Participants.

Twenty-three participants (7 males; mean age: 24.8±4.6, age range: 20–42 years) participated in the experiment. All participants were right-handed as measured by the Edinburgh Handedness Inventory [35]. Participants were recruited through a direct request within the common Institutional spaces. They participated to both the experiments in the period [July 2014—December 2015] and received a monetary reimbursement on completion of the study.

Stimuli and procedure.

The stimulus was an object of spherical shape (2 cm diameter) positioned on a black table in front of the participant at a distance of 30 cm from starting position of the hand, along the midsagittal plane. The concave base (12 cm diameter) was positioned at the participant right side at a 28 cm distance from the target location (see Fig 1, panel A).

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Fig 1. Descriptive example of the experimental setting.

Panel (A) shows the distance between the starting position of the hand and the object to be grasped, and the distance of the object (and the co-agent, i.e., the experimenter) from the concave base. Panel (B) shows a descriptive example of the participant position, which was identical in the behavioral and the fMRI experiment. Panel (C) shows an example of the reach-to-grasp movements performed (outside the scanner) in individual and social conditions. Panel (D) shows an example of the reach-to-grasp movements performed (inside the scanner) in individual and social conditions.

https://doi.org/10.1371/journal.pone.0184008.g001

Participants were tested individually in a softly lit room. They were laying supine on a bed, in order to control postural effects and matching behavioral and fMRI experimental settings (Fig 1, panel B). Before each trial, the right hand of each participant was resting on a starting pad (a brown velvet cloth 7 × 6 cm) with the index finger and the thumb gently opposed. Participants were asked to start the action after the presentation of a tone (880 Hz/200 ms). The action was divided into a reach-to-grasp and a place phase: the social aspect of the action was manipulated by considering two experimental conditions (Fig 1, panel C): (i) Individual. Each participant was asked to reach towards, grasp the stimulus (reach-to-grasp phase), and put it in a concave base (place phase). (ii) Social. Each participant was asked to reach towards, grasp the stimulus and pass it to another agent whose hand was placed on top of the concave base (place phase), so that the target location was identical to that of the individual condition in both phases. The concave base was equated, in terms of concavity and depth, to the hand of the co-agent as to avoid differences in movement accuracy. The co-agent was seated to the right side of the table with the hand supine resting on the end-pad. The vision of the participants was never occluded during the whole experiment.

Participants were informed about the type of trial (individual/social) by visually inspecting the presence/absence of the hand of the experimenter above the concave base adopted for individual trial. After each trial, the experimenter re-positioned the stimulus on the initial target location. The order of conditions was randomized between participants. Each subject performed 12 trials for each condition.

Analyses were confined to the reach to grasp phase, that is up to the moment the object was grasped. This was done in order to specifically target the ‘intentional’ component. Grasping the same object in order to move it or pass it are both intentional actions. The critical difference is in the intentional component: whereas moving an object contains a purely individual intention, passing an object to another person necessarily involves a social intention, as shown in the published literature.

Kinematics recordings.

A 3D-Optoelectronic SMART-D system (Bioengineering Technology and Systems, BTS) was used to track the kinematics of the participant’s right upper limb. Three light-weight infrared reflective markers (0.25 mm in diameter; BTS) were taped to the following points: 1) thumb (ulnar side of the nail); 2) index finger (radial side of the nail); and 3) wrist (dorsodistal aspect of the radial styloid process). Six video cameras (sampling rate 140 Hz) detecting the markers were placed in a semicircle at a distance of 1–1.2 m from the bed. The camera position, roll angle, zoom, focus, threshold, and brightness were calibrated and adjusted to optimize marker tracking, followed by static and dynamic calibration. For the static calibration, a 3-axes frame of 5 markers at known distances from each other was placed in the middle of the table. For the dynamic calibration, a 3-marker wand was moved throughout the workspace of interest for 60 s. The spatial resolution of the recording system was 0.3 mm over the field of view. The standard deviation of the reconstruction error was below 0.2 mm for the x-, y-, and z-axes.

Data processing.

Each trial was individually checked for correct marker identification, and the SMART-D Tracker software package (BTS) was used to provide a 3-D reconstruction of the marker positions as a function of time. Then, data were filtered using a finite impulse response linear filter (transition band = 1 Hz, sharpening variable = 2, cutoff frequency = 10 Hz). Movement onset was defined as the time at which the tangential velocity of the wrist marker crossed a threshold (5 mm/s) and remained above it for longer than 500 ms. For the reach-to-grasp phase, the end of movement was defined as the time at which the hand made contact with the object and quantified as the time at which the hand opening velocity crossed a threshold (−5 mm/s) after reaching its minimum value and remained above it for longer than 500 ms. For both social e individual reach-to-grasp tasks, the following kinematic parameters were extracted for each individual movement using a custom Matlab routine (Matlab 2014b, The 4 Math Works, Natick, MA, USA): the time interval between movement onset and end of movement (Movement Time–Mov_T), the time at which the tangential velocity of the wrist was maximum from movement onset (Time to Peak Wrist Velocity–T_peak_V) and its amplitude (Amplitude of Maximum Peak Velocity–Amp_peak_V), the time at which the acceleration of the wrist was maximum from movement onset (Time to Peak Acceleration–T_peak_A) and its amplitude (Amplitude of Maximum Peak Acceleration–Amp_peak_A), and the time at which the deceleration of the wrist was maximum from movement onset (Time to Peak Deceleration–T_peak_D) and its amplitude(Amplitude of Maximum Peak Deceleration–Amp_peak_D). Furthermore, a grasp-specific measure was assessed, specifically the time at which the distance between the 3D coordinates of the thumb and index finger was maximum, between movement onset and hand contact time (Time to Maximum Grip Aperture–T_max_grip_apert).

Statistical analysis.

Statistical analysis was performed using JASP software (Version 0.7.5.61; free downloadable at https://jasp-stats.org/download/). Gaussian distribution was confirmed for all kinematic indexes (α-level: p < 0.05, all ps ≥ 0.09), except for the T_peak_A, and Amp_peak_V (all ps ≤ 0.013). We used the Shapiro–Wilk test [36] allowing the use of parametric statistics for the major part of the kinematic indexes. For the two non-normally distributed indexes (i.e., T_peak_A, and Amp_peak_V), we performed non-parametric statistics, using the Wilcoxon signed rank test. The mean value for each kinematic parameter of interest was determined based on 12 individual observations for each participant and then entered into separate paired-samples t-tests for comparing social versus individual reach-to-grasp conditions.

Participants.

The same participants who took part in the behavioral experiment were tested also in the fMRI experiment.

Stimuli, procedure, and experimental design.

The stimulus, task and procedures were identical to those adopted for the behavioral experiment; timing and number of trials were modified in order to make the paradigm suitable for fMRI data collection. Participants were laying in the scanner bore with their head tilted at an approximate angle of 30°, supported by a foam wedge permitting direct viewing of the stimulus, and the co-agent arm. The co-agent was standing on the right side of the participant nearby the bore of the scanner and remained present for the entire experimental session (see Fig 1, panel D).

Participants were requested to perform the same action toward the stimulus, which was decomposed into two phases: i) reach-to-grasp phase, in which participants were instructed to reach and grasp the object and keep the hand still on the object until an acoustic go signal indicated the beginning of the subsequent stage; ii) place phase, in which participants were asked to put the grasped object into a concave base (individual condition) or to pass it to a co-agent (social condition), whose hand was on top of the concave base, positioned on the right side of the participant. The two phases were separated by a constant temporal interval of 2 s. Each trial started with a sound (880 Hz/200 ms) delivered by pneumatic MR-compatible headphones, and participants were instructed to start their action toward the stimulus only when the sound was delivered. For each experimental condition, fifty trials were administered, divided into two functional runs. As for the behavioral experiment, the fMRI analyses were confined to the reach-to-grasp phase. Participants were informed about the type of trial (social vs individual) by visually inspecting the presence/absence of the hand of the experimenter above the concave base adopted for individual trial.

The experiment was conducted by using an event-related design, with Inter Stimulus Interval (ISI) varying from 3 to 8 s with a “long exponential” probability distribution [37]. ISIs distribution was fully randomized across trials in each run.

Data acquisition and preprocessing.

The experiment was carried out on a whole body 1.5 T scanner (Siemens Avanto) equipped with a standard Siemens eight channels coil. Functional images were acquired with a gradient-echo, echo-planar (EPI) T2*-weighted sequence in order to measure blood oxygenation level-dependent (BOLD) contrast throughout the whole brain (37 contiguous axial slices, ascending interleaved sequence, 56 × 64 voxels, 3.5 mm × 3.5 mm × 4.0 mm resolution, FOV = 196 mm × 224 mm, flip angle = 90°, TE = 49 ms). Volumes were acquired continuously for each run with a repetition time (TR) of 3 s; 165 volumes were collected in each single scanning run, resulting in functional runs of 8 min and 15 s duration (16 min and 30 s of acquisition time in all). High-resolution T1-weighted image were acquired for each subject (3D MP-RAGE, 176 axial slices, no interslice gap, data matrix 256 × 256, 1 mm isotropic voxels, TR = 1900 ms, TE = 2.91 ms, flip angle = 15°). Data preprocessing was performed using SPM12 (Statistical Parametric Mapping, Wellcome Institute of Cognitive Neurology, London, UK) implemented in MATLAB 7.5.0 environment (MathWorks, Natick, MA, USA). ArtRepair toolbox (ArtRepair software Package, for SPM12) was adopted to correct for possible images corruption due to signal spikes induced by head motion. Motion correction was carried out by realigning data. Structural images were segmented and subsequently the image of grey matter was co-registered with all the functional images. Structural and functional images were then normalized adopting the template provided by the Montréal Neurological Institute (MNI) implemented in SPM12. No spatial smoothing was applied for classification purposes, because it introduces a certain degree of inter-voxel correlation, and may cause the loss of information useful for separating adjacent but functionally different brain areas.

Region of interest specification.

To avoid any circularity issue in regions of interest (ROI) selection [38], we did not rely on the functional data but selected four ROIs that were defined purely on anatomical grounds (using the SPM WFU pick atlas toolbox and the Talairach Daemon (TD) anatomical labels (gyral anatomy) atlas, transformed in MNI space; http://www.fil.ion.ucl.ac.uk/spm/ext/#WFU_PickAtlas).

We anatomically selected frontoparietal areas belonging to the pMNS and the MS, focusing on previous neuroimaging findings on action observation [9], showing a BOLD signal increase in areas of the pMNS and the MS (e.g., IFG, IPL, mPFC, and MTG) during social interaction with respect to individual condition. Thus, we bilaterally selected the IFG, the IPL, the mPFC, and the MTG. For a better delineation of the mPFC, we used the Automated Anatomical Labeling (AAL) atlas, selecting the Frontal_Sup_Medial regions.

To test the classifier performance outside of our network of ROIs, we defined two additional non-brain control ROIs in which no BOLD signal was expected and thus no consistent classification performance should be possible (see [39], for a similar methodological procedure). A 5 mm³ cubic region was selected within participants’ right ventricle (centroid MNI coordinates: [10, –12, 22]), plus a second 5 mm3 cubic ROI just outside the skull of the brain, near the right visual cortex region (centroid MNI coordinates: [56, –90, –10]). We anticipate that the pattern classification did not reveal any significant decoding in these two areas (i.e., right ventricle: M = .5 ± .01 SEM, t = -.16; outside the brain: M = .49 ± .02 SEM, t = -0.54).

Moreover, we considered the bilateral Posterior Cingulate Cortex (PCC) as a cortical control ROI. This area is particularly interesting because although its activity is associated with social cognitive processing such as self-reflection it seems not to be involved in the kind of social processing at stake here. In a recent review [40], the authors suggested that the PCC activity plays a key role in facing with moral issues regarding ourselves or others, or guilt that may come as a consequence of our actions. Crucially, Johnson and collaborators [41], found a dissociation between the medial prefrontal cortex (mPFC–here considered as part of the mentalising system), and the PCC: the activity of the mPFC was more associated with instrumental or agentic self-reflection (e.g., hopes and aspirations), whereas PCC activity was more associated with experiential self-reflection (e.g., duties and obligations). Furthermore, in their mini-review on the neural bases of mentalizing [22], the authors reported that the mPFC might be concerned with anticipating what a person is going to think and feel and thereby predict what they are going to do. Finally, the role played by the dorsal mPFC in executive inhibition, self-other distinction, prediction under uncertainty, and intention-related processing, has been reviewed in [42], where the authors argued that the involvement of the dorsal mPFC in these processes may explain why it is preferentially activated when people mentalize others' internal states. In light of this literature, we reasoned that the PCC could be a good cortical control ROIs for our study, because it is engaged in social situations that are different from that induced by our social interaction task. We anticipate that decoding results showed that the mean decoding accuracy (averaged across participants) from bilateral Posterior Cingulate Cortex (PCC) was M = 0.52 +/- 0.02 SE. The one tailed t-test (t(22) = 1.51, p = 0.07) showed that it was not possible to discriminate between individual and social intention from the voxel pattern activity within the PCC control ROI.

Classifier decoding.

After ROI extraction, the voxel time series were pre-processed through a series of commonly used steps: linear detrending, temporal filtering, and standardization. For every participant, each of the two runs was processed separately. Linear trends in each time series were removed, and a high-pass filter (0.01 Hz) was applied in order to remove low frequency drift in the signal. Then, time series were standardized in order to have zero mean and standard deviation one.

To investigate to what extent each selected ROI was involved in encoding the social intention of the action occurring during the first segment of the action we performed a decoding analysis using the fMRI signal within a temporal window of [3–6 s] from the onset of each trial, which better approximates the BOLD peak of the reach-to-grasp phase of the action.

We used Support Vector Machine (SVM) with linear kernel (using Matlab functions “svmtrain” and “svmclassify”) as multivoxel pattern classifier for discriminating between social vs. individual action intention. For each participant, we trained a linear classifier on the voxels within each selected ROI. The target condition was coded in a way to have a vector Ti ϵ{+1, −1}N, where i refers to the sample and N is the number of samples relative to both conditions in the classification (e.g., N = 100), in which all the samples corresponding to one target condition (i.e., Social) were labeled with +1, whereas all the other samples (i.e., Individual) with −1. The value of the regularization constant C was fixed to 1 (i.e., the default value). Cross-validation was used to estimate the test generalization performance. The SVM classifier was trained on the data set using a modified version of leave-one-out cross-validation, in order to maintain a balanced set of training and test examples. At each step of the cross-validation loop, two samples (one for each condition) were excluded from the training set and used to test generalization performance (see [43, 17]). Classifier accuracy, computed across the entire cross-validation loop on the test set, was used as statistical measures of binary classification.

Previous studies showed that t-test group analysis, with respect to nonparametric randomization tests, is a rather conservative estimate of significant decoding accuracy [39]). Therefore, we conducted a set of one-tailed t-tests, one for each ROI, on the classifier accuracy (against the chance level of 50%) to obtain group statistics regarding the discrimination between the two conditions included in each classification.

All further analyses on the classifier accuracies were restricted to the ROIs from which it was possible to significantly discriminate between social and individual intention. Crucially, in order to investigate the role of the different areas within the pMNS and the MS in distinguishing between individual and social intention, we performed a Repeated Measures Analysis Of Variance (RM-ANOVA) on the mean classifier accuracies, considering the ROI as a within subject factor.

Furthermore, in order to assess whether and to what extent the selected areas cooperate or are independent, we performed a correlation analysis, using Pearson correlation, among the decoding accuracies of the ROIs. We used false discovery rate (FDR) for multiple comparisons correction.

Decoding accuracies.

To investigate whether the voxel pattern activity within the considered pMNS and MS could convey information regarding the kind of intention motivating a reach-to-grasp action, we applied a linear SMV classifier to each a priori anatomically selected ROI. Preliminary analyses performed on each ROI and hemisphere did not show evidence on hemispheric differences across all the selected ROIs (see Table 2).

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Table 2. T statistics on the decoding accuracy from each ROI between left and right hemisphere.

https://doi.org/10.1371/journal.pone.0184008.t002

We had no a priory hypothesis about a major involvement of one hemisphere over the other in distinguishing between social and individual action. Thus, for each of the selected ROI, we pulled together the voxels belonging to both hemispheres. Classifier results showed that it was possible to decode the social intention of the action from all the selected ROIs (all ts ≥ 5.3; see, and Fig 3, panel B, and Table 3), but not, as anticipated, from the control ROIs (ts ≤ -.54).

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Fig 3. fMRI decoding results.

Panel (A) shows regions of interest (ROIs) used in the multivariate classifier analyses, transparently superimposed on top, lateral and mesial view of a standard template using BrainNet Viewer (http://www.nitrc.org/projects/bnv/) [44]. Panel (B) shows the mean linear SVM classification accuracy for discriminating between social and individual conditions of the reach-to grasp action phase as a function of the involved bilateral ROIs. Error bars indicate one standard error of the mean. All decoding accuracies are significantly greater than 0.5 (chance level = 50%; p < .001, after FDR correction for multiple comparisons). Panel (C) shows the significant correlation (p ≤ .001, after FDR correction for multiple comparisons) between the decoding accuracy of the IFG (pMNS) and that of the mPFC (MS). The greater was the discriminant information about the social intention conveyed by the IFG, the greater was that conveyed by the mPFC. Error bars indicate one standard error of the mean.

https://doi.org/10.1371/journal.pone.0184008.g003

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Table 3. T statistics (one-tailed one sample t-test) on the decoding accuracy of the selected ROIs.

https://doi.org/10.1371/journal.pone.0184008.t003

Interaction amongst the mirror and the mentalizing systems.

We performed an RM-ANOVA on the mean classifier accuracies, considering the ROI (4 levels) as a within-subjects factor. Results showed no main effects of the ROI (F(1.99, 43.76) = .13, p = .88, Greenhouse-Geisser corrected for sphericity). Thus, no significant differences among the decoding accuracies of the selected areas emerged.

Finally, we investigated whether and to what extent the decoding accuracies for the selected areas correlated to each other. After FDR correction for multiple comparisons, results showed that the decoding accuracy from the PFC was significantly correlated with that from the IFG (R = .62, p = .001). In summary, correlation results indicate that the mPFC and the IFG do interact. Specifically, the higher was the decoding accuracy from the mPFC, the higher was that from the IFG (see Fig 3, panel C).

The putative mirror neuron system (pMNS).

Concerning the pMNS, our results provide evidence of a direct involvement of these areas in mediating the social intention underlying the execution of the action: all regions included in the network (i.e., bilateral IFG and IPL) revealed distinct patterns of activity for the social and the individual condition. Only the first phase of the action (i.e., the reach-to-grasp phase) was considered in the analysis. Thus, despite the identical physical experimental setting of the reach-to-grasp phase during social and individual conditions (i.e., the object to be grasped was the same and was located at the same position), the intention underlying the action (i.e., individual vs social component) appeared to be represented by distinct voxel pattern activity. In other words, actions performed with and without a final social aim appeared to be coded differently within the pMNS. This is a novel finding that could answer a controversial question concerning the role of the pMNS in representing the social intentions during the execution of a social action.

The fact that these areas of the pMNS were encoding the action intention through distinct patterns of activity highlights their crucial role in mediating the construction of the final motor code for the appropriate execution of the action. This contribution becomes evident also when considering the difference between the kinematic profile characterizing the two conditions. Our findings are in line with other neuroimaging results investigating the neural correlates of joint action execution, and showing that the coordination of two agents for the achievement of a common goal recruits regions of the pMNS [48–49]. Due to a close link between perception and action provided by the pMNS brain regions, an agent could use simulation for predicting the intentions and consequences of actions performed by the co-agent, in order to adjust his own action planning and then successfully achieve the joint goal. In our experiment, the co-agent was always present and the participants were informed regarding the kind of action to be executed by visually inspecting the presence (i.e., social condition) or the absence (i.e., individual condition) of the co-agent’s hand. We argue that the pMNS could provide an automatic representation of an action based on the visual state of the other, by translating the perceived action (i.e., the hand movement) into motor and somatosensory representation of “how” and “what” the co-agent has done, in order to integrate this information into an appropriate common motor code. This argumentation is in line with the predictive coding account [50] for understanding the function of the mirror system: the most probable causes of an observed action can be inferred by minimizing the prediction error at each level of the cortical hierarchy involved during action observation.

For the execution of the reach-to-grasp action with a social intention, however, the pMNS might be only one component of a hierarchical system for abstracting the intention of the action (i.e., social vs. individual) and integrating it into appropriate motor commands [34, 51].

The metalizing system (MS).

The social nature of the intention underlying the reach-to-grasp action appeared to be decoded also within the MS, considered as responsible for the attribution of goals and intentions [51, 52]. During action observation, the MS appeared to become active when observers reflected on the intentionality of an observed action [53–55]. This suggests that MS regions contribute to the analysis of other people’s actions when the viewer decides to reflect upon their goals, intentions and beliefs [30, 51]. But why is the MS recruited for the execution of socially intended movements? Evidence from studying gaze-based social interactions suggests that activity in areas concerned with mentalizing is evident when participants follow the gaze of another person to engage in joint attention [56]. However, no study so far has elucidated the possibility that social information engaging the mentalizing system is conveyed as to influence movement kinematics. Our data on action execution may say something about this issue. We argue that the MS could provide a representation of an action intention by continuously monitoring both the self and the co-agent and that the nature of such process may be the integration of low-level embodied mechanisms within higher level inference-based mentalizing. This may serve to integrate mentalizing kind of information with that coming from low-level embodied processes, into an appropriate common motor code. Indeed, there is empirical evidence on the existence of shared neural circuits for mentalizing about the self and others [57]. Brain regions that are part of the mentalizing network are specifically engaged when we reflect about intentions [55].

Here, we suggest that the MS (in concert with the pMNS) may have the ability to inform regions responsible for shaping action kinematics regarding the ‘social’ intent motivating the action. Another possible role of the MS system might be to monitor, during the on-line control phase, that the social side of action is maintained throughout the movement.

Correlation amongst the mirror and the mentalizing systems.

In order to test the possible interactions among the areas of the two systems (the pMNS, and the MS) we investigated possible differences and correlations among their decoding accuracies. Our results showed that the selected areas are equally involved in representing the social intention of the action. Further analyses on decoding accuracies showed a different correlation dynamics among the two circuits: the decoding accuracy from the mPFC (MS) was positively correlated to that from the IFG (pMNS): the more informative was the activity pattern encoding action intention within the mPFC, the more informative was that within the IFG. These results confirm the idea that the pMNS and the MS can work in concert [31, 32, 57]. The pMNS might subserve the MS that, in turn, might integrate the representational code provided by the pMNS with its own in order to enrich the motor code with the information concerned with the “what” and the social “why” of an action. Indeed, studies focusing on action observation have shown that the pMNS and the MS do integrate [51]: the mirror system could translate the perceived action into motor and somatosensory representation [58–60] of how and what others do. Such simulated representation could be later interrogated by the MS to ‘reflect’ on why other people act [30]. Action observation and execution could be two faces of the same coin. Indeed, increasing the sensitivity of the fMRI data analysis (i.e., using a single subject analysis and no spatial smoothing), it has been shown that during the observation and execution of hand actions, the classical pMNS areas (i.e., ventral PM cortex and rostral IPL) together with additional brain areas like the MTG, contain “shared voxels” between execution and observation [61].

Hence, our findings are consistent with the idea that motor simulation and mentalizing (i.e., inferential mechanisms) are not mutually exclusive, but play complementary roles in understanding the intentions of other agents around us. Whether the role of the pMNS is subordinate to that of the MS is not clear. One hypothesis is that the pMNS may allow a rapid and pre-reflective understanding of another person’s intentions through sensorimotor processes, whereas the MS may allow higher-level reflective inferences, such as the attribution of complex distal intentions. Although a meta-analysis of functional MRI data suggested that these two systems are relatively independent [32], data from a number of activation or effective connectivity functional MRI studies [57, 62] do not support this view. The two systems may interact and cooperate.

Whether these two systems interact at a horizontal or vertical level within the cortical hierarchy requires further investigation. The unifying framework for motor control and social interaction [34] assumes a vertical hierarchy in the organization of movements with higher levels representing goals and intentions and lower levels representing kinematics and muscle-group selection. In this proposed hierarchical model it is possible to have several representations of the observed action ranging from the low-level kinematics of movement, to the representation of sequence of actions (at the intermediate level), to the task goal representation (i.e., the action intention) at the highest level. This model could be used in a forward and inverse way: the forward model can learn both elementary movements (lower level) and their sequential order (intermediate level) through sensorimotor learning; then, progressively higher levels could learn more abstract representations with the higher level learning task goals (i.e., intentions). Later, in the inverse model, the activation of a high-level goal, or intention, would progressively activate lower levels representations in such a way to generate the appropriate motor commands (at the lower level) to perform the required action. It is not clear, however, at which level of the hierarchical generative model the pMNS and the MS could interact. Neuroimaging studies based on effective connectivity, Diffusion Magnetic Resonance Imaging, and artificial simulations based on hierarchical generative models should help to understand how these two neural systems interact for recognizing the action intention and efficiently generating an appropriate motor command.

Overall, our findings challenge current theories in social cognition, blending motor simulation and inferential processes as part of the same hierarchical generative model for action’s intention understanding and generation of appropriate motor commands. Our findings suggest that the crosstalk between the MS and the pMNS, could be a crucial step in mediating the transition from intention understanding to action execution during social interactions, and could be a valuable insight for understanding which mechanism is impaired in disorders that show deficits in social cognition, such as autism spectrum disorder.