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\begin{document}

\title{Do actions structure auditory memory? Action-based event segmentation
effects on sensory responses, pupil dilation and sequential memory}



\author[1]{Marta Font-Alaminos}%
\author[1]{Nadia Paraskevoudi}%
\author[1]{Jordi Costa-Faidella}%
\author[1]{Iria SanMiguel}%
\affil[1]{Brainlab-Cognitive Neuroscience Research Group}%


\vspace{-1em}



  
  \date{June 16, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Our actions shape our everyday experience: what we experience, how we
perceive and remember it, is deeply affected by how we interact with the
world. Performing an action to deliver a stimulus engages
neurophysiological processes which are reflected in the modulation of
sensory and pupil responses. In this study, we hypothesized that these
processes shape memory encoding, parsing the experience by grouping
self- and externally-generated stimuli into differentiated events.
Participants encoded sound sequences, in which either the first or last
few sounds were self-generated and the rest externally-generated. We
tested recall of the sequential order of sounds that had originated from
the same (within event) or different sources (across events). Memory
performance was not higher for within event sounds, suggesting that the
memory representation was not structured by actions. However, during
encoding, we replicated the well-known electrophysiological response
attenuation, together with increased pupil dilation for self-generated
sounds. Moreover, we found that at the boundary between events,
physiological responses to the first sound originating from the new
source were determined by the direction of the source switch. The
results suggest that introducing actions, acts as a stronger contextual
shift than removing them, despite not directly contributing to memory
performance. The findings contribute to our understanding of how
interacting with sensory input shapes our experiences, by addressing the
unexplored relationships between action effects on sensory responses,
pupil dilation and memory encoding, and discarding a meaningful
contribution of low-level neurophysiological mechanisms associated to
action execution in the modulation of memory.%
\end{abstract}%



\sloppy


\textbf{1. Introduction}

The way we experience the world is fundamentally shaped by our actions.
Numerous studies have demonstrated that self-generated (SG) stimuli
elicit reduced subjective sensations and sensory responses compared to
stimuli that are externally-generated (EG; for reviews see Hughes et
al., 2013; Schr\selectlanguage{ngerman}öger et al., 2015). In the auditory domain, the effects
of self-generation on sensory responses have been mainly studied on
event-related potentials (ERPs), where an attenuation of the N1
component is consistently observed for SG sounds. Although the
self-generation effect has been mostly attributed to motor-driven
predictive processes engaged via forward modelling (Blakemore et al.,
1998; Frith et al., 2000; Wolpert et al., 1995), this modulation also
occurs in nonpredictive situations (e.g. Horváth et al., 2012;
Paraskevoudi \& SanMiguel, 2023a) indicating that other mechanisms could
be mediating the sensory attenuation. Recently, Paraskevoudi \&
SanMiguel (2023a, 2023b) suggested that subcortical neuromodulation
during motor actions (Aston-Jones \& Cohen, 2005; Eggermann et al.,
2014; McGinley et al., 2015; Vinck et al., 2015) may regulate the
sensory attenuation observed during actions. This hypothesis originated
from findings demonstrating a strong correlation between pupil diameter,
an indicator of locus coeruleus norepinephrine system (LC-NE) activity,
and actions (Lubinus et al., 2022; Yebra et al., 2019). Although
Paraskevoudi \& SanMiguel (2023a, 2023b) provided preliminary evidence
linking self-generation effects and pupil dilation during sound
processing, further research is required to identify the specific
contribution of LC-NE activity, proxied by pupil dilation, to the
self-generation effect.

Despite the elusive neural underpinnings of the self-generation effect,
existing research provides compelling evidence to suggest that active
production not only influences perception, but also memory (McCurdy et
al., 2017). For instance, played piano melodies and spoken words are
better remembered than the same stimuli when they are encoded in the
absence of overt actions, a phenomenon known as the production effect
(MacDonald \& MacLeod, 1998; MacLeod, 2011; MacLeod \& Bodner, 2017).
The production effect is frequently explained with the distinctiveness
model, which proposes that items containing more distinct components
have a higher probability of being recalled from memory than items with
fewer components (Ozubko \& Macleod, 2010). Once again, a recent study
has linked memory enhancements resulting from overt actions to the
activation of the LC-NE system, reflected in increased pupil dilation
(Yebra et al., 2019). All in all, it is possible that the
distinctiveness provided by the actions, along with the differential
sensory encoding of SG sounds, could be contributing to the memory
performance enhancement seen in the production effect. However, to date,
only three studies have combined the typical paradigms employed to
measure self-generation effects with a memory task in order to test the
relationship between the self-generation effects (on sensory processing)
and the production effects (on memory). Using slightly different memory
tasks and action-sound relationships, these studies have found
conflicting results ranging from no difference in memory (Font-Alaminos
et al., 2023) to memory enhancement (Paraskevoudi \& SanMiguel, 2023b)
and memory impairment (Paraskevoudi \& SanMiguel, 2023a) for SG compared
to EG sounds, despite the presence of reliable self-generation effects
on auditory ERPs in all three studies. Thus, further research is needed
to clarify the impact of actions on sensory processing and memory
encoding and identify the critical variables responsible for the
discrepancy in the observed effects.

An intriguing possibility is that actions may not simply modulate the
strength of memory traces to specific items, but perhaps they contribute
to structure the memory storage. Memory, a crucial aspect of human
cognition, enables individuals to store and retrieve past experiences
that shape their present and future behaviors. These experiences are
structured and organized in discrete units or events based on contextual
cues, a process called event segmentation (DuBrow \& Davachi, 2013).
Event segmentation allows for the effective organization and retrieval
of episodic memories by breaking them down into meaningful units or
events (Zacks \& Swallow, 2007). These meaningful units are separated by
event boundaries, identifiable moments where there is a noticeable
change in the content, context, or purpose of an experience (Kurby \&
Zacks, 2008). Minor changes in the physical environment or in the
ongoing task, such as on the background color or sound, can create these
event boundaries, highlighting their importance in the organization of
episodic memory (Kurby \& Zacks, 2008; Newtson \& Engquist, 1976; Speer
et al., 2003; Swallow et al., 2010). However, to our knowledge, no
studies have specifically investigated the potential of actions per se
to create event boundaries. Considering the current
self-generation-related research on sensory processing, neuromodulation,
and memory it seems plausible to assume that actions may not only
modulate the strength of memories for SG stimuli, but they may
additionally structure the encoding of sounds in memory, possibly
generating differentiated storages for SG and EG sounds.

The primary aim of this study was to examine the impact of motor actions
on memory encoding. We hypothesize that neuromodulatory mechanisms tied
to actions structure memory encoding and lead to the formation of
distinct memory representations for SG and EG auditory stimuli. Previous
studies have provided evidence for event segmentation during memory
encoding demonstrating that, when asked to remember the sequential order
of stimuli, items encoded as belonging to the same event present
increased memory performance compared to those belonging to different
events (Clewett et al., 2019, 2020; DuBrow \& Davachi, 2016; Pettijohn
et al., 2016; Radvansky \& Zacks, 2017). Here, we hypothesize that
differentiated events may be defined by the presence or absence of a
motor act to deliver the stimuli. In order to test this hypothesis, we
assessed temporal order memory performance for sound pairs encoded
within the same event versus across separate events.

Furthermore, we extended on previous research by investigating whether
SG sounds confer a memory advantage over EG sounds when delivered
concurrently to form unified events. Previous studies attempting to
relate self-generation and production effects have tested the effects of
self-generation on memory recall of discrete sounds (Font-Alaminos et
al., 2023; Paraskevoudi \& SanMiguel, 2023a, 2023b). Here, we tested the
effects on sequential memory, predicting that SG-SG pairs would elicit
better temporal order memory performance than EG-EG pairs. To examine
the effects of self-generation during encoding, we analyzed sensory ERP
components and expected to replicate the well-established finding of
attenuated sensory responses to SG sounds (Schröger et al., 2015). We
also measured pupil diameter to investigate the neuromodulatory effects
surrounding the motor actions and hypothesized that greater pupil
dilation would be observed for SG stimuli compared to EG stimuli during
sound encoding (Paraskevoudi \& SanMiguel, 2023a, 2023b).

To explore the influence of actions on event segmentation, we examined
whether a boundary was formed after a change in the sound's source (EG
or SG). In line with context change literature (Donchin \& Coles, 1988;
Polich, 2007), we predicted an enhanced P3 on the ERP and greater pupil
dilation for the boundary sound compared to non-boundary sounds.
Finally, we investigated the correlation between memory performance and
the magnitude of the self-generation and pupil dilation effects, and
explored whether a relationship exists between the effects on pupil and
on electrophysiological sensory responses. This study was pre-registered
as part of the Open Science Framework
(https://doi.org/10.17605/OSF.IO/5ZUGV).

\textbf{Methods}

\subsubsection*{Participants}

{\label{participants}}

The sample size for this study was determined based on previous similar
studies (Silva et al., 2019). Initially, a total of 28 healthy
participants were recruited to take part in the experiment. However, 3
participants had to be excluded due to a low signal-to-noise ratio in
the EEG data. As a result, the final sample for this study consisted of
25 participants (mean age: 21 years old; 8 males). All participants were
compensated for their time with a payment of 10 euros per hour.
Participants were mainly recruited among University of Barcelona
students. The study excluded participants with hearing impairments,
psychiatric or neurological disorders, age below 18 years or over 50
years, recent consumption of drugs or pharmaceuticals that affect the
central nervous system, or inability to comply with the task
instructions. In addition, participants who wore prescription glasses or
contact lenses were excluded to ensure clean pupillometry data. The
Bioethics Committee of the University of Barcelona (IRB00003099)
approved the study, and all participants provided informed consent after
completing a general health questionnaire.

\subsubsection*{Study Design}

{\label{study-design}}

We designed an electrophysiological and pupillometry experiment with a
temporal order memory task to investigate how self-generation affects
event segmentation. The order memory task had three phases: encoding,
retention, and retrieval (Figure 1). During the \emph{encoding} phase,
participants were presented with a sequence of nine sounds that included
both SG and EG sounds. Critically, all SG or EG sounds at encoding
appeared consecutively, creating two different events based on the
source of the sounds within the nine-sound sequence. Immediately after,
there was a short \emph{retention} period to memorize the sequential
order of the sounds. During the \emph{retrieval} phase, participants
were presented with a pair of sounds, and they had to indicate whether
they appeared in the same order as during encoding. The test sounds
belonged either to the same event (within condition) or to different
events (across condition) but were non-consecutive in the sequence. We
used visual stimuli to structure the task and indicate participants when
to perform actions to generate sounds. To ensure participants understood
the task, they underwent at least five practice trials using a
simplified version of the task before the start of the experiment (see
section 2.4.).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. Schematic description of the temporal order memory task with
externally-generated (EG) and self-generated (SG) sounds. This example
trial corresponds to the across-events condition. The timepoints mark
the duration of each phase of the trial. ITI corresponds to the
inter-trial interval.

\textbf{Encoding phase}

At the beginning of each trial, participants were presented with a row
of vertical lines on the screen. The number and distribution of vertical
lines varied from trial to trial as determined by the experimental
manipulations. Throughout the encoding period (14.85 s), a horizontal
line moved steadily across the screen from left to right, passing over
each vertical line in its path. Participants were instructed to press
the bottom-right button on a response pad with their right index finger
whenever the horizontal line crossed a vertical line. This action
produced a sound immediately after the button press. In each trial, the
encoding set consisted of nine sounds, some of which were delivered
consecutively after each button press (SG), while others were presented
passively (EG) either before or after the sequence of button presses.
This manipulation created two events in a sequence, each event formed by
SG or EG sounds. The change in the source that generated the auditory
stimuli, either EG or SG, acted as the contextual shift, and the first
sound delivered by the new source was the boundary sound. Participants
were instructed to pay attention to the order of all the sounds
regardless of the motor task. The sound onset asynchrony was set between
1.3 to 2 s semi randomly in steps of 0.05 s.

\textbf{Retention and Retrieval phase}

Following the encoding phase, participants were given a short retention
period of 2 s to retain the sequence's order. During the retrieval
phase, a pair of two nonconsecutive sounds of the encoding phase were
presented, with a 2 s onset separation interval. The pair of test sounds
could appear in the correct or in the inverse order of presentation and
could both be from the same encoding event (\emph{within} condition) or
could be one sound from each of the two encoding events
(\emph{across}condition). After 0.8 s following the onset of the second
test sound, participants were asked if the pair's order of presentation
was the same as at encoding. They had unlimited time to respond on the
response pad YES or NO using the middle and index fingers of their left
hand. The identity of the two buttons, either YES or NO, was
counterbalanced across participants. The inter-trial interval was set to
2 s after the participant's response.

\subsubsection*{Visual Stimuli}

{\label{visual-stimuli}}

The use of visual stimulation was necessary to ensure participants
followed instructions and maintained fixation during the experiment,
which allowed us to record their pupil response accurately. We presented
a fixation cross at the center of the screen throughout the experiment,
to indicate where participants should focus their gaze. The moving
horizontal line from left to right and the stationary vertical lines
appeared just below the fixation cross to indicate the progress of each
trial and the appropriate time to press the button. These visual stimuli
appeared within a small area on the center of the screen (visual angle
2.3\selectlanguage{ngerman}º) to eliminate any effects of gaze position on pupil diameter (Gagl
et al., 2011).

\subsubsection*{Auditory Stimuli}

{\label{auditory-stimuli}}

We developed a set of unique auditory stimuli for our experiment by
sourcing identifiable sounds from several freely available sound
databases (Adobe; FreeSound; Belin et al., 2000; Gygi \& Shafiro, 2010;
Hocking et al., 2013; Norman-Haignere et al., 2015). Initially, we
extracted 1110 different sounds, which we edited to have a duration of
250 ms and 10 ms exponential onset/offset ramp. To ensure the task's
feasibility, we manually selected sounds that were still recognizable
after editing (i.e., not noise) and categorized them into distinct
semantic groups. We grouped similar sounds into the same category to
create as many different categories of sounds as possible, resulting in
15 semantic categories with 24 sounds each from animal, environmental,
human, and music sounds (e.g. high pitch animal vocalizations, drums,
alarms, aspirated vowels, etc.). During practice trials, we presented
participants with pure tones of different frequencies ranging from 300
to 3700 Hz in 100 Hz steps instead of experimental sounds. All sounds
were played at a sampling rate of 44.1 kHz, 16 bits, mono, and an
intensity of 75 db.

We generated 24 unique sound sequences for each participant, consisting
of 9 different sounds randomly selected without replacement from 9 of
the 15 semantic categories to ensure variability. These unique sound
sequences were presented 9 times each with different experimental
manipulations (see section 2.2.6.). In each unique sound sequence, seven
of the sounds were presented only in the encoding phase (encoding-only
sounds), while the remaining two encoding sounds were also test sounds,
presented again during the retrieval phase. To prevent sequential
presentation of the 9 repetitions for each unique sequence, we
distributed the 216 trials into 24 blocks, each containing a single
repetition of a unique sound sequence.

\textbf{Experimental manipulations}

We manipulated several variables across each repetition of a unique
sound sequence. At encoding, we used two sound sources: SG and EG, with
half of the encoding sounds overall in the experiment belonging to each
type. We counterbalanced the order of the two events within each
sequence, thus half of the sequences began with EG sounds and then SG
sounds and vice versa. Additionally, we manipulated the position of the
test pairs at encoding. In half of the trials the test sounds appeared
in the third and sixth positions of the encoding sequence, and the other
half had them in the fourth and seventh positions. We also varied the
length of the events, which ranged from 2 to 7 sounds. The varied
lengths allowed us to counterbalance the appearance at encoding of both
retrieval test pair conditions (within and across) in each test
position. However, we included 24 catch trials (one for each block)
where we asked about the fifth and eighth positions, to ensure that
participants didn't notice that we were asking about the same positions
repeatedly. The retrieval phase of these catch trials was not included
in the analysis.

At retrieval, we created two test pair conditions: within and across. In
the within condition, half of the trials featured a SG-SG pair, while
the other half featured an EG-EG pair. In the across condition, half of
the trials presented a SG sound followed by an EG sound, and the other
half presented an EG sound followed by a SG sound. Finally, we presented
the test sounds in either the correct sequential order or the inverse
order, with each version used in half of the trials.

\textbf{Motor-only blocks}

In self-generation studies, to be able to assess the effects of actions
on auditory ERPs, it is standard procedure to eliminate the
motor-related activity from the SG sound ERPs prior to comparing the
auditory responses between SG and EG sounds (SanMiguel et al., 2013). To
subtract the motor component from the SG sound responses in the ERP
analysis, we added motor-only (M) blocks. These blocks were equal to the
first 4 blocks of experimental trials but without a retrieval phase and
without auditory stimuli. This ensured that the M evoked potentials were
elicited by similar conditions to the SG sounds. These sequences were
presented in 4 separate blocks of 9 sequences each, creating a total of
36 motor sequences which gave us a mean of 162 M items per participant
(2 to 7 M items per sequence). Before the beginning of each motor block,
we warned participants that they were in a motor only block and that,
consequently, no question or sounds would appear.

\subsubsection*{Apparatus}

{\label{apparatus}}

Binaural presentation of auditory stimuli was achieved using over-ear
headphones (Sennheiser, HD 558). Participants' button presses and
responses were recorded using a silent response pad (Korg nanoPAD2) to
avoid interference with the auditory stimuli. The instructional visual
stimulation was delivered through an AT Radeon HD 2400 monitor. The
experimental setup and control were conducted using MATLAB R2017a (The
Mathworks Inc., 2017) in conjunction with the Psychophysics Toolbox
(Brainard, 1997; Kleiner et al., 2007) and the Eyelink add-in toolbox
for eye tracker control (SR Research).

To record the EEG activity, we used a Neuroscan SynAmps RT amplifier and
CURRY 8 software (NeuroScan, Compumedics). We recorded data from 64
channels placed according to the international extended 10--20 electrode
system (Chatrian et al., 1985) by means of 64 Ag/AgCl electrodes
inserted in a nylon cap (Quick-Cap, Compumedics). The reference
electrode was placed at the nose, and we placed 4 additional electrodes
above and below the left eye and in the outer canthi of the eyes to
record the electrooculogram. We continually recorded the EEG activity at
a sampling rate of 500 Hz. Impedance levels were maintained below 5 k\selectlanguage{greek}Ω
\selectlanguage{english}to ensure proper signal quality.

To obtain the pupillometry, we recorded the pupil's area from the left
eye along horizontal and vertical gaze position at 1000 Hz sampling rate
using an EyeLink 1000 desktop mount (SR Research). Pupil area was
assessed using a center-of-mass algorithm in the centroid mode of the
eye tracker.

\subsubsection*{Procedure}

{\label{procedure}}

We conducted the experiment in an electrically and acoustically shielded
room. Participants sat with their head placed on a chinrest
approximately 60cm from the screen. To ensure that participants were
adequately prepared for the task, they were given a practice block
consisting of five trials of a modified version of the task where the
sounds were pure tones, the encoding phase had only six sounds and test
sounds were separated only by one sound. They were allowed to repeat
this block as many times as needed to ensure they understood the task.
The main experiment consisted of 24 blocks of experimental trials and
four blocks of motor-only trials, with the first motor block presented
before the first experimental block, and every six blocks thereafter. To
prevent participant fatigue, short breaks were included at least every
seven blocks. The experiment lasted approximately 90 minutes without
preparation or breaks.

\subsubsection*{Behavioral analysis}

{\label{behavioral-analysis}}

To evaluate participants' order memory performance, we calculated the
percentage of correctly answered retrieval questions (''is this the
correct order?'') for pairs of sounds that were presented either within
or across events, and for each specific combination of sound source
(EG-EG, SG-SG, SG-EG, EG-SG) separately. We conducted two-tailed t-tests
to compare the percentage of correct responses between conditions and
sound source combinations.

\subsubsection*{EEG preprocessing}

{\label{eeg-preprocessing}}

We measured electrophysiological activity to retrieve event-related
potentials (ERPs) in response to the encoding sounds and button presses
in the motor-only (M) trials. The raw EEG data was bandpass filtered
from 1 to 25 Hz using a Kaiser window (Kaiser \selectlanguage{greek}β \selectlanguage{english}5.653, filter order
1812) and manually examined to remove continuous atypical artifacts and
to identify faulty electrodes. We then applied Independent Component
Analysis (ICA) using the logistic infomax ICA algorithm (Onton \&
Makeig, 2006) to identify the eye-movement related components. The ICA
weights of those components were subtracted from the raw bandpass
filtered data. Remaining artifacts were rejected by applying a threshold
of 75 \selectlanguage{greek}μ\selectlanguage{english}V maximal signal-change per epoch and malfunctioning electrodes
were interpolated spherically. Participants with excessive artifacts in
their EEG recordings (\textgreater{} 60\% of epochs meeting the
rejection criteria) were excluded. All the catch trials and the encoding
trials with incorrect button presses were removed from the analysis.
Epochs of -200 to +600 ms relative to sound or button press onsets, with
a baseline correction from -200 to 0 ms, were extracted for each
encoding item (EG, SG, and M) and for encoding sounds that occurred in
the last position before a boundary (bB) and the boundary sounds (i.e.
after a switch in the sound source, the first sound elicited by the new
source, B), separately for EG (EGbB, EGB) and SG sounds (SGbB, SGB). All
SG epochs were corrected for motor activity (SG-M) by subtracting the M
epochs' averages to remove the activation resulting from motor actions.
For simplicity, however, we will from now on refer to SG-M as SG.

\subsubsection*{ERP analysis}

{\label{erp-analysis}}

In order to investigate the self-generation effects, we compared the
responses to EG sounds and the SG sounds by extracting the mean
amplitudes of the N1 component (window: 80-120 ms) at Cz and joined
mastoids (N1\textsubscript{mast}), the P2 component (window: 150-250 ms)
at Cz, and the N1 subcomponents Na (window: 80-100 ms) and Tb (window:
110-150 ms) at the joined temporal electrodes (T8 and T7), following the
methodology described in SanMiguel et al. (2013). We used two-tailed
t-tests to compare the amplitudes of EG and SG on all the defined
components: N1, N1\textsubscript{mast}, P2, Na and Tb.

To examine the potential boundary effect, we evaluated the sensory
response to boundary sounds by extracting the mean amplitudes of the N1
and P2 components at Cz. To assess context change we planned to extract
the mean amplitude in the P3 window at its maximum midline electrode
(Pz). However, contrary to our expectations, we observed a negative
instead of positive response at this spatiotemporal location. Moving
forward, we will refer to this component as the parietal negative
response (PNR, window: 392-432). We used 2x2 repeated measures ANOVAs
with the factors Source (EG vs. SG sounds) and Position (B vs. bB
sounds) to compare the amplitudes on all the defined components: N1, P2,
and PNR.

We conducted a data-driven analysis to explore the possibility of
detecting further effects that may not have been captured by the
targeted ERP analyses (Maris \& Oostenveld, 2007). To control for
multiple comparisons and identify clusters with significant group
differences in multiple dimensions (electrode-time samples), we used a
non-parametric cluster-based permutation analysis. We defined
neighboring sensors using a Delaunay triangulation over a 2D projection
of the electrode montage and set a minimum of 2 nearby electrodes per
cluster. This analysis was performed for all comparisons, and for each
comparison, a two-tailed t-test was performed on the extracted values of
each electrode-time sample of the epochs. The resulting amplitude values
were assessed using the non-parametric Monte Carlo method with 10000
random partitions. Clusters were created by grouping adjacent
electrode-time samples exceeding a significance level set to 0.05. The
sum of individual t-statistics was calculated within each cluster to
obtain a cluster-level statistic, and the significance level (p-value)
was calculated using the non-parametric Monte Carlo method.

We used EEGLAB (Delorme \& Makeig, 2004) for EEG preprocessing and ERP
analysis, Eeprobe (ANT Neuro) for ERP visualization and mean amplitude
extraction, and Fieldtrip (Oostenveld et al., 2011) for the data-driven
analysis.

\subsubsection*{Pupillometry
preprocessing}

{\label{pupillometry-preprocessing}}

We measured the pupil diameter in response to all the encoding items
following a similar pipeline as described in (Paraskevoudi \& SanMiguel,
2023a), using Fieldtrip (Oostenveld et al., 2011). The EyeLink software
detected missing data and blinks which were linearly interpolated using
a time window from -100 to 100 ms. Missed blinks were identified using
peak detection on the velocity of the pupil signal (Urai et al., 2017).
If they occurred less than 250 ms apart they were joined as a single
blink and the resulting blinks were then linearly interpolated. To
reduce measurement noise not likely to originate from physiological
sources, the interpolated pupil time series were filtered using a 0.05-4
Hz third-order Butterworth filter. The effect of blinks and saccades on
the pupil response was estimated through a deconvolution analysis and
removed from the data using linear regression (Knapen et al., 2016; Urai
et al., 2017). We used the residual bandpass filtered pupil time series
(z-scored) for the evoked analyses (Slooten et al., 2019). The data was
downsampled to 100 Hz and epoched from --0.5 to 1.5 ms with a baseline
correction of 500 ms before the sound onset. The average evoked response
for each participant was obtained for all the EG and SG sounds at
encoding, and for the boundary and the before boundary sounds separated
by sound source (EGB, EGbB, SGB, SGbB). To further explain the boundary
pupil data we decided a posteriori to conduct an exploratory (not
pre-registered) analysis to assess the overall pupil response for each
event type, depending on the event order within a sequence
(1\textsuperscript{st} or 2\textsuperscript{nd}). For this additional
analysis, we aggregated all preprocessed (except for baseline
correction) sound epochs belonging to the same event, and calculated the
overall mean pupil diameter for the event. We then calculated the grand
average for all first events (sounds before the boundary) and all second
events (sounds after the boundary), separately for EG events and SG
events. It is important to clarify that we did not perform any motor
correction in the pupillometry analysis because our goal was to evaluate
the impact of motor actions on the pupil response.

\subsubsection*{Pupillometry analysis}

{\label{pupillometry-analysis}}

Non-parametric permutation statistics were used to test for group-level
significance of the individual averages, following the same procedure as
for the EEG data except that for pupillometry the clusters were defined
based on the time dimension only. We tested for possible differences in
evoked pupil responses computing t-values of the difference between the
conditions of interest and thresholded these~\emph{t} ~values at
a~\emph{p} ~value of .05. We tested for the main effects of Source (EG
vs. SG sounds) and Position (B vs. bB sounds) as well as their
interaction (performed on the difference waves EGB-EGbB vs SGB-SGbB).
Adjacent time-samples that passed the threshold of the p-value
(\textless{} .05) were constituted as clusters. The sum of individual
t-statistics was calculated within each cluster to obtain a
cluster-level statistic, and the significance level (p-value) was
calculated using the non-parametric Monte Carlo method with 10000 random
partitions. Additionally, to evaluate the effects in the overall event
pupil response depending on event order we performed a 2x2 repeated
measures ANOVA on Source (EG vs. SG) and Order (1\textsuperscript{st} vs
2\textsuperscript{nd}) of the two events that formed each sequence.

\subsubsection*{Correlations}

{\label{correlations}}

To investigate our hypotheses on the relationship between the
behavioral, electrophysiological, and pupillometry results we tested the
possible correlations between these effects, considering only the
physiological effects that were significant in the previous analyses.
For the ERP responses and pupil data we included the self-generation
effect (amplitude difference of EG minus SG) and the boundary effect
separately by source (EGB minus EGbB; SGB minus SGbB) for each
significant component or cluster. To obtain one value to perform the
correlations on the significant clusters, we extracted the mean
amplitude of the significant time window at its maximal electrode for
the ERPs, and the peak of the difference wave for the pupil data. We
then correlated the electrophysiological and neuromodulatory (pupil
dilation) effects with the hypothetical production effects (the
difference in temporal order memory performance between across and
within), and directly with the memory performance in each combination of
sound sources (EG-EG, SG-SG, SG-EG, EG-SG) using Pearson's correlation
coefficients.

\subsection*{Results}

{\label{results}}

\begin{enumerate}
\item ~
  \subsubsection*{Behavioral}

  {\label{behavioral}}
\end{enumerate}

The present study examined the accuracy of participants' order memory by
analyzing the mean percentage of correct responses to the retrieval
question (''is this the correct order?'') for sound pairs encoded within
or across events, and for each sound source pair combination (EG\_EG,
SG\_SG, SG\_EG, EG\_SG, Figure 2). Two-tailed t-tests revealed no
significant difference in performance between sound pairs encoded within
versus across events, \emph{t} (24) = -.966, \emph{p =} .344. Examining
the within condition, there was no significant difference in performance
for sound pairs comprised of two encoded EG sounds versus two encoded SG
sounds, \emph{t} (24) = .536, \emph{p =} .597. Additionally, for the
across pairs, there was no significant difference in performance
depending on whether the first sound was EG or SG, \emph{t} (24) =
.324,\emph{p =} .748). These findings indicate that the participants'
order memory performance was not influenced by the sound source at
encoding nor by the presence of two encoding events.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2} . Behavioral performance on temporal order memory. Bar
plots illustrate both the within- and across-condition, while
considering each pair combination of sound sources, externally-generated
(EG) and self-generated (SG), during the encoding phase. Specifically,
the across-condition is represented by the combinations SG-EG and EG-SG,
while the within-condition is represented by EG-EG and SG-SG. Individual
data points are depicted alongside the bar plots and connected by
discontinuous lines.

\subsubsection*{Electrophysiological}

{\label{electrophysiological}}

Results of the targeted ERP analysis on the self-generation effects
showed significant differences between EG and SG on several components
(Figure 3). Specifically, the amplitude of the N1 component at Cz was
significantly suppressed for SG compared to EG (\emph{t} (24) =
-6.671,\emph{p \textless{}} .001, \emph{d =} .101). Conversely,
N1\textsubscript{mast} did not differ significantly between EG and SG
(\emph{t} (24) = .791, \emph{p =} .437). The Na and Tb components were
also significantly modulated, with SG enhancement for Na and attenuation
for Tb (\emph{t} (24) = 2.556, \emph{p =} .017, \emph{d =} .310
and\emph{t} (24) = -2.597, \emph{p =} .016, \emph{d =} .397,
respectively). Moreover, the P2 amplitude at Cz was significantly
suppressed for SG compared to EG (\emph{t} (24) = 6.985, \emph{p
\textless{}} .001, \emph{d =} .984). The cluster-based analysis showed
more negative values over frontocentral electrodes for the EG compared
to SG from 0 to 114 ms (t = -2.453, \emph{p =} .025) and more positive
values over frontocentral electrodes from 158 to 254 ms (t = 3.784,
\emph{p =} .001) and parietal electrodes from 528 to 598 ms (t = 3.181,
\emph{p =} .007), for the EG compared to SG. Thus, the negative cluster
(0 to 114 ms) encompassed the Na (80-100 ms) and N1 (80-120 ms)
components, and the first positive cluster (158 to 254 ms) encompassed
the P2 (150-250 ms) component.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} Electrophysiological results comparing the
externally-generated (EG) and self-generated (SG) stimuli at encoding.
The top section shows ERPs recorded on the analyzed electrodes. At Cz,
M1, and M2, the analyzed components are N1 and P2, while at T7 and T8,
the N1 subcomponents Na and Tb are examined. Significance is indicated
by asterisks. The bottom section displays topographical plots
representing three significant clusters identified through the ERP
data-driven analysis comparing the EG vs the SG stimuli. The gray
shading indicates the time windows corresponding to each cluster, the
highlighted cluster electrodes indicate whether a cluster is negative
(white) or positive (black) dots.

To examine the boundary effect, we examined responses to the boundary
(B) and before-boundary (bB) sounds and performed 2x2 repeated measures
ANOVAs (Source x Position) on N1 and P2 (at Cz), as well as on PNR at Pz
to test for possible interactions between self-generation and boundary
effects (Figure 4). Related to the N1, we obtained a main effect of
Source (\emph{F} (1, 24) = 38.184, \emph{p \textless{}}
.001,\(\eta_{p}^{2}\) = .614) but no main effect of Position nor an
interaction, thus reflecting the self-generation effect formerly
reported, that is, a reduction of the N1 amplitude for SG sounds
regardless of position. For the P2, we obtained a main effect of Source
(\emph{F} (1, 24) = 20.201, \emph{p \textless{}} .001,
\(\eta_{p}^{2}\) = .457), as expected, and Position (\emph{F} (1, 24)
= 22.657, p \textgreater{} .001, ??2 = .486), which we did not predict,
but no interaction. This indicates that P2 was significantly enhanced
for boundary sounds maintaining the P2 attenuation for the SG sounds
regardless of position. Finally, for the PNR there was a significant
interaction between Source and Position (\emph{F} (1, 24) =
8.417,\emph{p =} .008, \(\eta_{p}^{2}\) = .260). The post-hoc
comparisons showed a significant effect of Position only for the SG
sounds (\emph{t} (24) = -2.274, \emph{p} = .032, \emph{d} = 1.276),
indicating that the PNR was enhanced for the boundary SG sounds.
However, there was no effect of Position for the EG sounds (\emph{t}
(24) = .994, \emph{p} = .330). Thus, in terms of modulation of the PNR,
starting to perform actions seemed to mark a clear boundary while
stopping to perform actions did not. The cluster-based analysis revealed
a significant interaction temporally overlapping the N1-P2 complex
(window: .084 to .198 s, t = 4643.049, \emph{p =} .009). Post-hoc
comparisons revealed that SG sounds differed significantly depending on
Position, where the boundary SG sounds elicited more positive responses
compared to the before-boundary SG sounds (window: .084 to .198 s, t =
6237.125, \emph{p \textless{}} .001); contrary, EG sound responses were
not modulated by Position. The significant results from the
cluster-based analysis overlapped with the N1 and P2 windows from the
targeted ERP analysis, however, the cluster showed a posterior scalp
distribution.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4.} Electrophysiological results comparing the boundary
effect (boundary, B, minus before-boundary, bB) on externally-generated
(EG) and self-generated (SG) stimuli at encoding and the interaction
between boundary and self-generation effects. The top section displays
the recorded ERPs on the analyzed electrodes. At Cz, the N1 and P2
components were analyzed, while at Pz, the PNR was examined. The
left-most and middle columns depict the boundary effect for the EG and
SG sounds, respectively. In the rightmost column the difference waves
depicting the boundary effects are compared between the SG and EG sounds
(interaction). Asterisks indicate significant effects. The bottom
section exhibits topographical plots representing the two significant
clusters identified through the ERP data-driven analysis. The gray
shading indicates the time windows corresponding to each cluster, and
the electrodes included in each cluster are highlighted with dots, the
color indicating whether the cluster is negative (white) or positive
(black).

\subsubsection*{Pupillometry}

{\label{pupillometry}}

The pupil analysis amongst all the encoding sounds showed a significant
difference in pupil diameter between EG and SG sounds (Figure 5).
Specifically, cluster-based permutation t-tests revealed a larger pupil
diameter for SG sounds compared to EG sounds in the time window spanning
from --.130 to .890 s (\emph{t} = -750.562, \emph{p \textless{}} .001).

Regarding the boundary effect, the pupil response cluster-based
permutation t-tests revealed that the interaction between Position and
Source was significant, (window: --.230 to 1.500 s, t =
-1170.038,\emph{p \textless{}} .001). Further analysis on the
significant time window showed a simple main effect of Position on EG
sounds (window: .790 to 1.100 s, t = -590.941, \emph{p \textless{}}
.001). This indicated that the phasic pupil response to EGB sounds was
smaller than the response to EGbB sounds. Similarly, we found a simple
main effect of Position on SG sounds (window: --.240 to 1.100 s, t =
990.362, \emph{p \textless{}} .001). However, this indicated the
opposite effect; the phasic pupil response to the SGB sounds was larger
than to the SGbB sounds. Regarding Source, there was a simple main
effect on before-boundary sounds (window: .010 to .620 s, t = -310.524,
\emph{p \textless{}} .001). This goes in accordance with the previous
analysis amongst all the encoding sounds, showing enhanced pupil
diameter for SGbB compared to EGbB sounds. Finally, there was a simple
main effect of Source on boundary sounds (window: -.220 to 1.100 s, t =
-1131.452,\emph{p \textless{}} .001), indicating the same pattern of
effect on the Position on SG sounds, that is, larger phasic response for
the SGB compared to the EGB sounds. Overall, these findings suggest that
both position and source play a significant role in the boundary effect
on the pupil response. Additionally, the cluster-based analysis
identified a difference on the baseline period (window: -0.490 to -0.260
s, t = 169.787, \emph{p =} .021). However, due to the limited duration
of the baseline period, we deemed it insufficient for drawing meaningful
conclusions. Instead, considering that boundary effects on the baseline
period of the boundary sound might relate to the direction of the
boundary (from EG to SG or from SG to EG), to further explore this, we
examined the differences in the overall pupil response for each event
type taking event order into account, by conducting a 2x2 repeated
measures ANOVA on Source (EG vs. SG) and Order (1st vs. 2nd event) of
the two events comprising each sequence. The analysis revealed a
significant interaction between Source and Order (\emph{F} (1, 24) =
10.619, \emph{p =} .003, \(\eta_{p}^{2}\) = .307), main effect of
Source (\emph{F} (1, 24) = 50.755, \emph{p \textless{}} .001,
\(\eta_{p}^{2}\) = .679) and Order (\emph{F} (1, 24) = 39.074, \emph{p
\textless{}} .001,\(\eta_{p}^{2}\) = .619). In terms of the simple
main effects of Source, the overall event response was found to be
larger for the SG events compared to the EG events during both the
1\textsuperscript{st} event (\emph{t} (24) = -4.674, \emph{p
\textless{}} .001, \emph{d =} .363 ) and the 2\textsuperscript{nd} event
(\emph{t} (24) = -8.915, \emph{p \textless{}} .001, \emph{d =} .241) of
the sequence. Regarding the simple main effects of Order,
2\textsuperscript{nd} events had significantly lower pupil dilations
than 1\textsuperscript{st} events both for EG events (\emph{t} (24) =
8.691, \emph{p \textless{}} .001, ,\emph{d =} .145) and for SG events
(\emph{t} (24) = -4.553, \emph{p =}.048, \emph{d =} .240).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5.} Pupil responses during encoding. The top panel shows
the evoked pupil responses at encoding to all externally-generated (EG)
and self-generated (SG) stimuli. The bottom right panel shows the evoked
pupil responses for EG and SG depending on whether they were the last
stimulus before a boundary (bB) or the boundary stimulus (B). The bottom
left panel displays the overall mean pupil response of the entire
events, separately for EG and SG events depending on order of
presentation within a sequence (1\textsuperscript{st} event or
2\textsuperscript{nd} event). Error bars show the standard error of the
mean (SEM). Significance is marked by a black line encompassing the
significant time window.

\subsubsection*{Correlations}

{\label{correlations-1}}

Table 1 displays the correlations conducted in this study to explore the
possible relationships between the electrophysiological (ERPs) and
neuromodulatory (pupil dilation) effects of actions at encoding and the
memory performance effects at retrieval on the one hand; and the
possible relationships between the two physiological effects of actions
at encoding. The results indicate that the ERP SG effects (EG-SG) on
each significant component did not exhibit correlations with the effect
of actions on pupil dilation (EG-SG) or the effect of source on the
performance of within pairs (EG\_EG - SG\_SG). Additionally, the pupil
dilation effects did not demonstrate correlations with memory
performance. Regarding the boundary effect, we examined the correlation
between each significant physiological effect of the boundary (B-bB) and
the corresponding associated memory performance for that boundary type.
In cases where an interaction was present, we explored separately the
boundary effect for each sound source (EG and SG). Notably, we
discovered a significant negative correlation between the boundary
effect on memory performance (within minus across) and the modulation of
P2 for boundary sounds (\emph{r} = -.425, \emph{p =} .034). This finding
suggests that larger performance differences between across and within
events were associated with a smaller boundary effect at P2. However, no
correlations were found between the behavioral boundary effect and the
pupil, nor between the pupil and the ERP.

\textbf{Correlations}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
r\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
p\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{SG effects}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{SG effects}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Memory performance for within pairs (EG\_EG -- SG\_SG)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
N1 (EG - SG)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-.099\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
.638\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Na (EG - SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.133\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.526\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Tb (EG - SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.035\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.870\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
P2 (EG - SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.204\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.328\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Pupil (EG - SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.313\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.127\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Pupil (EG - SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
N1 (EG - SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
-.007\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.972\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Na (EG - SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.070\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.741\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Tb (EG - SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
-.061\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.773\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
P2 (EG - SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.040\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.848\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Boundary effect}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Boundary effect}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Memory performance (Within -- Across)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
P2 (B - bB)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{-.425}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{.034}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Memory performance (Within - Across EG-SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Parietal cluster (B - bB for SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
-.260\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.209\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
PNR (B - bB for SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.030\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.886\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Pupil (B - bB for SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
-.209\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.315\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Memory performance (Within -- Across SG-EG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Pupil (B - bB for EG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
-.287\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.164\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Pupil (B - bB for EG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
P2 (B - bB)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
-.218\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.296\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Pupil (B - bB for SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Parietal cluster (B - bB for SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
-.256\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.216\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
P2 (B - bB)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.055\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.792\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
PNR (B - bB for SG)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
-.236\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
.256\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 1.} Correlations between the memory performance, the
significant self-generation and boundary effects on ERP components and
the significant self-generation and boundary effects on pupil responses.

\subsection*{Discussion}

{\label{discussion}}

The present study's primary aim was to explore the possible influence of
motor actions to structure auditory memory. To do so, we manipulated the
presence or absence of actions during the encoding of sound sequences
and examined memory recall for the temporal order of presentation of
sound pairs from the encoded sequences. We recorded electrophysiological
and pupillary responses during the sound sequence encoding, aiming to
relate the known physiological effects of actions during sensory
processing to the possible effects of said actions on memory. We
hypothesized that the neurophysiological processes engaged by actions,
and the ensuing action-related modulation of sensory processing during
encoding would promote the differentiated storage of self- and
externally-generated sounds in memory. At the physiological level, we
observed distinct modulations in both ERPs and pupil diameter in
relation to the sounds encoded at the position coinciding with a change
in sound source (SG or EG). This suggests that indeed the presence or
absence of actions acted as a meaningful context for the sound encoding,
and that specific neurophysiological mechanisms marked the processing of
boundaries between the two different contexts. However, in contrast to
our initial hypothesis, memory performance was not affected in any way
by the actions performed during encoding. We conclude that the mere
presence or absence of overt actions during sound sequence encoding, and
the neurophysiological processes engaged by them, does not meaningfully
structure auditory memory representations.

Additionally, this experiment contributes to our understanding of the
role of motor actions in modulating the strength of memory traces, more
directly relating to the production effect literature. Previous studies
testing the influence of actions on auditory memory encoding have
reported either impaired, equal, or enhanced single-item recognition
memory for sounds encoded concurrently to the execution of an action
compared to sounds in isolation (Font-Alaminos et al., 2023;
Paraskevoudi \& SanMiguel, 2023a, 2023b). Importantly, in these studies,
there was no contingent relationship between actions and sounds
(Font-Alaminos et al., 2023; Paraskevoudi \& SanMiguel, 2023a). Their
findings suggest that the presence of action alone during sound encoding
does not reliably contribute to the production effect, and thus that
beyond action-sound coincidence, action-sound predictability may play a
fundamental role in the memory advantage for self-generated sounds. In
our study, we aimed to create conditions that more closely resemble
those of studies reporting the production effect. This resulted in SG
sounds being fully predictable in time, while EG sounds remained
unpredictable, resembling natural conditions where external inputs are
inherently unpredictable, but self-generated ones are not. This setup
also resembles more closely the paradigms in which the production effect
has been reported, where stimuli that receive a memory advantage are
generated in the context of well-established action-effect
relationships, such as using one's own voice (MacLeod, 2011), or playing
a musical instrument (Brown \& Palmer, 2012). Nevertheless, in the
present study the sound's identity remained equally unpredictable for SG
and EG sounds. Moreover, while the previous studies (Font-Alaminos et
al., 2023; Paraskevoudi \& SanMiguel, 2023a, 2023b) tested for effects
of actions on single-item recognition memory, here, we explored possible
effects on sequence memory, testing memory recall for sequential order.
Despite these changes, we still could not demonstrate any effects of the
presence of actions during sound encoding on memory performance. These
results, summed to the previous findings, start to corroborate that
actions alone, and the neurophysiological modulatory mechanisms
associated with them, do not underlie or even seem to meaningfully
contribute to the production effect, suggesting that additional factors
beyond the mere presence of motor actions are critically involved in
memory enhancement for SG stimuli. Considering the lack of feeling of
control reported by several participants in the present study, the
feeling of agency and control over the stimuli may be a key factor
contributing to memory improvement, rather than the mere generation of a
sound by a motor action during encoding. Recent studies have
demonstrated that the presence of agency enhances the ability to
remember the temporal order of events (Houser et al., 2022) and that
voluntarily initiating the onset of stimuli improves working memory and
speeds up visual and attentional processes (Loyola-Navarro et al.,
2022). These findings suggest that the cognitive aspect of control and
agency may play a crucial role in memory enhancement for self-generated
stimuli (Sturm et al., 2023).

To delve into the effects of self-generation during encoding, we
examined sensory ERP components. Consistent with well-established
findings on predictable SG sounds, our study replicated the attenuated
sensory responses to SG sounds on N1, Tb and P2 (Fu et al., 2006; Houde
et al., 2002; Paraskevoudi \& SanMiguel, 2023b). These results reinforce
the notion that self-generated stimuli are processed differently at the
sensory level compared to externally-generated stimuli. Notably, the
absence of N1 attenuation at the mastoids suggests that, if any, the
modulation of sound responses in areas located on the superior temporal
plane (i.e., primary auditory cortex, which should be reflected at the
mastoids, Horv\selectlanguage{ngerman}áth et al., 2012) is rather weak. Thus, the consistent
lack of modulation at the mastoids (Font-Alaminos et al., 2023;
Paraskevoudi \& SanMiguel, 2023a, 2023b) supports the idea of concurrent
modulation of sensory-specific and -unspecific components of the
auditory N1 during the SG effect (Horváth et al., 2012; SanMiguel et
al., 2013). Furthermore, we investigated the neuromodulatory effects
surrounding motor actions by measuring pupil diameter during sound
encoding. Our results revealed greater pupil dilation for SG stimuli
compared to EG stimuli, indicating differential activity of the LC-NE
system. This finding is in line with recent research highlighting the
distinct modulation of pupil responses during the execution of
goal-directed motor actions (Lubinus et al., 2022; Paraskevoudi \&
SanMiguel, 2023a, 2023b; Yebra et al., 2019). All in all, the
physiological data shows the clear engagement of distinct
neurophysiological processes during the processing of SG sounds, that
could have substantial impact on perceptual and memory processes.

However, we did not observe any significant correlations between the
electrophysiological, neuromodulatory effects and any memory performance
differences between EG and SG sounds. This suggests that the observed
modulations in sensory processing, as reflected by the attenuated
sensory ERP components and the pupil dilation for SG sounds, are not
meaningfully modulating the strength of memory traces. Regarding the
relationship between the different physiological effects observed during
encoding, it is worth noting that previous research has demonstrated
that larger pupil diameter for highly predictable self-produced sounds
significantly correlates with greater suppression of the Tb component
(Paraskevoudi \& SanMiguel, 2023b). We could not replicate this
correlation between the pupil and EEG measures. Although the two studies
were similar in terms of temporal predictability conditions of SG and EG
sounds, notably a significant correlation between Tb suppression and
pupil dilation was found only when participants could have an additional
sense of control over the stimulation, as they could choose out of
several sound categories which one they wanted to produce on each
instance (Paraskevoudi \& SanMiguel, 2023b). All in all, sufficient
converging negative evidence (Font-Alaminos et al., 2023; Paraskevoudi
\& SanMiguel, 2023a; and the present study) is starting to accumulate
indicating that 1) actions alone, and the associated sensory attenuation
reflected on ERPs and engagement of neuromodulatory mechanisms are not
the basis for the production effect; and 2) increase in LC-NE activity
engaged by making overt actions does not meaningfully contribute to
attenuation of SG sound ERPs.

Still, actions could potentially meaningfully contribute to structure
auditory memory. Returning to the main aim of this study, in contrast to
our initial hypothesis, we did not find any significant differences in
temporal order memory performance between the across and within
conditions. In principle, this finding indicates that actions did not
structure the encoding sequence in two differentiated events. However,
recent research by Pu et al. (2022) tested principles of event
segmentation manipulating several variables such as the length of events
and position of the test items on a task where boundaries were defined
as the change of color frame on a list of grey scaled pictures. Based on
one of their observations they proposed the concept of the local primacy
effect, which suggests that memory improvements, either across or within
events, are strongest at the beginning of an event and gradually
decrease as event positions move away from the event boundary. In our
experiment, due to the need to control for the distance between the two
test sounds in the encoding sequence, pairs of test sounds pertaining to
the within event condition were primarily positioned at later local
event positions, while pairs pertaining to the across events condition
were positioned at earlier positions. This discrepancy in event position
distribution may potentially explain our lack of differences between
conditions. Furthermore, our findings are consistent with the results
reported by Raccah et al. (2022) for the middle positions of temporal
order memory as a function of serial position during encoding. In their
study, where boundaries were defined as a change in male/female speaker
over a list of spoken words, they also found no significant difference
between across and within conditions on middle sequence positions.
However, when they modeled for serial position to account for primacy
and recency effects, they observed the expected segmentation effects.

Although we did not find differences in temporal order memory that could
indicate event segmentation based on actions, our analysis of the
electrophysiological data did reveal a boundary effect on the P2
component for both SG and EG boundary sounds, and additional boundary
effects were observed only for SG boundary sounds (at EG\selectlanguage{english}-SG boundaries),
specifically, an increased positivity over parietal cortex temporally
overlapping the N1-P2 complex, followed by an increased late parietal
negative response (PNR). This suggests that the processing of SG sounds
is more sensitive to modulations on sensory processing related to
changes in source-context within an encoding sequence. In other words,
introducing actions, and engaging the associated neurophysiological
effects, potentially acts as a stronger contextual shift than removing
them. Interestingly, our analysis of pupil diameter showed an opposing
pattern of boundary effects between sources (i.e., depending on the
direction of the boundary), with SG boundary sounds (at EG\selectlanguage{english}-SG
boundaries) eliciting the highest pupil diameter, EG boundary sounds (at
SG\selectlanguage{english}-EG boundaries) eliciting the lowest, and both SG and EG sounds at the
position immediately before the boundary falling in the middle range.
Upon investigating the possible reasons for these contrasting effects,
we observed EG boundary sounds were associated with larger pupil
diameters at baseline compared to SG boundary sounds. According to the
law of initial values (Lacey, 1956), the magnitude of a physiological
response to a stimulus is influenced by the baseline level of the
response. In the case of EG boundary sounds, it is possible that the
pupil did not dilate significantly because, coming from a sequence of SG
sounds, it was already relatively dilated, reaching a physiological
limit that hindered further dilation. Hence, when the baseline pupil
size is larger, an equivalent dilatory response may have a reduced
effect on the diameter, causing the dilation to appear smaller in
comparison (Gilzenrat et al., 2010). To further understand these
directional effects, we investigated the overall pupil response of the
two events conforming a sequence. Although our paradigm and the
exploratory analysis we performed does not exactly allow us to estimate
the tonic response, previous literature on tonic and phasic pupil
activity clearly shows an inverted U-shaped pattern, where the optimal
phasic response is obtained at intermediate levels of tonic activity
(McGinley et al., 2015; Yerkes \& Dodson, 1908). Here, the boundary
clearly occurs in contexts of different activity levels for one
direction and the other, indicating an interaction between boundary and
source which suggests that initiating actions marks more clearly the
boundary than ceasing them which could explain why we didn't observe
differences in temporal order memory.

Finally, examining the relationship between electrophysiological,
neuromodulatory, and behavioral responses in relation to self-generation
on boundary effects, we observed that only the ERP P2 component's
boundary effect exhibited a correlation with the performance difference
between within and across conditions. Nevertheless, we did not find any
associations between pupil response and EEG indicating that although
both demonstrated distinct boundary-related effects, they may be
reflecting different underlying processes in terms of providing
sequential structure. In conclusion, these findings imply that a change
in context was indeed processed at the sensory level by the
participants, however, it did not result in significant performance
differences on temporal order memory.

Our study had several limitations that may explain the lack of
significant behavioral results observed in the context of event
segmentation paradigms on the visual modality and shed light on the
complexity of event segmentation paradigms on auditory stimuli,
particularly self-generated sounds. Firstly, the predictability of
boundaries may have influenced participants' perception of events. We
expected the motor act to be sufficient to signal a change in context
and elicit an event boundary that could have consequences on memory.
Event segmentation is an automatic process that occurs with little
conscious control (Zacks \& Swallow, 2007). However, it has been
suggested that heightened attention is directed towards event boundaries
due to their association with unpredictability and the optimization of
information uptake (Kosie \& Baldwin, 2019; Kurby \& Zacks, 2008). Thus,
the predictable nature of our paradigm's boundaries, as the sequence of
actions to be performed was displayed at the beginning of the trial, may
have hindered the necessary attentional engagement required to create
distinct event boundaries.

Secondly, the length and repetition of events in our study could have
impacted the results. Due to the constrains of designing an auditory
temporal order memory task, we only utilized two repetitive event
patterns, which limited the complexity and variability of the events. It
remains unclear whether repeated pairing of two event types can lead to
their integration into a more complex event type or if they are
perceived as separate entities (Shin \& DuBrow, 2021). Furthermore,
while previous research demonstrated a robust (visual) boundary effect
even with decreasing event length (Pu et al., 2022), we were not able to
explore longer event lengths since pilot behavioral testing indicated a
sharp drop on auditory memory performance overall for longer events.
Therefore, including more varied event patterns could yield different
results.

Another divergence from previous sequential order memory tasks was the
absence of a specific task to assess associative memory for the source
of the items. Unlike other studies, our focus was solely on examining
the effects of the change in context in the form of a motor action.
Thus, the inclusion of an associative memory task could have biased our
results due to the increased attention on the contextual environment.
Future research should explore this aspect to gain a more comprehensive
understanding. Additionally, the dual task nature of our experiment,
with participants simultaneously performing a motor task to generate
sounds, may have acted as a distractor. Divided attention between the
motor task and memory encoding can interfere with episodic memory
processes, particularly memory for temporal order. Previous studies have
demonstrated that memory for temporal order requires greater attentional
resources and strategic processing than memory for individual items
(Mangels et al., 2001; Troyer \& Craik, 2000).

In conclusion, our study investigated the influence of motor actions on
memory encoding by examining order memory performance and
electrophysiological and pupillary responses during sequence encoding.
While the behavioral findings did not reveal significant differences in
order memory performance based on event segmentation, we observed
distinct modulations in sensory processing and pupil dilation related to
motor actions. These findings contribute to our understanding of the
production effect and suggest that factors beyond motor actions, such as
the feeling of agency, may play a crucial role in memory enhancement for
self-generated stimuli. Furthermore, the study's limitations, highlight
the complexity of event segmentation on auditory paradigms and the need
for future research to deepen our understanding of the underlying
mechanisms and the role of motor activity in shaping our experiences.

\subsection*{References}

{\label{references}}

Aston-Jones, G., \& Cohen, J. D. (2005). An Integrative Theory of Locus
Coeruleus-Norepinephrine Function: Adaptive Gain and Optimal
Performance. \emph{Annual Review of Neuroscience} , \emph{28} (1),
403--450. https://doi.org/10.1146/annurev.neuro.28.061604.135709

Belin, P., Zatorre, R. J., Lafaille, P., Ahad, P., \& Pike, B. (2000).
Voice-selective areas in human auditory cortex. \emph{Nature}
,\emph{403} (6767), 309--312. https://doi.org/10.1038/35002078

Blakemore, S.-J., Wolpert, D. M., \& Frith, C. D. (1998). Central
cancellation of self-produced tickle sensation. \emph{Nature
Neuroscience} , \emph{1} (7), 635--640. https://doi.org/10.1038/2870

Brainard, D. H. (1997). The Psychophysics Toolbox. \emph{Spatial Vision}
, \emph{10} (4), 433--436. https://doi.org/10.1163/156856897X00357

Brown, R. M., \& Palmer, C. (2012). Auditory--motor learning influences
auditory memory for music. \emph{Memory \& Cognition} , \emph{40} (4),
567--578. https://doi.org/10.3758/s13421-011-0177-x

Chatrian, G. E., Lettich, E., \& Nelson, P. L. (1985). Ten Percent
Electrode System for Topographic Studies of Spontaneous and Evoked EEG
Activities. \emph{American Journal of EEG Technology} , \emph{25} (2),
83--92. https://doi.org/10.1080/00029238.1985.11080163

Clewett, D., DuBrow, S., \& Davachi, L. (2019). Transcending time in the
brain: How event memories are constructed from
experience.\emph{Hippocampus} , \emph{29} (3), 162--183.
https://doi.org/10.1002/hipo.23074

Clewett, D., Gasser, C., \& Davachi, L. (2020). Pupil-linked arousal
signals track the temporal organization of events in memory.\emph{Nature
Communications} , \emph{11} (1), 1--14.
https://doi.org/10.1038/s41467-020-17851-9

Delorme, A., \& Makeig, S. (2004). EEGLAB: An open source toolbox for
analysis of single-trial EEG dynamics including independent component
analysis. \emph{Journal of Neuroscience Methods} , \emph{134} (1),
9--21. https://doi.org/10.1016/j.jneumeth.2003.10.009

Donchin, E., \& Coles, M. G. H. (1988). Is the P300 component a
manifestation of context updating? \emph{Behavioral and Brain Sciences}
,\emph{11} (3), 357--374. https://doi.org/10.1017/S0140525X00058027

DuBrow, S., \& Davachi, L. (2013). The influence of context boundaries
on memory for the sequential order of events. \emph{Journal of
Experimental Psychology: General} , \emph{142} (4), 1277--1286.
https://doi.org/10.1037/a0034024

DuBrow, S., \& Davachi, L. (2016). Temporal binding within and across
events. \emph{Neurobiology of Learning and Memory} , \emph{134} ,
107--114. https://doi.org/10.1016/j.nlm.2016.07.011

Eggermann, E., Kremer, Y., Crochet, S., \& Petersen, C. C. H. (2014).
Cholinergic Signals in Mouse Barrel Cortex during Active Whisker
Sensing. \emph{Cell Reports} , \emph{9} (5), 1654--1660.
https://doi.org/10.1016/j.celrep.2014.11.005

Font-Alaminos, M., Paraskevoudi, N., \& SanMiguel, I. (2023). Actions do
not clearly impact auditory memory. \emph{Frontiers in Human
Neuroscience} , \emph{17} , 1124784.
https://doi.org/10.3389/fnhum.2023.1124784

Frith, C. D., Blakemore, S. J., \& Wolpert, D. M. (2000). Abnormalities
in the awareness and control of action. \emph{Philosophical Transactions
of the Royal Society B: Biological Sciences} , \emph{355} (1404),
1771--1788. https://doi.org/10.1098/rstb.2000.0734

Fu, C. H. Y., Vythelingum, G. N., Brammer, M. J., Williams, S. C. R.,
Amaro, E., Jr, Andrew, C. M., Yaguez, L., van Haren, N. E. M.,
Matsumoto, K., \& McGuire, P. K. (2006). An fMRI Study of Verbal
Self-monitoring: Neural Correlates of Auditory Verbal
Feedback.\emph{Cerebral Cortex} , \emph{16} (7), 969--977.
https://doi.org/10.1093/cercor/bhj039

Gagl, B., Hawelka, S., \& Hutzler, F. (2011). Systematic influence of
gaze position on pupil size measurement: Analysis and
correction.\emph{Behavior Research Methods} , \emph{43} (4), 1171--1181.
https://doi.org/10.3758/s13428-011-0109-5

Gilzenrat, M. S., Nieuwenhuis, S., Jepma, M., \& Cohen, J. D. (2010).
Pupil diameter tracks changes in control state predicted by the adaptive
gain theory of locus coeruleus function. \emph{Cognitive, Affective, \&
Behavioral Neuroscience} , \emph{10} (2), 252--269.
https://doi.org/10.3758/CABN.10.2.252

Gygi, B., \& Shafiro, V. (2010). Development of the Database for
Environmental Sound Research and Application (DESRA): Design,
Functionality, and Retrieval Considerations. \emph{EURASIP Journal on
Audio, Speech, and Music Processing} , \emph{2010} , 654914.
https://doi.org/10.1155/2010/654914

Hocking, J., Dzafic, I., Kazovsky, M., \& Copland, D. A. (2013). NESSTI:
Norms for Environmental Sound Stimuli. \emph{PLOS ONE} , \emph{8} (9),
e73382. https://doi.org/10.1371/journal.pone.0073382

Horvath, J., Maess, B., Baess, P., \& Toth, A. (2012). Action-sound
coincidences suppress evoked responses of the human auditory cortex in
EEG and MEG. \emph{Journal of Cognitive Neuroscience} , \emph{24} (9),
1919--1931. https://doi.org/10.1162/jocn\_a\_00215

Houde, J. F., Nagarajan, S. S., Sekihara, K., \& Merzenich, M. M.
(2002). Modulation of the Auditory Cortex during Speech: An MEG
Study.\emph{Journal of Cognitive Neuroscience} , \emph{14} (8),
1125--1138. https://doi.org/10.1162/089892902760807140

Houser, T. M., Tompary, A., \& Murty, V. P. (2022). Agency enhances
temporal order memory in an interactive exploration
game.\emph{Psychonomic Bulletin \& Review} , \emph{29} (6), 2219--2228.
https://doi.org/10.3758/s13423-022-02152-7

Hughes, G., Desantis, A., \& Waszak, F. (2013). Mechanisms of
intentional binding and sensory attenuation: The role of temporal
prediction, temporal control, identity prediction, and motor
prediction.\emph{Psychological Bulletin} , \emph{139} (1), 133--151.
https://doi.org/10.1037/a0028566

Kleiner, M., Brainard, D., Pelli, D., Ingling, A., Murray, R., \&
Broussard, C. (2007). What's new in psychtoolbox-3. \emph{Perception}
,\emph{36} (14), 1--16.

Knapen, T., Gee, J. W. de, Brascamp, J., Nuiten, S., Hoppenbrouwers, S.,
\& Theeuwes, J. (2016). Cognitive and Ocular Factors Jointly Determine
Pupil Responses under Equiluminance. \emph{PLOS ONE} , \emph{11} (5),
e0155574. https://doi.org/10.1371/journal.pone.0155574

Kosie, J. E., \& Baldwin, D. (2019). Attentional profiles linked to
event segmentation are robust to missing information. \emph{Cognitive
Research: Principles and Implications} , \emph{4} (1), 8.
https://doi.org/10.1186/s41235-019-0157-4

Kurby, C. A., \& Zacks, J. M. (2008). Segmentation in the perception and
memory of events. \emph{Trends in Cognitive Sciences} , \emph{12} (2),
72--79. https://doi.org/10.1016/j.tics.2007.11.004

Lacey, J. I. (1956). The Evaluation of Autonomic Responses: Toward a
General Solution. \emph{Annals of the New York Academy of Sciences}
,\emph{67} (5), 125--163.
https://doi.org/10.1111/j.1749-6632.1956.tb46040.x

Loyola-Navarro, R., Moenne-Loccoz, C., Vergara, R. C., Hyafil, A.,
Aboitiz, F., \& Maldonado, P. E. (2022). Voluntary self-initiation of
the stimuli onset improves working memory and accelerates visual and
attentional processing. \emph{Heliyon} , \emph{8} (12), e12215.
https://doi.org/10.1016/j.heliyon.2022.e12215

Lubinus, C., Einhauser, W., Schiller, F., Kircher, T., Straube, B., \&
van Kemenade, B. M. (2022). Action-based predictions affect visual
perception, neural processing, and pupil size, regardless of temporal
predictability. \emph{NeuroImage} , \emph{263} , 119601.
https://doi.org/10.1016/j.neuroimage.2022.119601

MacDonald, P. A., \& MacLeod, C. M. (1998). The influence of attention
at encoding on direct and indirect remembering. \emph{Acta Psychologica}
, \emph{98} (2--3), 291--310.
https://doi.org/10.1016/s0001-6918(97)00047-4

MacLeod, C. M. (2011). I said, you said: The production effect gets
personal. \emph{Psychonomic Bulletin \& Review} , \emph{18} (6),
1197--1202. https://doi.org/10.3758/s13423-011-0168-8

MacLeod, C. M., \& Bodner, G. E. (2017). The production effect in
memory. \emph{Current Directions in Psychological Science} , \emph{26} ,
390--395. https://doi.org/10.1177/0963721417691356

Mangels, J. A., Picton, T. W., \& Craik, F. I. M. (2001). Attention and
successful episodic encoding: An event-related potential
study.\emph{Cognitive Brain Research} , \emph{11} (1), 77--95.
https://doi.org/10.1016/S0926-6410(00)00066-5

Maris, E., \& Oostenveld, R. (2007). Nonparametric statistical testing
of EEG- and MEG-data. \emph{Journal of Neuroscience Methods} ,\emph{164}
(1), 177--190. https://doi.org/10.1016/j.jneumeth.2007.03.024

McCurdy, M. P., Leach, R. C., \& Leshikar, E. D. (2017). The generation
effect revisited: Fewer generation constraints enhances item and context
memory. \emph{Journal of Memory and Language} , \emph{92} , 202--216.
https://doi.org/10.1016/j.jml.2016.06.007

McGinley, M. J., David, S. V., \& McCormick, D. A. (2015). Cortical
Membrane Potential Signature of Optimal States for Sensory Signal
Detection. \emph{Neuron} , \emph{87} (1), 179--192.
https://doi.org/10.1016/j.neuron.2015.05.038

Newtson, D., \& Engquist, G. (1976). The perceptual organization of
ongoing behavior. \emph{Journal of Experimental Social Psychology}
,\emph{12} , 436--450. https://doi.org/10.1016/0022-1031(76)90076-7

Norman-Haignere, S., Kanwisher, N. G., \& McDermott, J. H. (2015).
Distinct Cortical Pathways for Music and Speech Revealed by
Hypothesis-Free Voxel Decomposition. \emph{Neuron} , \emph{88} (6),
1281--1296. https://doi.org/10.1016/j.neuron.2015.11.035

Onton, J., \& Makeig, S. (2006). Information-based modeling of
event-related brain dynamics. \emph{Progress in Brain Research}
,\emph{159} , 99--120. https://doi.org/10.1016/S0079-6123(06)59007-7

Oostenveld, R., Fries, P., Maris, E., \& Schoffelen, J. M. (2011).
FieldTrip: Open source software for advanced analysis of MEG, EEG, and
invasive electrophysiological data. \emph{Computational Intelligence and
Neuroscience} , \emph{2011} . https://doi.org/10.1155/2011/156869

Ozubko, J. D., \& Macleod, C. M. (2010). The production effect in
memory: Evidence that distinctiveness underlies the
benefit.\emph{Journal of Experimental Psychology. Learning, Memory, and
Cognition} , \emph{36} (6), 1543--1547. https://doi.org/10.1037/a0020604

Paraskevoudi, N., \& SanMiguel, I. (2023a). Sensory suppression and
increased neuromodulation during actions disrupt memory encoding of
unpredictable self-initiated stimuli. \emph{Psychophysiology} ,\emph{60}
(1), e14156. https://doi.org/10.1111/psyp.14156

Paraskevoudi, N., \& SanMiguel, I. (2023b). \emph{The multifaceted
nature of self-generation effects: Independent and interactive effects
of actions, predictability, and delay on sensory processing and memory
encoding of sounds.} {[}Preprint{]}. Preprints.
https://doi.org/10.22541/au.168322155.53723594/v1

Pettijohn, K. A., Thompson, A. N., Tamplin, A. K., Krawietz, S. A., \&
Radvansky, G. A. (2016). Event boundaries and memory
improvement.\emph{Cognition} , \emph{148} , 136--144.
https://doi.org/10.1016/j.cognition.2015.12.013

Polich, J. (2007). Updating P300: An integrative theory of P3a and
P3b.\emph{Clinical Neurophysiology} , \emph{118} (10), 2128--2148.
https://doi.org/10.1016/j.clinph.2007.04.019

Pu, Y., Kong, X.-Z., Ranganath, C., \& Melloni, L. (2022). Event
boundaries shape temporal organization of memory by resetting temporal
context. \emph{Nature Communications} , \emph{13} (1), 622.
https://doi.org/10.1038/s41467-022-28216-9

Raccah, O., Doelling, K. B., Davachi, L., \& Poeppel, D. (2022).
Acoustic features drive event segmentation in speech. \emph{Journal of
Experimental Psychology: Learning, Memory, and Cognition} .
https://doi.org/10.1037/xlm0001150

Radvansky, G. A., \& Zacks, J. M. (2017). Event boundaries in memory and
cognition. \emph{Current Opinion in Behavioral Sciences} , \emph{17} ,
133--140. https://doi.org/10.1016/j.cobeha.2017.08.006

SanMiguel, I., Todd, J., \& Schroger, E. (2013). Sensory suppression
effects to self-initiated sounds reflect the attenuation of the
unspecific N1 component of the auditory ERP. \emph{Psychophysiology}
,\emph{50} (4), 334--343. https://doi.org/10.1111/psyp.12024

Schroger, E., Marzecova, A., \& Sanmiguel, I. (2015). Attention and
prediction in human audition: A lesson from cognitive
psychophysiology.\emph{European Journal of Neuroscience} , \emph{41}
(5), 641--664. https://doi.org/10.1111/ejn.12816

Shin, Y. S., \& DuBrow, S. (2021). Structuring Memory Through
Inference-Based Event Segmentation. \emph{Topics in Cognitive Science}
,\emph{13} (1), 106--127. https://doi.org/10.1111/tops.12505

Silva, M., Baldassano, C., \& Fuentemilla, L. (2019). Rapid memory
reactivation at movie event boundaries promotes episodic
encoding.\selectlanguage{english}\emph{The Journal of Neuroscience : The Official Journal of the
Society for Neuroscience} , \emph{July} .
https://doi.org/10.1523/JNEUROSCI.0360-19.2019

Slooten, J. C. V., Jahfari, S., Knapen, T., \& Theeuwes, J. (2019).
Correction: How pupil responses track value-based decision-making during
and after reinforcement learning. \emph{PLOS Computational Biology}
,\emph{15} (5), e1007031. https://doi.org/10.1371/journal.pcbi.1007031

Speer, N. K., Swallow, K. M., \& Zacks, J. M. (2003). Activation of
human motion processing areas during event perception. \emph{Cognitive,
Affective, \& Behavioral Neuroscience} , \emph{3} (4), 335--345.
https://doi.org/10.3758/CABN.3.4.335

Sturm, S., Costa\selectlanguage{english}-Faidella, J., \& SanMiguel, I. (2023). Neural
signatures of memory gain through active exploration in an
oculomotor\selectlanguage{english}-auditory learning task. \emph{Psychophysiology} , e14337.
https://doi.org/10.1111/psyp.14337

Swallow, M. K., Jeffrey, Zacks. M., \& Richard, Abrams. A. (2010). Event
Boundaries in Perception Affect Memory Encoding and Updating. \emph{J
Exp Psychol Gen.} , \emph{138} (2), p236.
https://doi.org/10.1037/a0015631

Troyer, A. K., \& Craik, F. I. M. (2000). The effect of divided
attention on memory for items and their context. \emph{Canadian Journal
of Experimental Psychology / Revue Canadienne de Psychologie
Experimentale} , \emph{54} (3), 161--171.
https://doi.org/10.1037/h0087338

Urai, A. E., Braun, A., \& Donner, T. H. (2017). Pupil-linked arousal is
driven by decision uncertainty and alters serial choice
bias.\emph{Nature Communications} , \emph{8} (1), 14637.
https://doi.org/10.1038/ncomms14637

Vinck, M., Batista-Brito, R., Knoblich, U., \& Cardin, J. A. (2015).
Arousal and Locomotion Make Distinct Contributions to Cortical Activity
Patterns and Visual Encoding. \emph{Neuron} , \emph{86} (3), 740--754.
https://doi.org/10.1016/j.neuron.2015.03.028

Wolpert, D. M., Ghahramani, Z., \& Jordan, M. I. (1995). An Internal
Model for Sensorimotor Integration. \emph{Science} , \emph{269} (5232),
1880--1882. https://doi.org/10.1126/science.7569931

Yebra, M., Galarza-Vallejo, A., Soto-Leon, V., Gonzalez-Rosa, J. J., de
Berker, A. O., Bestmann, S., Oliviero, A., Kroes, M. C. W., \& Strange,
B. A. (2019). Action boosts episodic memory encoding in humans via
engagement of a noradrenergic system. \emph{Nature Communications}
,\emph{10} (1), 1--12. https://doi.org/10.1038/s41467-019-11358-8

Zacks, J. M., \& Swallow, K. M. (2007). Event Segmentation.\emph{Current
Directions in Psychological Science} , \emph{16} (2), 80--84.
https://doi.org/10.1111/j.1467-8721.2007.00480.x

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