# Motor inhibition prevents motor execution during typing imagery: Evidence from an action-mode switching paradigm

Ladislas Nalborczyk, F.-Xavier Alario, Marieke Longcamp

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

## To cite this version:

Ladislas Nalborczyk, F.-Xavier Alario, Marieke Longcamp. Motor inhibition prevents motor execution during typing imagery: Evidence from an action-mode switching paradigm. Cognition, 2025, 254, pp.105997. 10.1016/j.cognition.2024.105997. hal-04772491

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Submitted on 8 Nov 2024

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Motor inhibition prevents motor execution during typing imagery: evidence from an
action-mode switching paradigm

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Ladislas Nalborczyk<sup>1,2,3</sup>, F.-Xavier Alario<sup>1</sup>, & Marieke Longcamp<sup>1</sup>

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<sup>1</sup> Aix Marseille Univ, CNRS, CRPN, Marseille, France

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<sup>2</sup> Cognitive Neuroimaging Unit, CEA DRF/I2BM, INSERM, Université Paris-Sud,
Université Paris-Saclay, NeuroSpin Center, Gif/Yvette, France

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<sup>3</sup> Inserm U 1127, CNRS UMR 7225, Sorbonne Universités, Institut du Cerveau, ICM,
Paris, France

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Author Note

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Correspondence concerning this article should be addressed to Ladislas Nalborczyk, CRPN, CNRS & Aix-Marseille University, 3 place Victor Hugo, 13331 Marseille Cedex 3, France. E-mail: ladislas.nalborczyk@gmail.com
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Abstract

Motor imagery is accompanied by a subjective multisensory experience. This sensory experience is thought to result from the deployment of internal models developed for the execution and monitoring of overt actions. If so, how is it that motor imagery does not lead to overt execution? It has been proposed that inhibitory mechanisms may prevent execution during imagined actions such as imagined typing. To test this hypothesis, we combined an experimental with a modelling approach. We conducted an experiment in which participants (N = 49) were asked to alternate between overt (executed) and covert (imagined) typing. We predicted that motor inhibition should lead to longer reaction and movement times when the current trial is preceded by an imagined vs. an executed trial. This prediction was borne out by movement times, but not by reaction times. We introduced and fitted an algorithmic model of motor imagery to disentangle potentially distinct inhibitory mechanisms underlying these effects. Results from this analysis suggest that motor inhibition may affect different aspects of the latent activation function (e.g., the shape of the activation function or the motor execution threshold) with distinct consequences on reaction times and movement times. Overall, these results suggest that typing imagery involves the inhibition of motor commands related to typing acts. Preregistration, complete source code, and reproducible analyses are available at https://osf.io/y9a3k/.

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*Keywords:* typing, typing imagery, motor imagery, motor inhibition, response inhibition, algorithmic modelling

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Wordcount (excluding abstract, references, tables, and figures): 6238
Introduction

Motor imagery, defined as the mental rehearsal of an action, is a cornerstone of human cognition. For most individuals, it is accompanied by a rich subjective multisensory experience. A prominent proposal is that during motor imagery, the sensory consequences of actions may be simulated mentally using pairs of internal models developed for the control of overt actions (e.g., Grush, 2004; Jeannerod, 1994, 2001). More precisely, internal forward models may predict the sensory consequences of (a copy of) motor commands issued from internal inverse models. This view is supported by a wealth of chronometric, electrophysiological, neuroimaging, and neurostimulation studies (for reviews, see Guillot, Di Rienzo, et al., 2012; Guillot, Hoyek, et al., 2012; Guillot & Collet, 2005).

This simulationnist perspective entails that the reuse of motor networks during motor imagery should be accompanied by mechanisms preventing execution (Jeannerod, 2006; O'Shea & Moran, 2017). Previous research has postulated at least three (non-exclusive) potential inhibitory mechanisms that might operate during motor imagery (Guillot, Di Rienzo, et al., 2012). First, the need to prevent execution could be integrated within the representation of the action to be produced internally so that only “subthreshold” motor commands are involved during motor imagery (see also Bach et al., 2022; Glover et al., 2020). Second, motor inhibition could be applied broadly to all ongoing actions and to all effectors involved in these actions (global motor inhibition). Third, motor inhibition may be applied in a finer-grained manner, only to some effector (effector-specific inhibition).

To investigate these inhibitory mechanisms, Rieger et al. (2017) developed a protocol called the action-mode switching paradigm in which participants have to rapidly alternate between various overt (executed) and covert (imagined) hand movements. The authors reasoned that, if motor imagery involves the inhibition of motor commands, then the inhibition applied during some imagined trial may persist to the next trial and may slow down its initiation, resulting in switching costs (for imagined-executed
61 vs. executed-executed sequences) or switching benefits (for imagined-imagined
62 vs. executed-imagined sequences). By further varying the effector used for the executed or
63 imagined action, this paradigm allows assessing the presence of effector-specific inhibition.
64 Using this paradigm, Rieger et al. (2017) showed that imagined hand movements involve
65 both global and effector-specific motor inhibition (see also Scheil & Liefooghe, 2018). It has
66 been further shown that these effects do not depend on the effector used to indicate the
67 onset and duration of imagined movements (Bart et al., 2020). These effects are influenced
68 by the proportion of imagined trials in mixed blocks (Bart et al., 2021b) and they decay
69 rapidly with increasing inter-trial interval (Bart et al., 2021a). In brief, the existence of
70 inhibitory mechanisms preventing motor execution during imagined hand reaching
71 movements has been successfully demonstrated and replicated in several distinct
72 experiments using this paradigm.

73 However, the evidence so far comes from ad-hoc pointing movements; it is unclear
74 whether the observed effects and inferred mechanisms generalise to more intricate and
75 automatised actions. Among our daily activities, typing provides an ideal test case of this
76 generalisation. Typing involves complex and often highly automatised sequential motor
77 actions, and it is ubiquitous in the everyday life of millions of persons. Cognitive models of
78 typing propose that all keystrokes programs of a word are activated in parallel prior to the
79 onset of execution, with a graded activation level allowing the ordering of the keystrokes
80 during execution (Logan & Crump, 2011; Pinet et al., 2016; Pinet et al., 2019; Rumelhart
81 & Norman, 1982; Snyder et al., 2014). Moreover, it has been hypothesised that during
82 typing imagery, forward models may predict the sensory consequences of (a copy of) typing
83 acts issued from inverse models (Dahm & Rieger, 2019). In support of the view of typing
84 imagery as mentally simulated typing, previous research suggests that the timing of typing
85 imagery is shorter but proportional to the timing of overt typing (Rieger, 2012) and that
86 typing imagery generally contains similar errors as overt typing, albeit to a lesser extent
87 (Dahm & Rieger, 2019; Rieger et al., 2011).
Here, we assessed the presence and scope (i.e., global and/or effector-specific) of motor inhibition during typing imagery with an adapted version of Rieger et al. (2017)'s action-mode switching paradigm. We asked participants to alternate between overt and covert typing of unimanual words. We reasoned that, if typing imagery involves the inhibition of motor commands (and if this inhibition persists until the next trial), then, compared to typing execution, typing imagery should slow down the initiation of the next trial (may this trial be imagined or executed). This should translate into a *switching cost* in imagined-executed vs. executed-executed sequences, and into a *switching benefit* in imagined-imagined vs. executed-imagined sequences. In contrast, if typing imagery does not involve the inhibition of motor commands, we expected to observe a standard switching cost (Kiesel et al., 2010; for reviews, see Monsell, 2003) in both executed and imagined trials. Following Rieger et al. (2017), we hypothesised that effector-specific inhibition should translate into longer reaction times (RTs) and movement times (MTs) when the same hand is repeated than when it is not repeated, only in sequences in which the first trial is an imagery trial.

Differences in RTs or MTs due to the action mode of the previous trial provide important cues regarding the inhibitory mechanisms involved. However, these raw contrasts may be inconclusive about the mechanistic implementation. For instance, motor imagery could slow down the initiation of subsequent movements either by upregulating the motor execution threshold or by delaying the spread of excitatory inputs (Nalborczyk et al., 2024). In order to disentangle potentially distinct underlying inhibitory mechanisms, we fitted a novel algorithmic model of motor imagery allowing to infer the effect of inhibition in the previous trial on the underlying timecourse of motor activation during typing imagery in the current trial (see the modelling section).
## Methods

In this section, we report how we determined our sample size, all data exclusions (if any), all manipulations, and all measures in the study (Simmons et al., 2012). A pre-registered version of our protocol is available online: https://osf.io/y9a3k/.

## Ethics information

The present research has been approved by the local ethics committee of Aix-Marseille University (agreement “HowFast”, number 2016-09-11-06). All participants provided informed consent and received course credits in exchange for their participation.

## Participants

We recruited 49 French-speaking undergraduate students in Psychology from Aix-Marseille University, ranging in age from 18 to 23 years (M = 19.41, SD = 1.32, 39F, 10M), and with no reported history of psychiatric or neurological disorder, speech disorder, or hearing deficit. As preregistered (https://osf.io/x5r67), this sample size was defined based on temporal constraints (i.e., two full weeks of data collection) and previous research (i.e., more than double the number of participants in Rieger et al., 2017).

## Linguistic material

Two classes of words were created based on the location of their constitutive letters on the keyboard (in relation to a median line located between the t-g-b and y-h-n letters on a regular AZERTY/QWERTY keyboard). Words made of letters located on the left of this median line were considered as “left-hand words” whereas words made of letters located on the right of this median line were considered as “right-hand words”. Left-hand words included words such as “averse” (rainfall) or “carafe” (carafe), whereas right-hand words included words such as “poumon” (lung) or “nylon” (nylon). These words were taken from the Lexique database (New et al., 2004). The groups of left-hand and
right-hand words were matched for per-word average bigram frequency, word frequency, number of letters, and number of syllables (the complete list of stimuli is available in the online supplementary materials).

139 **Design**

The experimental design was fully within-participant, with three crossed two-level factors: current action mode (i.e., executed vs. imagined trials), previous action mode (i.e., executed vs. imagined trials), and hand alternation/repetition (i.e., same hand vs. other hand), defining a total of eight sequences (i.e., pairs of successive trials) of interest.

144 **Procedure**

At the beginning of the experiment, typing expertise was assessed via an online copy task (Leijten & Van Waes, 2013; Van Waes et al., 2019) and summarised by computing average typing speed (ranging from 171 to 470.80 characters per minute, M = 313.25, SD = 65.13) and accuracy (ranging from 88% to 99% of correctly typed characters, M = 94.52, SD = 2.77). We also assessed typing habits via the questionnaire developed in Pinet et al. (2022) (see the online supplementary materials).

Afterwards, we provided participants with extensive instructions about typing imagery. Namely, we instructed them to imagine themselves from a first-person perspective typing the words, insisting on the multisensory nature of motor imagery. We asked them to focus on the tactile feelings and auditory percepts associated with typing (i.e., kinaesthetic motor imagery). Participants were also instructed to perform both the overt and covert typing tasks as fast and accurately as possible without correcting potential errors.

Participants started with a first training block which consisted only of overt typing trials. They continued with a second training block consisting only of covert typing trials followed by a third training block containing both overt and covert typing trials. Within each trial, the action mode (i.e., executed vs. imagined trials) was indicated by a
161 geometrical shape (i.e., rectangle vs. circle) surrounding the word. The association between
162 action mode and shape was counterbalanced across participants. The main experimental
163 part (post-training) followed, consisting of six blocks of 65 trials each, yielding a total of
164 390 trials per participant (Figure 1A). The order of word sequences (i.e., pairs of successive
165 words/trials) in each block was randomised using Euler tours (Bakermans & Behrens,
166 2021), ensuring that each of the eight trials sequences appeared equally often (i.e., 8 times
167 per block).

<figure><img src="image_3.png" /><figcaption>*Figure 1. A: Experimental procedure. The main experimental part (post-training) involved 6 blocks of 65 trials each. B: Illustrated timecourse of a single trial.*</figcaption></figure>

168 The timecourse of each single trial is depicted in Figure 1B. Participants first needed
169 to press the spacebar with their two thumbs for at least 500ms, visually represented by a
170 progress bar displayed at the center of the screen. After these 500ms, the word appeared
171 on the screen until the participant released the spacebar and started typing the word,
172 either overtly or covertly. As soon as they finished, they had to return their thumbs to the
173 spacebar. In each trial, we measured both the reaction time (RT) and the movement time
(MT). The reaction time was defined as the time interval between the appearance of the stimulus (i.e., the word) and the release of the spacebar by the participant (i.e., the disappearance of the stimulus from the screen). The movement time was defined as the time interval between the release of the spacebar by the participant and the next spacebar keypress (Figure 1B).

The first trial of each block was discarded (as it was not preceded by any other trial), yielding a total of 48 repetitions of each of the eight sequences of interest per participant. Following these six blocks, participants had to fill out the typing habits questionnaire developed in Pinet et al. (2022). The experimental procedure was developed using the PsychoPy software (Peirce et al., 2019) and took approximately 45 min. At the end of the experiment, participants were fully informed about the theoretical rationale for the study and compensated (in course credits) for their participation.

## Data analysis

Statistical analyses were conducted using R version 4.2.3 (2023-03-15) (R Core Team, 2017) and are reported with the **papaja** (Aust & Barth, 2017) and **knitr** (Xie, 2015) packages. To assess the effects of motor inhibition on RTs and MTs, we built and fitted several Bayesian multilevel generalised linear models using the **brms** package (Bürkner, 2017).<fnref n="1" /> Data were analysed using *current action mode* (2 levels, executed vs. imagined, recoded using a -0.5/+0.5 sum contrast), *previous action mode* (2 levels, executed vs. imagined, recoded using a -0.5/+0.5 sum contrast), and *hand alternation/repetition* (2 levels, same vs. different, recoded using a -0.5/+0.5 sum contrast) as within-subject categorical predictors, and the RT or MT as a dependent variable. We analysed RTs and MTs separately (i.e., we built separate models for each of these two measures). All models allowed intercepts and slopes to vary by participant.

<footnote n="1">¹ An introduction to Bayesian statistics is outside the scope of this paper. The interested reader is referred to Nalborczyk et al. (2019) for an introduction to Bayesian multilevel modelling using the *brms* package.</footnote>
Models were fitted using weakly informative priors (see the supplementary materials for code details). Four Markov Chain Monte-Carlo (MCMC) were ran for each model to approximate the posterior distribution, including each 5000 iterations and a warmup of 2000 iterations. Posterior convergence was assessed examining trace plots as well as the Gelman-Rubin statistic $\hat{R}$. Constant effect estimates were summarised via their posterior mean and 95% credible interval (CrI), where a credible interval can be considered as the Bayesian analogue of a classical confidence interval. When applicable, we also report Bayes factors (BFs), computed using the Savage-Dickey method, which consists in taking the ratio of the posterior density at the point of interest divided by the prior density at that point. These BFs can be interpreted as an updating factor, from prior knowledge (what we knew before seeing the data) to posterior knowledge (what we know after seeing the data).

As pre-registered, we excluded trials in which participants performed the wrong action mode, that is, trials in which participants typed some letters in the imagined typing condition or did not type in the executed typing condition. This amounted to 149 trials, that is, less than 0.01% of the total number of trials.

## Results

This section is divided into four parts. First, we present a visual exploration of the data. Second, we present results from confirmatory (preregistered) analyses, aiming at assessing the sequential effects of motor inhibition on reaction times (RTs) and movement times (MTs). Third, we present results from exploratory (non-preregistered) analyses, aiming at distinguishing participants that effectively performed the imagery task from those who did not. These were motivated by various indications that the task may not have been performed equally well by all participants. Fourth, we discuss results obtained by fitting a novel algorithmic model of motor imagery to these data and how it can be used to disentangle different underlying inhibitory mechanisms.
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## Visual exploration of the data

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Figure 2 shows the distributions of RTs, MTs, and total response times (TTs) for the different sequences of trials (i.e., pair of successive trials): imagined-imagined, executed-imagined, imagined-executed, and executed-executed sequences (from left to right). Given this visual arrangement, if imagined typing involves motor inhibition, this should translate into negative slopes from left to right columns, in each panel. In short, such general pattern was observed for MTs (and TTs), whereas the inverse pattern was observed for RTs.

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## Confirmatory (preregistered) analyses

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To estimate these effects while accounting for the skewness of the collected data (for more details, see the online supplementary materials), we fitted two multilevel distributional Log-Normal models (one model for RTs and one model for MTs), where “distributional” means that not only the means but also the standard deviations were allowed to vary across conditions (for more details, see for instance Williams et al., 2021). This was justified by the observation that the amount of variability (both within and between participants) strongly differed across executed and imagined trials (as can be seen from Figure 2). The distributional model allows estimating the effects of interest while taking into account the effects the predictor variables may have on dispersion, thus producing more precise (less biased) estimates of the effects on means or medians. Estimates from these models are reported in Table 1 and Table 2 and discussed in the next two paragraphs.

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**Reaction times.** RTs were slightly shorter when the previous trial was imagined rather than executed ($\beta$ = -0.006, 95% CrI [-0.013, 0], $BF_{10}$ = 1.65, $BF_{+}$ = 0.036) and longer when the current trial was imagined rather than executed ($\beta$ = 0.066, 95% CrI
<caption>Table 1</caption>

*Estimates from the multilevel Log-Normal model fitted on reaction times*

<table><thead><tr><th>Predictor</th><th>Estimate</th><th>SE</th><th>Lower</th><th>Upper</th><th>Rhat</th><th>BF<sub>10</sub></th><th>BF<sub>+</sub></th></tr></thead><tbody><tr><td>previous mode</td><td>-0.006</td><td>0.004</td><td>-0.013</td><td>0.000</td><td>1.001</td><td>1.65</td><td>0.036</td></tr><tr><td>current mode</td><td>0.066</td><td>0.004</td><td>0.059</td><td>0.073</td><td>1.000</td><td>6 &times; 10<sup>18</sup></td><td>&infin;</td></tr><tr><td>same hand</td><td>-0.004</td><td>0.003</td><td>-0.011</td><td>0.003</td><td>1.001</td><td>0.666</td><td>0.151</td></tr><tr><td>previous mode:current mode</td><td>-0.015</td><td>0.006</td><td>-0.027</td><td>-0.003</td><td>1.000</td><td>11.985</td><td>0.006</td></tr><tr><td>previous mode:same hand</td><td>-0.001</td><td>0.006</td><td>-0.013</td><td>0.010</td><td>1.000</td><td>0.614</td><td>0.665</td></tr><tr><td>current mode:same hand</td><td>-0.007</td><td>0.006</td><td>-0.018</td><td>0.005</td><td>1.000</td><td>1.107</td><td>0.154</td></tr><tr><td>previous mode:current mode:same hand</td><td>0.004</td><td>0.008</td><td>-0.012</td><td>0.021</td><td>1.000</td><td>0.946</td><td>2.360</td></tr></tbody></table>

*Note.* The 'Estimate' column represents the estimated group-level effect (slope) of each predictor included in the model (in terms of standardised AUCs). The 'Lower' and 'Upper' columns contain the lower and upper bounds of the 95% CrI, whereas the 'Rhat' column reports the Gelman-Rubin statistic. The last two columns report the BF in favour of the alternative hypothesis (relative to the null) and the directional (i.e., one-sided) BF, respectively.

[0.059, 0.073], BF<sub>10</sub> = 6 &times; 10<sup>18</sup>, BF<sub>+</sub> = Inf).<sup>2</sup> There was strong evidence in favour of a non-null interaction effect between the effect of the previous mode and the effect of the current mode (β = -0.015, 95% CrI [-0.027, -0.003], BF<sub>10</sub> = 11.985, BF<sub>+</sub> = 0.006), indicating that the effect of the previous mode was stronger in imagined trials than in executed trials (where the effect of the previous trial was null). This equates to a switch cost in executed-imagined compared to imagined-imagined sequences, whereas there was no evidence for such an effect in imagined-executed compared to executed-executed sequences. These results are more readily understandable visually and are presented in the original scale of the RTs in Figure 3.

<footnote n="2"><sup>2</sup> For one-sided hypotheses, BF<sub>+</sub> represents the ratio of the posterior probability of the effect being positive and the posterior probability of the effect being negative.</footnote>
<caption>Table 2</caption>

*Estimates from the multilevel Log-Normal model fitted on movement times*

<table><thead><tr><th>Predictor</th><th>Estimate</th><th>SE</th><th>Lower</th><th>Upper</th><th>Rhat</th><th>BF<sub>10</sub></th><th>BF<sub>+</sub></th></tr></thead><tbody><tr><td>previous mode</td><td>0.016</td><td>0.004</td><td>0.009</td><td>0.023</td><td>1.000</td><td>1041.083</td><td>23999.000</td></tr><tr><td>current mode</td><td>0.037</td><td>0.004</td><td>0.029</td><td>0.045</td><td>1.000</td><td>5 × 10<sup>15</sup></td><td>∞</td></tr><tr><td>same hand</td><td>0.001</td><td>0.004</td><td>-0.006</td><td>0.008</td><td>1.000</td><td>0.382</td><td>1.420</td></tr><tr><td>previous mode:current mode</td><td>-0.011</td><td>0.006</td><td>-0.023</td><td>0.001</td><td>1.000</td><td>2.97</td><td>0.036</td></tr><tr><td>previous mode:same hand</td><td>0.000</td><td>0.006</td><td>-0.012</td><td>0.012</td><td>1.000</td><td>0.61</td><td>0.904</td></tr><tr><td>current mode:same hand</td><td>-0.005</td><td>0.006</td><td>-0.017</td><td>0.007</td><td>1.000</td><td>0.826</td><td>0.282</td></tr><tr><td>previous mode:current mode:same hand</td><td>0.007</td><td>0.008</td><td>-0.009</td><td>0.024</td><td>1.000</td><td>1.195</td><td>4.079</td></tr></tbody></table>

*Note.* The 'Estimate' column represents the estimated group-level effect (slope) of each predictor included in the model (in terms of standardised AUCs). The 'Lower' and 'Upper' columns contain the lower and upper bounds of the 95% CrI, whereas the 'Rhat' column reports the Gelman-Rubin statistic. The last two columns report the BF in favour of the alternative hypothesis (relative to the null) and the directional (i.e., one-sided) BF, respectively.

**Movement times.** MTs were longer when the previous trial was imagined rather than executed ($\beta$ = 0.016, 95% CrI [0.009, 0.023], $BF_{10}$ = 1041.083, $BF_+$ = 23999) and longer when the current trial was imagined rather than executed ($\beta$ = 0.037, 95% CrI [0.029, 0.045], $BF_{10}$ = $5 \times 10^{15}$, $BF_+$ = Inf). This equates to a switching benefit in executed-imagined compared to imagined-imagined sequences, and to a switching cost in imagined-executed compared to executed-executed sequences. There was moderate evidence in favour of a non-null interaction effect between the effect of the previous mode and the effect of the current mode ($\beta$ = -0.011, 95% CrI [-0.023, 0.001], $BF_{10}$ = 2.97, $BF_+$ = 0.036), suggesting that the effect of the previous mode was smaller in imagined trials than in executed trials. These results are also depicted in Figure 3.

The predictions regarding effector-specific inhibition could not be appropriately tested, as highlighted by a reviewer. The participants included were not necessarily touch
268 typists. Therefore, some letters may have been typed with either the left or the right hand
269 according to the context or to individual preferences rather than to their keyboard
270 position; words containing central letters on the keyboard (e.g., T, G, or B) may have been
271 typed with both hands. This uncertainty blurs the experimental manipulation we
272 implemented and prevents us from drawing conclusions regarding effector-specific
273 inhibition effects. For the sake of completeness, though, we report the results from these
274 analyses in the supplementary materials.

275 **Preliminary summary.** To sum up, results from these analyses revealed that, as
276 predicted, imagery in the previous trial increased the *duration* of executed or imagined
277 movements, suggesting that typing imagery does involve the inhibition of motor
278 commands. However, contrary to our predictions and results from previous studies
279 studying pointing movements (e.g., Bart et al., 2020, 2021a, 2021b; Rieger et al., 2017), the
280 *initiation* of executed or imagined movements (i.e., the RT) was sped up rather than
281 slowed by typing imagery in the previous trial.

282 **Exploratory (non-preregistered) analyses - Assessing task compliance**

283 In this section, we report the results of exploratory (i.e., non-preregistered) analyses,
284 aiming at distinguishing participants who effectively performed the imagery task from
285 those who did not. How can we make sure that participants effectively performed the task
286 as expected in imagined trials? This is a common concern in studies involving covert
287 operations such as mental or motor imagery. Although there is no possible direct
288 verification, we considered an ensemble of a priori and a posteriori precautions. A priori,
289 we took great care of explaining the task to the participants in details and planned an
290 extensive training period during which the experimenter could monitor the (overt)
291 behaviour of the participant to provide more details about the task, if needed.

292 A posteriori (after data collection), we sought to separate participants who were
293 plausibly engaged in the imagery task from those who were not, based on the collected
294 movement times. We reasoned that if the imagined action follows a “faithful” mental
295 simulation of the corresponding overt action, we could expect the time it takes to imagine
296 an action to be correlated to the time it takes to execute the same action (similar to what
297 has been observed for handwriting or walking, for review see Guillot & Collet, 2005).

298 Figure 4 depicts the relation between the average movement time in executed and
299 imagined trials, averaged per word and participant, for a few exemplary participants. In
300 the data, we can distinguish between two profiles of participants: those who show a
301 positive relation between executed and imagined movements times (left panel, in green)
302 and those who do not (right panel, in orange), thereafter labelled as “on-task” and
303 “off-task” participants, respectively. We can then construct groups of participants (of
304 similar size) according to the correlation between the executed and imagined movement
305 times and assess the effect of interest according to this new grouping factor.

306 Figure 5 shows that the predicted effects of the previous action mode on MTs was
307 only found in participants with a non-null correlation between executed and imagined MTs
308 (middle and right panels) and was the strongest in participants with a strong positive
309 correlation between executed and imagined MTs (right panel). This visual intuition was
310 confirmed by fitting the model described in the previous section and adding interaction
311 terms with the correlation coefficient. This analysis revealed a strong interaction between
312 the effect of the previous action mode and the correlation coefficient, suggesting that higher
313 correlation coefficients (between executed and imagined MTs) were associated with higher
314 slowing effects of the previous action mode. This strongly suggests that typing imagery,
315 when performed correctly, does involve the inhibition of motor commands. However, this
316 figure also shows that even for “on-task” participants (right panel), we did not find the
317 effect predicted based on previous studies for the RTs. Finally, this analysis revealed that
318 participants with the strongest positive correlation coefficients had longer RTs on average.
319 Modelling latent inhibitory processes

320 Differences in RTs or MTs due to the action mode of the previous trial provide 321 important cues regarding the inhibitory mechanisms involved. However, these raw 322 contrasts may be inconclusive about the mechanistic implementation. For instance, motor 323 imagery could slow down the initiation of subsequent movements either by upregulating 324 the motor execution threshold or by delaying the spread of excitatory inputs (Nalborczyk 325 et al., 2024). We developed an algorithmic model intended to capture alternative inhibitory 326 mechanisms, and to distinguish between modulations of the motor execution threshold and 327 modulations in the shape of the underlying activation function (Nalborczyk et al., 2024). 328 This model provides a simplified overarching description of how the motor system is 329 involved over the timecourse of a trial during motor imagery, roughly corresponding to the 330 underlying activity of populations of excitatory and inhibitory neurons (adapted from 331 MacDonald et al., 2014, 2017).<fnref n="3" /> The overall level of motor activation is modelled as a 332 time-varying rescaled lognormal function defined as:

$$f(t; A, \mu, \sigma) = A \cdot \exp \left[ - \frac{(\ln t - \mu)^2}{2\sigma^2} \right], \quad t > 0$$

333 where A represents the amplitude (i.e., the maximum value) of the activation function 334 and is usually fixed to an arbitrary value (e.g., A = 1). This leaves three free parameters: 335 $\mu$ (the peak latency or “peak time” of the function), $\sigma$ (its width or “curvature”), and one

<footnote n="3">³ In the initial stages of the model development, we thought that this model could be used to account for both motor imagery and execution. However, it soon became clear that this model could not be applied as it is for motor execution because in such cases it leads to nonsensical predictions. For instance, assuming both a motor imagery threshold and a motor execution threshold make sense for motor imagery. However making the same assumption for motor execution would imply a stage of conscious (experienced) motor imagery preceding execution, which we think is not cognitively plausible. Primarily for this reason, we introduced this model as a model of motor imagery alone and therefore fitted it to imagined trials only.</footnote>
parameter for the imagery threshold $T$ (expressed relative to $A$ and such that $T < A$). From a psychological perspective, $A-T$ may be related to the vividness of motor imagery percepts, $\mu$ may be related to the speed at which these mental percepts are established, and $\sigma$ may be related to their duration (Nalborczyk et al., 2024). In this model, the reaction time is defined as the time at which the activation function crosses the threshold for motor imagery and the movement time is defined as the time “spent” above the threshold (Figure 6) (similar to what has been proposed for conscious access, e.g., Pereira et al., 2022).

We fitted this model to data coming from each participant individually. The fit procedure was adapted from Ratcliff and Smith (2004) and is commonly used to fit sequential sampling models to behavioural data (e.g., Servant et al., 2019). The model was simultaneously fitted to RTs and MTs quantiles (0.1, 0.3, 0.5, 0.7, 0.9), to take into account the entire RTs and MTs distributions (instead of only the mean or median values). The following loss function (likelihood-ratio chi-square statistic) was minimised:

$$G^2 = 2 \left( \sum_{i=1}^{2} \sum_{j=1}^{6} p_{ij} \log \frac{p_{ij}}{\pi_{ij}} \right)$$

where the outer summation over $i$ refers to RTs and MTs, and the inner summation over $j$ extends refers to the 6 bins bounded by RT/MT quantiles. The quantities $p_{ij}$ and $\pi_{ij}$ are the observed and predicted proportions of trials in RT/MT bin $j$. This $G^2$ statistic characterises the goodness-of-fit of the model to the joint distributions of RT and MT. It was minimised using differential evolution (Storn & Price, 1997) and 200 simulated trials per condition (with $F = 0.9$ and $CR = 0.95$) using the DEoptim package (Mullen et al., 2011). The code underlying this model and the fitting routine are implemented in the momimi package (available on Github: https://github.com/lnalborczyk/momimi).
<caption>Table 3</caption>

*Average (group-level) parameter estimates from the algorithmic model of motor imagery*

<table><thead><tr><th>Condition (trial sequence)</th><th>Execution threshold</th><th>Peak time</th><th>Curvature</th></tr></thead><tbody><tr><td>executed_imagined</td><td>1.34</td><td>1.23</td><td>0.68</td></tr><tr><td>imagined_imagined</td><td>1.24</td><td>1.22</td><td>0.68</td></tr></tbody></table>

Estimates from these models are reported in Figure 6 and Table 3. Recall that whereas in previous studies both RTs and MTs increased following imagined trials, in the present study group-level results showed that MTs increased but RTs decreased following imagined trials. Parameter estimates obtained by fitting the algorithmic model to these data suggest that these differences may be due to different underlying inhibitory mechanisms. More precisely, the shortening of RTs (jointly observed with the lengthening of MTs) following imagined trials is compatible with a *decrease* in the motor execution threshold (as reported in Table 3), whereas the lengthening of both RTs and MTs following imagined trials is compatible with a delay in the peak time of the activation function (as previously suggested by Nalborczyk et al., 2024).

Discussion

We investigated the presence of motor inhibition during typing imagery with an adapted version of Rieger et al. (2017)'s action-mode switching paradigm. Overall, our results show that typing imagery slows down the realisation of subsequent executed or imagined movements (as assessed by differences in MTs), but speeds up the initiation of subsequent executed or imagined movements (as assessed by differences in RTs). Taken together, these results suggest that motor inhibition prevents the execution of typing acts during typing imagery. The algorithmic modelling revealed a possible implementation of inhibition in terms of threshold modulation or delay. In the following, we discuss this
interpretation in more detail, including potential explanations for the discrepancies between our results and previous results obtained using the action-mode switching paradigm.

Unexpected effects on reaction times

Based on previous studies using a similar paradigm with imagined and executed pointing movements, we hypothesised that motor inhibition during imagined movements may slow down both the RT and MT. However, we did not observe such an effect on the RTs. The discrepancy between our results and those of previous studies may come from the nature of the task(s) participants had to realise during the time period considered as the “reaction time” in our experiment. During this time period, participants had to identify the action of the current trial (as indicated by the geometrical shape surrounding the word displayed on the screen), but they also had to *read the word*, and possibly process it at different linguistic stages, which differs considerably from the RT period of previous studies involving simpler hand movements and during which participants only had to identify the spatially congruent target to which they should next reach (or imagined reaching). We assessed this possibility in a second follow-up preregistered study (cf. supplementary materials). The overall procedure was similar to the main experiment, except for the linguistic material. In this experiment, we sought to design an experiment closer to the protocol of Rieger et al. (2017). To this end, we used bigrams instead of words while taking care of maximising the distance between left-hand and right-hand bigrams on the keyboard. Moreover, we added an association training during which participants learned to associate four bigrams to a geometrical shape. This was intended to remove or reduce the effect of reading the item on screen during the period considered as “reaction time” and, as such, to provide a more direct comparison to the procedure of Rieger et al. (2017). Overall, we observed no difference across sequences in average RTs or MTs, but we observed longer RTs and MTs variability following imagined trials, in a direction compatible with the expected effects on average RTs and MTs (cf. supplementary materials, Table S1 and S2).
Another possibility, suggested by a reviewer, is that cue repetition (i.e., the geometric
shape indicating the action mode) in successive trials of the same action mode may have
provided a perceptual benefit, which could counteract the hypothesised effect (especially on
RTs). In other words, the effect of task-switching may be confounded with the effect of
cue-switching (e.g., Schneider & Logan, 2011). Whereas this could explain why we did not
observe the predicted effect on RTs in II vs. EI sequences (where the effect of cue repetition
and action mode switching may cancel out), this could not explain the lack of effects on
RTs in IE vs. EE sequences. Indeed, in executed trials, perceptual benefits related to cue
(action mode) should be congruent (in the same direction) with inhibition effects and
should therefore results in longer RTs for IE vs. EE sequences, which is not what was
observed. Nevertheless, the effect of cue-switching could be assessed in a future extension
of the present study by using a 2:1 cue-task mapping to assess the impact of this
manipulation on the pattern of RTs/MTs and the model's parameters (in the vein of
Schmitz & Voss, 2014).

416 **Distinct inhibitory mechanisms**

We introduced and fitted an algorithmic model of motor imagery allowing to
disentangle distinct underlying inhibitory mechanisms. Results from this analysis showed
that motor inhibition may have different effects on patterns of RTs and MTs, suggesting
that it may affect different aspects of the latent activation function (i.e., the shape of the
activation function or the motor execution threshold). More precisely, the shortening of
RTs jointly observed with the lengthening of MTs following imagined trials (as observed in
the present study) is compatible with a *decrease* in the motor execution threshold (as
reported in Table 3), whereas the lengthening of both RTs and MTs following imagined
trials (as observed in previous studies such as Bart et al., 2020, 2021a, 2021b; Rieger et al.,
2017) is compatible with a delay in the peak time of the activation function (as previously
suggested by Nalborczyk et al., 2024).
As mentioned previously, task requirements during the RT period in the present study differed considerably from that of previous studies. These differences in task requirements may have impacted the patterns of RTs/MTs as well as the model's estimates. We further speculate that across typing and pointing, motor inhibition resulting from imagery affects different aspects of the execution/imagery initiation in the following trial. For instance, Haith et al. (2016) showed that motor preparation (i.e., preparing a movement) and motor initiation (i.e., initiating a movement once it is prepared) may be modulated independently and suggested that these two components have distinctly neural correlates. We therefore speculate that these two components of the RT (i.e., preparation + initiation) may be impacted differently in typing vs. pointing movements and may be mapped to distinct parameters of the algorithmic model. More precisely, because motor preparation is associated with activity in motor and premotor cortices and may share resources with motor execution, we might expect it to impact the shape of the activation function – this would correspond to modulations of the peak time and/or the curvature parameter. In contrast, motor initiation is not expected to affect the shape of the activation function and may therefore be related to modulations of the threshold parameter (whose modulations do not change the shape of the activation function), with correlates in brain regions such as the SMA (as suggested for motor initiation by Haith et al., 2016). Relating this discussion to the two tasks mentioned previously (typing vs. pointing), we could speculate that motor inhibition in the pointing action-mode switching tasks impacts the shape of the activation function (thus, slowing down motor preparation) whereas motor inhibition in the typing action mode switching tasks would impact the motor execution threshold (thus, slowing down or delaying motor initiation). This remains to be assessed in future studies combining behavioural measures with neuroimaging or electrophysiological measures of motor preparation and motor imagery.

Perhaps surprisingly, fitting the algorithmic model of motor imagery presented in Nalborczyk et al. (2024) to our data revealed that the motor execution threshold was
455 *decreased* in imagined-imagined sequences relative to executed-imagined sequences.

456 Whereas this interpretation may seem unintuitive at first, it reflects the *increase* of the
457 motor execution threshold in the first (previous) imagined trial, which then drifts back to
458 an average value. In other words, the motor execution threshold decreases in the second
459 trial of an imagined-imagined sequence (relative to the first trial) because it has increased
460 in the first trial of this sequence (relative to the trial preceding this sequence) and has no
461 reason to increase further. This could reflect strategic modulations of the execution
462 threshold in sequences of trials wherein the threshold is modulated according to task
demands in order to minimise a putative cost associated with upregulating the threshold
463 higher than necessary. This hypothesis is supported by considering what happens in
464 sequences of three successive trials (i.e., imagined-imagined-imagined
465 vs. executed-imagined-imagined sequences). In such sequences, the motor execution
466 threshold in the last trial of this sequence is *lower* in imagined-imagined-imagined than in
467 executed-imagined-imagined sequences (RTs/MTs are smaller/longer in III than EII
468 sequences, cf. Figure S3 in supplementary materials).
469

470 These inhibitory mechanisms may be subserved by brain regions such as the
471 pre-supplementary motor area (Kasess et al., 2008) or the right inferior frontal gyrus,
472 which may plausibly be responsible for weakening the motor commands that are emitted
473 during motor imagery (e.g., Angelini et al., 2015, 2016; Nalborczyk et al., 2022).
474 Downstream regions in the cerebellum (e.g., Lotze et al., 1999), in the brainstem (e.g.,
475 Jeannerod, 2001, 2006), or at the spinal level (e.g., Grosprêtre et al., 2016) may also
476 contribute to motor inhibition at a later stage. Alternatively, the need for explicit
477 inhibitory mechanisms counteracting motor commands has been questioned by studies
478 looking at population-level neuronal activity during motor preparation or motor imagery
479 (e.g., Dekleva et al., 2024; Kaufman et al., 2014). Analyses of intracranial recordings in
480 monkeys have revealed that neuronal activity within the primary motor cortex cancels out
481 at the population level during motor preparation, explaining how the motor cortex may
prepare movement without executing it (Kaufman et al., 2014). Dekleva et al. (2024)

further showed, in a implanted human tetraplegic patient, that motor imagery and motor

execution do not occupy completely orthogonal neural subspaces. Rather, population-level

neural activity can be decomposed in a common subspace plus orthogonal subspaces for
executed and imagined movements. This population-level view does not necessarily

contradict the algorithmic perspective of the model fitted on the present data, however.

Indeed, premotor areas (such as the dorsal premotor cortex) may be responsible for sending

“trigger” signals responsible for switching from the execution or imagery neural subspaces

(similar to what has been hypothesised for motor preparation and execution, e.g., Zimnik

& Churchland, 2021), with similar behavioural consequences (e.g., on imagined RTs and

MTs) than the threshold modulation perspective. Moreover, the “activation timecourse”

assumed by the algorithmic model may correspond to the population-level aggregate

activity within the primary motor cortex. Whether this assumption holds (or whether the

activation timecourse may be related to fluctuations in the vividness of motor imagery

percepts) remains to be assessed in future studies using TMS or intracranial measurements

together with continuous subjective reports.

Conclusions

In summary, we adapted the action mode switching paradigm (Rieger et al., 2017) to
typing imagery and observed that typing imagery slows down subsequent imagined or
executed typing movements. We further used and discussed a novel algorithmic model of
motor imagery allowing to infer the underlying continuous timecourse of motor activation
throughout the trial and to distinguish between different potential inhibitory mechanisms.

The assumptions and novel predictions derived from this framework remain to be assessed,
thus providing promising avenues for future research on the rich subjective experience of
motor imagery and the neural mechanisms supporting it.
## Acknowledgements

We want to thank Camille Grasso and Thibault Gajdos for insightful comments at various stages of the present research and Martina Rieger for her suggestions regarding the data analysis and interpretation. This work, carried out within the Institute of Convergence ILCB (ANR-16-CONV-0002), has benefited from support from the French government (France 2030), managed by the French National Agency for Research (ANR) and the Excellence Initiative of Aix-Marseille University (A*MIDEX). A CC-BY public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions. The funders have/had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

## Data availability statement

Data and supplementary materials are available via the Open Science Framework: https://osf.io/y9a3k/.
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<figure><img src="image_4.png" /><figcaption>*Figure 2.* Average reaction time (top), movement time (middle), and total response time (bottom) across conditions. The error bars represent the 25th and 75th percentiles. Each dot represents the by-participant median reaction time (top row) or movement time (bottom) computed across 96 trials per condition. Left panel: imagined trials. Right panel: executed trials. Sequences of trials (i.e., pair of successive trials) are represented on the x-axis, which corresponds to (from left to right): imagined-imagined, executed-imagined, imagined-executed, and executed-executed sequences.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>*Figure 3.* Estimated mean reaction time (top) and movement time (bottom) across conditions in Experiment 1. The error bars represent the 95% credible intervals. Sequences of trials (i.e., pair of successive trials) are represented on the x-axis, which corresponds to (from left to right): imagined-imagined, executed-imagined, imagined-executed, and executed-executed sequences.</figcaption></figure>
<figure><img src="image_6.png" /></figure>

*Figure 4.* Relation between the average movement time in executed and imagined trials. Each panel represents a participant, each dot/triangle represents the per-word average MT in executed and imagined trials for this participant. Dashed lines represent the isochrony lines for which executed and imagined MTs are equal. Participants classified as 'off-task' according to this correlation are depicted in orange on the left whereas participants classified as 'on-task' according to the correlation between executed and imagined MTs are depicted in green on the right.
<figure><img src="image_7.png" /><figcaption>*Figure 5.* Average (median) reaction time and movement time per condition for participants with a null correlation between average executed and imagined movement times (left panel), those with a weak positive correlation (middle panel), and those with a strong positive correlation (right panel).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>*Figure 6.* Average latent activation function as estimated by the model in imagined-imagined (blue) and executed-imagined (orange) sequences from stimulus onset (time = 0) to RT (first threshold crossing) onto to MT (difference between the second and first threshold crossing). The horizontal dotted lines represent the motor execution thresholds whereas the dashed horizontal lines represent the motor imagery thresholds. Only participants with a strong positive correlation between executed and imagined MTs (N = 16) were included.</figcaption></figure>