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\begin{document}

\title{Adaptation Patterns and Their Associations with Mismatch Negativity: A
Roving Electroencephalogram Paradigm with Expectations Maintained}



\author[1]{Brian W. L. Wong}%
\author[2]{Shuting Huo}%
\author[3]{Urs Maurer}%
\affil[1]{BCBL}%
\affil[2]{The Hong Kong Polytechnic University}%
\affil[3]{The Chinese University of Hong Kong}%


\vspace{-1em}



  
  \date{June 16, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Adaptation is defined as reduced neural activation following repeated
stimulus presentation. While numerous previous electroencephalogram
studies used either one repetition or multiple repetitions to elicit
adaptation in event-related potentials, the adaptation patterns under
controlled expectations manifested in the two main auditory components,
N1 and P2, are still largely unknown. In addition, multiple repetitions
were used in mismatch negativity experiments to measure deviance
detection, but how adaptation in different time points contributes to
the mismatch negativity remains unclear. Therefore, in the present
study, 37 healthy adults participated in an electroencephalogram
experiment with a pure tone roving paradigm and a random stimulus
arrangement to maintain expectations. The amplitudes were traced along
with the first ten tones in the auditory event-related potentials
components, N1 and P2, to examine the adaptation patterns. Results
showed an L-shaped adaptation in the N1 with a large decrease after the
first repetition (N1 initial adaptation), and a continuous, linear
amplitude increase in the P2 even after the first repetition (P2
subsequent adaptation), possibly indicating memory trace formation.
Regression results showed that the peak amplitudes of the N1 initial
adaptation and the P2 subsequent adaptation significantly explained the
variance in the mismatch negativity amplitude. The results indicate
distinct adaptation patterns for multiple repetitions in different
components and suggest that the mismatch negativity combines two
processes as indicated by the initial adaptation in the N1 and a
continuous memory trace effect in the P2. Separating the two processes
may be relevant for models of cognitive processing and clinical
disorders.%
\end{abstract}%



\sloppy


Full title: Adaptation Patterns and Their Associations with Mismatch
Negativity: A Roving Electroencephalogram Paradigm with Expectations
Maintained

Short title/running head: Adaptation and MMN

Authors: Brian W. L. WONG\textsuperscript{1,2}, Shuting
HUO\textsuperscript{1,3}, Urs MAURER\textsuperscript{1,4,5}

\textsuperscript{1} Department of Psychology, The Chinese University of
Hong Kong, Hong Kong, China

\textsuperscript{2} BCBL, Basque Center on Brain, Language and
Cognition, Donostia-San Sebasti\selectlanguage{ngerman}án, Spain

\textsuperscript{3} Department of Applied Social Sciences, The Hong Kong
Polytechnic University, Hong Kong, China

\textsuperscript{4} Brain and Mind Institute, The Chinese University of
Hong Kong, Hong Kong, China

\textsuperscript{5} Centre for Developmental Psychology, The Chinese
University of Hong Kong, Hong Kong, China

Email addresses:

Brian W. L. Wong: b.wong@bcbl.eu

Shuting Huo: shuting.huo@polyu.edu.hk

Urs Maurer: umaurer@cuhk.edu.hk

Address for correspondence:

Urs Maurer

Associate Professor

Rm 325, Sino Building

The Chinese University of Hong Kong

Email: umaurer@cuhk.edu.hk

Phone: (+852) 39431035

Word count: 7,918

Figure count: 7

\emph{Keywords} : Adaptation, expectation, electroencephalogram (EEG),
mismatch negativity (MMN), repetition positivity (RP)

Acknowledgment: We thank WONG Kwun Pok Oscar for assisting in some data
collection in this study.

\textbf{Abstract}

Adaptation is defined as reduced neural activation following repeated
stimulus presentation. While numerous previous electroencephalogram
studies used either one repetition or multiple repetitions to elicit
adaptation in event-related potentials, the adaptation patterns under
controlled expectations manifested in the two main auditory components,
N1 and P2, are still largely unknown. In addition, multiple repetitions
were used in mismatch negativity experiments to measure deviance
detection, but how adaptation in different time points contributes to
the mismatch negativity remains unclear. Therefore, in the present
study, 37 healthy adults participated in an electroencephalogram
experiment with a pure tone roving paradigm and a random stimulus
arrangement to maintain expectations. The amplitudes were traced along
with the first ten tones in the auditory event-related potentials
components, N1 and P2, to examine the adaptation patterns. Results
showed an L-shaped adaptation in the N1 with a large decrease after the
first repetition (N1 initial adaptation), and a continuous, linear
amplitude increase in the P2 even after the first repetition (P2
subsequent adaptation), possibly indicating memory trace formation.
Regression results showed that the peak amplitudes of the N1 initial
adaptation and the P2 subsequent adaptation significantly explained the
variance in the mismatch negativity amplitude. The results indicate
distinct adaptation patterns for multiple repetitions in different
components and suggest that the mismatch negativity combines two
processes as indicated by the initial adaptation in the N1 and a
continuous memory trace effect in the P2. Separating the two processes
may be relevant for models of cognitive processing and clinical
disorders.

\emph{Keywords} : Adaptation, expectation, electroencephalogram (EEG),
mismatch negativity (MMN), repetition positivity (RP)

\textbf{1. Introduction}

\textbf{1.1 Adaptation}

Repeated presentation of the same stimulus typically leads to reduced
neural activation, which is called neural adaptation (Grill-Spector et
al., 2006; Ringo, 1996), repetition suppression (Desimone, 1996;
Grill-Spector et al., 2006), sensory gating (Boutros \& Belger, 1999;
Freedman et al., 1987), habituation (Bourbon et al., 1987; Loveless,
1983; c.f. Barry et al., 1992), refractoriness (Berry \& Meister, 1998;
Budd et al., 1998; Rosburg \& Mager, 2021) and some other terms with
varying usage across the literature. In the present study, we refer it
to adaptation. Adaptation can be observed across different stimuli in
both visual and auditory modalities, for example, in the repetition of
faces (see Schweinberger \& Neumann, 2016 for a review), symbols (e.g.,
Soltész \& Sz\selectlanguage{polish}ű\selectlanguage{english}cs, 2014), and tones (e.g., Todorovic \& de Lange, 2012).

Adaptation can be measured by an amplitude decrement of components of
the event-related potential (ERP) that is recorded from the scalp using
electroencephalography (EEG). In the auditory modality, ERP components,
including N1 and P2, are typically measured at fronto-central
electrodes. N1 is a negative component occurring between around 60--160
ms (Woods, 1995) and P2 is a positive component that peaks at around
150--250 ms (Crowley \& Colrain, 2004). Distributed over fronto-central
areas (Crowley \& Colrain, 2004; Woods, 1995), these components
typically have a polarity reversal over inferior posterior electrodes,
when measured against an average reference, as their sources are mainly
in the temporal lobe (Crowley \& Colrain, 2004; Fogarty et al., 2020;
N\selectlanguage{ngerman}äätänen \& Picton, 1987). The auditory N1 and P2 adaptations, as
reflected by amplitude decrement, were found in a vast number of
previous studies using pairs of tones or long trains of tones (e.g., N1:
Bourbon et al., 1987; Boutros et al., 1999; Budd et al., 1998; Lagemann
et al., 2012; Näätänen \& Picton, 1987; Rosburg, 2004; Rosburg \& Mager,
2021; Rosburg et al., 2006; N1 and P2: Hari et al., 1982; Herrmann et
al., 2016; Peter et al., 2019; Polich, 1986; Rosburg et al., 2022;
Rosburg et al., 2010; Sambeth et al., 2004). Regarding the adaptation
pattern, most studies showed an N1 decrease stabilized after the
2\textsuperscript{nd} or 3\textsuperscript{rd} sound in a stimulus
sequence but no further decrease for subsequent sounds (e.g., Barry et
al., 1992; Bourbon et al., 1987; Boutros et al., 1999; Budd et al.,
1998; Lagemann et al., 2012; Rosburg, 2004), although some studies found
a gradual response decrease (e.g., Öhman \& Lader, 1972). An initial
amplitude decrease was shown in the P2 in some studies (Rosburg \&
Sörös, 2016; Rosburg et al., 2010). However, some studies also reported
an increase of a slow positive wave from 50 to 250 ms post-stimulus
onset along with stimulus repetition, which reflected ``repetition
positivity'' (RP; Cooper et al., 2013; Recasens et al., 2015; more
details of RP will be discussed below). Since the RP corresponds to an
increase of P2 and a decrease of N1, it seems to be contradictory to the
diminishing P2. Therefore, more research on this topic is necessary to
understand the adaptation patterns.

\textbf{1.2 Adaptation and MMN}

Adaptation is related to a component formed by an ERP difference wave
called mismatch negativity (MMN). First described by Näätänen et al.
(1978), the MMN usually occurs when a stimulus with different properties
(e.g., frequency, intensity, duration, etc.) called deviant presented in
a sequence of repeated stimuli called standards. It has been studied
extensively in the auditory modality, and it is measured by subtracting
the ERP to standards that appear mostly near the end of a stimulus train
from that to deviants (Garrido et al., 2009b). The MMN peaks at about
100--250 ms from the point of deviation (Kujala \& Näätänen, 2001). For
the scalp location, the MMN is usually maximal at fronto-central areas
and reverses polarity at the mastoids (Kujala \& Näätänen, 2001), while
its source is in temporal and frontal areas (Alho, 1995). Attention is
not required to trigger the MMN as it is associated with pre-attentive
processing in the auditory modality (Näätänen et al., 2001).

Importantly, the formation of the MMN, which is measured by the
amplitude from a difference waveform generated by subtracting the
average response to standards from that to deviants, is not only due to
the more negative amplitude in the deviant trials, but also more
positive (or less negative) amplitudes caused by adaptation in the
standard trials. For example, if the amplitudes of the N1 and P2
components become less negative or more positive along repetitions, the
MMN will be larger assuming that the amplitude of the deviant is
unchanged. However, the MMN will be smaller if the N1 and P2 amplitudes
become more negative and less positive along repetitions. Indeed, the
adaptation hypothesis (Jääskeläinen et al., 2004; May et al., 1999; May
\& Tiitinen, 2010) has been proposed to explain the mechanism of the
MMN. The adaptation hypothesis emphasizes that MMN is induced by the
attenuation and delay of the N1 due to adaptation to the repetitive
standard stimuli (May et al., 1999; May \& Tiitinen, 2010). Therefore,
the MMN does not reflect any higher-level comparison processing or
mismatch detection per se, but just a release from stimulus-specific
adaptation (Fishman, 2014). On the contrary, the model-adjustment
hypothesis (Näätänen \& Alho, 1995; Näätänen et al., 2005; Näätänen \&
Winkler, 1999; Sussman \& Winkler, 2001; Winkler et al., 1996) proposes
that the MMN represents the outcome of change detection between the
deviants and the memory trace formed by standards. Originally, this
hypothesis only applied to repeated sounds of the same standards, but
later it was extended to include the detection of regularity violation
to account for the existence of the MMN in some experiments using
standard stimuli with predictable patterns, or regularities, such as a
sequence of tones with increasing frequencies, and deviants which
violate the regularities, for instance, a repeated or decreased tone
(Winkler, 2007). The occurrence of the MMN in this type of experimental
design supported the model-adjustment hypothesis but not the adaptation
hypothesis because no repeated sound could cause adaptation in the
regularities (Garrido et al., 2009b).

While the aforementioned studies attempted to discriminate the two
postulations of MMN mechanisms, it is important to note that they are
not mutually exclusive. Indeed, a predictive coding framework could
explain the mechanisms of MMN by encompassing the adaptation hypothesis
and the model-adjustment hypothesis (Carbajal \& Malmierca, 2018;
Friston, 2005; Garrido et al., 2009b; Winkler, 2007). It suggests that
our brain extracts regularities in a sequence of stimuli and forms a
hypothesis about upcoming stimuli based on the regularities. If a
deviant happens, the MMN is elicited because a prediction error is
generated due to a violation of the hypothesis about the stimulus and
regularities by the deviant. The predictive coding framework
incorporates the adaptation hypothesis by suggesting that when standard
stimuli can be predicted more precisely by the top-down process, less
weight is assigned to bottom-up influences, leading to a stronger
adaptation shown by a weaker and later N1 (Garrido et al., 2009b). From
the predictive coding perspective, adaptation and MMN can be considered
as microscopic and macroscopic correlates of the same deviance-detection
process when the repetition rule is involved (Carbajal \& Malmierca,
2018). On the other hand, the predictive coding framework shares the
same idea with the model-adjustment hypothesis that MMN is due to a
comparison between the predicted inputs based on memory trace and the
actual inputs. A mismatch between these two inputs leads to a prediction
error, and the prediction model has to be adjusted. Therefore, in the
predictive coding framework, the MMN represents a failure to predict
bottom-up inputs and an ensuing failure to suppress prediction error
(Garrido et al., 2009b).

Repetition positivity (RP) is a component that supports the predictive
coding mechanism of MMN. RP is formed by subtracting the ERPs of fewer
repetitions of standard stimuli from the ERPs with more repetitions. It
may be pertinent to the phenomenon that an increase in standard stimulus
repetition may lead to an increase in MMN (Sams et al., 1983).
Crucially, the RP is not only limited to the N1 time window, and this is
inconsistent with the adaptation hypothesis of MMN as it proposes that
the N1 adaptation effect could explain the MMN completely (Haenschel et
al., 2005). In the study by Haenschel et al. (2005), RP, which could
account for a large proportion of the MMN, occurred early from the P1
time window, and it was larger when repetitions of standards increased.
Based on these characteristics, they postulated that the RP is an ERP
correlate of adaptation, a mechanism that underlies memory trace
formation in the primary auditory cortex (A1). According to the
predictive coding account, the RP is considered an index of prediction
error suppression stemming from the congruence between sensory and
predicted input (Baldeweg, 2007). Research studies that investigate the
RP are relatively scarce compared with those examining MMN. Therefore,
more studies need to be conducted before the relationship between RP,
adaptation, and MMN can be firmly established.

\textbf{1.3 Expectation Maintenance and the Present Study}

Importantly, previous studies have suggested that stimulus expectations
may affect the components related to adaptation and MMN. Specifically,
the N1 amplitude of stimulus repetition was found to be enhanced by
expectations of the stimuli (Hari et al., 1979), while the regular
combination of standards and deviants might induce a pattern since
grouping could happen pre-attentively even though the stimulus onset
asynchrony (SOA) was as long as one second, which might affect the MMN
amplitude (Herholz et al., 2009). In addition, explicit top-down
expectations were found to diminish the MMN (Chennu et al., 2013;
Costa-Faidella et al., 2011b; Lecaignard et al., 2021). Recent findings
have shown that stimulus expectations generated by participants may play
a role in adaptation when attention is involved (e.g., Tang et al.,
2018), or even not involved (e.g., Barbosa \& Kouider, 2018; Kuravi \&
Vogels, 2017; Todorovic et al., 2011). Therefore, the top-down
processing may modulate the repetition effect. This underscores the
importance of maintaining or controlling expectations when examining the
adaptation effect. Nonetheless, restrictions imposed on stimulus
arrangements could lead to expectations. For example, in typical MMN
experiments, the consecutive occurrence of deviants is avoided, and the
maximum tone repetition is set as a fixed number. These restrictions
might induce participants to generate expectations regarding whether the
next stimulus is a deviant or standard. These expectations may
potentially affect components related to adaptation and the MMN.
Therefore, in the present study, the expectations was maintained through
a stable presentation probability of the stimuli to ensure a pure
measurement of adaptation uninfluenced by expectation. Specifically, an
experimental paradigm in which no restriction was imposed on the
stimulus arrangement, besides an 85\%-standard and 15\%-deviant rule,
and long trains of maximally 30 standards, was utilized.

In addition, a roving paradigm was adopted in the present study. In this
paradigm, a deviant becomes the subsequent standard to ensure that the
properties of the deviants and standards are the same. Therefore, the
MMN must reflect the inherent differences between the standards and
deviants, without being influenced by any other physical properties.
Additionally, the memory trace of the standards needs to be
re-established and strengthened during each train of standards (Cooper
et al., 2013). This paradigm has been used in some previous studies
(e.g., Costa-Faidella et al., 2011a; Cowan et al., 1993; Garrido et al.,
2008; Haenschel et al., 2005; Recasens et al., 2015).

The present study used ``initial adaptation'' to represent the amplitude
decrease from the 1\textsuperscript{st} to the
2\textsuperscript{nd}tones and ``subsequent adaptation'' to capture the
following decrease from the 2\textsuperscript{nd} to the final tones in
each sequence of the same stimuli. Overall, using an auditory
experimental paradigm with controlled expectations, the present study
focuses on two research questions: 1) How are the initial and subsequent
adaptation patterns manifested in N1 and P2? 2) To what extent can these
adaptation patterns explain the MMN? These two research questions were
examined using an EEG roving experiment in healthy adults, coupled with
adaptation pattern, correlation, and regression analyses. Regarding the
first research question, we predicted that the N1 and P2 components
would show reduced amplitudes in the initial tones that signify
adaptation based on previous studies (Budd et al., 1998; Rosburg \&
Sörös, 2016). However, increased amplitudes might be observed in P2 due
to the RP, indicating a memory trace effect (Haenschel et al., 2005).
Concerning the second research question, we hypothesized that the
correlations of the MMN (negative values) and adaptation effects
manifested by N1 (negative values) and P2 (positive values) would be
positive and negative respectively. Moreover, the initial and subsequent
adaptation effects might be important predictors of the MMN as the
adaptation and the memory trace proposed by the model adjustment account
may both play a role in the MMN.

\textbf{2. Materials and Methods}

\textbf{2.1 Participants}

We recruited 40 adults to participate in the experiment. Three
participants were excluded due to excessive muscle movement or electric
noise. Therefore, the data of the remaining 37 participants (\emph{age
range} : 19--26 years; \emph{mean age} : 20.84 years; 19 females) were
analyzed. Two of them were left-handed, while the others were
right-handed. All participants were undergraduate students studying at
the Chinese University of Hong Kong (CUHK). Written consent was obtained
before the experiment started. They either received course credits or
cash for compensation. This study was approved by The Joint Chinese
University of Hong Kong--New Territories East Cluster Clinical Research
Ethics Committee (The Joint CUHK-NTEC CREC) (reference no.: 2019.048).

\textbf{2.2 Stimuli and Procedure}

The experiment was programmed using E-Prime 3.0 (Psychology Software
Tools, Pittsburgh, PA). All stimuli were pure sinusoidal tones, with
seven different frequencies (500 Hz, 550 Hz, 600 Hz, 650 Hz, 700 Hz, 750
Hz, and 800 Hz). The duration of each tone was 70 ms (with 5 ms rise and
fall times), and the stimulus onset asynchrony (SOA) was fixed at 650
ms. All tones were presented at 70 dB. As a roving paradigm was used,
deviants (the 1\textsuperscript{st} tone in each sequence) would become
subsequent standards.

EEG data were recorded using NetStation, with a Net Amps 300 amplifier
and 128-channel Electrical Geodesics Incorporated (EGI) nets against a
Cz reference. The sampling rate was 500 Hz, and the online filter
setting was 0.1 Hz high-pass. The impedance was kept below 50 k\selectlanguage{greek}Ω \selectlanguage{english}for
each electrode. The data recording took place in a sound-attenuated
laboratory. Before the experiment began, participants were told that
some sounds would be played when they were watching a silent movie, but
they should ignore the sounds. Also, they were instructed to limit body
movements during the experiment to minimize muscle artifacts.

In the present experiment, the first tone was randomly played among the
seven types of tones with different frequencies. After that, there was
an 85\% chance that the next tone would be the same as the first tone
(standard) and a 15\% chance that the next tone would be different from
the first tone (deviant). This criterion was kept unchanged throughout
the experiment. The only exception was that if 30 tones were played
consecutively, the next tone must have been a deviant, which happened
only 2.73 times on average (\emph{SD} = 1.59 times; \emph{range} : 0--5
times) among around 300 sequences for each participant. This restriction
was to ensure that there were sufficient trials to be analyzed. The
purpose of increasing the maximum stimulus number in each train (30)
compared with previous studies (mostly 10 or fewer) and using a random
stimulus arrangement was to maintain participants' expectations of the
stimulus arrangement. Throughout the experiment, participants watched a
silent movie, ``Tom and Jerry'', via a laptop during the experiment,
when the tones were played by two speakers 80 cm away from them. The
present experiment ended after 2,000 tones had been played, which were
divided into two blocks with 1,000 tones each. There was a small pause
between the two blocks for the experimenter to check the impedance of
all the electrodes and lower it if needed. The duration of the
experiment was around 20 minutes. Figure 1 shows the experimental
paradigm.

After the EEG experiment, a tone discrimination task run by E-Prime 3.0
was conducted to confirm that participants were able to discriminate
tones with a frequency difference of 50 Hz. In each trial, participants
listened to a pair of tones. The task began with eight practice trials
to ensure that participants understood the task, followed by 104
experimental trials. Among the 104 trials, the tone pairs were different
in 48 trials and they were the same in 56 trials. The properties of the
tones were the same as those in the EEG experiment, and they were played
in a pseudorandomized sequence. All different tone pairs in the
experimental trials had 50 Hz discrepancy, the least frequency
difference that appeared in the EEG task, but tone pairs with larger
frequency differences appeared in practice trials. Participants were
told to press ``f'' on the keyboard when the two tones were the same and
press ``j'' when they were different. Participants were emphasized that
they should make the judgment as accurately and quickly as possible. In
each trial, a fixation cross first appeared for one second, followed by
a pair of tones (70 ms each), with an inter-tone interval of 280 ms.
Then, a question mark appeared on the screen, which remained until
participants had judged whether the two tones were the same or different
by pressing the corresponding button. After that, the fixation cross
reappeared as the next trial began. Accuracy and reaction time were
shown immediately after the response in practice trials, but not in the
main experiment. This task lasted for around five minutes. The average
accuracy was 94.49\% (\emph{SD} = 5.38\%; \emph{d'} = 3.56), indicating
that participants were able to discriminate the tones used in the
experiment. No participant was excluded based on their performance in
this task.

\textbf{2.3 EEG Preprocessing}

Brain Vision Analyzer (version 2.2.1.8266; Brain Products, Gilching,
Germany) was used for data preprocessing and waveform analyses. The data
preprocessing steps for each participant began with segmentation,
followed by filtering (0.3 Hz high-pass with a slope of 24 dB/oct and 30
Hz low-pass with a slope of 48 dB/oct), bad channel selection, raw data
inspection, independent component analysis (ICA) for eye blink and
lateral eye movement corrections based on a restricted Infomax
algorithm, bad channel interpolation with spherical splines,
re-referencing to the average signals recorded from all electrodes, and
artifact rejection (if an extreme voltage (\textgreater{}+80 or
\textless{}-80 \selectlanguage{greek}μ\selectlanguage{english}V) was detected, a segment before and after 200 ms of
that detection point was excluded). Overall, an average of 24\% of
trials were rejected. The average remaining trial numbers in the three
conditions related to the adaptation effects, namely the deviants
(1\textsuperscript{st} tones), the 2\textsuperscript{nd} tones, and the
final tones in the 3\textsuperscript{rd} to
30\textsuperscript{th}positions, were 230 (152--297), 197 (133--250),
and 139 (98--172) respectively. The average remaining trial numbers for
the 3\textsuperscript{rd} to 10\textsuperscript{th} tones, which are
relevant to the adaptation pattern analyses, are recorded in Table S1.
We included only the final tones in the 4\textsuperscript{th} to
30\textsuperscript{th} positions for the adaptation and MMN analyses,
rather than all the tones within these positions. This approach ensures
a relatively balanced comparison between standards and deviants by
maintaining a similar number of each. A fixed delay of 18 ms was
corrected due to the anti-aliasing filter of the amplifier (see the
advisory notice about the timing affected by anti-alias filter effects
used in Net Amps 300 amplifiers, 26 November 2014, EGI for more
details). The epoch was from 168 ms pre-stimulus to 632 ms
post-stimulus. Baseline correction was conducted based on the data in
the pre-stimulus period.

\textbf{2.4 Data Analyses}

Based on previous literature related to adaptation and MMN (e.g., B\selectlanguage{ngerman}ühler
et al., 2017; Jaffe-Dax et al., 2017; Jost et al., 2015), the following
25 frontocentral electrodes were pooled for analyses: E19, E11, E4, E20,
E12, E5, E118, E13, E6, E112, E10, E16, E18, E30, E7, E106, E105, E37,
E31, E129, E80, E87, E55, E36, E104 (see the top-left corner of Figure 3
for the electrode positions). The time window for the detection of the
local peak amplitude of N1 was defined by the global field power (GFP)
and global dissimilarity peaks of subsequent maps of the grand average
data. GFP is the root mean square (RMS) across the average-referenced
electrode values, or the standard deviation of all electrodes at a given
time point (Murray et al., 2008), while global dissimilarity is an index
of configuration differences between the maps, notwithstanding their
strength (Lehmann \& Skrandies, 1980; Murray et al., 2008). The GFP and
global dissimilarity were generated from Brain Vision Analyzer and
Cartool (Brunet et al., 2011). The N1 time windows of the deviants, the
2\textsuperscript{nd} tones, and the final tones were 70--218 ms,
76--118 ms, and 76--140 ms respectively. On the other hand, since there
were no clear GFP peaks in the P2, its time windows were defined by the
peaks between N1 and N2. The P2 time windows of the corresponding
conditions were 120--348 ms, 96--240 ms, and 98--254 ms respectively
(see Figure S1 for the illustrations of how these P2 time windows were
defined).

In addition, the MMN waveform was formed by subtracting the amplitudes
of the final tones in the 4\textsuperscript{th} to
30\textsuperscript{th} positions from that of the deviants in the same
frontocentral electrodes (deviants--final tones), and the time window
based on the GFP and the global dissimilarity peaks was defined as
76--196 ms. Local peak MMN amplitude was identified automatically using
Brain Vision Analyzer. Similarly, for the adaptation effects, the local
peak amplitude in each tone position and time window was identified
automatically using Brain Vision Analyzer. We defined the initial
adaptation as the peak amplitude decrease between the deviants and the
tones in the 2\textsuperscript{nd} position in the frontocentral
electrodes (deviants--2\textsuperscript{nd} tones). On the other hand,
the subsequent adaptation was defined as the subtraction of the peak
amplitudes of the final tones in the 4\textsuperscript{th}to
30\textsuperscript{th} positions from that of the tones in the
2\textsuperscript{nd} position in the same electrodes
(2\textsuperscript{nd} tones--final tones). Figure 1 illustrates the
calculation methods of initial adaptation, subsequent adaptation, and
MMN.

The adaptation curves of the first ten tones of N1 and P2 were first
plotted based on the peak amplitudes in the corresponding time window,
as after the tenth tone, the trial number (\textless{} 50 trials on
average) might not be enough to generate reliable results. Then, a
paired sample t-test between each consecutive tone pair with
Holm-Bonferroni corrections (Holm, 1979) and a repeated-measures ANOVA
on the amplitude based on an a priori linear trend analysis across the
first ten tones in each stimulus train were conducted to understand the
adaptation pattern manifested in N1 and P2. On the other hand, the
existence of an MMN was tested by a one-sample t-test against zero for
the amplitude of the difference wave. Additionally, the relationship
between initial adaptation, subsequent adaptation effects, and MMN was
examined through correlation and backward stepwise linear regression
analyses. Specifically, in the regression model, the N1 and P2 initial
adaptations and subsequent adaptations were the independent variables
(IVs), and the MMN peak amplitude was the dependent variable (DV). The
elimination criterion of the backward regression was
\emph{p}\textgreater{} .10. This means that the IV with the lowest
partial correlation with the DV, which reached the criterion
\emph{p}\textgreater{} .10, was removed in each step until all variables
that remained in the model were \emph{p} \textless{} .10. All
statistical tests mentioned above were conducted using IBM SPSS
Statistics Version 25 (IBM, Armonk, NY, USA).

\textbf{3. Results}

Figure 2 shows the adaptation patterns depicted by the peak amplitudes
of N1 and P2 along the first ten tones. Details are described below.

\textbf{3.1 N1 Adaptation}

The adaptation curve of the first ten tones from the average N1 peak
data was plotted based on the peak amplitudes in the N1 time window. A
steep decrease (less negative) in N1 amplitude was found between the
1\textsuperscript{st} and 2\textsuperscript{nd} tones, then they reached
a plateau (Figure 2a). Paired-sample t-tests were conducted to compare
the average N1 peak amplitude elicited by each successive tone pair.
Results showed that the N1 amplitude of the 2\textsuperscript{nd} tones
(\emph{M}= -0.71 \selectlanguage{greek}μ\selectlanguage{english}V, \emph{SD} = 0.56 \selectlanguage{greek}μ\selectlanguage{english}V) was significantly less
negative than that of the 1\textsuperscript{st} tones (\emph{M} = -1.51
\selectlanguage{greek}μ\selectlanguage{english}V, \emph{SD}= 0.77 \selectlanguage{greek}μ\selectlanguage{english}V; \emph{t} \textsubscript{36} = -6.68, \emph{p}
\textless{} .001, \emph{d} = 0.72). Moreover, there was a rebound
effect, as shown by a significant increase (more negative) in the N1
amplitude from the 2\textsuperscript{nd} to the 3\textsuperscript{rd}
tones (\emph{M} = -0.99 \selectlanguage{greek}μ\selectlanguage{english}V, \emph{SD} = 0.60 \selectlanguage{greek}μ\selectlanguage{english}V; \emph{t}
\textsubscript{36} = 3.91,\emph{p} \textless{} .001, \emph{d} = 0.43).
No other significant difference was found in other tone pairs (all
\emph{p} \textgreater{} .007 based on the Holm-Bonferroni correction).
Based on the trend analysis across the 1\textsuperscript{st} to
10\textsuperscript{th}tones, a significant linear trend was found
(\emph{F} \textsubscript{1,36}= 17.06, \emph{p} \textless{} .001,
\emph{MSE} = 4.40,\selectlanguage{greek}\emph{η\textsubscript{p}\textsuperscript{2}}\selectlanguage{english} = 0.32).
However, no significant linear trend was found across the
2\textsuperscript{nd} to 10\textsuperscript{th} tones (\emph{F}
\textsubscript{1,36} = 0.08,\emph{p} = .78, \emph{MSE} =
0.02,\selectlanguage{greek}\emph{η\textsubscript{p}\textsuperscript{2}}\selectlanguage{english} = 0.002),
demonstrating that there was no evidence showing that a continued
amplitude decrement was followed by the initial decrease from the
1\textsuperscript{st} to 2\textsuperscript{nd} tones (Figure 2a).

\textbf{3.2 P2 Adaptation}

Likewise, the average peak amplitudes of the P2 were first extracted in
the first ten positions to generate adaptation curves. The P2 amplitude
showed an increasing trend along with the first ten stimuli (Figure 2b).
Paired-sample t-tests on amplitude showed no significant differences
between individual tone pairs based on the Holm-Bonferroni-adjusted
\emph{p} -value (\emph{p} \textgreater{} .006). However, a significant
linear trend was shown in the trend analysis from the
1\textsuperscript{st} to 10\textsuperscript{th} tones (\emph{F}
\textsubscript{1,36} = 26.90, \emph{p} \textless{} .001,\emph{MSE} =
7.43, \selectlanguage{greek}\emph{η\textsubscript{p}\textsuperscript{2}}\selectlanguage{english} = 0.43), and also
from the 2\textsuperscript{nd} to 10\textsuperscript{th} tones (\emph{F}
\textsubscript{1, 36} = 33.28, \emph{p} \textless{} .001,\emph{MSE} =
7.64, \selectlanguage{greek}\emph{η\textsubscript{p}\textsuperscript{2}}\selectlanguage{english} = 0.48). These
results illustrate that a continuous amplitude increase happened in the
P2 time window, which was not only limited to the initial two tones
(Figure 2b). The ERPs of the first ten tone positions are shown in
Figure 3.

\textbf{3.3 MMN}

A one-sample t-test of the difference wave of deviants (tones in the
1\textsuperscript{st} position) minus final tones in the
4\textsuperscript{th} to 30\textsuperscript{th} positions confirmed that
the average peak amplitude was significantly smaller than zero
(\emph{M}= -1.01 \selectlanguage{greek}μ\selectlanguage{english}V, \emph{SD} = 0.60 \selectlanguage{greek}μ\selectlanguage{english}V; \emph{t} \textsubscript{36} =
-10.28,\emph{p} \textless{} .001), showing that the MMN was elicited.
The MMN waveform and topography with fronto-central negativity and
temporal positivity are shown in Figure 4.

\textbf{3.4 Adaptation and MMN}

The descriptive statistics of the amplitudes of the N1 and P2 initial
adaptation and subsequent adaptation effects and the MMN are shown in
Table 1.

\textbf{3.4.1 Correlation Analysis}

The results of the Pearson correlation of the variables are shown in
Table 2. A significant, positive, and strong correlation between the
amplitudes of MMN and N1 initial adaptation was found (\emph{r}
\textsubscript{35} = .67, \emph{p} \textless{} .001), which means that
participants who exhibited a stronger N1 initial adaptation effect
showed a larger MMN (Figure 5a). No other significant correlation
related to the MMN amplitude was found (all \emph{p} \textgreater{} .008
based on Holm-Bonferroni correction).

\textbf{3.4.2 Regression Analysis}

To further investigate the relationship between adaptation and MMN, a
backward stepwise regression was performed to clarify which and how each
adaptation effect contributes to the MMN. First, the four adaptation
variables measured by amplitudes were entered into the regression model,
while the MMN peak amplitude acted as the DV. Only N1 initial adaptation
(\selectlanguage{greek}\emph{β} \selectlanguage{english}(standardized beta) = 0.72; \emph{p} \textless{} .001) and P2
subsequent adaptation amplitudes (\selectlanguage{greek}\emph{β} \selectlanguage{english}= 0.33; \emph{p} = .009)
survived in the final model (\emph{F} \textsubscript{2,34} =
20.50;\emph{p} \textless{} .001; see Figure 5b for the correlation
between the P2 subsequent adaptation effect and MMN). According to the
squared semipartial correlation, which indicates the unique contribution
of an independent variable to the dependent variable (Aloe \& Becker,
2010), these two variables explained more than 50\% of the MMN variance
(49.98\% and 10.43\% respectively). The standardized coefficients of
each variable and the adjusted R squares of the initial and final models
of the amplitudes of adaptation and MMN are shown in Table 3.

To confirm that the initial and subsequent adaptation effects did not
partial each other out, partial correlations were conducted. The partial
correlation between the MMN and the N1 adaptation amplitudes when the N1
subsequent adaptation effect was controlled, and the partial correlation
between the MMN and the P2 subsequent adaptation amplitudes when the P2
initial adaptation effect was controlled, remained the same as the
corresponding correlations reported above. Hence, the initial and
subsequent adaptation effects that were relevant to the regression
analysis did not partial each other out. The results were reported in
Table S2.

\textbf{3.4.3 Additional Analyses}

Although we tried to balance the trial number by including only the
final standards but not all standards in our analyses, the trial numbers
were still unequal between the deviant, 2\textsuperscript{nd} tone, and
final tone conditions. Therefore, the same analyses were conducted with
a fixed number of trials in these three conditions based on the trial
number of the final tone condition (the condition with the least trial
numbers) remained in each participant. The results were similar, with N1
initial adaptation and P2 subsequent adaptation amplitudes predicting
the MMN amplitude in the regression model. However, N1 subsequent
adaptation was also a significant predictor. Detailed results are
reported in Supplementary Information.

\textbf{3.4.4 Topographic Analysis}

To explore whether the topographic distributions of the two adaptation
effects were different (and thereby having different underlying neural
sources; Michel \& Murray, 2012), a Topographic Analysis of Variance
(TANOVA) was conducted in RAGU (Koenig et al., 2011) by comparing
individual N1 initial adaptation (peak amplitudes of deviants--peak
amplitudes of the 2\textsuperscript{nd}tones) and P2 subsequent
adaptation (peak amplitudes of the 2\textsuperscript{nd} tones--peak
amplitude of final tones in the 4\textsuperscript{th} to
30\textsuperscript{th} positions) with 5000 randomization runs (see
Figure 6 for the corresponding scalp topographies and Figure 7 for the
scalp topographies for N1 subsequent adaptation and P2 initial
adaptation). In order to focus on the topographic distribution, the maps
were normalized for strength (GFP = 1). The result revealed that the
topographic maps of N1 initial adaptation and P2 subsequent adaptation
were significantly different (\emph{p} \textless{} .05), indicating that
the topographic distributions, and therefore the underlying sources of
these two adaptation effects, were disparate. The gradients of the N1
initial adaptation effect were steepest over left superior temporal
regions, while the gradients of the P2 subsequent adaptation effect were
steepest over bilateral frontal regions. These findings are in agreement
with the initial adaptation taking place in auditory cortex regions and
the model reestablishment effect in bilateral frontal regions.

\textbf{4. Discussion}

Neural adaptation is a robust phenomenon that reflects one mechanism of
how the brain makes use of previous experience with stimuli (e.g.,
Grill-Spector et al., 2006). In the auditory domain, most studies have
utilized stimulus pairs or a series of stimulus repetitions to
investigate adaptation, without specifically examining the adaptation
patterns across individual tones. In contrast, the present study
examined N1 and P2 adaptation patterns, with a comparison between
initial and subsequent repetitions. While previous MMN studies used
different paradigms and methods to discriminate the adaptation and model
adjustment accounts of MMN, and some of them attempted to control the
adaptation effect to examine a pure model adjustment component of MMN
(e.g., Jacobsen \& Schr\selectlanguage{ngerman}öger, 2001), the present study investigated the
relationship between MMN and adaptation patterns in N1 and P2.
Importantly, the expectations was maintained by presenting tones with a
fixed probability in a long train. The adaptation effects reflected by
the amplitudes were found in the N1 during the initial tone repetitions.
For the P2 amplitude, a linear increase with repetitions was shown,
which might indicate a memory trace or model reestablishment effect. In
terms of amplitudes, the MMN was mainly explained by the N1 adaptation
effect induced by the initial tones and the continuous P2 adaptation
effect triggered by subsequent tones. Details of the adaptation patterns
are discussed below.

\textbf{4.1 N1 Adaptation at the Initial Stage}

The steep amplitude decrease from the 1\textsuperscript{st} tone to the
2\textsuperscript{nd} tone in the N1 adaptation effect matched our
hypothesis and most previous findings (e.g., Bourbon et al., 1987; Budd
et al., 1998; Lagemann et al., 2012; Recasens et al., 2015; Rosburg,
2004; Rosburg et al., 2006). This means that N1 adaptation occurs
quickly with simple tone stimuli that are presented in rapid succession
when the expectations are maintained. Nevertheless, these results may
not generalize to conditions with longer inter-stimulus intervals (ISI)
as previous studies have shown that the steep amplitude decrease only
appeared when the ISI was short, but the further decrease was observable
when the ISI was longer (e.g., Fruhstorfer et al., 1970; Özesmio et al.,
2000; Sambeth et al., 2004; c.f. Bourbon et al., 1987; Budd et al.,
1998; Rosburg et al., 2010). In addition, a previous study has found
that the major contributor of the N1 was the temporal lobe in the
one-second ISI condition, but in longer ISI conditions, sources in brain
regions other than the auditory cortex have become more active (Hari et
al., 1982). As we used a short ISI (580 ms) in the present study, the
steep decrease might be ascribed to the ISI.

Additionally, a rebound was shown between the 2\textsuperscript{nd} tone
and the 3\textsuperscript{rd} tone according to the adaptation curve
formed by N1 amplitude (Figure 2a). This was not expected by the
adaptation account, but it might be related to a similar effect found in
a previous study using dynamic causal modeling (DCM) to examine the
adaptation effect represented by the intrinsic connections in A1
(Garrido et al., 2009a). They interpreted the initial decrease of
intrinsic connectivity from the 1\textsuperscript{st} tone to the
2\textsuperscript{nd} tone as reduced estimated precision of
predictions, triggered by the deviants, and the subsequent increase as a
gradual recovery due to learning. Thus, the rebound found between the
2\textsuperscript{nd} and 3\textsuperscript{rd} tones in the N1
adaptation effect in the present study may be in accordance with the
interpretation from the study by Garrido et al. (2009a) that the
adaptation effect happened in the A1 may incorporate the predictive
component, and the brain only needs a few repetitions to generate
predictions. Importantly, the present finding extended this
interpretation to the circumstance where expectations are maintained. In
other words, the predictive component in the adaptation effect can be
automatically generated.

\textbf{4.2 P2 Adaptation: A Memory Trace Effect}

The P2 amplitude showed a continuously increasing trend along the first
ten tones, which did not match our hypothesis and some previous findings
where a steep decrease of P2 amplitude was found in the first repetition
only and a plateau afterwards (Rosburg \& Sörös, 2016; Rosburg et al.,
2010). However, this continuous P2 increase can be interpreted as
repetition positivity (RP), and it was also found in two studies by
Costa-Faidella et al. (2011a, 2011b). In addition, Recasens et al.
(2015) found a repetition enhancement in the sustained field time window
(230--270 ms after stimulus onset), a time window close to P2. These
discrepancies might be caused by the degree of expectations and SOA due
to the different paradigms. In the study by Rosburg and Sörös (2016), a
fixed train of eight tones was adopted, with the sixth tone as a deviant
and the others as standards. Therefore, participants were able to
predict the tone pattern. In contrast, in the present study,
participants could not expect what the next tone would be as a deviant
might happen anytime with a 15\% probability. In addition, the studies
by Costa-Faidella et al. (2011a, 2011b) and Recasens et al. (2015) also
adopted a paradigm with three different numbers of tones in local trains
that might lead to less solid expectation. Hence, expectations may
modulate the adaptation pattern revealed by P2. Another possible reason
for the discrepancies is the different times allowed for recovery from
adaptation. A longer ISI (976 ms) between each tone and the insertion of
interval (6000--7000 ms) between trains of tones were used in the study
by Rosburg and Sörös (2016), whereas a shorter ISI (580 ms) was adopted
without any inter-train interval in the present study, and short ISIs
were also used by Costa-Faidella et al. (2011a, 2011b) and Recasens et
al. (2015). Hence, the continuous increase of P2 may only appear when
the time for recovery from adaptation is limited, while a sharp decrease
of P2 in the initial two tones may be observed when more time is
allowed. The above two factors may also explain the steep decrease in P2
amplitude found in Rosburg et al. (2010), who adopted a similar design
as Rosburg and Sörös (2016). Furthermore, the different paradigms used
between studies might also contribute to the discrepancies. Besides the
present study, the studies by Costa-Faidella et al. (2011a, 2011b) and
Recasens et al. (2015) also adopted a roving paradigm, which might
facilitate memory trace strengthening and reestablishment because the
tones were unpredictable. In contrast, in paradigms that contain
discontinuous stimulus trains or more predictable standard tones, such
as the stimulus pair or traditional oddball paradigms, less memory trace
strengthening and reestablishment were needed (Cooper et al., 2013).
This further substantiated the relationship between the RP and memory
trace as sensory memory needs to be updated constantly during the roving
paradigm (Cooper et al., 2013). As these postulations are speculative as
a direct comparison has yet to be made, factors that modulate the
relationship between RP and adaptation deserve more attention in future
research. However, the important implication is that the adaptation
effect examined in the stimulus pair and traditional oddball paradigms
may not be generalized to the roving paradigm and the circumstance when
expectations are maintained.

As mentioned above, the increase in P2 can be interpreted as an RP.
Although the topography of the P2 subsequent adaptation shows a negative
frontal-central component (Figure 6b), this was due to the reverse
calculation method of the adaptation effect (2\textsuperscript{nd} tone
-- final tone instead of final tone -- 2\textsuperscript{nd} tone).
Nonetheless, the RP found in the present study had a later onset
compared with a previous study by Haenschel et al. (2005), in which they
compared the RP between two, six, and 36 standard repetitions in healthy
adults. They found that the RP, which happened from around 50--250 ms
post-stimulus, was larger in conditions with more standard repetitions,
and RP contributed to most of the MMN. Therefore, they postulated that
RP is an ERP correlate of adaptation, a mechanism that facilitates
memory trace formation in the A1. However, it was unknown whether the
larger RP and MMN appeared after 36 standard repetitions were due to a
longer memory trace or a higher precision of the prediction made by the
participants because, after a certain time, participants might easily
predict that a deviant would appear after 36 standard repetitions if
there had been no deviant at the 2\textsuperscript{nd} and
6\textsuperscript{th} positions. However, in the present study, where
participants could not predict whether the next tone would be deviant or
not, RP, as shown by a continuous amplitude increase along with stimuli,
was still found, but with a later onset in the P2 time window, compared
with that (around 50 ms) in the study by Haenschel et al. (2005). While
this did not refute the interpretation by Haenschel et al. (2005) that
RP signifies adaptation, a mechanism underlying memory trace formation,
the present finding suggested that the latency of the RP may be
modulated by the precision of the prediction. Specifically, a later
onset may be shown in the RP when the precision of the prediction
decreases. Conversely, the RP had an earlier onset because of a higher
precision of the prediction model in participants in the study by
Haenschel et al. (2005) due to a predictable stimulus pattern.

\textbf{4.3 Adaptation and MMN}

According to the regression analysis, amplitudes of N1 initial
adaptation and P2 subsequent adaptation both predicted the MMN
significantly, and they explained 49.98\% and 10.43\% of the MMN
variance respectively. While the effect of N1 initial adaptation matched
the adaptation hypothesis of MMN, the contribution of P2 subsequent
adaptation amplitudes to MMN implied that the adaptation hypothesis of
MMN may not be sufficient because it proposed that the N1 adaptation
alone could explain the MMN. In addition, it is difficult to use a pure
adaptation mechanism to account for the N1 rebound effect. Therefore,
although the present study emphasizes the contribution of the adaptation
effect to MMN, it does not refute the model adjustment or the predictive
coding accounts. On the contrary, the main findings of the present study
supported a predictive coding account of MMN, which corroborated with
most previous findings (e.g., Alain et al., 1999; Herholz et al., 2009;
Symonds et al., 2017; Wacongne et al., 2011). Importantly, the findings
suggested that MMN is made up of two different processes that correspond
to the RP: One is the less negative inclination in N1 during the first
repetition that may signify a purer adaptation effect, whereas the other
one is the P2 subsequent adaptation with a positive deflection
reflecting memory trace formation and model reestablishment. Crucially,
the N1 initial adaptation effect may reflect the degree of model
adjustment after the appearance of deviants, while the MMN quantifies
the degree of error detected from the model. Hence, the positive
correlation between N1 initial adaptation and MMN implied that
participants who were the most sensitive to the tone change also had the
most precise models. In addition, the increasing P2 along with
repetitions is consistent with the predictive coding account, which
proposes that the prediction error is minimized due to the matching
between the top-down predictions and bottom-up inputs along tone
repetitions (Friston, 2005), and this explanation integrates both
adaptation and model adjustment accounts (Garrido et al., 2008).
Notably, the present study illustrated that the predictive coding
account was supported even when the expectations were maintained by
random stimulus arrangement based on the adaptation findings and the
different topographies of N1 initial adaptation and P2 subsequent
adaptation.

Based on our regression findings, it is important to take different
components and time windows into consideration when examining the
relationship between adaptation and MMN. For the N1, the initial
adaptation contributes to the MMN, but for the P2, the subsequent
adaptation predicts it. In addition, the present findings suggested that
when investigating adaptation or its relationship to MMN, future studies
should focus on both N1 and P2. Examining only one component may
obstruct the discovery of comprehensive adaptation effects. Importantly,
isolating the N1 and P2 processes may illuminate the relationship
between adaptation and MMN.

\textbf{4.4 Implications, Limitations, and Future Studies}

This study is theoretically significant because it sheds light on the
relationship between stimulus repetition, adaptation, and MMN. While
some previous studies examined the adaptation and MMN separately (e.g.,
Budd et al., 1998; Haenschel et al., 2005), the relationship between
adaptation and MMN was elucidated by tracing the adaptation pattern
along a sequence of trials. It is also practically significant as
understanding the adaptation patterns and the mechanism of MMN may
enhance the effectiveness of clinical applications, such as the
diagnosis of dyslexia using the adaptation pattern and MMN, as some
previous studies revealed weaker adaptation and smaller MMN in
dyslexics, compared with healthy controls (e.g., Baldeweg et al., 1999;
Jaffe-Dax et al., 2017).

It is important to note that the adaptation effect and MMN can be
influenced by many factors, not only including stimulus characteristics
such as the frequency difference of the tones (e.g., Butler, 1968), the
tone duration (e.g., Lanting et al., 2013), the number of repetitions
(e.g., Baldeweg, 2007) and the ISI (e.g., Budd et al., 1998; Herrmann et
al., 2016; Lanting et al., 2013; Pereira et al., 2014) but also
variables related to the participants such as expectations and attention
(e.g., Costa-Faidella et al., 2011a; Hari et al., 1979; Herholz et al.,
2009; Todorovic et al., 2011). While it may be a limitation that the
present study did not manipulate some of these variables, almost all of
these factors remained constant throughout the experiment so they should
not bias the results. However, future studies can examine how they may
modulate the MMN and adaptation findings.

For limitations, since we only focused on the frequency differences in
the present study, it is unknown whether the adaptation patterns and the
relationship between adaptation effects and MMN can be generalized to
deviants with other features (e.g., intensity, duration, and abstract
pattern, etc.), although based on previous findings, we would expect
adaptation plays a less important role in MMN when the repetition rule
is not involved (Carbajal \& Malmierca, 2018). In addition, future
studies should consider adding a control condition, in which the same
tones as the standards of the experimental condition are presented but
they are embedded in different tones to avoid adaptation, to distinguish
the adaptation and prediction error components (Carbajal \& Malmierca,
2018). Overall, more studies are needed to examine how the variables
mentioned above interact with adaptation and MMN when expectations are
maintained.

\textbf{5. Conclusion}

To conclude, the present study utilized a roving paradigm that
maintained expectations and showed that the adaptation patterns were
diverse in the N1 and P2 time windows. Additionally, the N1 initial
adaptation and the P2 subsequent adaptation, or the RP, contributed to
MMN significantly. Hence, while the N1 initial adaptation does play a
role in MMN, it cannot explain the entire MMN as proposed by the
adaptation hypothesis, without taking the RP into account.
Theoretically, the present study showed that under a paradigm that
maintains expectations, the precision of prediction is still pertinent
to the adaptation effects reflected by the N1 amplitude and RP.
Practically, the diverse adaptation patterns in the N1 and P2 components
and their discrete relationships to MMN imply the importance of
isolating the two MMN processes signified by N1 and P2 when
investigating adaptation and MMN in future studies.

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Author Notes

Funding: This work was supported by the General Research Fund of the
Research Grants Council of Hong Kong awarded to the corresponding author
(RGC-GRF 14600919).

Conflict of interest: None

Name and email address for reprints: Urs MAURER
(umaurer@psy.cuhk.edu.hk)

CRediT taxonomy:

Conceptualization: Brian W. L. Wong, Urs Maurer; Methodology: Brian W.
L. Wong, Urs Maurer; Investigation: Brian W. L. Wong; Software: Brian W.
L. Wong; Formal analysis: Brian W. L. Wong, Urs Maurer; Writing -
original draft preparation: Brian W. L. Wong; Writing - review and
editing: Brian W. L. Wong, Shuting Huo, Urs Maurer; Funding acquisition:
Urs Maurer; Resources: Urs Maurer; Supervision: Urs Maurer

Tables

\textbf{Table 1}

\emph{Descriptive Statistics of the Peak Amplitudes of MMN and
Adaptation in N1 and P2 (N =37)}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Variable & \emph{Mean} & \emph{SD}\tabularnewline
\midrule
\endhead
MMN amplitude & -1.01 & 0.60\tabularnewline
N1 initial adaptation amplitude & -0.79 & 0.72\tabularnewline
N1 subsequent adaptation amplitude & 0.06 & 0.44\tabularnewline
P2 initial adaptation amplitude & 0.08 & 0.79\tabularnewline
P2 subsequent adaptation amplitude & -0.20 & 0.41\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2}

\emph{Pearson Correlations between the Peak Amplitudes of MMN and
adaptations in N1 and P2 (N = 37)}\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& 1 & 2 & 3 & 4 & 5\tabularnewline
\midrule
\endhead
1. MMN amplitude & - & .67\textsuperscript{*} & -.07 & .23 &
.22\tabularnewline
2. N1 initial adaptation amplitude & - & - & -.47\textsuperscript{*} &
.51\textsuperscript{*} & -.16\tabularnewline
3. N1 subsequent adaptation amplitude & - & - & - & .24 &
.46\textsuperscript{*}\tabularnewline
4. P2 initial adaptation amplitude & - & - & - & - & -.32\tabularnewline
5. P2 subsequent adaptation amplitude & - & - & - & - & -\tabularnewline
\bottomrule
\end{longtable}

\emph{Note.} \textsuperscript{*}Significant \emph{p} -values with
Holm-Bonferroni corrections.

\textbf{Table 3}

\emph{Standardized Coefficients and Adjusted R\textsuperscript{2} in the
Regression Analysis of the Amplitudes of Adaptation Effects and MMN (N =
37)}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Variable & Model & Model\tabularnewline
\midrule
\endhead
& Initial & Final\tabularnewline
N1 initial adaptation amplitude & 0.83\textsuperscript{*} &
0.72\textsuperscript{*}\tabularnewline
N1 subsequent adaptation amplitude & 0.20 & -\tabularnewline
P2 initial adaptation amplitude & -0.06 & -\tabularnewline
P2 subsequent adaptation amplitude & 0.23 &
0.33\textsuperscript{*}\tabularnewline
Adjusted R\textsuperscript{2} & .52 & .52\tabularnewline
\bottomrule
\end{longtable}

\emph{Note.} \textsuperscript{*}\emph{p} \textless{} .01

Figure Captions

Figure 1. Roving paradigm used in the present study, with the
calculation methods of the amplitudes of initial adaptation, subsequent
adaptation, and MMN.

Figure 2. The adaptation curves based on the N1 (a) and P2 (b) peak
amplitudes in each position of the first ten tones (error bars indicate
standard deviations; \emph{N} = 37).
\textsuperscript{*}significant\emph{p} -values based on paired-sample
t-tests of each consecutive tone pair with Holm-Bonferroni corrections.

Figure 3. The ERPs of the first ten tones indicated by different colors
(\emph{N} = 37). Data were pooled from 25 fronto-central electrodes
(black dots at the top-left corner).

Figure 4. (a) Waveform of MMN, deviants and last standards in
4\textsuperscript{th} to 30\textsuperscript{th} tones in each sequence
(\emph{N} = 37). The average MMN waveform (black) calculated by
subtracting the amplitudes in the final position of each train of
stimuli (4\textsuperscript{th} position or after) from that in the
deviants. Waveforms of deviants and the final tones are shown in red and
blue respectively. The highlighted part indicates the MMN time window
(76--196 ms). (b) Topography of MMN. From left to right, the topographic
maps of deviants, final tones in 4\textsuperscript{th} to
30\textsuperscript{th} positions, and the corresponding MMN.

Figure 5. Scatter plots of significant correlations or regressions
between MMN and the variables related to the adaptation effects,
including (a) N1 initial adaptation amplitude and (b) P2 subsequent
adaptation amplitude. Each dot indicates a datum from each participant
(\emph{N} = 37).

Figure 6. Scalp topographies based on the average individual peak
amplitude (\emph{N} = 37). (a) From left to right, the topographic maps
of deviants, 2\textsuperscript{nd} tones, and the ensuing initial
adaptation in the N1. (b) From left to right, the topographic maps of
2\textsuperscript{nd} tones, final tones (4\textsuperscript{th} to
30\textsuperscript{th} positions), and the ensuing subsequent adaptation
in the P2.

Figure 7. Scalp topographies based on the average individual peak
amplitude (\emph{N} = 37). (a) From left to right, the topographic maps
of 2\textsuperscript{nd} tones, final tones (4\textsuperscript{th} to
30\textsuperscript{th} positions), and the ensuing subsequent adaptation
in the N1. (b) From left to right, the topographic maps of deviants,
2\textsuperscript{nd} tones, and the ensuing initial adaptation in the
P2. Please note the different scales used in different topographies for
clarity.

Figures

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