Giedre Stripeikyte

Michael Pereira

Giulio Rognini

Jevita Potheegadoo

Olaf Blanke
email: olaf.blanke@epfl.ch

Nathan Faivre
email: nathan.faivre@univ-grenoble-alpes.fr

Increased functional connectivity of the intraparietal sulcus underlies the attenuation of numerosity estimations for self-generated

Previous studies have shown that self-generated stimuli in auditory, visual, and somatosensory domains are attenuated, producing decreased behavioral and neural responses compared to the same stimuli that are externally generated. Yet, whether such attenuation also occurs for higher-level cognitive functions beyond sensorimotor processing remains unknown. In this study, we assessed whether cognitive functions such as numerosity estimations are subject to attenuation in 56 healthy participants (32 women).

We designed a task allowing the controlled comparison of numerosity estimations for self (active condition) and externally (passive condition) generated words. Our behavioral results showed a larger underestimation of self-compared to externally-generated words, suggesting that numerosity estimations for self-generated words are attenuated. Moreover, the linear relationship between the reported and actual number of words was stronger for self-generated words, although the ability to track errors about numerosity estimations was similar across conditions. Neuroimaging results revealed that numerosity underestimation involved increased functional connectivity between the right intraparietal sulcus and an extended network (bilateral supplementary motor area, left inferior parietal lobule and left superior temporal gyrus) when estimating the number of self vs. externally generated words. We interpret our results in light of two models of attenuation and discuss their perceptual versus cognitive origins.

3

SIGNIFICANCE STATEMENT

We perceive sensory events as less intense when they are self-generated compared to externally-generated ones. This phenomenon, called attenuation enables us to distinguish sensory events from self and external origins. Here, we designed a novel fMRI paradigm to assess whether cognitive processes such as numerosity estimations are also subject to attenuation. When asking participants to estimate the number of words they had generated or passively heard, we found bigger underestimation in the former case, providing behavioral evidence of attenuation. Attenuation was associated with increased functional connectivity of the intraparietal sulcus, a region involved in numerosity processing.

Together, our results indicate that attenuation of self-generated stimuli is not limited to sensory consequences but also impact cognitive processes such as numerosity estimations.

INTRODUCTION

The ability to distinguish self-versus externally-generated stimuli is crucial for selfrepresentation [START_REF] Kircher | The Self in Neuroscience and Psychiatry, The self in neuroscience and psychiatry[END_REF][START_REF] Legrand | The bodily self: The sensori-motor roots of pre-reflective selfconsciousness[END_REF]. A typical mechanism by which stimuli generated by oneself and those caused by external sources are distinguished is sensory attenuation, whereby self-generated stimuli are perceived as less intense. Indeed, previous studies have shown that self-produced stimuli in the auditory [START_REF] Baess | Selective suppression of self-initiated sounds in an auditory stream: An ERP study[END_REF][START_REF] Timm | Motor Intention Determines Sensory Attenuation of Brain Responses to Self-initiated Sounds[END_REF], visual [START_REF] Hughes | ERP correlates of action effect prediction and visual sensory attenuation in voluntary action[END_REF][START_REF] Benazet | Attenuation of visual reafferent signals in the parietal cortex during voluntary movement[END_REF] and somatosensory domains [START_REF] Shergill | Modulation of somatosensory processing by action[END_REF][START_REF] Kilteni | Functional connectivity between the cerebellum and somatosensory areas implements the attenuation of self-generated touch[END_REF], are perceived as less intense compared to the same stimuli when they are externally generated. Such attenuation was shown at the behavioral level and at the neural level in sensory cortical regions (e.g., auditory cortex [START_REF] Rummell | Attenuation of responses to self-generated sounds in auditory cortical neurons[END_REF][START_REF] Jack | Inner speech is accompanied by a temporally-precise and content-specific corollary discharge[END_REF]) as well as the thalamus, cerebellum, supplementary motor area and inferior parietal cortex [START_REF] Hickok | Computational neuroanatomy of speech production[END_REF][START_REF] Lima | Roles of Supplementary Motor Areas in Auditory Processing and Auditory Imagery[END_REF][START_REF] Bansal | The function and failure of sensory predictions[END_REF][START_REF] Brooks | Predictive Sensing: The Role of Motor Signals in Sensory Processing[END_REF].

Previous studies have shown that attenuation not only applies to stimuli that are generated by overt actions, but also extends to covert processes such as motor imagery [START_REF] Kilteni | Motor imagery involves predicting the sensory consequences of the imagined movement[END_REF] or inner speech [START_REF] Scott | Inner speech captures the perception of external speech[END_REF][START_REF] Whitford | Neurophysiological evidence of efference copies to inner speech[END_REF]. In the latter case, attenuation of the electrophysiological [START_REF] Whitford | Neurophysiological evidence of efference copies to inner speech[END_REF][START_REF] Jack | Inner speech is accompanied by a temporally-precise and content-specific corollary discharge[END_REF] and behavioral [START_REF] Scott | Inner speech captures the perception of external speech[END_REF] responses corresponding to the test stimulus (phoneme) was observed when it matched the imagined cued phoneme. These studies used phonemes that are integrated into the early stages of hierarchical speech processing implying primary sensory cortices [START_REF] Liebenthal | Neural substrates of phonemic perception[END_REF][START_REF] Dewitt | Phoneme and word recognition in the auditory ventral stream[END_REF], where attenuation effects have been demonstrated [START_REF] Rummell | Attenuation of responses to self-generated sounds in auditory cortical neurons[END_REF]. An outstanding question is whether attenuation of self-generated stimuli is limited to sensory consequences (e.g., weakened percept) or whether it can also impact cognitive processes that are non-perceptual in nature such as abstract judgments or performance monitoring.

To answer this question, we investigated the cognitive function of numerosity estimations, defined as approximate judgments when counting is not involved [START_REF] Dehaene | The Number Sense[END_REF].

Properties of numerosity estimations such as innateness, amodality, or precision that linearly decreases with increasing numerosity have been described [START_REF] Anobile | Number As a Primary Perceptual Attribute: A Review[END_REF][START_REF] Burr | Psychophysical evidence for the number sense[END_REF]. Extensive neuroimaging work has also established that brain areas in the intraparietal sulcus (IPS) region play a key role in numerosity processing (for review, see [START_REF] Arsalidou | Is 2+2=4? Meta-analyses of brain areas needed for numbers and calculations[END_REF]. If attenuation for self-related functions extends to higher-level cognitive processes such as numerosity estimations, we would expect the number of items that are self-generated to be underestimated compared to externally-generated items in relation to modulation of IPS activity. Thus, this study aimed at investigating whether numerosity estimations of self-generated words were attenuated compared to numerosity estimations of passively heard words, thereby demonstrating that self-attenuation applies to cognitive processes beyond sensory processing.

To investigate which brain regions and networks showed activity related to attenuation processes, we designed a functional magnetic resonance imaging (fMRI) paradigm allowing the controlled comparison of numerosity estimations of either self-generated words (active condition) during a phonetic verbal fluency task or externally-generated words (passive condition) while passively listening to a stream of words. Additionally, we asked participants to evaluate the error they could have made in their estimation (performance monitoring).

Assuming that attenuation occurs during numerosity estimations (active condition), we generated words and explored possible effects on performance monitoring. As performance monitoring is better for self-initiated vs. observed processes [START_REF] Pereira | Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging[END_REF], we expected to find a stronger relationship between the reported and actual number of words in the active vs. passive condition. At the neural level, considering that sensory attenuation of self-generated stimuli involves the corresponding sensory brain regions (e.g., primary auditory cortex for attenuated sounds, see [START_REF] Rummell | Attenuation of responses to self-generated sounds in auditory cortical neurons[END_REF][START_REF] Jack | Inner speech is accompanied by a temporally-precise and content-specific corollary discharge[END_REF], we expected to find reduced BOLD signal during the active vs. passive condition in areas responsible for numerosity processing including the IPS.

METHODS

Participants

Three independent participant groups were tested in this study. First, we performed a behavioral pilot experiment in a mock scanner, where we tested 17 (6 women) participants (age range: 18 -28 years; M = 23 years, CI(95%) = [23,24]); schooling level varied between 13 and 21 years; M = 17 years, CI(95%) = [16,18]). During the main fMRI experiment, we studied 25 (14 women) participants (age range: 18 -37 years; M = 23 years, CI(95%) = [22,26]; schooling level varied from 12 to 22 years: M = 17 years, CI(95%) = [16,18]). An additional control experiment in the mock scanner was performed, where 14 (12 women) participants were tested (age range: 18 -29 years; M = 24 years, CI(95%) = [22,26]); schooling level varied between 10 and 23 years; M = 16 years, CI(95%) = [14,18]). All participants were right-handed according to the Edinburgh Hand Preference Inventory [START_REF] Oldfield | The assessment and analysis of handedness: The Edinburgh inventory[END_REF] and native French-speaking healthy volunteers with no history of neurological or psychiatric disease and no recent reported history of drug use. Participants had normal or corrected-to-normal vision and no claustrophobia. All participants were naive to the purpose of the study, gave informed consent in accordance with institutional guidelines and the Declaration of Helsinki, and received monetary compensation (20 CHF / hour). The study was approved by the local ethical committee of the canton Geneva (protocol ID: 2015-00092).

Experimental task

The experiment was performed in a mock scanner environment (both for the behavioral pilot experiment and the training for the main fMRI experiment) and in the MRI scanner (main fMRI experiment). The task consisted in phonetic verbal fluency and passive word listening parts, followed by numerosity and error estimations (Figure 1). The task comprised two conditions (20 trials each) during which participants either covertly generated words (active condition) or listened to pre-recorded words (passive condition). Each trial started with a randomly jittered inter-trial interval varying between 4 s and 4.75 s, followed by an audio cue (2 s) indicating which of the active or passive condition will follow, and a recorded cue letter (2 s). Next, the word generation phase lasted between 20 s and 35 s; in the active condition, participants had to covertly generate words starting with the cued letter (Figure 1, top). For each word they covertly generated, participants were asked to press a response button, which gave us access to the actual number of generated words. In the passive condition, a series of pre-recorded audio words were played to the participants, who were asked to press the response button for every word that contained the cue letter (Figure 1, bottom). We avoided asking participants to detect words starting with the cued letter, as this could have influenced word generation in the next active trials with the same cued letter. In both conditions, the end of the generation/listening phase was indicated by an audio cue (0.5 s). After that, participants first reported the estimation of a total number of words generated (active condition) or heard (passive condition). For this, they used two buttons that moved a slider displayed on the screen. The slider was presented as a random integer (ranging from 0 to 20), which could be changed in value by pressing the response box buttons (left button -decrease the value, right button -increase the value). This was followed by an error estimation, where participants were asked to evaluate their performance on the numerosity estimation by estimating the magnitude of the error they thought they might have made (in number of words). For this, an automatically sliding bar was presented, and the participant had to select with one button press the desired value (e.g., +/-2 words error). Values varied from +/-0 words (e.g., numerosity response judged as correct) to +/-5 words mistaken. The total time for numerosity and error estimations were restricted to a maximum of 7 s. Except for the numerosity and error estimation periods, participants were asked to perform the task with eyes closed.

Stimuli

All stimuli were prepared and presented using Matlab 2016b (mathworks.com) and the Psychtoolbox-3 toolbox (psychtoolbox.org;[START_REF] Brainard | The Psychophysics Toolbox[END_REF][START_REF] Kleiner | What's new in Psychtoolbox-3?[END_REF][START_REF] Pelli | The VideoToolbox software for visual psychophysics: Transforming numbers into movies[END_REF].

Twenty different cue letters were used for active and passive conditions. The same cue letter was used once during the active and once during the passive condition in the counterbalanced order. Played back words during the passive condition were chosen from a list of 420 French words [START_REF] Ferrand | Normes d'associations verbales pour 366 noms d'objets concrets[END_REF]. The audio stimuli presented during the task were recorded by male and female native French speakers in a neutral manner and registered in wav format with 11025Hz sampling frequency. The gender of the voice pronouncing words in the passive condition was matched to the participants' gender. During the experiment, participants were equipped with MRI compatible earphones and report buttons for the right hand.

Procedure

Participants were trained in a mock scanner prior to the main fMRI experiment in order to familiarize themselves with the task. They were asked to perform four trials of the task during the training (twice for each condition), with the cue letters 'j' and 'k'. These letters were not used later during the main fMRI experiment. We note that participants were instructed by the experimenter not to count nor use any strategy to try to remember the number of words generated/heard while performing numerosity and error estimations.

The main experiment consisted of three runs lasting approximately 15 min, each with short breaks in-between. The total duration of a trial varied between 40 and 55 s due to pseudorandomized time for the word generation/ listening phase. This time variability was introduced to avoid habituation and predictability for the number of generated/heard words and to decorrelate hemodynamic activity related to word generation/listening and numerosity estimations. The experiment was designed in 10 blocks with 4 trials of the same condition per block. The blocks of the active condition trials always preceded the blocks of the passive condition, allowing us to use the number and pace of generated words that were recorded based on participant's button presses to playback the words during the next block of the passive condition. The order of the number of words played with their corresponding pace during the passive condition was shuffled within the block. This was done to ensure that participants could not recognize whether the number and pace of the played words matched the preceding active condition block. Additionally, to avoid repetition effects between blocks, the cue letters between two consecutive blocks were different, meaning that the cue letters in a given active block were not used in the following passive block.

After the main experiment, a standard phonemic verbal fluency (generation time of 60 s, cue letter 'p') test [START_REF] Lezak | Neuropsychological assessment[END_REF] was performed overtly to verify that subjects understood the task correctly. Overall, the experiment lasted approximately 1h 30 min (MRI session) and 1h in the mock scanner (pilot session). The pilot mock scanner study contained the same procedure as the main MRI experiment, except for the shorter breaks between the runs since there was no scanning involved.

The sample size for the main fMRI experiment was based on a recent study from our group in which similar conditions were contrasted (e.g., judgement of self-generated vs. observed decisions; [START_REF] Pereira | Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging[END_REF].

Control order experiment

An additional control experiment in the mock scanner was performed to rule out the possibility that repetition effects occurred between consecutive blocks. It consisted only of passive condition trials, which were divided into 'random' and 'repeated' blocks. The 'random' block trials were designed by pseudo-randomly generating the number of words played (mean of 10 words/block ranging from 6 to 20 words per trial) and their playing pace (total time of playing varied between 20 s and 35 s) to match the main fMRI experiment. 'Repeated' blocks consequently followed 'random' blocks, with the same number of words and word playing pace as in the preceding random block but with a shuffled order. This control experiment consisted of 2 'random' and 2 'repeated' blocks, with 4 trials/block, and lasted approximately 30 min.

Behavioral performance measures

Most statistical frameworks to analyze performance monitoring have been developed for discrimination tasks with a binary response (e.g., Fleming and Lau, 2014 for a review). In the following, we propose two indices of numerosity performance and performance monitoring to analyze ordinal data (e.g., number of words). In addition to the prerequisites described below, these indices were defined at the single-trial level so they could serve as parametric regressors of interest in the fMRI analysis.

The numerosity performance index is a normalised accuracy ratio reflecting how correctly participants estimated the number of words during the generation/listening phase. For each trial, we wanted signed numerosity performance to be proportional to the difference between the reported number of generated/heard words (numerosity estimation; [N]) and the actual number of words generated/heard [W]. We normalized this difference by the sum of numerosity estimation and actually generated/heard number of words [N+W] (equation 1) to give more weight to errors made about low numbers of words (e.g., an error of +/-2 given a numerosity estimation of 8 has higher magnitude than an error of +/-2 given a numerosity estimation of 16; Figure 1-1 A). This normalization allowed us to assess attenuation effects independent of the number of generated words, given that the precision of numerosity estimation linearly decreases with increasing number of items (e.g., [START_REF] Piazza | Tuning curves for approximate numerosity in the human intraparietal sulcus[END_REF]. Negative numerosity performance values thus reflected an underestimation of generated/heard words, and positive values reflected an overestimation of generated/heard words. In contrast, null numerosity performance values reflected correct answers about the number of generated/heard words.

𝑛𝑢𝑚𝑒𝑟𝑜𝑠𝑖𝑡𝑦 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 𝑁 -𝑊 𝑁 + 𝑊 (equation 1)
Additionally to the numerosity performance index, we derived separate measures of accuracy [N-W] and accuracy ratio [(N-W)/W] to assess the generalizability of our findings.

Performance monitoring reflected how well participants estimated an error about their previous performance. We defined it as the absolute value of the difference between the error estimation [E] and (accuracy [N-W]), normalized by the sum of the numerosity estimation and words generated/heard [N+W]) (equation 2). Normalization was done in order to consider the difficulty; the same error made when estimating the low number of words or high number of words should be penalized proportionally. A performance monitoring value of 0 reflected ideal error tracking, whereby participants correctly estimated the error made during the numerosity estimation. An increase in performance monitoring value represented an increase in error magnitude while estimating the difference between numerosity estimation and the actual number of words generated/heard (Figure 1-1 B).

𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 𝑚𝑜𝑛𝑖𝑡𝑜𝑟𝑖𝑛𝑔 = | 𝐸 -|𝑁 -𝑊| 𝑁 + 𝑊 | (equation 2)
Data cleaning was performed prior to statistical analysis: trials for which participants did not generate at least five words or failed to answer numerosity or error estimations within the time limit were excluded from behavioral and fMRI analysis (in total, 2.44 ± 1.87 trials/subject were excluded). The threshold of 5 words was selected according to the working memory capacity of 5 ± 2 items [START_REF] Cowan | The magical mystery four: How is working memory capacity limited, and why?[END_REF]. Considering all participants' data, 6.1% of all trials were discarded.

Behavioral data analysis

All continuous variables (numerosity performance, accuracy, accuracy ratio and performance monitoring) were analyzed using linear mixed-effects regressions with condition ("active", "passive" or "random", "repeat" for the control order experiment) as a fixed effect and a random intercept by participant and condition. The inclusion of additional random effects was guided by model comparison and selection based on Bayesian Information Criteria. Analyzes were performed using the lme4 [START_REF] Bates | Fitting Linear Mixed-Effects Models Using lme4 | Bates[END_REF] and lmerTest [START_REF] Kuznetsova | lmerTest Package: Tests in Linear Mixed Effects Models[END_REF] packages in R (R-project.org). The significance of fixed effects was estimated using Satterthwaite's approximation for degrees of freedom of F statistics [START_REF] Luke | Evaluating significance in linear mixed-effects models in R[END_REF].

Bayesian analysis was performed using the brms package [START_REF] Bürkner | brms: An R package for Bayesian multilevel models using Stan[END_REF] in R to examine the occurrence of possible repetition effects between random and repeated passive blocks in the control experiment. Namely, we conducted a Bayesian linear model of numerosity performance with condition ("random", "repeated") as fixed effects and a random intercept by participant and condition with four chains of 10000 iterations including 2000 warmup samples. We made a prior assumption that underestimation would be observed in the passive condition (prior with Gaussian distribution of mean = -0.032 and SD = 0.156), based on the difference between numerosity performance during the active and passive conditions observed in the main fMRI experiment.

Data simulation of absolute accuracy during numerosity estimations

To examine weather absolute accuracy (correct numerosity estimations of words generated/heard) of numerosity estimations observed in the experimental data may be approximated by a noisy sampling process rather than counting, we performed data simulations. More specifically, we simulated 1000 numerosity estimations (as the main experiment contained 40 trials/subject * 25 subjects) from a normal distribution centred on 10 and standard deviation (SD) varying from 0.5 to 6 with 0.1 steps. We rounded each item to the nearest integer and counted how many times the number 10 was obtained. The simulation was iterated 10000 times.

fMRI data preprocessing

Anatomical and functional images were processed and analyzed using SPM-12 (Welcome Centre for Human Neuroimaging, London, UK). Pre-processing steps included slice time correction, field-map distortion correction, realignment and unwarping to spatially correct for head motions and distortions, co-registration of structural and functional images, normalization of all images to common Montreal Neurological Institute (MNI) space, and spatial smoothing with a Gaussian kernel with a full-width at half maximum (FWHM) of 4 mm. Quality assurance of all EPI images was performed with the criteria of maximum 2 mm translation and 2° rotation between volumes. In addition, an excessive movement was estimated with the mean framewise displacement (FD) [START_REF] Power | Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion[END_REF] with the exclusion threshold of 0.5 mm. None of the subjects had a higher mean FD (0.2 ± 0.06 mm) than the set threshold.

fMRI data analysis

We used a two-level random-effects analysis. In the first-level analysis, condition-specific effects were estimated according to a general linear model (GLM) fitted for each subject. An average mask of grey matter from all subjects was built using FSL (fsl.fmrib.ox.ac.uk/fsl) and used to mask out white matter and non-brain tissues. The GLM was built using six boxcar regressors corresponding to the duration of the word generation/listening phase, numerosity estimation and error estimation in the active and passive conditions. Parametric modulators of numerosity performance and performance monitoring were included in the numerosity estimation and error estimation regressors, respectively. Further, we added regressors of no interest corresponding to audio instructions, button presses and excluded trials, plus six regressors for head motion (translation and rotation).

At the second-level (group level), we performed a one-way analysis of variance (ANOVA) with F-tests to assess main effects common to active and passive conditions and t-tests to analyze the difference between conditions (active vs. passive) for each regressor of interest: numerosity estimation and error estimation. We used a voxel-level statistical threshold of p<0.001 and corrected for multiple comparisons at the cluster level using family-wise error (FWE) correction with the threshold of p<0.05. We used the anatomical automatic labelling (AAL) atlas for brain parcellation [START_REF] Tzourio-Mazoyer | Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain[END_REF].

Functional connectivity analysis

Psychophysiological Interaction (PPI) analysis was employed to identify modulations of functional coupling between a seed region and other brain regions by experimental conditions (active vs. passive) [START_REF] Friston | Psychophysiological and modulatory interactions in neuroimaging[END_REF]. To perform this analysis we used generalized psychophysiological interaction (gPPI) toolbox version 13.1 [START_REF] Mclaren | A generalized form of contextdependent psychophysiological interactions (gPPI): A comparison to standard approaches[END_REF]. Spheres of 6 mm radius were formed around the peak coordinates of the right IPS (x = 29; y = -65; z = 50) and the left IPS (x = -27; y = -66; z = 47) clusters that were identified in the second-level analyses. First-level (individual) GLM analyses were performed, including task regressors of numerosity estimation or error estimation (psychological term) and time course of the seed region (physiological term). As for other aforementioned fMRI analyses, we performed t-tests to compare the differences between conditions.

Data and code availability

The MRI data that support the findings of this study is available on a public repository on zenodo.org (https://doi.org/10.5281/zenodo.4925909). Behavioral data, analysis and task codes are available here: https://gitlab.epfl.ch/lnco-public/cognitive-attenuation.

RESULTS

Behavioral results

By design, the number of generated and heard words was matched between conditions We then assessed the linear relationship between the reported (numerosity estimation) and actual number of words (Figure 2 B). Besides a main effect of words (F (1,908) = 1042, p < 0.001) and of condition (F (1,198) = 8.25, p = 0.0045), we found an interaction between condition and generated/heard words (F (1,802) = 4.3, p = 0.038). This interaction was driven by a steeper slope in the active condition (M = 0.62, CI(95%) = [0.57, 0.67]) compared to the passive condition (M = 0.55, CI(95%) = [0.50, 0.60]) (Figure 2 C), which indicates better numerosity tracking for self-generated words during the main fMRI experiment (a slope of value 1 reflecting ideal performance). We note that this interaction did not reach significance in our pilot experiment (F (1,350) = 1.10, p = 0.66), which calls for interpreting this result with caution.

Next, we investigated whether performance monitoring varied between conditions, by quantifying how well participants were able to track the error made when estimating the number of generated/heard words. We found no differences in performance monitoring This confirms the absence of evidence supporting an effect of attenuation on performance monitoring.

Finally, we carried out a control task for word generation outside the scanner, during which participants had to perform a standard verbal fluency task overtly with the cue letter 'p'.

Comparing the number of words generated, starting with the letter 'p' overtly (outside the scanner) and covertly (during the scanning), we did not observe any significant differences between the number of words generated overtly (M = 12.6 , SD = 3.79, CI(95%) = [10.9, 14.1]) and covertly (M = 12.3, SD = 3.96, CI(95%) = [10.6, 13.9]; paired t-test; t (24) = -0.4; p=0.69). This control task confirms that subjects did generate words covertly and comparably to overt fluency, as instructed.

Control order experiment. The global underestimation (M = -0.06, SD = 0.11, CI(95%) = [-0.08, -0.05]) was replicated as in the main experiment. This underestimation was not different (F(1,117) = 0.03, p = 0.87) between 'random' (M = -0.06, SD = 0.12, CI(95%) = [-0.09, -0.04]) and 'repeated' (M = -0.07, SD = 0.12, CI(95%) = [-0.09, -0.04]) blocks, with a Bayes factor BF10 = 0.09 supporting the absence of difference between conditions.

Data simulation of absolute accuracy during numerosity estimations.

As can be seen on the Figure 3, the value of 10 occurred in 19.5% of all data points when drawn from a normal distribution with SD = 2, similarly to the observed empirical data.

fMRI results

Numerosity performance

Brain activity during the numerosity estimation phase was widespread, irrespective of the experimental condition (Table 1-1). Differences between conditions revealed widespread relative deactivations in the active compared to the passive condition in the bilateral parietal cortex including IPS, middle-superior temporal gyri, precuneus, cerebellum, middle cingulate gyri, SMA, insula, middle-superior frontal gyri, hippocampus, caudate nucleus, putamen (for the detailed list of all areas see Table 1).

To avoid contaminating the results with inherent differences between the active and passive conditions that are not specific to numerosity processes, we looked for brain activity parametrically modulated by numerosity performance. We found a single brain region with such a pattern of activity, namely the right IPS, whose activity was negatively correlated with numerosity performance (main effect F = 20.0, p FWE = 0.042, k vox = 148, x = 29, y = -65, z = 50; Figure 4 A,B). The same pattern was found bilaterally when using a less stringent threshold (p<0.005 peak level uncorrected; Figure 4 C,Table 2). Unlike our prediction, IPS activation did not differ between the active and passive conditions (p FWE > 0.05), suggesting that this effect was independent of whether words were heard or actively generated.

Using PPI analysis, we investigated whether the functional connectivity of the right IPS with other brain regions differed between the active and passive conditions. This analysis revealed that bilateral supplementary motor area (SMA), left inferior parietal lobule (IPL) and left superior temporal gyrus (STG) had increased connectivity with the right IPS in the active compared to the passive condition (Figure 5, Table 3). No effect in the other direction was observed.

Performance monitoring

Besides numerosity performance, we also quantified brain activity during the error estimation phase. We found widespread cortical and subcortical activations during this phase compared to baseline, irrespective of the experimental condition (Table 4-1).

Moreover, bilateral insula and right putamen were significantly less active during the active compared to the passive condition (Figure 6, Table 4). The left caudate nucleus showed the opposite pattern, with higher activity in the passive than in the active condition.

To avoid confounding factors between the active and passive condition, we looked more specifically at parametric modulations of error monitoring. We observed that BOLD signal in the left IPS was more related to performance monitoring in the active compared to the passive condition (t = 4.02, p FWE = 0.041, k vox = 198, x = -27, y = -66, z = 47; Figure 7).

Interestingly, this activation cluster spatially overlapped with the observed activation cluster related to the main effect of numerosity performance when applying a lower statistical threshold (exploratory analysis, p<0.005 peak level uncorrected, pFWE < 0.05 cluster level) (see Figure 4 C).

Lastly, results from the PPI analysis using the left IPS as a seed ROI did not reveal any functional connectivity differences between the active and passive conditions during error estimation.

DISCUSSION

The present study examined whether attenuation of self-generated stimuli impacts cognitive processes beyond the traditionally investigated sensory processes, namely numerosity estimations and performance monitoring. To this end, we developed an experimental paradigm allowing the controlled comparison of numerosity estimations and performance monitoring regarding self-and externally-generated words while acquiring fMRI data. We found that participants more strongly underestimated the number of selfcompared to externally-generated words, providing behavioral evidence that numerosity estimations are indeed subject to attenuation. As expected, numerosity performance was associated with hemodynamic activity changes in IPS. Furthermore, a network including the bilateral SMA, left IPL and left STG showed increased functional connectivity with the right IPS during numerosity estimations for self-generated words, suggesting that numerosityrelated attenuation involves this neural network. Finally, by asking participants to monitor the accuracy of their own numerosity estimations, we found equivalent performance monitoring for self-and externally-generated words. Although no difference was found at the behavioral level, we found that performance monitoring was associated with increased IPS activity in the active vs. passive condition.

Behavioral and neural markers of attenuated numerosity estimations

Participants underestimated the number of words they generated or heard. Such underestimations were previously described regarding the numeric estimation of perceptual quantities (e.g., number of dots or sequences of sounds) to discrete measures (e.g., Arabic numeral) [START_REF] Castronovo | Numerical Estimation in Blind Subjects: Evidence of the Impact of Blindness and Its Following Experience[END_REF][START_REF] Reinert | Unbounded number line estimation as a measure of numerical estimation[END_REF]. In the present study, we found that word numerosity underestimation was stronger in the active compared to the passive condition, in line with our hypothesis that attenuation of self-generated stimuli may extend to higher-level cognitive functions [START_REF] Kilteni | Motor imagery involves predicting the sensory consequences of the imagined movement[END_REF][START_REF] Jack | Inner speech is accompanied by a temporally-precise and content-specific corollary discharge[END_REF]. Of note, this underestimation could not be due to simple repetition effects between successive blocks of active and passive condition trials, as a control experiment showed no differences in numerosity estimations between repeated blocks of passive condition trials.

Attenuation of self-generated stimuli has been mostly investigated for sensory processes and refers to the diminished behavioral and neural responses associated with selfgenerated compared to externally-generated stimuli [START_REF] Timm | Motor Intention Determines Sensory Attenuation of Brain Responses to Self-initiated Sounds[END_REF][START_REF] Benazet | Attenuation of visual reafferent signals in the parietal cortex during voluntary movement[END_REF]. Importantly, attenuation has recently also been described for imagined actions in the absence of overt actions (e.g., [START_REF] Kilteni | Motor imagery involves predicting the sensory consequences of the imagined movement[END_REF][START_REF] Jack | Inner speech is accompanied by a temporally-precise and content-specific corollary discharge[END_REF]). For example, imagined self-touch was felt as less intense compared to externally applied touch [START_REF] Kilteni | Motor imagery involves predicting the sensory consequences of the imagined movement[END_REF].

Similarly, imagined speech elicited reduced electrophysiological signals related to auditory processing [START_REF] Whitford | Neurophysiological evidence of efference copies to inner speech[END_REF]. These studies, however, investigated cognitive processes intimately linked to sensorimotor systems (e.g., imagined movement and touch), yet, to the best of our knowledge, it was unknown whether comparable mechanisms of attenuation also affect cognition beyond sensorimotor processing, such as numerosity estimations with no or less obviously implicated sensorimotor processes (e.g., [START_REF] Dehaene | The Number Sense[END_REF]. Here we show that differential attenuation can be observed for cognitive processes such as numerosity estimations that depend on whether they are self-generated or not. As of today, there is no consensus to explain how the brain attenuates expected (e.g., self-generated) stimuli while remaining sensitive to unexpected ones (for a recent review and unifying theoretical account see [START_REF] Press | The Perceptual Prediction Paradox[END_REF]. Among the so-called cancellation theories, the internal forward model [START_REF] Farrer | Experiencing oneself vs another person as being the cause of an action: The neural correlates of the experience of agency[END_REF][START_REF] Miall | Forward models for physiological motor control[END_REF] proposes that corollary discharges related to action are used to predict the sensory consequences of that action.

When such predictions match the actual sensory feedback from the action, its sensory consequences are attenuated, and the action is perceived as self-generated [START_REF] Wolpert | Motor prediction[END_REF]. The forward model thus proposes to link sensory attenuation to the sensory predictions generated by a neural comparator. An analogous mechanism has been proposed to account for attenuation for covert actions such as motor imagery [START_REF] Kilteni | Motor imagery involves predicting the sensory consequences of the imagined movement[END_REF] or inner speech [START_REF] Tian | Mental imagery of speech and movement implicates the dynamics of internal forward models[END_REF]. The present data show that attenuation exists beyond overt and covert actions, raising the possibility that the forward model extends to repetitive cognitive activity (e.g., fluency and related numerosity estimations).

One possibility, similar to what has been described for inner speech, would be that numerosity underestimation stems from a weaker perceptual representation of selfgenerated words. In other words, attenuation may not impact numerosity estimations directly, but rather through a decreased representation of self-generated words, which in turn may lead to attenuation of numerosity estimations.

A second account could be that mental operations have a gating effect [START_REF] Cromwell | Sensory gating: A translational effort from basic to clinical science[END_REF], independent from predictive mechanisms, thereby directly affecting the strength of the mental representations of self-generated words. A similar gating mechanism has recently been shown to offer a plausible alternative to forward model accounts of sensory self-attenuation [START_REF] Thomas | Action enhances predicted touch[END_REF]. Finally, the general mechanisms put forward by the predictive coding account of active inference to explain sensory attenuation may also apply to non-perceptual cognitive attenuation such as the one we observed [START_REF] Brown | Active inference, sensory attenuation and illusions[END_REF].

At the neural level, the IPS showed activity related to numerosity performance, in line with previous studies [START_REF] Cohen | Cerebral Networks for Number Processing: Evdence from a Case of Posterior Callosal Lesion[END_REF][START_REF] Piazza | Exact and approximate judgements of visual and auditory numerosity: An fMRI study[END_REF]. This relation, however, was not modulated by condition (active vs. passive), as one could have expected based on previous reports showing that sensory attenuation coincides with decreased activity in the corresponding sensory area (e.g., attenuation in primary auditory cortex during selfgenerated auditory stimuli; [START_REF] Rummell | Attenuation of responses to self-generated sounds in auditory cortical neurons[END_REF][START_REF] Jack | Inner speech is accompanied by a temporally-precise and content-specific corollary discharge[END_REF]. Importantly, we found differences in functional coupling during numerosity estimations of the IPS with a network of brain regions between the active and passive conditions. The increase in coupling during the active condition occurred between the IPS and a network comprising the SMA, IPL and STG, known to be involved in predictive processing, for example of self-generated auditory and imagery speech [START_REF] Lima | Roles of Supplementary Motor Areas in Auditory Processing and Auditory Imagery[END_REF][START_REF] Tian | Mental imagery of speech implicates two mechanisms of perceptual reactivation[END_REF]. Although previous neuroimaging studies have mainly shown attenuated brain activity for self-generated actions (e.g., [START_REF] Jack | Inner speech is accompanied by a temporally-precise and content-specific corollary discharge[END_REF], increases in functional connectivity such as we describe have been reported in the primary auditory cortex during attenuated speech (van de [START_REF] Van De Ven | Neural network of speech monitoring overlaps with overt speech production and comprehension networks: A sequential spatial and temporal ICA study[END_REF], or of somatosensory cortex during attenuated touch [START_REF] Kilteni | Functional connectivity between the cerebellum and somatosensory areas implements the attenuation of self-generated touch[END_REF]. Thus, increased functional connectivity in a network centred on the key numerosity region, IPS, and that has been associated with speech-related processing further supports that numerosity underestimation stems from a process related to attenuation when participants self-generate words.

Besides numerosity underestimation, we observed that participants formed more accurate numerosity judgments for self-vs. externally-generated words: while participants' word estimations were lower in the active condition than in the passive condition, the relation between their estimated number of words and the actual number of words was better in the active condition vs. passive condition, suggesting a sharper representation of the number of self-generated words. This result corroborates recent findings showing better monitoring for decisions that are committed rather than observed [START_REF] Pereira | Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging[END_REF]. This improved monitoring of self-generated words could be related to the sharpening of expected representations known in the sensory domain [START_REF] Kok | Less Is More: Expectation Sharpens Representations in the Primary Visual Cortex[END_REF] or to a selfgeneration effect underlying the facilitation of information encoding and enhanced recall for self-generated stimuli [START_REF] Bertsch | The generation effect: A metaanalytic review[END_REF][START_REF] Slamecka | The generation effect: Delineation of a phenomenon[END_REF]. We note that this effect was not replicated during the mock pilot experiment, which included a smaller sample of participants with a globally higher absolute accuracy. It is possible that this subtle effect could not be observed in such conditions, which warrants future investigations and cautious interpretation.

Performance monitoring is modulated at the neural but not behavioral level

Previous research has shown that both humans and non-human primates [START_REF] Beran | Rhesus macaques (Macaca mulatto) monitor uncertainty during numerosity judgments[END_REF]Duyan andBalcı, 2019, 2018) can monitor the quality of their numerosity estimations.

Thus, in addition to asking participants to estimate the number of words they generated or heard, we also asked them to estimate their own error during this process (performance monitoring). Although our behavioral results showed similar performance monitoring between conditions, we observed that performance monitoring was associated with hemodynamic activity in the left IPS predominantly in the active condition. Interestingly, this region is not typically associated with performance monitoring such as the prefrontal cortex or the insula/inferior frontal gyrus [START_REF] Vaccaro | Thinking about thinking: A coordinate-based metaanalysis of neuroimaging studies of metacognitive judgements[END_REF]. Since activity in the left IPS is related to numerosity estimation [START_REF] Cappelletti | rTMS over the intraparietal sulcus disrupts numerosity processing[END_REF][START_REF] Dormal | Contribution of the right intraparietal sulcus to numerosity and length processing: An fMRI-guided TMS study[END_REF], this parametric modulation could therefore represent a substrate for monitoring specific to numerosity estimations. By investigating global fMRI activity during error estimations, we also observed decreased activation in the insula and putamen and increased activity in the caudate nucleus when comparing the active vs. passive conditions. To note, the anterior insula and putamen were activated during both conditions but were attenuated in the active compared to the passive conditions. While the anterior insula activations are consistent with previous literature on performance monitoring [START_REF] Ullsperger | Conscious perception of errors and its relation to the anterior insula[END_REF][START_REF] Bastin | Direct Recordings from Human Anterior Insula Reveal its Leading Role within the Error-Monitoring Network[END_REF], the findings of modulated activity in the striatum were unexpected.

Both areas are known as essential for the control of goal-directed decision-making [START_REF] Balleine | The role of the dorsal striatum in reward and decision-making[END_REF][START_REF] Kim | The role of the dorsal striatum in choice impulsivity[END_REF]. Further links between performance monitoring and activity modulation between conditions in the striatum regions should be explored in future studies.

Methodological considerations

As generating vs. listening to words inherently involves different behavioral and neural processes, several aspects of our design should be considered when interpreting the current findings as reflecting attenuation per se rather than experimental confounds. At the behavioral level, the larger underestimation during the active condition was not due to difficulty differences between conditions as task difficulty was matched by design, and absolute accuracy (correct numerosity estimations of words generated/heard) was similar in both conditions. Of note, a steeper slope during numerosity estimations and number of words generated in the active condition suggests that participants better tracked their performance even when underestimating more compared to the passive condition.

Although we could not explicitly control whether participants counted the number of words they generated or heard, instructions were given not to do so. Yet, counting seems unlikely as absolute accuracy reached only 19.5% of all trials, which is comparable to a recent study with a similar setup [START_REF] Serino | Thought consciousness and source monitoring depend on robotically controlled sensorimotor conflicts and illusory states[END_REF], and much lower than what is typically observed when participants are instructed to count (e.g, Kansaku et al., 2007). Furthermore, a similar level of absolute accuracy was obtained on simulated data with numerosity estimations drawn from a normal distribution with SD = 2, suggesting that behavior in this task is approximated by a noisy sampling process rather than counting. At the neural level, to ensure that the inherent differences between conditions did not contaminate our results, we focused analyses on the numerosity and error estimation phases which were identical between conditions, instead of the word generation/listening phases. In addition, we did not base our conclusions on a direct contrast between the two conditions, but on correlations between neural activity and numerosity or error estimates.

Conclusion

Based on behavioral and neuroimaging data, we propose that higher-level cognitive functions such as numerosity estimations about the number of self-generated words are attenuated. Such attenuation involves a functional network including a key-numerosity region (IPS) and speech-related regions including the SMA, IPL and STG. While attenuating the sensory consequences of one's actions is of crucial importance for aspects of the self, such as the sense of agency, attenuating the products of one's mental activities may also be relevant to distinguish them from external sources of information. Our paradigm offers a promising tool to investigate attenuation processes related to the self in cognition and to compare and distinguish them from sensory attenuation processes. It may be of relevance to the study of clinical cases in which attenuation in sensory and cognitive domains may be is altered, including patients with psychotic symptoms like thought insertion whereby thoughts are not considered as one's own, but of somebody else. Table 1. Numerosity estimation: difference between active and passive conditions. Brain areas showing a difference in activity between the active and passive condition during the numerosity estimation phase, independent of parametric modulation. The activation clusters of numerosity estimation main effect are detailed in Table 1-

Figures

Tables

  fMRI data acquisition MRI data were acquired using a Siemens Magnetom Prisma 3 T scanner with a 64-channel head coil. T1 weighted (1 mm isotropic) scans were acquired using a Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) sequence (TR = 2300 ms; TI = 900 ms; TE = 2.25 ms; flip-angle = 8 degrees; GRAPPA = 2; FOV = 256 × 256 mm; 208 slices). Functional scans were obtained using echo-planar (EPI) sequence (multiband acceleration = 6; TR = 1000 ms; TE = 32 ms; flip-angle = 58 degrees; FOV = 224 × 224 mm; matrix = 64 x 64; slice thickness = 2 mm; number of slices = 66). The number of functional image volumes varied according to the experiment duration (2278 ± 61 volumes).

(

  active: M = 10.7, SD = 3.6, CI(95%) =[10.4, 11.1]; passive: M = 10.7, SD = 3.5, CI(95%) =[10.4, 11.0], F (1,884) = 0.005, p = 0.94). The duration of word generation and listening did not differ between conditions either (active: M = 27.4, SD = 5.6, CI(95%) = [26.9, 27.9]; passive: M = 28.4, SD = 7.0, CI(95%) = [27.7, 29.0], F (1,26) = 2.04, p = 0.16). Having shown that conditions were similar in terms of difficulty, we turned to the analysis of numerosity performance and performance monitoring. Numerosity performance indices revealed that globally, participants underestimated the number of words (M = -0.05, SD = 0.11, CI(95%) = [-0.06, -0.05]). This underestimation was significantly larger (F (1,24) = 5.85, p = 0.023, 18 subjects out of 25 showed the effect) in the active condition (M = -0.07, SD = 0.11, CI(95%) = [-0.08, -0.06]) compared to the passive condition (M = -0.04, SD = 0.12, CI(95%) = [-0.05, -0.03]) (Figure 2 A). Comparable results were obtained during the pilot experiment in the mock scanner in an independent group of subjects (active: M = -0.05, SD = 0.01, CI(95%) = [-0.06, -0.03]); passive: M = -0.03, SD = 0.10, CI(95%) = [-0.04, -0.02]; F (1,16) = 5.80 p = 0.016). These two experiments suggest that numerosity estimations for self-generated words are attenuated. We further show that the larger underestimation in the active compared to the passive condition was also found when considering the measures of accuracy (active: M = -1.48, SD = 2.38, CI(95%) = [-1.69, -1.26]); passive: M = -0.94, SD = 2.59, CI(95%) = [-1.17, -0.71]; F(1,24) = 4.43 p = 0.045) and accuracy ratio as dependent variables (active: M = 0.89, SD = 0.20, CI(95%) = [0.87, 0.91]); passive: M = 0.95, SD = 0.23, CI(95%) = [0.93, 0.97]; F(1,24) = 7.44 p = 0.011).

  (F (1,24) = 1.09, p = 0.3) between the active (M = 0.07, SD = 0.06, CI(95%) = [0.07, 0.08]) and passive (M = 0.08 , SD = 0.06, CI(95%) = [0.07, 0.09]) conditions during the main fMRI experiment, nor during the mock scanner pilot experiment (F (1,16) = 0.78, p = 0.39; active: M = 0.07, SD =0.06, CI(95%) = [0.06, 0.08]; passive: M = 0.06, SD =0.06, CI(95%) = [0.06, 0.07]).

Figure 1 .

 1 Figure 1. Schematic representation of the general task flow: active (top) and passive (bottom) conditions consisting of instruction, word generation/listening, numerosity and error estimation. Each condition was repeated 4 times per block. Auditory and visual instructions were provided in French. Simulated distribution of derived behavioral performance measures using numerosity and error estimation values are depicted in Figure 1-1.

Figure 2 .

 2 Figure 2. Behavioral results. (A) Individual numerosity performance in the active (red) and passive (blue) conditions. A value of zero represents ideal performance, where the reported and actual number of words match. Each dot represents a participant mean numerosity performance. Grey lines connect performance from the same participant across conditions. (B) Mixed-effects linear regression between numerosity estimation (reported number of words) and an actual number of words. Reference dashed line with a slope equal to 1 represents ideal performance. Thick lines represent the global model fit, while thin lines depict individual estimates. Individual plots are depicted in Figure 2-1. (C) Mean slope coefficient estimates with 2.5% and 97.5% confidence intervals.

Figure 3 .

 3 Figure 3. Percentage of simulated accurate numerosity estimations (occurrences of value 10) in normal distribution centred on 10 with varying standard deviation. Simulation was iterated 10000 times. Reference dashed line of 19.5% absolute numerosity estimations accuracy (as observed in the experiment empirical data) occurs with the standard deviation

Figure 4 .

 4 Figure 4. Neural correlates of numerosity performance. (A) Main effect (active+passive conditions) of the parametric modulation of numerosity performance during numerosity estimation in the right intraparietal sulcus (IPS, depicted in green). (B) Box plot of the corresponding individual beta estimates in the right IPS. Each dot represents a participant mean beta estimates. Grey lines connect beta estimates from the same participant across conditions. (C) Overlap between the parametric modulation of numerosity performance during numerosity estimation (depicted in green) main effect (active + passive conditions) at the lower statistical threshold of peak level uncorrected p<0.005 (exploratory analyses) and parametric modulation of performance monitoring during the error estimation (active vs. passive condition) (depicted in purple); peak level uncorrected p<0.001.

Figure 5 .

 5 Figure 5. PPI analysis: numerosity estimation. (A) Increased functional connectivity in the active vs. passive condition between the right intraparietal sulcus seed (IPS, depicted in green) and bilateral supplementary motor area (SMA), left inferior parietal lobule (IPL) and superior temporal gyri (STG), depicted in red. (C) Box plots of the corresponding beta estimates for each significant PPI cluster. Each dot represents a participant mean beta estimates. Grey lines connect beta estimates from the same participant across conditions.

Figure 6 .

 6 Figure 6. Neural correlates of performance monitoring. (A) Decreased neural activity during the active vs. passive condition in the right putamen and insula bilaterally (depicted in blue).

Figure 7 .

 7 Figure 7. Parametric modulation of performance monitoring. (A) Positive association between the trial-wise performance monitoring and BOLD signal during performance monitoring in the active vs. passive condition in the left intraparietal sulcus (IPS, depicted in purple). (B) Box plot of corresponding beta estimates in the left IPS in the active (blue) and passive (red) conditions. Each dot represents a participant mean beta estimates. Grey lines connect beta estimates from the same participant across conditions.

  1. Voxel level p<0.001 uncorrected, cluster threshold at p<0.05 FWE corrected. BA -Broadmann area, k -cluster size, R -right hemisphere, L -left hemisphere, R -right hemisphere Table2. Parametric modulation of numerosity performance (exploratory analyses at lower statistical threshold). Brain areas in which the BOLD signal correlates with the trial-by-trial intensity in blue represent linear increase of underestimation (negative values of numerosity performance). (B) Simulated performance monitoring when the number of words is constant (10 or 15 words). The performance monitoring value is depicted in the heatmap changing from blue (being correct) to red (worse performance monitoring).

Figure 2 - 1 .

 21 Figure 2-1. Mixed-effects linear regression between numerosity estimation (reported number of words) and an actual number of words depicted for each participant. Reference dashed line with a slope equal to 1 represents ideal performance. Thick lines represent the individual model fit per condition (active: red and passive: blue). Each dot represents a participant's single trial performance (overlapping dots are depicted in higher color intensity).

Table 1- 1 .

 1 Numerosity estimation: main effect (active + passive conditions). Brain areas activated during the numerosity estimation phase independent of parametric modulation.Voxel level p<0.001 uncorrected, cluster threshold at p<0.05 FWE corrected. BA -Broadmann area, k -cluster size, R -right hemisphere, L -left hemisphere, R -right hemisphere

Table 4 -

 4 1. Error estimation: main effect (active + passive conditions). Brain areas with activations during the error estimation phase, independent of parametric modulation. Voxel level p<0.001 uncorrected, cluster threshold at p<0.05 FWE corrected. BA -Broadmann area, k -cluster size, R -right hemisphere, L -left hemisphere, R -right hemisphere

	BA	Anatomical Label		k	x	y	z	p FWE	T
		Active < passive
		Middle temporal g.
		Precuneus
		Superior temporal g.
		Postcentral
	7	Middle cingulum
	6 40 31 13 24 21	Cerebellum SMA Insula Precentral Supramarginal g. Angular g. Superior parietal
	4	Putamen
	32 39	Middle frontal g. Caudate	L/R	113587	20	8	-12	<0.001 9.75
	3	Superior fontal g.
	5 9 23 27 8 18	Superior parietal g. Calcarine Rolandic percular Supramarginal g. Thalamus Hippocampus Fusiform g.
	44	Inferior temporal g.
		Lingual g.
		Cuneus
		Occipital g.
		Thalamus	L	314	-11	-23	15	<0.001 6.84
	10 Middle frontal g.	L	442	-36	50	27	0.004	4.97
	10 Middle frontal g.	R	399	29	53	32	0.005	4.69

Table 1 .

 1 Numerosity estimation: difference between active and passive conditions. Brain areas showing a difference in activity between the active and passive condition during the numerosity estimation phase, independent of parametric modulation. The activation clusters of numerosity estimation main effect are detailed in Table1-1. Voxel level p<0.001 uncorrected, cluster threshold at p<0.05 FWE corrected. BA -Broadmann area, k -cluster size, R -right hemisphere, L -left hemisphere, R -right hemisphere

	BA	Anatomical Label		k	x	y	z	p FWE	T
		Active > passive
		Caudate nucleus	L	336	-15	30	-6	0.004 5.76
		Active < passive
	45 47 13	Insula, inferior frontal g. triangular part	L	392	-33	24	-6	0.002	5.3
		Putamen	R	209	20	12	-6	0.043	5.2
	13 47 45	Insula, inferior frontal g. triangular part	R	482	32	27	0	0.000	4.3

Table 4 .

 4 Error estimation: difference between active and passive conditions. Brain areas showing a difference in activity between the active and passive condition during the error estimation phase, independent of parametric modulation. The activation clusters of error estimation main effect are detailed in Table4-1. Voxel level p<0.001 uncorrected, cluster threshold at p<0.05 FWE corrected. BA -Broadmann area, k -cluster size, R -right hemisphere, L -left hemisphere

Acknowledgments

The authors are grateful for the technical support during data acquisition to the Foundation Campus Biotech Geneva (FCBG) Human Neuroscience Platform of the Campus Biotech. This work was supported by the Bertarelli Foundation (grant number 532024), the Swiss National Science Foundation (grant number 3100A0-112493), National Center of Competence in Research (NCCR) "Synapsy -The Synaptic Bases of Mental Diseases" (grant number 51NF40 -185897), and two generous donors advised by Carigest SA. Nathan Faivre has received funding from the European Research Council (ERC) under the European Union Horizon 2020 research and innovation programme (grant number 803122).

Conflict of interest statement

The authors declare no competing interests.

Author Contributions

GS, NF, GR, OB developed the study concept and contributed to the study design. Testing, data collection and data analysis were performed by GS. GS, MP, JP and NF provided methodological support. GS, NF, MP, and OB drafted the paper; all authors provided critical revisions and approved the final version of the paper for submission. behavioral measures of numerosity performance during the numerosity estimation phase.

Voxel level p<0.005 uncorrected, cluster threshold at p<0.05 FWE corrected. BA -Broadmann area, k -cluster size, R -right hemisphere, L -left hemisphere Table 3. PPI analysis: numerosity estimation. Brain areas identified through PPI analysis seeding from the right intraparietal sulcus during numerosity estimation. Voxel level p<0.001 uncorrected, cluster threshold at p<0.05 FWE corrected. BA -Broadmann area, kcluster size, R -right hemisphere, L -left hemisphere Table 4. Error estimation: difference between active and passive conditions. Brain areas showing a difference in activity between the active and passive condition during the error estimation phase, independent of parametric modulation. The activation clusters of error estimation main effect are detailed in Table 4-1