Manuel De Vega

Léo Dutriaux
email: leodutriaux@gmail.com

Iván Z Moreno

Enrique García-Marco

Alix Seigneuric

Valérie Gyselinck

Crossing hands behind your back reduces recall of manual action sentences and alters brain dynamics

Keywords: Postural interference, embodiment, memory, event-related potentials, beta rhythms

The embodied meaning approach posits that understanding action-related language recruits motor processes in the brain. However, the functional impact of these motor processes on cognition has been questioned. The present study aims to provide new electrophysiological (EEG) evidence concerning the role of motor processes in the comprehension and memory of action language. Participants read lists of sentences including manual-action or attentional verbs, while keeping their hands either in front of them or crossing them behind their back.

Results showed that posture impacted selectively the processing of manual action sentence, and not of attentional sentences, in three different ways: 1) EEG fronto-central beta rhythms, a signature of motor processes, were desynchronized while reading action sentences in the hands-in-front posture compared to the hands-behind posture. The estimated source was the posterior cingulate cortex, involved in proprioceptive regulation. 2) Recall of nouns associated with manual sentences decreased when learning occurred in the hands-behind posture. 3) ERPs analysis revealed that the initial posture at learning modulates neural processes during subsequent recall of manual sentences in the left superior frontal gyrus, which is related to motor processes. These results provide decisive evidence for the functional involvement of embodied simulations in the encoding and retrieval of action-related language.

According to the embodied cognition framework, the activation of a concept consists of a sensorimotor simulation of its meaning. For instance, understanding a sentence like 'you cut the bread' may involve activations in the hand motor and premotor cortex as well as the excitation of hand muscles. Action-related language and especially that referring to manual actions and to manipulable objects have received much empirical support by a high number of behavioral (e.g., [START_REF] Borreggine | The Action-Sentence Compatibility Effect: It's All in the Timing[END_REF][START_REF] De Vega | Action Sentences Activate Sensory Motor Regions in the Brain Independently of Their Status of Reality[END_REF][START_REF] Glenberg | Grounding language in action[END_REF], neuroimaging (e.g., [START_REF] Aziz-Zadeh | Congruent embodied representations for visually presented actions and linguistic phrases describing actions[END_REF][START_REF] De Vega | Action Sentences Activate Sensory Motor Regions in the Brain Independently of Their Status of Reality[END_REF][START_REF] Moody | Effects of implied physical effort in sensory-motor and pre-frontal cortex during language comprehension[END_REF][START_REF] Tettamanti | Negation in the brain: Modulating action representations[END_REF], electroencephalographic (e.g., [START_REF] Aravena | Applauding with closed hands: neural signature of action-sentence compatibility effects[END_REF][START_REF] Moreno | Understanding action language modulates oscillatory mu and beta rhythms in the same way as observing actions[END_REF][START_REF] Moreno | Brain dynamics in the comprehension of action-related language. A time-frequency analysis of mu rhythms[END_REF][START_REF] Van Elk | The functional role of motor activation in language processing: motor cortical oscillations support lexicalsemantic retrieval[END_REF], non-invasive brain stimulation (e.g., [START_REF] Buccino | Listening to action-related sentences modulates the activity of the motor system: a combined TMS and behavioral study[END_REF][START_REF] Papeo | Effects of TMS on different stages of motor and non-motor verb processing in the primary motor cortex[END_REF][START_REF] Tomasino | Action verbs and the primary motor cortex: A comparative TMS study of silent reading, frequency judgments, and motor imagery[END_REF] and neurological patient studies (e.g., Boulenger et al., 2008;[START_REF] Herrera | Action naming in Parkinson's disease patients on/off dopamine[END_REF]. In spite of these findings, the notion of embodied meaning remains controversial. According to some critics, grounded representations lack functional relevance in language comprehension.

In this paper we will present new behavioral and neurobiological evidence of the functional link between grounding and long-term memory for action sentences.

Several electroencephalographic (EEG) studies have investigated the grounding of action-related language. Some brain rhythms have been considered signatures of motor processes; in particular, mu (8-13 Hz) and beta rhythms (15-25 Hz), recorded over frontocentral electrodes, provide rich temporal information of the underlying neural motor processes. These rhythms are highly synchronized during motor rest and become desynchronized when participants move, observe others' movements or even imagine their own movements, especially if these movements are manual [START_REF] Cochin | Perception of motion and qEEG activity in human adults[END_REF][START_REF] Mcfarland | Mu and beta rhythm topographies during motor imagery and actual movements[END_REF][START_REF] Pfurtscheller | Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks[END_REF][START_REF] Pineda | The functional significance of mu rhythms: Translating "seeing" and "hearing" into "doing[END_REF]. Most importantly, mu rhythms (and to a lesser extent beta rhythms) are also desynchronized when participants process action language, providing strong evidence of the neural dynamics of the motor system occurring online [START_REF] Lam | Making sense: motor activation and action plausibility during sentence processing[END_REF][START_REF] Moreno | Understanding action language modulates oscillatory mu and beta rhythms in the same way as observing actions[END_REF][START_REF] Moreno | Brain dynamics in the comprehension of action-related language. A time-frequency analysis of mu rhythms[END_REF][START_REF] Van Elk | The functional role of motor activation in language processing: motor cortical oscillations support lexicalsemantic retrieval[END_REF]. Similarly, a magnetoencephalography (MEG) study found somatotopically differentiated mu and beta rhythm modulations while reading hand and foot verbs [START_REF] Niccolai | Grasping Hand Verbs: Oscillatory Beta and Alpha Correlates of Action-Word Processing[END_REF]. Another line of studies collected event-related potentials (ERP) in the context of action-sentence compatibility tasks, reporting a reduction both in early motor potential (MP) and in later re-afferent potential (RAP) when participants were asked to perform a response with a hand shape mismatching the action in the sentence they had just listened to [START_REF] Aravena | Applauding with closed hands: neural signature of action-sentence compatibility effects[END_REF][START_REF] Ibáñez | Motor-language coupling: Direct evidence from early Parkinson's disease and intracranial cortical recordings[END_REF]. For instance, pressing a button with the open hand after hearing "Rocio applauded" increased the MP and RAP compared to listening to the same sentences and pressing the button with the closed hand. In sum, the above studies have shown that several neural signatures of motor activity are modulated by manual action language suggesting the involvement of motor networks in the processing of the action language's meaning.

However, critics of the embodied meaning approach raise doubts about the significance of the above behavioral and neural evidence [START_REF] Chatterjee | Disembodying cognition[END_REF][START_REF] Dove | Three symbol ungrounding problems: Abstract concepts and the future of embodied cognition[END_REF][START_REF] Mahon | What is embodied about cognition? Language[END_REF][START_REF] Mahon | A critical look at the embodied cognition hypothesis and a new proposal for grounding conceptual content[END_REF][START_REF] Patterson | Where do you know what you know? The representation of semantic knowledge in the human brain[END_REF]. According to these authors, it is not clear whether grounded representations are functional or even necessary aspects of meaning. First, most of the neurological evidence is basically correlational [START_REF] Mahon | A critical look at the embodied cognition hypothesis and a new proposal for grounding conceptual content[END_REF]. Indeed, the increase of BOLD signal in motor and premotor areas or the mu/beta rhythm modulations associated with action-related verbs does not imply that the motor engagement is a causal feature of conceptual meaning. It could be the case that the actual meaning of words is processed elsewhere by multimodal neural mechanisms, and secondarily spreads activation to sensory-motor regions [START_REF] Mahon | What is embodied about cognition? Language[END_REF][START_REF] Patterson | Where do you know what you know? The representation of semantic knowledge in the human brain[END_REF]. Second, most behavioral studies provide online sensory-motor effects, but do not show any long-term functional consequence of embodied grounding. For instance, dual-task experiments report interactions between the comprehension of manual action language and the performance on a concurrent hand movement using online measures such as reaction times or kinematics indexes [START_REF] Borreggine | The Action-Sentence Compatibility Effect: It's All in the Timing[END_REF][START_REF] De Vega | The comprehension of action-related sentences may cause interference rather than facilitation on matching actions[END_REF][START_REF] Glenberg | Grounding language in action[END_REF][START_REF] Taylor | Inferences about action engage action systems[END_REF]. However, some replicability issues have recently been raised for these dual task paradigms [START_REF] Morey | Testing the actionsentence compatibility effect: A pre-registered, multi-lab, Collaborative replication[END_REF][START_REF] Papesh | Just Out of Reach : On the Reliability of the Action-Sentence Compatibility Effect[END_REF]. Even more relevant here, they do not provide any evidence that embodiment affects other significant cognitive measures, such as performance in long-term memory tasks. This is especially important for theories of embodied cognition since they assume that sensorimotor processes are not only involved during conceptual processing, but also in semantic memory. As acknowledged recently in the literature [START_REF] Ostarek | Towards Strong Inference in Research on Embodiment -Possibilities and Limitations of Causal Paradigms[END_REF][START_REF] Ostarek | Six Challenges for Embodiment Research[END_REF], new paradigms are needed to provide decisive evidence for or against the involvement of sensorimotor processes in higherlevel cognition.

To date only a few studies addressed directly the grounding of long-term memory on the motor system for action-related concepts [START_REF] Canits | Effects of grasp compatibility on long-term memory for objects[END_REF][START_REF] Dutriaux | How to change your memory of an object with a posture and a verb[END_REF][START_REF] Dutriaux | Aging and posture in the memory of manipulable objects[END_REF][START_REF] Dutriaux | Learning is better with the hands free: The role of posture in the memory of manipulable objects[END_REF][START_REF] Van Dam | Embodied grounding of memory: toward the effects of motor execution on memory consolidation[END_REF][START_REF] Vitale | Enhancing Motor Brain Activity Improves Memory for Action Language: A tDCS Study[END_REF]. For instance, van [START_REF] Van Dam | Embodied grounding of memory: toward the effects of motor execution on memory consolidation[END_REF] have shown that recognition for manipulable objects is modulated by motor actions performed in the interval between study and recognition phases. Specifically, they revealed a better memory for words that denoted objects for which the functional use was congruent with the action performed during this interval (e.g., pepper mill-twisting action, doorbell-pressing action) than for words that denoted objects for which the functional use was incongruent.

More recently, [START_REF] Dutriaux | Learning is better with the hands free: The role of posture in the memory of manipulable objects[END_REF] manipulated the hand posture during the encoding phase, asking their participants to learn lists of pictures (Experiment 1) or nouns (Experiment 2) of manipulable and non-manipulable objects. During this phase, they had to adopt either a resting posture (hands in front of them) or a supposedly interfering posture (hands behind the back). After a short distracting task, they had to perform a free recall of the objects. Results have shown that holding the hands behind the back decreases the memory of manipulable objects, but not of non-manipulable objects. This so-called Postural Interference effect (PI effect) shows that posture selectively interfered with motor simulation of manipulable objects and subsequently decreased memory for those objects. Importantly, [START_REF] Dutriaux | Learning is better with the hands free: The role of posture in the memory of manipulable objects[END_REF] have found this effect with both pictures and words. In another experiment, [START_REF] Dutriaux | How to change your memory of an object with a posture and a verb[END_REF] extended these results showing that the memory of words denoting manipulable objects was impaired by the interfering posture when the objects were presented in a short phrase associated with a manual action verb, but not when associated with an attentional verb. This PI effect at the sentence level suggests in addition that a posture which does not favour action interferes with motor simulation elicited by an action verb, and thus decreases the memory of manipulable objects.

However, the question of the exact mechanism underlying the PI effect remains open. Indeed, it is difficult to conclude from these results whether the effect of posture arises directly from the neural motor system (e.g., inhibition of M1 in the hand-behind posture), or from the more peripheral proprioceptive information. Second, although the PI effect can be considered as the first behavioral evidence of the involvement of motor processes in long-term memory of action related language, the experimental design did not allow to determine the stage at which the motor recruitment occurs; namely, only at encoding or both at encoding and retrieval.

Finally, using a motor compatibility paradigm, [START_REF] Canits | Effects of grasp compatibility on long-term memory for objects[END_REF] failed to find any effect on the long-term memory of manipulable objects. They used pictures or names of manipulable objects associated with either precision grip (e.g., "key") or power grip (e.g., "hammer"), with instructions to categorize them as artificial or natural, responding either with a precision or a whole hand grip device. They found an online compatibility effects on reaction times, but they failed to find any negative effect of incompatibility on the unexpected free recall of images (Experiment 1) and names (Experiment 2 and 3), or on pictures recognition (Experiment 4). Thus, contrary to the previously described studies, [START_REF] Canits | Effects of grasp compatibility on long-term memory for objects[END_REF] did not observe an effect of motor interference on the long-term memory of manipulable objects.

Therefore, further research is needed to assess the role of sensorimotor processes in long-term memory of manipulable objects.

To gather further evidence on the motor engagement of conceptual meaning and memory, the present study uses a methodological approach similar to that of [START_REF] Dutriaux | How to change your memory of an object with a posture and a verb[END_REF], involving the manipulation of hand posture in a memory task for sentences. In order to get neural signatures of the motor processes, we recorded EEG measures in addition to collecting behavioral data. Participants read lists of sentences with a manual action verb (e.g., "to hang a cane") or with an attentional verb (e.g., "to see a cane") to be recalled. In the learning phase they were asked either to rest their hands on the table (front posture) or to cross them behind their back (behind posture). The EEG data collected during both the learning and the retrieval phases were analyzed with ERP (evoked activity) and time-frequency (induced activity) protocols, to provide detailed data on the event-related brain dynamics.

Complementarily, source estimation was implemented for those significant effects obtained with ERP or time-frequency. If the motor system is functionally involved in action related language and memory, we can expect: 1) To replicate the behavioral PI effect; 2) To obtain a selective effect of posture on motoric brain activity in the learning phase, while reading action sentences, but not while reading attentional sentences, and 3) Similarly, to observe delayed effect of posture during the retrieval of action sentences, but not of attentional sentences. Let us be more specific about the last two hypotheses. In the learning phase, we predict that reading action sentences in the front posture desynchronizes fronto-central mu and/or beta rhythm, normally associated with action language [START_REF] Lam | Making sense: motor activation and action plausibility during sentence processing[END_REF][START_REF] Moreno | Understanding action language modulates oscillatory mu and beta rhythms in the same way as observing actions[END_REF][START_REF] Moreno | Brain dynamics in the comprehension of action-related language. A time-frequency analysis of mu rhythms[END_REF][START_REF] Van Elk | The functional role of motor activation in language processing: motor cortical oscillations support lexicalsemantic retrieval[END_REF], whereas reading the same action sentences in the behind posture should reduce the modulation of these rhythms. The rationale underlying this prediction is that the proprioceptive information of the behind posture restrains the motor control system and, consequently, interferes with the motor simulations in action language (see e.g., [START_REF] Ambrosini | Body posture differentially impacts on visual attention towards tool, graspable, and non-graspable objects[END_REF][START_REF] Sirigu | Motor and visual imagery as two complementary but neurally dissociable mental processes[END_REF]. Therefore, if the motor simulations are functional for action language comprehension, the behind posture should weaken the memory traces of action verbs (associated with mu/beta modulations), resulting in impaired performance in recall. No such effect would be expected, however, if motor simulation does not contribute to action language comprehension. Concerning the ERP, we predict that reading action verbs in the behind posture compared to the front posture would increase the demand of neural resources, modulating the N2 and P3 components related to conflict monitoring processes. Indeed, reading action sentences while keeping the hands behind the back might trigger conflict monitoring processes between doing and not doing, which have been shown to involve the N2 and P3 components [START_REF] Botvinick | Conflict monitoring and cognitive control[END_REF][START_REF] Botvinick | Conflict monitoring and anterior cingulate cortex: an update[END_REF][START_REF] Kühn | When doing nothing is an option: The neural correlates of deciding whether to act or not[END_REF].

Concerning the recall phase, if motor activation directly contributes to the construction of a memory trace for action sentences in the front posture, then their retrieval should also activate motor processes. Thus, in the recall phase we can expect similar modulations on EEG rhythms (mu and beta) and ERPs waveform to those observed in the learning phase, namely a selective effect of hands posture for retrieving action sentences and no effect of posture on attentional sentences. Alternatively, if the motor simulation produced at learning is short-lived and epiphenomenal (e.g., not contributing to the formation of longterm memory traces), then the EEG motor signatures should not be modulated during retrieval. Finally, the source estimation of the obtained EEG effects both in the learning and the recall phase will allow us to explore whether the observed effects of posture on action language are associated with hand-related motor and premotor networks. However, given the exploratory nature of source analyses with the PI paradigm, other possible sources could be found. For instance, the episodic memory demands of the task may induce differential activations in the hippocampus and parahippocampal regions, as action sentences might be better recalled than attentional sentences. This effect could be interactively modulated by posture. Alternatively, the expected effects of posture could involve proprioceptive networks (i.e., precuneus, posterior cingulate cortex, intraparietal sulcus) rather than or in addition to motoric sources.

METHOD

A behavioral pilot was performed with the French version of the materials, to verify the robustness of the PI effect despite the changes in procedure and material due to different constraints inherent to an EEG study. As expected, the behavioral results were consistent with what had been previously observed (see Supplementary Material), which enabled us to perform the study described below. Although no part of the study procedures was preregistered prior to the research being conducted, the present procedure is based on this pilot study. Stimuli, scripts for presentation of the stimuli, further details on the method, scripts for analyses, and behavioral data are available at https://osf.io/7np8a/. EEG data are available at https://zenodo.org/record/4458608.

Participants

Thirty undergraduate students of Psychology at the University of La Laguna participated in this study. Data from four participants were excluded due to their low performance in the recall task (< -2.5 SD), leaving twenty-six participants for the analysis (24 women, two men; mean age = 24.4 years, SD = 2.47). A power analysis using the R package Superpower [START_REF] Caldwell | Power Analysis with Superpower[END_REF] showed that a sample size of 26 is sufficient to observe the interaction effect observed in the pilot study with p =.05 and 85% power. All participants were right-handed, had Spanish as their mother tongue, and did not report any neurological disease, visual problems, or medicine intake. They gave informed consent and received course credit for volunteering. The Research Ethics Committee of the University of La Laguna approved this study, and the experiment was conducted according to the principles expressed in the Declaration of Helsinki. We report how we determined our sample size, all data exclusions, all inclusion/exclusion criteria, whether inclusion/exclusion criteria were established prior to data analysis, all manipulations, and all measures in the study

Materials

We used 30 Spanish manual action verbs and 30 attentional verbs, as well as 120 nouns referring to manipulable objects, to construct two sets of 120 sentences with the format "infinitive form of verb + article + noun". Within each set of sentences, each verb was used twice and each noun only once. The sentences in the two sets differed only in the combination of nouns and verbs, such that if a given noun appeared with a manual verb in set 1 (e.g., colgar un bastón / to hang a cane) it was associated with an attentional verb in set 2 (e.g., observar un bastón / to observe a cane), and vice versa. Manual verbs and attentional verbs did not differ in frequency (manual M = 37 words/million, SD = 68; attentional M = 30 words/million, SD = 58; Mann-Whitney U = 1682, n1 = n2 = 30, p = .23) or length (manual M = 7, SD = 1.8; attentional M = 6.45, SD = 1.4; t (58) = 1.54, p = .13), according to the Spanish EsPal norms [START_REF] Duchon | EsPal: One-stop shopping for Spanish word properties[END_REF]. We also used Google Search as an indicator of the strength of association between each verb with each associated object. Mann-Whitney U tests across verb types were not significant in either list (set 1: manual M = 38529 Google Search results, SD = 72154; attentional M = 33893 Google Search results, SD = 94357; U = 2160, n1 = n2 = 60, p = .06; set 2: manual M = 82461 Google Search results, SD = 305069; attentional M = 125400 Google Search results, SD = 406372; U = 1693, n1 = n2 = 30, p = .57). Three normative studies were performed involving 60 participants, none of which participated in the EEG experiment, who were divided in three groups with instructions to judge in 7-points Likert scales either the familiarity, the 'manuality' (does the verb refer to a manual action) or the concreteness of the verbs. In the selected materials, the familiarity was similar for the two types of verbs (manual = 6.24 ± 0.51; attentional = 5.96 ± 0.75; t (58) = 1.73, p = .09), while, as expected, the manual action verbs were judged as more 'manual' (manual = 6.01, SD = 0.47; attentional = 1.66, SD = 0.77; F (1, 118) = 1411, p < .0001) and more concrete than the attentional verbs (manual = 5.74 ± 0.51; attentional = 4.26 ± 0.63; t (58) = 10.20, p < .001). The noun frequency and length values were M = 13.5, SD = 31.5 and M = 6.7, SD = 1.9, respectively.

Design and Procedure

The experiment involved a 2 Verb (manual, attentional) x 2 Hand posture (front, behind) within-participant design. Half of the participants were assigned randomly to one of the two sets of 120 experimental sentences. The sentences were divided into 12 experimental blocks of 11 sentences each: 5 manual and 5 attentional sentences as experimental materials, and 1 filler sentence that was added at the beginning of each block to discard recall primacy effects and excluded from the analysis. An additional block of 5 sentences was used as practice. Each experimental block consisted of: a) learning phase, including the 10 experimental sentences in random order, plus the initial filler sentence, b) one-minute distractive task, c) recall phase. The experiment was programmed using the Neuropsydia module for Python [START_REF] Makowski | Neuropsydia.py: A Python Module for Creating Experiments, Tasks and Questionnaires[END_REF].

In the learning phase, all the sentences in each block were presented visually word-byword, according to the sequence illustrated in Figure 1 (A). The large blank interval (6000  6600 ms.) following the last sentence word was necessary to recover the oscillation baseline after the verbal stimulus (see [START_REF] Moreno | Brain dynamics in the comprehension of action-related language. A time-frequency analysis of mu rhythms[END_REF]. To minimize mental rehearsal of the previously read sentence during this period, a secondary task was introduced. Namely, a onesecond sound consisting of a low-or high-pitch cello tone was presented, and participants were asked to mentally count the low pitch tones across the whole block of trials (a number between 0 and 2). They had to give their number orally at the end of the learning phase, after the last sentence and before the distractive task. Crucially, the participants were asked to memorize the sentences while keeping their hands on the table in a relaxed posture, or while holding one of their wrists with the other hand behind their back. The posture was changed every two blocks, and the order of posture was counterbalanced across participants.

The one-minute distractive task, which followed each block and preceded the recall phase, consisted of several trials in which the participants had to detect whether a target lower case letter was present among a group of upper-case letters. This task only aimed to avoid recency effects in memory and was not analyzed.

The recall phase (always performed with the hands in front) consisted of a cued recall task, in which the participants were given the verb visually (e.g., colgar / to hang), and they had to retrieve the missing noun (e.g., bastón / cane). Participants gave their responses orally when they were prompted by the '*' mark appearing in the screen and the experimenter recorded them for further classification, as Figure 1 (B) illustrates.

EEG recording

Participants' EEG was recorded continuously using a Compumedics Neuroscan (version 4.5) system from 60 active electrodes mounted in a Quick-Cap elastic cap following the 10/20 system, with additional electrodes placed on the left and right mastoids. All EEG electrodes were referenced online to an electrode at vertex (Cz). The signal was amplified (SynAmps2) and digitized at a sampling rate of 500 Hz. A band-pass filter from 0.1 to 100 Hz was applied online. The impedance of the electrodes was kept below 5 kΩ.

Preprocessing and analysis

ERP. For the ERP analyses, the EEG data preprocessing was conducted using Matlab Toolbox Fieldtrip (for details, see [START_REF] Oostenveld | FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data[END_REF]. Each participant dataset was low pass-filtered at 30 Hz. Artifacts due to blinks were corrected using ICA (Independent Component Analyses) algorithm. EEG segments were extracted with an interval of 200 ms. preceding the onset of the target word and extending 800 ms. afterwards.

The target word was the noun (e.g., cane) in the Learning Phase and was the verb in the Recall Phase (e.g., hang). Artifact rejection of segments due to muscular artifact or alpha noise was performed manually. A total of 10.11% of trials were excluded; no differences in Verb [F(1,25) = .75, p = .395]). For the computation of ERPs, artifact-free segments were finally averaged separately for each of the 4 experimental conditions (Front-Manual, Front-Attention, Behind-manual and Behind-attention) for both Learning phase and Recall phase.

We analyzed the ERP separately for verbs and nouns in the Learning Phase and timelocked to verbs in the Recall Phase. In all cases, the analyses were performed following a two-step procedure. First, Verb x Posture repeated measure ANOVAs for epochs between -200 and 800, with respect to the target word, were performed, for each data point and scalp site (500 x 60). To control for false positive effects (p < .05), time windows were considered reliable only when observed for at least 14 consecutive time points (28 ms) and four neighboring channels. In a second step, the time periods fitting the criterion were decomposed using the non-parametric cluster randomization approach implemented in the Matlab Toolbox Fieldtrip [START_REF] Oostenveld | FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data[END_REF]. This approach was chosen because of its adequacy to estimate the scalp distribution of significant differences between two conditions. For each pair-wise comparison the average activity at each scalp site for the intervals showing reliable interaction was introduced as input to the randomization test, giving as output a number of spatially contiguous sites in which the difference between the two conditions reached significance (p< .05). For each phase, two pair-wise comparisons (behind-attentional vs.

behind-manual and front-attentional vs. front-manual) were planned to decompose reliable interactions and to examine the effects of Posture for each verb type. This exploratory approach was selected due to the relative novelty of the object of study.

TF. EEG segments were extracted with an interval of 1500 ms. preceding the onset of the target word and extending 3000 ms. afterwards. Artifacts due to blinks were corrected using ICA (Independent Component Analyses) algorithm. Electrodes that had been removed were then recovered based on the average activity at neighboring electrodes. The target word was the verb both in the Learning Phase and in the Recall Phase (e.g., hang). A total of Two time-frequency (TF) analyses of the EEG data were conducted with the 2 Verb x 2 Posture experimental design, one for the learning phase data and the other for the retrieval phase data. The TF representations (TFRs) of single trial data for the selected epoch (-1500 to 3500 ms.) were computed using a multitaper approach [START_REF] Mitra | Analysis of Dynamic Brain Imaging Data[END_REF]. TFRs were constructed for the 2-30 Hz frequency range, using a 2.5 Hz frequency smoothing and 400 ms. time smoothing. Power changes were computed in frequency steps of 1 Hz and time of 10 ms. Averaged TFRs were computed for each condition and each participant separately by averaging the overall power of trials in every time frequency bin for each individual electrode.

The resulting participant's averaged power changes in the post stimulus interval were expressed as an absolute change with respect to the baseline interval (-500 to 0 ms. relative to verb onset).

Differences between conditions were tested for statistical significance using a clusterbased random permutation procedure to identify consistent differences between conditions in time-frequency clusters. This approach allows us to control for multiple comparisons based on physiologically plausible assumptions [START_REF] Maris | Nonparametric statistical testing of EEG-and MEG-data[END_REF]. Significant timefrequency pairs were clustered in the domains of frequency and time if they showed the same direction of effect. The significance of each cluster-level statistic was estimated by comparing the cluster statistics to a permutation distribution derived from the data. The permutation distribution is the distribution of the test statistic under the null hypothesis that the distributions of the two conditions are identical. The permutation distribution was obtained by randomly permuting the data 2000 times.

We selected three regions of interest (ROIs): the frontal area, the fronto-central area, and the parieto-occipital area. The frontal ROI was obtained by averaging the five near electrodes (F7, F3, Fz, F4, F8). The fronto-central ROI included electrodes over the sensorymotor cortex (FC1, FC2, C3, Cz, C4), where motor-related mu or beta rhythms have been reported in previous studies [START_REF] Mcfarland | Mu and beta rhythm topographies during motor imagery and actual movements[END_REF][START_REF] Moreno | Brain dynamics in the comprehension of action-related language. A time-frequency analysis of mu rhythms[END_REF][START_REF] Muthukumaraswamy | Mu rhythm modulation during observation of an object-directed grasp[END_REF][START_REF] Oberman | EEG evidence for mirror neuron dysfunction in autism spectrum disorders[END_REF]. Finally, the parietooccipital ROI (PO1, Pz, PO2, P3, P4) was selected with the aim of exploring possible differences in alpha rhythms related to visual or attentional processing [START_REF] Moreno | Brain dynamics in the comprehension of action-related language. A time-frequency analysis of mu rhythms[END_REF].

In all these ROIs the pairwise contrasts front-manual vs. behind-manual and front-attentional vs. behind-attentional conditions were obtained to verify whether the interference of the posture differentially modulates the TF signatures for the manual and the attentional verbs.

Source analysis ERP.

To estimate the likely brain sources, the LAURA (Local Auto-Regressive Average; by Grave de Peralta Menendez, Murray, Michel, Martuzzi, & Gonzalez Andino, 2004) distributed source estimation approach was applied using Cartool software (http://brainmapping.unige.ch/Cartool. php). A realistic head model based on a standard MRI (MNI brain from Montreal Neurological Institute), was used by applying the SMAC method, which transforms the MRI to a best-fitting sphere. Thereafter, 5005 solution points were defined in regular distances within the gray matter (6 x 6 x 6 mm) and a 3-shell spherical lead field was calculated for this set of solution points and for the 60 scalp electrodes. From this lead field, LAURA current density estimates were computed for the average ERP activity in time intervals showing significant waveform differences. Voxel-wise t-tests were run to compare the mean LAURA source estimates of the two conditions differing in scalp topography. To estimate the Brodmann areas which best fit the LAURA results, the location of each maximum t-value (positive or negative) was compared with the location of the Brodmann areas in MNI space.

TF.

A beamforming approach was used (Dynamic Imaging of Coherent Sources; DICS) to identify the neural origins of the oscillatory activity corresponding to the significant clusters obtained in the TF analysis [START_REF] Gross | Taskdependent oscillations during unimanual and bimanual movements in the human primary motor cortex and SMA studied with magnetoencephalography[END_REF][START_REF] Liljeström | Neuromagnetic localization of rhythmic activity in the human brain: a comparison of three methods[END_REF]. Sources were reconstructed using the frequency and time ranges that characterized the statistically significant clusters from the TF analysis, re-referencing the data to the average of all electrodes (common average reference). For each comparison a frequency analysis was carried out using the multitaper method based on discrete prolate spheroidal sequences (Slepian sequences) [START_REF] Mitra | Analysis of Dynamic Brain Imaging Data[END_REF]. Standard electrode locations were used in the source reconstruction for all participants, and the electrodes were aligned to a volume conduction model. The volume conduction model was calculated using the boundary element method with a standard MNI/SPM brain (Montreal Neurological Institute (MNI), Montreal, Quebec, Canada; http://www. bic.mni.mcgill.ca/brainweb) [START_REF] Oostenveld | FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data[END_REF]. This standard volume conduction model was used for all participants. Spatial filters were computed (overall participants) at a discrete grid of source points with 1 cm resolution using all the trials from all six conditions involved in the relevant contrasts. Next, source power values were computed using this common filter for each participant in each condition. Power values were then log-transformed to normalize the power values distribution and reduce the influence of outliers. Grand averaged sources were calculated by averaging the individual sources across participants and conditions. Contrasts were defined as the absolute difference in power between the two conditions. For the evaluation of the differences between conditions for the source reconstructed power data, paired t-tests were computed at each source location, and then neighboring source locations with significant (uncorrected) t-values were clustered. Then, the sum of the t-values in each cluster was calculated, and we selected the largest sum of the t-values.

RESULTS

No part of the study analyses was pre-registered prior to the research being conducted.

Behavioral analysis

Fig. 2 shows the mean percentages of words recalled for each experimental condition.

The results showed a main effect of Verb (F (1, 25) = 12.14, p < .01, ηp² = .33) with better recall of objects associated with a manual verb than with an attentional verb. The expected interaction between Verb and Posture was also found (F (1, 25) = 10.71, p < .01, ηp² = .30).

The pairwise contrasts under the Hochberg correction revealed better recall for Front-Manual (M = 34.36%, sd = 18.94) than for Behind-Manual condition (M = 27.44%, sd = 16.04; t (75) = 2.54, p < .05), and no differences raised between Front-Attentional (M = 23.59%, sd = 19.11) and Behind-Attentional (M = 24.74%, sd = 17.41; t (75) = 0.60, p = .55). Also, in Front Posture there was a better recall for Manual than for Attentional sentences (t(75) = 4.01, p < .01) but there were no differences in the Behind Posture between Manual and Attentional sentences (t(75) = 0.82, p = 0.42). However, given the fact that the two-time windows yielded similar statistical effects (see Figure 3. Learning phase. ERP analysis for the contrast between Verbs for the extended temporal window 134-344 ms. time-locked to the object noun. (A) The ERP plot for Manual and Attentional Verbs in the Front and in the Behind posture for some selected electrodes (midline: Fz, Cz and lateral: PO3, PO4). (B) Scalp topographies of ERP statistically significant cluster between Manual and Attentional Verbs in the front (left map) and behind (right map) . (C) Source reconstructions of the significant effects, revealing a cluster in the ACC (Ba 25) more active for Manual verbs and another cluster in the parahippocampal gyrus (Ba 35, 36) more active for Attentional verbs. Activations are projected on standard MNI coordinates.

Time-Frequency.

Only comparisons that reached statistical significance in the paired ttests of the TF analysis will be reported. One potential concern might be that our sensor-level oscillatory analysis compared baseline corrected power values between the different conditions, while our source-level oscillatory analysis compared power values that were not baseline corrected. This could mean that differences in baseline power might (at least partially) drive the effects for our sensor-level analysis, making them less comparable to the effects found at the level of oscillatory sources. To address this issue, we used the same random permutation testing procedure employed in our main analyses to test power differences in the baseline intervals used for the sensor-level analysis. We did not find any significant difference in baseline power among conditions over the frontal, fronto-central, or parieto-occipital ROI. We thus concluded that our mu or beta effects for the sensor-level analysis are not driven by differences in the level of baseline power between conditions, and these results highlight the same oscillatory effects found in our source-level analysis. These results are presented in detail below.

There was a significant main effect of Posture at the fronto-central region (p = .03).

Crucially, the paired t-contrasts only revealed significant differences between the Frontmanual and Behind-manual conditions at this region. The individual power spectra for the Front-manual condition showed a strong decrease in power in the mu frequency range (8-13 Hz) and, to a lesser extent, in the low beta frequency range (15-20 Hz), starting around 200 ms. after the verb onset and extending across the rest of the sentence (Fig. 4A, top left). Also, for the Behind-manual condition, a strong decrease in power in the mu frequency range was present starting around 200 ms after the verb onset and extending across the rest of the sentence. By contrast, in the low beta range, a wide decrease was presented from 200 ms after the verb onset until 1800 ms, followed by an increase in power that extended across the rest of the epoch (Fig. 4A, top right). The comparison of the spectral power indicates no clear difference in the mu frequency range between Front-manual and Behind-manual, although showed difference in the low beta frequency range (Front-manual < Behind-manual) across the whole sentence (Fig. 4A, bottom left). Finally, the statistical contrast only substantiated a significant negative cluster in the beta frequency range of 15-20 Hz, extending from 1500 to 1900 ms after the verb onset (p = .03), indicating a larger decrease in power in Front-manual than in Behind-manual condition. Notice that the cluster starts approximately after the noun offset (Fig. 4A, bottom right), indicating a sentence-level integration process rather than a neural signature of the noun, which was shared by the two verb conditions. The scalp topography of the significant negative cluster for the beta frequency range showed a frontocentral distribution (Fig. 4 B). The cluster level randomization test showed no significant differences over the frontal or parieto-occipital ROI for this comparison, indicating that the effects are constrained to the fronto-central region.

The source reconstruction of the significant differential cluster in the beta frequency band for the Front-manual vs. Behind-manual comparison shows the strongest activity in coordinates: x = 19 y = -45 z = 1 (Ba 37), corresponding to the posterior cingulate cortex (PCC) and extending to the fusiform gyrus and parahippocampal areas, for the time window 1500-1900 ms. relative to the verb onset, and the frequency range 17.45 ± 2.5 Hz (Fig. 4 C). The upper panel shows the TF analysis in Front-manual (FM) and Behind-manual (BM) conditions for the selected electrodes over the fronto-central region (FC1, FC2, C3, Cz, C4), in the 2-30 Hz frequency range. The left lower panel depicts the FM -BM difference, and the right lower panel the statistically significant cluster for this difference in the beta frequency range of 14-24 Hz. (B) Scalp topography of the FM-BM difference in beta rhythm. The white stars corresponded to the significant electrodes. (C) The source reconstruction of the beta effects reveals a significant negative cluster (FM < BM) around the PCC (Ba 37). Source activations are projected on a standard brain (MNI/SPM).

Recall Phase

The time-frequency analysis of the EEG did not provide significant effects of posture, verb or their interaction. However, the ERP analysis yielded statistically significant results. In the first step, point-wise ANOVAs over ERP segments time-locked to the presentation of the Verb yielded an early time-window showing reliable interactive effects between Verb and Posture. In this time-window there is a positive waveform, extending approximately between 172 and 222 ms after the Verb onset (p = 0.022, cirange = 0.009) with a broad fronto-central distribution, which corresponds to a P2 component (See Fig. 5A).

For this time-window with interactive effects, randomization tests revealed significant positivity for Manual compared to Attentional Verbs (p = 0.003, cirange = 0.003). There is a positive cluster for Manual compared to Attentional Verbs in the Front Posture (p = 0.002, cirange = 0.0028) and no differences in the Behind Posture. No other differences arise. Fig. 5B illustrates the scalp distribution of the differences between pairs of conditions underlying the significant interactions in this time window obtained by the non-parametric cluster-based randomization test.

Finally, the time-window in which the Verb x Posture interaction was obtained [172-222 ms] was chosen to explore the likely brain sources. Paired t-contrast on LAURA inverse solution revealed a likely intracranial generator of the topographical differences. When we contrasted manual verbs in the two postures, a main source was obtained in the left superior frontal gyrus (Ba 8; coordinates: x = -17 y = 32 z= 45), maximum t[25] = 2.77, p = .01.

DISCUSSION

The present EEG study investigated the grounding of action-related language by asking participants to read for subsequent recall simple sentences with either a manual or an attentional verb while holding their hands in front of them or behind their back. The behind posture should prevent from acting, and thus reduce the possibility to call for motor processes generally associated with action related sentences. Thus, according to embodied cognition, the behind posture should decrease motor activity while reading action sentences, and subsequently decrease memory and motor activity during the recall of these sentences.

As expected, the results showed differential effects of the hands posture for manual and attentional verbs both during the learning and the recall phase, which were manifest both on behavioral measures and electrophysiological indexes. First, a typical PI effect has been observed in memory performance, showing that the behind posture impaired the recall of sentences with manual action verbs but did not affect the recall of sentences with attentional verbs, confirming previous findings in the literature [START_REF] Dutriaux | How to change your memory of an object with a posture and a verb[END_REF][START_REF] Dutriaux | Learning is better with the hands free: The role of posture in the memory of manipulable objects[END_REF]. Second, reading sentences with manual verbs under restraining hand movements (behind posture), compared to the front posture, reduced beta rhythm desynchronization, generally associated with motor processes, clearly indicating that the behind posture interferes with the motor processes usually recruited to simulate actions during comprehension of action language [START_REF] Lam | Making sense: motor activation and action plausibility during sentence processing[END_REF][START_REF] Moreno | Understanding action language modulates oscillatory mu and beta rhythms in the same way as observing actions[END_REF][START_REF] Moreno | Brain dynamics in the comprehension of action-related language. A time-frequency analysis of mu rhythms[END_REF][START_REF] Van Elk | The functional role of motor activation in language processing: motor cortical oscillations support lexicalsemantic retrieval[END_REF]. Third, a main novelty of this study was the delayed effect of posture observed at the recall phase, consisting of a selective increase in positive amplitude around the P2 component of the ERPs for sentences with manual verb compared to sentences with attentional verb in the front posture, whereas there was no difference in waveform modulation in the behind posture. These results demonstrate for the first time that the behavioral PI effect induced by body posture on memory for action verbs is associated with differential brain dynamics both at the time of encoding and retrieval, and constitute strong evidence that embodied processes play a functional role in language and memory. A more detailed discussion of the results at learning and at retrieval follows.

Learning

As stated in our hypotheses, having one's hands behind the back should decrease neural motor activity during the encoding of action sentences. Consistently, manual action verbs induced more desynchronization in fronto-central beta rhythms, indexing neural motor processes, in the front posture than in the behind posture. This suggests that the behind posture causes interference on the motor simulation of manual actions. By contrast, the posture did not modulate beta rhythms for attentional verbs. The modulation of Rolandic beta rhythms by posture in manual action language occurred quite late, beyond the end of the sentence (Fig. 2 A, bottom right). This means that the behind posture interferes on the motor simulation underlying manual sentences, at the sentence integration stage or during the sentence wrap-up rather than being a purely lexical motor 'resonance' associated with manual verbs. This is consistent with previous studies with action sentences that also reported a late post-verb modulation of motoric rhythms [START_REF] Moreno | Brain dynamics in the comprehension of action-related language. A time-frequency analysis of mu rhythms[END_REF], or an N400 effect associated with motor incompatibility in the object name following the action verb, rather than the verb itself [START_REF] Santana | Temporal constraints on the comprehension of motor language. An embodied semantics approach[END_REF]).

Fronto-central beta rhythms desynchronization is usually associated with the activation of the motor and premotor cortex (e.g., [START_REF] Pfurtscheller | Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks[END_REF][START_REF] Pineda | The functional significance of mu rhythms: Translating "seeing" and "hearing" into "doing[END_REF].

Yet, the estimated source of the current modulation of beta rhythms by posture, was the right PCC extending to temporal medial regions, which are not typical motor or premotor sources.

The PCC, is a highly interconnected hub with many functions, including the regulation of both external and proprioceptive body inputs [START_REF] Guterstam | Posterior Cingulate Cortex Integrates the Senses of Self-Location and Body Ownership[END_REF][START_REF] Leech | Fractionating the default mode network: Distinct contributions of the ventral and dorsal posterior cingulate cortex to cognitive control[END_REF]. Therefore, the PCC activity is reasonable for a task that manipulated a static proprioceptive component such as posture. Yet, note that the PCC source of beta rhythms is not a general marker of postural effect but was restricted to trials with manual action sentences. Other studies in the literature consistently reported that proprioceptive information could interact with motor processes, associated with language comprehension. For instance, participants' own facial expression modulates the processing of emotional pictures [START_REF] Strack | Inhibiting and facilitating conditions of the human smile: A nonobtrusive test of the facial feedback hypothesis[END_REF] and texts [START_REF] Havas | Cosmetic use of botulinum toxin-a affects processing of emotional language[END_REF]; hand posture (either a power or a precision grip) primes a categorization task when it is coherent with the grip evoked by the target object [START_REF] Borghi | Are visual stimuli sufficient to evoke motor information?[END_REF], and hand posture modulates performance in motor imagery tasks [START_REF] De Lange | Posture influences motor imagery: An fMRI study[END_REF][START_REF] Ionta | The influence of hands posture on mental rotation of hands and feet[END_REF][START_REF] Shenton | Mental motor imagery and the body schema: evidence for proprioceptive dominance[END_REF]. In other words, ascending proprioceptive information seems relevant when the linguistic context demands motor simulations. The source of PCC apparently extends to the neighboring fusiform gyrus and parahippocampal area, and this allows other interpretations of the beta modulation by posture to be considered. For example, it could be related to memory processes, which would be consistent with the observed better recall for front-manual than for behind-manual trials. Note, however, that typical memoryrelated modulations of the EEG oscillations correspond to posteriorly distributed theta rhythms (4-7 Hz) rather than the current fronto-central beta rhythms. In any case, the low spatial resolution of the EEG-based source estimation leaves open the need for more detailed studies on the functional neuroanatomy of postural effects on action language.

Unexpectedly, mu desynchronization, another signature of motor processes, was not differentially affected by posture or by verb. In other words, there was similar mu rhythms desynchronization in the front and behind postures (see Fig. 4 A) as well as in manual and attentional verbs during sentence encoding (see Supplementary Material). This is consistent with the findings of Donno et al (2020), who reported an impact of hands posture on the modulation of beta rhythms but not on the modulation of mu rhythms. Yet, this lack of effect conflicts with the results of previous EEG studies which reported mu desynchronization for action verbs [START_REF] Lam | Making sense: motor activation and action plausibility during sentence processing[END_REF][START_REF] Moreno | Understanding action language modulates oscillatory mu and beta rhythms in the same way as observing actions[END_REF][START_REF] Moreno | Brain dynamics in the comprehension of action-related language. A time-frequency analysis of mu rhythms[END_REF][START_REF] Van Elk | The functional role of motor activation in language processing: motor cortical oscillations support lexicalsemantic retrieval[END_REF]. One possible reason for this, is that in our study the sentence final word was always a manipulable object name, which can induce mu desynchronization itself both in the manual and in the attentional verb contexts [START_REF] Bechtold | Event-related desynchronization of mu and beta oscillations during the processing of novel tool names[END_REF][START_REF] Kumar | Mu rhythm desynchronization reveals motoric influences of hand action on object recognition[END_REF][START_REF] Proverbio | Tool perception suppresses 10-12Hz μ rhythm of EEG over the somatosensory area[END_REF].

The ERP analyses showed a main effect of verb over the object name, consisting of a modulation of the complex N2 and P3, which are markers of inhibitory or conflict resolution processes. The estimated sources of the N2-P3 effect for verb might shed some light on the neural differences between manual and attentional verbs. Sentences with manual verbs induced more activation in the ACC, a region involved in conflict monitoring processes [START_REF] Botvinick | Conflict monitoring and cognitive control[END_REF][START_REF] Botvinick | Conflict monitoring and anterior cingulate cortex: an update[END_REF] and in decisions between doing or not doing something [START_REF] Kühn | When doing nothing is an option: The neural correlates of deciding whether to act or not[END_REF], whereas sentences with attentional verbs are associated with more activation in the parahippocampal gyrus, a region typically related to spatial and episodic memory in humans [START_REF] Allen | The evolution of episodic memory[END_REF][START_REF] Miller | Neural Activity in Human Hippocampal Formation Reveals the Spatial Context of Retrieved Memories[END_REF]. This suggests a neural dissociation between the two types of verbs during learning but does not directly support our hypothesis of embodiment since posture did not modulate the ERPs. On the other hand, the electrophysiological main effect of verb at learning has an echo in memory performance, with a better recall -although globally low-of objects presented with a manual action verb than with an attentional verb. This behavioral effect is similar to the effect reported by [START_REF] Dutriaux | How to change your memory of an object with a posture and a verb[END_REF], which can be attributed to the fact that manual verbs are more concrete and imageable than attentional ones [START_REF] Paivio | Concrete image and verbal memory codes[END_REF].

Recall

An embodied account of memory would predict that the decrease of neural motor activity observed during the learning phase should impact both memory performance and neural motor activity during recall. And indeed, consistently with our hypotheses, keeping the hands behind the back at the learning phase had a selective effect on the subsequent memory task, impairing only the recall of sentences with manual verbs, what suggests that the motor system is functionally involved both in the encoding and the retrieval of action language. The PI effect is consistent with dual-task studies, which reported that language comprehension could influence the kinematics [START_REF] Boulenger | Subliminal display of action words interferes with motor planning: A combined EEG and kinematic study[END_REF] or the speed [START_REF] Borreggine | The Action-Sentence Compatibility Effect: It's All in the Timing[END_REF][START_REF] De Vega | The comprehension of action-related sentences may cause interference rather than facilitation on matching actions[END_REF][START_REF] Glenberg | Grounding language in action[END_REF][START_REF] Taylor | Inferences about action engage action systems[END_REF] of a concurrent motor response, but differs from them in several aspects. First, dual-task studies involve the comprehension of action verbs or sentences while concurrently performing a matching or mismatching motor response. By contrast, the participants in the current paradigm did not perform any motor action during language comprehension, but just kept a resting posture which implies certain 'readiness' for (hands in front) or 'blocking' of manual action (hands behind). Second, the dual-task paradigms mainly reveal online effects of action language on motor response speed or kinematics, whereas here we obtained for the first time both online effects (in learning) and consistently long-term effects (in recall) of posture on action language. The neural mechanisms underlying effects in dual-task studies could differ from those observed with the PI paradigm. Namely, in the former studies the observed interaction would be due to the fact that action language and motor responses share motor processes, producing either response facilitation or interference. By contrast, in the PI paradigm, upward proprioceptive information might feed forward into the motor system, either facilitating or interfering with the motor plan demanded by manual action language. Also, consistently with our hypotheses and with the behavioral data, the electrophysiological results in the retrieval phase showed that the posture kept at learning modulates the P2 component of the ERP only in trials with manual action verbs. Specifically, the P2 amplitude was larger for manual than attentional verbs in the front posture, indexing the memory advantage of the former, but this difference was entirely suppressed in the behind posture. The P2 is a fronto-central positivity peaking around 250 ms, whose precise functional significance and its neural source remain imperfectly understood. It has been linked to topdown predictive processes in language [START_REF] Federmeier | Thinking ahead: The role and roots of prediction in language comprehension[END_REF][START_REF] Federmeier | Both sides get the point: Hemispheric sensitivities to sentential constraint[END_REF] or to memory performance [START_REF] Dunn | The Relation of ERP Components to Complex Memory Processing[END_REF]. Both interpretations can be compatible with an embodied account of memory and language. The reconstructed source associated with the P2 modulation leads to a SFG activation (around BA8), which is adjacent to the premotor cortex. In sum, the results support the hypothesis that the decrease of motor activity at learning selectively impacts neural activity during the retrieval of manual action sentences. Some limitations of this study should be noted. It would have been useful to test whether recalled and forgotten items differ in neural signatures such as mu rhythms modulation. Unfortunately, the relatively low performance in the recall task, although not surprising for such material, did not allow such a reliable analysis. Yet, a preliminary analysis of recalled vs. forgotten trials in which the posture was collapsed, suggests that mu and beta rhythms are modulated by recalled but not by forgotten sentences, with a source in the left supramarginal gyrus (Ba 40) in the case of action sentences (see Supplementary Material).

The same lack of power prevented us from testing the correlation between motor activity at learning or at retrieval with the behavioral data, but this study paves the way for future studies designed specifically to test this idea.

In conclusion, this study demonstrated that motor representations activated online during the comprehension of action language play a functional role in long-term memory.

Numerous previous studies had provided evidence that the comprehension of action language induces resonance in the motor and premotor cortex. However, doubts persist on whether these online processes are a functional feature of meaning or rather an epiphenomenon. The rationale of the present research was to disturb motor processes by manipulating the hand posture and test performance in memory while collecting EEG. We gathered evidence supporting each of our hypotheses. We unequivocally obtained a selective impact of hand posture on memory performance just for manual action sentences, that is, when the motor simulation was disturbed by keeping the hands behind the back, the recall of manual sentences was impaired, confirming a PI effect. Convergently, keeping the hands behind the back selectively impedes beta rhythms desynchronization over fronto-central electrodes while reading manual action sentences, indicating that the activity of the motor cortex was interfered during the learning phase. Finally, the ERPs at the retrieval phase also revealed a selective impact of posture on the P2 component for manual action verbs, indicating a reduction of top-down prediction when the sentences had been encoded with the hands behind the back. In sum, this research can be taken as a strong evidence in favor of the functional role of motor simulations in meaning and memory and also opens new avenues on the role of proprioceptive information on the comprehension of action language and its long-term impact on performance.

Declarations of interest: None

Figure 1 .

 1 Figure 1. Structure of a trial. (A) Sequence of events in the learning phase [translation: to hang / the / cane]; and (B) in the recall phase.

  artifact rejection arise among factors in either the Learning Phase (Posture [F(1,25) = 1.99, p = .17], Verb [F(1, 25) = 3.36, p = .08]) nor in Recall Phase; (Posture [F(1,25) = 4.2, p = .051],

  23.92% of trials were excluded. The average number of trials rejected due to artifacts did not significantly differ among conditions in the learning phase (Verb [F (1, 25) = 2.06, p =.16], [F (1, 25) =.01, p=.91]) nor in the recall phase (Verb [F (1, 25) = .76, p =.39], or accuracy [F (1, 25) = 2.15, p=.15]).

Figure 2 .

 2 Figure 2. Behavioral results. Mean percentage of recalled sentences as a function of Verb and Posture. Vertical segments represent standard errors of the mean.

Figure 4 .

 4 Figure 4. Learning phase. TF analysis for the contrast Front-manual -Behind-manual. (A)The upper panel shows the TF analysis in Front-manual (FM) and Behind-manual (BM) conditions for the selected electrodes over the fronto-central region (FC1, FC2, C3, Cz, C4), in the 2-30 Hz frequency range. The left lower panel depicts the FM -BM difference, and the right lower panel the statistically significant cluster for this difference in the beta frequency range of 14-24 Hz. (B) Scalp topography of the FM-BM difference in beta rhythm. The white stars corresponded to the significant electrodes. (C) The source reconstruction of the beta effects reveals a significant negative cluster (FM < BM) around the PCC (Ba 37). Source activations are projected on a standard brain (MNI/SPM).

Figure 5 .

 5 Figure 5. Recall phase. ERP analysis for the interaction Verb x Posture for a time-window of 172-222 ms after the verb onset. (A) The ERPs for Manual and Attentional verbs at the front and the behind posture for some selected central (Fz, Cz) and lateral electrodes (PO3, PO4). (B) Scalp topographies of ERP for the difference between Manual and Attentional verbs in the front and in the behind posture. The crosses correspond to the significant electrodes. (C) Source reconstructions for the interaction Verb x Posture in the Superior Frontal Gyrus (BA 8; x = -17 y = 32 z= 45). Activations are projected on standard MNI coordinates.

  Investigation, Data Curation, Writing -Review & Editing, Visualization. Alix Seigneuric: Conceptualization, Methodology, Writing -Review & Editing. Valérie Gyselinck: Conceptualization, Methodology, Resources, Writing -Original Draft, Writing -Review & Editing, Supervision, Project administration, Funding acquisition.

Acknowledgement

We would like to thank Martina Bracco for her help in the preparation of the manuscript.

Funding

This work was supported by the French National Research Agency (grant number ANR-13-APPR-0009 given to Valérie Gyselinck); and the Spanish Ministerio de Ciencia, Innovación y Universidades and the European Regional Development Funds (grant number RTI2018-098730-B-100 given to Manuel de Vega).

Supplementary Material) and have similar topographical distribution (see Fig. 2 B), we will present here the results for the extended time window from 134 to 344 ms.

All effects during this time window were decomposed using the non-parametric cluster randomization approach. The main effects of Verb showed a positive cluster (p = .003, cirange = .003). Neither Posture (p = .36) nor the interaction Posture x verb (p =