Pragmatic representations of self- and others’ action in the monkey putamen

Domain: neuroscience
Published: 2026-01-19
Editorial outcome: minor_revision

Abstract
Social coordination in primates relies on parieto-frontal networks encoding self- and others’ actions. These areas send convergent projections to the putamen, but its role in representing self- and others’ actions remains unknown. We recorded neuronal activity from anatomically characterized putamen regions of two male macaques during a Mutual Action Task (MAT), where a monkey and a human took turns grasping a multi-affordance object based on sensory cues. Cortico-striatal synaptic input, indexed by local field potentials, mirrored known cortical dynamics during sensory instructions and movement, while single neurons selectively encoded the monkey’s action, the human’s action, or both. Grip type was encoded only during the monkey’s trials. Viewing the partner’s action was neither necessary nor sufficient, as neurons fired even when it occurred in darkness but not when viewed through a transparent barrier. Thus, the possibility for actual interaction characterizes the pragmatic role of the putamen in gating cortical representations of self- and other’s actions in social contexts.

Introduction
Coordinating one’s own actions with those of others is a fundamental skill in both humans and other nonhuman primates. To this end, we draw on contextual cues, physical objects, and other’s observed behavior to decide whether to act and which action to perform. Importantly, such a variety of physical1 and social2 stimuli affords a large set of potential motor actions, which can ultimately be selected and converted into overt motor responses, or inhibited when inappropriate in the current context.

To date, neuroscientific research on these abilities has been limited to the cerebral cortex, where several parietal and frontal regions host neuronal mechanisms underlying visuomotor transformations for grasping objects3–6 and planning motor responses to others’ observed actions during social interactions2,7,8. Indeed, neurons with partially mixed selectivity for graspable objects, self, and others’ actions have been reported in the inferior parietal9, ventral10, and mesial11,12 premotor cortices, as well as in the prefrontal cortex13. These regions form a cortical network where different sensory information is remapped onto a motor code, affording possible motor responses to perceived stimuli14. This latter conclusion relies on evidence that the visual responses of neurons in these areas are down-modulated or completely abolished when a transparent barrier is interposed between the observer and the observed object6,9,10 or another’s action7,15. Since a barrier does not alter the visual features of the stimulus but selectively eliminates the possibility of interacting with it, these findings demonstrate the pragmatic nature of the visual responses in the cortical grasping network, that is, their role in affording object-directed actions as well as motor responses to others during social interactions14.

Recent neuroanatomical studies have shown that all the aforementioned parietal, premotor, and prefrontal nodes of the cortical grasping network send convergent projections to specific, overlapping territories of the putamen16,17. This nucleus serves as the main entry point of the basal ganglia, a system of deep brain structures of considerable clinical interest due to their crucial role in selecting and controlling goal-directed and habitual actions18–20. The extensive cortico-striatal projections from the main nodes of the cortical grasping network to the putamen suggest that this nucleus may function as a key coordination hub for large-scale, functionally specialized networks dedicated to the selection and control of manipulative actions. Surprisingly, the possible contribution of putaminal neurons to the selection and organization of grasping actions remains unknown. Even less explored is their activity during the observation of other’s action, which is known to induce a balanced facilitation and suppression of corticospinal neuron activity21, as well as of neurons in the parietal9 and premotor11,22 cortical regions. These modulations may result from cortico-striatal loops hypothesized in previous studies8,23, potentially enabling the selection or suppression of one’s own motor actions while observing those of others—especially when social coordination in a shared space is required.

To address these issues, we designed a Mutual Action Task (MAT) in which a monkey and an experimenter, facing each other on opposite sides of a table, took turns grasping or observing their partner grasp the same multi-affordance object using either a precision grip (PG) or a whole-hand prehension (WH), based on a previously presented visual instruction. We recorded neuronal activity using multiple linear 32-channel probes from the sector of the putamen that receives anatomically-verified projections from cortical territories involved in forelimb motor control. The cortico-striatal synaptic input, indexed by local field potentials (LFPs) modulations24, faithfully reflected known cortical dynamics during sensory instructions and motor planning, while single-neuron activity selectively encoded the monkey’s own action, the experimenter’s action, or both. The grip type was clearly encoded by nearly a quarter of the neurons during the monkey’s own trials, but not during the partner’s trials. Control conditions revealed that viewing the partner’s action was not necessary for most recorded neurons, as they still discharged even when the partner acted in the dark. Notably, a sizeable fraction of these neurons exhibited reduced or abolished discharge when the observed action occurred behind a transparent plastic barrier, supporting a pragmatic role for the monkey putamen in social coordination.

Results
Histological and connectional characterization of the recorded region
We used silicon and stainless-steel 32-channel linear electrode arrays (“Methods”) in Mk1 and Mk2, respectively, employing a chronic (Supplementary Fig. 1a) or semi-chronic (Supplementary Fig. 1b) vertical approach. We targeted a region of the putamen corresponding to the stereotactic coordinates of the previously identified territory receiving projections from multiple areas of the cortical grasping network17, as verified by post-mortem 3D reconstruction of the recorded regions (Fig. 1a). The recordings spanned the entire dorso-ventral extension of the putaminal forelimb representation25, confirmed by the reconstruction of electrode tracks in Nissl-stained coronal sections of the two monkeys’ brains (Fig. 1b). In Mk2, we also conducted an anatomical tracing study at the end of the neurophysiological experiments to directly characterize the connectivity pattern of the core region (yellow in Fig. 1a) where neurons were previously recorded.

The results confirmed that the medial aspect of the putamen (Fig. 1c) receives projections specifically from the forelimb motor regions of the cingulate cortex26, the mesial and dorsal premotor cortices6, the ventral premotor cortex10,27,28, the primary motor cortex29, as well as from regions in the ventral and dorsal bank of the intraparietal sulcus and the inferior and superior parietal lobules30,31. The most lateral part of the putamen exhibits a similar connectivity pattern (Fig. 1d), with strong input from the primary motor and dorsal premotor cortices (Fig. 1e), supporting a prominent role of the putaminal investigated regions in forelimb motor control.

Monkey’s exploit the contextual instructions to perform the Mutual Action Task with an experimenter
We collected neuronal and behavioural data in 32 sessions (n = 18 in Mk1, n = 14 in Mk2) during the MAT (Fig. 2a). At the beginning of each trial, both partners gently pressed a button with their right hand (contralateral to the monkey’s recorded hemisphere) and, in complete darkness, received an auditory cue instructing who was required to act in the ongoing trial (Agent cue, Fig. 2a) and, next, a visual cue specifying which grip type (precision or whole-hand grip, Grip cue, Fig. 2a) to use for grasping a multi-affordance object (Fig. 2b). Afterward, the light turned on (Object vision, Fig. 2a) and when the cue sound ceased (Go signal) the previously instructed agent (i.e. monkey or experimenter) had to reach and grasp the object with the appropriate grip type and lift it (Action), while the partner remained still. In half of the trials, the light switched off at the onset of the reaching movement (Action vision, Fig. 2a), requiring the agent to perform the action in complete darkness. After each correctly executed trial, the monkey was automatically rewarded with a drop of juice (Fig. 2a). This task allowed us to assess the effect of three main factors—the agent identity (monkey and experimenter), the grip type (precision and whole-hand grip), and action visibility (full light and in the dark)—which, combined, resulted in 8 main conditions. We collected at least 15 correctly performed trials for each condition per session.

Behavioural evidence demonstrates an overall robust acquisition of the task rules by both animals (Fig. 2c), with an average percentage of correctly performed trials higher than 80% (83% for Mk1, 87% for Mk2). Specifically, monkeys acted and refrained from acting coherently with the auditory cue (Mk1, χ2 = 2179, p = 10−475; Mk2, χ2 = 2132, p = 1.7 × 10−465) and correctly performed the previously instructed type of grip (Mk1, χ2 = 1437, p = 1.22 × 10−314; Mk2, χ2 = 1102, p = 1.14 × 10−241). Furthermore, the merely executive errors in task performance were very rare, and the monkeys made very few errors during the other’s trials. These findings demonstrate a similar and high level of accuracy and compliance with the task instructions in both animals, during both the execution and observation tasks.

The analyses of reaction times, reaching-grasping times, and lifting times revealed a substantial similarity in the execution of the MAT between the two animals (Fig. 2d). For both monkeys, the reaction time did not differ between trials instructed with the PG or WH cue. In contrast, in PG trials, which require a higher degree of motor control relative to WH trials, the reaching time was significantly longer in Mk2 (t = 8.47, p = 2.09 × 10−6) and the lifting time was longer in both animals (Mk1: t = 11.47, p = 2 × 10−9; Mk2: t = 9.11, 9.66 × 10−7).

Local Field Potentials (LFPs), but not single neurons, encode contextual instructions
Since most of the input to the putamen nucleus derives from the cortex, and LFP signals are largely driven by synaptic modulations24, we first assessed the changes in power within each the three main LFP frequency bands (low, 2–8 Hz; medium, 13–28 Hz; high, 60–100 Hz) during self and other trials of the MAT (Fig. 3a). The dynamics of LFP modulation observed during the MAT closely resemble those previously reported in motor cortical regions using various visuomotor paradigms32–34.

During self-trials, a sizeable and progressively increasing proportion of recorded channels exhibited significant power suppression relative to baseline within the low and medium frequency bands as the task advanced from agent cue to grip cue, and finally to the visual presentation of the object; at that stage, virtually all channels showed marked suppression in both low and medium frequency bands (Fig. 3b). In contrast, power in the high frequency band significantly increased at the Go signal, with nearly all channels exhibiting modulation in this band during both the reaching and grasping phases of monkey’s own action. Consistently, a subset of the modulated channels also displayed grip selectivity within the low and medium bands prior to movement onset, and in the low and high frequency bands during reaching and grasping (Fig. 3c).

Very similar modulations across the different frequency bands were observed during other-trials (Fig. 3d). However, no increase in low-band power was evident during the partner’s action, and the enhancement in the high frequency band appeared earlier – starting at the Go signal and continuing into the reaching phase. Moreover, virtually no recording site showed grip selectivity during other-trials (Fig. 3e). We further investigated the relationship between LFP modulations during self and other trials by computing the correlation coefficients for each site and frequency (see Methods), where maximal self-other similarity is indicated by values approaching 1 and maximal dissimilarity by values approaching −1. Most sites exhibited robust and positive correlations in the lower frequency bands (<30 Hz) during both PG- and WH-grip trials (Fig. 3f).

Interestingly, while LFP modulations in the low and medium frequency bands encoded contextual task cues, this was not the case for high frequency bands, which are thought to reflect neuronal spiking activity24. Indeed, single-neuron analyses (see Methods) revealed that among 560 isolated neurons (415 from Mk1 and 145 from Mk2) 245 were task-related, and very few of them exhibited purely cue-related activity (n = 11). Some were modulated during the agent and/or grip cues as well as during the subsequent action execution phase (n = 34), whereas the vast majority (n = 200) displayed purely action-related responses (Fig. 3g). By looking at the tuning of the recorded neurons in a time-resolved manner (200-ms sliding window in steps of 20 ms), no significant coding for contextual task cues was detected before the object became visible. Notably, the highest fraction of neurons exhibiting selectivity for agent identity, grip type, and action vision was found during the object grasping phase (Fig. 3h).

Taken together, these findings suggest that cortico-striatal synaptic input may convey early contextual information but influences putaminal spiking activity primarily during self and other’s action. We therefore focused subsequent analyses on self- and other-related neuronal responses during the movement epochs of the MAT.

Putamen neurons encoding self and other’s action
Action-related neurons were classified based on their response to actions performed by the two agents. Self-type neurons (ST, n = 153, 65%) were modulated exclusively during the monkey’s own action (Example neuron 1, Fig. 4a). Other-type neurons (OT, n = 30, 13%) responded only to the experimenter’s action (Example neuron 2, Fig. 4a). Self-and-other-type neurons (SOT, n = 51, 22%) responded during the action of both agents (Example neuron 3, Fig. 4a). Most action-related neurons exhibited facilitated discharge during self-trials (Fig. 4b), whereas those responding during other-trials showed a more balanced distribution of facilitation and suppression (Fig. 4c). Facilitated neurons displayed a sharp increase in firing rate tightly aligned with movement onset, particularly during self-trials. In contrast, suppressed neurons exhibited an earlier and more gradual reduction of their activity, especially during other trials (Supplementary Fig. 3). These findings suggest that while self-type neuronal responses are likely driven by direct cortico-striatal projections35, other-type responses may be more strongly shaped by local inhibitory mechanisms. Interestingly, all but one of the other-related neurons (OT and SOT combined) were recorded simultaneously with at least one self-type neuron (Supplementary Fig. 4). Thus, despite the lack of electromyographic recordings, we could verify the absence of modulation among ST neurons during other trials, which makes it unlikely that other-related responses were due to covert muscle activation, consistent with previous direct evidence from monkey action observation studies11,36–38.

Examining the relationship between SOT neuron response during self and other trials revealed that the majority (73%) exhibited a coherent facilitation (FF, n = 24) or suppression (SS, n = 13) in both contexts, while the remaining neurons displayed opposite modulations (FS and SF, Fig. 4d). Considering SOT neurons with coherent modulations during self and other trials, the timing of peak activity and discharge onset were not significantly different (peak, t = −0.31, p = 0.76; onset, t = −0.37, p = 0.71) and showed moderately positive and significant correlations (Fig. 4e). These findings emphasize the similarity in SOT neuron dynamics across self- and other-trials. Nevertheless, using a Bayesian classifier trained to distinguish self from other trials (“Methods”), we found that SOT neurons exhibiting coherent facilitation or suppression (FF and SS) carried as much information about agent identity as SOT neurons modulated in an opposite manner (FS and SF) for the two agents (Fig. 4f). This indicates that even neurons with similar responses to both agents’ actions retain information about who is acting, highlighting a strong and widespread agent-specific coding in primate putamen neurons.

Next, we investigated whether agent-specific coding of actions also reflects the way in which grasping is performed—namely, using either a PG or a WH grip—as previously demonstrated in all cortical areas projecting to the putamen5,27,39. Among action-related neurons active during the monkey’s own actions (n = 204), approximately one quarter (23.5%) encoded grip type, with a similar fraction preferring PG (n = 30, 14.7%, example Neuron 4, Fig. 4g) and WH (n = 24, 11.8%, example Neuron 5, Fig. 4g). As the vast majority of grip-selective neurons exhibited facilitated discharge (n = 47, 87%), we further examined the population dynamics of these neurons (Fig. 4h), confirming the absence of grip-type coding prior to movement onset and a sharp tuning to grip type from reach onset through grasp execution.

Importantly, grip-selective neurons did not significantly vary their tuning depending on whether the action was performed in the light or in the dark (Supplementary Fig. 5), supporting the inherently motor nature of their discharge. Among action-related neurons active during the experimenter’s actions (n = 81), only a few (n = 8, 9.9%) encoded the grip type. Two of these were SOT neurons, but their grip selectivity was different during self and other trials. Thus, although putamen neurons display significant tuning for grip type during monkey’s own actions, they do not generalize this coding feature to others’ actions (Fig. 4i).

No need to see, but to share the space for action
The visual input from the monkey’s own hand during action execution exerts a facilitatory effect on the discharge of parietal40 and premotor37 neurons, particularly when comparing those responsive to the action of others with purely motor neurons. This finding suggests that cortical SOT neurons process visuomotor information regarding both self and others. However, whether this property also applies to putaminal neurons—which are direct targets of projections from the core areas of the cortical grasping network—remains to be determined.

To address this issue, we compared neuronal responses during MAT trials performed by the monkey in the dark and in full light (Fig. 5a), allowing us to manipulate the visibility of the monkey’s own action. Among 204 neurons responding during monkey’s own action, the vast majority showed no significant difference in discharge intensity between the light versus dark condition (Fig. 5b and example neuron 6, Fig. 5c), regardless of whether they were responsive (SOT) or non-responsive (ST) to other’s action (c2 = 0.6, p = 0.44), in striking contrast to previous findings reported in the parietal and premotor areas37,40. Even at the population level the response of neurons facilitated (n = 155, 76%) and suppressed (n = 49, 24%) during monkey’s own action showed no significant difference between grasping in the light and in the dark (Fig. 5d), indicating that putaminal neuron activity is essentially unaffected by the visual feedback from monkey’s own acting hand.

Next, we asked whether visual information was actually necessary for putaminal neurons to encode the partner’s action. In fact, we can easily be aware of another’s action even if it occurs in the dark or behind an occluder, provided that prior visual or auditory cues are available, and neurons responsive to others’ observed action can fire with a notable degree of independence from actual action visibility36,38,41,42, suggesting remarkable generative properties within the cortical action observation network23. To address this issue in the putamen, we compared neuronal responses during MAT trials performed by the experimenter in the dark and in full light (Fig. 5e). Among 81 neurons responsive during the partner’s action in the light (SOT, n = 51; OT n = 30), the overwhelming majority (n = 76, 94%) showed no difference in their discharge when the experimenter’s action occurred in the dark (Fig. 5f and example neuron 7, Fig. 5g). Even at the population level, the response of both facilitated (n = 44, 54%) and suppressed (n = 37, 46%) neurons during partner’s action showed no significant difference between light and dark conditions (Fig. 5h), indicating that the encoding of others’ action by neurons of the motor putamen can occur entirely independently of visual information.

Finally, given their marked independence from visual input, we aimed to further scrutinize the relevance of putaminal neuron activity during the partner’s action. Based on substantial evidence from neurophysiological studies of the areas belonging to the cortical grasping network7,9,10,15, we hypothesized that other-related neural responses in the striatal node of the cortico-basal ganglia loop play a pragmatic role, being modulated by the possibility of interacting with the target object. To directly test this hypothesis, we employed an additional experimental condition in which the experimenter’s action was performed in full light behind a transparent plastic barrier interposed between the monkey’s hand and the target (Fig. 6a). Note that the barrier did not alter neither the task contingencies nor the available visual information with respect to the social condition, but introduced a physical separation between the monkey and the experimenter’s target, rendering it unreachable for the monkey. Out of 70 putaminal neurons tested in these conditions (42 SOT and 28 OT, Fig. 6b), 22 (31.5%) discharged similarly with and without the barrier, 5 increased their firing with the barrier (7.5%), while the response of the remaining (61%) was either reduced (n = 8, 11%), or abolished (n = 35; 50%; example neuron 8, Fig. 6c) during the barrier with respect to the social condition. Population responses showed a similar effect of the barrier among facilitated and suppressed neurons (Fig. 6d), indicating that the mere vision of the partner’s action is not only unnecessary, but even not sufficient to trigger putaminal neuron activity. Rather, what appears critical is the possibility for the monkey to interact with the target of the partner’s action, thereby highlighting the pragmatic nature of other-related putaminal neuron response.

Methods
Animal models
This study involved two purpose-bred, socially housed adult male monkeys (Macaca mulatta, 9 and 12 Kg). Prior to recordings, the monkeys were habituated to sitting in a primate chair and interacting with the experimenters. Following this initial phase, they underwent specific positive reinforcement training to perform the Mutual Action Task (MAT), described below, using the hand contralateral to the hemisphere to be recorded.

In preparation for neural data collection, the monkeys underwent a surgical procedure for the implantation of a recording chamber over the region of interest, leaving the skull intact. The recording probes were implanted during subsequent surgeries. All surgeries were performed in stereotaxic and aseptic conditions, under general anaesthesia induced by intramuscular injection of ketamine (5 mg/Kg) and medetomidine hydrochloride (0.05 mg/Kg) and maintained with 2% isofluorane vaporized in 100% oxygen. The monkey’s vital parameters were continuously monitored with a multiparametric monitor. To ensure hydration, a constant intravenous infusion of saline solution was provided, and vitamin A gel was applied to prevent eye dryness during anaesthesia. Monkeys received analgesics, broad-spectrum antibiotics, and anti-inflammatory drugs during and after surgical procedures. Multielectrode linear probes were subsequently implanted in both monkeys during different surgeries.

All experimental procedures were conducted in agreement with the European (Directive 2010/63/EU) and Italian (D. lgs 26/2014) legislation for the Protection of Animals Used for Scientific Purposes, received the approval of the Veterinarian Animal Care and Use Committee of the University of Parma and of the Italian Ministry of Health.

Behavioural paradigm
During the MAT, each monkey sat in its primate chair positioned at one end of a table, with the experimenter sat in front of it on the opposite side, taking on the role of a collaborative partner. Both subjects shared an operational space containing a multi-affordance object (Fig. 2b) that could be reached and manipulated by both partners. The object (5.5 cm-diameter, 6 cm-high cylinder, centrally topped by a 4.5 cm × 4.5 cm × 1.5 cm parallelepiped) positioned 16 cm away from each subject’s initial hand position, afforded two distinct grip types: a precision grip (PG), involving thumb-index pinching on the central part of the object, and a whole hand prehension (WH), requiring wrapping the hand around its cylindrical body. A visual cue displayed on a 2 × 1 cm OLED screen embedded in the central part of the parallelepiped on top of the object indicated the required grip type: an empty square for PG and a filled square for WH. Metallic plates around each component of the object activated distinct capacitive circuits upon contact, thereby triggering a specific TTL signal for effective execution of PG and WH, recording and storing the detected contact times along with other behavioural and task events.

Each monkey was trained separately with the experimenter to interpret a sound as a Go or No-Go cue: a high-pitched tone (1200 Hz sine wave) instructed Mk1 to act and Mk2 to remain still; conversely, a low-pitched tone (a 300 Hz) instructed Mk2 to act and Mk1 to remain still. Each monkey’s No-Go cue served as the Go cue for the experimenter, who performed the action while the monkey remained still.

Each trial began in complete darkness, with the monkey and the experimenter holding their right hand on a starting button. After 1 s (inter-trial interval), the cue sound was presented (sound onset), conveying an opposite Go or No-Go instruction to the partners. 770 ms after sound onset, the OLED screen displayed either an empty or a full square, instructing the agent to perform a PG or WH, respectively. After 730 ms, ambient light was turned on (light onset), making the object visible. Following an additional 570 ms, the cue sound ceased (go signal), indicating to the partner for whom the sound served as the Go cue to release the button, reach for, and grasp the object using the specified type of prehension within 1 s. The object had to be held for at least 500 ms in order for the monkey to receive the reward. In half the trials, the light it was switched off upon button release to eliminate visual feedback during monkey’s or experimenter’s movement (dark condition); in the other half, the light remained on until task completion (light condition). In addition to the basic social condition in which the MAT could be performed by both partners, we also included a control condition in which a transparent plastic barrier was interposed between the monkey’s hand and the target (barrier condition), thereby making it impossible for the monkey to interact with the target.

Whenever the monkey successfully completed all steps of a Go trial or remained motionless during a No-Go trial while the experimenter performed the actions, a fixed amount of liquid reward was automatically delivered; otherwise, the trial was aborted. A trial was considered correct only if both partners behaved appropriately. The task phases were automatically controlled and monitored using a LabView-based software, which also enabled trial termination in the event of behavioural error by either partner. Specifically, the monkey could commit the following types of errors during self trials: early start, releasing the button before sound offset; not touch, releasing the button without contacting the target object on time; no lift, touching the object but failing to lift it; no hold up, lifting the object but failing to keep it pulled up for at least 500 ms. During experimenter’s trials, we evaluated possible wrong start errors, consisting in detaching the hand from the starting button during the experimenter’s trials.

In each session, we collected 15 trials for each of the 8 experimental conditions (2 grip types × 2 agents × 2 visual feedback conditions), resulting in 120 correctly performed trials.

Neural recording techniques
Each monkey was surgically implanted with a custom-made bio-compatible plastic recording chamber (Mk1: 4.5 × 5 × 2.5 cm; Mk2: 2.8 × 3.3 × 2.5 cm), designed from 3D cranial reconstructions based on 7T MRI data using 3D Slicer. Chambers featured parallel grooves (1 mm-wide, 2 mm-spacing) housing up to 8 (Mk1) or 4 (Mk2) Omnetics connector blocks interfacing multielectrode arrays and the wireless datalogger headstages (Deuteron Technologies Ltd) for neural data collection. After full recovery from chamber implantation, different probe insertion strategies were employed in the two animals.

In Mk1, five 32-channel linear silicon probes (ATLAS Neuroengineering, IrOx contacts, 250 mm inter-site spacing, 24 mm length, 30 × 100 mm cross-section, 0.23–0.29 MW impedance) were chronically implanted through a small craniotomy performed within the chamber in two different surgeries. We used a dedicated insertion device60 to position and release probes with a vertical approach at the desired location based on MRI-based reconstruction of stereotaxic coordinates. Probes featured a pointed tip to minimize tissue dimpling and were connected via flexible polyimide ribbon cables to the Omnetics connectors61. A collagen-based dural regeneration matrix (DuraGen® Plus) was placed around insertion sites, and liquid bone cement was then poured into the recording chamber, which rapidly solidified to secure the probes in place and prevent any contamination. Two months after the first surgery and the subsequent recording period, another surgery was performed to explant the first two probes and implant three new probes of the same type.

In Mk2, during the chamber placement surgery, bone cement was poured into the chamber (13-mm thickness) and allowed to solidify. Then, during two distinct surgeries spaced four months apart, four polyimide guide tubes (36 mm in length; outer diameter: 820 mm; inner diameter 760 mm) were implanted through the cement and the intact bone with a stereotaxically manipulated drill bit to ensure vertical trajectory to reach the desired depth and location. The polyimide tubes served as permanent guides, allowing for the precise, repeated insertion of individual linear multisite probes (32-channel Pt/Ir Plexon pig-tailed V-Probes, length: 45 mm from the first recording site; inter-electrode spacing: 200 μm; recording site diameter: 15 μm). Each probe was sheathed in a stainless-steel ground tube and connected to an Omnetics interface via flexible cable. Probes were inserted weekly in the awake animal and semi-chronically fixed using Kwik-CastTM silicone sealant.

Once the logger was connected to the electrode arrays, all components were enclosed in a protective chamber-mounted cap54. Neural signals were bandpass-filtered (2-7000 Hz), digitized at 32 kHz and stored locally on a 64 GB MicroSD card. The logger communicated with a PC via a USB-connected transceiver featuring four digital inputs and one digital output to synchronize neural and behavioral data.

Recording of behavioural events and definition of the epochs of interest
Behavioural events were detected using distinct contact-sensitive devices that signalled 1) the detachment of each subject’s hand from the starting button, 2) the hand-target contact (depending on the type of prehension), and 3) the object lifting. These events generated specific TTL signals, which were sent to a PC equipped with a dedicated LabView-based software that monitored the behavioural performance and controlled the generation of instructional cues and reward delivery. All input and output signals were synchronously recorded with the neural data for subsequent statistical analysis.

Neural signal analyses considered the following epochs defined based on task events: (1) baseline, ranging from 600 to 100 ms before the cue sound presentation, when the monkey was still with its hand on the starting position in the dark, prior to trial onset; (2) agent cue and grip cue, 500 ms following the sound onset and the visual cue onset, respectively. To characterize the motor phase of the MAT, we identified the following additional epochs of interest: (1) pre-movement, corresponding to the 300 ms interval before the hand detached from the starting button; (2) reaching-grasping, corresponding to the interval ranging from the detachment of the hand from the starting button to target object contact; and (3) lifting-holding, ranging from object contact to 300 ms after this event. We ensured that we restricted the analysis only the period ending 300 ms after the contact with the object in order to encompass the initial lifting and holding phase while excluding any potential response associated with arm retraction or reward delivery62,63.

Local field potentials analysis
All signal analyses were performed offline using custom software written in MATLAB (MathWorks, Natick, MA, United States). LFP recordings were downsampled from 32 kHz to 1 kHz and band-pass filtered between 2–100 Hz using a second-order Butterworth filter, along with a 50 Hz notch filter (Q = 35). Time-frequency decomposition (Fig. 3a) was carried out using the multitaper method (Chronux toolbox http://chronux.org/; time-bandwidth product TW = 3, K = 5 tapers), with 300-ms a moving window and a step size of 30 ms. The resulting spectral power was log-transformed by converting it to decibel (dB), calculated as ten times the base-10 logarithm of the spectral power magnitude at each frequency. Power values were segmented into the pre-defined epochs and interpolated to a uniform time width for trial alignment. For baseline normalization, the mean power from a 500-ms pre-agent cue baseline window was subtracted:

Pnorm dB(f,t) = PdB(f,t) − Pbaseline dB(f) (1)

Then, for visualization and comparison across trials, sites and configurations, the values were scaled to the [–1, 1] range using the trial-wise maximum absolute value:

Pscaled(f,t) = Pnorm(f,t) / max(|Pnorm(f,t)|) (2)

To assess whether LFP power varied significantly across task epochs (Fig. 3b–e), a non-parametric one-way ANOVA (Kruskal–Wallis test) was applied to compare the LFP power distribution across the seven task epochs (P < 0.05, followed by Wilcoxon signed-rank tests to assess directionality of possible modulations (enhancement or suppression, P < 0.05 Bonferroni corrected), performed independently for each combination of frequency band (low, 2–8 Hz; medium, 13–28 Hz; high, 60–100 Hz), probe (n = 5), and recording site (32 per probe). For each probe and depth, we compiled a dataset of average LFP power values aggregated across recording sessions, grip types (PG and WH grip), trials (up to 15 per condition). For epochs exhibiting significant (positive or negative) deviation from baseline power, possible differences in LFP power between grip types (e.g., PG vs. WH grip) were tested using paired Wilcoxon signed-rank tests. Sites showing condition-specific effects were logged with corresponding depth, probe, and day identifiers for spatial mapping of task-selective modulations.

To assess representational similarity of LFP activity between agents (i.e. monkey and experimenter) (Fig. 3f), we computed site-wise Pearson correlation coefficients for time-frequency power between the compared conditions. For each frequency (f) at the same site (i), correlation coefficient (ρ(f,i)) was computed by correlating the time-frequency power values from the two conditions:

ρ(f,i) = corr(Pcond1(f,i,:), Pcond2(f,i,:)) (3)

The resulting correlation values were aggregated (100 bins, range –1 to 1) to visualize the distribution of condition-specific similarity across the frequency spectrum. This resulted in a frequency-by-correlation heatmap, where color intensity reflects the proportion of recording sites exhibiting a given level of similarity. The distribution of ρ values across sites was tested for significance using one-sample t-tests.

To evaluate possible differences in LFP modulation across depths (Supplementary Fig. 2a), average power within the three frequency bands was computed during the reaching epoch and aggregated per site. Regression analysis was performed by fitting:

y = β0 + β1 * d (4)

where y is normalized power and d is the site depth. To quantify spatial gradients in task-related activity, we extracted the slope (β1), the coefficient of determination (R2), and the Spearman correlation coefficient (ρ).

Day-to-day temporal consistency of LFP modulations (Supplementary Fig. 2b) was assessed in a similar manner to depth-related analysis: normalized power was averaged across sites for each configuration and recording day, and modulation trends over time were evaluated using linear regression and correlation metrics.

Single-unit analyses
Single units were identified using dedicated offline sorting software (Offline Sorter™ by Plexon Inc) by imposing a 3-standard-deviation negative threshold relative to the signal-to-noise ratio on the band-pass filtered signal (4 pole Bessel filter, 300–7000 Hz) for waveform detection. The temporal stability of each isolated unit throughout the task was verified by projecting spike waveforms into a 3D space defined by the first two principal components and the acquisition time across the entire session. We excluded from our analyses only unstable or non-physiologically plausible waveforms, but not those belonging to units with very low spike count.

Recordings obtained from the same implanted probe across multiple sessions on different days were compared to determine whether and when waveforms with the same spike shape, interspike interval, and response profile during the task could be detected across multiple days from the same (or adjacent) recording site. Next, to avoid resampling biases, neurons consistently identified in the same or adjacent channels based on these criteria were considered only once in the data set, using as a criterion the signal-to-noise ratio (i.e. the amplitude of the averaged waveform for the unit relative to the threshold of each session).

Cue-related activity was assessed using a 2 × 3 repeated-measures ANOVA (factors: Grip and Epoch) separately for each agent condition, possibly followed by post-hoc tests (p < 0.01, Tuckey-corrected). Action-related responses during the monkey’s own and/or the experimenter’s action were assessed with a 2 × 4 repeated-measures ANOVA (factors: Grip and Epoch), possibly followed by post-hoc tests (p < 0.01, Tuckey corrected). The sign of the modulation of each recorded neurons was assessed relative to its mean baseline firing rate to characterize significant modulations as either positive (facilitated neurons) or negative (suppressed neurons). All neurons showing a significant main effect of the factor Grip or an interaction between Grip and Epoch (p < 0.01, Tuckey corrected), were considered as grip-selective action-related neurons. For each neuron, the best grip was defined as the grip type associated with the mean firing rate across the significantly modulated motor epochs (i.e. reaching and grasping) that deviated the most, in absolute terms, from the neuron’s baseline firing rate. Action-related neurons were further distinguished based on their agent selectivity as: self-type (ST), if their activity differed significantly from the baseline exclusively during monkeys’ own trials in the dark condition; other-type (OT), if they responded significantly only during the experimenter’s trials in the light; or self-and-other-type (SOT), if they responded significantly during both conditions. All other comparisons were carried out with ad-hoc ANOVAs with the same statistical thresholds.

Heatmaps and population analyses
Heatmaps (Figs. 4b, 4c, 6d) were generated to illustrate the temporal activation profile of individual neurons within selected subpopulations. Each row represents the net soft-normalized activity of a single unit, averaged across 30 trials (n = 15 for each grip type), shown separately for self- and other-trials. Soft normalization was performed by subtracting the baseline firing rate from the binned firing rate of each neuron and then dividing the resulting values by the absolute maximum across all conditions for that neuron plus a constant (5 spikes/s), to stabilize variance and reduce the influence of low firing rate units. All final plots were generated using a bin size of 200 ms and a step size of 20 ms.

To examine population activity during the trial (Fig. 3h), we assessed neuronal selectivity between pairwise comparison of the two levels of each investigated factor at successive time bins using a two-tailed sliding t-test (α = 0.01).

Population plots (Figs. 4–6) were obtained by averaging the single-neuron activity used for the heatmaps in 200-ms bins, stepped every 20 ms.

Preference indices
To quantitatively assess the degree of preference expressed by single neurons for the grip type during self and other trials, a preference index (PI) was calculated as follows:

PI = (RPG − RWH) / (RPG + RWH) (5)

where RPG and RWH are the average responses of the neuron in the reaching/grasping epochs of PG and WH conditions, respectively. The PI values range from 1 (complete selectivity for PG condition) to −1 (complete selectivity for WH), and a value of zero corresponds to identical discharges in the two conditions.

Correlation analyses
Correlation analyses were performed by means of a two-tailed Pearson’s correlation test, carried out on peak of activity timing and discharge onset. The peak time was calculated on the same activity vector computed for the heatmap and defined as the bin with the highest (or lowest, for suppressed neurons) value of mean firing rate across the 15 trials of each type of grip. The discharge onset was defined as the first of a series of consecutive bins up to the peak of activity in the preferred grip condition that were significantly different from the baseline (one-tailed t-test, p < 0.05).

Decoding analyses
We employed the Neural Decoding Toolbox64 to measure the decoding accuracy of a Poisson naive Bayes classifier trained and tested to discriminate self from other’s action using different sets of neurons. For each neuron, data were first converted from raster format into binned format. Specifically, we created binned data that contained the average firing rate in 200-ms bins sampled at 20 ms intervals for each trial (data point). We obtained a population of binned data characterized by a number of data points corresponding to the number of trials per conditions (i.e. 30 × 2 = 60 data points for self/other decoding) in an N-dimensional space (where N is the total number of neurons considered for each analysis). Next, we randomly grouped all the available data points into a number of splits corresponding to the number of data points per condition, with each split containing a “pseudo-population”, that is, a population of neurons that could be partially recorded separately but treated as if they were recorded simultaneously. Before sending the data to the classifier, we pre-selected those features (neurons) that showed a difference between conditions with p < 0.5. The classifier was trained using a leave-one-split-out cross-validation procedure (30 folds for self-other decoding), with accuracy defined as the proportion of correctly classified trials during testing.

Tracers’ injections and histological procedures
At the end of the recordings, Mk2 underwent surgery, as described above, for the injection of retrograde neural tracers into the putaminal zones where the last neural activity had been recorded. Tracers were slowly pressure-injected through a stainless steel, 31-gauge beveled needle connected via a polyethylene tube to a Hamilton syringe (Reno, NV, USA). The needle tip was lowered 4.5 mm below the end of the implanted guide tubes. At the level of the medial guide tube, we injected Fast Blue (FB, 3% in distilled water, Drilling Plastics GmbH, Breuberg, Germany), whereas at the level of the lateral guide tube we injected cholera toxin B subunit, conjugated with AlexaFluor™488 (CTB-g, 1% in phosphate-buffered saline; Molecular Probes).

After 28 days of survival period for tracers’ transport in Mk2 and after the end of the experiments in Mk1, the animals underwent deep anaesthesia induced by ketamine and medetomidine, followed by a lethal dose of sodium thiopental. Subsequently, they were perfused through the left cardiac ventricle in sequential stages with approximately 2 litres of saline (over 10 min), 5 litres of 3.5% formaldehyde (over 30 min), and 3 litres of 5% glycerol (over 20 min). All perfusion solutions were prepared in 0.1 M phosphate buffer at pH 7.4.

The brains were then coronally blocked on a stereotaxic apparatus, extracted from the skulls, and placed in 10% buffered glycerol for 3 days, followed by 20% buffered glycerol for 4 days. Finally, the brains were frozen and cut into coronal sections of 60 mm of thickness and collected in five series (sections spaced 300 μm apart). In Mk2, in which FB and CTBg were injected, one series of each fifth section was processed for visualizing CTBg with immunohistochemistry, as described in previous studies16. In both Mk1 and Mk2, two series were stained using the Nissl method (0.1 thionin in 0.1 M acetate buffer at pH 3.7).

The distribution of FB and CTBg labelled neurons in the cortex of Mk2 was plotted in sections every 600 μm together with the outer and inner cortical borders, using a computer-based charting system. The location of the electrodes’ tracks in Mk1 and Mk2 was verified in Nissl-stained sections and then plotted in sections every 600 μm with the outer border of the striatum. Data from individual sections were then imported into the 3D reconstruction software to obtain volumetric reconstruction of the injected hemisphere and of the recorded striatum. The areal attribution of the labelled neurons was based on criteria and maps described in previous studies65. The cortical input to the injected striatal zone was expressed in terms of the percentage of labelled neurons found in each cortical area with respect to the overall cortical labelling found for each tracer’s injection in the injected hemisphere.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript describes a study of primate putamen neurons in a motor task involving execution and observation of different actions under different conditions. The design varied agent (self, other) and grip type (precision, full-hand), instructed by auditory and visual cues, as well as performance under observation vs in-darkness conditions. The monkey was rewarded on all (self, other) correct trials. LFP low/medium frequency-band signals, likely reflecting cortical input, depended on contextual information provided by the audio-visual cues, with higher selectivity on self compared to other trials. The majority of putamen single-neurons did not show cue-related activity but activity related to the motor responses, with prominent population coding only of self-vs-other and self grip type. Most neurons were selective for self actions although meaningful numbers of neurons also encoded either only other’s actions or both self and other’s actions. These neurons showed differential sensitivity to visible vs non-visible context and whether or not a barrier was introduced. Overall, these are interesting and well-conducted experiments. The manuscript is well-written and clear. Analyses are appropriate. An unusual feature of the study is the connectivity characterization using retrograde tracing from the putamen recording site, which provides strong support for the characterization of the recorded neurons as being part of a motor-coordination network. The manuscript by itself does not necessarily offer a strong mechanistic advance. However, its value and importance lie, in my view, in introducing to the literature rare and precious evidence on the response properties of primate putamen neurons. This is important not only for advancing understanding of the primate including human functional networks for action execution and social action-coordination, but also relevant from a clinical perspective given the basal ganglia’s role in various neurological diseases. The data will be of interest also to computational neuroscientists who critically require such data to build more refined theories of neural motor control systems. A few comments should be addressed before publication.\n\nMajor points\n1. One concern of a potential confound, or at least an alternative explanation, for the authors’ interpretation of a ‘pragmatic’ representation is whether the activity of putamen neurons reflects covert muscle activation. This interpretation would explain why the neurons do not respond when a barrier is introduced. This could be assessed by recordings using muscle electrodes. Although it may not be possible to conduct these control tests (which should not preclude publication of this interesting data set), this issue should at least be discussed.\n2. It would be helpful if the authors could provide evidence to confirm that the monkeys actually observed the partner’s actions, e.g. eye-fixation data. If no such data is available, it would be important to discuss the issue, i.e. whether differences in putamen activity on self and other trials might be related to differences in visual stimulation or attention.\n3. Perhaps the most puzzling finding is that activity of neurons responding to other’s action did not reflect whether or not the action was visible. One the one hand, this result is consistent with the absence of visibility-related modulation in own-action coding neurons. However, it remains unclear whether the monkeys actually processed the other’s action. The authors should discuss whether additional controls might be necessary to validate this finding, e.g. a task in which the monkey receives reward only if it correctly responds to the other’s action. An alternative explanation that should be discussed is also that putamen neurons coding other’s action in the dark might not be motor related but instead provide a timing signal of an event that predicts reward. This could be tested by recording neurons ‘during extinction’, i.e. when other’s action is no longer rewarded.\n4. Related to the above point, it would be important to test whether some neurons respond selectively during reward period. More specifically, do neurons respond differentially based on whether reward results from own vs other’s action? This is of considerable interest as a previous study showed agency-dependent reward coding in the monkey striatum (Baez-Mendoza, Harris & Schultz, 2013, PNAS).\n\nMinor points\n5. Please define pragmatic on first use, i.e. as it relates to a neural representation and also clarify this in line 315.\n6. The introduction should refer to classical primate neurophysiology studies on the putamen, e.g., by Kimura, Romo.\n7. Typo line 191: thee", "reviewer_2": "In the current study, the authors investigated neural coding of self vs. other hand motor behavior in putamen. They introduced three experimental conditions: self vs. other, precision grip vs. whole hand, light vs. dark. The main finding is that putamen neurons encode the agent, grip type, but not the light variable. For self-action, putamen encodes specific grip type, but not the agent. These findings extend previous research of cortical neurons, with some of them consistent with those in cortex, but there are also difference. Thus overall this study has novelty, and extend our understandings of hand action coding from cortex to subcortical areas. The experiments are nicely designed. The paper is clearly written.\n\nMajor comments:\n1. The other-action neurons do not distinguish between the light and dark condition, which is a bit puzzling and unexpected to me. As the author pointed out, this result may imply that the animals infer or imagine that the experimenter performs the task, without really “seeing” it. A control could be something like the experimenter hold their hand without reaching the task although he/she needs to perform reach during that trial. For example, similar to their last control experiment, the transparent obstacle could be placed between the experimental and the target. Theoretically the neuron will not respond in this case, isn’t it?\n2. Related to the first point, it is also not expected to me that the other-action neurons do not encode the grip type. I thought these neurons should encode something shared between self and other action, if they are mirror neurons, so that they should distinguish precision grip or whole hand for the experimenters. Yet this is not the case. What does this finding imply? The author could provide more insight and discussion.\n3. I have difficult to understand the logic of the LFP result. If the LFP reflects input from cortex to putamen, then the context information about agent reflected in the LFP, implying encoded in cortex, is also expected in putamen (spiking activity). Yet this information is not reflected in the spiking activity of putamen. So where does this context information go? Does it go to elsewhere?\n\nMinor comments:\n1. Reverse figure sequence: Figure 1 behavior; Figure 2 Putamen recording?\n2. Figure 2, figures are a bit small, better incorporate the supplementary figure 2 about the two types of grip\n3. I suggest to have a summary figure (e.g. box and arrows), showing possible functional difference between cortical areas and putamen, based on their current findings. This would help readers to visualize their conclusions and hypothesis.", "reviewer_3": "Rotunno and colleagues carefully designed a well-controlled MAT task to examine the neural representation of self and others’ actions. It is striking that spiking activity and LFPs in the putamen—an area anatomically close to the fronto-parietal network—reflect others’ actions in a manner similar to self-actions, particularly when the animal can potentially interact with the other. I was convinced that putamen neurons were modulated by multiple task-related factors. However, the distinct functional roles of the putamen compared to cortical regions during the MAT task remain somewhat unclear. The quality of the collected data is high, and I believe these original findings will attract considerable interest in the field of social neuroscience. I hope the following suggestions may help strengthen the manuscript.\n1.The behavioral results in Fig. 2 are robust, clearly showing that the animals learned the task rule, with consistently low error rates across individuals. However, how did performance differ between self-action and other-action trials? Could differences in neuronal firing between self and other trials be partly explained by task difficulty or cognitive demand?\n2. To better address the role of the putamen and associated cortical areas in task performance, it would be informative to focus on error trials. How did spiking activity and LFPs differ between correct and error trials?\n3. The authors convincingly show that anatomical projections from the fronto-parietal network differ between medial and lateral putamen. It is striking that medial putamen, but not lateral putamen, receives dominant projections from F7, a key mirror neuron area. To further support the claim that others’ action representations in putamen arise from cortical connectivity, I suggest directly comparing spiking and LFP patterns between medial and lateral recording sites. Comparing effects across Figures 3–6 (e.g., the counts of cell numbers based on response selectivity, mean PSTHs, etc.) between medial and lateral putamen would considerably strengthen the argument.\n4. The effect of the barrier on representations of others’ actions in putamen activity is especially intriguing. Related to the above point, how were barrier-sensitive neurons distributed across the putamen? Were they concentrated in medial regions?\n5. The frequency distributions of the grip Preference Index during self- versus other-action trials are notably different. How do the authors interpret this divergence in putamen cell activity? Is the result consistent with findings from cortical recordings within the fronto-parietal network? Further discussion would be valuable."}, "author_response": "We sincerely thank all the Reviewers for appreciating and understanding the novelty and relevance of our work, as well as for the constructive comments, which we have addressed in the revised version of the manuscript.\nWe believe that these revisions significantly enhanced the clarity and robustness of the paper. Point-by-point responses to each Reviewer’s comment are provided below, and the main changes/addition to the text are highlighted in red font in the revised manuscript.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThis manuscript describes a study of primate putamen neurons in a motor task involving execution and observation of different actions under different conditions. The design varied agent (self, other) and grip type (precision, full-hand), instructed by auditory and visual cues, as well as performance under observation vs in-darkness conditions. The monkey was rewarded on all (self, other) correct trials. LFP low/medium frequency-band signals, likely reflecting cortical input, depended on contextual information provided by the audio-visual cues, with higher selectivity on self compared to other trials. The majority of putamen single-neurons did not show cue-related activity but activity related to the motor responses, with prominent population coding only of self-vs-other and self grip type. Most neurons were selective for self actions although meaningful numbers of neurons also encoded either only other’s actions or both self and other’s actions. These neurons showed differential sensitivity to visible vs non-visible context and whether or not a barrier was introduced.\nOverall, these are interesting and well-conducted experiments. The manuscript is well-written and clear.\nAnalyses are appropriate. An unusual feature of the study is the connectivity characterization using retrograde tracing from the putamen recording site, which provides strong support for the characterization of the recorded neurons as being part of a motor-coordination network. The manuscript by itself does not necessarily offer a strong mechanistic advance. However, its value and importance lie, in my view, in introducing to the literature rare and precious evidence on the response properties of primate putamen neurons. This is important not only for advancing understanding of the primate including human functional networks for action execution and social action-coordination, but also relevant from a clinical perspective given the basal ganglia’s role in various neurological diseases. The data will be of interest also to computational neuroscientists who critically require such data to build more refined theories of neural motor control systems. A few comments should be addressed before publication.\nR. We deeply thank the Reviewer for having recognized and emphasized all the main advancements and strengths of our work.\nMajor points\n1. One concern of a potential confound, or at least an alternative explanation, for the authors’ interpretation of a ‘pragmatic’ representation is whether the activity of putamen neurons reflects covert muscle activation. This interpretation would explain why the neurons do not respond when a barrier is introduced. This could be assessed by recordings using muscle electrodes. Although it may not be possible to conduct these control tests (which should not preclude publication of this interesting data set), this issue should at least be discussed.\nR. Recording muscle activity is not possible since both animals have been euthanized to obtain anatomical reconstruction. We did not record EMG because in previous works of our (Bonini et al. 2014 J Neurosci; Maranesi et al. 2015 J Neurosci; Livi et al. 2019 PNAS) and others’ groups (Kraskov et al. 2009) it was repeatedly established that the presence of the experimenter that performs the task instead of the monkey in specific and predictable trials acts, as a matter of fact, as a “social barrier” that inhibits the monkey from moving and prevents EMG activity to occur. In line with this literature, we added in the revised manuscript a Supplementary Figure (Supplementary Figure 4) where we show that self-type neurons - which are active during monkey’s own action, when EMG activity is obviously high – remain silent during experimenter’s action, when simultaneously recorded other-related neurons discharge.\nAlthough indirectly, this evidence suggests that the activity of other-related putamen neurons is hardly accountable for subtle muscle activation.\nAll these aspects, the reference to the new Supplementary figure, and the relevant literature, have been included in the Results section titled “Putamen neurons encoding self and other’s action” and commented in a paragraph dedicated to possible alternative interpretations at the end of the Discussion section (see text in red font).\n2. It would be helpful if the authors could provide evidence to confirm that the monkeys actually observed the partner’s actions, e.g. eye-fixation data. If no such data is available, it would be important to discuss the issue, i.e. whether differences in putamen activity on self and other trials might be related to differences in visual stimulation or attention.\nR. This is an important point that we carefully considered before starting the experiments. Since we wanted to keep the behavior as natural as possible, avoiding restraining the monkeys’ head (see Lanzarini et al. 2025 Science) and leaving visual exploration completely free, we couldn’t measure eye position during the task. Of course, the monkey was required to obtain information from the contextual cues to correctly perform the task, which implied paying attention to the workspace shared with the experimenter. Nevertheless, there might be differences in the amount of gaze toward the workspace during self- and other-trials. Previous data from our (Maranesi et al. 2013 Eur J Neurosci) and other (Papadourakis and Raos 2019 Cereb Cortex) groups, showed that gaze dependence of action observation responses in the premotor cortex (which is a strong source of projections to the putamen) is generally low. Furthermore, neurophysiological experiments on action observation that used free-gaze paradigms reported the highest visual responses and selectivity for object/grip features (Mazurek et al. 2018 J Neurosci; Papadourakis and Raos 2019 Cereb Cortex). Thus, free visual exploration seems to be an important condition to maximize the recruitment of motor brain regions during action observation, and this is why we decided to perform the experiments using this approach.\nConcerning the possible differences in putamen neuron activity between self and other trials, it is unlikely that they could be due to visual stimulation or attention because other-related neurons responded even during trials in the dark. We elaborated more on these issues in the dedicated paragraph at the endo of the Discussion section (see text in red font).\n3. Perhaps the most puzzling finding is that activity of neurons responding to other’s action did not reflect whether or not the action was visible. On the one hand, this result is consistent with the absence of visibility-related modulation in own-action coding neurons. However, it remains unclear whether the monkeys actually processed the other’s action. The authors should discuss whether additional controls might be necessary to validate this finding, e.g. a task in which the monkey receives reward only if it correctly responds to the other’s action. An alternative explanation that should be discussed is also that putamen neurons coding other’s action in the dark might not be motor related but instead provide a timing signal of an event that predicts reward. This could be tested by recording neurons ‘during extinction’, i.e. when other’s action is no longer rewarded.\nR. Concerning the first point, it should be noted that the monkey was required to remain still during experimenter’s turns to receive the reward, which was a constant of all correctly performed trials. This makes absolutely reasonable the Reviewer’s alternative explanation: since the reward was a constant of all self and other correctly performed trials, one would expect that reward-prediction neurons would fire similarly during both agents’ trials. Indeed, SOT neurons exhibit some similarities in their peak response time and discharge onset (see Fig. 4e), which would fit with the interpretation proposed by the Reviewer. However, if neuronal discharge was a timing rather than a motor signal, these similarities should be maintained also when comparing other and barrier conditions, as the timing of the reward did not vary between them. The lack of response in the barrier condition, however, strikingly contrasts with this interpretation, bringing support to the pragmatic hypothesis: we added these considerations at the end of the Discussion section (see text in red font) and we thank the Reviewer for pointing this out.\n4. Related to the above point, it would be important to test whether some neurons respond selectively during reward period. More specifically, do neurons respond differentially based on whether reward results from own vs other’s action? This is of considerable interest as a previous study showed agency-dependent reward coding in the monkey striatum (Baez-Mendoza, Harris & Schultz, 2013, PNAS).\nR. We thank the Reviewer for raising this very interesting point. In the present manuscript, we deliberately excluded from the analysis the reward epoch in order to avoid confounding reward-related responses with task-related activity, as specified in the Methods section. Nonetheless, we totally agree that this issue is of considerable interest, thus we followed the Reviewer’s suggestion conducting a preliminary analysis (2 x 3 repeated measures ANOVA, factors: Epoch and Condition), focused on a comparison of baseline and reward epochs (500 ms epoch following reward delivery, as in Baez-Mendoza et al. 2013) during self-, partner-social, and partner-barrier conditions. This analysis was applied to all neurons in the dataset, including the non-task-related ones because these latter could respond to reward delivery as this epoch was excluded from the main analyses. The following figure summarizes the main findings.\nIt is worth noting that both task-related (52%) and task-unrelated (34%) neurons exhibits possible reward-related responses. In addition, as predicted by the Reviewer, most of the neurons appear to show agency-dependence as in Báez-Mendoza’s paper. Indeed, possible mouth-related motor responses may occur during reward delivery, but they should be present in all the three conditions: this never occurred, as illustrated in the figure above, thereby supporting the idea of agent-dependent reward coding.\nHowever, reward coding through the lens of our task is a more complex phenomenon. For example, the activity of at least part of the neurons responding selectively during self-trials may actually be associated with the arm returning to the button after task execution (an action that could evoke an arm-related motor response even in task-unrelated neurons) rather than to the reward delivery, which occurs simultaneously. In addition, we found that most of the neurons encoding the reward following other-trials exhibited specificity for the social or barrier condition, suggesting a more complex, context-dependent form of agent-based reward coding in the putamen nucleus.\nWe deeply thank the Reviewer for the great suggestion, and given the interest, richness, and complexity of these findings we decided to further investigate this important point and publish it in a dedicated paper.\nMinor points\nPlease define pragmatic on first use, i.e. as it relates to a neural representation and also clarify this in line 315.\nR. We did this on the first occurrence of the term in the introduction, specifying that the pragmatic nature of visual responses pertains to “their role in affording object-directed actions as well as motor responses to others during social interactions”.\n6. The introduction should refer to classical primate neurophysiology studies on the putamen, e.g., by Kimura, Romo.\nR. Done, thanks for pointing this out.\n7. Typo line 191: thee.\nR. Done.\nReviewer #2 (Remarks to the Author):\nIn the current study, the authors investigated neural coding of self vs. other hand motor behavior in putamen.\nThey introduced three experimental conditions: self vs. other, precision grip vs. whole hand, light vs. dark.\nThe main finding is that putamen neurons encode the agent, grip type, but not the light variable. For self-\naction, putamen encodes specific grip type, but not the agent. These findings extend previous research of\ncortical neurons, with some of them consistent with those in cortex, but there are also difference. Thus overall\nthis study has novelty, and extend our understandings of hand action coding from cortex to subcortical areas.\nThe experiments are nicely designed. The paper is clearly written.\nR. We deeply thank this Reviewer for recognizing the novelty of our work, the accuracy of the experimental design, and the care we devoted to the writing.\nMajor comments:\n1. The other-action neurons do not distinguish between the light and dark condition, which is a bit\npuzzling and unexpected to me. As the author pointed out, this result may imply that the animals infer or\nimagine that the experimenter performs the task, without really “seeing” it. A control could be something like\nthe experimenter hold their hand without reaching the task although he/she needs to perform reach during that\ntrial. For example, similar to their last control experiment, the transparent obstacle could be placed between\nthe experimental and the target. Theoretically the neuron will not respond in this case, isn’t it?\nR. Regarding the first point, we did not have a condition in which the experimenter voluntarily omits to act, but it could have been interesting to analyze the response during erroneous omitted experimenter’s action (we did it for omitted monkey’s action in a previous study, see Bonini et al. 2014 Curr Biol); however, it is not feasible with the current data set because of the small number of total errors of both agents, which prevents us from having sufficient statistical power (see also our Response to point 2 of Reviewer 3).\nConcerning the second point, we did not test the condition in which a barrier was interposed between the experimenter and the target. However, based on our interpretation of the generative properties of the putamen neurons - derived from previous evidence from other regions of the cortical grasping network - it should not be surprising to observe agent-selective neuronal responses even in the absence of anyone’s movement, since this has been reported in the ventral premotor cortex (see for example Bonini et al. 2014, Curr Biol), a powerful source of input to the putamen.\nAlthough putamen neurons’ responses during other trials could have alternative interpretations (as also pointed out by Reviewer 1), these interpretation do not appear to fit with our findings (see our responses to points 3 and 4 of Reviewer 1), suggesting that OT and SOT neurons’ response during other trials cannot be explained in terms of reward prediction or task staging but, most likely, reflects a pragmatic representation of action.\n2. Related to the first point, it is also not expected to me that the other-action neurons do not encode the\ngrip type. I thought these neurons should encode something shared between self and other action, if they are\nmirror neurons, so that they should distinguish precision grip or whole hand for the experimenters. Yet this is\nnot the case. What does this finding imply? The author could provide more insight and discussion.\nR. In line with our recent theoretical contributions (Orban et al. 2021; TiCS; Bonini et al. 2022; 2023 TiCS), we do not use the term “mirror” in the paper. We strongly believe that most cortical cells encoding others’ actions are better understood within a broader, dynamic “other-to-self” framework, rather than as a simple and rigid “one-to-one matching” between self and other actions evoked by the original “mirror” concept.\nRegarding grip tuning, we actually show that a few other-related neurons discharge differently during the experimenter’s grip types, but their tuning is completely mismatched between self and other actions. This finding is not at odds with many reports on cortical “mirror” neurons which – when properly tested – reveal no match between self and other grip-type coding (Pani et al. 2014 J Cogn Neurosci; Papadourakis and Raos 2019 Cereb Cortex; Maranesi et al. 2024 Prog Neurobiol). We stated this explicitly in the Discussion and we have further elaborated this concept in the revised version.\n3. I have difficult to understand the logic of the LFP result. If the LFP reflects input from cortex to\nputamen, then the context information about agent reflected in the LFP, implying encoded in cortex, is also\nexpected in putamen (spiking activity). Yet this information is not reflected in the spiking activity of putamen.\nSo where does this context information go? Does it go to elsewhere?\nR. We agree that the presence of context-related information in the LFP but not in the spiking activity may at first seem counterintuitive. However, as extensively shown by existing literature (see Buzsáki et al. 2012, Nat Rev Neurosci) even in primate motor cortex (Confais et al., 2020; Rule et al. 2017) LFPs and spikes reflect different aspects of neuronal processing: specifically, LFPs primarily capture the aggregate synaptic and dendritic currents that constitute the facilitatory and inhibitory input to a local network, which not necessarily turn into changes in spiking activity of individual neurons.\nAs such, input information may be clearly represented in the LFP signal, as it is driven by spiking activity of other areas but not necessarily reflected in local spiking activity of the target area. This local filtering may be supported by inhibitory interneurons and local circuitries that we have hypothesized and considered in different sections of the paper (e.g. “while self-type neuronal responses are likely driven by direct cortico-striatal projections35, other-type responses may be more strongly shaped by local inhibitory mechanisms” in the Results section, or “extends to the basal ganglia the possible function to modulate the decision of whether or not to respond to the action of another agent, while delegating to the cortico-cortical sensorimotor circuit the specification of the details (such as the grip type) concerning how to do so, based on object- or agent-specific affordances” in the Discussion).\nMinor comments:\n1. Reverse figure sequence: Figure 1 behavior; Figure 2 Putamen recording?\nR. We prefer to keep the original sequence of the figures as it also reflects the scientific path we followed, from previous neuroanatomical evidence lacking a physiological counterpart to the current functional investigation of anatomically-characterized regions.\n2. Figure 2, figures are a bit small, better incorporate the supplementary figure 2 about the two types of grip.\nR. We did it, thanks for the suggestion.\n3. I suggest to have a summary figure (e.g. box and arrows), showing possible functional difference between\ncortical areas and putamen, based on their current findings. This would help readers to visualize their\nconclusions and hypothesis.\nR. We totally agree with the Reviewer, but similar figures have already been presented in previous review articles in which hypotheses tested in this work were advanced (see Figure 1 in Bonini 2017 Neuroscientist and Figure 3 in Bonini et al. 2022 TiCS). At this stage, we believe that the available data provides a significant advancement in the understanding of the functional properties of putamen neurons, but more work is still needed to advance our understanding of the relationship between cortical areas and the putamen.\nReviewer #3 (Remarks to the Author):\nRotunno and colleagues carefully designed a well-controlled MAT task to examine the neural representation\nof self and others’ actions. It is striking that spiking activity and LFPs in the putamen—an area anatomically\nclose to the fronto-parietal network—reflect others’ actions in a manner similar to self-actions, particularly\nwhen the animal can potentially interact with the other. I was convinced that putamen neurons were modulated\nby multiple task-related factors. However, the distinct functional roles of the putamen compared to cortical\nregions during the MAT task remain somewhat unclear. The quality of the collected data is high, and I believe\nthese original findings will attract considerable interest in the field of social neuroscience. I hope the following\nsuggestions may help strengthen the manuscript.\nR. We thank the Reviewer for recognizing the quality of our data and for the thoughtful suggestions, which we have carefully addressed as detailed below.\n1. The behavioral results in Fig. 2 are robust, clearly showing that the animals learned the task rule, with\nconsistently low error rates across individuals. However, how did performance differ between self-action and\nother-action trials? Could differences in neuronal firing between self and other trials be partly explained by\ntask difficulty or cognitive demand?\nR. We thank the Reviewer for raising this point, which gave us the opportunity to add another category of errors in the behavioral task (wrong start) related to erroneous detachment of monkey’s hand from the starting position during experimenter’s trials. In fact, this type of errors, compared with similar errors during monkey’s trials (i.e. early start and no touch, combined), might reveal a greater difficulty for the monkey in inhibiting the tendency to act during experimenter’s relative to self-trials, which in turn could explain some differences in neuronal firing between the two types of trials.\nHowever, as shown in the new version of Fig. 2c that integrates the percentage of wrong start during experimenter’s trial, in both monkeys the frequency of these errors is similar or even lower during other relative to self trials. These findings do not support the hypothesis that difference in neuronal firing between self and other trials can be explained by higher task difficulty or cognitive demand.\n2. To better address the role of the putamen and associated cortical areas in task performance, it\nwould be informative to focus on error trials. How did spiking activity and LFPs differ between correct and\nerror trials?\nR. We fully agree that an analysis of error trials would be informative for further clarifying the role of the putamen and associated cortical areas. However, as also noted by the Reviewer in the previous point, both animals made an extremely low number of errors throughout the task. The total number of errors across all sessions is reported in the manuscript, and the distribution of so few trials across sessions does not allow us a meaningful comparison of the activity between correct and error trials.\n3. The authors convincingly show that anatomical projections from the fronto-parietal network differ\nbetween medial and lateral putamen. It is striking that medial putamen, but not lateral putamen, receives\ndominant projections from F7, a key mirror neuron area. To further support the claim that others’ action\nrepresentations in putamen arise from cortical connectivity, I suggest directly comparing spiking and LFP\npatterns between medial and lateral recording sites. Comparing effects across Figures 3–6 (e.g., the counts of\ncell numbers based on response selectivity, mean PSTHs, etc.) between medial and lateral putamen would\nconsiderably strengthen the argument.\nR. We agree that anatomical projections from the fronto-parietal network show partially distinct topographies between medial and lateral putamen. However, our tracer data indicate that the contribution of F7 is in fact very limited in both cases, while the majority of premotor labeling involved other areas such as F5, F3 and F2. Following FB injection, F7 labeling appeared more clustered and accounted for about 0.8% of the total labeling; by contrast, after CTBg injection, labeling was more scattered and did not reach the 0.5% threshold used to display areas in panel e. We thank the Reviewer for pointing this out: to avoid any confusion, we have now specified this in the legend of Fig. 1.\nDespite the small quantitative differences observed in tracer injections, we followed the Reviewer’s suggestion and directly compared the distribution of different types of neurons based on their response type between medial and lateral probes, in all implants of both monkeys. Chi-square tests on the relative frequencies of ST, SOT and OT neurons showed no significant difference between medial and lateral probes (ST: χ² = 0.0163, p = 0.9; SOT: χ² = 0.108, p = 0.74; OT: χ² = 0.064, p = 0.80). Furthermore, since we used linear probes for neuronal recordings, we also compared possible inhomogeneity in the depth distribution of ST, SOT and OT neurons between dorsal, intermediate and ventral recording sites. Chi-square tests revealed an overall homogeneity in ST (χ² = 2.69, p = 0.26) and SOT neurons (χ² = 0.90, p = 0.636) distributions across different depths. On the contrary, OT neurons showed an under-representation at dorsal positions and an over-representation at intermediate sites (χ² = 19.40, p<0.001).\nThe figure below illustrates these findings.\nDepth distribution of task-related neurons. a,b, Percentage distributions of ST (light blue), OT (green), and SOT (yellow) and barrier-related (coral) neurons across dorsal, intermediate and ventral sites. Chi-square tests revealed no significant differences in ST (χ² = 2.69, p = 0.26), SOT (χ² = 0.90, p = 0.636) and barrier-sensitive (χ² = 0.64, p = 0.73) distributions across depths, indicating that neuronal classes are homogeneously represented in the dorso-ventral extension of the putamen. By contrast, OT neurons exhibit a significant over-representation at intermediate sites and an under-representation in dorsal sites (χ² = 19.40, p<0.001).\nWe prefer not to include these data in the manuscript because the only significant result regarding OT neurons is likely due to their very low number relative to the others, thus being probably a false positive result (see also the small difference in LFP depth topography in Supplementary Figure 2 and barrier sensitive neurons in the next point).\n4. The effect of the barrier on representations of others’ actions in putamen activity is especially\nintriguing. Related to the above point, how were barrier-sensitive neurons distributed across the putamen?\nWere they concentrated in medial regions?\nR. To address this point, we examined whether barrier-sensitive neurons were differentially distributed across the putamen. In medial probes, 14 out of 19 neurons (74%) were classified as barrier-sensitive, whereas in lateral probes 34 out of 51 (67%) showed barrier sensitivity. A chi-square test indicated no significant difference between these proportions (χ² = 0.099, p = 0.75). As for the previous point (see above), we leveraged our linear probes to test the possible non-uniform depth distribution of barrier-sensitive neurons and did not find any significant inhomogeneity (χ² = 0.64, p = 0.73). Together, these findings indicate that barrier-sensitive neurons are homogenously distributed in the putamen nucleus.\n5. The frequency distributions of the grip Preference Index during self- versus other-action trials are\nnotably different. How do the authors interpret this divergence in putamen cell activity? Is the result consistent\nwith findings from cortical recordings within the fronto-parietal network? Further discussion would be\nvaluable.\nR. We thank this Reviewer that, together with Reviewer 2, raised this point. Indeed, the mismatch between self and other’s grip coding in putamen neurons is consistent with findings from cortical recordings and we further elaborated this aspect in the revised Discussion section of the manuscript (please, see our extended response to the same issue at point 2 of Reviewer 2).\nWe sincerely thank all the reviewers for their endorsement of our manuscript and reviewer 3 for the additional comments, which we have addressed in the revised version as detailed below.\nWe hope that our paper is now suitable for publication in Nature Communications."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all my comments.", "reviewer_2": "The authors have addressed my questions well. I don't insist with my some of my previous minor suggestions. Supporting for publication.", "reviewer_3": "I appreciate the authors’ responses to my comments.\nRegarding your response to my third comment, I understand that the projection from F7 is relatively minor in both the medial and lateral putamen. The authors compared neuronal activity profiles between these two regions but could not draw any firm conclusion about meaningful differences worth including in the manuscript.\nIf that is the case, presenting the “differential projection data between the medial and lateral putamen” may be misleading in supporting the electrophysiological findings. For publication, I recommend either omitting these data altogether or focusing solely on one subregion—medial or lateral putamen—to demonstrate that the putamen, as a whole, is connected with broad motor control cortical regions.\nRegarding your response to my fifth comment, I am convinced that the mismatch between self and other’s grip coding also occurs in some cortical regions. However, it remains unclear whether the pattern of inconsistency is consistent between the cortex and the putamen. My point differs from that raised in comment 2 by Reviewer 2. I suggest that examining the consistency of this “self–other inconsistency pattern” between the cortex and putamen could provide strong evidence as to whether the same information is represented in both structures or whether some transformation occurs.\nThe authors have not fully addressed this issue, and I recommend that they discuss it in greater detail in the Discussion section."}, "author_response": "REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have addressed all my comments.\nR. We thank the reviewer for the positive feedback.\nReviewer #2 (Remarks to the Author):\nThe authors have addressed my questions well. I don't insist with my some of my previous minor suggestions. Supporting for publication.\nR. We thank the reviewer for the positive feedback.\nReviewer #3 (Remarks to the Author):\nI appreciate the authors’ responses to my comments.\nR. We thank the reviewer for the positive feedback on our responses.\n1) Regarding your response to my third comment, I understand that the projection from F7 is relatively minor in both the medial and lateral putamen. The authors compared neuronal activity profiles between these two regions but could not draw any firm conclusion about meaningful differences worth including in the manuscript.\nIf that is the case, presenting the “differential projection data between the medial and lateral putamen” may be misleading in supporting the electrophysiological findings. For publication, I recommend either omitting these data altogether or focusing solely on one subregion—medial or lateral putamen—to demonstrate that the putamen, as a whole, is connected with broad motor control cortical regions.\nR. We fully agree with the reviewer’s conclusion that the main purpose of the tracing study is to demonstrate that the putamen, as a whole, is connected with the main nodes of the cortical grasping network. Indeed, despite unavoidable quantitative differences, this is precisely what our data show, and it was the original aim of our analysis. For this reason, we believe it would not be appropriate to omit informative data that further support and strengthen our main conclusion, in line with the reviewer’s remark. To further emphasize this point and avoid confusion, we have revised the text in the Results section to remove any reference to comparative descriptions of the tracing data, and we now conclude the section with this sentence: ““The most lateral part of the putamen exhibits a similar connectivity pattern (Fig. 1d), with strong input from the primary motor and dorsal premotor cortices (Fig. 1e), supporting a prominent role of the putaminal investigated regions in forelimb motor control.”\n2) Regarding your response to my fifth comment, I am convinced that the mismatch between self and other’s grip coding also occurs in some cortical regions. However, it remains unclear whether the pattern of inconsistency is consistent between the cortex and the putamen. My point differs from that raised in comment 2 by Reviewer 2. I suggest that examining the consistency of this “self–other inconsistency pattern” between the cortex and putamen could provide strong evidence as to whether the same information is represented in both structures or whether some transformation occurs. The authors have not fully addressed this issue, and I recommend that they discuss it in greater detail in the Discussion section.\nR. We thank the reviewer for the clarification regarding the specificity of their point above. It is now clearer what they meant, and we further elaborated this point in the discussion section as follows: ““The lack of congruence in single-neuron grip tuning between self and other trials is consistent with previous findings in the premotor53 and parietal9,54 cortices. However, the proportion of putaminal neurons with visual (10%) and motor (25%) selectivity for the grip type is considerably lower than that reported for all cortical areas for which data are available. Together with our anatomical connectivity data, these findings suggest that within the cortico-striatal system, cortico-cortical sensorimotor circuits specify the detailed features of potential motor plans (e.g., grip type) based on object- or agent-specific affordances, whereas the basal ganglia modulate the decision of whether or not to respond to another agent’s action – and not necessarily with a matched action14,55.”\nRefs:\n[9] Maranesi, M., Lanzilotto, M., Arcuri, E. & Bonini, L. Mixed selectivity in monkey anterior intraparietal area during visual and motor processes. Prog Neurobiol 236, 102611 (2024).\n[14] Bonini, L., Rotunno, C., Arcuri, E. & Gallese, V. The mirror mechanism: linking perception and social interaction. Trends Cogn Sci 27, 220–221 (2023).\n[53] Papadourakis, V. & Raos, V. Neurons in the Macaque Dorsal Premotor Cortex Respond to Execution and Observation of Actions. Cereb. Cortex 29, 4223–4237 (2019).\n[54] Pani, P., Theys, T., Romero, M. C. & Janssen, P. Grasping execution and grasping observation activity of single neurons in the macaque anterior intraparietal area. J Cogn Neurosci 26, 2342–2355 (2014).\n[55] Orban, G. A., Sepe, A. & Bonini, L. Parietal maps of visual signals for bodily action planning. Brain Struct Funct 226, 2967–2988 (2021)."}]

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