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# A robust temporal map of speech monitoring from planning to articulation

Lydia Dorokhova, Benjamin Morillon, Cristina Baus, Pascal Belin, Anne-Sophie Dubarry, F.-Xavier Alario, Elin Runnqvist

► To cite this version:

Lydia Dorokhova, Benjamin Morillon, Cristina Baus, Pascal Belin, Anne-Sophie Dubarry, et al. A robust temporal map of speech monitoring from planning to articulation. Speech Communication, 2024, 165, pp.103146. 10.1016/j.specom.2024.103146. hal-04772609

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https://hal.science/hal-04772609v1

Submitted on 8 Nov 2024

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# A robust temporal map of speech monitoring from planning to articulation

Lydia Dorokhova<sup>a</sup>, Benjamin Morillon<sup>b</sup>, Cristina Baus<sup>c</sup>, Pascal Belin<sup>d</sup>, Anne-Sophie Dubarry<sup>e</sup>, F.-Xavier Alario<sup>e</sup>, Elin Runnqvist<sup>a,*</sup>

## ARTICLE INFO

**Keywords:**

Error monitoring

Response-conflict

Language production

EEG

MVPA

## ABSTRACT

Speakers continuously monitor their own speech to optimize fluent production, but the precise timing and underlying variables influencing speech monitoring remain insufficiently understood. Through two EEG experiments, this study aimed to provide a comprehensive temporal map of monitoring processes ranging from speech planning to articulation.

In both experiments, participants were primed to switch the consonant onsets of target word pairs read aloud, eliciting speech errors of either lexical or articulatory-phonetic (AP) origin. Experiment I used pairs of the same stimuli words, creating lexical or non-lexical errors when switching initial consonants, with the degree of shared AP features not fully balanced but considered in the analysis. Experiment II followed a similar methodology but used different words in pairs for the lexical and non-lexical conditions, fully orthogonalizing the number of shared AP features.

As error probability is higher in trials primed to result in lexical versus non-lexical errors and AP-close compared to AP-distant errors, more monitoring is required for these conditions. Similarly, error trials require more monitoring compared to correct trials. We used high versus low error probability on correct trials and errors versus correct trials as indices of monitoring.

Across both experiments, we observed that on correct trials, lexical error probability effects were present during initial stages of speech planning, while AP error probability effects emerged during speech motor preparation. In contrast, error trials showed differences from correct utterances in both early and late speech motor preparation and during articulation. These findings suggest that (a) response conflict on ultimately correct trials does not persist during articulation; (b) the timecourse of response conflict is restricted to the time window during which a given linguistic level is task-relevant (early on for response appropriateness-related variables and later for articulation-relevant variables); and (c) monitoring during the response is primarily triggered by pre-response monitoring failure. These results support that monitoring in language production is temporally distributed and rely on multiple mechanisms.

## 1. Introduction

Speaking is a complex process that engages both cognitive and motor components, encompassing semantic and lexical retrieval as well as articulatory programming and execution. Extensive research has provided evidence that both cognitive and motor aspects of speech are monitored continuously for fluent production. For instance, naturally

occurring and laboratory induced speech errors show patterns suggesting the anticipation of potential undesired outcomes during speech planning. In particular, contextually inappropriate responses such as taboo words or non-lexical speech errors occur below chance even in controlled error protocols (Severens et al., 2011; Hartsuiker et al., 2005). Monitoring is also evident in speakers' behavior in response

<footnote><sup>a</sup> Aix-Marseille Université, CNRS, LPL, Laboratoire Parole et Langage, 5 avenue Pasteur, Aix-en-Provence, 13100, France</footnote>

<footnote><sup>b</sup> Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Faculty of Medicine, 27, Boulevard Jean Moulin, Marseille, 13005, France</footnote>

<footnote><sup>c</sup> Department of Cognition, Development and Educational Psychology, University of Barcelona, Edifici de Ponent, Pg. de la Vall d'Hebron, 171, Barcelona, 08035, Spain</footnote>

<footnote><sup>d</sup> Aix-Marseille Université, CNRS, INT, Institut de Neurosciences de la Tumone, Faculty of Medicine, 27, Boulevard Jean Moulin, Marseille, 13005, France</footnote>

<footnote><sup>e</sup> Aix-Marseille Université, CNRS, CRPN, Centre de Recherche en Psychologie et Neuroscience, 3, place Victor-Hugo, Marseille, 13331, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: lydia.dorokhova@gmail.com (L. Dorokhova), benjamin.morillon@univ-amu.fr (B. Morillon), baus.cristina@gmail.com (C. Baus), pascal.belin@univ-amu.fr (P. Belin), anne-sophie.dubarry@univ-amu.fr (A.-S. Dubarry), francois-xavier.alario@univ-amu.fr (F.-X. Alario), elin.runnqvist@univ-amu.fr (E. Runnqvist).</footnote>
to their own speech errors, including accurate self-reporting of errors in various environments (Postma and Noordanus, 1996; Gauvin et al., 2016); post-error increases in response latencies (Ganushchak and Schiller, 2006); and self-repairs (Levelt, 1983). It has been observed that certain speech error repairs occur too rapidly to be attributed to the interception and planning of corrections after the error is produced, suggesting that errors are intercepted before becoming overt (Levelt, 1983; Hartsuiker and Kolk, 2001). Furthermore, studies involving modulated speech feedback have demonstrated error monitoring during articulation, as participants adapt their speech production (pitch or formants) to compensate for perceived distortions in feedback (e.g. Savariaux et al. (1995) and Niziolek and Guenther (2013)). Somatosensory feedback has also been altered to the same effect (e.g., Tremblay et al. (2003)). Overall, speech error patterns reveal the coexistence of both cognitive and motor dimensions in speech monitoring.

Concerning the temporal dynamics of monitoring, it has been shown that error-to-cutoff times display a bimodal distribution, with an interruption of an erroneous segment occurring either shortly after the error or around 500 ms later (Nooteboom and Quené, 2017). This implies the existence of at least two distinct time points during which monitoring processes occur or interact with the production process. Employing time-sensitive electroencephalographic recordings (EEG), prior literature has revealed three relevant time points to observe EEG activity in speech-monitoring tasks: speech planning in its initial stages, speech motor planning, and speech articulation. In the context of initial stage speech planning, several studies have examined the EEG signal following stimulus presentation and preceding motor response preparation. In a speech production task designed to prime errors, trials resulting in errors showed an increased negativity between 350 ms and 600 ms after the appearance of a written word pair to be pronounced aloud (Moller et al., 2006). Additionally, semantic response conflict on correct trials in a phoneme detection task elicited a negativity around 450 ms after stimulus presentation (Ganushchak and Schiller, 2008a). Concerning speech motor planning, previous EEG studies have analyzed the signal following the presentation of a speech-cue, immediately before the response. In one study, within the 50–150 ms and 230–300 ms time windows erroneous trials exhibited more negative potentials compared to correct productions (Moller et al., 2006). In another study, correct trials primed to result in taboo word errors resulted in an increased negativity in the 550–625 ms time window compared to correct trials primed to result in neutral errors (e.g. Severens et al. (2011)). Lastly, concerning speech articulation, previous studies observed that during picture naming, incorrectly named pictures resulted in a larger ERN compared to correctly named pictures, with the ERN also being influenced by semantic naming context (Ganushchak and Schiller, 2008b; Masaki et al., 2001; Riès et al., 2011; Baus et al., 2020).

Despite these valuable insights, a comprehensive understanding of the temporal dynamics of monitoring is hindered by the focus on specific variables and time frames of production in each study. The majority of previous studies have targeted monitoring through comparisons involving overt errors, lacking insight into how monitoring operates in contexts where errors are probable but ultimately avoided (but see Severens et al. (2011)). As such, it remains unclear whether monitoring occurs at multiple time points for overt errors only or also for correct trials where errors are likely. Additionally, while some previous research has explored the impact of meaning-related variables (e.g., semantic relatedness, taboo status) on speech monitoring, the influence of other linguistic variables susceptible of producing response conflict, such as lexical or articulatory-phonetic variables, is still not well understood, and to our knowledge no study has explored more than one linguistic variable in the same study. Thus, the extent to which the time course of monitoring is similar for all aspects of speech or varies based on the level of representation is still unclear. Some hints to the answer to these open questions can be obtained through the results of two recent fMRI studies that examined

correct utterances produced in contexts of high lexically or articulatory-phonetic driven error probability respectively, and that also examined erroneous as opposed to correct utterances (Runnqvist et al., 2021; Todorović et al., 2024). It was observed that cerebellar structures (Crus I) related to predictive internal modeling were active for both monitoring of correct but error prone utterances and for overt errors compared to correct utterances across lexical and articulatory-phonetic variables. In contrast, additional frontal and medial frontal structures were recruited for error prone utterances at the articulatory-phonetic level and for overt errors, indicating that distinct mechanisms are at play depending on the distance from articulation. Finally, across both studies, the anterior cingulate cortex was only differentially activated for the contrasts involving overt errors, supporting the involvement of a different feedback control related mechanism for overt errors. While the observed differential brain activity in these studies provides evidence supporting dissociations in monitoring mechanisms depending on whether errors are probable or overt and depending on linguistic variables (see also Runnqvist (2023), Teghipco et al. (2023), Okada et al. (2018), Hansen et al. (2019a,b) and Volfart et al. (2022)), it does not offer information about the specific timing of when these mechanisms are engaged during speech production. Doing so was the objective of the current study.

To this end, we analyzed all comparable aspects of two similar experiments using EEG, restricting our interpretations to robust effects that replicated across both. In both experiments, participants were primed to switch the consonant onsets of target word pairs read aloud, eliciting speech errors of either lexical or articulatory-phonetic (AP) origin. To control for physical variance in the stimuli across conditions, Experiment I used pairs of the same stimuli words, creating lexical or non-lexical errors when switching initial consonants, with the degree of shared AP features not fully balanced but considered in the analysis. To fully orthogonalize the number of shared AP features, Experiment II followed a similar methodology but used different words in pairs for the lexical and non-lexical conditions.

As error probability is higher in trials primed to result in lexical versus non-lexical errors and AP-close compared to AP-distant errors, more monitoring is required for these conditions. Similarly, error trials require more monitoring compared to correct trials. We thus used high versus low lexical or AP error probability on correct trials, and errors versus correct trials as indices of monitoring.

In order to cover the entire speech production process as reflected both by externally triggered events (e.g., stimulus and speech cue appearance, see Fig. 1) and internally initiated events (e.g., the response), the EEG signal was segmented into three distinct epochs (stimulus-locked, speech-cue-locked, and response-locked, see Fig. 1). Finally, we performed both event-related potential (ERP) analyses and multivariate pattern (MVPA) analyses on the data. The reason behind this analysis approach was that MVPA can be more sensitive to subtle variations in neural activity. It can detect distributed patterns of neural activation that ERPs might miss, making it especially useful when studying cognitive processes that involve complex and distributed neural networks (e.g., King and Dehaene (2014)).

## 2. Methods and materials

The dataset collected and made available by the authors for this study can be accessed from the Open Science Framework (OSF) repository (Dorokhova et al., 2024).

### 2.1. Participants

The study received appropriate ethical approval (filed under id "RCB: 2011-A00562-39") at the regional ethical committee "Comité de Protection des Personnes Sud Méditerranée I".
<figure><img src="image_4.png" /><figcaption>Fig. 1. Schematic representation of the procedure and epoching of EEG signal. Please note that the red font in the word pairs is only for illustration purposes, in the actual experiments all the words were presented in white font. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

## Experiment I

Twenty-nine right-handed native speakers of French (22 women) with normal or corrected-to-normal vision took part in the experiment in exchange for monetary compensation (mean age = 21, min = 19, max = 23). No participant reported any history of language or neu- rological disorders. One participant was excluded from the analyses because they had participated in another study using the same task only a few weeks before the experimental session. Four participants were excluded from further analyses because of excessive noise or EEG data loss. Thus, 24 participants were included in the final analysis.

## Experiment II

Fourty-four right-handed native speakers of French with normal or corrected-to-normal vision took part in the experiment in exchange for monetary compensation. No participant reported any history of language or neurological disorders. Thirteen participants were excluded from the analyses due to different issues: behavioral (5 participants had an error rate outside of the criteria: 1%<n<50%), problems during EEG recordings (8 participants, excessive noise or EEG data loss). Thus, 31 participants (29 women, mean age = 23, min = 19, max = 31) were included in the final analysis.

## 2.2. Stimuli

### Experiment I

Target stimuli consisted of 160 printed French nouns (those used in Runnqvist et al. (2016)) to be presented in pairs. For illustrative purposes, the examples in the text are given in English. To control for differences due to auditory stimulation, motor activity, or artic- ulator specific modulations of the signal (e.g., Szirtes and Vaughan (1977)), the same words were to be produced across conditions (albeit combined differently to prime **lexical** and **non-lexical errors**). Thus, across participants, each word was used twice in combination with another word (e.g., *mole sail*, *mole fence*). Exchanging the first letters

of these combinations would result in a new word pair in one case (*sole mail*, lexical error outcome) and in a non-word pair in the other case (*fole mence*, non-lexical error outcome). An orthographic criterion was used for selecting stimuli, but even when applying a phonological criterion post-hoc only 7/160 non-lexical items resulted in real words for one of the words in a pair when changing orthography (which sometimes also entailed a change in wordclass, e.g., for the pair *caverne bouton* the primed noun error *couton* does not exist but *coûtons* is a conjugated form of the verb *couter*). All combinations were used in both possible orders across participants (e.g., *mole sail* and *sail mole*). Further, all combinations for which the exchange of initial phonemes resulted in new word-pairs (*mole sail*) were used also in reversed order (*sole mail*). The words in the target pairs were selected with the criterion that they should be unrelated. Despite this effort, for 9/320 word pairs (4 lexical and 5 non-lexical) there was some form of semantic relationship between the two words (e.g., *sick sinus. chicken tavern*). A given participant was only presented with one combination for each word (lexical or non-lexical outcome), and was only presented with one of the words differing in only the first sound (*mole* or *sole*). This resulted in the creation of eight experimental lists with 80 word pairs (40 lexical and 40 non-lexical error outcome) counterbalanced across participants. Finally, all word pairs were coded for the degree of shared phonetic features (place, manner of articulation and voicing) of initial consonants of words in a pair being assigned a number from 0 (AP distant words, e.g., [m] is labial, nasal and voiced and [s] is dental, fricative and voiceless) to 2 (AP close words, e.g., [m] and [b] both being labial and voiced). Of the 80 word pairs, on average across the 8 lists 25.5 word pairs did not share any features, 40.25 word pairs shared one feature and 14.25 shared 2 features. As a first step, to control for a possible confound between our lexical and AP variables, we controlled whether the stimuli across the lexical and non-lexical conditions differed in the average amount of shared features (SF) and this was not the case (lexical 0.9 shared features vs. non-lexical 0.8 shared features). The two-tailed independent samples t-test comparing
average SF values between the lexical and non-lexical conditions (n = 8 for both groups) yielded a non-significant result (p = 0.11). As a subsequent step, we introduced AP Proximity as a three-level factor (No SF, 1 SF and 2 SF) during the analysis of errors and reaction times. This allowed us to investigate potential interactions between this factor and the lexical status, as detailed in the 2.5 Analysis and 3 Results sections. More importantly for the current purposes, coding the phonetic proximity between our word pairs also allowed us to assess the impact of this AP variable known to modulate speech error rates (e.g., Nooteboom and Quené (2008) and Oppenheim and Dell (2008)) on participants' electrophysiological recordings.

During the experiment, three priming word pairs preceded each target word pair. The first two shared the initial consonants, and the third pair had further phonological overlap with the error being primed (*sun mall – sand mouth – soap mate – mole sail*). Note that, to induce errors, the order of the two initial consonants (/s/ and /m/) is different for the primes and the target. Participants were also presented with 153 filler pairs that had no specific relationship to their corresponding target pairs. One to three such filler pairs were presented to participants before each prime and target sequence. Thus, each participant was presented with 473 unique word combinations (80 targets, 240 primes and 153 fillers). Each list contained three blocks in which these 473 words were repeated three times in different orders. Participants were instructed to read all target word pairs aloud, all prime pairs silently, 35% of the filler pairs aloud and 65% of the filler pairs silently. Prior to the commencement of the experimental trials, participants underwent a task familiarization phase. This entailed exposure to a concise task sample, comprising 10 instances, under the direct supervision of the experimenter.

## Experiment II

240 French monosyllabic (120) and bisyllabic (120) nouns were presented in pairs. Pairs were constant; there was no cross-combination of words as in Experiment I. Just as in Experiment I, exchanging the initial consonants of words in a pair gave a **lexical** or **non-lexical** outcome. We applied a phonological criterion for selecting the stimuli. In this stimuli set we manipulated the number of shared phonetic features of the onsets of words in pairs: half of the stimuli were **AP close** (2 shared feature among 3 possible: place, manner and voicing) and half were **AP distant** (no shared features), AP close and AP distant pairs were distributed equally across the lexical status conditions. Thus, here were no intermediate values (1 feature in common) as in Experiment I, because we aimed to maximize the effect by using the extremes. Words in pairs were always presented in the same order. Thus, the list was composed of 120 words pairs, where 60 were bisyllabic, 60 - monosyllabic, each syllabic condition contained 30 lexical and 30 non-lexical outcome pairs, and each lexical condition contained 15 AP close and 15 AP distant pairs. As in Experiment I, each target pair was preceded by 3 primes with the inverted order of onsets of words in pairs. Participants also saw 234 filler pairs, as in Experiment I, 1 to 3 of such pairs were presented before each sequence of primes and targets. The list was repeated 2 times with different order of sequences and primes. Participants were instructed to read all target word pairs aloud, all prime pairs silently, 49% of the filler pairs aloud and 51% of the filler pairs silently. Participants underwent pre-experiment task familiarization, involving a supervised exposure to a 10-instance task sample.

## 2.3. Procedure

### Experiment I & Experiment II

The experiment was controlled by Eprime 2.0 software (Schneider et al., 2002). Word pairs were presented centered on the computer screen with a black courier new font, size 18, on a white background. Each word pair remained on the screen for 700 ms and words presented for silent reading were followed by a blank screen for 200 ms. All

targets and 35% of the filler items in the Experiment I and 50% of the filler items in Experiment II were followed by (a) a question mark remaining on the screen for 500 ms. (b) an exclamation mark presented 500 ms after the presentation of the question mark and remaining on the screen for 1000 ms, (c) a blank screen for 500 ms before the next trial started (see Schematic representation of the task in Fig. 1). Participants were seated at a distance of approximately 50 cm from the computer screen. They wore a microphone attached to the head in Experiment I, the microphone was placed on the table in front of them in Experiment II. They were instructed to silently read the word pairs as they appeared, but to name aloud the last word pair they had seen whenever a question mark was presented, and before the appearance of an exclamation mark. Hence, the purpose of the question mark cue was to prompt speech, and the purpose of the exclamation mark cue was to prompt speed. Productions were recorded both through E-prime and the software Audacity® to be processed off-line.

## 2.4. Electrophysiological recordings

### Experiment I & Experiment II

The EEG was recorded from 64 Ag/AgCl Active-Two pre-amplified electrodes (BIOSEMI, Amsterdam; 10–20 system positions). The sampling rate was 1024 Hz for Experiment I (online filters: DC to 208 Hz, 3 db/octave) and 2048 for Experiment II. Two additional electrodes placed close to Cz, the Common Mode Sense (CMS) active electrode and the Driven Right Leg (DRL) passive electrode, were used to form a feedback loop that maintains the average potential of the participant as close as possible to the AD-box reference potential. Two additional electrodes placed over the left and right mastoid were used to reference the signals (average mastoids reference). The vertical EOG was obtained by subtracting the signal of C29 (corresponding to FP2) from the signal of an external electrode placed underneath the left eye. The horizontal EOG was recorded with two external electrodes positioned over the two outer canthi.

## 2.5. Analyses

### 2.5.1. Behavior

#### Experiment I & Experiment II

**Annotation.** A person naive to the purpose of the experiment transcribed all spoken productions, then inspected and coded vocal response on-sets of all individual recordings using Check-vocal (Protopapas, 2007). Check-vocal is a software that allows for semi-automatic codification of the response accuracy and timing based on two sources of information: the speech waveform and the spectrogram. The transcriptions were scored as correct, disfluencies, partial responses (e.g., only one word produced), full omissions, and erroneous productions. The latter were classified as **priming related errors** or **other errors**. **Priming related errors** included full exchanges (*mill pad* ⇒ *pill mad*), partial exchanges (anticipations, e.g., *mill pad* ⇒ *pill pad*, perseverations, e.g., *mill pad* ⇒ *mill mad*, other partial exchanges, e.g., *mill pad* ⇒ *mill pack*). repaired and interrupted exchanges (*mill pad* ⇒ *pi..mill pad*), full and partial competing errors (*mill pad* ⇒ *pant milk/pant pad*), and other related errors (*mill pad* ⇒ *mad pill*). **Other errors** included diverse phonological substitutions that were unrelated to the priming manipulation (e.g., *mill pad* ⇒ *chill pant/gri..mill pad/..pant*).

#### Experiment I

**Data overview.** Data of 24 participants initially presented 5760 trials, where each of the 80 pairs was repeated three times, resulting in 240 trials per participant. The lexicality condition was equally distributed in halves of the total number of trials, while the Articulatory-Phonetic (AP) condition introduced three levels (as detailed in 2.2 Stimuli). This configuration yielded 1834 pairs with no shared features, 2898
pairs with one shared feature, and 1028 pairs with two shared features between the initial consonants of the word pairs.

To ensure data quality, an initial filtering step excluded trials featuring full omissions, leading to the removal of 327 trials (5.68%). Subsequently, instances with RTs less than 100 ms or exceeding 1000 ms were identified as outliers and eliminated, accounting for 99 trials (1.72%). The final data set consisted of 5334 trials, distributed across conditions as follows: lexical (2663 trials), non-lexical (2671 trials), 0 shared features (1701 trials), 1 shared feature (2687 trials), and 2 shared features (946 trials).

Prior to statistical analysis, orthogonal contrasts were implemented for the AP condition using Helmert coding via the R built-in function contr.helmert. For the lexicality factor and priming_related_errors factor, sum coding (contr.sum) was applied (Chambers et al., 1990 through R documentation).

## Experiment II

*Data overview.* Data of 31 participants initially presented 7440 trials, wherein each of the 120 pairs was repeated twice, resulting in 240 trials per participant. The lexicality and AP conditions were evenly distributed in halves of the total trial count (3720 per condition).

Following the same data filtering process as in Experiment I, the exclusion of full omissions led to a reduction in the number of trials to 7344 (1.29% excluded). The removal of RT outliers (those falling outside the range of 100 ms < RT < 1000 ms) accounted for 144 trials (1.44%). The resultant dataset comprised 7200 trials, distributed across conditions as follows: lexical (3579 trials), non-lexical (3621 trials), AP close (3588 trials), and AP distant (3621 trials).

Given the balanced nature of all conditions, sum contrasts were employed for all factors using sum coding (contr.sum) (Chambers et al., 1990 via R documentation) prior to conducting the statistical analysis in R Core Team (2022).

## Experiment I & Experiment II

*Statistical models.* The data analysis was conducted using the RStudio (R Core Team, 2022) and key analytical tasks were performed utilizing specific packages including 'Matrix' (version 1.5-1, Bates et al. (2021)), 'lme4' (version 1.1-34, Bates et al. (2015)), 'lmerTest' (version 3.1-3, Kuznetsova et al. (2021)), 'multcomp' (version 1.4-25, Hothorn et al. (2021)), dplyr (version 1.1.2, Wickham et al. (2021)), ggplot2 (version 3.4.3, Wickham (2016)), each of which facilitated critical statistical and visualization procedures. For the analysis of errors, we employed a mixed-effects logistic regression model using the glmer function of lme4 package in R Core Team (2022). The initially proposed model:

$$ \texttt{glmer Substance \sim (1 + Lexicality | Subject) + (1 + AP | Subject) + (1 | WordPair), family = binomial} $$

involved two primary predictor variables, specifically Lexicality and AP, along with their interaction. Furthermore, the model accounted for a diverse set of random effects, encompassing random slopes and intercepts for both Lexicality and AP based on each individual subject in conjunction with a random intercept for the Word pair. However, due to convergence issues encountered during the modeling process, the complexity of the initial model needed to be streamlined. As a result, the model, shown below, aimed to scrutinize the effects of lexicality, AP condition, and their interaction on priming-related errors:

$$ \texttt{glmer Substance \sim (1 + Lexicality | Subject) + (1 + AP | Subject), family = binomial} $$

This model featured fixed effects for lexicality and AP condition as well as their interaction, while random intercepts for subjects and Word Pairs captured both individual differences and item-specific effects.

Additionally, two separate lmer functions were employed. The first aimed to uncover Reaction Time (RT) discrepancies between overt errors and correct productions:

$$ \texttt{lmer(RT \sim priming\_related\_errors + (1 + priming\_related\_errors | Subject) + (1 | WordPair))} $$

This model included random intercepts for both subjects and items, along with a random slope for priming_related_errors within subjects, addressing subject-specific variations in how these errors influenced Reaction Time.

The second lmer model was exclusively applied to correct trials and focused on examining the interplay between Reaction Time (RT) and the variables of Lexicality and Articulatory-Phonetic (AP), along with their interaction.

$$ \texttt{lmer(RT \sim lexicality + AP + lexicality * AP + (1 | Subject) + (1 | WordPair))} $$

This model integrated random intercepts for subjects and items to accommodate individual differences and item-specific effects. The final model is an outcome of simplifying a more complex model that originally included random slopes and intercepts for both Lexicality and AP with respect to each individual subject. The decision to simplify was prompted by issues with convergence that were encountered during the modeling process.

### 2.5.2. EEG signal processing

#### Experiment I & Experiment II

*Preprocessing.* The EEG data was processed using the EEGLAB toolbox (Delorme and Makeig, 2004) in MATLAB (Inc., 2020). Continuous EEG data were filtered offline through a 0.1 Hz to 30 Hz band-pass filter. Activity from the left and right mastoid electrodes were used offline to re-reference scalp recordings. For the analyses we defined three epochs of interest: ERPs were either (1) time locked to the stimulus and segmented into 800 ms epochs (–100 to 700 ms), (2) locked to the speech cue into 500 ms epochs (–100 to 400 ms) or (3) locked to the response into 1500 ms epochs (–1000 to 500 ms) (Fig. 1). Only segments without artifacts (activity ± 75 µV) were included. The epochs were then averaged and referenced to a 100 ms pre-stimulus, pre-speech-cue and pre-response baseline, respectively.

*ERP analyses.* As a next step we conducted a peak search within the epochs using ERP lab (Lopez-Calderon and Luck, 2014). The different conditions (overt (priming related) errors/correct; AP close/distant and lexical/non-lexical outcome) were averaged prior to this analysis, and we considered only the response peaks that were observed in both Experiments for subsequent analyses. For this, grand average waves of each epoch of both Experiments were inspected visually for the appearance of positive or negative peaks. Then the approximate time interval was given to the peak latency research function in ERP Measurement tool to obtain the exact peak latency value. These values were compared through two Experiments: when the difference in peak latency was less than 15 ms in between two Experiments, the mean value between the two peaks was used, when the difference was bigger, the peaks were not retained for further analyses. Subsequently, these peaks were used as centers of 100 ms time-windows. When it was impossible to use 100 ms time-windows, the largest possible symmetrical window was defined. The sum up of all the considered time-windows is reported in Table 1.
<table><caption>**Table 1**
Summary of time windows around common peaks found across Experiment I and II.</caption><thead><tr><th>Epoch</th><th>Window start</th><th>Window end</th></tr></thead><tbody><tr><td rowspan="2">Stimulus locked</td><td>62</td><td>162</td></tr><tr><td>142</td><td>242</td></tr><tr><td rowspan="3">Speech-cue locked</td><td>164</td><td>264</td></tr><tr><td>226</td><td>326</td></tr><tr><td>372</td><td>400</td></tr><tr><td rowspan="4">Response locked</td><td>-793</td><td>-693</td></tr><tr><td>-483</td><td>-383</td></tr><tr><td>-115</td><td>-15</td></tr><tr><td>62</td><td>162</td></tr></tbody></table>

**Statistical models.** We utilized the same software and packages mentioned in the 2.5.1 Behavior Statistical models section to analyze EEG signal data. Each window of each Experiment was analyzed with Linear Mixed-Effects Models on 9 fronto-central electrodes of interest (FC1, FCZ, FC2, C1, Cz, C2, CP1, CPz, CP2) same as in Grisoni et al. (2019) and on all electrodes (57 after excluding frontal electrodes F7, AF7, Fp1, Fpz, Fp2, and AF8). Separate regressions were applied to each of three conditions: (1) overt errors vs. correct:

lmer (Mean amplitude ~ priming_related_errors*Electrode + (1|subject))

(2) lexical vs. non-lexical primed error outcome on correct trials:

lmer (Mean amplitude ~ lexicality*Electrode + (1|subject))

and (3) Articulatory-phonetic proximity (AP) (close (2 shared features) vs. distant (no shared features) on correct trials) -only the conditions of 0 and 2 shared features were contrasted in the analysis of AP proximity to ensure comparability across the two experiments:

lmer (Mean amplitude ~ AP*Electrode + (1|subject))

In summary, each time-window was analyzed with three separate models to investigate the effects of errors, lexicaity and AP proximity respectively (see below). Even though we conducted two separate experiments and focused on the cross-validated findings, we decided to apply a Bonferroni correction to handle the issue of multiple comparisons. Summarized p-values for both corrected and uncorrected regressions are available in the Appendix A, specifically in Table A.1 for 9 fronto-central electrodes of interest, and Table A.2 for all 57 electrodes.

MVPA. Multivariate Pattern Analysis (MVPA) was also conducted on both Experiments on the same time-windows as in the ERP analysis, with the skelearn software (Pedregosa et al., 2011). We used a nested cross-validation scheme to ensure robust model evaluation. Both the inner and outer cross-validation loops consisted of 5 folds, thereby minimizing the risk of overfitting and ensuring that the model was trained and tested on different subsets of the data. Before model training, all features were standardized to have zero mean and unit variance. Feature selection was performed using an ANOVA F-test to retain the top 100 features, which were most predictive of the outcome. We utilized the LogisticRegression model from the scikit-learn library, with the 'lbfgs' solver and an L2 penalty term. The regularization hyperparameter was tuned using a grid search approach over the range of values [0.01, 0.1, 0.25, 0.5, 0.75, 1.0]. This tuning was performed within the inner cross-validation loop, optimizing for the highest average ROC-AUC score. The segmented data was fitted into a 2D space-time Riemannian manifold to then run a logistic regression to classify across trials each of our three binary variables: (1) overt errors vs. correct, (2) lexical vs. non-lexical status and (3) AP close vs. AP distant. Splitting of the data was performed using a stratified folding approach, to prevent

models from biasing toward the most numerous class. The outer loop of the nested cross-validation was carried out 10 times and averaged per subject. This analysis was repeated for each of the three contrasts and on each time-window. Analyses were performed at the single-subject level and followed by standard parametric one-tail paired t-tests at the group level (distribution of ROC_AUC values across subject compared to chance level (50%)). The final model pipeline can be consulted at the Open Science Framework (OSF) repository (Dorokhova et al., 2024).

# 3. Results

## 3.1. Behavior

### Experiment I

#### Errors.

Participants made errors in 230 trials (3.99% of all data). After the filtering described in 2.5 Analyses, this number was reduced to 222 (4.16% of filtered data) of which 124 errors were priming related (2.32% of filtered data). More priming related errors were made in the lexical condition (114, 2.14%) than in the non-lexical one (10, 0.18%). The dispatch of errors in articulatory – phonetic proximity groups was as follows: 26 errors out of 1701 trials without shared features AP (1.53%), 75 errors out of 2687 trials with 1 SF (2.79%), and 23 errors out of 946 trials with 2 SF (2.43%). The effect of lexicaity was significant ($z = 6.31; p < .001$) on priming-related errors according to the Generalized Linear Mixed-Effects Model (see 2.5 Analyses for description). There was no effect of the articulatory-phonetic condition (1SF: $z = .02; p = .9$; 2SF: $z = .49; p = .62$) and no interaction with the lexicaity condition (1SF: $z = 1.45; p = .14$; 2SF: $z = .18; p = .85$) (see Fig. 2).

RT. Participants were slower in overall error trials (mean $RT_{222} = 612$ ms) that included priming related errors (mean $RT_{124} = 570$ ms)) than in correct trials (mean $RT_{5112} = 515$ ms). The effect of priming related errors on the RT was significant ($d.f. = 20.59; t = -3.03; p < .01$). On correct trials, no significant difference in RT was observed between lexical (mean $RT_{2489} = 515$ ms and non-lexical condition (mean $RT_{2623} = 515$ ms), ($d.f. = 598.8; t = .41, p = .68$). Participants were gradually slower through the AP condition ranging from mean $RT_{1640} = 508$ ms for 0 SF, $RT_{2568} = 518$ ms for 1 SF to $RT_{904} = 522$ ms for 2 SF. The AP effect was significant (1SF: $d.f. = 580.48; t = 2.73; p = .006$; 2SF: $d.f. = 586.79; t = 2.3; p = .02$), but no interaction with lexicaity was observed (1SF: $d.f. = 587.19; t = -1.35; p = .17$; 2SF: $d.f. = 606.73; t = .03; p = .97$) (see Fig. 3).

### Experiment II

Errors. Participants made errors in (1100) trials (14.78% of all data), after the filtering described in 2.5 Analyses, this number was reduced to 912 with 372 of priming related errors (5.16% of filtered data). More errors were committed in the lexical condition (259, 3.59%) compared to the non-lexical (113, 1.56%) and in the close articulatory-phonetic condition (236, 3.27%) compared to the distant articulatory-phonetic condition (136, 1.88%). The effects of lexicaity and articulatory-phonetic condition were significant ($z = 4.42; p < .001$; $z = -3.35; p < .001$ respectively) on priming-related errors without interaction of the variables ($z = 1.2; p = .23$).

RT. Participants were slower in overall error trials (mean $RT_{912} = 628$ ms) that included priming related errors (mean $RT_{372} = 633$ ms)) than in correct trials (mean $RT_{6278} = 531$ ms). The effect of priming related errors on the RT was significant ($d.f. = 31.99; t = -8.6; p < .001$). On correct trials, no significant difference in RT was observed between lexical (mean $RT_{3057} = 532$ ms) and non-lexical condition (mean $RT_{3221} = 530$ ms), ($d.f. = 113.53; t = .97; p = .33$). Participants were slower in the close AP condition (mean $RT_{3028} = 538$ ms) than in the distant AP condition ($RT_{3250} = 525$ ms). The AP effect was significant ($d.f. = 113.51; t = -3.1; p < .01$), but no interaction with lexicaity was observed (1SF: $d.f. = 113.52; t = -1; p = .28$).
PRIMING RELATED ERRORS

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Percentages of priming related errors by Lexicality (lexical, non-lexical) and AP conditions (0 shared features -SF-, 1 SF, 2SF). Panel A for Experiment I, panel B for Experiment II. Each portion is displayed with respect to the number of trials within the condition (i.e., the potential maximum number of errors. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

REACTION TIMES

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Reaction Time (RT) Distribution by Articulatory phonetic (AP) Condition (0 shared features -SF-, 1 SF, 2SF) on Correct Trials (Left) and RT Distribution for Correct Responses and Overt Priming-Related Errors (Right). Panel A corresponds to Experiment I, while Panel B corresponds to Experiment II.</figcaption></figure>

3.2. EEG signal results

At the neural level, we investigated significant effects of lexicality
(lexical vs. non-lexical error priming), phonetic articulatory (AP close
vs. distant onsets) and error (overt errors vs. correct). We investi-
gated them with both a univariate (ERP) and a multivariate (MVPA)
method. Each analysis was performed on the two experiments, the
three distinct types of epochs (stimulus-locked, speech-cue-locked, and
response-locked) and either all electrodes or a ROI analysis including
9 fronto-central electrodes of interest (see Methods; Grisoni et al.
(2019)). Below we especially focus on the effects that were significant
across both experiments, but the effects that did not replicate through

Experiments are visualized and marked with asterisk in Fig. 4 for
response-locked epochs, in Fig. 5 for speech cue-locked epochs.

ERP results. Three time windows in the response-locked epoch elicited significant differences for overt errors vs. correct condition in both datasets in the ROI analysis of 9 fronto-central electrodes. The first two time windows occurred before production onset. Firstly, the waveform of correct trials differed significantly from errors in the time window between [-483 to -383 ms] (Figure ??). This effect was followed by a significant difference between errors and correct trials during the pre-response positive drop [-115 ms, -15 ms] (Fig. 4), ([-483 ms, -383 ms] and [-115 ms, -15 ms]). A third significant difference
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Summary of results: (top) MVPA, (middle) mean difference waves of all electrodes and (bottom) of the 9 fronto-central electrodes and their topographic maps across Experiment I (panel A.) and Experiment II (panel B.) in response-locked epochs. Colors are used to differentiate the conditions: blue for lexicality, green for AP proximity and red for overt error vs. correct trials. Bold stars mark significant bonferroni corrected p-values if observed in both Experiments, asterisks significant bonferroni corrected p-values if observed in one Experiment, empty diamonds significant uncorrected p-values. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

between correct trials and errors occurred after the onset of articulation ([62 ms, 162 ms]) (see Figure ??, Fig. 4). For the stimulus-locked epochs, no significant effects were observed consistently across both experiments. In the ‘all electrodes’ analysis, the initial window of the speech cue-locked epoch [164 to 264 ms] exhibited a significant effect of overt errors, albeit not reaching significance after applying

Bonferroni correction. The ERP analysis did not reveal any significant effect -cross-validated across Experiments-of the Articulatory-Phonetic (AP) and Lexicality effects. Supplementary tables of p-values can be found in the Appendix A.

**MVPA results.** Both datasets showed significant decoding for the first time window (164–264 ms) of the speech cue-locked epoch for AP
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Summary of results: (top) MVPA, (middle) mean difference waves of all electrodes and (bottom) of the 9 fronto-central electrodes and their topographic maps across Experiment I (panel A.) and Experiment II (panel B.) in speech cue-locked epochs. Colors are used to differentiate the conditions: blue for lexicality, green for AP proximity and red for overt error vs. correct trials. Bold stars mark significant bonferroni corrected p-values if observed in both Experiments, asterisks significant bonferroni corrected p-values if observed in one Experiment, empty diamonds significant uncorrected p-values. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

close vs. distant (Experiment 1: t = 3.10; [ROC_AUC] = 0.53; p < 0.01; Experiment 2: t = 4.70; [ROC_AUC] = 0.57; p < 0.01) and overt errors vs. correct (Experiment 1: t = 2.42; [ROC_AUC] = 0.52; p = 0.01; Experiment 2: t = 3.29; [ROC_AUC] = 0.56; p < 0.01). Furthermore, both datasets showed significant decoding for the response-locked epochs: the first window (–793, –693 ms) revealed a significant lexicality effect

(Experiment 1: t = 2.85; [ROC_AUC] = 0.53; p < 0.01; Experiment 2: t = 5.7; [ROC_AUC] = 0.61; p < 0.01) and the second window (–483; –383 ms) revealed a significant overt errors vs. correct effect (Experiment 1: t = 3.52; [ROC_AUC] = 0.53; p < 0.01; Experiment 2: t = 4.35; [ROC_AUC] = 0.59; p < 0.01). No significant effects consistent across both experiments were observed for the stimulus-locked epochs.
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Grand average wave of FCz electrode for correct (green) and overt error (red) trials in Experiment I (panel A.) and Experiment II (panel B.) in response-locked epochs. Stars indicate significant effect in GLM on 9 fronto-central electrodes (FC1 FCz FC2 C1 Cz C2 CP1 CPz CP2). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Left: Grand average wave of FCz electrode for correct (green) and overt error (red) trials in Experiment I (panel A.) and Experiment II (panel B.) in speech cue-locked epochs. Right: Grand average wave of FCz electrode for AP close (red, 2 shared features-SF) and AP distant (green, 0 SF) correct trials in Experiment I (panel A.) and Experiment II (panel B.) in speech cue-locked epochs. Asterisks and diamonds indicate significant effect in one or the other GLM (9 fronto-central electrodes or all electrode) of the ERP. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

Additional significant decoding results, not replicated between the two experiments, are depicted in Figs. 4, 5, and also in Table A.3 and Fig. B.2 in the Appendix B (see Figs. 6 and 7).

## 4. Discussion

The principal aim of this study was to thoroughly investigate the temporal dynamics of the monitoring process, encompassing speech planning, speech-motor preparation, and articulation. Our specific focus was to explore potential variations in the temporal dynamics of monitoring of (a) correctly produced utterances with high error probability inflicted by either lexical or articulatory-phonetic related variables; and (b) incorrect utterances. To accomplish this, we analyzed all comparable aspects of two similar EEG experiments. To have a comprehensive temporal map of the entire speech production process, the three contrasts of interest (high vs low lexically driven error probability, high vs low articulatory-phonetic driven error probability, and

errors vs correct trials) were examined across three distinct epochs that allowed us to assess speech planning, speech motor preparation and articulation. Finally, we employed two types of analyses (ERP and MVPA) to increase our chances of detecting subtle effects, as well as effects distributed over several scalp locations. It is worth noting that our emphasis was on robust effects that consistently emerged across both experiments, ensuring the reliability of the results. In what follows we will discuss the results we obtained for the initial stages of speech planning, speech motor planning and articulation.

### Initial stages of speech planning

The initial stages of speech planning were examined through the stimulus-locked epochs and through the early part of the response-locked epochs (see Fig. 1). There was an effect of lexicality on the early pre-response part of the response-locked time window [−793 – 693]. That is, based on the electrophysiological response across all electrodes, the MVPA distinguished above chance those correct trials that were more error prone due to the lexical response competition
<caption>Table A.1</caption>

Summary of p-values of 9 fronto-central electrode GLM. Significant values in one experiment are in bold, significant in both experiments are on gray background.

<table><caption>Table A.2</caption><thead><tr><th rowspan="3"></th><th rowspan="3"></th><th rowspan="2">Time-window</th><th colspan="2">Overt error</th><th colspan="2">Lexicality</th><th colspan="2">AP proximity</th></tr><tr><th colspan="2">p-values</th><th colspan="2">p-values</th><th colspan="2">p-values</th></tr><tr><th></th><th></th><th>(uncorrected)</th><th></th><th>(uncorrected)</th><th></th><th>(uncorrected)</th></tr></thead><tbody><tr><td rowspan="10">Exp.1</td><td rowspan="2">Stimulus</td><td>62 162</td><td>0.132</td><td>0.066</td><td>0.904</td><td>0.452</td><td>0.460</td><td>0.230</td></tr><tr><td>142 242</td><td>0.212</td><td>0.106</td><td>0.643</td><td>0.322</td><td>0.505</td><td>0.253</td></tr><tr><td rowspan="3">Speech cue</td><td>164 264</td><td>2.141</td><td>0.714</td><td>0.353</td><td>0.118</td><td>0.141</td><td><strong>0.047</strong></td></tr><tr><td>226 326</td><td>1.989</td><td>0.663</td><td>0.754</td><td>0.251</td><td>0.120</td><td><strong>0.040</strong></td></tr><tr><td>372 400</td><td><strong>0.010</strong></td><td><strong>0.003</strong></td><td>2.545</td><td>0.848</td><td>0.098</td><td><strong>0.033</strong></td></tr><tr><td rowspan="4">Response</td><td>-793 -693</td><td>0.806</td><td>0.269</td><td>0.483</td><td>0.161</td><td>1.739</td><td>0.580</td></tr><tr><td>-483 -383</td><td><strong>0.001</strong></td><td><strong>&lt;0.001</strong></td><td>1.083</td><td>0.361</td><td><strong>0.044</strong></td><td><strong>0.015</strong></td></tr><tr><td>-115 -15</td><td><strong>&lt;0.001</strong></td><td><strong>&lt;0.001</strong></td><td>0.783</td><td>0.261</td><td>0.349</td><td>0.116</td></tr><tr><td>62 162</td><td><strong>0.003</strong></td><td><strong>0.001</strong></td><td>2.099</td><td>0.700</td><td><strong>0.036</strong></td><td><strong>0.012</strong></td></tr><tr><td rowspan="10">Exp.2</td><td rowspan="2">Stimulus</td><td>62 162</td><td>0.490</td><td>0.245</td><td>0.890</td><td>0.445</td><td>0.634</td><td>0.317</td></tr><tr><td>142 242</td><td>1.560</td><td>0.780</td><td>0.578</td><td>0.289</td><td>1.745</td><td>0.872</td></tr><tr><td rowspan="3">Speech cue</td><td>164 264</td><td>1.884</td><td>0.628</td><td><strong>0.048</strong></td><td><strong>0.016</strong></td><td>1.919</td><td>0.640</td></tr><tr><td>226 326</td><td>1.475</td><td>0.492</td><td>0.132</td><td><strong>0.044</strong></td><td>2.850</td><td>0.950</td></tr><tr><td>372 400</td><td>1.566</td><td>0.522</td><td>0.139</td><td><strong>0.046</strong></td><td>0.764</td><td>0.255</td></tr><tr><td rowspan="4">Response</td><td>-793 -693</td><td>0.669</td><td>0.223</td><td>1.560</td><td>0.520</td><td>0.324</td><td>0.108</td></tr><tr><td>-483 -383</td><td><strong>0.007</strong></td><td><strong>0.002</strong></td><td>0.215</td><td>0.072</td><td>1.856</td><td>0.619</td></tr><tr><td>-115 -15</td><td><strong>&lt;0.001</strong></td><td><strong>&lt;0.001</strong></td><td>0.855</td><td>0.285</td><td><strong>0.015</strong></td><td><strong>0.005</strong></td></tr><tr><td>62 162</td><td><strong>0.002</strong></td><td><strong>0.001</strong></td><td>1.646</td><td>0.549</td><td>0.574</td><td>0.191</td></tr></tbody></table>

Summary of p-values of all electrode GLM. Significant values in one experiment are in bold, significant in both experiments are on gray background.

<table><thead><tr><th rowspan="3"></th><th rowspan="3"></th><th rowspan="2">Time-window</th><th colspan="2">Overt error</th><th colspan="2">Lexicality</th><th colspan="2">AP proximity</th></tr><tr><th colspan="2">p-values</th><th colspan="2">p-values</th><th colspan="2">p-values</th></tr><tr><th></th><th></th><th>(uncorrected)</th><th></th><th>(uncorrected)</th><th></th><th>(uncorrected)</th></tr></thead><tbody><tr><td rowspan="10">Exp.1</td><td rowspan="2">Stimulus</td><td>62 162</td><td>1.100</td><td>0.367</td><td>1.716</td><td>0.572</td><td>0.853</td><td>0.284</td></tr><tr><td>142 242</td><td>0.243</td><td>0.081</td><td>2.480</td><td>0.827</td><td>2.855</td><td>0.952</td></tr><tr><td rowspan="3">Speech cue</td><td>164 264</td><td>0.097</td><td><strong>0.032</strong></td><td>0.905</td><td>0.302</td><td>0.729</td><td>0.243</td></tr><tr><td>226 326</td><td>0.161</td><td>0.054</td><td>2.834</td><td>0.945</td><td>1.670</td><td>0.557</td></tr><tr><td>372 400</td><td>0.129</td><td>0.064</td><td>1.842</td><td>0.921</td><td>1.944</td><td>0.972</td></tr><tr><td rowspan="4">Response</td><td>-793 -693</td><td>0.124</td><td>0.062</td><td>1.876</td><td>0.938</td><td>1.689</td><td>0.844</td></tr><tr><td>-483 -383</td><td>2.734</td><td>0.911</td><td>1.035</td><td>0.345</td><td>1.637</td><td>0.546</td></tr><tr><td>-115 -15</td><td>2.351</td><td>0.784</td><td>1.351</td><td>0.450</td><td>1.537</td><td>0.512</td></tr><tr><td>62 162</td><td><strong>0.014</strong></td><td><strong>0.005</strong></td><td>2.175</td><td>0.725</td><td>1.272</td><td>0.424</td></tr><tr><td rowspan="10">Exp.2</td><td rowspan="2">Stimulus</td><td>62 162</td><td>0.500</td><td>0.167</td><td>1.414</td><td>0.471</td><td>0.853</td><td>0.284</td></tr><tr><td>142 242</td><td>0.283</td><td>0.094</td><td>0.780</td><td>0.260</td><td>2.855</td><td>0.952</td></tr><tr><td rowspan="3">Speech cue</td><td>164 264</td><td><strong>0.028</strong></td><td><strong>0.009</strong></td><td>1.950</td><td>0.650</td><td>0.729</td><td>0.243</td></tr><tr><td>226 326</td><td>0.070</td><td><strong>0.023</strong></td><td>1.391</td><td>0.464</td><td>1.670</td><td>0.557</td></tr><tr><td>372 400</td><td>0.315</td><td>0.157</td><td>1.941</td><td>0.970</td><td>1.944</td><td>0.972</td></tr><tr><td rowspan="4">Response</td><td>-793 -693</td><td>1.085</td><td>0.542</td><td>1.939</td><td>0.969</td><td>1.689</td><td>0.844</td></tr><tr><td>-483 -383</td><td>2.265</td><td>0.755</td><td>0.148</td><td><strong>0.049</strong></td><td>1.637</td><td>0.546</td></tr><tr><td>-115 -15</td><td>1.962</td><td>0.654</td><td>0.217</td><td>0.072</td><td>1.537</td><td>0.512</td></tr><tr><td>62 162</td><td>1.400</td><td>0.467</td><td>1.203</td><td>0.401</td><td>1.272</td><td>0.424</td></tr></tbody></table>

from those that were less error prone due to an absence of lexical competition. Interestingly, this effect falls almost in the same time-window where previous studies had observed ERP effects of semantic response conflict (Ganushchak and Schiller, 2008a) and of response conflict (Moller et al., 2006). Taken together, these findings seem to indicate that the kind of response competition affecting early stages of speech planning is related to the meaning or appropriateness of a potential response (i.e., competing responses that are semantically related as opposed to unrelated, or that are real words as opposed to non-words are all more appropriate). Additionally, this time window resulted in an overt errors vs. correct effect in Experiment I, similar to Moller et al. (2006), but the absence of this effect in Experiment II, despite a larger number of observations, indicates its lesser robustness. One possibility is that the effect was more robust in their study because all their critical trials were primed to result in lexical errors and were thus always response appropriate. To gain further insights on the nature of the effect of lexical error probability that we observed, we will briefly consider the results of the fMRI study by Runnqvist et al. (2021), using the exact same task as here and the same stimuli as in our Experiment

1. In that study, lexically driven error probability engaged the Crus I of the cerebellum, which was linked to the internal modeling of upcoming speech as a means of error monitoring. Interestingly, and consistent with this interpretation of predictive internal modeling, another study using EEG found that the readiness-potential (RP), was modulated by predictability (Grisoni et al., 2019). Although we did not observe a distinguishable RP in our data, the timing of our MVPA lexicality effect preceding the verbal response is consistent with this component that is usually observed preceding a motor response. Thus, linking our findings with a previously found cerebellar origin of the effect and with modulations related to predictability occurring in similar time windows in previous studies, a plausible interpretation is that monitoring during the initial stages of speech planning is carried out through predictive internal modeling (e.g. Runnqvist et al. (2016, 2021) and Pickering and Garrod (2013)).

## Speech motor preparation

The speech motor preparation period was investigated through the speech-cue locked epochs and the late pre-response part of response-locked epochs. Leveraging multivariate pattern analysis (MVPA), we
<table><caption>Table A.3</caption><caption>Summary of p-values of standard parametric one-tail paired t-test of the distribution of MVPA ROC_AUC values compared to chance level. Significant values are in bold, significant in both experiments are on gray background.</caption><thead><tr><th></th><th>Time-window</th><th>Overt error</th><th>Lexicality</th><th>AP proximity</th></tr><tr><th></th><th></th><th colspan="3">p-values</th></tr></thead><tbody><tr><td rowspan="10">Exp.1</td><td>Stimulus</td><td>62 162</td><td>0.350</td><td>0.431</td><td>0.178</td></tr><tr><td></td><td>142 242</td><td><strong>0.027</strong></td><td>0.271</td><td>0.809</td></tr><tr><td>Speech cue</td><td>164 264</td><td><strong>0.002</strong></td><td>0.095</td><td><strong>&lt;0.001</strong></td></tr><tr><td></td><td>226 326</td><td>0.750</td><td>0.374</td><td>0.359</td></tr><tr><td></td><td>372 400</td><td>0.099</td><td>0.073</td><td><strong>0.005</strong></td></tr><tr><td>Response</td><td>-793 -693</td><td><strong>0.001</strong></td><td><strong>&lt;0.001</strong></td><td>0.072</td></tr><tr><td></td><td>-483 -383</td><td><strong>&lt;0.001</strong></td><td>0.345</td><td>0.992</td></tr><tr><td></td><td>-115 -15</td><td>0.101</td><td>0.130</td><td>0.089</td></tr><tr><td></td><td>62 162</td><td>0.081</td><td><strong>0.003</strong></td><td><strong>0.007</strong></td></tr><tr><td rowspan="10">Exp.2</td><td>Stimulus</td><td>62 162</td><td>0.085</td><td>0.692</td><td>0.111</td></tr><tr><td></td><td>142 242</td><td>0.644</td><td><strong>0.028</strong></td><td>0.494</td></tr><tr><td>Speech cue</td><td>164 264</td><td><strong>0.011</strong></td><td>0.060</td><td><strong>0.002</strong></td></tr><tr><td></td><td>226 326</td><td>0.697</td><td>0.278</td><td>0.520</td></tr><tr><td></td><td>372 400</td><td>0.578</td><td>0.214</td><td>0.629</td></tr><tr><td>Response</td><td>-793 -693</td><td>0.097</td><td><strong>0.004</strong></td><td>0.055</td></tr><tr><td></td><td>-483 -383</td><td><strong>0.001</strong></td><td>0.863</td><td>0.590</td></tr><tr><td></td><td>-115 -15</td><td><strong>0.012</strong></td><td>0.569</td><td>0.420</td></tr><tr><td></td><td>62 162</td><td>0.583</td><td>0.151</td><td>0.201</td></tr></tbody></table>

observed significant decoding rates for both overt errors as compared to correct trials and for high as compared to low articulatory-phonetic error probability on correct trials within a [164 264 ms] time-window after the speech cue. This time window is similar to the one where previously more negative event-related potential (ERP) for errors were reported (Moller et al., 2006). Our MVPA findings seem to mirror the EEG findings of Moller et al. (2006) in what concerns overt errors, but also extend their findings by showing that articulatory phonetic conflict also impacts the same time window. In the study of Moller et al. (2006), the spatial source of the EEG effect was localized to the medial frontal cortex (SMA), with a potential involvement of the anterior cingulate region. Additionally, the SMA activation during speech planning (post-speech cue) was previously linked by Moller et al. (2006) to conflicts in articulatory gestures planning, which is in line with recent findings by Todorović et al. (2024) and the appearance of the pre-SMA region activation in situations involving increased articulatory-motor complexity (e.g., Alario et al. (2006)). In summary, our findings are consistent with the SMAs known implication in phonetic encoding and articulation complexity, and provides support for the hypothesis that also response conflict involving articulatory phonetic representations may lead to greater SMA activation during speech motor planning. Connecting both sets of findings (fMRI and EEG), the SMA might work in concert with frontal and parietal structures and the superior cerebellum in a forward modeling loop preparing for motor execution (e.g., Riecker et al. (2005) and Todorović et al. (2024)). Regarding the late pre-response window of the response locked epochs, we observed a significant difference between errors and correct trials in the ERPs [−115 to −15 ms]. We propose two plausible interpretations for this effect: The first interpretation is consistent with the findings discussed in the speech-planning section regarding the readiness potential (RP) from Grisoni et al. (2017). In fact, this window corresponds to the greatest negativity of the RP, preceding the positive drop related to motor actions. The effect on this window supports the idea of prediction mechanisms as a monitoring component and implies that errors are inherently more unpredictable than correct trials. Alternatively, the effect may also be attributed to proprioceptive error detection involving somatosensory speech targets and stemming from the preparation of articulatory muscles before the onset of speaking (Riès et al., 2020; Guenther and Hickok, 2016).

### Articulation

The articulation-related effects were examined by analyzing the post-response part of the response-locked epochs. The contrast of overt errors vs. correct trials yielded a significant effect in the time window [62–162 ms] that is consistent with the error-related negativity

(ERN) (Ganushchak and Schiller, 2008a). We thus interpret the effect in the [62–162 ms] time window as the ERN, which is typically associated with conflict monitoring in the Anterior Cingulate Cortex (ACC) (Delaheene et al., 1994; Falkenstein et al., 1991; Nozari et al., 2011, but see Burle et al. (2008)). However, in previous fMRI and intracerebral EEG studies the ACC was found to be active only for overt errors (and not for internal conflict) (Runnqvist et al., 2021; Todorović et al., 2024; Bonini et al., 2014), potentially indicating a feedback-related function coming into play in the time-window when the error is executed (Loh et al., 2020). Surprisingly, multivariate pattern analysis (MVPA) did not show significant decoding rates for overt errors vs. correct trials in this time window, while the event-related potential (ERP) effects were robustly significant across experiments and even after applying Bonferroni correction. This discrepancy raises questions about the underlying factors reflected by these two types of analyses and will be discussed further.

To summarize the global picture from both experiments: Internal monitoring of correct trials reveals early differences, discernible only during speech planning, suggesting that conflict on correct trials related to response appropriateness or meaning is resolved or substantially diminished by the time motor planning and articulation take place. On the other hand, articulatory-phonetic conflict on correct trials triggers differences only during speech motor preparation, but this conflict is also resolved or greatly diminished before the actual response. In contrast, overt errors trigger differences during both early and late speech motor preparation, as well as during articulation. Thus, when examining correct trials, there seems to be a temporal coincidence between the moment of conflict emergence and the moment when the object of this conflict becomes task-relevant (i.e., response appropriateness or meaning related conflict arises when participants read and process meaning, and articulatory-phonetic conflict arises during speech motor planning). Note that while these results indeed suggest that processes operating on the already activated linguistic representations to be produced unfold in a sequential manner according to their task relevance, they may be compatible with both sequential and parallel processing in language production (e.g., Fairs et al. (2021)). For instance, Fairs et al. (2021) proposed that while all dimensions of words are subject to a first pass of parallel activation (ignition) due to their holistic nature, selection and checking processes are likely to proceed sequentially during later reverberation processes. Concerning the mechanism underlying these internal monitoring effects, the combined evidence of this and previous studies suggest that this monitoring is carried out through predictive internal modeling. If correct, such internal modeling seems to generate error signals in a temporally distributed
<figure><img src="image_11.png" /><figcaption>**Fig. B.1.** Grand average wave of FCz electrode for non-lexical (green) and lexical (red) trials (left column); for AP close (red) and AP distant (green) correct trials (middle column); for correct (green) and overt error (red) trials (right column) in Experiment I (panel A) and Experiment II (panel B) in stimulus-locked (top panel), speech cue-locked (central panel) and response-locked (down panel) epochs. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

and task-relevant fashion (as opposed to only, for instance, upon phonological encoding). Conversely, overt errors show both pre-response and response related differences in processing, suggesting that when the error signal of the predictive internal modeling is not enough to stop an error, additional, presumably more feedback dependent, processes are triggered during articulation. That is, the persistence of the effect related to overt errors suggests that errors may be detected multiple times, possibly through different processes. Concretely, error detection may occur through internal modeling before the response, followed by proprioceptive feedback, and finally, feedback-related mismatch. This interpretation aligns with fMRI studies that have identified the cerebellum, housing the internal models, as playing a significant role for monitoring of both error probability and overt errors (Runnqvist et al., 2021; Todorović et al., 2024), while other structures seemed to be exclusively triggered by overt errors (e.g., the ACC).

In a more comprehensive discussion of the present study, two intriguing questions remain unanswered. The first question pertains to determining the most relevant event during speech production for precisely accounting for the underlying monitoring processes. Specifically, we consider two types of segmentation: one based on external events,

such as stimulus presentation and speech cue appearance, and the other based on the participant's initiated response. While the response-locked epochs provide dynamic windows across participants, the stimulus-locked epochs remain stable. To effectively analyze groups of individuals, alignment in processes is essential to observe and statistically quantify the effects. Remarkably, the response-locked segmentation yielded a greater number of significant effects in both experiments and revealed the lexical effect during the speech planning stage, which coincided with the window of the stimulus-locked segmentation where this effect was not observed. This finding suggests that the initiation of the response may be better suited as the departure point that allows for alignment among individuals, as external events may introduce perceptual differences at multiple levels (e.g., reading speed, encoding period, lexical retrieval speed), potentially disaligning the group from a process-oriented perspective.

The second question emerges from the diverging results obtained from the two types of analyses conducted on the data. For example, while both the ERP analysis and MVPA yielded consistent cross-validated results in the early pre-response window [−483 to −383 ms], the strong error-related negativity (ERN) effect observed in the post-response window with the ERP analysis was not observed with MVPA.
<figure><img src="image_12.png" /><figcaption>**Fig. B.2.** Summary of results of all performed analyses: MVPA, mean difference waves of all and 9 fronto-central electrodes and their topographic maps across Experiment I (panel A) and Experiment II (panel B.) in stimulus-locked epochs. Color code is used to differentiate the conditions: blue for lexicality, green for articulatory – phonetic proximity and red for overt error vs. correct contrast. Asterisks mark significant Bonferroni corrected p-values if observed in one Experiment. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

The differences observed between the MVPA and ERP analysis results warrant further discussion. One potential reason for these discrepancies could be the tuning of the MVPA model, which might result in overfitting or underfitting. Overfitting occurs when the model captures noise rather than the underlying signal, while underfitting happens when the model is too simplistic to capture the complexity of the data.

Moreover, while ERP effects might be strong, they can be diluted by weaker signals when integrated into the MVPA model. This integration can lead to overfitting to noise, thereby affecting the performance and interpretability of the MVPA results. The complementary nature of MVPA and ERP analyses allows for a more comprehensive understanding of the neural processes, as each method captures different aspects of the data.

It is also important to acknowledge the general difficulty in interpreting differences between these types of models. Despite the challenges, such comparisons are valuable and can provide deeper insights into the neural mechanisms underlying the observed effects.

The nuanced understanding gained from these complementary methods emphasizes the need for careful consideration of the strengths and limitations of each analytical approach.

In conclusion, while our study highlights informative comparisons between MVPA and ERP analyses, it also underscores the complexities involved in interpreting their differences. Future research should continue to explore these complementary methods to enhance our understanding of neural dynamics.

Finally, some potential limitations of our study should be pointed out. First, it is important to acknowledge that one of our experiments had a smaller sample size, which can be associated with more exaggerated effect sizes. While we believe our findings are robust, the smaller sample size might have influenced some of the replication differences observed across experiments. Future studies with larger sample sizes would be beneficial to confirm the robustness of these results and mitigate any potential biases due to sample size variations. Second, while our approach of replication across experiments at multiple levels
<table><caption>**Table C.4**
Experimental lists A,B,C,D of Experiment I.</caption><thead><tr><th colspan="3">A</th><th colspan="3">B</th></tr><tr><th>lexical</th><th>nonlexical</th><th></th><th>lexical</th><th>nonlexical</th><th></th></tr></thead><tbody><tr><td>OSF</td><td>cadeau rocher</td><td>OSF</td><td>belote ciment</td><td>OSF</td><td>carreau roulette</td></tr><tr><td>OSF</td><td>douche salle</td><td>OSF</td><td>bordée fournée</td><td>OSF</td><td>caverne mouton</td></tr><tr><td>OSF</td><td>lierre poupe</td><td>OSF</td><td>cœur robe</td><td>OSF</td><td>ceinture région</td></tr><tr><td>OSF</td><td>malade sinus</td><td>OSF</td><td>cote lueur</td><td>OSF</td><td>doc geste</td></tr><tr><td>OSF</td><td>marine fission</td><td>OSF</td><td>dague four</td><td>OSF</td><td>fête lobe</td></tr><tr><td>OSF</td><td>ministre seringue</td><td>OSF</td><td>disque suite</td><td>OSF</td><td>glace fuite</td></tr><tr><td>OSF</td><td>natte pièce</td><td>OSF</td><td>façon gardon</td><td>OSF</td><td>lanière fêtard</td></tr><tr><td>OSF</td><td>note puits</td><td>OSF</td><td>filleul monteur</td><td>OSF</td><td>lapin fusée</td></tr><tr><td>OSF</td><td>rage cap</td><td>OSF</td><td>fosse masque</td><td>OSF</td><td>lecteur joker</td></tr><tr><td>OSF</td><td>ragot fumeur</td><td>OSF</td><td>jointure boulette</td><td>OSF</td><td>maçon journée</td></tr><tr><td>OSF</td><td>tenue voiture</td><td>OSF</td><td>jonction loto</td><td>OSF</td><td>pelle risque</td></tr><tr><td>1SF</td><td>titre voile</td><td>OSF</td><td>lamelle têtard</td><td>OSF</td><td>rampe soeur</td></tr><tr><td>1SF</td><td>butte lave</td><td>OSF</td><td>manière cuisson</td><td>1SF</td><td>verbe tour</td></tr><tr><td>1SF</td><td>clé bol</td><td>OSF</td><td>panne roc</td><td>1SF</td><td>boisson caresse</td></tr><tr><td>1SF</td><td>crochet briquet</td><td>OSF</td><td>pion vase</td><td>1SF</td><td>case sueur</td></tr><tr><td>1SF</td><td>dentier répit</td><td>OSF</td><td>recteur tracas</td><td>1SF</td><td>casque fraise</td></tr><tr><td>1SF</td><td>dune lieu</td><td>OSF</td><td>rouleau tonton</td><td>1SF</td><td>cordée frimeur</td></tr><tr><td>1SF</td><td>durée pédale</td><td>OSF</td><td>tête liège</td><td>1SF</td><td>coupe frange</td></tr><tr><td>1SF</td><td>flic coin</td><td>1SF</td><td>barreau légion</td><td>1SF</td><td>fonction troupier</td></tr><tr><td>1SF</td><td>foire prime</td><td>1SF</td><td>brique vieux</td><td>1SF</td><td>gamelle ponton</td></tr><tr><td>1SF</td><td>gag troupe</td><td>1SF</td><td>contre braise</td><td>1SF</td><td>linge bourse</td></tr><tr><td>1SF</td><td>garage palette</td><td>1SF</td><td>coussin bouton</td><td>1SF</td><td>lion vote</td></tr><tr><td>1SF</td><td>gaule tare</td><td>1SF</td><td>fable place</td><td>1SF</td><td>molosse lardon</td></tr><tr><td>1SF</td><td>lutin bocal</td><td>1SF</td><td>filon croupier</td><td>1SF</td><td>pause mouche</td></tr><tr><td>1SF</td><td>matin passage</td><td>1SF</td><td>forge course</td><td>1SF</td><td>pelote fracas</td></tr><tr><td>1SF</td><td>nature ration</td><td>1SF</td><td>fraction paresse</td><td>1SF</td><td>poussin savoir</td></tr><tr><td>1SF</td><td>pillage sommier</td><td>1SF</td><td>gerbe plaque</td><td>1SF</td><td>sable poudre</td></tr><tr><td>1SF</td><td>raison maquette</td><td>1SF</td><td>lampe boule</td><td>1SF</td><td>tilleul buisson</td></tr><tr><td>1SF</td><td>rosier gâteau</td><td>1SF</td><td>moisson lavoir</td><td>1SF</td><td>toison boulet</td></tr><tr><td>1SF</td><td>serre valve</td><td>1SF</td><td>peinture musée</td><td>1SF</td><td>biche dieux</td></tr><tr><td>1SF</td><td>soir lac</td><td>1SF</td><td> pomme mâche</td><td>1SF</td><td>bosse montre</td></tr><tr><td>1SF</td><td>tonus boucan</td><td>1SF</td><td>sapin poker</td><td>1SF</td><td>bouleau moto</td></tr><tr><td>1SF</td><td>verger bison</td><td>1SF</td><td> singe veste</td><td>1SF</td><td>crique pousse</td></tr><tr><td>2SF</td><td>banque marque</td><td>1SF</td><td> touche grange</td><td>1SF</td><td>flaque somme</td></tr><tr><td>2SF</td><td>casse tube</td><td>2SF</td><td>colosse primeur</td><td>2SF</td><td>gorge canne</td></tr><tr><td>2SF</td><td>ciel fil</td><td>2SF</td><td>fiche selle</td><td>2SF</td><td>pilon bouture</td></tr><tr><td>2SF</td><td>coteau poupon</td><td>2SF</td><td>foudre soupe</td><td>2SF</td><td>pointure conteur</td></tr><tr><td>2SF</td><td>dame rose</td><td>2SF</td><td>gousse cause</td><td>2SF</td><td>tâche piège</td></tr><tr><td>2SF</td><td>faveur semelle</td><td>2SF</td><td>poison couture</td><td>2SF</td><td>traction piment</td></tr><tr><td>2SF</td><td>mec bise</td><td>2SF</td><td>taverne poulet</td><td>2SF</td><td>vague foule</td></tr><tr><th colspan="3">C</th><th colspan="3">D</th></tr><tr><th>lexical</th><th>nonlexical</th><th></th><th>lexical</th><th>nonlexical</th><th></th></tr><tr><td>OSF</td><td>boule fosse</td><td>OSF</td><td>cadeau voiture</td><td>OSF</td><td>cil nièce</td></tr><tr><td>OSF</td><td>braise fiche</td><td>OSF</td><td>casse voile</td><td>OSF</td><td>farine galette</td></tr><tr><td>OSF</td><td>colosse monteur</td><td>OSF</td><td>faveur maquette</td><td>OSF</td><td>foin dalle</td></tr><tr><td>OSF</td><td>façon musée</td><td>OSF</td><td>lutin fission</td><td>OSF</td><td>gare sac</td></tr><tr><td>OSF</td><td>grange forge</td><td>OSF</td><td>marine semelle</td><td>OSF</td><td>loir toile</td></tr><tr><td>OSF</td><td>liège pion</td><td>OSF</td><td>natte coin</td><td>OSF</td><td>lune cage</td></tr><tr><td>OSF</td><td>mâche touche</td><td>OSF</td><td>ragot palette</td><td>OSF</td><td>lute cube</td></tr><tr><td>OSF</td><td>masque contre</td><td>OSF</td><td>raison pédale</td><td>OSF</td><td>maison cocher</td></tr><tr><td>OSF</td><td>vase cote</td><td>OSF</td><td>rosier sommier</td><td>OSF</td><td>poteau vison</td></tr><tr><td>1SF</td><td>veste gerbe</td><td>OSF</td><td>serre rose</td><td>OSF</td><td>purée rumeur</td></tr><tr><td>1SF</td><td>barreau cuisson</td><td>OSF</td><td>verger poupon</td><td>1SF</td><td>rentier pommier</td></tr><tr><td>1SF</td><td>bordée couture</td><td>1SF</td><td>butte cap</td><td>1SF</td><td>salade dépit</td></tr><tr><td>1SF</td><td>course brique</td><td>1SF</td><td>ciel tare</td><td>1SF</td><td>saveur massage</td></tr><tr><td>1SF</td><td> filleul tracas</td><td>1SF</td><td>coteau bocal</td><td>1SF</td><td>sillage râteau</td></tr><tr><td>1SF</td><td>filon primeur</td><td>1SF</td><td>crochet fumeur</td><td>1SF</td><td>sinistre nation</td></tr><tr><td>1SF</td><td>four tête</td><td>1SF</td><td>dentier passage</td><td>1SF</td><td>tag loupe</td></tr><tr><td>1SF</td><td>fraction têtard</td><td>1SF</td><td>douche puits</td><td>1SF</td><td>barque clic</td></tr><tr><td>1SF</td><td>jointure poker</td><td>1SF</td><td>dune prime</td><td>1SF</td><td>blé taule</td></tr><tr><td>1SF</td><td>jonction fournée</td><td>1SF</td><td>foire troupe</td><td>1SF</td><td>butin coupon</td></tr><tr><td>1SF</td><td>lamelle gardon</td><td>1SF</td><td>gag marque</td><td>1SF</td><td>fagot toucan</td></tr><tr><td>1SF</td><td>lueur singe</td><td>1SF</td><td>garage ration</td><td>1SF</td><td>gosier toiture</td></tr><tr><td>1SF</td><td>manière loto</td><td>1SF</td><td>gaule lac</td><td>1SF</td><td>patin femelle</td></tr><tr><td>1SF</td><td>peinture ciment</td><td>1SF</td><td>lierre valve</td><td>1SF</td><td>pierre manque</td></tr><tr><td>1SF</td><td>place gousse</td><td>1SF</td><td>malade répit</td><td>1SF</td><td>pote groupe</td></tr><tr><td>1SF</td><td>plaque foudre</td><td>1SF</td><td>matin rocher</td><td>1SF</td><td>pote fiel</td></tr><tr><td>1SF</td><td>recteur légion</td><td>1SF</td><td>mec lave</td><td>1SF</td><td>raveau mission</td></tr><tr><td>1SF</td><td>robe lampe</td><td>1SF</td><td>nature bison</td><td>1SF</td><td>rampe bec</td></tr><tr><td>1SF</td><td>roc disque</td><td>1SF</td><td>note lieu</td><td>1SF</td><td>rap dieu</td></tr><tr><td>1SF</td><td>rouleau boulette</td><td>1SF</td><td>rage bise</td><td>1SF</td><td>rature local</td></tr></tbody></table>

(continued on next page)
<table><caption>Table C.4 (continued).</caption><tr><td>1SF</td><td>sapin lavoir</td><td>1SF</td><td>soir tube</td><td>1SF</td><td>somme pelle</td><td>1SF</td><td>tasse salve</td></tr><tr><td>1SF</td><td>selle pomme</td><td>1SF</td><td>tenue sinus</td><td>1SF</td><td>tilleul fracas</td><td>1SF</td><td>venue raquette</td></tr><tr><td>1SF</td><td>soupe coeur</td><td>1SF</td><td> titre fil</td><td>1SF</td><td>traction fêtard</td><td>1SF</td><td>verre bave</td></tr><tr><td>1SF</td><td>vieux dague</td><td>1SF</td><td>tonus seringue</td><td>1SF</td><td>vague dieux</td><td>1SF</td><td>vitre dose</td></tr><tr><td>2SF</td><td>belote poulet</td><td>2SF</td><td>banque poupe</td><td>2SF</td><td>boisson mouton</td><td>2SF</td><td>berger minus</td></tr><tr><td>2SF</td><td>cause panne</td><td>2SF</td><td>clé pièce</td><td>2SF</td><td>canne pause</td><td>2SF</td><td>bonus meringue</td></tr><tr><td>2SF</td><td>coussin paresse</td><td>2SF</td><td>dame bol</td><td>2SF</td><td>caverne troupier</td><td>2SF</td><td>brochet dédale</td></tr><tr><td>2SF</td><td>moisson bouton</td><td>2SF</td><td>durée gâteau</td><td>2SF</td><td>pelote boulet</td><td>2SF</td><td>mise nuits</td></tr><tr><td>2SF</td><td>poison tonton</td><td>2SF</td><td>flic salle</td><td>2SF</td><td>poussin caresse</td><td>2SF</td><td>parage criquet</td></tr><tr><td>2SF</td><td>suite fable</td><td>2SF</td><td>ministre boucan</td><td>2SF</td><td>sable fuite</td><td>2SF</td><td>patte col</td></tr><tr><td>2SF</td><td>taverne croupier</td><td>2SF</td><td>pillage briquet</td><td>2SF</td><td>toison ponton</td><td>2SF</td><td>souche frime</td></tr></table>

<table><caption>Table C.5
Experimental lists E,F,G,H of Experiment I.</caption><thead><tr><th colspan="2">E</th><th colspan="2">F</th></tr><tr><th>lexical</th><th>nonlexical</th><th>lexical</th><th>nonlexical</th></tr></thead><tbody><tr><td>OSF</td><td>cap rage</td><td>OSF</td><td>cocher radeau</td><td>OSF</td><td>cité biche</td></tr><tr><td>OSF</td><td>fission marine</td><td>OSF</td><td>dalle souche</td><td>OSF</td><td>fêtard lanière</td></tr><tr><td>OSF</td><td>fumeur ragot</td><td>OSF</td><td>loupe pierre</td><td>OSF</td><td>fuite glace</td></tr><tr><td>OSF</td><td>pièce natte</td><td>OSF</td><td>meringue sinistre</td><td>OSF</td><td>fusée lapin</td></tr><tr><td>OSF</td><td>poupe lierre</td><td>OSF</td><td>minus salade</td><td>OSF</td><td>geste doc</td></tr><tr><td>OSF</td><td>puits note</td><td>OSF</td><td>mission farine</td><td>OSF</td><td>journée maçon</td></tr><tr><td>OSF</td><td>rocher cadeau</td><td>OSF</td><td>nièce patte</td><td>OSF</td><td>lobe fête</td></tr><tr><td>OSF</td><td>salle douche</td><td>OSF</td><td>nuits pote</td><td>OSF</td><td>mouton caverne</td></tr><tr><td>OSF</td><td>seringue ministre</td><td>OSF</td><td>rap cage</td><td>OSF</td><td>région ceinture</td></tr><tr><td>OSF</td><td>sinus malade</td><td>OSF</td><td>rumeur fagot</td><td>OSF</td><td>risque pelle</td></tr><tr><td>OSF</td><td>voile titre</td><td>OSF</td><td>toile vitre</td><td>OSF</td><td>roULEtte carreau</td></tr><tr><td>1SF</td><td>voiture tenue</td><td>OSF</td><td>voiture venue</td><td>OSF</td><td>soeur rampe</td></tr><tr><td>1SF</td><td>bison verger</td><td>OSF</td><td>bave lutte</td><td>OSF</td><td>tour verbe</td></tr><tr><td>1SF</td><td>bocal lutin</td><td>OSF</td><td>col blé</td><td>1SF</td><td>boUet toison</td></tr><tr><td>1SF</td><td>bol clé</td><td>OSF</td><td>criquet brochet</td><td>1SF</td><td>bourse linge</td></tr><tr><td>1SF</td><td>boucan tonus</td><td>OSF</td><td>dédale purée</td><td>1SF</td><td>boisson tilleul</td></tr><tr><td>1SF</td><td>briquet crochet</td><td>OSF</td><td>dépit rentier</td><td>1SF</td><td>caresse boisson</td></tr><tr><td>1SF</td><td>coin flic</td><td>OSF</td><td>dieu lune</td><td>1SF</td><td>fracas pelote</td></tr><tr><td>1SF</td><td>gâteau rosier</td><td>1SF</td><td>dose rame</td><td>1SF</td><td>fraise casque</td></tr><tr><td>1SF</td><td>lac soir</td><td>1SF</td><td>foin clic</td><td>1SF</td><td>frange coupe</td></tr><tr><td>1SF</td><td>lave butte</td><td>1SF</td><td>frime poire</td><td>1SF</td><td>frimeur cordée</td></tr><tr><td>1SF</td><td>lieu dune</td><td>1SF</td><td>galette parage</td><td>1SF</td><td>joker lecteur</td></tr><tr><td>1SF</td><td>maquette raison</td><td>1SF</td><td>gare taule</td><td>1SF</td><td>lardon molosse</td></tr><tr><td>1SF</td><td>palette garage</td><td>1SF</td><td>groupe tag</td><td>1SF</td><td>mouche pause</td></tr><tr><td>1SF</td><td>passage matin</td><td>1SF</td><td>local butin</td><td>1SF</td><td>ponton gamelle</td></tr><tr><td>1SF</td><td>pédale durée</td><td>1SF</td><td>massage patin</td><td>1SF</td><td>poudre sable</td></tr><tr><td>1SF</td><td>prime foire</td><td>1SF</td><td>nation rature</td><td>1SF</td><td>savoir poussin</td></tr><tr><td>1SF</td><td>ration nature</td><td>1SF</td><td>pommier sillage</td><td>1SF</td><td>sueur case</td></tr><tr><td>1SF</td><td>répit dentier</td><td>1SF</td><td>raquette maison</td><td>1SF</td><td>troupier fonction</td></tr><tr><td>1SF</td><td>rose dame</td><td>1SF</td><td>râteau gosier</td><td>1SF</td><td>vote lion</td></tr><tr><td>1SF</td><td>sommier pillage</td><td>1SF</td><td>sac loir</td><td>2SF</td><td>bouture pilon</td></tr><tr><td>1SF</td><td>tare gaule</td><td>1SF</td><td>salve verre</td><td>2SF</td><td>canne gorge</td></tr><tr><td>1SF</td><td>troupe gag</td><td>1SF</td><td>toucan bonus</td><td>2SF</td><td>conteur pointure</td></tr><tr><td>1SF</td><td>valve serre</td><td>1SF</td><td>vision berger</td><td>2SF</td><td>foule vague</td></tr><tr><td>2SF</td><td>bise mec</td><td>2SF</td><td>barque manque</td><td>2SF</td><td>montre bosse</td></tr><tr><td>2SF</td><td>fil ciel</td><td>2SF</td><td>cil fiel</td><td>2SF</td><td>moto bouleau</td></tr><tr><td>2SF</td><td>marque banque</td><td>2SF</td><td>coupon poteau</td><td>2SF</td><td>piège tâche</td></tr><tr><td>2SF</td><td>poupon coteau</td><td>2SF</td><td>cube tasse</td><td>2SF</td><td>piment traction</td></tr><tr><td>2SF</td><td>semelle faveur</td><td>2SF</td><td>femelle saveur</td><td>2SF</td><td>pousse crique</td></tr><tr><td>2SF</td><td>tube casse</td><td>2SF</td><td>mise bec</td><td>2SF</td><td>somme flaque</td></tr></tbody></table>

<table><thead><tr><th colspan="2">G</th><th colspan="2">H</th></tr><tr><th>lexical</th><th>nonlexical</th><th>lexical</th><th>nonlexical</th></tr></thead><tbody><tr><td>OSF</td><td>contre masque</td><td>OSF</td><td>case vote</td><td>OSF</td><td>cage lune</td></tr><tr><td>OSF</td><td>cote vase</td><td>OSF</td><td>casque montre</td><td>OSF</td><td>cocher maison</td></tr><tr><td>OSF</td><td>fiche braise</td><td>OSF</td><td>conteur molosse</td><td>OSF</td><td>cube lutte</td></tr><tr><td>OSF</td><td>forge grange</td><td>OSF</td><td>foule bosse</td><td>OSF</td><td>dalle foin</td></tr><tr><td>OSF</td><td>fosse boule</td><td>OSF</td><td>fraise biche</td><td>OSF</td><td>dépit salade</td></tr><tr><td>OSF</td><td>gerbe veste</td><td>OSF</td><td>frange gorge</td><td>OSF</td><td>galette farine</td></tr><tr><td>OSF</td><td>monteur colosse</td><td>OSF</td><td>fusée maçon</td><td>OSF</td><td>loupe tag</td></tr><tr><td>OSF</td><td>musée façon</td><td>OSF</td><td>geste verbe</td><td>OSF</td><td>massage saveur</td></tr><tr><td>OSF</td><td>pion liège</td><td>OSF</td><td>piège lion</td><td>OSF</td><td>nation sinistre</td></tr><tr><td>OSF</td><td> touche mâche</td><td>OSF</td><td>tâche mouche</td><td>OSF</td><td>nièce cil</td></tr><tr><td>1SF</td><td>boulette rouleau</td><td>OSF</td><td>bourse crique</td><td>OSF</td><td>pommier rentier</td></tr><tr><td>1SF</td><td>brique course</td><td>1SF</td><td>bouture cordée</td><td>OSF</td><td>râteau sillage</td></tr><tr><td>1SF</td><td>ciment peinture</td><td>1SF</td><td>buisson carreau</td><td>OSF</td><td>rumeur purée</td></tr><tr><td>1SF</td><td>coeur soupe</td><td>1SF</td><td>coupe soeur</td><td>OSF</td><td>sac gare</td></tr><tr><td>1SF</td><td>couture bordée</td><td>1SF</td><td>dieux vague</td><td>OSF</td><td>toile loir</td></tr></tbody></table>

(continued on next page)
<table><caption>Table C.5 (continued).</caption><tr><td>1SF</td><td>cuisson barreau</td><td>1SF</td><td>fil titre</td><td>1SF</td><td>doc risque</td><td>OSF</td><td>vision poteau</td></tr><tr><td>1SF</td><td>dague vieux</td><td>1SF</td><td>fumeur crochet</td><td>1SF</td><td>fétard traction</td><td>1SF</td><td>bave verre</td></tr><tr><td>1SF</td><td>disque roc</td><td>1SF</td><td>lac gaule</td><td>1SF</td><td>flaque poudre</td><td>1SF</td><td>bec rame</td></tr><tr><td>1SF</td><td>foudre plaque</td><td>1SF</td><td>lave mec</td><td>1SF</td><td>fracas tilleul</td><td>1SF</td><td>clic barque</td></tr><tr><td>1SF</td><td>fournée jonction</td><td>1SF</td><td>lieu note</td><td>1SF</td><td>frimeur pilon</td><td>1SF</td><td>coupon butin</td></tr><tr><td>1SF</td><td>gardon lamelle</td><td>1SF</td><td>marque gag</td><td>1SF</td><td>glace pousse</td><td>1SF</td><td>dieu rap</td></tr><tr><td>1SF</td><td>gousse place</td><td>1SF</td><td>passage dentier</td><td>1SF</td><td>joker pointure</td><td>1SF</td><td>dose vitre</td></tr><tr><td>1SF</td><td>lampe robe</td><td>1SF</td><td>prime dune</td><td>1SF</td><td>journée fonction</td><td>1SF</td><td>femelle patin</td></tr><tr><td>1SF</td><td>lavoir sapin</td><td>1SF</td><td>puits douche</td><td>1SF</td><td>lardon gamelle</td><td>1SF</td><td>fiel pote</td></tr><tr><td>1SF</td><td>légion recteur</td><td>1SF</td><td>ration garage</td><td>1SF</td><td>lobe rampe</td><td>1SF</td><td>groupe poire</td></tr><tr><td>1SF</td><td>loto manière</td><td>1SF</td><td>répit malade</td><td>1SF</td><td>moto lanière</td><td>1SF</td><td>local rature</td></tr><tr><td>1SF</td><td>poker jointure</td><td>1SF</td><td>rocher matin</td><td>1SF</td><td>pelle somme</td><td>1SF</td><td>manque pierre</td></tr><tr><td>1SF</td><td>pomme selle</td><td>1SF</td><td>seringue tonus</td><td>1SF</td><td>piment ceinture</td><td>1SF</td><td>mission radeau</td></tr><tr><td>1SF</td><td>primeur filon</td><td>1SF</td><td>sinus tenue</td><td>1SF</td><td>région lecteur</td><td>1SF</td><td>raquette venue</td></tr><tr><td>1SF</td><td>singe lueur</td><td>1SF</td><td>tare ciel</td><td>1SF</td><td>roulette bouleau</td><td>1SF</td><td>salve tasse</td></tr><tr><td>1SF</td><td>tétard fraction</td><td>1SF</td><td>troupe foire</td><td>1SF</td><td>savoir lapin</td><td>1SF</td><td>taule blé</td></tr><tr><td>1SF</td><td>tête four</td><td>1SF</td><td>tube soir</td><td>1SF</td><td>sueur linge</td><td>1SF</td><td>toiture gosier</td></tr><tr><td>1SF</td><td>tracas filleul</td><td>1SF</td><td>valve lierre</td><td>1SF</td><td>tour fête</td><td>1SF</td><td>toucan fagot</td></tr><tr><td>2SF</td><td>bouton moisson</td><td>2SF</td><td>bol dame</td><td>2SF</td><td>boulet pelote</td><td>2SF</td><td>col patte</td></tr><tr><td>2SF</td><td>croupier taverne</td><td>2SF</td><td>boucan ministre</td><td>2SF</td><td>caresse poussin</td><td>2SF</td><td>criquet parage</td></tr><tr><td>2SF</td><td>fable suite</td><td>2SF</td><td>briguet pillage</td><td>2SF</td><td>fuite sable</td><td>2SF</td><td>dédale brochet</td></tr><tr><td>2SF</td><td>panne cause</td><td>2SF</td><td>gâteau durée</td><td>2SF</td><td>mouton boisson</td><td>2SF</td><td>frime souche</td></tr><tr><td>2SF</td><td>pairesse coussin</td><td>2SF</td><td>pièce clé</td><td>2SF</td><td>pause canne</td><td>2SF</td><td>meringue bonus</td></tr><tr><td>2SF</td><td>poulet belote</td><td>2SF</td><td>poupe banque</td><td>2SF</td><td>ponton toison</td><td>2SF</td><td>minus berger</td></tr><tr><td>2SF</td><td>tonton poison</td><td>2SF</td><td>salle flic</td><td>2SF</td><td>troupier caverne</td><td>2SF</td><td>nuits mise</td></tr></table>

<table><caption>Table C.6
Experimental list of Experiment II.</caption><thead><tr><th colspan="2">lexical</th><th colspan="2">nonlexical</th></tr><tr><th>OSF</th><th>2SF</th><th>OSF</th><th>2SF</th></tr></thead><tbody><tr><td>balade sonnet</td><td>bague digue</td><td>ballet sapeur</td><td>bande membre</td></tr><tr><td>boule fosse</td><td>banque marque</td><td>bourse foin</td><td>baroque patin</td></tr><tr><td>braise fiche</td><td>bêche peau</td><td>bride farce</td><td>beige poste</td></tr><tr><td>cadeau rocher</td><td>bêcheur piquet</td><td>courant record</td><td>beurrier passion</td></tr><tr><td>durée fiction</td><td>belote poulet</td><td>dépit fourmis</td><td>bible daube</td></tr><tr><td>festin dada</td><td>carte torse</td><td>façade dépense</td><td>cerne forces</td></tr><tr><td>fête bac</td><td>casse tube</td><td>ferme brute</td><td>coin tact</td></tr><tr><td>gaule sang</td><td>cause panne</td><td>glace faune</td><td>compte taux</td></tr><tr><td>grange forge</td><td>ciel fil</td><td>gué signe</td><td>confit puma</td></tr><tr><td>lavoir sapin</td><td>coteau coupon</td><td>lamelle sofa</td><td>coup puce</td></tr><tr><td>liqueur pétale</td><td>croupier taverne</td><td>légume pédale</td><td>croyante tampon</td></tr><tr><td>ueur secteur</td><td>daron bélier</td><td>levier souris</td><td>dopage barbu</td></tr><tr><td>mâche touche</td><td>derme ton</td><td>mari talent</td><td>double tombe</td></tr><tr><td>masque contre</td><td>faveur semelle</td><td>mer cape</td><td>fiston sondage</td></tr><tr><td>matin taxi</td><td>gage coût</td><td>miracle sérum</td><td>galion bascule</td></tr><tr><td>ministre seringue</td><td>gallon bourde</td><td>modèle famille</td><td>gamin bonheur</td></tr><tr><td>musée façon</td><td>garage bateau</td><td>muette top</td><td>guide corne</td></tr><tr><td>natte pièce</td><td>malais barquette</td><td>nombre pont</td><td>maillet baleine</td></tr><tr><td>note pain</td><td>mec bise</td><td>nymphe peuple</td><td>mot bave</td></tr><tr><td>poker jointure</td><td>mouture boisson</td><td>potion jumelle</td><td>muret bacille</td></tr><tr><td>rage pôle</td><td>nature marine</td><td>rabat ficus</td><td>niveau moquette</td></tr><tr><td>ragot fondeur</td><td>pairesse coussin</td><td>roque pelle</td><td>palier cuiller</td></tr><tr><td>râpe paille</td><td>poing coupe</td><td>rythme poivre</td><td>plumet tournée</td></tr><tr><td>soupe liège</td><td>primeur tension</td><td>sucre loutre</td><td>pouce corde</td></tr><tr><td>tacle rôt</td><td>prune baume</td><td>tapis visage</td><td>proue berge</td></tr><tr><td>tenue voiture</td><td>suite fable</td><td>tireuse vanille</td><td>sœur feutre</td></tr><tr><td>terrine vernis</td><td>tétard ponton</td><td>toge rêne</td><td>tabou pavot</td></tr><tr><td>vase cote</td><td>torche prône</td><td>veau cure</td><td>tofu cobra</td></tr><tr><td>verre toile</td><td>tracas cantine</td><td>vieux taupe</td><td>tonne ponce</td></tr><tr><td>village panneau</td><td>veste gerbe</td><td>visite pétrin</td><td>vers gifle</td></tr></tbody></table>

(time window selection, consideration of significant effects) ensures that the effects observed are robust and generalizable, it is possible that this rather stringent approach made certain true but more subtle effects go undetected. As we focused on the discussion of the effects that we did observe here, it does not compromise our conclusions, and can be addressed in future studies by, for instance, including the time windows reported as significant in one of our two experiments in more focused and hypothesis driven analyses.

## Conclusion

Results replicated across two experiments revealed robust electrophysiological effects of high versus low lexically and articulatory

phonetically driven error probability on correct trials, and of overt speech errors versus correct trials. Temporal dissociations were observed across these contrasts with lexicality affecting the early stages of speech planning, articulatory phonetic proximity the early stages of speech motor preparation, and overt errors affecting both early and late stages of speech motor preparation as well as articulation. These results suggest the presence of temporally distributed predictive internal modeling in charge of monitoring before articulation, and of an additional mechanism relying on somatosensory and auditory feedback recruited successively in the case of the occurrence of a speech error.

## CREDIT authorship contribution statement

**Lydia Dorokhova:** Writing – original draft, Visualization, Formal analysis, Data curation. **Benjamin Morillon:** Supervision, Formal analysis. **Cristina Baus:** Supervision, Formal analysis. **Pascal Belin:** Supervision, Funding acquisition. **Anne-Sophie Dubarry:** Methodology. **F.-Xavier Alario:** Writing – original draft, Supervision, Funding acquisition, Conceptualization. **Elin Runnqvist:** Writing – original draft, Validation, Supervision, Methodology, Formal analysis, Data curation, Conceptualization.

## Funding

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). E.R. has benefited from support from the French government, managed by the French National Agency for Research (ANR) through a research grant (ANR-18-CE28-0013).

## Declaration of competing interest

The author certify there is no conflict of interest.

## Appendix A. Tables

See Tables A.1–A.3.

## Appendix B. Figures

See Figs. B.1 and B.2.
# Appendix C. Experimental lists

See Tables C.4–C.6.

## Data availability

The dataset collected and made available by the authors for this study can be accessed from the Open Science Framework (OSF) repository (Dorokhova et al., 2024)

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