# Task-specific temporal prediction mechanisms revealed by motor and electroencephalographic indicators

Alana Arrouet, José Eduardo Marques-Carneiro, Pierre Marquet, Anne Giersch

► To cite this version:

Alana Arrouet, José Eduardo Marques-Carneiro, Pierre Marquet, Anne Giersch. Task-specific temporal prediction mechanisms revealed by motor and electroencephalographic indicators. NeuroImage, 2025, 306, pp.120982. 10.1016/j.neuroimage.2024.120982. inserm-05515022

HAL Id: inserm-05515022

https://inserm.hal.science/inserm-05515022v1

Submitted on 17 Feb 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

# Task-specific temporal prediction mechanisms revealed by motor and electroencephalographic indicators

Alana Arrouet<sup>a,b,c,d</sup>, José Eduardo Marques-Carneiro<sup>a,b</sup>, Pierre Marquet<sup>c,e,f</sup>, Anne Giersch<sup>a,b,d,g,* ID</sup>

<figure><img src="image_4.png" /></figure>

## ARTICLE INFO

**Keywords:**

Temporal predictions

Motor

Perception

Sequential effects

Contingent negative variation

## ABSTRACT

Time prediction is pervasive, and it is unclear whether it is supra-modal or task-specific. This study aimed to investigate the role of motor temporal prediction in preparing to stop a movement following a sensory stimulus. Participants performed a straight-line movement with their finger until a target signal, which occurred after a short or long foreperiod. In one task, participants changed movement direction between trials (multidirectional task), while in the other, they always moved in the same direction (unidirectional task). The motor trajectory and EEG signals were continuously recorded. During the foreperiod, participants slowed down their movement, reflecting preparation to stop. To assess the influence of motor temporal prediction we examined how a given trial influences performance on the subsequent trial (sequential effect) when the movement changes or stays the same (multi- vs. unidirectional). In the unidirectional task, but not in the multidirectional task we found sequential effects on several behavioural parameters. In contrast, sequential effects were observed in both tasks on EEG results. This study revealed a temporal prediction related to motor movement (behavioural indicators), and a temporal preparation while waiting for the target (EEG indicator). These findings highlight the importance of considering various temporal prediction mechanisms.

## 1. Introduction

Timing is ubiquitous in the brain, and there is no dedicated area for time. Performing a motor action requires planning motor elements in time and processing sensory feedback at the right time. Such timing activities require the cerebellum, the supplementary motor area, the parietal and prefrontal cortices (Coull and Giersch, 2022), i.e. areas also involved in other mental activities. This makes it difficult to understand how time intervenes in the different functions of our every day life. This is the case for motor control, which requires a high level of temporal precision. To achieve optimal motor control when learning a motor skill, we may repeat the same movement over and over. Repetition on a trial-to-trial basis has been shown to enhance performance on a

short-term scale (Bertelson, 1961; Vleugels et al., 2020). It may be useful especially when playing music or dancing, where the timing of the movement is critical (Levitin et al., 2018). In such conditions, previous experience may help to execute a movement in the required time. However, the same movement in real life can be done while our body position changes, or the environmental conditions change. For example, a violinist may play the same note by moving the bow from right to left or from left to right. Skilled performers are supposed to be efficient even in altered conditions (Johnson, 1961; Ranganathan et al., 2020), meaning they can execute their action efficiently even when the action has to be adjusted to external conditions, such as a different body position, or the use of a new instrument. The question is: when the motor program changes, is it still possible to use the temporal information from

<footnote><sup>a</sup> INSERM U1114, Cognitive Neuropsychology and Pathophysiology of Schizophrenia, 1 place de l'Hôpital, 67091 Strasbourg Cedex, France</footnote>

<footnote><sup>b</sup> INSERM U1329, team Psychiatry of STEP (Strasbourg Translational nEuroscience and Psychiatry), 1 place de l'Hôpital, 67091 Strasbourg Cedex, France</footnote>

<footnote><sup>c</sup> CERVO Brain Research Centre, Québec, Canada</footnote>

<footnote><sup>d</sup> Université de Strasbourg, Strasbourg, France</footnote>

<footnote><sup>e</sup> Department of Psychiatry and Neurosciences, Université Laval, Québec, Canada</footnote>

<footnote><sup>f</sup> Joint International Research unit between Université Laval and Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland</footnote>

<footnote><sup>g</sup> Hôpitaux Universitaires de Strasbourg, 1 place de l'Hôpital, 67091 Strasbourg, Cedex, France</footnote>

<footnote>* Corresponding author at: U1329, team Psychiatry of STEP (Strasbourg Translational nEuroscience and Psychiatry), 1 place de l'Hôpital, 67091 Strasbourg Cedex, France.</footnote>

<footnote>E-mail address: giersch@unistra.fr (A. Giersch).</footnote>
past experiences?

The question is not trivial, inasmuch previous studies suggested that past time experience has strong influences on the current time expectations, whether tested with simple or choice reaction time (RT) tasks (Bertelson and Boons, 1960; Los et al., 2014; Salet et al., 2022; Steinborn et al., 2008). The strength of these effects may give the impression that they are ubiquitous. Whether the influence of past time experience generalizes to the case where the movement changes remains nonetheless an open question. The response to that question may depend on the extent to which time prediction mechanisms are integral to movement planning. Biomechanical, internal constraints can be expected to be integral to the movement (Miall et al., 1993; Rasman et al., 2024). In contrast, if the movement must be completed in e.g. 1.2 s to play a specific musical note, this is an external constraint, not a biomechanical one. The external constraint may or may not be integrated in the motor program itself. It might be processed independently and only coupled with the motor program. If at least part of the temporal prediction is integral to the motor program, it should be difficult to re-use this prediction when the movement requires a different motor program. For example, if the movement changes direction, different muscles are involved, with a different sequence, and potentially a different timing of muscle contraction (Miall et al., 1993). This should be the case even if the external constraint remains the same, i.e. if the movement should be finished at the same time. In this study, we gather behavioural and electroencephalographic (EEG) indicators during the expectancy period (rather than after) preceding the stopping of a movement, and we compare these indicators when the movement is repeated or changed from trial-to-trial.

The investigation of temporal prediction abilities is based on the widely used variable foreperiod paradigm, where a first stimulus indicates the start of the trial, followed at a short or a long foreperiod by a target. It is well established in the literature that the later the target is presented, the faster the participant will react to it (Coull, 2009; Martin et al., 2017; Niemi and Näätänen, 1981; Nobre et al., 2007; Woodrow, 1914). According to hazard-based theories, the ‘variable foreperiod effect’ can be explained as follows: as time passes, the probability of the target appearing increases, and the benefit of the passage of time allows for a higher level of readiness. Alternatively, the formalized Multiple Trace Theory of Temporal Preparation (fMTP) suggests that temporal preparation in variable foreperiod paradigm is shaped by memory traces of previous trials, with past experiences optimizing the motor system readiness (Los et al., 2017; Salet et al., 2022). A discussion regarding these models is beyond the aim of our manuscript, but the latter interestingly considers the role of the motor system.

Many authors raised the question of whether improved performance in variable foreperiod paradigms (faster RTs and higher accuracy) was due to facilitated perceptual processes and/or to an enhanced motor preparation in response to the target. Temporal expectations have been found to enhance the reaction to predictable targets over time (Correa et al., 2005, 2006a; Lange and Röder, 2006; Ramirez et al., 2021) and some experiments emphasize the role of motor preparation in temporal expectation (Thomaschke and Dreisbach, 2013; Volberg and Thomaschke, 2017). To demonstrate the association between temporal preparation and motor readiness, Thomaschke et al. (2011) used a speeded choice RT where participants responded to a target using either the left or right hand. The authors manipulated contingencies between foreperiods and either stimulus features (shape) or responses (right or left). For example, a specific shape was more frequently associated with a given foreperiod. Thomaschke et al. (2011) observed the typical benefits of temporal predictability on performance only when foreperiods were linked to response sides, not to shapes. However, it is questionable to what extent RT are enough to quantify in equal measure the advantage provided by motor versus perceptual temporal prediction, inasmuch RT is inherently associated with a motor response. It may have limited the chance of seeing any foreperiod effect in the case of a shape contingency.

It is the limitations in the use of RT that led us to adapt the variable foreperiod task to introduce a motor component and to answer our question. As a reminder, we wish to know to which extent the temporal prediction associated with a motor action is independent of the motor program. In the present study, we created two distinct motor tasks. Concretely, we instructed participants to perform a straight-line movement. They initiated the movement after a first tactile stimulus (which we will name the ‘start signal’ in the remainder of the text) and stopped it as fast as possible after a second tactile stimulus (which we will call the ‘target signal’ in the remainder of the text). It should be noted that this procedure differs from the stop signal procedure, whose aim it is to explore how the initiation of a task can be stopped, or not, depending on the occurrence of a stop signal (Logan and Cowan, 1984). In our task, participants were asked to halt a movement in all trials, unlike the stop signal approach, which focuses on how movement or thought execution can be prevented in a minority of trials by a signal that comes as a rare event. Additionally, in our protocol participants had no other task than stopping their movement after the target signal. To avoid any confusion with the stop signal procedure, we named our signals according to the variable foreperiod literature.

In the present study we chose to use a motor action to access the motor kinematics parameters reflecting motor preparation. We also devised two different tasks with vs. without a change in movement, to test the idea of a temporal prediction attached to the motor program. In one task participants moved in the same direction on each trial; in the other participants changed the direction of their movement from one trial to the next. Repeating a movement allows to rely on the temporal information specifically attached to the previous movement. How much the temporal information is attached to the motor program is explored by manipulating the delay between the start signal and target signal. The task includes two conditions: in one, the action has to be stopped after a short foreperiod; in the other, it has to be stopped after a long foreperiod. This means that there is no trivial repetition of a strictly identical movement. Instead, the movement can be short or long. In case of an additional change in the movement direction, there is also a change in the internal biomechanical constraints that affect the time needed to achieve the movement. Those constraints are known to the system, and the movement can be adapted accordingly. For example, the model of motor control proposed by Miall et al. (1993) includes a Smith Predictor, which generates specific predictions in order to anticipate delays that are inherent to the motor system. The Smith Predictor may thus help to adjust the motor program in order to realize the action in the expected time (Rasman et al., 2024).

However, usual models of motor control distinguish between the feed-forward inverse model, which helps to build a motor program before movement execution, and the forward model, which predicts the sensory consequences of the action (Wolpert and Kawato, 1998). Some studies (Miall and Jackson, 2006) have suggested that changes in the sensory feedbacks might not necessarily reflect in the motor program itself, but whether or not this applies to time prediction is unclear. In our task, time expectation regarding the target signal should affect the movement kinematics by indicating when to slow down in anticipation of the target signal. This motor preparation should speed up the movement stop. According to hazard-based models, and since the probability of the target signal is 50 % at short foreperiod and 100 % at long foreperiod, preparation should be optimal only at long foreperiod, resulting in the usual variable foreperiod effect on the motor stop (Correa et al., 2006a; Martin et al., 2017; Niemi and Näätänen, 1981). From the standpoint of the fMTP, memory traces from previous trials lead to gradual motor preparation, allowing for a more efficient motor reaction at long foreperiods (Los et al., 2017; Salet et al., 2022). Both models predict faster motor times at long foreperiods. This may occur whether the expectation is integral to the feed-forward inverse program or only used to adjust the reaction to the target signal with the forward model. Our point is that if the feed-forward program includes the time prediction, it may be difficult to transfer this prediction from one action to the
following one, when the movement changes. We investigate this by
exploring trial-to-trial effects in the variable foreperiod paradigm.

In the typical variable foreperiod task, the influence of recent temporal experience on current temporal expectation has been well described. Such ‘sequential effects’ are as follows: with a short current foreperiod, RT tends to be longer if the preceding foreperiod was long compared to when it was short. In contrast, when the current foreperiod is long, the preceding foreperiod has no effect on RT (Los, 2010; Niemi and Näätänen, 1981; Woodrow, 1914; Zahn et al., 1963). Various mechanisms could underlie sequential effects. The hazard-based theories cannot directly account for such effects, but dual-process models (Vallesi et al., 2007) assume a mechanism that is distinct from the one originating the variable foreperiod effect, and is based on the automatic prediction that two consecutive trials will be similar. When two short foreperiods occur in succession, this corresponds to the expectation, whereas if a short foreperiod follows a long one, participants are not prepared to respond because they expected another long foreperiod. This surprise effect results in slower RTs in the long-short trial sequence compared to the short-short sequence. On the other hand, when the current foreperiod is long and follows a short foreperiod, predictions can be updated when the target does not appear after the short foreperiod. This way preparation can be optimal whatever the foreperiod of the preceding trial (Correa et al., 2006a; Karlin, 1959; Van der Lubbe et al., 2004). The fMTP (Los et al., 2017; Salet et al., 2022) proposes a slightly different explanation. According to the fMTP, sequential effects arise from the replay of the previous foreperiod. If the previous trial had a long foreperiod, an anticipatory response is prevented on the current trial by impeding a response during the short foreperiod. This also explains slower RT in long-short trial sequences compared to short-short sequences (Los, 2013; Los and Heslenfeld, 2005; Los and Van Den Heuvel, 2001).

The sequential effects are observed in simple and choice RT tasks, although Steinborn et al. (2008) suggested them to be stronger in simple RT procedures. Our tasks only require to react to the target signal without imposing a choice between different responses. This holds true even when the movement direction changes from trial-to-trial, as participants choose the direction for the next movement only after responding to the current target signal. Whatever the precise mechanisms of the sequential effects, the foreperiod of the previous trial can influence performance only if this foreperiod is expected, i.e. integrated in the preparation. Looking at sequential effects is thus a way to verify to which amount external time constraints are taken into account when planning an action. To that aim it is not enough to look at the response occurring after the stimulus, which might reflect the processing of the stimulus rather than the movement planning itself. We looked also at motor parameters preceding the processing of the signal, as well as at EEG signals known to be associated with the waiting period. Motor parameters prior to the target signal can be expected to be inherently associated with feed-forward motor planning, but not EEG signals. Exploring these two types of parameters (motor and EEG) in parallel and at the same time period allows us to contrast parameters differentially related to motor planning.

Among EEG signals, the contingent negative variation (CNV) is of
special interest since it has been associated with temporal preparation
processes. The CNV starts after the presentation of the first signal in a
trial and persists until the onset of the target (Breska and Deouell, 2014;
Correa et al., 2006a; Griffin and Nobre, 2003; Mento et al., 2013; Walter
et al., 1964). The CNV is observed in many different tasks, like duration
estimation (Kononowicz and Penney, 2016), sustained attention tasks
(Kropp et al., 2001; Li et al., 2024), or in the stop signal procedure
(Arjona et al., 2014). The CNV brain sources are multiple, as both the
dorsolateral prefrontal cortex, the supplementary motor cortex, primary
motor cortex, anterior cingulated cortex, basal ganglia, thalamus, orbito-
frontal cortex, and even parietal areas and cerebellum are involved in
the genesis of CNV (Basile et al., 2002; Fattapposta et al., 2024).
Moreover, it has been shown that the CNV is influenced by various

factors like the intensity of the warning signal (Loveless and Sanford,
1975), eye movements (Tecce, 1972; Weerts and Lang, 1973), expected
rewards and motivation (Brunia et al., 2011; Frömer et al., 2021;
McAdam et al., 1969), and attention (Arjona et al., 2014; Brunia, 1993;
Mento, 2017). It is thus likely that the CNV reflects more than motor
preparation. This is the case even in tasks that do not explicitly require
attention, like in our motor task. In particular Mento et al. (2013) used a
variable foreperiod task and dissociated the CNV from decisional pro-
cesses and motor response. In their task, the foreperiod was constant in
successive blocks, but no response was required. A CNV was observed
during the foreperiod, and its amplitude became more negative as the
task progressed, reflecting progressive learning of the temporal structure
of the trials throughout the task. This implicit learning occurred even
though the onset of the target was neither associated with a motor
response nor with decision-making, suggesting the possibility of a
motor-independent prediction. In parallel, sequential effects also influ-
ence the amplitude of the CNV in the variable foreperiod task: during the
short foreperiod, the CNV amplitude is more negative if the previous
foreperiod was also short rather than long (Capizzi et al., 2013; Los and
Heslenfeld, 2005; Mento, 2013, 2017; Van der Lubbe et al., 2004). In all,
the CNV is well suited to explore time expectation mechanisms beyond
motor preparation.

Preliminary results had been obtained in a motor task in which
participants had to change their movement from trial to trial. The results
showed that sequential effects were absent in kinematic parameters or
behavioural responses when the movement changed from trial-to-trial.
In contrast, sequential effects were observed on behavioural responses
in the same participants with a simple RT task. Given these results and
the fact that the movement was changed from trial to trial, it was
possible that sequential effects disappeared because the program had to
be reset. However, these results were obtained on a limited number of
trials and required replication, especially given the supposed automa-
ticity and replicability of the sequential effects. The unidirectional task
was chosen to maximize our chance of observing sequential effects,
while keeping the original multidirectional task unchanged. In the
present study we contrast the CNV and the behavioural results in the
tasks in which the movement does or does not change direction. Since
the CNV reflects more than just motor preparation, sequential effects on
CNV amplitude should be observed regardless of whether the motor
program changes from trial-to-trial. Such results would show that the
time characteristics of the previous trial have been retained. In contrast,
the sequential effects at the behavioural level may be present or not,
depending on the tasks. If preliminary results are replicated sequential
effects disappear when the movement changes direction, it would
replicate preliminary results and confirm that external time constraints
do not necessarily influence the movement program. Moreover,
sequential effects should be observed when the movement direction
stays the same from trial-to-trial. Such a contrast between the two tasks
would indicate that external time constraints can be integrated within
the motor program (when the movement stays the same) but are not
necessarily transferred to the next program (when the movement
changes). To which amount this concerns the feed-forward inverse
program or the adjustment of the action after the target signal is verified
by looking at sequential effects on motor parameters preceding the
target signal onset. If sequential effects are observed on parameters
recorded before the stimulus onset, it would suggest that sequential ef-
fects affect the feed-forward inverse program, whereas if it affects only
parameters recorded after the target signal it would mean that sequen-
tial effects are selectively used to adjust the reaction to the sensory
information.

## 2. Material and methods

2.1. Participants

25 participants were recruited for this experiment, including 23
right-handed and 19 women. We calculated the sample size based on the sequential effects observed in a preliminary study using a simple RT task, with the formula: $N = 2*(\text{phi}*\text{sd})^2/(m1-m2)^2$, where: $\text{phi} = 3.242$ (for a power of 90 %), $\text{sd} = 25.42$ (the standard deviation of the RT difference between short foreperiods depending on the preceding trial), $m1$ and $m2 = 315$ ms and 347 ms, respectively (a 32 ms difference between RTs for short foreperiods preceded by either a short or long foreperiod). The calculation resulted in a minimum of 13 participants. Given the use of a new task and EEG, the use of 16 participants in the manuscripts of Mento (2017), and the need to assert a lack of sequential effects in the multidirectional task, we multiplied the sample size by 2. This number corresponds to the one announced in the document written for the ethical committee. The average age (mean ± SD) was $22.72 \pm 3.43$ and years of education were $15.08 \pm 1.58$. Any neurological, psychiatric or addiction disorders was an exclusion criterion. None of the participants used cannabis in the last 2 months prior to the experiment, or any other psychotropic treatment. Participants with a history of head injury with a loss of consciousness lasting >15 min were not included.

All participants gave their informed written consent, and the study was conducted in accordance with the Declaration of Helsinki. The protocol received an agreement from the ethics committee of the University of Strasbourg (Unistra/CER/2019-24).

## 2.2. Equipment

Participants sat down in a dark and quiet room, in front of a box of 63 cm high, 60 cm wide and 47 cm deep. Before the beginning of the protocol, we showed the inside of the box to the participants so they could see that it was empty. Then, we instructed participants to put their dominant hand inside the box, fist closed, and index finger extended, so that they could start/stop linear movements inside the box. Precision Micro-Drives® vibro-tactile motors were attached to the forearm and wrist of the participants' dominant arm. They were used to apply tactile stimuli to indicate when the movement should be started or stopped. The motors were connected to a Chronos E-Prime 3.0 stimulation box. The whole task was controlled by a Dell computer model OptiPle x9020 AIO and programmed on E-Prime 3.0. The intensity of the vibrations was set at 3.0 V, with a frequency of 230 Hz. Participants were provided with rubber earplugs to prevent them from hearing the vibrations. Before the beginning of the tasks, the box was covered with a thick black material so that participants could not see inside, and no light could come into the box. To record participants' index finger trajectories in the dark box, a red LED was attached to the participant's index fingernail, and the trajectory of the LED was recorded with a GoPro HERO7 black camera placed at a height of 63 cm. The video recording parameters were linear field of view, 120 Hz resolution, 16:9 full HD format.

## 2.3. Procedure

Our protocol consisted of three experimental tasks.

In the two motor experimental tasks of interest, participants were instructed to perform a movement in a straight line following a tactile vibration of 100 ms applied to the forearm (start signal) and to stop it as fast as possible following a tactile vibration of 100 ms applied to the wrist (target signal). The different locations of the vibrations (forearm and wrist) made it easy for the participants to distinguish the start and target signals. The target signal occurred at variable foreperiods after the start signal, either 1000 ms (short foreperiod) or 1700 ms (long foreperiod). The foreperiods were set to ensure that the shortest one (1000 ms) allows participants to perform a long enough action. The long foreperiod (1700 ms) was selected to be distinct from the short one while remaining short enough to keep participants from reaching the edge of the box.

The two experimental tasks differed mainly in the type of performed movement. In the multidirectional task, participants had to change the

direction of their movement from one trial to another, as they might have done if they were exploring a maze. As in a maze, movements were restricted to the X and Y axes (horizontal and vertical directions), and participants chose one of these directions on each trial. In the unidirectional task, participants began each trial from the same position, ensuring that they always moved in the same direction. To make the beginning of the next trial unpredictable, we used a variable inter-trial interval (ITI) ranging from 2000 to 2300 ms. In each experimental task there were 240 trials, 120 with a short and 120 with a long foreperiod, randomly distributed.

Before starting each task, a training phase of 10 trials was conducted to familiarize participants with the tasks. During this phase, the experimenter visually ensured that participants initiated their movement with the start signal and stopped with the target signal. The speed of their movements was also visually monitored during this training phase to prevent them from going too fast and reaching the box edge, or too slow and showing a jerky trajectory. The experimenter thus ensured that all participants made movements with similar speeds, even if those were not strictly identical. On occasion, some participants did collide with a box side. In such cases, the instruction was to completely stop the movement and remain still until the next trial. This instruction allowed us to identify these trials as anticipations, enabling us to exclude them from further analysis.

In the third control task, participants had to press a button as fast as possible in response to a target. This task allowed us to verify that our sample showed the temporal prediction indicators (variable foreperiod and sequential effects) commonly observed on RTs in a typical variable foreperiod protocol. As this control task was not relevant to our main question, this task's methodology and results can be found in the supplementary material - Section 1.

## 2.4. Electroencephalography

During the tasks, EEG activity was recorded using the ActiveTwo Biosemi system (Amsterdam, Netherlands) with 64 actives Ag/AgCl electrodes mounted according to the 10-20 international system. Electrodes were distributed over the scalp according to the international 10-20 system. The ground electrode was composed of the Common Mode Sense (CMS) and the Driven Right Leg (DRL) electrodes. Acquisition quality was ensured by keeping the electrode offset (average voltage relative to the CMS) below 50 mV. Flat-type electrodes were placed on the left and right earlobes for future referencing. No online filter was applied, and the signal was sampled at 2048 Hz.

This EEG system is based on active electrodes, which transform impedance as follows: the input impedance is very high (300 Mohm), while the output impedance is very low (< 1 Ohm). This way, the interference currents flow via very low impedances (the output of the active electrode) and cannot generate significant interference voltages. In addition, the electrode impedance does not affect the level of interference.

## 2.5. Data analyses

### 2.5.1. Behavioural data

We recorded participants' motor trajectories and analysed behavioural indicators from the collected video images on MATLAB 2021b.

We segmented our videos into 240 trials of 3000 ms. The beginning of each trial was defined by the onset of the start signal (i.e. the first tactile vibration on the forearm). It was recorded by the camera thanks to a blue LED inside the box that lit up at the onset of the forearm vibration and lasted for 100 ms, i.e. until the offset of the vibration. Similarly, when the target signal occurred (i.e. the second tactile vibration on the wrist), a green LED inside the box lit up during the entire wrist vibration.

We analysed the trajectory of the finger (i.e. of the led) with MATLAB. We established several criteria to enable the script to identify the
indicators defined in the following. The criteria are described below.

2.5.1.1. Stopping latency or RT. We collected participants' stopping latencies i.e. the delay they needed to stop their movement after the target signal. The stopping latency was used as a RT in our protocol. RT is the indicator commonly used to assess participants' performance in variable foreperiod paradigms (see our control task in supplementary material – Section 1) and allowed us to extrapolate typical behavioural indicators of temporal prediction abilities in our two motor tasks. In both experimental tasks, the stopping point was defined as the first point where all our criteria (see below) were met. If no point satisfied the stopping criteria, the trial was excluded from further analyses. If the movement had been stopped before 150 ms after the target signal, this stop was considered as anticipated, and the trial was excluded from further analyses. As a matter of fact, the time it takes to process the target, to make a decision and to send a motor command back means that the reaction is necessarily delayed relative to the target stimulus (Jana et al., 2020). Trials that were 'anticipated' and subsequently excluded include true anticipations as well as instances where participants reached the limits of the box. When they hit the box limits before the end of the trial, they were instructed to halt their movement before the target signal occurred, and the action was thus considered an anticipated stop.

RT was defined as the time difference between the onset of the target signal and the moment participants stopped their movement. At the stopping point, the participants' speed was never exactly zero. If their index finger was shaking a little, a movement was detected and the speed, though small, was not equal to zero (see Fig. 1). Therefore, we had to define criteria allowing us to consider the movement to be stopped. A second difficulty was that not all participants were moving at exactly the same speed, and we could not define a unique speed threshold below which the movement would be considered as being stopped. Therefore, we used slowdown percentages rather than exact speed values. To ensure that the criteria allowed us to detect the stopping point correctly, each trial in each participant was visually inspected using a graphical representation of the speed over time. We verified whether the stopping point placed by our script was where common sense would have placed it.

For the multidirectional task, an average of 2 trials out of 120 trials per participant were excluded due to an early stop in case of a short and long foreperiod respectively. A total of 90 and 89 trials per participant were included on average for the short and long foreperiod respectively.

For the unidirectional task, an average of 6 and 9 trials out of 120 trials per participant were excluded due to an early stop in the short and long foreperiod respectively. A total of 87 and 82 trials per participant were included on average for the short and long foreperiod respectively.

2.5.1.1.1. Stopping point criteria in the multidirectional task. An example of how speed varies during the trajectory in a trial is depicted in Fig. 1. We refer to each maximum and minimum speed as a 'speed peak' (see Fig. 1). In the remainder of the text, when criteria refer to both minimum and maximum speed, the generic term 'peak' will be used. Otherwise, we will specify whether the criterion concerns a maximum or a minimum speed.

A first criterion for the stopping point was that speed had to be slowed down by at least 40 % compared to the previous maximum speed. For example, if the previous maximum speed was 10 cm/sec, the speed at the stopping point had to be lower or equal to 6 cm/sec. This criterion allowed us to detect a significant slowdown, but was not enough to determine a stopping point, as it may happen that a participant's speed decreases, without necessarily stopping. We added a second criterion to ensure that the participant was not re-accelerating. A stopping point was identified as such only if the next two speed peaks were less than twice the speed at the stopping point. According to the previous example, if the speed at the stopping point was 6 cm/sec, then the speed at the two next speed peaks had to be strictly lower than 12 cm/sec. These two criteria still did not guarantee that the movement was truly stopped. We needed to establish a speed threshold below which we could consider the participants' index finger was no longer moving. As emphasized, due to tremors, the speed was never equal to zero. Instead of using zero speed, we decided to use the tremors-related speed as a threshold at the stopping point. To determine the tremor-related speed, we used the median speed peak of each trial (between 0 and 3 s). The median speed peak was reliable because trajectory-related speed changes exhibited a linear variation with few peaks, whereas tremors-related peaks displayed irregular patterns with multiple small peaks (see Fig. 1). Due to the high number of peaks during tremor, the median speed peak was always tremors-related. The third criterion was that the speed at the stopping point had to be less than or equal to 1.5 times the median speed peak of this trial. If we take the previous example, with a speed of 6 cm/sec at the stopping point, the median speed peak of the trial had to be equal or lower than 9 cm/sec. The combination of these three criteria allowed our program to detect stopping points in an objective and accurate way, as verified with visual inspection.

2.5.1.1.2. Stopping point criteria in the unidirectional task. The

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** There is an initial acceleration due to the start signal. During this acceleration, speed can vary, leading to speed peaks. Green arrows indicate maxima, while blue arrows indicate minima. After 1 second, the target signal occurs (red line on the graph), and the delay needed to stop the action represents the stopping latency (i.e. RT). Even though participants no longer continue their movement, small speed peaks, due to tremors, are still detected.</figcaption></figure>
criteria for the stopping point had to be adapted for the unidirectional task. In this task, after having stopped their movement, participants returned to the starting position before the onset of the next trial. The stop was therefore not as clear and the standstill period not as long as in the multidirectional task. Hence, we adapted the stopping criteria as follows: at the stopping point, the speed of the movement had to be slowed down by at least 30 % compared to the previous maximum speed (instead of 40 % in the multidirectional task). The second criterion was the same as in the multidirectional task: a stopping point was identified as such only if the next two speed peaks were less than twice the speed at the stopping point. When the standstill period was not long enough, the two next speed peaks after the stop could not be less than twice the speed at the stopping point. As a matter of fact, when participants went back to their starting position after stopping their movement, they usually did it so quickly that the following speed peak was the highest speed peak of the trial. If a short standstill period was detected, then a stopping point was identified as such only if the speed at the next maximum was at least 50 % of the maximum speed of this trial. Finally, to ensure that the movement had truly stopped, we added a last criterion based on the speed itself at the stopping point, which had to be lower or equal to 35 % of the maximum speed associated with returning to the starting position. The value of 35 % was verified by visual inspection: stopping points would have been missed with a lower percentage.

2.5.1.2. Deceleration points. Participants slow down their movement in anticipation of the movement end. Such slowing down is characteristic of the movement kinematics and indicates how and when the movement end is prepared for in the motor program (Duque et al., 2017; Woodworth, 1899). The start of this slowing down was taken as a proxy for the moment when preparation to stop the movement started. In the following, we will refer to this slowing down as the ‘deceleration point’ for the sake of simplicity. Since we have two foreperiods in each task, we were interested in the deceleration point before the onset of the target signal at 1000 ms (short foreperiod) which we will call the ‘first deceleration point’. The second deceleration point between 1000 ms and 1700 ms (long foreperiod) did not bring much more to the results, and we focus on the first deceleration point for the sake of simplicity. Results related to the second deceleration point can be found in the supplementary material – Section 2. Before 1000 ms, participants could not know when the target signal would occur, and for this reason a first deceleration point was expected in all trials (short and long foreperiods). The criteria used to identify the first deceleration point were identical between the tasks. First deceleration points were defined as the first slowing down where all our criteria were met.

2.5.1.2.1. First deceleration point (150 – 1000 ms) criteria. For the first deceleration point, we defined a temporal period from 150 until 1000 ms. At least 150 ms are needed for the start signal to be processed and a motor command to be sent back (Jana et al., 2020). Hence deceleration point below 150 ms was considered as an anticipation. If the target signal was expected at 1000 ms, slowing down should start before 1000 ms. As for the stopping point criteria, we verified the consistency of our criteria, described hereafter, by visual inspection, for each trial in each participant.

A first criterion was that the speed at the first deceleration point had to be at least equal to 30 % of the maximal speed in the time window of interest. Visual inspection suggested the criterion of 30 % of the maximum speed was a good compromise between the risk of omission and false alarms. The two additional criteria ensured that participants were decelerating their movement and not preparing to accelerate further. The speed at the next speed peak had to be at least 10 % slower than the speed at the first deceleration point, and/or the acceleration at the next speed peak had to be at least 30 % lower than the acceleration at the first deceleration point.

Several additional indicators were derived from the deceleration point. In order to describe the data, we measured the speed at this point

and the latency, i.e. the delay between the start of the trial and the first deceleration point (results regarding the latency can be found in the supplementary material – Section 2). However, the main parameter was the deceleration slope in between the first deceleration point and the target signal (in case of a short foreperiod), which evaluates the efficiency of the slowing down in anticipation of the target signal. We calculated the relative deceleration slope according to this formula: [(speed at the first deceleration point – speed at 950 ms) / speed at the first deceleration point] in all trials (similar results were observed when we did not divide the slope by the speed at the first deceleration point). We used the speed at 950 ms instead of the speed at 1000 ms to avoid a potential effect of the onset of the target signal in trials with a short foreperiod.

### 2.5.2. Electrophysiological data

The EEG analysis was performed on MATLAB R2021b with the EEGLab toolbox (Delorme and Makeig, 2004).

The pre-processing steps were applied to each participant and for each task separately. First, we referenced our signal with our bipolar electrodes attached to the right and left ears. Then we reduced the sampling rate from 2048 to 512 Hz to keep a good temporal resolution while reducing the analyses time. The data was band-pass filtered between 0.01 and 30 Hz (Mento, 2017). We then applied additional cleaning and filtering steps to create an optimal dataset for the application of the ICA ‘runica’ algorithm proposed by EEGLab (Luck, 2022). An average of 3 ± 3 electrodes per participant were interpolated (a maximum of 12 electrodes interpolated in two participants whose EEG signal was particularly noisy; none concerned our electrodes of interest).

We segmented our trials 200 ms before the onset of the start signal and up to 2000 ms after. The baseline was defined as the average EEG signal from 200 ms before to the onset of the start signal i.e. [−200 0] ms. Subsequently, we rejected artefacts appearing 200 ms before the onset of the start signal and until the target signal in the long foreperiod i.e. [−200 1700 ms] using a single voltage threshold ± 150 μV. An average of 110 out of 120 trials were included for both the short and long foreperiods in the multidirectional task, vs. 113/120 and 112/120 trials in the unidirectional task, for the short and long foreperiods respectively. For three participants, the data was too noisy due to sweating artefacts, and was excluded from the analyses, bringing our group to 22 participants for our EEG data.

### 2.5.3. Statistical analyses

Statistical analyses were carried out in R (RStudio Team, 2016). For all the indicators, we worked with the median values of each participant (to avoid difficult decisions about outliers cutoff values). The graphs display the data averaged across participants. Error bars represent the standard error of the mean (SEM).

For our statistical comparisons we performed repeated measures ANOVAs to examine interaction effects that are well-established in the literature (Correa et al., 2006a; Martin et al., 2017; Mento, 2017). When the assumption of normal distribution was not met in our datasets, we normalized them before conducting the ANOVA. To do so, we used the ‘boxcox’ function from the ‘bestnormalize’ R package, which enabled our datasets to conform to a normal distribution (Box and Cox, 1964). This approach was predetermined in the ethical document. To keep a normal distribution while preserving a mean and a standard deviation different from 0 to 1 respectively, we applied inverse z-scores to our datasets before conducting the statistical analysis. Overall, the level of significance was set to α = 0.05. The partial eta-squared (η²_p) values are added as a measure of effect size.

## 3. Results

### 3.1. Reaction times

We verified whether the previous and current foreperiods had an
<figure><img src="image_6.png" /><figcaption>Fig. 3. Relative deceleration slopes in all current trials (i.e. regardless of their foreperiod) in both motor tasks, depending on the previous foreperiod. This indicator shows how participants decelerate from the first deceleration point to the onset of the target signal at short foreperiod (we take into account the moment just preceding the onset, in order to avoid vibration-related artefacts). Opaque shapes with error bars show the mean relative deceleration slope ± SEM, while transparent shapes reflect individual participants' data points in each condition. Statistical significance is denoted by ** for p-value < 0.01.</figcaption></figure>

= 0.04, η²ₚ = 0.19], confirming the difference between the two tasks.

### 3.3. CNV

We verified whether sequential effects were observed on the CNV amplitude in both tasks, despite the absence of sequential effect on the behavioural indicators in the multidirectional task. To do so, we performed a two-factor ANOVA with the within-group factors 'task' (unidirectional vs. multidirectional) and 'previous foreperiod' (short vs. long) on the mean amplitude of the CNV on the current trial (measured

between 800 and 1000 ms) (Fig. 4). In this time window we are right before the onset of the target signal in case of a short foreperiod. Since participants do not know whether the foreperiod will be short or long we averaged data over both foreperiods. We focused our analysis on FCz, Cz and C1 electrodes which are traditionally used to observe the CNV (Capizzi et al., 2013; Foerster et al., 2022; Kononowicz and Penney, 2016; Kononowicz and Van Rijn, 2011; Macar et al., 1999).

The two-factor ANOVA on CNV amplitude revealed a main effect of 'previous foreperiod' [F(1,21) = 9.63, p = 0.005, η²ₚ = 0.31], with a less negative CNV amplitude when the previous foreperiod was long (–1 ua

<figure><img src="image_7.png" /><figcaption>Fig. 4. CNV amplitudes in µV in multi- and unidirectional tasks (panel A and B respectively), averaged across three electrodes (FCz, Cz, and C1) in all current trials (i. e. regardless of their foreperiod). The shaded area around the lines corresponds to the SEM. 0 ms on the abscissa marks the onset of the start signal (green line), while 1000 ms marks the onset of the target signal at the short foreperiod (red line). Statistical analyses were conducted on the average CNV amplitude in the 800–1000 ms window. Statistical significance is indicated by * for p-values < 0.05.</figcaption></figure>
± 3.2) rather than short (−2.4 ua ± 4). We found no main effect of 'task': [F(1,21) = 0.71, p = 0.4, η²ₚ = 0.03] nor any interaction between the factors [F(1,21) = 0.22, p = 0.6, η²ₚ = 0.01]. This was expected, since we had anticipated sequential effects on the CNV in both tasks. Nonetheless, to make sure that a sequential effect can indeed be found in both tasks, we performed one-factor ANOVAs in each task, which are available in supplementary material – Section 2.

As a reminder, no sequential effect is expected at the long foreperiod. The CNV are nonetheless illustrated in supplementary material – Section 2. The analysis shows that there is no difference in the CNV amplitude at the long foreperiod between the two tasks, and only a trend in the decrease of the CNV amplitude at long relative to short foreperiod.

## 4. Discussion

The aim of this study was to understand whether a temporal prediction is specifically attached to the motor action. We combined two motor tasks with the variable foreperiod paradigm. In the unidirectional task, participants repeated the same movement direction from trial-to-trial, while in the multidirectional task, they were asked to change direction from trial-to-trial. We replicated the usual variable foreperiod effect in both motor tasks (see supplementary material – Section 1 for the control task), despite our change of protocol. Moreover, we collected kinematic parameters during the foreperiod, in addition to typical indicators of temporal prediction abilities (RT and CNV) and assessed sequential effects. The latter are thought to be robust markers of automatic temporal prediction abilities (Kong et al., 2015; Vallesi et al., 2014). Parameters similarly influenced by sequential effects in both tasks indicate a temporal prediction independent from the specific motor program, while those observed only in the unidirectional task may reflect a motor-related temporal prediction.

Regarding the EEG results, sequential effects were observed on the CNV amplitude in our control task (see supplementary material – Section 1) and in both motor tasks. Regardless of the motor direction taken by participants from trial-to-trial, we observed an impact of the previous foreperiod on participants' EEG-related preparation on the current trial. The presence of sequential effects on the CNV amplitude in a variable foreperiod task has been extensively described in the literature, contributing to establishing CNV as an EEG indicator of temporal prediction abilities (Breska and Deouell, 2014; Mento, 2017; Van der Lubbe et al., 2004; Walter et al., 1964). Our results are consistent with those previous findings.

Sequential effects were not expected when the current foreperiod was long. For both the hazard-based theory and for the fMTP there is no influence from the preceding foreperiods at long foreperiods (see supplementary material – Section 2), and this is also what is usually seen in the literature (Correa et al., 2006b; Los et al., 2017; Niemi and Näätänen, 1981; Vallesi et al., 2007; Zahn et al., 1963). This might be seen as in contradiction with the study of McAdam et al. (1969). However, in this study participants were required to emit an explicit prediction regarding the next foreperiod on each trial. The authors measured the impact of this prediction rather than the implicit impact of the recent foreperiod, which probably explains the discrepancy with our results and the literature.

Regarding the behavioural results, we observed sequential effects on RT in the control task (see supplementary material – Section 1) and in the unidirectional task only. In addition, in the unidirectional task, we observed sequential effects on the anticipatory deceleration of the movement. In the multidirectional task, these sequential effects were not present. In other words, sequential effects are observed when the same movement is repeated from trial-to-trial but disappear when the movement direction changes. This suggests that sequential effects on behavioural indicators reflect the existence of a motor-related temporal prediction. We first detail behavioural results before discussing why we observe a difference in sequential effects between behavioural and EEG

responses.

Slowing down (decelerating) before the end of a movement has long been described (Woodworth, 1899). It is intrinsic to the motor program itself as it helps to avoid any unwanted collision, e.g. before reaching an obstacle. The timing and the way this slowdown occurs indicate how the end of the movement is prepared for and provide important insights into the motor preparation occurring before the target signal. The characteristics of the deceleration allow us to assess sequential effects, which demonstrate how motor-related predictions are adjusted based on recent experiences, whether the movement direction changes (multidirectional task) or remains the same (unidirectional task).

In the unidirectional task, with short foreperiods, we observed sequential effects on the relative deceleration slope, likely reflecting a temporal prediction. As a reminder, the relative deceleration slope was calculated as (speed at the first deceleration point – speed before target signal at short foreperiod) / speed at the first deceleration point. If the previous foreperiod had been short, the relative deceleration slope was steeper than if the previous foreperiod had been long. A steeper slope indicates increased deceleration without requiring additional time. The slope is steeper because it starts from a higher speed (at the deceleration point). This result may seem counterintuitive. After a short foreperiod, the next foreperiod is predicted to be short, due to sequential effects. A shorter foreperiod would be expected to lead to earlier deceleration (see supplementary material – Section 2), and slower speed. This was not observed. What is observed in the unidirectional task is increased deceleration when the previous foreperiod was short as compared to long. It is as if the repetition of the movement from trial-to-trial helps to optimize the deceleration phase. In contrast, when participants change direction from one trial to the next (multidirectional task), no sequential effects were observed on behaviour. The change in movement appears to preclude an optimization of the deceleration phase.

Behavioural parameters do not seem to be sensitive to the prediction of the onset of the target signal, i.e. the sensory signal, which was identical in the uni- and multidirectional tasks. If they were, we would have observed sequential effects in both tasks. One might assume that since deceleration occurs during the trajectory itself, it is not surprising that the sequential effects on this indicator reflect a motor-related temporal prediction. Yet the detrimental impact of the direction changes on sequential effects persists on RT. RT is closer to the typical measure in variable foreperiod tasks. It is a compound measure since it stems from a motor response to a sensory stimulus (Faugeras and Naccache, 2016; Niu et al., 2018). As such, it should be sensitive to both the prediction of the onset of the sensory signal and motor preparation. Especially the moment at which the target signal occurred on the previous trial might have been expected to be considered in the current trial. This is not the case, suggesting that motor prediction needs to be renewed when the movement changes. This need may explain why movement and RT are slower in the multidirectional task than in the unidirectional task. It might have been argued that the lack of sequential effect in the multidirectional task relates to the fact that responses in our protocol require motor inhibition (stopping the movement) rather than motor activation (pressing a button). However sequential effects have been widely documented across various task types (Brulé et al., 2021; Los, 2010; Marques-Carneiro et al., 2020; Wehrman et al., 2018). Importantly they are replicated in the unidirectional task, consistent with the literature emphasizing the importance of motor optimization in the sequential effects (Correa et al., 2006b; Los et al., 2017; Niemi and Näätänen, 1981; Salet et al., 2022; Vallesi et al., 2007; Zahn et al., 1963). It is thus unlikely that the need to stop the movement explains the lack of sensitivity to the prediction of the target signal onset.

Our behavioural results seem especially sensitive to the motor anticipation. The fact that in the unidirectional task, we found sequential effects on the relative deceleration slope and not on its onset (latencty) suggests that temporal prediction abilities play a role on how the movement is executed and not on its initiation. How this fits with explorations regarding the Smith predictor remain to be determined, given
the difference in the tasks across protocols (Miall and Jackson, 2006). However, our results clearly suggest the existence of a specific motor-related temporal prediction. Inasmuch as this effect precedes the target signal, it suggests an effect on the inverse ‘feed-forward program’ rather than on the forward model that adjusts the movement to sensory feedbacks.

It could be argued that, for some reason, time expectations for long foreperiods differ between the uni- and multidirectional tasks, thus explaining the lack of sequential effects in the multidirectional task. If the long foreperiod is not expected as efficiently in the multidirectional task as in the unidirectional task, its influence in the subsequent trial might decrease. As a matter of fact, some older studies using foreperiods longer than 2 s have shown decreases in the CNV amplitudes for the longest foreperiods (Loveless and Sanford, 1975; McAdam et al., 1969). However, such long foreperiods can be expected to require executive functions to maintain expectation (Zélanti and Droit-Volet, 2011), and are clearly longer than the longest foreperiod used in our study (1700 ms). Our results do not show a decrease in expectation with time, albeit there is a tendency. Most importantly there was no difference between tasks for long foreperiods. The foreperiod effects show consistent time expectations in both tasks, as shown by the decreases in RT with increasing foreperiods, and the similar CNV amplitude between tasks at long foreperiods (see supplementary material).

Our task being novel, we might discuss its sensitivity. For example, one might assume that our parameters are not sufficiently sensitive to small-amplitude sequential effects in the multidirectional task due to variability in the movement. It is important to emphasize that deceleration points were detected as easily in both tasks: on average, velocity peaks were found in 95 % of trials for each participant in the multidirectional task and in 94 % of trials for each participant in the unidirectional task. Also, the main effect of variable foreperiod was found to be significant in both tasks (see the results and the supplementary material – Section 2), suggesting that the parameters were sensitive enough in both the uni- and multidirectional tasks.

Nevertheless, temporal prediction is still implemented in the multi-directional task, as evidenced by the results related to the amplitude of the CNV. The amplitude of the CNV was less negative when the previous foreperiod was long rather than short. This observation holds true in both of our motor tasks. While the CNV could reflect motor preparation (Macar and Besson, 1985; Walter et al., 1964), it might offer insights into a more general preparation, irrespective of the specific movement, due to the need to prepare in time for the target signal. Many studies have suggested a distinction between early and late CNV components, the former being related to attention orientation after the first stimulus (Weerts and Lang, 1973), and the later to motor preparation (Loveless and Sanford, 1975). It has been made clear that the late CNV differs from the lateralized preparation potential (LRP) (Ulrich et al., 1998) though. Ulrich et al. (1998) cued the movement to be executed with information on hand, direction, and force of the movement. They showed that, in contrast with LRP, the late CNV increased with the amount of prior information. They proposed that the CNV represents the assembling of the motor program, including any external influence helping to build the program. Gomez et al. (2003) used source localization and suggested that the late CNV is involved also in perceptual preparation, with a perceptual and motor coordination mediated by the frontal cortex. It is beyond the goal of the present study to discuss the exact functions reflected by the CNV. However, we can note that the slope of deceleration is a purely motor-related signal preceding the target signal. This motor parameter is modulated by sequential effects in the unidirectional task, but not in the multidirectional task, whereas the CNV amplitude is modulated by sequential effects in both tasks. Our results suggest that the CNV is unlikely reflecting a pure motor measure, and those results are in accordance with the literature. The CNV amplitude could reflect proactive anticipation of the target’s onset, preparing for the detection, decision and response that need to be made as soon as the target appears (Correa et al., 2005, 2006c; Vangkilde et al., 2012). Future studies may

benefit from the present procedure to distinguish between perceptual and motor functions. Such future studies may also address a limitation of our approach, which is the fact that the uni- and multidirectional tasks differ not only by the direction change between trial, but also by the need to decide the direction in the multidirectional task. Devising intermediate experimental steps (e.g. with no choice but still a change in direction) may help to explore further the question of the distinction between the perceptual and motor components of time prediction and understanding at which point sensory and motor time prediction become disconnected, like they are in the multidirectional task.

## 5. Conclusion

Our results suggest that CNV amplitude is influenced by additional mechanisms not reflected in our behavioural indicators. In this sense, they confirm the existence of different mechanisms of temporal prediction abilities, as behavioural performance is affected by experimental manipulations, whereas EEG is not. In all, our results suggest two types of temporal prediction at the same temporal scale: one is motor-related, linked to the movement itself and visible on behavioural indicators (in the unidirectional task and before the target onset), while the other involves mechanisms that are independent from the movement and visible on the EEG indicator. These results raise questions regarding how the different mechanisms of the prediction interact, and whether motor prediction is embedded in a more general one. Similarly, given the neurodevelopmental component of temporal prediction abilities (Bender et al., 2005; Johnson et al., 2015; Mento and Valenza, 2016), it will be interesting to investigate whether these mechanisms of the prediction are established simultaneously during development or if one precedes the other. Most importantly, our task provides a useful tool to distinguish different types of temporal prediction impairments in pathological conditions.

## CRediT authorship contribution statement

Alana Arrouet: Writing – original draft, Validation, Software, Methodology, Investigation, Formal analysis, Data curation. José Eduardo Marques-Carneiro: Validation, Software, Methodology. Pierre Marquet: Validation, Supervision. Anne Giersch: Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

## Acknowledgement

This work was funded by an INSERM-Grand-Est region PhD grant, and by the Canada Excellence Research Chair in Neurophotonics through the International Joint Unit on Neurodevelopment and Child Psychiatry. We thank Sander Los and two anonymous reviewers for their help in improving the manuscript.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2024.120982.

## Data availability

We share the link to our data and code
## References

Arjona, A., Escudero, M., Gómez, C.M., 2014. Updating of attentional and premotor allocation resources as function of previous trial outcome. Sci. Rep. 4 (1), 4526. https://doi.org/10.1038/srep04526.

Basile, L.F.H., Ballester, G., De Castro, C.C., Gattaz, W.F., 2002. Multifocal slow potential generation revealed by high-resolution EEG and current density reconstruction. Int. J. Psychophysiol. 45 (3), 227-240. https://doi.org/10.1016/S0167-8760(02)00014-4.

Bender, S., Weisbrod, M., Bornfleth, H., Resch, F., Oelkers-Ax, R., 2005. How do children prepare to react? Imaging maturation of motor preparation and stimulus anticipation by late contingent negative variation. Neuroimage 27 (4), 737-752. https://doi.org/10.1007/j.neuroimage.2005.05.020.

Bertelson, P., 1961. Sequential redundancy and speed in a serial two-choice responding task. Q. J. Exper. Psychol. 13 (2), 90-102. https://doi.org/10.1080/17470216108416478.

Bertelson, P., Boons, J.-P., 1960. Time uncertainty and choice reaction time. Nature 187 (4736), 531-532. https://doi.org/10.1038/187531a0.

Box, & Cox. (1964). *An Analysis of Transformations*.

Breska, A., Deouell, L.Y., 2014. Automatic bias of temporal expectations following temporally regular input independently of high-level temporal expectation. J. Cogn. Neurosci. 26 (7), 1555-1571. https://doi.org/10.1162/jocn_a_00564.

Brulé, S., Herlin, B., Pouget, P., Missal, M., 2021. Ketamine reduces temporal expectation in the rhesus monkey. Psychopharmacol.(Berl.) 238 (2), 559-567. https://doi.org/10.1007/s00213-020-05706-6.

Brunia, C.H.M., 1993. Waiting in readiness : gating in attention and motor preparation. Psychophysiology 30 (4), 327-339. https://doi.org/10.1111/j.1469-8986.1993.tb02054.x.

Brunia, C.H.M., Hackley, S.A., Van Boxtel, G.J.M., Kotani, Y., Ohgami, Y., 2011. Waiting to perceive : reward or punishment? Clin. Neurophysiol. 122 (5), 858-868. https://doi.org/10.1016/j.clinphys.2010.12.039.

Capizzi, M., Correa, Á., Sanabria, D., 2013. Temporal orienting of attention is interfered by concurrent working memory updating. Neuropsychologia 51 (2), 326-339. https://doi.org/10.1016/j.neuropsychologia.2012.10.005.

Correa, Á., Lupiáñez, J., Madrid, E., Tudela, P., 2006a. Temporal attention enhances early visual processing : a review and new evidence from event-related potentials. Brain Res. 1076 (1), 116-128. https://doi.org/10.1016/j.brainres.2005.11.074.

Correa, Á., Lupiáñez, J., Tudela, P., 2005. Attentional preparation based on temporal expectancy modulates processing at the perceptual level. Psychonom. Bull. Rev. 12 (2), 328-334. https://doi.org/10.3758/BF01396380.

Correa, Á., Lupiáñez, J., Tudela, P., 2006b. The attentional mechanism of temporal orienting : determinants and attributes. Exp. Brain Res. 169 (1), 58-68. https://doi.org/10.1007/s00221-005-0131-x.

Correa, Á., Sanabria, D., Spence, C., Tudela, P., Lupiáñez, J., 2006c. Selective temporal attention enhances the temporal resolution of visual perception : evidence from a temporal order judgment task. Brain Res. 1070 (1), 202-205. https://doi.org/10.1016/j.brainres.2005.11.094.

Coull, J.T., 2009. Neural substrates of mounting temporal expectation. PLoS Biol. 7 (8), 8-11. https://doi.org/10.1371/journal.pbio.1000166.

Coull, J.T., Giersch, A., 2022. The distinction between temporal order and duration processing, and implications for schizophrenia. Nat. Rev. Psychol. 1 (5), 257-271. https://doi.org/10.1038/s41459-022-00038-y.

Delorme, A., Makeig, S., 2004. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Method. 134 (1), 9-21. https://doi.org/10.1016/j.jneumeth.2003.10.009.

Duque, J., Greenhouse, I., Labruna, L., Ivry, R.B., 2017. Physiological markers of motor inhibition during human behavior. Trend. Neurosci. 40 (4), 219-236. https://doi.org/10.1016/j.tins.2017.02.006.

Fattapposta, F., Pauletti, C., Mannarelli, D., De Pascalis, V., Ciorciari, J., Crewther, D., White, D., Knyazev, G., 2024. Contingent Negative Variation (CNV). In: Valeriani, M., De Tommaso, M. (Eds.), Psychophysiology Methods. Springer US, p. 23-32. https://doi.org/10.1007/978-1-0716-3545-2_2. Vol. 206.

Faugeras, F., Naccache, L., 2016. Dissociating temporal attention from spatial attention and motor response preparation : a high-density EEG study. Neuroimage 124, 947-957. https://doi.org/10.1016/j.neuroimage.2015.09.051.

Foerster, F.R., Chidharom, M., Bonnefond, A., Giersch, A., 2022. Neurocognitive analyses reveal that video game players exhibit enhanced implicit temporal processing. Commun. Biol. 5 (1), 1082. https://doi.org/10.1038/s42003-022-04033-0.

Frömer, R., Lin, H., Dean Wolf, C.K., Inzlicht, M., Shenhav, A., 2021. Expectations of reward and efficacy guide cognitive control allocation. Nat. Commun. 12 (1), 1030. https://doi.org/10.1038/s41467-021-21315-z.

Gomez, C., Marco-Pallarés, J., Grau, C., 2003. Preparatory visuo-motor cortical network of the contingent negative variation estimated by current density. Neuroimage 20, 216-224. https://doi.org/10.1016/S1053-8119(03)00295-7.

Griffin, I.C., Nobre, A.C., 2003. Orienting attention to locations in internal representations. J. Cogn. Neurosci. 15 (8), 1176-1194. https://doi.org/10.1162/089892903322598139.

Jana, S., Hannah, R., Muralidharan, V., Aron, A.R., 2020. Temporal cascade of frontal, motor and muscle processes underlying human action-stopping. Elife 9, e50371. https://doi.org/10.7554/eLife.50371.

Johnson, H.W., 1961. Skill = speed × accuracy × form × adaptability. Percept. Mot. Skills. 13 (2), 163-170. https://doi.org/10.2466/pms.1961.13.2.163.

Johnson, K.A., Burrowes, E., Coull, J.T., 2015. Children can implicitly, but not voluntarily, direct attention in time. PLoS One 10 (4), e0123625. https://doi.org/10.1371/journal.pone.0123625.

Karlin, L., 1959. Reaction time as a function of foreperiod duration and variability. J. Exp. Psychol. 58 (2), 185-191. https://doi.org/10.1037/h0049152.

Kong, D., Asplund, C.L., Ling, A., Chee, M.W.L., 2015. Increased automaticity and altered temporal preparation following sleep deprivation. Sleep 38 (8), 1219-1227. https://doi.org/10.1007/s10565-sleep.4896.

Kononowicz, T.W., Penney, T.B., 2016. The contingent negative variation (CNV): timing isn't everything. Curr. Opin. Behav. Sci. 8, 231-237. https://doi.org/10.1016/j.cobeha.2016.02.022.

Kononowicz, T.W., Van Rijn, H., 2011. Slow potentials in time estimation: the role of temporal accumulation and habituation. Front. Integr. Neurosci. 5. https://doi.org/10.3389/fint.2011.00048.

Kropp, P., Linstedt, U., Niederberger, U., Gerber, W.D., 2001. Contingent negative variation and attentional performance in humans. Neurol. Res. 23 (6), 647-650. https://doi.org/10.1179/016164101101198956.

Lange, K., Röder, B., 2006. Orienting attention to points in time improves stimulus processing both within and across modalities. J. Cogn. Neurosci. 18 (5), 715-729. https://doi.org/10.1162/jocn.2006.18.5.715.

Levitin, D.J., Grahn, J.A., London, J., 2018. The psychology of music : rhythm and movement. Annu. Rev. Psychol. 69 (1), 51-75. https://doi.org/10.1146/annurev-psych-122216-011740.

Li, Y., You, Y., Yu, B., Lu, Y., Zhou, H., Tang, M., Zuo, G., Xu, J., 2024. The impact of cue and preparation prompts on attention guidance in goal-directed tasks. Front. Hum. Neurosci. 18, 1397452. https://doi.org/10.3389/fnhum.2024.1397452.

Logan, G.D., Cowan, W.B., 1984. On the ability to inhibit thought and action : a theory of an act of control. Psychol. Rev. 91 (3), 295-327. https://doi.org/10.1037/0033-295X.91.3.295.

Los, S.A., 2010. Foreperiod and sequential effects: theory and data. In: Nobre, A.C., Coull, J.T. (Eds.), Attention and Time, 1re éd. Oxford University PressOxford, p. 289-302. https://doi.org/10.1093/acprof:oso/9780199563456.003.0021.

Los, S.A., 2013. The role of response inhibition in temporal preparation : evidence from a go/no-go task. Cognition 129 (2), 328-344. https://doi.org/10.1016/j.cognition.2013.07.013.

Los, S.A., Heslenfeld, D.J., 2005. Intentional and unintentional contributions to nonspecific preparation : electrophysiological evidence. J. Exper. Psychol.: Gener. 134 (1), 52-72. https://doi.org/10.1037/0096-3445.134.1.52.

Los, S.A., Kruijne, W., Meeter, M., 2014. Outlines of a multiple trace theory of temporal preparation. Front. Psychol. 5. https://doi.org/10.3389/fpsyx.2014.01058.

Los, S.A., Kruijne, W., Meeter, M., 2017. Hazard versus history : temporal preparation is driven by past experience. J. Exper. Psychol.: Hum. Percept. Perform. 43 (1), 78-88. https://doi.org/10.1037/xhp0000279.

Los, S.A., Van Den Heuvel, C.E., 2001. Intentional and unintentional contributions to nonspecific preparation during reaction time foreperiods. J. Exper. Psychol.: Hum. Percept. Perform. 27 (2), 370-386. https://doi.org/10.1037/0096-1523.27.2.370.

Loveless, N.E., Sanford, A.J., 1975. The impact of warning signal intensity on reaction time and components of the contingent negative variation. Biol. Psychol. 2 (3), 217-226. https://doi.org/10.1016/0301-0511(75)90021-6.

Luck, S.J. (2022). *Applied Event-Related Potential Data Analysis*. https://doi.org/10.18115/D5QG92.

Macar, F., Besson, M., 1985. Contingent negative variation in processes of expectancy, motor preparation and time estimation. Biol. Psychol. 21 (4), 293-307. https://doi.org/10.1016/0301-0511(85)90184-X.

Macar, F., Vidal, F., Casini, L., 1999. The supplementary motor area in motor and sensory timing : evidence from slow brain potential changes. Exp. Brain Res. 125 (3), 271-280. https://doi.org/10.1007/s002210050683.

Marques-Carneiro, J.E., Polgári, P., Koning, E., Seyller, E., Martin, B., Van der Burg, E., Giersch, A., 2020. Where and when to look : sequential effects at the millisecond level. Attent. Percept. Psychophys. 82 (6), 2821-2836. https://doi.org/10.3758/s13414-020-01995-3.

Martin, B., Franck, N., Cermolacce, M., Falco, A., Benair, A., Etienne, E., Weibel, S., Coull, J.T., Giersch, A., 2017. Fragile temporal prediction in patients with schizophrenia is related to minimal self disorders. Sci. Rep. 7 (1), 1-10. https://doi.org/10.1038/s41598-017-07987-y.

McAdam, D.W., Knott, J.R., Rebert, C.S., 1969. Cortical slow potential changes in man related to interstimulus interval and to pre trial prediction of interstimulus interval. Psychophysiology 5 (4), 349-358. https://doi.org/10.1111/j.1469-8986.1969.tb02833.x.

Mento, G., 2013. The passive CNV : carving out the contribution of task-related processes to expectancy. Front. Hum. Neurosci. 7. https://doi.org/10.3389/fnhum.2013.00827.

Mento, G., 2017. The role of the P3 and CNV components in voluntary and automatic temporal orienting : a high spatial-resolution ERP study. Neuropsychologia 107 (May), 31-40. https://doi.org/10.1016/j.neuropsychologia.2017.10.037.

Mento, G., Tarantino, V., Sarlo, M., Bisiacchi, P.S., 2013. Automatic temporal expectancy : a high-density event-related potential study. PLoS One 8 (5), e62896. https://doi.org/10.1371/journal.pone.0062896.

Mento, G., Valenza, E., 2016. Spatiotemporal neurodynamics of automatic temporal expectancy in 9-month old infants. Sci. Rep. 6 (1), 36525. https://doi.org/10.1038/srep36525.

Miall, R.C., Jackson, J.K., 2006. Adaptation to visual feedback delays in manual tracking : evidence against the Smith Predictor model of human visually guided action. Exp. Brain Res. 172 (1), 77-84. https://doi.org/10.1007/s00221-005-0306-5.

Miall, R.C., Weir, D.J., Wolpert, D.M., Stein, J.F., 1993. Is the cerebellum a smith predictor? J. Mot. Behav. 25 (3), 203-216. https://doi.org/10.1080/0022895.1993.9942050.

Niemi, P., Näätänen, R., 1981. Foreperiod and simple reaction time. Psychol. Bull. 89 (1), 133-162. https://doi.org/10.1037/0033-2909.89.1.133.
Niu, X., Li, J., Cao, Q., 2018. Electrophysiological correlates of integrating choice and response time during sequential decision making. Neurosci. Lett. 682, 45-49.
https://doi.org/10.1016/j.neulet.2018.06.019.

Nobre, A., Correa, A., Coull, J., 2007. The hazards of time. Curr. Opin. Neurobiol. 17 (4), 465-470. https://doi.org/10.1016/j.conb.2007.07.006.

Ramirez, L.D., Foster, J.J., Ling, S., 2021. Temporal attention selectively enhances target features. J. Vis. 21 (6), 6. https://doi.org/10.1167/jov.21.6.6.

Ranganathan, R., Lee, M.-H., Newell, K.M., 2020. Repetition without repetition : challenges in understanding behavioral flexibility in motor skill. Front. Psychol. 11, 2018. https://doi.org/10.3389/fpsyg.2020.02018.

Rasman, B.G., Blouin, J.-S., Nasrabadi, A.M., Van Woerkom, R., Frens, M.A., Forbes, P.A., 2024. Learning to stand with sensorimotor delays generalizes across directions and from hand to leg effectors. Commun. Biol. 7 (1), 384. https://doi.org/10.1038/s42003-024-06029-4.

RStudioTeam, 2016. RStudio: Integrated Development For R. [Logiciel]. RStudio, Inc. URL
http://www.rstudio.com/.

Salet, J.M., Kruijne, W., Van Rijn, H., Los, S.A., Meeter, M., 2022. FMTP: a unifying computational framework of temporal preparation across time scales. Psychol. Rev. 129 (5), 911-948. https://doi.org/10.1037/rev0000356.

Steinborn, M.B., Rolke, B., Bratzke, D., Ulrich, R., 2008. Sequential effects within a short foreperiod context : evidence for the conditioning account of temporal preparation. Acta Psychol 129 (2), 297-307. https://doi.org/10.1016/j.actpsy.2008.08.005.

Tecce, J.J., 1972. Contingent negative variation (CNV) and psychological processes in man. Psychol. Bull. 77 (2), 73-108. https://doi.org/10.1037/h0032177.

Thomaschke, R., Dreisbach, G., 2013. Temporal predictability facilitates action, not perception. Psychol. Sci. 24 (7), 1335-1340. https://doi.org/10.1177/0956797612469411.

Thomaschke, R., Kiesel, A., Hoffmann, J., 2011. Response specific temporal expectancy : evidence from a variable foreperiod paradigm. Attent. Percept. Psychophys. 73 (7), 2309-2322. https://doi.org/10.3758/s13414-011-0179-6.

Ulrich, R., Leuthold, H., Sommer, W., 1998. Motor programming of response force and movement direction. Psychophysiology 35 (6), 721-728. https://doi.org/10.1017/S0048577298970615.

Vallesi, A., Arbula, S., Bernardis, P., 2014. Functional dissociations in temporal preparation : evidence from dual-task performance. Cognition 130 (2), 141-151.
https://doi.org/10.1016/j.cognition.2013.10.006.

Vallesi, A., Shallice, T., Walsh, V., 2007. Role of the prefrontal cortex in the foreperiod effect : TMS evidence for dual mechanisms in temporal preparation. Cerebr. Cortex 17 (2), 466-474. https://doi.org/10.1093/cercor/bhj163.

Van der Lubbe, R.H.J., Los, S.A., Jaskowski, P., Verleger, R., 2004. Being prepared on time : on the importance of the previous foreperiod to current preparation, as reflected in speed, force and preparation-related brain potentials. Acta Psychol. 116 (3), 245-262. https://doi.org/10.1016/j.actpsy.2004.03.003.

Vangkilde, S., Coull, J.T., Bundesen, C., 2012. Great expectations : temporal expectation modulates perceptual processing speed. J. Exper. Psychol.: Hum. Percept. Perform. 38 (5), 1183-1191. https://doi.org/10.1037/a0026343.

Vleugels, L.W.E., Swinnen, S.P., Hardwick, R.M., 2020. Skill acquisition is enhanced by reducing trial-to-trial repetition. J. Neurophysiol. 123 (4), 1460-1471. https://doi.org/10.1152/jn.00741.2019.

Volberg, G., Thomaschke, R., 2017. Time-based expectations entail preparatory motor activity. Cortex 92, 261-270. https://doi.org/10.1016/j.cortex.2017.04.019.

Walter, W.G., Cooper, R., Aldridge, V.J., McCALLUM, W.C., Winter, A.L., 1964. Contingent negative variation : an electric sign of sensori-motor association and expectancy in the human brain. Nature 203 (4943), 380-384. https://doi.org/10.1038/203380a0.

Weerts, T.C., Lang, P.J., 1973. The effects of eye fixation and stimulus and response location on the Contingent Negative Variation (CNV). Biol. Psychol. 1 (1), 1-19.
https://doi.org/10.1016/0301-0511(73)90010-0.

Wehrman, J.J., Wearden, J.H., Sowman, P., 2018. Short-term effects on temporal judgement : sequential drivers of interval bisection and reproduction. Acta Psychol. 185, 87-95. https://doi.org/10.1016/j.actpsy.2018.01.009.

Wolpert, D.M., Kawato, M., 1998. Multiple paired forward and inverse models for motor control. Neur. Netw. 11 (7-8), 1317-1329. https://doi.org/10.1016/S0893-6080 (98)00066-5.

Woodrow, H., 1914. The measurement of attention. Psycholog. Monogr. 17 (5), i-158.
https://doi.org/10.1037/h0093087.

Woodworth, R.S., 1899. Accuracy of voluntary movement. Psycholog. Rev.: Monogr. Supplem. 3 (3), i-114. https://doi.org/10.1037/h0092992.

Zahn, T.P., Rosenthal, D., Shakow, D., 1963. Effects of irregular preparatory intervals on reaction time in schizophrenia. J. Abnorm. Soc. Psychol. 67 (1), 44-52. https://doi.org/10.1037/h0049269.

Zélanti, P.S., Droit-Volet, S., 2011. Cognitive abilities explaining age-related changes in time perception of short and long durations. J. Exp. Child Psychol. 109 (2), 143-157.
https://doi.org/10.1016/j.jecp.2011.01.003.