# Fast frequency modulation is encoded according to the listener expectations
in the human subcortical auditory pathway

Alejandro Tabas  Stefan Kiebel  Max Planck Institute for Human Cognitive and
Brain Sciences, Leipzig, Germany  Michael Marxen  Katharina von Kriegstein

###### Abstract

Expectations aid and bias our perception. In speech, expected words are easier
to recognise than unexpected words, particularly in noisy environments, and
incorrect expectations can make us misunderstand our conversational partner.
Expectations are combined with the output from the sensory pathways to form
representations of speech in the cerebral cortex. However, it is unclear
whether expectations are propagated further down to subcortical structures to
aid the encoding of the basic dynamic constituent of speech: fast frequency-
modulation (FM). Fast FM-sweeps are the basic invariant constituent of
consonants, and their correct encoding is fundamental for speech recognition.
Here we tested the hypothesis that subjective expectations drive the encoding
of fast FM-sweeps characteristic of speech in the human subcortical auditory
pathway. We used fMRI to measure neural responses in the human auditory
midbrain (inferior colliculus) and thalamus (medial geniculate body).
Participants listened to sequences of FM-sweeps for which they held different
expectations based on the task instructions. We found robust evidence that the
responses in auditory midbrain and thalamus encode the difference between the
acoustic input and the subjective expectations of the listener. The results
indicate that FM-sweeps are already encoded at the level of the human auditory
midbrain and that encoding is mainly driven by subjective expectations. We
conclude that the subcortical auditory pathway is integrated in the cortical
network of predictive speech processing and that expectations are used to
optimise the encoding of even the most basic acoustic constituents of speech.

## Introduction

Expectations can have dramatic effects on sensory processing [1]. A prime
example is speech perception, where expectations influence processing on many
different levels. For instance, word recognition is often completed before the
entire word has been heard [2], and strongly affected by semantic context [3],
word prevalence [4], and prior knowledge [5].

Predictive coding is one of the leading frameworks explaining how expectations
affect perceptual encoding [6, 7, 8]. Predictive coding has been suggested to
form the basis for encoding of speech in the auditory system [5, 9, 10, 11,
12]. Predictive coding formulates perception as a constant process of
hypothesis testing [7, 8]: neural centres at high-order levels perform
predictions on the sensory world via a generative model, while neurons at
lower levels test these predictions against the actual sensory input [13, 14].
A key hypothesis of the framework is that sensory neurons at lower levels do
not encode the features of the stimuli but prediction error: the difference
between the sensory input and the expectations of the observer on that sensory
input. When the input matches the expectations, prediction error neurons do
not need to communicate with higher-level centres, speeding up recognition and
optimising the neural code.

In the human auditory cortex, speech sounds are encoded as prediction error
[15, 16, 17, 18, 19, 20, 21]; whether that is also the case in the subcortical
auditory pathway is unclear. Anatomical and physiological properties make the
subcortical auditory pathway a prime candidate for predictive coding: The
subcortical auditory pathway contains massive cortico-thalamic and cortico-
collicular efferent systems [22, 23, 24, 25] that are well suited to transmit
complex expectations to subcortical nuclei. Moreover, neural populations in
the subcortical auditory pathway are endowed with much shorter time constants
and faster access to acoustic information than neural populations in the
cerebral cortex [26]. This feature renders subcortical auditory pathways
better suited to test hypotheses on the incoming fast dynamics of speech
sounds [27, 28, 29].

Stimulus-specific adaptation (SSA) has been used as a first attempt to test
for predictive coding in the subcortical pathways. SSA is a phenomenon where
individual neurons adapt to repetitions of a pure tone but show recovered
responses to a frequency deviant [30]. SSA is present in single neurons of the
rodent’s auditory thalamus (medial geniculate body; MGB) [31, 32, 33, 34] and
auditory midbrain (inferior colliculus; IC) [31, 35, 36, 37, 38, 39], and in
neural populations of the human IC and MGB [40, 41, 42, 43, 37, 44]. SSA can,
however, be explained both by neural habituation or predictive coding (see
[45] for a review). In the case of pure tones, we have recently used a novel
SSA paradigm which revealed that SSA in human IC and MGB is driven largely by
subjective expectations of the listeners, as hypothesised by predictive coding
but not by neural habituation [40].

In contrast to pure tones, speech sounds comprise highly dynamic elements.
These elements cannot be fully characterised by mixtures of static pure tones.
The most ubiquitous of these dynamic elements are fast frequency-modulated
(FM)-sweeps [46, 47]. Combinations of three fast FM-sweeps of different
average frequency acoustically characterise consonants preceding a vowel.
Accurately perceiving the modulation direction and rate of those FM-sweeps is
crucial for speech comprehension; for instance, the phonemes /ba/ and /da/
differ only on the modulation direction of one of their comprised FM-sweeps
[46].

Whilst pure tones are encoded according to their frequency along the tonotopic
axis already at the basilar membrane [48], FM-sweeps are encoded in FM-
direction and FM-rate selective neurons [49]. In humans, the lowest level in
the auditory hierarchy with evidence for fast FM-direction [50, 51] and rate
[52] selectivity is in auditory cortex. Although FM-sensitive neurons have
been reported in the rodent IC and MGB [53, 54, 55, 49, 56], it is currently
unclear whether FM is also encoded in subcortical stations of the human
auditory pathway. If that was the case, an important question is whether FM-
sweeps, as basic constituent of speech sounds, are encoded according to the
subjective expectations of the listener.

Given the paramount importance of fast FM-sweeps for speech comprehension and
the privileged temporal properties of the subcortical elements of the auditory
pathway, we addressed two key questions. First, whether FM-rate and FM-
direction are already encoded in neural populations of the human IC and MGB.
Second, whether fast FM-sweeps are encoded in IC and MGB according to the
principles of predictive coding; i.e., as prediction error with respect to the
subjective expectations of the listener. To address these questions we
measured blood-oxygen level dependent (BOLD) responses in two key subcortical
structures of the auditory pathway, IC and MGB, while participants listened to
sequences of FM-sweeps. The FM-sweeps were constructed as closely as possible
to FM-sweeps characteristic of speech sounds, and designed in such a way that
they all elicited the same pitch percept and the same average activity along
the tonotopic axis [57, 58]. To test if encoding was mediated by predictive
coding, we introduced abstract rules that assigned different likelihoods to
different FM-sweeps without affecting stimulus statistics [40]. We reasoned
that, if FM-sweeps were encoded according to their objective properties, an
FM-sweep embedded in a specific statistical context should elicit the same
activation no matter the expectations that participants have on its
occurrence. Reversely, if FM-sweeps were encoded according to the principles
of predictive coding, BOLD responses should directly depend on how well the
sensory input fits the expectations of the listeners.

Once established that fast FM-sweeps are encoded as prediction error in IC and
MGB, we addressed two additional questions. First, we investigated whether the
same encoding mechanisms operate in primary (or lemniscal) and secondary (or
non-lemniscal) MGB. Previous findings in the animal literature indicated that
SSA and predictive coding are stronger in secondary subdivisions of the
subcortical pathways (e.g., [31], a finding that is not apparent in humans
[40, 41]). Second, we tested whether the topographic distribution of neural
populations encoding prediction error in IC and MGB were replicated in FM-
sweeps and pure tones. Similar topographies in both stimulus families would
indicate a common mechanism for subcortical predictive coding.

## Results

### Experimental Design and Hypotheses

The stimuli were three fast FM-sweeps: One sweep with a fast negative FM-rate
(frequency span $\Delta f=-200$ Hz), one with a fast positive FM-rate
(frequency span $\Delta f=200$ Hz), and one with a slow positive FM-rate
(frequency span $\Delta f=100$ Hz; Fig 1A-B). We used 50 ms long sweeps in the
frequency range of $f\sim 1500\,$Hz so that they had the typical properties of
formant transitions in speech [47]. The sweep average frequencies were
adjusted so that all FM-sweeps elicited the same average activity along the
tonotopic axis and were perceived as having the same pitch [58, 57]; this
design guaranteed that FM-direction and FM-rate selective neurons were
necessary to differentiate between any two sweeps in the paradigm.

We arranged the stimuli in sequences of 8 FM-sweeps with 7 repetitions of the
same sweep (standard) and one deviating sweep (deviant) (Fig 1C). Participants
were instructed to report, with a button press, the position of the deviant
within the sequence as fast and accurately as possible after identifying the
deviant. Each sequence was characterised by the position of the deviant and
$\Delta^{2}f=|\Delta f_{\text{deviant}}-\Delta f_{\text{standard}}|$: the
absolute difference between the frequency spans of the deviant and the
standard. With the three FM-sweeps we built three different combinations: one
with $\Delta^{2}f=100$ Hz, where the two FM-sweeps have the same direction
(up) but different modulation rate; one with $\Delta^{2}f=300$ Hz, where the
two FM-sweeps have different rate and different direction; and one with
$\Delta^{2}f=400\,$Hz, where to two FM-sweeps have the same modulation rate
($|\Delta f|=200$ Hz) but different direction (Fig 1B).

Figure 1: Experimental design and hypotheses. A) Example of an FM-sweep with
positive FM-rate $\Delta f$ B) The three FM-sweeps used in the experiment (in
dark blue) in comparison to an hypothetical family of seven sweeps with
increasing modulation rate. All sweeps had the same duration of 50 ms. They
were characterised by differences in the frequency span $\Delta f$. C) Example
trial. Each trial consisted of a sequence of seven repetitions of one FM-sweep
(standards; blue) and one other FM-sweep (deviant; red). In each trial, a
single deviant was located in positions 4, 5, or 6 of the sequence.
Participants reported, in each trial, the position of the deviant right after
they identified it. Each participant completed up to 540 trials in total, 60
per deviant position and $\Delta^{2}=|\Delta f_{\text{deviant}}-\Delta
f_{\text{standard}}|$. Sweeps within a sequence were separated by 700 ms
inter-stimulus-intervals (ISIs). D) Schematic view of the expected underlying
responses in the auditory pathway for the sequence shown in C, together with
the definition of the experimental variables ($std0$: first standard; $std1$:
repeated standards preceding the deviant; $std2$: standards following the
deviant; $dev\,x$: deviant in position $x$). E) Expected responses in the
auditory pathway nuclei corresponding to the hypotheses: h1) responses reflect
adaptation by habituation only; h2) responses reflect prediction error with
respect to the participant’s expectations.

Expectations for each of the deviant positions were modulated by two abstract
rules that were disclosed to the participants: 1) all sequences have a
deviant, and 2) the deviant is always located in position 4, 5, or 6. The
three deviant positions were used the same number of times along the
experiment, so that the three deviant positions were equally likely at the
beginning of the sequence. Therefore, the likelihood of finding a deviant in
position 4 ($dev4$) after hearing 3 standards is $1/3$. However, if the
deviant is not located in position 4, it must be located in either position 5
or 6, which makes the likelihood of finding a deviant in position 5 ($dev5$)
after hearing 4 standards $1/2$. The likelihood of finding a deviant in
position 6 ($dev6$) after hearing 5 standards is $1$.

To address the first research question, whether neural populations of human IC
and MGB encode FM-rate and FM-direction, we tested whether these two nuclei
show SSA to the FM-sweeps used in the experiment; namely, if neural responses
in IC and MGB adapt to repeated FM-sweeps while preserving high responsiveness
to FM-sweeps that deviate from the standards in FM-rate or FM-direction (Fig
1D). Since all sweeps were designed to elicit the same average activation
across the tonotopic axis and elicited the same pitch percept, neural
populations showing SSA to these FM-sweeps necessarily comprise neurons that
are sensitive to FM-rate and FM-direction.

To address the second research question, whether IC and MGB responses encode
FM-sweeps as prediction error with respect to the listener expectations, we
used Bayesian model comparison. We considered two models. The first model
assumed that adaptation to repeated fast FM-sweeps was mostly driven by
habituation to the stimulus sequence properties, independently of
participant’s expectations; namely, that neural populations habituate to
repetitions of the standard, but show recovered responses to deviant
irregardless of their position (habituation hypothesis; Fig 1E, h1). The
second model assumed that adaptation was driven by predictive coding; namely,
that neural responses to the deviants reflect prediction error with respect to
the expectations of the participants (predictive coding hypothesis; Fig 1E,
h2).

We measured BOLD responses in participants’ IC and MGB with an fMRI-sequence
at 3-Tesla. The sequence was optimised to measure BOLD at relatively high
spatial resolution (1.75 mm isotropic) while maintaining a high SNR (around
25).

### Behavioural Responses

All participants showed accuracies over 0.96 to all deviant positions (Fig
2A). Accuracy was slightly higher for the two more expected deviant positions,
but differences between conditions were not significant ($p>0.1$,
uncorrected). Reaction times (Fig 2B) showed a behavioural benefit of
expectations: Participants reacted faster to more expected deviants (average
$RT=770$ ms, $558$ ms and $246$ ms for deviants at positions 4, 5, and 6,
respectively (Fig 2C); all differences were significant with $p<0.0001$,
corrected for 3 comparisons).

Figure 2: Performance and reaction times. Mean accuracy (A) and reaction times
(B) across deviant positions. Grey circles represent the average value per
participant and deviant position. Violin plots are the kernel density
estimations of the reaction times for each deviant position. **: $p<0.005$,
**** $p<0.00005$; all $p$-values corrected for 3 comparisons.

### Human IC and MGB show stimulus specific adaptation (SSA) to FM-sweeps

We first studied whether the IC and MGB show SSA to fast FM-sweeps to test if
the two nuclei are sensitive to FM-rate and FM-direction in humans. We
estimated BOLD responses using a general linear model (GLM) with 6 different
regressors: the first standard ($std0$), the standards after the first
standard but before the deviant ($std1$), the standards after the deviant
($std2$), and deviants at positions 4, 5, and 6 ($dev4$, $dev5$, and $dev6$,
respectively; Fig 1D). The conditions $std1$ and $std2$ were parametrically
modulated [59] according to their positions to account for possible variations
in the responses over subsequent repetitions (see Methods and Fig S1).

To compute SSA, we determined which voxels within the ICs and MGBs adapted to
the standard (i.e., adaptation) and recovered responsiveness to deviants
(i.e., deviant detection). SSA regions were then defined as the intersection
between adaptation and deviant detection regions. ICs and MGBs were identified
based on structural MRI data and an independent functional localiser (see
Methods; IC and MGB ROIs; coloured patches in Fig 3). Within these ROIs, we
used non-parametric ranksum tests ($N=18$; one sample per participant) to find
which voxels showed significant adaptation to repeated standards (contrast
$std0>0.5\,std1+0.5\,std2$). The associated $p$-maps were thresholded so that
the false-discovery-rate $\text{\emph{FDR}}<0.05$. Surviving voxels
constituted the _adaptation_ ROIs (blue and purple patches in Fig 3). The same
procedure was used to delimit the _deviant detection_ ROIs (red and purple
patches in Fig 3): the set of voxels within each anatomical ROI that responded
significantly stronger to deviants than to repeated standards (contrast
$dev4>0.5\,std1+0.5\,std2$; note that we compare the responses to the repeated
standards with $dev4$ as this is the deviant position for which participants
have the lowest expectation). The four anatomical ROIs showed significant
adaptation (peak $p\leq 0.0001$) and deviant detection (peak $p<0.0001$;
cluster size, exact peak $p$-values and MNI coordinates are shown in Table 1;
all $p$-values corrected for four comparisons).

Figure 3: Mesoscopic stimulus specific adaptation (SSA) in bilateral IC and
MGB. Regions within the MGB and IC ROIs adapted to the repeated standards
(adaptation; blue shows adaptation only, purple shows SSA, which includes
adaptation) and recovered responses to deviants (deviant detection; red shows
deviant detection only, purple shows SSA, which includes deviant detection).
Stimulus specific adaptation (i.e., recovered responses to a deviant in voxels
showing adaptation; SSA) occurred in bilateral MGB and IC (purple). Maps were
computed by thresholding the contrast $p$-maps at $\text{\emph{FDR}}<0.05$.
Yellow patches show voxels included in the anatomical masks computed with a
functional localiser that showed neither adaptation nor deviant detection.

SSA regions were computed combining the unthresholded _adaptation_ and
_deviant detection_ $p$-maps. The uncorrected $p$-value associated to SSA for
a given voxel was $p_{\text{SSA}}=\max{(p_{\text{adaptation}},p_{\text{deviant
detection}})}$. SSA $p$-maps where thresholded to $FDR<0.05$ to compute the
_SSA_ ROIs (Fig 3, purple). The four anatomical ROIs had extensive SSA regions
(cluster sizes larger than 90 $mm^{3}$; peak $p\leq 0.0003$; exact peak
$p$-values and MNI coordinates are shown in Table 1; all $p$-values corrected
for four comparisons).

contrast | ROI | cluster size | MNI coordinates (mm) | peak-level $p$-value
---|---|---|---|---
adaptation | left IC | 130 voxels | $[-4,-35,-9]$ | $p=1\times 10^{-4}$
| right IC | 124 voxels | $[4,-35,-9]$ | $p=8\times 10^{-5}$
| left MGB | 152 voxels | $[-14,-25,-7]$ | $p=8\times 10^{-5}$
| right MGB | 146 voxels | $[14,-26,-6]$ | $p=1\times 10^{-4}$
deviant detection | left IC | 92 voxels | $[-6,-33,-10]$ | $p=9\times 10^{-5}$
| right IC | 91 voxels | $[6,-33,-8]$ | $p=7\times 10^{-5}$
| left MGB | 136 voxels | $[-14,-24,-7]$ | $p=5\times 10^{-5}$
| right MGB | 140 voxels | $[11,-27,-5]$ | $p=2\times 10^{-5}$
SSA | left IC | 91 voxels | $[-4,-35,-9]$ | $p=3\times 10^{-4}$
| right IC | 91 voxels | $[6,-33,-9]$ | $p=2\times 10^{-4}$
| left MGB | 136 voxels | $[-14,-25,-7]$ | $p=2\times 10^{-4}$
| right MGB | 140 voxels | $[12,-26,-5]$ | $p=1\times 10^{-4}$
Table 1: Statistics and MNI coordinates of the adaptation and deviant
detection contrasts in the four regions of interest. All $p$-values FDR-
corrected for the number of voxels in each anatomical ROI and further
corrected for 4 comparisons within each contrast.

Significant SSA was also found at the single-subject level in 15 of the 18
participants ($p\leq 0.048$ for each of the 15 participants, corrected for the
596 voxels included in a global subcortical auditory ROI that comprised
bilateral IC and MGB), but not all participants showed significant SSA in all
ROIs (IC-L: 8 participants, $p\leq 0.049$; IC-R, MGB-L, MGB-R: 6 participants
each, with $p\leq 0.048$; all $p$-values corrected for the number of voxels in
the ROI and further corrected for four ROIs).

### Human IC and MGB are sensitive to FM-direction and FM-rate

In the next step, we specifically tested whether the IC and MGB are similarly
sensitive to FM-rate and FM-direction. To do that we analysed the data
corresponding to: 1) trials where the standard and deviant differed only in
modulation direction but not in absolute modulation rate; and 2) trials where
the standard and deviant differed only in modulation rate but not in
direction. If IC and MGB encode direction and rate, we would expect similar
results in both partitions of the data. Conversely, if human IC and MGB are
only sensitive to one of the two properties, we would expect null effects in
the partition of the data where the standard and deviants differ only in that
property.

Results were similar in both partitions of the data (Fig 4), demonstrating
that the human IC and MGB encode both FM-direction and FM-rate.

Figure 4: Summary BOLD responses for partitions of the data where deviant and
standard differed only in direction or rate. Average z-score in each of the
four SSA ROIs to the different experimental conditions in trials where the
standard and deviant differed only in direction (orange) or rate (yellow).
Violin plots are kernel density estimations of the distribution of $z$-scores,
averaged over voxels and runs of each ROI. Each distribution holds 17 samples,
one per participant (one participant was excluded from this analysis because
there were not enough trials available, see Methods for details). Black error
bars show the mean and standard error of the distributions.

We further corroborated that the levels of SSA were comparable for both types
of FM changes at the single-subject level. In order to characterise FM-
sensitivity with a number for each subject and FM-sweep combination, we used
the SSA index [30] $SI$ (Eq (1); note that $SI>0$ is equivalent to the deviant
detection contrast used in Fig 3).

$SI=\frac{dev4-\frac{1}{2}(std1+std2)}{dev4+\frac{1}{2}(std1+std2)}$ (1)

We measured the difference in $SI$ to FM-direction ($SI_{dir}$) and FM-rate
($SI_{rate}$) in the voxels of the subject-specific SSA regions calculated in
the previous section for each of the 15 subjects for which we obtained
significant SSA. If FM-direction and FM-rate are both encoded in IC and MGB,
we would expect no difference between these two partitions of the data. We
measured the difference using Cohen’s $d=(\langle SI_{dir}\rangle-\langle
SI_{rate}\rangle)/\sigma$), where $\langle SI\rangle$ is the average of $SI$
and $\sigma$ is the pooled standard deviation. The difference ranged between
$d\geq-0.33$ and $d\leq 0.475$ across participants. The expected value of the
difference ($E[d]=0.02\pm 0.05$) overlapped with zero, indicating once again
that both FM-direction and FM-rate are already encoded in the subcortical
auditory pathway.

### Expectations drive the encoding of FM-sweeps in IC and MGB

To address our second question, we evaluated whether the average BOLD
responses to deviants in the three different positions were affected by
participant’s subjective expectations within the SSA regions. In congruence
with the predictive coding hypothesis (Fig 1E, h2), the response profile
showed reduced responses for more expected deviants (Fig 5).

Formal statistical testing confirmed that responses to different deviant
positions were different in all ROIs for all contrasts among deviant
positions: $dev4\neq dev5$ ($|d|\geq 0.99$ and $p<0.006$), $dev4\neq dev6$
($|d|\geq 2.39$ and $p<0.00005$), and $dev5\neq dev6$ ($|d|\geq 1.74$ and
$p<0.0003$; all $p$-values corrected for $3\times 4$ 12 comparisons). Exact
$p$-values and effect sizes are listed in Table 2. All statistical tests
included one sample per participant, ROI, and deviant position.

Figure 5: Summary BOLD responses. Average z-score in each of the four SSA ROIs to the different regressors. Violin plots are kernel density estimations of the distribution of $z$-scores, averaged over voxels and runs of each ROI. Each distribution holds 18 samples, one per participant. Black error bars show the mean and standard error of the distributions. Significance bars were computed by pooling across standard-deviant combinations. $Std0$, first standard; $std1$: standards preceding the deviant; $std2$: standards following the deviant; $dev4$, $dev5$ and $dev6$: deviants at positions 4, 5, and 6, respectively (Fig 1D). * $p<0.05$, ** $p<0.005$, *** $p<0.0005$, **** $p<0.00005$; all $p$-values corrected for 12 comparisons.  | IC-L
---|---
| dev5 | dev6
dev4 | $d=0.89$ | $p=0.017$ | $d=2.35$ | $p=4\times 10^{-5}$
dev5 |  | $d=1.75$ | $p=4\times 10^{-4}$
| IC-R
| dev5 | dev6
dev4 | $d=0.86$ | $p=0.022$ | $d=2.24$ | $p=10^{-4}$
dev5 |  | $d=1.74$ | $p=5\times 10^{-4}$
| MGB-L
| dev5 | dev6
dev4 | $d=1.20$ | $p=0.0075$ | $d=2.46$ | $p=10^{-4}$
dev5 |  | $d=1.68$ | $p=0.0015$
| MGB-R
| dev5 | dev6
dev4 | $d=1.17$ | $p=0.0073$ | $d=2.91$ | $p=2\times 10^{-5}$
dev5 |  | $d=2.40$ | $p=3\times 10^{-4}$
Table 2: Statistics of the average BOLD response differences between deviant
positions. Effect size is expressed as Cohen’s $d$. Statistical significance
was evaluated with two-tailed Ranksum tests between the distributions of the
mean response in each ROI across participants ($N=18$), pooling across
standard-deviant combinations. All $p$-values in the table are corrected for
$3\times 4=12$ comparisons.

To corroborate that differences were present at the single-subject level we
run a correlation analysis for each of the 15 participants for which we
obtained significant SSA. In each participant, we computed the Pearson’s
correlation between the BOLD responses elicited by each deviant location with
its likelihood of occurrence (namely, $1/3$ for deviant 4, $1/2$ for deviant
5, and $1$ for deviant 6). If BOLD responses reflect prediction error, we
would expect a negative correlation between the likelihood and the responses.
We found significantly negative correlations in all 15 participants
($\rho\in[-0.87,-0.42]$, all $p<0.03$; all Pearson tests had $9\times 3=27$
samples, 3 per run).

### FM-sweeps are encoded as prediction error in the IC and MGB

We used Bayesian model comparison to formally evaluate the response properties
in each voxel of the IC and MGB ROIs. This approach provides for a
quantitative assessment of the likelihood that each of the two hypotheses (Fig
1E) can explain the responses in each voxel. This analysis is sensitive to
possible region-specific effects that could have been averaged out when
aggregating the z-scores across voxels in each ROI.

Following the methodology described in [60, 61], we first calculated the log-
likelihood of each model in each voxel of the two ICs and MGBs in each
participant. Each model yields different predictions on the relative
amplitudes to different positions in the sequences (Fig 1E). We tested h1 and
h2 to adjudicate between the habituation and predictive coding explanations of
the responses. H1 assumed an asymptotic decay of the standards and recovered
responses to the deviants; h2 assumed that the responses to both deviants and
standards would depend on the participant’s expectations (Fig 6; for exact
values, see Methods). Participant-specific log-likelihoods were used to
compute the Bayes factor $K$ (i.e., the ratio of the posterior likelihoods)
between h1 and h2.

Figure 6: Design of the Bayesian models. Each model was defined according to
the relative amplitudes it predicted for the different sounds in the
sequences. H1 assumed asymptotic habituation to consecutive standards and
recovered responses to deviants. H2 assumed that responses to the stimuli
depended on how predictable they were.

H2 was the best explanation for the data in the majority of voxels of the four
ROIs (Figures 7 and 8): h2 was more likely than h1 in 99% and 80% of the left
and right IC, respectively, and in all voxels of the left and right MGB.

Figure 7: Bayesian model comparison. Bayes’ factor $K$ for the model
comparison h2/h1. Values $K>1$ are more favourable to h2; values $K<1$ mean
that h1 is a better explanation of the data than h2. $K=1$ is indicated by the
fine grey line. Violin plots are kernel-density estimations of the
distribution of $K$ across voxels (i.e., one sample per voxel).  Figure 8:
Topographic distribution of each model. Bayes’ factor $K$ between h2
(predictive coding) and h1 (habituation) in each of the voxels of the
subcortical ROIs in a logarithmic scale. Voxels with negative $\log K$ values
($K<1$; blue) are best explained by h1; voxels with positive $\log K$ values
($K>1$; red) are best explained by h2.

To test whether the effect was present at the single-subject level, we
computed $K$ independently for each subject in the subject-specific bilateral
IC and MGB. Since we performed the group analyses over the entire ROIs (and
not only the SSA regions), here we used the full anatomical ROIs of each
participant. We measured for how many voxels within each region and
participant h2 was the better explanation of the data. H2 was the better
explanation of the data (more than 50% of voxels) in 16 (IC-L), 13 (IC-R), 13
(MGB-L), and 15 (MGB-R) participants. In 7 (IC-L), 5 (IC-R), 10 (MGB-L), and 8
(MGB-R) participants H2 was the better explanation even in more than 75% of
voxels.

### FM-sweeps are encoded as prediction error in primary and secondary MGB

The auditory pathway is anatomically subdivided into two sections: the primary
(lemniscal) or secondary (non-lemniscal) pathways. The primary pathway is
characterised by neurons that carry auditory information with high fidelity
and it is generally regarded as responsible for the transmission of bottom-up
sensory input [48]. The secondary pathway has wider tuning curves and it is
generally regarded as responsible for the integration of contextual and
multisensory information [48].

Both IC and MGB comprise regions that participate in both, the primary and
secondary pathways [48]. The primary subdivision of the IC is its central
nucleus, while the cortices constitute the secondary subdivisions. The primary
subdivision of the MGB is its ventral section, while the medial and dorsal
sections constitute the secondary subdivisions.

In rodents, SSA and prediction error to pure tones are significantly stronger
in secondary subdivisions (e.g., [31]). In humans, prediction error is
similarly strong in primary and secondary MGB for pure tones [40]. Here we
test for differential representations of prediction error to FM-sweeps in MGB.

Distinguishing between the primary and secondary subsection of the IC and MGB
non-invasively is technically challenging [62]. A recent study [63]
distinguished two distinct tonotopic gradients of the MGB. The ventral
tonotopic gradient was identified as the ventral or primary (vMGB) subsection
of the MGB (see Fig 9A, green). Although the parcellation is based only on the
topography of the tonotopic axes and their anatomical location, the region is
the best approximation to-date of the vMGB in humans. No parcellation of the
IC is available to-date.

Figure 9: Analyses of BOLD responses in ventral MGB. A) Masks from [63] of the
ventral MGBs (green); blue indicates the remainder of the anatomical MGB ROIs.
B) The distribution of the SSA index $SI$ across each of the two subdivisions
of the MGB ROIs; $SI>0$ is usually interpreted as SSA in the animal literature
[30]. C) Histograms showing Bayes’ factor K for the comparison between h2 and
h1 (Fig 1E) in each of the subdivisions. No systematic functional differences
are apparent between primary and secondary MGB.

Both primary and secondary subdivisions of bilateral MGB showed SSA. SSA
strength was measured in each voxel using the SSA index (Eq (1)).
Distributions of the $SI$ across the voxels of each of the subdivisions were
comparable in both hemispheres (Fig 9B) demonstrating that SSA is not confined
to nor stronger in the secondary MGB.

Predictive coding (h2) was the best explanation for the responses of all
voxels in the two subdivisions of the left MGB, and in 95% and 97% of the
primary and secondary subdivisions of the right MGB, demonstrating that FM-
sweeps are encoded as prediction error in both, primary and secondary
subdivisions of bilateral MGB. Moreover, the distributions of the Bayes’
factor $K$ between the predictive coding (h2) and adaptation (h1) hypotheses
were comparable across subdivisions (Fig 9C).

### Prediction error to FM-sweeps and pure tones has similar topographic
distributions in the IC

To study whether the same neural populations are in charge of encoding
prediction error to FM-sweeps and pure tones, we compared the topographic
distribution of the Bayes factor $K$ between the h2 and h1 in our data with
the topographic distribution of the Bayes factor $K$ we obtained in a previous
experiment, where we measured BOLD responses to the same experimental paradigm
as here but using pure tones [40]. We computed the correlation between both
$K$ across voxels of each of the four ROIs, as defined by the anatomical atlas
from [64].

Distribution of $K$ to both families of stimuli was strongly correlated across
voxels of bilateral IC (left, $\rho=0.47$, $p=10^{-7}$; right, $\rho=0.34$,
$p=10^{-4}$; $p$-values corrected for 4 comparisons), but not across voxels of
the MGBs (left, $\rho=-0.11$, $p=0.22$; right, $\rho=0.15$, $p=0.1$;
uncorrected $p$-values).

## Discussion

####

The effects of expectations on speech recognition are readily evident in our
daily lives. However, the neural mechanisms underlying the integration of
expectations in at early stages of the speech processing pipeline are poorly
understood. Here we have investigated how fast FM-sweeps, the dynamic building
blocks of speech sounds, are encoded in the human subcortical auditory
pathway, and how the subjective expectations of the listener influence their
processing. Our study provided four main findings: first, we showed that the
human IC and MGB comprise FM-direction and FM-rate selective neuronal
populations. Second, we showed that responses in IC and MGB were driven by
subjective expectations of the participants, demonstrating that the IC and MGB
are integrated in a global network of predictive coding. The findings were
robust and present at the single-subject level, demonstrating the
generalisation power of the result. Third, we showed that the expectations
determined the responses to FM-sweeps in primary and secondary subdivisions of
bilateral MGB. Last, we showed that the topographic distribution of neural
populations encoding the FM-sweeps as prediction error was similar to that of
pure tones in the IC.

Combined, our results provide first demonstration that the human IC and MGB
are actively engaged in the predictive processing of the building blocks of
speech, fast FM-sweeps. This confirms the long-standing hypothesis that
predictive coding combines high-level expectations with the exquisite temporal
properties of the subcortical auditory pathway to promote the encoding of low-
level features of the speech signal [65, 28]. This mechanism might be
responsible for boosting encoding efficiency and aiding speech recognition.

####

Neurons that respond selectively to FM-direction and FM-rate have been located
in rodents in the IC [66, 67, 68], MGB [54, 49], and auditory cortex [53, 55,
69, 56]. In contrast, FM-selectivity has only been reported in humans in
auditory cortex [52] or higher-order areas of the cerebral cortex [50, 51].
Here, we have established that neural populations in the human IC and MGB show
SSA to FM-direction and FM-rate. Since our FM-sweeps were matched in duration,
pitch, and expected elicited activity along the tonotopic axis, this finding
provides first evidence for FM-selectivity as early as in the IC in humans.

####

Animal studies have extensively shown that the SSA index to pure tones in IC
and MGB increases with increasing rarity and frequency difference of the
deviant with respect to the standard [70, 71, 72, 73, 39, 74, 33, 34, 75].
These studies implicitly assume that sensory neurons form expectations based
on the local statistics of the stimuli. Whether adaptation driven by such
potential expectations is a true reflection of predictive coding is, however,
still a matter of debate [76, 77]. Modelling studies have demonstrated that
identical behaviours can be produced by synaptic fatigue without the need of
expectations [78, 79, 80]. Manipulating expectations orthogonally to stimulus
regularities is the only way to assess if prediction error is computed with
respect to a global model of the sensory world [45].

To-date, the only evidence (see [45] for a review) that subcortical nuclei
encode stimuli according to subjective expectations independently of stimulus
regularities was provided by our previous study on pure tones in human IC and
MGB [40]. Here we used fast FM-sweeps that were explicitly designed to elicit
the same activation across the tonotopic axis [58] to ensure that participants
had to make use of FM-direction and FM-rate selective neurons to differentiate
the deviant from the standards. The current findings demonstrate that the same
principles apply to the encoding of dynamic FM-sweeps, even though FM is
decoded much later than pure tone frequency in the auditory system [49].

Our results also showed that the topographic distribution of voxels encoding
pure tones and FM-sweeps according to the principles of predictive coding were
highly correlated in the IC, but not in the MGB. This divergence might
indicate a different functional role of the IC and the MGB with respect to
both families of stimuli; however, it might also be caused by a greater
variability in the anatomical location and orientation of the MGB across
subjects [62] and should be considered with caution until replications are
available.

####

Although the expectations we induced in our participants had a relatively
simple structure, they had a comparable level of abstraction as expectation
stemming from grammatical rules [81], semantic context [3], or familiarity
with the speaker’s style [82]. An integrated inverted hierarchy could
propagate linguistic predictions all the way to formant transitions [45, 12,
28], and use neural centres in the IC and MGB to compute prediction error with
respect to these predictions.

The expectations induced by our paradigm are most likely generated in the
cerebral cortex. However, since we optimised our paradigm to study prediction
error rather than the generation of expectations, we cannot test whether the
subcortical responses we measured are driven or not by corticofugal
projections. This possibility would be consistent with the massive
corticofugal connections from cerebral cortex to MGB and IC [25, 83], and with
results from animal studies where the deactivation of unilateral auditory
cortex [32] or the TRN [84] led to reduction of SSA in the ventral MGB (but
also see contradictory findings in non-lemniscal MGB [74] and non-lemniscal IC
[85]).

####

Despite the fact that the MGB is at a higher processing stage than the IC, we
found similar prevalence of predictive coding in both nuclei for FM-sweeps
(Fig 7) as well as for pure tones [40]. These results contrast with a study in
rodents concluding that the MGB encodes prediction error more strongly than
the IC [31]. We speculate that this fundamental difference between the human
and rodent studies is caused by the introduction of abstract rules in our
paradigm: if prediction error is computed with respect to high-level
expectations, there is no reason for prediction error to vary across the
hierarchy, since the same expectations are used to compute prediction error at
all levels. Rodent studies use passive listening tasks where expectations are
induced by repetition. Without an explicit high-level model, prediction error
can only be computed with respect to implicit local models that monitor local
stimulus statistics and that may vary in complexity across processing stages.

Our study provides also novel insight into characteristics of SSA. Previous
studies on subcortical SSA rested almost exclusively on pure tones [76, 77,
45]. Only three studies considered whether SSA generalised to other acoustic
properties. Thomas et al. [86] reported SSA to FM-rate in the IC of the big
brown bat; however, since big brown bats use FM for echolocation and the
authors used stimuli in the rate range of echolocation signals, it was unclear
whether this behaviour would generalise to auditory FM. Gao et al. [37]
measured SSA using ramped and damped broadband noises in the IC, demonstrating
that neurons in the IC adapt to intensity modulation. Last, Duque et al. [70]
measured SSA to intensity, and showed that neurons in the IC do not adapt to
nominal loudness. Our findings complement these results showing that the human
IC and MGB adapt to fast FM characteristic of speech without loudness or
spectral changes, and provides first evidence for SSA to acoustic properties
other than pitch and loudness in the subcortical pathways.

####

Nuclei in the auditory pathway are organised in primary (or lemniscal) and
secondary (or non-lemniscal) subdivisions. The lemniscal division of the
auditory pathway has narrowly tuned frequency responses and is considered as
responsible for the transmission of bottom-up information; the non-lemniscal
division presents wider tuned frequency responses, is involved in multisensory
integration, and is more heavily targeted by corticofugal connections [48].
Because of these properties, it has been previously suggested that prediction
error may be encoded exclusively in secondary subdivisions of the IC and MGB
[72, 76, 31]. In agreement with this hypothesis, SSA is stronger in secondary
subdivisions of the rodent’s IC [38, 37, 87, 72, 88] and MGB [33, 74, 87].

In contrast, our results indicated an apparent lack of specialisation across
subdivisions of the MGB during the encoding of FM-sweeps. Namely, both primary
and secondary MGB were similarly responsive to FM, and they both encoded FM as
prediction error. Similar results were apparent in our previous study when we
investigated the encoding of pure tones [40]. This lack of specialisation
would fit with the idea that expectations are used in the subcortical pathways
to aid encoding: to optimise the resources of the subcortical stations
requires to make use of the the narrow receptive fields of the primary
subdivisions [48].

The fundamental difference between our results and the findings in animals
might stem from a number of reasons. First, our design involved an active
task: lemniscal pathways might only be strongly modulated by predictions when
they carry behaviourally relevant sensory information. Second, the modulation
of the subcortical auditory pathway might be fundamentally different in humans
compared to other mammals, as they have to accomplish processing of such
complex and dynamic signals as speech. Last, given the strength of the SSA
effects reported in this study, it is possible that regions with weak SSA
might have been contaminated with signal stemming from areas with strong SSA
due to smoothing and interpolation necessary for the analysis of fMRI data.

####

Given the paramount role of predictions on speech perception [17, 5, 3, 89],
atypical predictive coding in the subcortical sensory pathway could have
profound repercussion at the cognitive level [90, 91, 45]. For instance,
developmental dyslexia, a disorder characterised by difficulties with
processing speech sounds, has been attributed to altered adaption dynamics to
stimulus regularities [92, 93, 94], altered responses in the left MGB [91,
94], and atypical left hemispheric cortico-thalamic pathways [95, 96].
Understanding the mechanisms underlying the predictive processing of low-level
features of speech in subcortical sensory pathways is an essential
prerequisite to understand dysfunction.

## Methods

This study was approved by the Ethics committee of the Technische Universtät
Dresden, Germany (ethics approval number EK 315062019). All listeners provided
written informed consent and received monetary compensation for their
participation.

### Participants

Eighteen German native speakers (12 female), aged 19 to 31 years (mean 24.6),
participated in the study. None of them reported a history of psychiatric or
neurological disorders, hearing difficulties, or current use of psychoactive
medications. Normal hearing abilities were confirmed with pure tone audiometry
(250 Hz to 8000 Hz); all participants had hearing threshold equal to or below
15 dB SPL in the frequency range of the FM sweeps (1000 Hz-3000 Hz).
Participants were also screened for dyslexia (German SLRT-II test [97], RST-
ARR [98], and rapid automatised naming (RAN) test of letters, numbers,
objects, and colours [99]) and autism (Autism Spectrum Quotient; AQ [100]).
All scores were within the neurotypical range (SLRT:
$\min(\max(PR_{\text{words}},PR_{\text{pseudowords}}))=21$, higher than the
cut-off value of 16, following the same guidelines as [101]; RST-ARR: all
$PR\geq 31$, higher than the cut-off value of 16; RAN: maximum of 3 errors and
$RT<36$ seconds in each of the four categories; AQ: all participants $AQ\leq
31$, under or equal to the cut-off value of 32).

Since we had no estimations of the possible sizes of the effects, we maximised
our statistical power by recruiting as many participant as we could fit in the
MRI measurement time allocated to the study. This number was fixed to twenty
before we started data collection, but two participants dropped out of the
study during data collection. We maximised the amount of data collected for
participant to reduce random error to a minimum and maximise the likelihood of
measuring effects at the single-subject level.

### Experimental paradigm

All sounds were 50 ms long (including 5 ms in/out ramps) sinusoidal FM sweeps.
The frequency sweeps lasted for 40 ms and were preceded and followed by 5 ms
long segments of constant frequency that overlapped with the in/our ramps. The
sweeps were assembled in the frequency space to avoid discontinuities of the
final waveforms.

We used a total of three sweeps during the experiment: a _fast up_ sweep with
starting frequency $f_{0}=1000$ Hz and ending frequency $f_{1}=1200\,$Hz
($\Delta f=200$ Hz); a _slow up_ sweep with $f_{0}=1070$ Hz and $f_{1}=1170$
Hz ($\Delta f=100$ Hz), and a fast down sweep with $f_{0}=1280$ Hz and
$f_{1}=1080$ Hz ($\Delta f=-200$ Hz). The sweeps had different average
frequencies to ensure that they elicited the same average spectral activity
along the tonotopic axis and the same pitch percept (see [58] for details).

From those 3 sweeps we constructed 6 standard-deviant frequency combinations
that were used the same number of times across each run, so that all sweeps
were used the same number of times as deviant and standards. Each sequence
consisted of 7 repetitions of the standard sweep and a single instance of the
deviant sweep. Sweeps were separated by 700 ms inter-stimulus-intervals (ISI),
the shortest possible ISI that allowed the participants to predict the fully
expected deviant [40], amounting to a total duration of 5300 ms per sequence.

In each trial of the fMRI experiment, participants listened to one tone
sequence and reported, _as fast and accurately as possible_ using a button box
with three buttons, the position of the deviant (4, 5 or 6). The inter-trial-
interval (ITI) was jittered so that deviants were separated by an average of 5
seconds, up to a maximum of 11 seconds, with a minimum ITI of 1500 ms. We
chose such ITI properties to maximise the efficiency of the response
estimation of the deviants [102] while keeping a sufficiently long ITI to
ensure that the sequences belonging to separate trials were not confounded.

All but one participant completed 9 runs of the main experiment across three
sessions; participant 18 completed only 8 runs for technical reasons. Each run
contained 6 blocks of 10 trials. The 10 trials in each block used one of the 6
possible sweep combinations, so that all the sequences within each block had
the same standard and deviant. Thus, within a block only the position of the
deviant was unknown, while the deviant’s FM-direction and FM-rate were known.
The order of the blocks within the experiment was randomised. The position of
the deviant was pseudorandomised across all trials in each run so that each
deviant position happened exactly 20 times per run but an unknown amount of
times per block. This constraint allowed us to keep the same a priori
probability for all deviant positions in each block. In addition, there were
23 silent gaps of 5300 ms duration (i.e., null events of the same duration as
the tone sequences) randomly located in each run [102]. Each run lasted around
10 minutes, depending on the reaction times of the participant.

Due to an undetected bug in the presentation code, information on the exact
sweep combination used in each trial was unavailable for some runs. The bug
affected the first three runs of participants 1, 2, 4, and 5; and the first
six runs of participant 3. This information was not relevant for the analyses
that aggregated the data across sweep combinations, and affected only the
analyses of Fig 4, where we excluded the affected runs of participants 1, 2,
4, and 5, and participant 3 altogether.

We also run a functional localiser that was designed to activate the
participant’s IC and MGB. Each run of the functional localiser consisted on 20
blocks of 16 seconds and lasted for about 6.5 minutes. Ten of the blocks were
silent; the remaining blocks consisted on presentations of 16 sounds of one
second duration each. Sounds were taken from a collection of 85 natural sounds
collected by [62]. Participants were instructed to press a key when the same
sound was repeated twice to ensure that they attended the sounds; behavioural
data from the functional localiser was not used in the analysis.

Each session consisted on three runs of the main experiment, interspersed with
two runs of the functional localiser. All runs were separated by breaks of a
minimum of 1 minute to allow the participants rest. Fieldmaps and a whole-head
EPI (see  _Data acquisition_) were acquired between the third and fourth run.
In the first session, we also measured an structural image before the
fieldmaps. The first run of the first session was preceded by a _practice run_
of four randomly chosen trials to ensure the participants had understood the
task. We acquired fMRI during the practice run in order to allow the
participants to undertake the training with MRI-noise.

### Data acquisition

MRI data were acquired using a Siemens Trio 3 T scanner (Siemens Healthineers,
Erlangen, Germany) with a 32-channel head coil.

Functional MRI data were acquired using echo planar imaging (EPI) sequences.
We used partial coverage with 24 slices. The volume was oriented in parallel
to the superior temporal gyrus such that the slices encompassed the IC, the
MGB, and the superior temporal gyrus. In addition, we acquired one volume of
an additional whole-head EPI with the same parameters (including the FoV) and
84 slices during resting to aid the coregistration process (see  _Data
preprocessing_).

The EPI sequence had the following acquisition parameters: TR = 1900 ms, TE =
42 ms, flip angle 66∘, matrix size $88\times 88$, FoV 154 mm$\times$154 mm,
voxel size 1.75 mm isotropic, bandwidth per pixel $1386$ Hz/px, and
interleaved acquisition. During functional MRI data acquisition, cardiac
signal was acquired using a scanner pulse oximeter (Siemens Healthineers,
Erlangen, Germany).

Structural images were recorded using an MPRAGE [103] T1 protocol with 1 mm
isotropic resolution, TE = 1.95 ms, TR = 1000 ms, TI = 880 ms, flip angle 1 =
8∘, FoV = 256 mm$\times$256 mm.

Stimuli were presented using MATLAB (The Mathworks Inc., Natick, MA, USA) with
the Psychophysics Toolbox extensions [104] and delivered through an
Optoacoustics (Optoacoustics Ltd, Or Yehuda, Israel) amplifier and headphones
equipped with active noise-cancellation. Loudness was adjusted independently
for each participant to a comfortable level before starting the data
acquisition.

### Data preprocessing

The preprocessing pipeline was coded in Nipype 1.5.0 [105], and carried out
using tools of the Statistical Parametric Mapping toolbox, version 12;
Freesurfer, version 6 [106]; the FMRIB Software Library, version 5 (FSL)
[107]); and the Advanced Normalization Tools, version 2.3 (ANTS) [108]. All
data were coregistered to the Montreal Neurological Institute (MNI) MNI152 1
mm isotropic symmetric template.

First, we realigned the functional runs. We used SPM’s _FieldMap Toolbox_ to
calculate the geometric distortions caused in the EPI images due to field
inhomogeneities. Next, we used SPM’s _Realign and Unwarp_ to perform motion
and distortion correction on the functional data. Motion artefacts, recorded
using SPM’s ArtifactDetect, were later added to the design matrix (see
_Estimation of the BOLD responses_).

Next, we used Freesurfer’s recon-all routine to calculate the boundaries
between grey and white matter (these are necessary to register the functional
data to the structural images) and ANTs to compute the transformation between
the structural images and the MNI152 symmetric template.

Last, we coregistered the functional data to the structural image with
Freesurfer’s _BBregister_ , using the boundaries between grey and white matter
of the structural data and the whole-brain EPI as an intermediate step. Data
was analysed in the participant space, and then coregistred to the MNI152
template. Note that, since the resolution of the MNI space (1 mm isotropic)
was higher than the resolution of the functional data (1.75 mm isotropic), the
transformation resulted in a spatial oversampling.

All the preprocessing parameters, including the smoothing kernel size, were
fixed before we started fitting the general linear model (GLM) and remained
unchanged during the subsequent steps of the data analysis.

Physiological (heart rate) data was processed by the PhysIO Toolbox [109],
that computes the Fourier expansion of each component along time and adds the
coefficients as covariates of no interests in the model’s design matrix.

### Estimation of the BOLD responses

First level analyses were coded in Nipype and carried out using SPM. Second
level analyses were carried out using custom code in MATLAB. The coregistered
data were first smoothed using a 2 mm full-width half-maximum kernel Gaussian
kernel with SPM’s _Smooth_.

The first level GLM’s design matrix for the main experiment included 6
regressors: first standard (std0), standards before the deviant (std1),
standards after the deviant (std2), and deviants in positions 4, 5, and 6
(dev4, dev5, and dev6, respectively; Fig 1). Conditions std1 and std2 were
modelled using linear parametric modulation [59], whose linear factors were
coded according to the position of the sound within the sequence to account
for effects of habituation [40]. The first level GLM’s design matrix for the
functional localiser included 2 conditions: sound and silence. On top of the
main regressors, the design matrix also included the physiological PhysIO and
artefact regressors of no-interest.

### Definition of the anatomical and SSA ROIs

We used a recent anatomical atlas of the subcortical auditory pathway [64] to
compute prior regions corresponding to the left IC, right IC, left MGB, and
right MGB, respectively. The atlas comprises three different definitions of
the ROIs calculated using 1) data from the big brain project, 2) postmortem
data, and 3) fMRI in vivo-data. To compute the prior coarse region for each
nuclei we combined the three masks and inflated the resulting regions with a
Gaussian kernel of 1 mm fwdh.

The final IC and MGB regions were computed by combining the prior coarse
regions with the results from the contrast $sound>silence$ of the functional
localiser. Within each region, we thresholded the contrast to increasingly
higher values until the number of surviving voxels equalled the volume of the
region reported in [64]; namely, 146 voxels for each of the ICs, and 152 for
each of the MGBs.

We used the coefficients of the GLM or beta estimates from the first level
analysis to calculate the adaptation (Fig 3, blue patches) and deviant
detection (red patches) ROIs, defined as the sets of voxels within the IC and
MGB ROIs that responded significantly to the contrasts $std0>0.5std1+0.5std2$
and $dev4>0.5std1+0.5std2$, respectively. Significance was defined as
$p<0.05$, family-wise-error (FDR)-corrected for the number of voxels within
each of the IC/MGB ROIs. SSA voxels are defined as voxels that show both,
adaptation and deviant detection; thus, we calculated an upper bound of the
$p$-value maps for the SSA contrast as the maximum of the uncorrected
$p$-values associated to the adaptation and deviant detection contrasts. The
SSA ROIs (Fig 3, purple patches) were calculated by FDR-correcting and
thresholding the resulting $p$-maps at $\alpha=0.05$. All calculations were
performed using custom-made scripts (see Data and code availability).

### Bayesian model comparison

The Bayesian analysis of the data consisted as well of first and second level
analyses. In the first level, we used SPM via nipype to compute the log-
evidence in each voxel of each participant for each of the four models (see
Fig 6). The models were described using one regressor with parametric
modulation whose coefficients corresponded to a simplified view of the
expected responses according to each model (Table 3). The expected responses
of each model were the same in all trials that had the same standard-deviant
combination and deviant position. Given the model amplitude(s) $a_{n}$ and the
timecourse of a voxel $y$, SPM calculates the log-evidence of the linear model
$y=\sum\beta_{n}a_{n}+\xi$, where $\beta_{n}$ are the linear coefficients of
each regressor and $\xi$ are noise terms.

h1 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8
---|---|---|---|---|---|---|---|---
$a_{0}$ | deviant at 4 | $1/1$ | $1/2$ | $1/3$ | $1$ | $1/3$ | $1/4$ | $1/5$ | $1/6$
deviant at 5 | $1/1$ | $1/2$ | $1/3$ | $1/4$ | $1$ | $1/4$ | $1/5$ | $1/6$
deviant at 6 | $1/1$ | $1/2$ | $1/3$ | $1/4$ | $1/5$ | $1$ | $1/5$ | $1/6$
h2 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8
$a_{0}$ | deviant at 4 | $1/2$ | $0$ | $0$ | $2/3$ | $0$ | $0$ | $0$ | $0$
deviant at 5 | $1/2$ | $0$ | $0$ | $1/3$ | $1/2$ | $0$ | $0$ | $0$
deviant at 6 | $1/2$ | $0$ | $0$ | $1/3$ | $1/2$ | $0$ | $0$ | $0$
Table 3: Amplitudes of the models used for Bayesian Model Comparison. H1
assumes an asymptotic decay ($a_{0}\propto 1/n$ where $m$ is the position of
the stimulus in the sequence) in the responses for all standards, a full
response to deviants, and a recovery from the last standard before the deviant
and the first standard after the deviant that is sufficient to make the
responses to both standards comparable. H2 assumes that the responses scale
with predictability ($a_{0}=1-p$, where $p$ is the likelihood of finding the
heard stimuli in each position). The models were defined exactly as in [40].

Log-evidence maps for each participant were combined following the random-
effects-equivalent procedure described in [60, 61] to compute the posterior
probability maps associated to each model at the group level. We combined the
maps using custom scripts (see Data and code availability). Histograms shown
in Figures 7 and 9 are kernel-density estimates computed with the distribution
of the posterior probabilities across voxels for each of the SSA ROIs.

### Statistical analysis

All pairwise comparisons reported in the study were evaluated for significance
using two-tailed Ranksum tests. Unless stated otherwise, $p$-values for all
analyses that comprised multiple testing were corrected using the Holm-
Bonferroni method. A result was deemed statistically significant when the
corrected $p<0.05$.

### Data and code availability

Derivatives (beta maps and log-likelihood maps, computed with SPM) and all
code used for data processing and analysis are publicly available in
https://osf.io/f5tsy/.

## References

  * [1] F. P. de Lange, M. Heilbron, and P. Kok, “How Do Expectations Shape Perception?,” Trends in Cognitive Sciences, vol. 22, no. 9, pp. 764–779, 2018.
  * [2] W. Marslen-Wilson, “Linguistic Structure and Speech Shadowing at Very Short Latencies,” Nature, vol. 244, pp. 522–523, aug 1973.
  * [3] M. H. Davis, M. A. Ford, F. Kherif, and I. S. Johnsrude, “Does semantic context benefit speech understanding through ”top-down” processes? Evidence from time-resolved sparse fMRI,” Journal of Cognitive Neuroscience, vol. 23, no. 12, pp. 3914–3932, 2011.
  * [4] S. C. Sereno, C. C. Brewer, and P. J. O’Donnell, “Context Effects in Word Recognition: Evidence for Early Interactive Processing,” Psychological Science, vol. 14, no. 4, pp. 328–333, 2003.
  * [5] E. Sohoglu, J. E. Peelle, R. P. Carlyon, and M. H. Davis, “Predictive top-down integration of prior knowledge during speech perception,” Journal of Neuroscience, vol. 32, no. 25, pp. 8443–8453, 2012.
  * [6] R. P. N. Rao and D. H. Ballard, “Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects,” Nature Neuroscience, vol. 2, no. 1, pp. 79–87, 1999.
  * [7] K. Friston, “Learning and inference in the brain.,” Neural networks: the official journal of the International Neural Network Society, vol. 16, pp. 1325–52, nov 2003.
  * [8] K. Friston, “A theory of cortical responses.,” Philosophical transactions of the Royal Society of London. Series B, Biological sciences, vol. 360, no. 1456, pp. 815–36, 2005.
  * [9] M. J. Pickering and S. Garrod, “An integrated theory of language production and comprehension,” Behavioral and Brain Sciences, vol. 36, no. 4, pp. 329–347, 2013.
  * [10] G. R. Kuperberg and T. F. Jaeger, “What do we mean by prediction in language comprehension?,” Language, Cognition and Neuroscience, vol. 31, pp. 32–59, jan 2016.
  * [11] T. E. Cope, E. Sohoglu, W. Sedley, K. Patterson, P. S. Jones, J. Wiggins, C. Dawson, M. Grube, R. P. Carlyon, T. D. Griffiths, M. H. Davis, and J. B. Rowe, “Evidence for causal top-down frontal contributions to predictive processes in speech perception,” Nature Communications, vol. 8, p. 2154, dec 2017.
  * [12] K. J. Friston, N. Sajid, D. R. Quiroga-Martinez, T. Parr, C. J. Price, and E. Holmes, “Active listening,” Hearing Research, vol. 399, p. 107998, jan 2021.
  * [13] M. W. Spratling, “A review of predictive coding algorithms,” Brain and Cognition, vol. 112, pp. 92–97, 2017.
  * [14] G. B. Keller and T. D. Mrsic-Flogel, “Predictive Processing: A Canonical Cortical Computation,” Neuron, vol. 100, no. 2, pp. 424–435, 2018.
  * [15] H. Blank and M. H. Davis, “Prediction Errors but Not Sharpened Signals Simulate Multivoxel fMRI Patterns during Speech Perception.,” PLoS Biology, vol. 14, no. 11, p. e1002577, 2016.
  * [16] S. Ylinen, M. Huuskonen, K. Mikkola, E. Saure, T. Sinkkonen, and P. Paavilainen, “Predictive coding of phonological rules in auditory cortex: A mismatch negativity study,” Brain and Language, vol. 162, pp. 72–80, 2016.
  * [17] H. Blank, M. Spangenberg, and M. H. Davis, “Neural Prediction Errors Distinguish Perception and Misperception of Speech,” The Journal of Neuroscience, vol. 38, pp. 6076–6089, jul 2018.
  * [18] Y. Vidal, P. Brusini, M. Bonfieni, J. Mehler, and T. A. Bekinschtein, “Neural signal to violations of abstract rules using speech-like stimuli,” eNeuro, vol. 6, no. 5, 2019.
  * [19] C. Signoret, L. M. Andersen, Ö. Dahlström, R. Blomberg, D. Lundqvist, M. Rudner, and J. Rönnberg, “The Influence of Form- and Meaning-Based Predictions on Cortical Speech Processing Under Challenging Listening Conditions: A MEG Study,” Frontiers in Neuroscience, vol. 14, no. September, pp. 1–15, 2020.
  * [20] E. Sohoglu and M. H. Davis, “Rapid computations of spectrotemporal prediction error support perception of degraded speech,” eLife, vol. 9, pp. 1–25, 2020.
  * [21] S. Hovsepyan, I. Olasagasti, and A. L. Giraud, “Combining predictive coding and neural oscillations enables online syllable recognition in natural speech,” Nature Communications, vol. 11, no. 1, pp. 1–12, 2020.
  * [22] C. C. Lee and S. M. Sherman, “On the classification of pathways in the auditory midbrain, thalamus, and cortex,” Hearing Research, vol. 276, no. 1-2, pp. 79–87, 2011.
  * [23] B. R. Schofield, “Central Descending Auditory Pathways,” in Auditory and Vestibular Efferents. (D. Ryugo and R. Fay, eds.), ch. 9, pp. 261–290, Springer Handbook of Auditory Research, 2011.
  * [24] J. A. Winer, “Decoding the auditory corticofugal systems,” Hearing Research, vol. 207, no. 1-2, pp. 1–9, 2005.
  * [25] J. a. Winer, “The human medial geniculate body,” Hearing Research, vol. 15, no. 3, pp. 225–247, 1984.
  * [26] M. A. Steadman and C. J. Sumner, “Changes in neuronal representations of consonants in the ascending auditory system and their role in speech recognition,” Frontiers in Neuroscience, vol. 12, no. OCT, pp. 1–16, 2018\.
  * [27] A. L. Giraud, C. Lorenzi, J. Ashburner, J. Wable, I. Johnsrude, R. Frackowiak, and A. Kleinschmidt, “Representation of the temporal envelope of sounds in the human brain,” Journal of Neurophysiology, vol. 84, no. 3, pp. 1588–1598, 2000.
  * [28] K. von Kriegstein, R. D. Patterson, and T. D. Griffiths, “Task-Dependent Modulation of Medial Geniculate Body Is Behaviorally Relevant for Speech Recognition,” Current Biology, vol. 18, no. 23, pp. 1855–1859, 2008.
  * [29] A. F. Osman, C. M. Lee, M. A. Escabí, and H. L. Read, “A hierarchy of time scales for discriminating and classifying the temporal shape of sound in three auditory cortical fields,” Journal of Neuroscience, vol. 38, no. 31, pp. 6967–6982, 2018.
  * [30] N. Ulanovsky, L. Las, and I. Nelken, “Processing of low-probability sounds by cortical neurons,” Nature Neuroscience, vol. 6, no. 4, pp. 391–398, 2003\.
  * [31] G. G. Parras, J. Nieto-Diego, G. V. Carbajal, C. Valdés-Baizabal, C. Escera, and M. S. Malmierca, “Neurons along the auditory pathway exhibit a hierarchical organization of prediction error,” Nature Communications, vol. 8, no. 1, 2017.
  * [32] P. Bauerle, W. von der Behrens, M. Kossl, and B. H. Gaese, “Stimulus-Specific Adaptation in the Gerbil Primary Auditory Thalamus Is the Result of a Fast Frequency-Specific Habituation and Is Regulated by the Corticofugal System,” Journal of Neuroscience, vol. 31, no. 26, pp. 9708–9722, 2011\.
  * [33] F. M. Antunes, I. Nelken, E. Covey, and M. S. Malmierca, “Stimulus-Specific Adaptation in the Auditory Thalamus of the Anesthetized Rat,” PLoS ONE, vol. 5, no. 11, p. e14071, 2010.
  * [34] L. A. Anderson, G. B. Christianson, and J. F. Linden, “Stimulus-Specific Adaptation Occurs in the Auditory Thalamus,” Journal of Neuroscience, vol. 29, no. 22, pp. 7359–7363, 2009.
  * [35] B. L. Robinson, N. S. Harper, and D. McAlpine, “Meta-adaptation in the auditory midbrain under cortical influence,” Nature Communications, vol. 7, p. 13442, dec 2016.
  * [36] Y. A. Ayala, A. Udeh, K. Dutta, D. Bishop, M. S. Malmierca, and D. L. Oliver, “Differences in the strength of cortical and brainstem inputs to SSA and non-SSA neurons in the inferior colliculus.,” Scientific reports, vol. 5, p. 10383, 2015.
  * [37] P. P. Gao, J. W. Zhang, J. S. Cheng, I. Y. Zhou, and E. X. Wu, “The inferior colliculus is involved in deviant sound detection as revealed by BOLD fMRI,” NeuroImage, vol. 91, pp. 220–227, 2014.
  * [38] D. Pérez-González, O. Hernández, E. Covey, and M. S. Malmierca, “GABA A-mediated inhibition modulates stimulus-specific adaptation in the inferior colliculus,” PLoS ONE, vol. 7, no. 3, 2012\.
  * [39] L. Zhao, Y. Liu, L. Shen, L. Feng, and B. Hong, “Stimulus-specific adaptation and its dynamics in the inferior colliculus of rat.,” Neuroscience, vol. 181, pp. 163–74, may 2011.
  * [40] A. Tabas, G. Mihai, S. Kiebel, R. Trampel, and K. Von Kriegstein, “Abstract rules drive adaptation in the subcortical sensory pathway,” eLife, vol. 9, pp. 1–19, 2020.
  * [41] R. Cacciaglia, C. Escera, L. Slabu, S. Grimm, A. Sanjuán, N. Ventura-Campos, and C. Ávila, “Involvement of the human midbrain and thalamus in auditory deviance detection,” Neuropsychologia, vol. 68, pp. 51–58, 2015.
  * [42] M. Cornella, A. Bendixen, S. Grimm, S. Leung, E. Schröger, and C. Escera, “Spatial auditory regularity encoding and prediction: Human middle-latency and long-latency auditory evoked potentials,” Brain Research, vol. 1626, pp. 21–30, nov 2015.
  * [43] C. Escera and M. S. Malmierca, “The auditory novelty system: An attempt to integrate human and animal research,” Psychophysiology, vol. 51, no. 2, pp. 111–123, 2014.
  * [44] S. Grimm, C. Escera, L. Slabu, and J. Costa-Faidella, “Electrophysiological evidence for the hierarchical organization of auditory change detection in the human brain,” Psychophysiology, vol. 48, no. 3, pp. 377–384, 2011\.
  * [45] A. Tabas and K. von Kriegstein, “Adjudicating Between Local and Global Architectures of Predictive Processing in the Subcortical Auditory Pathway,” Frontiers in Neural Circuits, vol. 15, no. March, pp. 1–14, 2021.
  * [46] A. M. Liberman, P. C. Delattre, L. J. Gerstman, and F. S. Cooper, “Tempo of frequency change as a cue for distinguishing classes of speech sounds,” Journal of Experimental Psychology, vol. 52, no. 2, pp. 127–137, 1956.
  * [47] A. M. Liberman and M. Studdert-Kennedy, “Phonetic Perception,” in Perception, pp. 143–178, Springer Berlin Heidelberg, 1978.
  * [48] B. Hu, “Functional organization of lemniscal and nonlemniscal auditory thalamus,” Experimental Brain Research, vol. 153, no. 4, pp. 543–549, 2003.
  * [49] R. I. Kuo and G. K. Wu, “The Generation of Direction Selectivity in the Auditory System,” Neuron, vol. 73, no. 5, pp. 1016–1027, 2012.
  * [50] I.-H. Hsieh, P. Fillmore, F. Rong, G. Hickok, and K. Saberi, “FM-selective Networks in Human Auditory Cortex Revealed Using fMRI and Multivariate Pattern Classification,” Journal of Cognitive Neuroscience, vol. 24, no. 9, pp. 1896–1907, 2012.
  * [51] M. F. Joanisse and D. D. DeSouza, “Sensitivity of human auditory cortex to rapid frequency modulation revealed by multivariate representational similarity analysis,” Frontiers in Neuroscience, vol. 8, no. SEP, pp. 1–10, 2014.
  * [52] H. Okamoto and R. Kakigi, “Encoding of frequency-modulation (FM) rates in human auditory cortex,” Scientific Reports, vol. 5, pp. 1–9, 2015.
  * [53] L. I. Zhang, A. Y. Y. Tan, C. E. Schreiner, and M. M. Merzenich, “Topography and synaptic shaping of direction selectivity in primary auditory cortex,” Nature, vol. 424, pp. 201–205, jul 2003.
  * [54] B. Lui and J. R. Mendelson, “Frequency modulated sweep responses in the medial geniculate nucleus,” Experimental Brain Research, vol. 153, pp. 550–553, dec 2003.
  * [55] C.-q. Ye, M.-m. Poo, Y. Dan, and X.-h. Zhang, “Synaptic mechanisms of direction selectivity in primary auditory cortex,” Journal of Neuroscience, vol. 30, no. 5, pp. 1861–1868, 2010.
  * [56] J. B. Issa, B. D. Haeffele, E. D. Young, and D. T. Yue, “Multiscale mapping of frequency sweep rate in mouse auditory cortex,” Hearing Research, vol. 344, pp. 207–222, 2016.
  * [57] I. Nabelek, A. Nabelek, and I. J. Hirsh, “Pitch of Short Tone Bursts of Changing Frequency,” The Journal of the Acoustical Society of America, vol. 45, no. 1, pp. 293–293, 1970.
  * [58] A. Tabas and K. Von Kriegstein, “Neural modelling of the encoding of fast frequency modulatio,” PLoS Computational Biology, vol. xxx, p. xxxxx, 2021\.
  * [59] J. P. O’Doherty, A. Hampton, and H. Kim, “Model-based fMRI and its application to reward learning and decision making,” Annals of the New York Academy of Sciences, vol. 1104, pp. 35–53, 2007.
  * [60] M. Rosa, S. Bestmann, L. Harrison, and W. Penny, “Bayesian model selection maps for group studies,” NeuroImage, vol. 49, pp. 217–224, jan 2010.
  * [61] K. E. Stephan, W. D. Penny, J. Daunizeau, R. J. Moran, and K. J. Friston, “Bayesian model selection for group studies,” NeuroImage, vol. 46, no. 4, pp. 1004–1017, 2009.
  * [62] M. Moerel, F. De Martino, K. Uğurbil, E. Yacoub, and E. Formisano, “Processing of frequency and location in human subcortical auditory structures.,” Scientific reports, vol. 5, p. 17048, 2015.
  * [63] G. Mihai, M. Moerel, F. De Martino, R. Trampel, S. Kiebel, and K. von Kriegstein, “Modulation of tonotopic ventral mgb is behaviorally relevant for speech recognition.,” eLife, 2019.
  * [64] K. R. Sitek, O. F. Gulban, E. Calabrese, G. A. Johnson, A. Lage-castellanos, M. Moerel, S. S. Ghosh, and F. D. Martino, “Mapping the human subcortical auditory system using histology , postmortem MRI and in vivo MRI at 7T,” eLife, vol. 8, p. e48932, 2019.
  * [65] I. B. Yildiz, K. von Kriegstein, and S. J. Kiebel, “From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems,” PLoS Computational Biology, vol. 9, no. 9, 2013.
  * [66] S. R. Hage and G. Ehret, “Mapping responses to frequency sweeps and tones in the inferior colliculus of house mice,” European Journal of Neuroscience, vol. 18, no. 8, pp. 2301–2312, 2003.
  * [67] A.-A. Li, A.-Y. Zhang, Q.-C. Chen, and F.-J. Wu, “Effects of modulation range and presentation rate of FM stimulus on auditory response properties of mouse inferior collicular neurons.,” Sheng li xue bao : [Acta physiologica Sinica], vol. 62, no. 3, pp. 210–8, 2010.
  * [68] H.-R. A. P. Geis and J. G. G. Borst, “Intracellular responses to frequency modulated tones in the dorsal cortex of the mouse inferior colliculus,” Frontiers in Neural Circuits, vol. 7, pp. 2002–2016, feb 2013.
  * [69] M. Trujillo, M. M. Carrasco, and K. Razak, “Response properties underlying selectivity for the rate of frequency modulated sweeps in the auditory cortex of the mouse,” Hearing Research, vol. 298, pp. 80–92, 2013.
  * [70] D. Duque, X. Wang, J. Nieto-Diego, K. Krumbholz, and M. S. Malmierca, “Neurons in the inferior colliculus of the rat show stimulus-specific adaptation for frequency, but not for intensity,” Scientific Reports, vol. 6, no. April, pp. 1–15, 2016.
  * [71] D. Duque and M. S. Malmierca, “Stimulus-specific adaptation in the inferior colliculus of the mouse : anesthesia and spontaneous activity effects,” Brain Structure and Function, vol. 220, pp. 3385–3398, 2015.
  * [72] Y. A. Ayala, A. Udeh, K. Dutta, D. Bishop, M. S. Malmierca, and D. L. Oliver, “Differences in the strength of cortical and brainstem inputs to SSA and non-SSA neurons in the inferior colliculus.,” Scientific reports, vol. 5, p. 10383, 2015.
  * [73] Y. A. Ayala, D. Pérez-gonzález, D. Duque, I. Nelken, and M. S. Malmierca, “Frequency discrimination and stimulus deviance in the inferior colliculus and cochlear nucleus,” Frontiers in Neural Circuits, vol. 6, p. 119, 2013.
  * [74] F. M. Antunes and M. S. Malmierca, “Effect of Auditory Cortex Deactivation on Stimulus-Specific Adaptation in the Medial Geniculate Body,” Journal of Neuroscience, vol. 31, no. 47, pp. 17306–17316, 2011.
  * [75] M. S. Malmierca, S. Cristaudo, D. Pérez-González, and E. Covey, “Stimulus-specific adaptation in the inferior colliculus of the anesthetized rat.,” The Journal of Neuroscience, vol. 29, no. 17, pp. 5483–5493, 2009.
  * [76] M. S. Malmierca, L. A. Anderson, and F. M. Antunes, “The cortical modulation of stimulus-specific adaptation in the auditory midbrain and thalamus: a potential neuronal correlate for predictive coding.,” Frontiers in systems neuroscience, vol. 9, p. 19, 2015.
  * [77] G. V. Carbajal and M. S. Malmierca, “The Neuronal Basis of Predictive Coding Along the Auditory Pathway : From the Subcortical Roots to Cortical Deviance Detection,” Trends in Hearing, vol. 22, pp. 1–33, 2018.
  * [78] D. Eytan, N. Brenner, and S. Marom, “Selective adaptation in networks of cortical neurons.,” The Journal of Neuroscience, vol. 23, no. 28, pp. 9349–9356, 2003.
  * [79] R. Mill, M. Coath, T. Wennekers, and S. L. Denham, “A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences,” PLoS Computational Biology, vol. 7, no. 8, 2011.
  * [80] R. Mill, M. Coath, T. Wennekers, and S. L. Denham, “Characterising stimulus-specific adaptation using a multi-layer field model,” Brain Research, vol. 1434, pp. 178–188, jan 2012.
  * [81] T. C. Gunter, A. D. Friederici, and H. Schriefers, “Syntactic Gender and Semantic Expectancy,” Journal of Cognitive Neuroscience, vol. 12, no. 4, 2000.
  * [82] S. Regel, S. Coulson, and T. C. Gunter, “The communicative style of a speaker can affect language comprehension? ERP evidence from the comprehension of irony,” Brain Research, vol. 1311, no. July 2014, pp. 121–135, 2010.
  * [83] J. A. Winer, “Three Systems of Descending Projections to the Inferior Colliculus,” in The Inferior Colliculus, pp. 231–247, New York: Springer-Verlag, 2005.
  * [84] X.-J. Yu, X.-X. Xu, S. He, and J. He, “Change detection by thalamic reticular neurons.,” Nature Neuroscience, vol. 12, no. 9, pp. 1165–1170, 2009.
  * [85] L. A. Anderson and M. S. Malmierca, “The effect of auditory cortex deactivation on stimulus-specific adaptation in the inferior colliculus of the rat,” European Journal of Neuroscience, vol. 37, no. 1, pp. 52–62, 2013.
  * [86] J. M. Thomas, C. Morse, L. Kishline, A. O’Brien-Lambert, A. Simonton, K. E. Miller, and E. Covey, “Stimulus-specific adaptation in specialized neurons in the inferior colliculus of the big brown bat, Eptesicus fuscus,” Hearing Research, vol. 291, no. 1-2, pp. 34–40, 2012.
  * [87] D. Duque, M. S. Malmierca, and D. M. Caspary, “Modulation of stimulus-specific adaptation by GABA(A) receptor activation or blockade in the medial geniculate body of the anaesthetized rat.,” The Journal of physiology, vol. 592, no. Pt 4, pp. 729–43, 2014.
  * [88] Y. A. Ayala and M. S. Malmierca, “The effect of inhibition on stimulus-specific adaptation in the inferior colliculus,” Brain Structure and Function, vol. 223, pp. 1391–1407, 2018.
  * [89] M. H. Davis and I. S. Johnsrude, “Hearing speech sounds: Top-down influences on the interface between audition and speech perception,” Hearing Research, vol. 229, pp. 132–147, jul 2007.
  * [90] J. McFadyen, R. J. Dolan, and M. I. Garrido, “The influence of subcortical shortcuts on disordered sensory and cognitive processing,” Nature Reviews Neuroscience, vol. 21, no. May, 2020.
  * [91] B. Diaz, F. Hintz, S. J. Kiebel, and K. von Kriegstein, “Dysfunction of the auditory thalamus in developmental dyslexia,” Proceedings of the National Academy of Sciences, vol. 109, no. 34, pp. 13841–13846, 2012.
  * [92] T. K. Perrachione, S. N. Del Tufo, R. Winter, J. Murtagh, A. Cyr, P. Chang, K. Halverson, S. S. Ghosh, J. A. Christodoulou, and J. D. Gabrieli, “Dysfunction of Rapid Neural Adaptation in Dyslexia,” Neuron, vol. 92, no. 6, pp. 1383–1397, 2016.
  * [93] M. Ahissar, Y. Lubin, H. Putter-Katz, and K. Banai, “Dyslexia and the failure to form a perceptual anchor,” Nature Neuroscience, vol. 9, no. 12, pp. 1558–1564, 2006.
  * [94] B. Chandrasekaran, J. Hornickel, E. Skoe, T. Nicol, and N. Kraus, “Context-Dependent Encoding in the Human Auditory Brainstem Relates to Hearing Speech in Noise: Implications for Developmental Dyslexia,” Neuron, vol. 64, no. 3, pp. 311–319, 2009.
  * [95] C. Müller-Axt, A. Anwander, and K. von Kriegstein, “Altered Structural Connectivity of the Left Visual Thalamus in Developmental Dyslexia,” Current Biology, vol. 27, pp. 3692–3698, 2017.
  * [96] N. Tschentscher, A. Ruisinger, H. Blank, B. Diaz, and K. von Kriegstein, “Thalamus and the Motion-Sensitive Planum Temporale in Developmental Dyslexia,” The Journal of Neuroscience, vol. 39, no. 9, pp. 1720–1732, 2019.
  * [97] K. Moll and K. Landerl, “Lese-und rechtschreibtest (slrt-ii). weiterentwicklung des salzburger lese-und rechtschreibtests (slrt), 2., korrigierte auflage mit erweiterten normen,” 2014.
  * [98] N. Ibrahimović and S. Bulheller, Rechtschreibtest RST-ARR: aktuelle Rechtschreibregelung: Lückendiktate.  Pearson Assessment & Information, 2013.
  * [99] M. B. Denckla and R. Rudel, “Rapid “Automatized” Naming of Pictured Objects, Colors, Letters and Numbers by Normal Children,” Cortex, vol. 10, no. 2, pp. 186–202, 1974.
  * [100] S. Baron-Cohen, S. Wheelwright, J. Hill, Y. Raste, and I. Plumb, “The ”Reading the Mind in the Eyes” Test revised version: A study with normal adults, and adults with Asperger syndrome or high-functioning autism,” Journal of Child Psychology and Psychiatry and Allied Disciplines, vol. 42, no. 2, pp. 241–251, 2001.
  * [101] K. Gutschmidt, S. Wenninger, F. Montagnese, and B. Schoser, “Dyslexia and cognitive impairment in adult patients with myotonic dystrophy type 1: a clinical prospective analysis,” Journal of Neurology, 2020.
  * [102] K. Friston, E. Zarahn, O. Josephs, R. Henson, and A. Dale, “Stochastic Designs in Event-Related fMRI,” NeuroImage, vol. 10, no. 5, pp. 607–619, 1999.
  * [103] M. Brant-Zawadzki, G. D. Gillan, and W. R. Nitz, “MP RAGE: A three-dimensional, T1-weighted, gradient-echo sequence - Initial experience in the brain,” Radiology, vol. 182, no. 3, pp. 769–775, 1992.
  * [104] D. H. Brainard, “The Psychophysics Toolbox,” Spatial Vision, vol. 10, no. 4, pp. 433–436, 1997.
  * [105] K. Gorgolewski, C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. S. Ghosh, “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python,” Frontiers in Neuroinformatics, vol. 5, 2011.
  * [106] B. Fischl, D. H. Salat, E. Busa, M. Albert, M. Dieterich, C. Haselgrove, A. Van Der Kouwe, R. Killiany, D. Kennedy, S. Klaveness, A. Montillo, N. Makris, B. Rosen, and A. M. Dale, “Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain,” Neuron, vol. 33, no. 3, pp. 341–355, 2002.
  * [107] M. Jenkinson, C. F. Beckmann, T. E. Behrens, M. W. Woolrich, and S. M. Smith, “Fsl,” NeuroImage, vol. 62, no. 2, pp. 782–790, 2012.
  * [108] B. B. Avants, N. J. Tustison, G. Song, P. A. Cook, A. Klein, and J. C. Gee, “A reproducible evaluation of ANTs similarity metric performance in brain image registration,” NeuroImage, vol. 54, no. 3, pp. 2033–2044, 2011\.
  * [109] L. Kasper, S. Bollmann, A. O. Diaconescu, C. Hutton, J. Heinzle, S. Iglesias, T. U. Hauser, M. Sebold, Z. M. Manjaly, K. P. Pruessmann, and K. E. Stephan, “The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data,” Journal of Neuroscience Methods, vol. 276, pp. 56–72, 2017.

## Supplementary materials

Figure S1: Schematic of the GLM’s design matrix. An example of the GLM design
matrix section corresponding to the regressors of interest prior to the
convolution with the haemodynamic response function. The example includes two
trials with two different deviant locations. The six regressors of interest
were standard 0, standard 1, standard 2, deviant 4, deviant 5 (not shown),
deviant 6. The standards were parametrically modulated and the modulation was
equal to the inverted index of the standard within the sequence (i.e., 4 for
the first $std1$, 3 for the second $std1$, etc; note that, since the
modulation was mean-corrected before the fitting of the GLM, the absolute
values of the modulation are not relevant).