click evoked otoacoustic emissions. Hear. Res. 233, 117–123.
doi: 10.1016/j.heares.2007.08.002
de Lange, F. P., Heilbron, M., and Kok, P. (2018). How do expectations shape
perception? Trends Cogn. Sci. 22, 764–779. doi: 10.1016/j.tics.2018.06.002
Delorme, A., and Makeig, S. (2004). EEGLAB: an open source toolbox for analysis
of single-trial EEG dynamics including independent component analysis.
J. Neurosci. Methods 134, 9–21. doi: 10.1016/j.jneumeth.2003.10.009
Dowling, W. J., Lung, K. M., and Herrbold, S. (1987). Aiming attention in pitch
and time in the perception of interleaved melodies. Percept. Psychophys. 41,
642–656. doi: 10.3758/bf03210496
Dragicevic, C. D., Aedo, C., Leon, A., Bowen, M., Jara, N., Terreros, G., et al. (2015).
The olivocochlear reflex strength and cochlear sensitivity are independently
modulated by auditory cortex microstimulation. J. Assoc. Res. Otolaryngol. 16,
223–240. doi: 10.1007/s10162-015-0509-9
Dragicevic, C. D., Marcenaro, B., Navarrete, M., Robles, L., and Delano, P. H.
(2019). Oscillatory infrasonic modulation of the cochlear amplifier by selective
attention. PLoS One 14:e0208939. doi: 10.1371/journal.pone.0208939
Escera, C., and Malmierca, M. S. (2014). The auditory novelty system: an attempt
to integrate human and animal research. Psychophysiology 51, 111–123. doi:
10.1111/psyp.12156
Ferber-Viart, C., Duclaux, R., Collet, L., and Guyonnard, F. (1995). Influence of
auditory stimulation and visual attention on otoacoustic emissions. Physiol.
Behav. 57, 1075–1079. doi: 10.1016/0031-9384(95)00012-8
Francis, N. A., Zhao, W., and Guinan, J. J. Jr. (2018). Auditory attention
reduced ear-canal noise in humans by reducing subject motion, not by medial
olivocochlear efferent inhibition: implications for measuring otoacoustic
emissions during a behavioral task. Front. Syst. Neurosci. 12:42. doi: 10.3389/
fnsys.2018.00042
Friston, K. (2005). A theory of cortical responses. Philos. Trans. R. Soc. Lond. B Biol.
Sci. 360, 815–836. doi: 10.1098/rstb.2005.1622
Froehlich, P., Collet, L., Chanal, J. M., and Morgon, A. (1990). Variability of the
influence of a visual task on the active micromechanical properties of the
cochlea. Brain Res. 508, 286–288. doi: 10.1016/0006-8993(90)90408-4
Froehlich, P., Collet, L., and Morgon, A. (1993). Transiently evoked otoacoustic
emission amplitudes change with changes of directed attention. Physiol. Behav.
53, 679–682. doi: 10.1016/0031-9384(93)90173-d
Giard, M. H., Collet, L., Bouchet, P., and Pernier, J. (1994). Auditory selective
attention in the human cochlea. Brain Res. 633, 353–356. doi: 10.1016/0006-
8993(94)91561-x
Gill, P., Woolley, S. M., Fremouw, T., and Theunissen, F. E. (2008). What’s that
sound? Auditory area CLM encodes stimulus surprise, not intensity or intensity
changes. J. Neurophysiol. 99, 2809–2820. doi: 10.1152/jn.01270.2007
Glasberg, B. R., and Moore, B. C. (1990). Derivation of auditory filter shapes
from notched-noise data. Hear. Res. 47, 103–138. doi: 10.1016/0378-5955(90)
90170-t
Goodman, S. S., Mertes, I. B., Lewis, J. D., and Weissbeck, D. K. (2013). Medial
olivocochlear-induced transient-evoked otoacoustic emission amplitude shifts
in individual subjects. J. Assoc. Res. Otolaryngol. 14, 829–842. doi: 10.1007/
s10162-013-0409-9
Grimm, S., and Escera, C. (2012). Auditory deviance detection revisited: evidence
for a hierarchical novelty system. Int. J. Psychophysiol. 85, 88–92. doi: 10.1016/j.
ijpsycho.2011.05.012
Guinan, J. J. Jr. (2006). Olivocochlear efferents: anatomy, physiology, function,
and the measurement of efferent effects in humans. Ear Hear. 27, 589–607.
doi: 10.1097/01.aud.0000240507.83072.e7
Guinan, J. J. Jr. (2018). Olivocochlear efferents: their action, effects, measurement
and uses, and the impact of the new conception of cochlear mechanical
responses. Hear. Res. 362, 38–47. doi: 10.1016/j.heares.2017.12.012
Guinan, J. J. Jr., Backus, B. C., Lilaonitkul, W., and Aharonson, V. (2003).
Medial olivocochlear efferent reflex in humans: otoacoustic emission (OAE)
measurement issues and the advantages of stimulus frequency OAEs. J. Assoc.
Res. Otolaryngol. 4, 521–540. doi: 10.1007/s10162-002-3037-3
Haenschel, C., Vernon, D. J., Dwivedi, P., Gruzelier, J. H., and Baldeweg, T. (2005).
Event-related brain potential correlates of human auditory sensory memorytrace formation. J. Neurosci. 25, 10494–10501. doi: 10.1523/JNEUROSCI.1227-
05.2005
Hafter, E. R., Schlauch, R. S., and Tang, J. (1993). Attending to auditory filters
that were not stimulated directly. J. Acoust. Soc. Am. 94(2 Pt 1), 743–747.
doi: 10.1121/1.408203
Harkrider, A. W., and Bowers, C. D. (2009). Evidence for a cortically mediated
release from inhibition in the human cochlea. J. Am. Acad. Audiol. 20, 208–215.
doi: 10.3766/jaaa.20.3.7
Heilbron, M., and Chait, M. (2018). Great expectations: is there evidence for
predictive coding in auditory cortex? Neuroscience 389, 54–73. doi: 10.1016/j.
neuroscience.2017.07.061
Huffman, R. F., and Henson, O. W. Jr. (1990). The descending auditory pathway
and acousticomotor systems: connections with the inferior colliculus. Brain Res.
Brain Res. Rev. 15, 295–323. doi: 10.1016/0165-0173(90)90005-9
Jaeger, M., Bleichner, M. G., Bauer, A. R., Mirkovic, B., and Debener, S. (2018).
Did you listen to the beat? Auditory steady-state responses in the human
electroencephalogram at 4 and 7 hz modulation rates reflect selective attention.
Brain Topogr. 31, 811–826. doi: 10.1007/s10548-018-0637-8
Jager, K., and Kossl, M. (2016). Corticofugal modulation of DPOAEs in gerbils.
Hear. Res. 332, 61–72. doi: 10.1016/j.heares.2015.11.008
Joris, P. X., and Yin, T. C. (1992). Responses to amplitude-modulated tones in the
auditory nerve of the cat. J. Acoust. Soc. Am. 91, 215–232. doi: 10.1121/1.402757
Lisowska, G., Smurzynski, J., Morawski, K., Namyslowski, G., and Probst, R.
(2002). Influence of contralateral stimulation by two-tone complexes, narrowband and broad-band noise signals on the 2f1-f2 distortion product otoacoustic
emission levels in humans. Acta Otolaryngol. 122, 613–619. doi: 10.1080/
000164802320396286
Frontiers in Neuroscience | www.frontiersin.org 11 April 2020 | Volume 14 | Article 362
fnins-14-00362 April 10, 2020 Time: 17:59 # 12
Riecke et al. Peripheral Processing of Predictable Sounds
Lopez-Poveda, E. A. (2018). Olivocochlear efferents in animals and humans: from
anatomy to clinical relevance. Front. Neurol. 9:197. doi: 10.3389/fneur.2018.
00197
Macmillan, N. A., and Creelman, C. D. (1991). Detection Theory: A User’s Guide.
Cambridge, MA: Cambridge UP.
Maison, S., Micheyl, C., and Collet, L. (2001). Influence of focused auditory
attention on cochlear activity in humans. Psychophysiology 38, 35–40. doi:
10.1111/1469-8986.3810035
Malmierca, M. S., Nino-Aguillon, B. E., Nieto-Diego, J., Porteros, A., PerezGonzalez, D., and Escera, C. (2019). Pattern-sensitive neurons reveal encoding
of complex auditory regularities in the rat inferior colliculus. Neuroimage 184,
889–900. doi: 10.1016/j.neuroimage.2018.10.012
Martikainen, M. H., Kaneko, K., and Hari, R. (2005). Suppressed responses to selftriggered sounds in the human auditory cortex. Cereb. Cortex 15, 299–302.
doi: 10.1093/cercor/bhh131
Michie, P. T., LePage, E. L., Solowij, N., Haller, M., and Terry, L. (1996). Evoked
otoacoustic emissions and auditory selective attention. Hear. Res. 98, 54–67.
doi: 10.1016/0378-5955(96)00059-7
Mondor, T. A., and Bregman, A. S. (1994). Allocating attention to frequency
regions. Percept. Psychophys. 56, 268–276. doi: 10.3758/bf03209761
Naatanen, R. (1982). Processing negativity: an evoked-potential reflection of
selective attention. Psychol. Bull. 92, 605–640. doi: 10.1037/0033-2909.92.3.605
Naatanen, R., Tervaniemi, M., Sussman, E., Paavilainen, P., and Winkler, I. (2001).
"Primitive intelligence" in the auditory cortex. Trends Neurosci. 24, 283–288.
doi: 10.1016/s0166-2236(00)01790-2
Norman, M., and Thornton, A. R. (1993). Frequency analysis of the contralateral
suppression of evoked otoacoustic emissions by narrow-band noise. Br. J.
Audiol. 27, 281–289. doi: 10.3109/03005369309076705
Perrot, X., Ryvlin, P., Isnard, J., Guenot, M., Catenoix, H., Fischer, C., et al. (2006).
Evidence for corticofugal modulation of peripheral auditory activity in humans.
Cereb. Cortex 16, 941–948. doi: 10.1093/cercor/bhj035
Picton, T. W., and Hillyard, S. A. (1974). Human auditory evoked potentials. II.
Effects of attention. Electroencephalogr. Clin. Neurophysiol. 36, 191–199. doi:
10.1016/0013-4694(74)90156-4
Picton, T. W., Hillyard, S. A., Galambos, R., and Schiff, M. (1971). Human auditory
attention: a central or peripheral process? Science 173, 351–353. doi: 10.1126/
science.173.3994.351
Probst, R., Lonsbury-Martin, B. L., and Martin, G. K. (1991). A review of
otoacoustic emissions. J. Acoust. Soc. Am. 89, 2027–2067. doi: 10.1121/1.400897
Puel, J. L., Bonfils, P., and Pujol, R. (1988). Selective attention modifies the active
micromechanical properties of the cochlea. Brain Res. 447, 380–383. doi: 10.
1016/0006-8993(88)91144-4
Rao, R. P., and Ballard, D. H. (1999). Predictive coding in the visual cortex:
a functional interpretation of some extra-classical receptive-field effects. Nat.
Neurosci. 2, 79–87. doi: 10.1038/4580
Robles, L., and Ruggero, M. A. (2001). Mechanics of the mammalian cochlea.
Physiol. Rev. 81, 1305–1352. doi: 10.1152/physrev.2001.81.3.1305
Rummell, B. P., Klee, J. L., and Sigurdsson, T. (2016). Attenuation of responses
to self-generated sounds in auditory cortical neurons. J. Neurosci. 36, 12010–
12026. doi: 10.1523/JNEUROSCI.1564-16.2016
Ryan, S., and Kemp, D. T. (1996). The influence of evoking stimulus level on the
neural suppression of transient evoked otoacoustic emissions. Hear. Res. 94,
140–147. doi: 10.1016/0378-5955(96)00021-4
Schafer, E. W., and Marcus, M. M. (1973). Self-stimulation alters human sensory
brain responses. Science 181, 175–177. doi: 10.1126/science.181.4095.175
Scharf, B., Magnan, J., and Chays, A. (1997). On the role of the olivocochlear
bundle in hearing: 16 case studies. Hear. Res. 103, 101–122. doi: 10.1016/s0378-
5955(96)00168-2
Schroger, E., Marzecova, A., and SanMiguel, I. (2015). Attention and prediction in
human audition: a lesson from cognitive psychophysiology. Eur. J. Neurosci. 41,
641–664. doi: 10.1111/ejn.12816
Skoe, E., Krizman, J., Spitzer, E., and Kraus, N. (2013). The auditory brainstem
is a barometer of rapid auditory learning. Neuroscience 243, 104–114. doi:
10.1016/j.neuroscience.2013.03.009
Slabu, L., Grimm, S., and Escera, C. (2012). Novelty detection in the human
auditory brainstem. J. Neurosci. 32, 1447–1452. doi: 10.1523/JNEUROSCI.
2557-11.2012
Smith, D. W., Aouad, R. K., and Keil, A. (2012). Cognitive task demands modulate
the sensitivity of the human cochlea. Front. Psychol. 3:30. doi: 10.3389/fpsyg.
2012.00030
Smith, S. B., and Cone, B. (2015). The medial olivocochlear reflex in children
during active listening. Int. J. Audiol. 54, 518–523. doi: 10.3109/14992027.2015.
1008105
Srinivasan, S., Keil, A., Stratis, K., Osborne, A. F., Cerwonka, C., Wong, J., et al.
(2014). Interaural attention modulates outer hair cell function. Eur. J. Neurosci.
40, 3785–3792. doi: 10.1111/ejn.12746
Srinivasan, S., Keil, A., Stratis, K., Woodruff Carr, K. L., and Smith, D. W. (2012).
Effects of cross-modal selective attention on the sensory periphery: cochlear
sensitivity is altered by selective attention. Neuroscience 223, 325–332. doi:
10.1016/j.neuroscience.2012.07.062
Terreros, G., and Delano, P. H. (2015). Corticofugal modulation of peripheral
auditory responses. Front. Syst. Neurosci. 9:134. doi: 10.3389/fnsys.2015.00134
Todorovic, A., and de Lange, F. P. (2012). Repetition suppression and expectation
suppression are dissociable in time in early auditory evoked fields. J. Neurosci.
32, 13389–13395. doi: 10.1523/JNEUROSCI.2227-12.2012
Todorovic, A., van Ede, F., Maris, E., and de Lange, F. P. (2011). Prior expectation
mediates neural adaptation to repeated sounds in the auditory cortex: an MEG
study. J. Neurosci. 31, 9118–9123. doi: 10.1523/JNEUROSCI.1425-11.2011
Vaughan, H. G. Jr., and Ritter, W. (1970). The sources of auditory evoked responses
recorded from the human scalp. Electroencephalogr. Clin. Neurophysiol. 28,
360–367. doi: 10.1016/0013-4694(70)90228-2
Veuillet, E., Duverdy-Bertholon, F., and Collet, L. (1996). Effect of contralateral
acoustic stimulation on the growth of click-evoked otoacoustic emissions in
humans. Hear. Res. 93, 128–135. doi: 10.1016/0378-5955(95)00212-x
Walsh, K. P., Pasanen, E. G., and McFadden, D. (2014). Selective attention reduces
physiological noise in the external ear canals of humans. I: auditory attention.
Hear. Res. 312, 143–159. doi: 10.1016/j.heares.2014.03.012
Walsh, K. P., Pasanen, E. G., and McFadden, D. (2015). Changes in otoacoustic
emissions during selective auditory and visual attention. J. Acoust. Soc. Am. 137,
2737–2757. doi: 10.1121/1.4919350
Ward, L. M., and Mori, S. (1996). Attention cueing aids auditory intensity
resolution. J. Acoust. Soc. Am. 100, 1722–1727. doi: 10.1121/1.416069
Wen, B., Wang, G. I., Dean, I., and Delgutte, B. (2009). Dynamic range adaptation
to sound level statistics in the auditory nerve. J. Neurosci. 29, 13797–13808.
doi: 10.1523/JNEUROSCI.5610-08.2009
Winkler, I., Denham, S. L., and Nelken, I. (2009). Modeling the auditory scene:
predictive regularity representations and perceptual objects. Trends Cogn. Sci.
13, 532–540. doi: 10.1016/j.tics.2009.09.003
Wittekindt, A., Kaiser, J., and Abel, C. (2014). Attentional modulation of the
inner ear: a combined otoacoustic emission and EEG study. J. Neurosci. 34,
9995–10002. doi: 10.1523/JNEUROSCI.4861-13.2014
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