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\begin{document}

\title{Neural signatures of memory gain through active exploration in an
oculomotor-auditory learning task}



\author[1]{Stefanie Sturm}%
\author[1]{Jordi Costa-Faidella}%
\author[2]{Iria SanMiguel}%
\affil[1]{Universitat de Barcelona Departament de Psicologia Clinica i Psicobiologia}%
\affil[2]{Universitat de Barcelona}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Active engagement improves learning and memory, and self- vs. externally
generated stimuli are processed differently: perceptual intensity and
neural responses are attenuated. Whether the attenuation is linked to
memory formation remains to be understood. This study investigates
whether active oculomotor control over auditory stimuli -- controlling
for movement and stimulus predictability -- benefits associative
learning, and studies the underlying neural mechanisms. Using EEG and
eyetracking we explored the impact of control during learning on the
processing and memory recall of arbitrary oculomotor-auditory
associations. Participants (N=23) learned associations through active
exploration or passive observation, using a gaze-controlled interface to
generate sounds. Our results show faster learning progress in the active
condition. ERPs time-locked to the onset of sound stimuli showed that
learning progress was linked to an attenuation of the P3a component. The
detection of matching movement-sound pairs triggered a target-matching
P3b response. There was no general modulation of ERPs through active
learning. However, participants could be divided into different learner
types: those who benefited strongly from active control during learning
and those who did not. The strength of the N1 attenuation effect for
self-generated stimuli was correlated with memory gain in active
learning. Our results show that control helps learning and memory and
modulates sensory responses. Individual differences during sensory
processing predict the strength of the memory benefit. Taken together,
these results help to disentangle the effects of agency, unspecific
motor-based neuromodulation, and stimulus predictability on ERP
components and establish a link between self-generation effects and
active learning memory gain.%
\end{abstract}%



\sloppy


\textbf{Neural signatures of memory gain through active exploration in
an oculomotor-auditory learning task}

\subsubsection*{\texorpdfstring{Stefanie Sturm\textsuperscript{a,b},
Jordi Costa-Faidella\textsuperscript{a,b,c} \& Iria
SanMiguel\textsuperscript{a,b,c}}{Stefanie Sturma,b, Jordi Costa-Faidellaa,b,c \& Iria SanMiguela,b,c}}

{\label{stefanie-sturmab-jordi-costa-faidellaabc-iria-sanmiguelabc}}

\emph{Affiliations:}

\textsuperscript{\textbf{a}} Brainlab -- Cognitive Neuroscience Research
Group, Departament de Psicologia Clinica Psicobiologia, Universitat de
Barcelona, Barcelona, Spain

\textsuperscript{\textbf{b}} Institut de Neuroci\selectlanguage{ngerman}ències, Universitat de
Barcelona, Barcelona, Spain

\textsuperscript{\textbf{c}} Institut de Recerca Sant Joan de Déu,
Esplugues de Llobregat, Spain.

\emph{Corresponding author:}

Iria SanMiguel

Department of Clinical Psychology and Psychobiology

P. Vall d'Hebron 171

08035 Barcelona, Spain

Tel: (0034) 93 312 5050

Email: isanmiguel@ub.edu

\emph{Declarations of interest:} \textbf{None.}

\textbf{Abstract}

Active engagement improves learning and memory, and self- vs. externally
generated stimuli are processed differently: perceptual intensity and
neural responses are attenuated. Whether the attenuation is linked to
memory formation remains to be understood. This study investigates
whether active oculomotor control over auditory stimuli -- controlling
for movement and stimulus predictability -- benefits associative
learning, and studies the underlying neural mechanisms. Using EEG and
eyetracking we explored the impact of control during learning on the
processing and memory recall of arbitrary oculomotor-auditory
associations. Participants (N=23) learned associations through active
exploration or passive observation, using a gaze-controlled interface to
generate sounds. Our results show faster learning progress in the active
condition. ERPs time-locked to the onset of sound stimuli showed that
learning progress was linked to an attenuation of the P3a component. The
detection of matching movement-sound pairs triggered a target-matching
P3b response. There was no general modulation of ERPs through active
learning. However, participants could be divided into different learner
types: those who benefited strongly from active control during learning
and those who did not. The strength of the N1 attenuation effect for
self-generated stimuli was correlated with memory gain in active
learning. Our results show that control helps learning and memory and
modulates sensory responses. Individual differences during sensory
processing predict the strength of the memory benefit. Taken together,
these results help to disentangle the effects of agency, unspecific
motor-based neuromodulation, and stimulus predictability on ERP
components and establish a link between self-generation effects and
active learning memory gain.

\emph{Keywords: self-generation, multisensory associations, active
learning, gaze-controlled interface}

\subsection*{Introduction}

{\label{introduction-1}}

We learn faster when we are actively engaged with the material -- this
is not just a folk wisdom, but it has been reproduced in a plethora of
experimental settings (Markant et al., 2016). Active learning benefits
for memory are, however, a very diverse phenomenon, and in order to
study their cognitive and neural underpinnings, we have to choose a
focus. In this study, we have asked ourselves whether memory gain
through active learning could be linked to the previously established
differences in the neural and perceptual processing of self- versus
externally generated stimuli (Baess et al., 2011; Blakemore et al.,
1998, 2000; Schäfer \& Marcus, 1973). Specifically, we decided to study
the effects of being in control over auditory stimuli on the learning
progress of visuo-auditory contingencies, in an experimental setup that
controls for the conflating factors of predictability and movement. We
designed a variation of the classic self-generation paradigm (Schäfer \&
Marcus, 1973) using eye movement sonification. In a memory task,
participants learned associations between movement-sound pairs. Their
learning progress was tracked across several stages of learning on a
behavioural and neural level.

Our first aim was to test whether actively controlling stimuli, beyond
effects of movement and predictability, would lead to associative
learning benefits on a behavioural level. Previous studies have shown
that active control during complex tasks such as spatial navigation, as
well as simpler experimental setups such as recognition memory tasks,
can facilitate learning (Harman et al., 1999; James et al., 2002;
Plancher et al., 2013). A related, somewhat more clearly defined
phenomenon is the ``production effect'': Stimuli produced by oneself are
remembered better than externally produced stimuli (Brown \& Palmer,
2012; MacLeod et al., 2010). Even minimal amounts of control, such as
controlling the pacing of information, have been found to improve memory
(Markant et al., 2014). The effects of control are easily conflated with
the effects of movement during learning, since in most studies on this
question, participants use hand movements in order to control stimuli in
the active condition, while not moving at all in the passive condition
(Craddock et al., 2011; Harman et al., 1999; Liu et al., 2007; Luursema
\& Verwey, 2011; Meijer \& Van der Lubbe, 2011). Nevertheless, some
studies have found memory benefits for active learning even when
controlling for the factor of movement (Plancher et al., 2013; Trewartha
et al., 2015). Theoretical and experimental accounts of the role of
choice during learning suggest that controlling the flow -- the pacing,
the order -- of information is crucial for the memory gain, since the
learner is able to develop hypotheses and test them, or revisit items
that they feel unsure about (Gureckis \& Markant, 2012; Kruschke, 2008;
Markant et al., 2016; Markant \& Gureckis, 2010; Schulze et al., 2012).
This is corroborated by the fact that motor activity unrelated to
strategic control over the learning strategy does not improve memory
performance (Voss et al., 2011). In order to get a better view of the
role of control in the learning process of arbitrary motor-auditory
contingencies, we developed a learning paradigm in which participants
had to return several times to the same set of stimuli and were tested
on their memory performance in between rounds of learning. We
hypothesised that we would encounter a memory advantage for stimuli
learned under active exploration. We expect that this memory advantage
will express itself in participants learning the associations faster in
the active condition.

A second aim of this study was to investigate the neural mechanisms
underlying the putative memory benefits for active learning. To that
aim, we isolated neurophysiological effects of control over acoustic
stimuli from unspecific neuromodulatory effects caused by movements and
effects of stimulus predictability. Control during stimulus generation
could modulate brain responses at different levels of learning, and we
probed a series of possible mechanisms.

Control over stimuli, accompanied by a Sense of Agency (SoA), is known
to impact stimulus processing. Electrophysiological responses to
self-generated stimuli tend to be attenuated relative to externally
generated stimuli, even when the stimuli evoking the response are
physically identical (Blakemore et al., 2000; Gentsch \& Schütz-Bosbach,
2011; Hughes et al., 2013b; Hughes \& Waszak, 2011; Kilteni et al.,
2020; Mifsud et al., 2018; SanMiguel et al., 2013). Although they can be
observed in all sensory modalities (auditory: Baess et al., 2011;
visual: Hughes \& Waszak, 2011; tactile: Kilteni et al., 2020, for some
examples), attenuation effects on sensory processing have been
extensively studied in the auditory domain, often comparing evoked
electrophysiological responses to self-generated and
externally-generated acoustic stimuli (Horváth, 2015; Schäfer \& Marcus,
1973). Using electroencephalogram (EEG), a number of neuro-electrical
markers of self-generated processing have been established: An
attenuation of certain event-related potentials (ERPs), i.e. a
diminished amplitude of different peaks that characterise the early
cortical processing of self- as opposed to externally generated sounds.
Attenuation for self-generated sounds has been observed in the N1
component (Bäß et al., 2008; Elijah et al., 2018; Mifsud et al., 2016;
Neszmélyi \& Horváth, 2017; Oestreich et al., 2016; Pinheiro et al.,
2019; van Elk et al., 2014), the P2 component (Horváth \& Burgyán, 2013;
Knolle et al., 2012), and the Tb component (Paraskevoudi \& SanMiguel,
2022; SanMiguel et al., 2013; Saupe et al., 2013). The nature of these
effects is often assumed to be predictive, since efference copies of
motor commands are thought to serve as a basis for precise anticipation
of sensory stimulation (Miall \& Wolpert, 1996). Correctly predicted
sensory stimulation is thought to elicit smaller neural responses than
wrongly predicted or surprising input, in line with the predictive
coding theory of neural processing (Blakemore et al., 1998; Kilner et
al., 2007). However, previous research has shown that motor activity
during sensory processing also has unspecific modulatory effects that
are not related to predictability -- just being in motion affects the
way we perceive stimuli (Horváth et al., 2012), and movement effects can
be a conflating factor when trying to study the effects of
predictability and control (Hazemann et al., 1975; Horváth, 2013;
Paraskevoudi \& SanMiguel, 2021; Press \& Cook, 2015). Recent studies
that investigated specifically effects of agency, controlling for
predictability and movement, have found both attenuation and enhancement
effects on the P2 component (Bolt \& Loehr, 2021; Han et al., 2021), and
modulations of the P3 component have also been observed (Burnside et
al., 2019; Kühn et al., 2011). We hypothesized that if the known effects
typically observed in the self-generation paradigms on the N1, P2 and P3
component are indeed related to agency and control, we should be able to
reproduce them with our design, even though we used an unconventional
experimental paradigm: Instead of hand or finger movements, participants
used their eye movements to generate sounds. By using a gaze-controlled
interface, we were able to compare an experimental condition in which
participants \emph{controlled} a cursor using their eye movements
(``agent condition'') with a condition in which
participants\emph{followed} a cursor with their gaze (``observer
condition''), minimizing the motor differences between conditions. Eye
movements are mostly automatic and usually used towards visual goals,
and we have no expectations of auditory consequences of our eye
movements (Mifsud \& Whitford, 2017; Slobodenyuk, 2016). Importantly,
two studies using self-generation paradigms have used saccades to
generate sounds and found either no attenuation for eye-movement
initiated sounds (Mifsud \& Whitford, 2017) or weakened attenuation of
the N1, but not the P2 component (Mifsud et al., 2016).
Electrophysiological responses to gaze fixations have been measured in
the context of brain-computer interfaces and gaze-controlled games (Ihme
\& Zander, 2011; Protzak et al., 2013), and certain markers of voluntary
gaze control have been established: Voluntary gaze fixations that were
made consciously in order to control an interface were characterised by
a slow negative parieto-occipital wave evoked by the fixation which was
absent or much decreased in fixations that did not control the interface
(Protzak et al., 2013; Shishkin et al., 2016).

Rather than control affecting stimulus processing on a basic level, we
also considered the possibility that control would specifically modulate
learning processes. Repeated presentation of a given movement-sound
pair, as was the case in our paradigm, leads to neural changes over time
related to the learning progress -- we develop internal models of the
associations that we have learned (Kilner et al., 2007), and the sound's
predictability based on the preceding movement increases gradually.
Effects of predictability on ERP components strongly resemble those of
self-generation: predictability often leads to sensory attenuation
(Alink et al., 2010; Grotheer \& Kovács, 2016; Kaiser \& Schütz-Bosbach,
2018; Summerfield et al., 2008), and in fact sensory attenuation for
self-generated stimuli is more pronounced when the outcome of the
self-generated action matches the agents' expectation (Hughes et al.,
2013a; Stenner et al., 2014). Controlling for temporal predictability
can help us to understand the functional separation of modulations of
established ERP components by self-generation (Klaffehn et al., 2019).
By studying the evolution of ERP components in relation to learning we
can shed light on the effects of increased predictability beyond the
self-generation effects, which should be observable from the start of
the learning process. In line with previous studies, we expected to find
an increased attenuation of the N1 (Kaiser \& Schütz-Bosbach, 2018) and
P2 component during late stages of learning. Furthermore, modulations of
the P3 component -- with less clear directionality -- have been observed
as a function of learning (Polich, 2007; Turk et al., 2018). If control
was to facilitate learning progress, we would expect stronger or earlier
effects of learning when participants have control over the stimuli.

Further insight into the neural mechanisms behind the active learning
memory advantage can be gained by studying evoked responses to
incongruent sounds. In our paradigm, participants are regularly tested
on their memory of movement-sound pairs; in those test trials, they are
required to passively observe a cursor movement and listen to a sound,
and judge whether the two are a matching pair or not, based on their
previously learned associations. We hypothesised that control during
acquisition strengthens the internal representation of the
movement-sound association, so violations of the latter should elicit
larger prediction error signals (Knolle et al., 2013; Mathias et al.,
2015). Based on the previous literature, we expected incongruent stimuli
to elicit mismatch responses like the N200 or an orienting response like
the P3a (Knolle et al., 2013; Winkler et al., 2009). Alternatively,
sounds congruent with learned associations can elicit ``matching''
responses: The P3b component in particular is thought to reflect the
matching of a stimulus with a predicted item, and has been found to be
larger with increased predictability (Molinaro \& Carreiras, 2010; Roehm
et al., 2007; Vespignani et al., 2010). This component is also referred
to as ``late positive component'' (LPC), which is believed to reflect an
explicit recollective process (Friedman \& Johnson Jr., 2000), typically
elicited by designs in which participants have to make a response
related to the stimulus (Yang et al., 2019). It is considered part of
the classical ``old/new'' effect; stimuli presented in a test phase
which appear familiar to the participant elicit a stronger LPC (Woodruff
et al., 2006). The LPC has been found to be a predictor of learning
outcomes (Turk et al., 2018). We expected the strength of either the
matching-responses to correctly predicted or the mismatch responses to
incongruent sounds to be modulated by the factor of control during the
learning phase.

Once a motor-auditory association is established, the increased
predictability of sounds that comes with learning should affect sound
processing similarly regardless of whether sounds are presented during
learning or during a test trial: we expected that sensory responses --
specifically the N1 and P2 component -- would be attenuated during late
stages of learning, and we hypothesised that this effect could be
modulated by the mode of acquisition of the motor-auditory associations.
Previous studies have shown that during memory tests, stimuli that were
previously self-generated can cause motor-reactivation even in the
absence of movememt (Butler et al., 2011). The distinctiveness account
of the production effect (Hommel, 2005) suggests that motor activation
during learning builds stronger, more distinctive memory traces, which
is thought to be reflected in more efficient learning; how we learned
something affects how we will process it in the future. Movement during
sound processing affects our memory of the sound, but is it necessary
for the movement to be causally linked to the sound in order for this
effect to come into play? If the latter was indeed necessary, we would
expect to see an effect of agency -- rather than movement -- on the
neural processing of the stimulus or the strength of the memory trace.
Alternatively, if we do not find modulations by agency, that would give
support to the idea that movement does not need to be causal to the
stimulus in order to affect its processing or memory encoding (Horváth
et al., 2012).

In the present study, our goal was to improve our understanding of how
active control over sound stimuli affects their immediate sensory
processing and encoding in memory. Towards these aims, we studied the
way in which control during learning improves memory, and how it
modulates neural responses during sound processing and memory encoding.
Last but not least, our goal was to reveal a link between
self-generation effects during sound processing and memory benefits of
active control.

\subsection*{Methods}

{\label{methods}}

\subsubsection*{Participants}

{\label{participants}}

Twenty-five healthy undergraduate university students from the
University of Barcelona volunteered in the study. Two participants were
excluded from the analysis due to their low behavioural performance,
based on a cut-off point determined by simulating the responses of 25
randomly responding individuals and choosing the highest performing one
as the threshold (56\% correctness in the behavioural task). The final
sample included twenty-three participants (14 women, M = 21 years old,
range: 18--31). No participant self-reported any hearing impairment,
psychiatric disorder or use of nervous system-affecting substances at
least 48 hours prior to the experiment. All participants gave written
informed consent for their participation after the nature of the study
was explained to them and they were monetarily compensated (10 Medical
Association (Declaration of Helsinki) with the exception of
pre-registration and was accepted by the Bioethics Committee of the
University of Barcelona.

\subsubsection*{Experiment design}

{\label{experiment-design}}

\emph{Experiment structure}

{\label{experiment-structure}}

The experiment consisted of two types of trials: acquisition trials and
test trials. During acquisition trials, participants had 20 s to learn
associations between movement directions of a white cursor over a grid
of 9 red squares (Fig. 1), and 8 different sounds that were played
depending on the cursor movements (see section on sound generation
below). During test trials, participants were tested on their memory for
the movement-sound associations.

The movement-sound associations were learned either as agents or as
observers. Agent and observer experimental conditions differed only
during acquisition trials. During acquisition trials in the agent
condition, the cursor was controlled by the participant's gaze, while in
the observer condition the cursor was animated by the computer. Thus, in
the agent condition, the acquisition process required active
exploration. Participants were instructed to perform saccades over the
squares and generate as many different sounds as possible. In the
observer condition, the cursor was animated using previously recorded
eye movements from the same participant, and participants were asked to
follow the cursor's movements and memorise the relationships between
movements and sounds.

Following each acquisition trial, participants were tested on their
memory of the movement-sound associations in a series of 6 test trials.
During test trials, participants were presented with a short animation
of the cursor moving from one square to another in a straight line
(executing one of the 8 possible movements). After a delay of 750 ms
(matching the pattern of acquisition trials, see section ``Visual
stimulation and gaze-controlled sound generation''), one of the 8 sounds
familiar from acquisition, either congruent or incongruent with the
previously learned associations, was presented. 50\% of test trials
presented congruent movement-sound pairs. The order of the animations
and sounds was based on a computer-generated, randomised list. At the
end of each test trial, participants responded whether the movement and
sound were a congruent pair by pressing one out of two buttons on a midi
keyboard placed in front of them.

One acquisition trial and 6 test trials were considered a ``learning
block''. During 7 consecutive learning blocks, participants were
presented with the same movement-sound associations. Groups of 7
learning blocks with contingent movement-sound associations are referred
to here as ``contingency blocks''. After the termination of the
7\textsuperscript{th} learning block, the contingency block was finished
and new sounds were loaded, so participants had to start their learning
process anew. Contingency blocks alternated between the agent and
observer conditions. The order of the conditions was counterbalanced
across participants.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} \textbf{Experiment structure.} \textbf{a.} Structure
of a single learning block, consisting of one 20 s acquisition trial,
followed by 6 test trials. During acquisition trials, participants
either actively explored (agent condition) or passively observed
(observer condition) the relationships between movement directions of a
cursor and 8 different sound stimuli. In test trials, participants were
tested on their memory of the associations. \textbf{b.} Structure of a
contingency block. Each contingency block consisted of 7 learning
blocks. The first three were considered the ``early learning stage'',
and the last three were considered the ``late learning
stage''.\textbf{c.} Structure of the experiment: The experiment
consisted of 14 contingency blocks, 7 of which belonged to the agent
condition and 7 of which to the observer condition.

In order to make the cursor move in a ``gaze-like'' style in the
observer condition, it was computer-animated using the participant's own
movements in acquisition trials of the preceding agent contingency
block. In case the experiment started with the observer condition, we
used the eye movement recordings from the training block, which always
involved active exploration. In order to make eye movements less
recognisable to the participant, we randomized the order of previously
recorded trials across the learning blocks.

\emph{Training}

{\label{training}}

Before starting the experiment, participants underwent two stages of
training. First, a ``free training'' session with the purpose of
adjusting the eye tracker, allowing the participants to familiarise
themselves with the equipment, and learn how to use the gaze-controlled
cursor. Participants sat facing a screen at 60 cm distance from their
eyes. Their head position was stabilized for eye tracking via a chin and
forehead rest, and they were wearing a pair of headphones connected to
the experiment computer.

Participants were then instructed to move their gaze across the screen
and ``explore'' the sounds that they were able to trigger by moving the
cursor (for details, see section ``Gaze-controlled sound generation'').
During the free training, the experimenter ensured that the participant
understood how to use the gaze-controlled cursor and was familiar with
the experiment structure. The duration of the free training was variable
but lasted typically around 5 minutes.

The subsequent ``structured training'' followed the same pattern as an
agent experimental block, but with only 3 instead of 6 test trials.

\emph{Visual stimulation and gaze-controlled sound generation}

{\label{visual-stimulation-and-gaze-controlled-sound-generation}}

Before the start of the free training and before every agent
experimental block, the eye tracker was calibrated collecting fixation
samples from known target points in order to map raw eye data to the
participant's gaze position (standard in-built Eyelink calibration
procedure). After the calibration was successful, the experiment screen
appeared: a grid of 9 red squares over a black background. Each red
square's side had a visual angle of 5\selectlanguage{ngerman}° 18' 0.99'', with gaps of 1° 28'
0.39'' between squares. The center of each red square was marked by a
small black square with a side length of 0° 49' 0.11''. The gaze
position of the participant appeared on the screen as a white dot
(radius = 0° 19' 0.64''). A fixation on a square was defined as the gaze
resting within a radius of 0° 29' 0.47'' around the edges of the square.
The distance between the chin and forehead rest and the screen was 60
cm, as suggested by the Eyelink 1000 user manual, which translates to an
eye-screen distance of about 70 cm.

During the free training, the structured training and the agent
experimental condition, participants were able to generate sounds by
moving their gaze from one square on the screen to another, adjacent
square. The possible movement directions that could trigger a sound
were: vertical up and down, horizontal left and right, and diagonal
up-right, up-left, down-right, and down-left. A participant could move
their gaze from one square to another, and in order to trigger a sound,
a fixation on the target square with a duration of 750 ms was required.
In the case that the participant interrupted the fixation before the
delay period of 750 ms ended, no sound was played.

\emph{Sound stimuli}

{\label{sound-stimuli-1}}

Sound stimuli were synthesized speech sounds created with Google
text-to-speech API through Python set to a male Spanish speaker with a
sampling rate of 16000 Hz. The sound stimuli were then manually
manipulated in Praat using the Vocal Toolkit (Boersma, 2002) to have the
same duration and flat pitch. Sounds were normalized and resampled to
96000 Hz. Each sound was a 500 ms /CV/ syllable delivered at 70 dB,
formed by a random combination of one of 8 different pitches, vowels and
consonants. Pitch (in Hz) was either 90, 120, 150, 180, 210, 240, 270 or
300; the consonant was either {[}f{]}, {[}g{]}, {[}l{]}, {[}m{]},
{[}p{]}, {[}r{]}, {[}s{]} or {[}t{]}; the vowel was either {[}a{]},
{[}e{]}, {[}i{]}, {[}o{]} or {[}u{]}. Per participant, 14 sets of 8
different sounds were generated. In each contingency-block, 8 sounds
were randomly paired with the 8 possible movement directions.

\emph{Apparatus}

{\label{apparatus}}

An ATI Radeon HD 2400 monitor and Sennheiser KD380 PRO noise cancelling
headphones were used for presentation of visual and auditory stimuli,
respectively. A midi keyboard, the Korg nanoPAD2, was used to record
participants' responses. This keyboard was chosen because key presses
don't produce any sounds. The presentation of the stimuli and recording
of participants' responses was controlled using MATLAB R2017a (The
Mathworks Inc.), the Psychophysics Toolbox extension (Brainard, 1997;
Kleiner et al., 2007; Pelli, 1997), and the Eyelink add-in toolbox for
eyetracker control.

EEG was recorded using Curry 8 Neuroscan software and a Neuroscan
SynAmps RT amplifier (NeuroScan, Compumedics, Charlotte, NC, USA).
Continuous DC recordings were acquired using Ag/AgCl electrodes attached
to a nylon cap (Quick-Cap; Compumedics, Charlotte, NC, USA) at 64
standard locations following the 10\% extension of the international
10-20 system (Chatrian, Lettich, \& Nelson, 1985; Oostenveld \&
Praamstra, 2001). Further electrodes were placed on the tip of the nose
(online reference), and above and below the left eye (vertical
electrooculogram, VEOG). Further two electrodes were placed next to the
outer canthi of both eyes referenced to the common reference (horizontal
electrooculogram, HEOG). The ground electrode was located at AFz.
Impedances were required to be below 10 k\selectlanguage{greek}Ω \selectlanguage{english}during the whole recording
session and data was sampled at 500 Hz.

Horizontal and vertical gaze position of the left eye were recorded
using the EyeLink 1000 desktop mount (SR Research) at a sampling rate of
1,000 Hz.

\subsubsection*{Behavioural data
analysis}

{\label{behavioural-data-analysis}}

We analysed the percentage of correct responses (\%Correct) to the
question of whether the movement-sound pair presented in a test trial
was congruent (``Did they match?''). Missing responses were counted as
false. Test trials presenting unseen sound-movement pairs were excluded
from the analysis to avoid forced guessing. After performing this
exclusion, we calculated the \%Correct of each participant per learning
block, distinguishing between associations acquired in the agent and
observer condition. We performed a repeated-measures ANOVA with the
factors agency (agent/observer) and learning block (seven levels).

During initial stages of learning, participants were expected to perform
very poorly on the memory task due to the little exposure to the
associations. During late stages, they were expected to be proficient.

\subsubsection*{EEG data analysis}

{\label{eeg-data-analysis}}

\emph{Preprocessing}

{\label{preprocessing}}

EEG data was preprocessed using EEGLAB (Delorme \& Makeig, 2004). After
a high-pass filter was applied to the data (0.5 Hz high-pass, Kaiser
window, Kaiser \selectlanguage{greek}β \selectlanguage{english}5.653, filter order 1812), the continuous recording of
each participant was inspected, and non-stereotypical artefacts were
manually rejected. Then, eye movements were removed from the data using
Independent Component Analysis (SOBI algorithm). Independent components
representing eye movement artefacts were rejected based on visual
inspection and the remaining components were projected back into
electrode space. A low-pass filter was applied (30 Hz low-pass, Kaiser
window, Kaiser \selectlanguage{greek}β \selectlanguage{english}5.653, filter order 1812). Malfunctioning electrodes
were interpolated (spherical interpolation). A -100 ms to 500 ms epoch
was defined around each sound both during acquisition and test trials
(-100 to 0 ms baseline correction). A 75 \selectlanguage{greek}μ\selectlanguage{english}V maximal signal-change per
epoch threshold was used to reject remaining artefacts. Participant
averages were calculated for each event of interest, as well as the
grand averages using all participants. We obtained ERPs for acquisition
sounds in agent and observer acquisition mode, as well as early (blocks
1 to 3) and late (blocks 5 to 7) learning stages. For test sound ERPs,
we calculated averaged ERPs for test sounds acquired in agent versus
observer mode, early versus late learning stages, and congruent versus
incongruent test sounds (relative to the associations between movements
and sounds learned in acquisition trials). The mean number of trials per
subject-level average was 361, with a standard deviation of 185 trials.

\emph{Statistical analyses}

{\label{statistical-analyses}}

Both in acquisition and test sounds, statistical comparisons were
conducted to extract agency and learning stage effects and their
interactions. In test sounds, we analysed the effects of congruency and
the interaction between congruency and the factors agency and learning
stage.

\emph{Data-driven approach}

A mass-univariate non-parametric randomization procedure was used as a
first statistical assessment of the EEG data (Maris, 2004; Maris and
Oostenveld, 2007). For this procedure, a Delaunay triangulation was used
to define clusters of neighbouring electrodes over a 2D projection of
the electrode montage, connecting nearby electrodes independently of the
physical distance between them. Clusters were defined in order to
contain a minimum of two electrodes. Two dimensional (time, electrode)
analyses were conducted on the ERP amplitudes between 0 and 400 ms
post-stimulus.

For each of the comparisons performed, the amplitude at each time point
and electrode underwent a 2-tailed dependent t-test. The significance
probability (p-value) of the t-statistic was determined by calculating
the proportion of 2D samples from 10000 random partitions of the data
that would have a larger test statistic as a result than the actually
observed test statistic (Monte Carlo method). Then, clusters were
created by grouping adjacent 2D points exceeding a significance level of
0.05 (two-tailed). A cluster-level statistic was calculated by taking
the sum of the t-statistics within every cluster. The significance
probability of the clusters was assessed with the described
non-parametric Monte Carlo method. Corrected values of p below 0.05 were
considered significant. For each significant cluster we report its
temporal spread, cluster statistic and p value.

Using this procedure, statistical comparisons were conducted both in
acquisition and test sounds comparing the agent and the observer
conditions (subtracting observer from agent condition) to test for
agency effects and comparing the early and late learning stages
(subtracting early from late learning stages) to test for learning
effects. Subsequently, we tested for interactions between agency and
learning stage comparing the difference between agent and observer
across learning stages and the difference between learning stages across
agency conditions. In test sounds, we tested for effects of congruency
contrasting congruent and incongruent sounds. Finally, we investigated
if congruency effects were modulated by the factors agency and learning
stage by comparing the difference between congruent and incongruent
trials (incongruent subtracted from congruent) in the agent versus
observer condition, and in the late versus early learning stage.

As discussed frequently (e.g.~Sassenhagen \& Draschkow, 2019),
cluster-based statistical analyses controlling for multiple comparisons
(Maris, 2004; Maris and Oostenveld, 2007) may lead to an overestimation
of the temporal and spatial characteristics of the effects, so it is
recommendable to avoid very specific time-space claims about the data.
We are aware of these limitations, and we try to relate the findings
from the cluster-based analysis to classic ERP components based on the
shapes and scalp topographies of the obtained waveforms.

\emph{Targeted-component analysis}

As a complementary, ERP-focused approach, we examined the responses for
all comparisons of interest in the N1, P2 and P3 time windows at the Fz,
Cz and Pz electrodes. The windows were defined after visual inspection
of the data by locating the highest negative or positive (depending on
the component of interest) peak in the usual latencies and electrodes
for each component, and defining a window centered on the peak and
adjusted to the width of the component, as reported by previous works
(SanMiguel et al., 2013). We observed morphological differences between
responses to acquisition sounds and test sounds, thus windows were
defined separately for the two types of sounds, based on the peaks
observed in either the average of all acquisition sounds or all test
sounds. The N1 was measured at the Fz electrode in the window 80-120 ms
in acquisition sounds and 110-140 ms in test sounds. The P2 was measured
at Cz in the window 180-240 ms in acquisition sounds and 210-270 ms in
test sounds. Both components showed reversed polarity at the mastoid
electrodes in these windows. The P3 component was measured at the Fz
electrode (P3a) and the Pz electrode (P3b), respectively, in the 310-390
ms time-window in acquisition sounds and the 340-400 ms time-window in
test sounds.

We ran repeated-measures ANOVAs in order to test for differences on the
mean amplitude of each component at the selected electrodes between
conditions of interest. Specifically, for the acquisition sounds, we ran
a two-way ANOVA with the factors \emph{agency} (two levels: agent and
observer) and \emph{learning stage} (two levels: early and late) for
each ERP component of interest.

We ran two separate ANOVAs for each component for the test sounds. A
one-way ANOVA with the factor movement-sound congruency (levels:
congruent and incongruent) and a two-way ANOVA with the factors agency
and learning stage. We also analysed whether the effects of congruency
were modulated by agency and learning stage by using the differences
between amplitudes in congruent and incongruent trials as the dependent
variable in a two-way ANOVA with the factors agency and learning stage.

\emph{Correlation analysis}

We aimed to identify electrophysiological markers related to the
performance benefits associated with active learning amongst a small set
of pre-defined candidate ERP components. Thus, we tested for significant
correlations, using Pearson's coefficient, between each participant's
effect of agency on the percentage of correct responses and the effect
of agency on the N1, P2 and P3 in acquisition sounds (amplitude of
acquisition sound ERPs in agent condition -- observer condition). Given
that agency effects on the behavioural data were restricted to the early
learning stage (see results), we used only the performance data from the
early learning stage (\%Correct in agent--\%Correct in passive) for this
correlation analysis.

\subsection*{Behavioural results}

{\label{behavioural-results}}

Behavioural results show that active exploration led to faster learning
and better memory performance. However, given enough training, passive
viewing led to similarly good performance (fig. 2).

A two-way ANOVA was run with the factors agency (agent versus observer)
and learning block (1 to 7) and the dependent variable \%Correct.

We found a significant main effect of agency {[}F\textsubscript{(1,
22)}= 11.865, p \textless{} .001, \selectlanguage{greek}η\selectlanguage{english}\textsubscript{p}\textsuperscript{2}
= 0.32{]} and learning block {[}F\textsubscript{(6,132)} = 16.534, p
\textless{} .001, \selectlanguage{greek}η\selectlanguage{english}\textsubscript{p}\textsuperscript{2} = 0.39{]}, and a
significant interaction between the two factors
{[}F\textsubscript{(6,132)} = 6.635, p \textless{} .001,
\selectlanguage{greek}η\selectlanguage{english}\textsubscript{p}\textsuperscript{2} = 0.20{]}. Post-hoc t-tests
(Bonferroni corrected) showed that memory performance in the agent
condition was significantly higher than in the observer condition in the
first, second and fourth learning block. The effect of agency on
\%Correct was inversely correlated with learning block
(r\textsubscript{(5)} = -3.922, p = .01). Post-hoc t-tests showed that
the difference between agent and observer was significantly larger in
the first learning block than in the last learning block
{[}t\textsubscript{(22)} = 4.291, p \textless{} 0.001, \emph{d} =
1.105{]}. The effect of learning was significantly smaller in the agent
compared to the observer condition {[}t\textsubscript{(22)} = -4.291, p
\textless{} 0.001, \emph{d} = -0.656{]} (subtracting learning block 1
from learning block 7 in agent versus observer condition). This shows
that agency accelerates memory encoding for arbitrary audiovisual
associations.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} \textbf{Effect of agency and learning block on
\%Correct.a.} \%Correct mean and standard error across all participants
(N=23) in the agent condition \emph{(solid line} ) and observer
condition (\emph{dotted line} ) across learning blocks. Asterisks mark
learning blocks in which significant differences between conditions were
found in post-hoc tests. \textbf{b.} Difference between the agent and
observer conditions for each participant across learning blocks
\emph{(black dots} ), as well as the median and interquartile range
across participants (\emph{boxplots} ). The difference between the agent
and observer conditions is inversely correlated with learning block. An
exponential curve was fitted to the data (\emph{blue solid line} ) to
illustrate the decreasing effect of agency with the progress of
learning. The effect of agency decreases rapidly during the first three
learning blocks and then remains constantly low in the remaining four
learning blocks.

\subsection*{Electrophysiological
results}

{\label{electrophysiological-results}}

For each effect and interaction studied, we present here two
complementary approaches: a data-driven analysis using cluster-based
permutation tests, and an ERP component driven analysis using ANOVAs to
assess effects on targeted ERP components.

\subsubsection*{Acquisition sounds}

{\label{acquisition-sounds}}

Learning progress was reflected in ERPs as an attenuation of the P3a
component. The cluster-based analysis comparing early and late
acquisition sounds (late -- early learning stage) revealed a negative
cluster with a fronto-central distribution (T = 4659.8, p \textless{}
.01; 220 ms to 400 ms), encompassing the P2 and P3a components (figure
3a). The targeted-component ANOVA yielded a significant main effect of
learning stage on the amplitudes of the P3a component at Fz
{[}F\textsubscript{(1,22)} = 14.436, p \textless{} .001,
\selectlanguage{greek}η\selectlanguage{english}\textsubscript{p}\textsuperscript{2} = 0.40{]}, reflecting more
negative values with increased learning stage.

Regarding the effect of agency, we found more positive ERPs in parietal
electrodes in the agent compared to the observer condition (figure 3b).
The cluster-based permutation test comparing the agent and observer
conditions detected a significant positive cluster with an
occipito-parietal distribution (T = 10900, p \textless{} .01; 60 ms to
400 ms). The cluster temporally encompasses the P2 and P3 components,
revealing overall more positive amplitudes in the agent condition. The
targeted-component ANOVA detected a significant main effect of agency on
the P3b component at Pz {[}F = 25.706, p \textless{} .0001,
\selectlanguage{greek}η\selectlanguage{english}\textsubscript{p}\textsuperscript{2} = 0.54{]}. The occipito-parietal
distribution of the effect led us to suspect that the observed
differences are due to motor differences related to the control of the
sound stimuli. To explore this possibility, we conducted further
analyses, which can be found in the supplementary materials.

In order to study possible interactions between agency and learning
stage using cluster-based permutation tests, we subtracted the early
learning stage from the late learning stage in both the agent and
observer condition and then ran a cluster-based analysis (late minus
early learning stage in agent condition versus late minus early learning
stage in observer condition). Studying this interaction, possible
confounding factors due to eye movement differences (see discussion in
supplementary materials) were eliminated from the data. However, this
analysis yielded no significant results. We also tested for interaction
effects in the targeted-component ANOVAs, but this also did not produce
any significant interaction effects. All in all, no interactions between
the factors agency and learning stage were detected.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} \textbf{Effects of learning stage and agency on
acquisition sound ERPs and topographical cluster plots.}
\textbf{a.}Grand-averaged ERP wave forms of the late learning stage
(\emph{blue} ) and the early learning stage (\emph{red} ) acquisition
sounds, plus the difference wave (early subtracted from late;
\emph{black} ) at the Fz electrode. The P3a time window is highlighted.
The asterisk indicates a significant difference across learning stages.
Below, a series of 5 topographical plots shows the difference between
late and early learning stage (late - early) across time and sensor
space. Electrodes belonging to the significant negative cluster are
highlighted in white.\textbf{b.} Grand-averaged ERP wave forms of the
agent condition (\emph{blue} ) and the observer condition (\emph{red} )
acquisition sounds, plus the difference wave (observer subtracted from
agent;\emph{black} ) at the Pz electrode. The P3b time window is
highlighted. The asterisk indicates a significant difference across
agency conditions. Below, a series of 4 topographical plots shows the
difference between agent and observer condition (agent - observer)
across time and sensor space. Electrodes belonging to the significant
positive cluster are highlighted in black.

\subsubsection*{Test sounds}

{\label{test-sounds}}

Congruency between movement and sound triggered a late positive
deflection that was absent in incongruent sounds (figure 4). The
cluster-based permutation test comparing congruent and incongruent test
sounds detected a significant positive cluster (T = 4946.6, p = .004;
240 ms to 400 ms) that starts at occipito-parietal electrodes and
spreads across the entire skull. The cluster temporally encompasses the
P2 and P3 components, revealing overall more positive amplitudes for
congruent sounds. The targeted-component ANOVAs found a significant main
effect of congruency on the P3 component in Fz
{[}F\textsubscript{(1,22)} = 12.342, p=.001,
\selectlanguage{greek}η\selectlanguage{english}\textsubscript{p}\textsuperscript{2} = 0.36{]} and Pz {[}F = 20.589, p
\textless{} .001, \selectlanguage{greek}η\selectlanguage{english}\textsubscript{p}\textsuperscript{2} = 0.48{]},
corresponding to the P3a and the P3b. The effects of congruency were not
modulated by agency or by learning stage.

We found no effects of agency nor learning stage on test sounds, and no
interactions. Both the cluster-based approach and the targeted-component
analysis yielded no significant results.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4. Effects of congruency on test sound ERPs.} Congruent
(\emph{blue} ) and incongruent (\emph{red} ) test sound evoked
grand-averaged ERPs, as well as their difference wave (congruent --
incongruent, in \emph{black} ) are plotted at the Fz and the Pz
electrodes. A series of topographical plots showing the difference
between congruent and incongruent sounds across time and sensor space
illustrates the spread of the significant positive cluster across time.
Sensors exhibiting significant differences across conditions are
highlighted in \emph{black.}

\subsubsection*{Correlations between neural and behavioural effects of
agency}

{\label{correlations-between-neural-and-behavioural-effects-of-agency}}

Differences in \%Correct between the agent and observer condition
exhibited a great variability across individuals (M = 9.7\%, SD = 7.1\%,
range = 0-26\%). Thus, active learning was more efficient than learning
from observation for some, but not all participants. Aiming to uncover
the neural underpinnings of active learning benefits, we tested for
possible correlations between the benefit that a participant would have
of agent over observer acquisition for their memory of the
movement-sound associations, and the degree to which agency modulated
auditory ERPs during early stages of acquisition, when the active
learning benefits were maximal.

We found a significant correlation between agency effects on N1
amplitude of acquisition sounds at Fz in the early learning stage and
agency effects in memory performance {[}r\textsubscript{(21)} = 0.6, p =
.002{]}. The stronger the N1 agency effect that a participant
experienced during early acquisition, the stronger the memory advantage
for the agent vs. the observer conditions (figure 5).

In order to visualize this finding, we decided to split the participants
into two groups, using the median of the difference between agent and
observer \%Correct as a cutoff point (M = 7.8\%).

Participants who had stronger behavioural agency effects than the median
were considered ``active learners'' (N = 11), and participants that fell
below that median of behavioural effects were considered ``indifferent
learners'' (N = 12).

We compared the overall performance (agent + observer condition) of
active and indifferent learners. Active learners performed overall
better in the behavioural task (M = 78\%, SD = 13\%) as compared to
indifferent learners (M = 68\%, SD = 11\%). A two-sample t-test showed
that this difference is statistically significant
{[}t\textsubscript{(21)} = 3.844, p \textless{} .001, \emph{d} =
0.80{]}.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5.} \textbf{Correlation between memory performance and
neural responses. a.} ERPs contrasting the agent and observer condition
at the Fz electrode in participants who exhibited a strong memory
benefit in the agent relative to the observer condition (active
learners) and in participants that did not exhibit a strong memory
advantage for the agent condition (indifferent learners). Below,
topographical plots that show the distribution of the effect in the N1
time window. \textbf{b.} The difference between the N1 amplitude in the
agent versus observer conditions during the first learning stage is
plotted on the y-axis, and the difference between the agent and the
observer condition in the \%Correct during the memory task is plotted on
the x-axis. A linear regression is fitted to the data (\emph{blue} ). A
dotted line indicates the median on the x-axis, based on which
participants were sorted into the ``active learners'' and ``indifferent
learners'' groups.

\subsection*{Discussion}

{\label{discussion}}

The aim of this study was to investigate the neural mechanisms
underlying the benefits of active control for associative learning while
controlling for the factors of movement and predictability, which in the
existing literature often conflate the effects of agency during
learning. Using a gaze-controlled interface in a motor-auditory
associative memory task, we showed that control over stimuli alone --
controlling for unspecific neuromodulatory effects through movement and
stimulus predictability -- can lead to learning benefits on a
behavioural level.

We found higher movement-sound association memory accuracy for
associations studied with active oculomotor control of visual
exploration versus objects studied passively. This active-learning
advantage for memory occurred despite the fact that visuo-auditory
information was matched between the agent and observer study conditions.
However, some participants did not seem to follow this pattern,
exhibiting small or no differences between the agent and observer
learning condition. We found that we could distinguish amongst
participants based on their learner type, that is to say that some
participants indeed exhibited the expected memory benefit for active
learning, while others did not. Interestingly, this behavioural
difference was correlated with the individual participant's degree of N1
attenuation, a well-established marker of self-generation during sound
perception. We found that the stronger sensory processing differences
between self- and externally generated stimuli -- represented by the
attenuation of the N1 component -- a participant exhibited, the more
they would benefit from control during learning, and the better their
overall performance.

A large body of research shows that, chiefly, being in control of
information during learning is beneficial for memory encoding. Beyond
the well-known production effect (Brown \& Palmer, 2012; MacDonald \&
MacLeod, 1998; MacLeod et al., 2010), an advantage for active,
self-directed over passive learning methods is an established fact in
educational contexts (Tomporowski et al., 2015) and has been observed in
different modalities and domains of learning (Butler et al., 2011;
Cohen, 1989; Gathercole \& Conway, 1988; James et al., 2002; Kuhn et
al., 2000; Schulze et al., 2012). Low-intensity exercise or simple
motor-activity (such as walking or finger tapping) produces mixed
results in relation to memory performance, with some studies finding
memory benefits (Schaefer et al., 2010; Schmidt-Kassow, Deusser, et al.,
2013; Schmidt-Kassow et al., 2010, 2014; Schmidt-Kassow, Heinemann, et
al., 2013) and others memory impairment (Lajoie et al., 1996; Li et al.,
2001; Lindenberger et al., 2000; Yogev-Seligmann et al., 2008). In many
of the studies on the benefits of production for memory the effects of
movement and the effects of being in control cannot be interpreted
separately (Mama \& Icht, 2016; Ozubko et al., 2012; Rummell et al.,
2016). Some studies have tried to single out the effect of agency from
conflating factors and found benefits for learning and memory (Chi,
2009; Gureckis \& Markant, 2012; Markant et al., 2016). In this study we
tried to relate self-generation effects during sensory processing to
memory benefits of active learning. Taking into consideration the
substantial evidence suggesting that self-generation effects are in part
due to unspecific neuromodulation through motor activity, we asked
ourselves whether or not the established self-generation effects would
be reproducible in a paradigm that specifically singles out the effect
of agency while controlling for movement. Additionally, we used eye
movements for sound generation. Eye movements don't trigger sounds in
real life, so participants had to learn the associations between their
movements and the different sounds from scratch. The fact that the
production effect was reproduced in this set-up suggests that agency
contributes significantly to the phenomenon, beyond the effects of
coincidental proximity to a motor act. Specifically, we found that the
attenuation of the N1 component could predict the strength of the active
learning memory benefits an individual participant would experience.

The production effect is frequently explained with the distinctiveness
account -- the idea that retrieval of an event from memory is
facilitated if the event is embedded in a network of associations rather
than remembered in isolation (Hommel, 2005). An alternative explanatory
approach is the idea that being in control is rewarding, that motivation
is higher, and that it activates more strongly those areas of the brain
that process reward (Leotti \& Delgado, 2011), facilitating memory
encoding. It has been hypothesized that feeling in control over
something makes it self-relevant, which by default might be remembered
better (Kim \& Johnson, 2012). In experiments comparing the memory
encoding of stimuli that are either under the control of the participant
or under the control of the experimenter, there is also an inherent
information processing advantage in control conditions: Self-directed
learners can decide when they want to see what information. They can
select the information that has the biggest effect on reducing their
uncertainty and optimise the flow of information according to their
needs. This makes the learning experience more efficient (Gureckis \&
Markant, 2012; Markant \& Gureckis, 2010; Schulz \& Bonawitz, 2007). In
this study, the correlational finding between the attenuation of the N1
component and the memory performance of individual subjects suggests
that whatever differences in performance we find are at least partly due
to perceptual differences during learning, rather than conflating
factors such as information efficiency.

It is not yet well understood how active production leads to improved
memory performance on a neural level, and so far there are few
established links between sensorimotor processing and memory gains. Our
study contributes to this discussion by delivering evidence towards a
link between the way we process a self-generated stimulus and the
strength of its memory trace. Linking the differences in memory encoding
that were found on a behavioural level to the differences in sensory
processing during the learning phases of our experimental task, we were
able to establish a connection between self-generation effects on ERP
components and the production effect on memory. Memory performance was
correlated with the degree of attenuation of the N1 component in self-
versus externally generated sounds. We can draw two tentative
conclusions from this: That there are individual differences in the
strength of the self-generation effect on the N1 component, and that
there is a link between the processing of self-generated sounds and
their memory encoding.

Due to physical differences between eye movements in the agent and
observer condition, we were not able to interpret the effect of agency
on acquisition sound ERPs directly. Nevertheless, we were able to study
whether the effects of the other two factors, learning stage and
congruency, were modulated by agency. Contrary to our expectations, we
did not find that the effects of learning progress and identity
predictability (i.e. congruency with learned associations) themselves on
neural processing were modulated by agency. Observing the change of ERP
components over the course of the learning process, we found an
attenuation of the P3a component in acquisition. We expected that faster
learning through agency during acquisition might speed up this process,
which would have led to a stronger attenuation earlier during learning.
Studying test sounds, which we manipulated to be either congruent or
incongruent with the learned movement-sound associations, we found a
late positivity for congruent sounds. A movement-sound association
strengthened by agency during learning should have reflected in a
stronger congruency effect overall. Neither of these effects was
modulated by agency during learning.

The P3a component is an orienting response typically associated with
novel stimuli (Polich, 2007). We found an attenuation of the P3a with
learning. Why was the attenuation effect not enhanced, or established
earlier in the learning process, by agency during acquisition? The
behavioural results suggest that the effect of agency should be most
visible in the early and intermediate stages of learning, while towards
the end, both conditions become similar. We could speculate that we
would have found an earlier attenuation in the agent condition, had we
been able to perform a more fine-grained analysis. Our design allowed us
to separate into early and late learning stages. Maybe an analysis using
more levels for this factor -- which in our case was not possible due to
an insufficient number of trials -- would have detected an effect during
intermediate stages of learning.

The congruency effects that we found were not exactly what we had
anticipated, but they were nevertheless conclusive. We had expected that
sounds that were incongruent with the learned associations between
movements and sounds would trigger some form of mismatch response,
possibly an audio-visual mismatch negativity (avMMN), which has been
observed in response to violations of cross-modal predictions, similar
to what was found by Winkler and colleagues (Winkler et al., 2009). We
hypothesized that the way in which associations have been learned
(either passively or as motor-associations) would impact the strength of
the prediction error elicited by violations of those learned
associations. Specifically, we expected to observe differences in
certain ERPs that had previously been linked to deviant or target
processing, like the N2b and the P3a (Knolle et al., 2013). We expected
that deviating from an association learned as linked to a motor act will
trigger a more efficient processing and yield stronger N2b and P3a
responses. What we found instead was that test sounds that were
congruent with learned associations between movements and sounds
triggered a late positive component with a central distribution, which
we could call P3. The P3 is often considered an index of context or
internal model updating (Polich, 2007; Reed et al., 2022), and depending
on the nature of the experimental task, it has also been observed as a
response to target stimuli (Hillyard \& Kutas, 1983; Nieuwenhuis et al.,
2005; O'Connell et al., 2012; Twomey et al., 2016; Verleger et al.,
2017). We found a P3 triggered by congruent sounds, so if we want to
integrate this finding into existing theories, we should consider it a
marker of model updating based on a positive match -- participants see
an animation, predict the upcoming sound, and when the prediction is
matched, the model is reinforced. Alternatively, we could think of this
component as a late positive component (LPC). This component has been
hypothesized to be correlate of the working memory updating processes
(Donchin, 1981; Donchin \& Coles, 1988; Polich, 2007). It has been found
in experiments where stimuli are task-relevant or response-dependent
(Pritchard, 1981; Snyder \& Hillyard, 1976). In one experiment, it was
elicited when participants had to detect and respond to deviant stimuli,
but not when they were instructed to ignore deviants (Maidhof et al.,
2010). The LPC may reflect participants detecting a stimulus they had
been looking out for (Mathias et al., 2015). Just like in this
experiment, Mathias and colleagues found that the LPC was not modulated
by active or passive acquisition mode, which they see as support for the
idea that the LPC depends on the stimulus' task relevance rather than
the degree of deviation from a memory representation.

\subsection*{Conclusion}

{\label{conclusion}}

We found that active control during the learning of movement-sound
associations using a gaze-controlled interface facilitates memory
encoding. We found that the degree of attenuation of the N1 component
for self-generated sounds correlated with the behavioural performance of
each participant: the stronger the sensory processing differences during
learning, the stronger the memory gain for active learning, and the
better the overall performance on the memory task. This finding suggests
that memory benefits of active learning are at least in part linked to
perceptual differences during sensory processing, and that there may be
a continuum of variation in the self-generation N1 attenuation effect
across the population that allows us to assess different learner
``profiles''. Although we did not find across-the-board modulation of
neural responses by the factor of agency during learning, we see neural
responses being modulated by increasing stimulus predictability, and we
found that during memory recall, matching association pairs triggered a
target matching response.

\textbf{CRediT authorship contribution statement}

\textbf{Stefanie Sturm:} ~Conceptualization, Data acquisition, Formal
Analysis, Methodology, Software, Investigation, Writing -- Original
draft preparation, Visualization\emph{.~\textbf{Jordi Costa-Faidella:}}
Conceptualization, Formal Analysis, Methodology, Software, Writing --
Original draft preparation, Supervision. Iria SanMiguel:
Conceptualization, Formal Analysis, Methodology, Software, Writing --
Original draft preparation, Supervision, Project administration, Funding
acquisition\emph{.~}

\textbf{Acknowledgements}

This work is part of the project PSI2017-85600-P, funded by MCIN/AEI/
10.13039/501100011033 and by ``ERDF A way of making Europe''; it has
additionally been supported by the MDM-2017-0729-18-2M Maria de Maeztu
Center of Excellence UBNeuro, funded by MCIN/AEI/ 10.13039/501100011033,
and by the Excellence Research Group 2017SGR-974 funded by the
Secretaria d'Universitats i Recerca del Departament d'Empresa i
Coneixement de la Generalitat de Catalunya. ISM was supported by grant
RYC-2013-12577, funded by MCIN/AEI/ 10.13039/501100011033 and by ``ESF
Investing in your future''. SS was supported by a grant for the
recruitment of new research staff (FI-2019) from the Generalitat de
Catalunya.

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