Neuronal activity of the anterior cingulate cortex during an observation-based decision making task in monkeys

Behavioural Brain Research 230 (2012) 48–61

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Neuronal activity of the anterior cingulate cortex during an observation-based decision making task in monkeys Mariana F.P. de Araujo a, Etsuro Hori a, Rafael S. Maior a,b, Carlos Tomaz b, Taketoshi Ono a, Hisao Nishijo a,∗ a b

System Emotional Science, Graduate School of Medicine and Pharmaceutical Science, University of Toyama, Sugitani 2630, Toyama, 930-0194, Japan Laboratory of Neurosciences and Behavior, Department of Physiological Sciences, Institute of Biology, University of Brasília, Brasilia, DF, Brazil

a r t i c l e

i n f o

Article history: Received 21 November 2011 Received in revised form 23 January 2012 Accepted 31 January 2012 Available online 7 February 2012 Keywords: Social cognition Decision-making Medial prefrontal cortex Mirror neurons

a b s t r a c t The medial prefrontal cortex (mPFC) including the anterior cingulate sulcus is implicated in both decisionmaking and social cognition, suggesting that this area may play a central role in decision-making based on social context. In the present study, neural activity was recorded from the monkey anterior cingulate cortex (ACC) while the monkeys chose one of two identical figures based on the choice previously made by a robot arm. Monkeys observed that the robot touched one of the two figures in the left or right side of a touch screen. Every time the robot chose the correct option, the same pair appeared on another touch screen for the monkey. Then, the monkey had to touch the figure in the same side to obtain reward. Neuronal responses were compared by one-way ANOVA among 17 intervals distributed in 4 phases: baseline before the trial, observation phase (robot arm choices and feedback signals), inter-phase interval (between observation and following execution phases), and execution phase (monkeys choices and associated outcomes). Of 264 neurons recorded, 164 (62.12%) responded in one or more intervals of the task. Of these, 16 responded during the observation-phase, 5 during the inter-phase interval, 98 during the execution-phase and 18 on both observation and execution phases. Furthermore, neuronal activity of 69 (26.14%) neurons during action observation was correlated with that during real action (execution). This type of neurons might correspond to mirror neurons. The results indicated that the ACC processes information about self and others actions and outcomes, which may support social-based decision-making. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Decision-making is a set of brain processes by which an animal tries to find an optimal outcome for a determined situation. It mainly consists of selecting an action among the available options and evaluating the outcomes associated to the chosen action [1]. Several studies provide evidence that the medial prefrontal cortex (mPFC), including the anterior cingulate cortex (ACC), is involved in many processes related to decision making, such as goal-based action selection [2], conflict monitoring [3] and error processing [4,5]. The mPFC, however, does not exclusively encode information about actions and their associated outcomes. The mPFC, especially its anterior part (amPFC), is also involved in social cognition. Functional imaging studies indicated mPFC activation in several tasks

∗ Corresponding author at: System Emotional Science, Graduate School of Medicine, University of Toyama, Sugitani 2630, Toyama, 930-0194, Japan. Tel.: +81 76 434 7215; fax: +81 76 434 5012. E-mail address: [email protected] (H. Nishijo). 0166-4328/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.bbr.2012.01.060

involving mental state attributions [6–8]. Furthermore, lesions in the monkey ACC, its rostral part more specifically, decreased social interaction [9,10]. The mirror neuron system (MNS) also plays a fundamental role in social cognition. Mirror neurons are cells that fire both when an animal acts and when it observes the same action being performed by another individual [11]. The activity of such neurons may underlie our ability to imitate others and understand the actions of others. Two regions form the core mirror neuron circuitry: the ventral premotor cortex and the rostral part of the inferior parietal lobule [12,13]. Recently, Uddin et al. [14] proposed that the MNS and medial cortical structures (including the mPFC) contribute to social cognition in different ways: the mPFC is responsible for mental state attributions of other individuals and evaluative simulation, whereas the MNS supports a physical other-to-self mapping that is fundamental for the understanding of physical actions done by intentional agents. In the present study, we focused on the posterior part of the mPFC (pmPFC) including the cingulate motor cortex [15], which has been implicated in reward-based action selection [16,17]. This area is interconnected with the amPFC involved in social cognition

M.F.P. de Araujo et al. / Behavioural Brain Research 230 (2012) 48–61

[18,19], while it also has bidirectional connections with the premotor cortex, involved in the MNS [12,20]. These findings suggest that the pmPFC integrates information from both the amPFC and core MN circuitry to make decisions based on a social context. Furthermore, since mirror neurons are not exclusively found in the core MN circuitry [21], this social based decision-making processing might rely on mirroring mechanisms in the pmPFC. Therefore, the aim of the present study was to investigate the involvement of the pmPFC, more specifically of the posterior part of the ACC, in social based decision-making. Neuronal activity was recorded from the monkeys’ ACC (observers) while they engaged in a choice task based on observation of the choices previously made by a demonstrator (a robot arm). 2. Material and methods 2.1. Subjects and experimental apparatus Two male adult monkeys (Macaca fuscata), weighing around 6.5 kg, were used in the experiment. The monkeys were housed in individual home cages and supplied with lab chaw ad libitum. The animals were deprived of water in their cages and obtained water only as a reward during the daily experimental procedure. Supplemental water and vegetables were given after each day’s session. To check the monkey’s health, their weight was routinely monitored. The monkeys were treated in strict compliance with the policies of the National Institutes of Health on the Care of Humans and Laboratory Animals, and the Guidelines for the Care and Use of Laboratory Animals at the University of Toyama. Every effort was made to minimize the number of animals used and their suffering. During the experimental sessions, the monkeys (observers) sat on a restraining chair connected to a 15 touch panel monitor (IntelliTouch, Elo Touch Systems, Tyco Electronics) (Fig. 1A). The animals had access to the monitor through a window of the panel covering the front side of the chair. A cage was positioned in front of the chair. It had a 17 touch screen panel (IntelliTouch, Elo Touch Systems, Tyco Electronics) attached to the inner wall opposite to the monkey. The distance between the screen on the wall and the monkey was 150 cm. The wall close to the monkey was transparent, allowing the monkeys to look at the screen. Inside the cage, centered in front of the touch screen, there was a robot arm (demonstrator) (Fig. 1A). 2.2. Training and task procedure The monkeys were first trained to press one figure presented on the upper left or right region of the touch panel connected to monkey chair to receive a reward (water). After the subjects associated figure pressing with reward, they were trained to first watch the demonstrator (robot arm) pressing one of two identical figures displayed in the upper left and right sides of its touch panel, since the monkeys were required to make a choice based on what they saw. At each trial, the left or right figure was randomly assigned as correct by feedback signals after pressing of the robot. The exactly same figures as those for the robot were then presented to the observers. The monkeys had to choose the figure in the correct side, that is, the same side of the screen as that for the robot. The sequence of the events in one trial was the following (Fig. 1B). After a tone (1 s) indicating the beginning of a trial, a fixation cross appeared on the demonstrator’s screen for 0.5 s, followed by the two figures. During this period, the monkeys were required to fixate on the cross at the center of the screen. The robot arm then moved to left or right in order to touch one of the figures. If it touched the incorrect one (which occurred randomly in around 50% of the trials), the trial ended with incorrect feedback signals for 1 s (blue-colored screen and error tone). If it touched the correct one, correct feedback signals were presented (green screen and correct tone). After a 2.5 s interval from the correct feedback onset, a similar sequence of events took place at the monkey’s touch panel: after a fixation cross (0.5 s), the same figures were displayed on the observer’s monitor. During this period, the monkeys were also required to fixate on the cross at the center of the screen. The monkeys then had to choose one of the two figures by touching the region of the screen where the correct figure was displayed. If the monkeys chose the correct one, the same correct feedback signals (0.7 s) were presented, followed by the reward (delivered by the opening of a solenoid bulb for 1 s, which corresponded to 1.5 ml of water). If they chose the incorrect one, the incorrect feedback signals were presented (0.7 s) and no reward was delivered. Intertrial intervals were inserted after the reward (20 s) and after the incorrect feedback (40 s). In 50% of the trials of each experimental session, the correct option was in the left side of the screen, and in the other 50% of the trials, it was in the right side of the screen. The order of the trials and the robot’s first choices were randomly assigned. However, after an incorrect choice, the robot changed the chosen side, so it did not make two successive errors. This design was chosen in order to mimic, as close as possible, the behavior of a biological agent.

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If the monkey exhibited signs of fatigue, such as closing its eyes for several seconds or moving its eyes or hand slowly, the experimental session was terminated immediately. In most cases, it was terminated within 2–3 h. The subjects were trained until they made more than 90% of correct choices in one session consisting of 64 trials. 2.3. Surgery The monkey was trained in the task for 3 h/day, 5 days/week. The monkeys required about 16–20 months of training to reach the performance criterion. Upon completion of the training period, a head-restraining device (a U-shaped frame made of epoxy resin) was attached to the skull under aseptic conditions [22,23]. The subjects were anesthetized with sodium pentobarbital (35 mg/kg, i.m.). The frame was anchored with dental acrylic to titan bolts inserted into the skull. During the surgery, heart and respiratory functions and rectal temperature were monitored (LifeScope14, Nihon Kohden, Tokyo, Japan). A blanket heater was used to keep body temperature at 36 ± 0.5 ◦ C. Antibiotics were administrated topically and systemically for 1 week to prevent infections. One week after surgery, the subjects were re-trained with their heads painlessly fixed to a stereotaxic apparatus by the surgically implanted head holder. Performance criterion was attained again within 1 week. An infrared charge-coupled device (CCD) camera for eye-movement monitoring was firmly attached to the chair by a steel rod. During both training and recording sessions, the monkey’s eye position was monitored with a 33-ms time resolution by an eye-monitor system [24]. After the re-training period, the subjects were anesthetized with medetomizine hydrochloride (0.5 mg/kg, i.m.) and a hole was opened in the animal’s skull above the target area (i.e., anterior cingulate bank), so the electrode could be inserted for the recording sessions. The location of the anterior cingulate sulcus was determined based on stereotaxic coordinates of a M. mulata brain atlas [25] and confirmed by MR images taken with a tungsten marker (500 ␮m diameter) stereotaxically inserted above this region. 2.4. Recording procedures and data acquisition Before the recording sessions, a hole for semi-chronic recording was drilled through the cranioplastic and the underlying skull above the target area under anesthesia. The exposed dura was excised, and the hole was covered with hydrocortisone ointment (Rinderon-VG® ointment, Shionogi Co., Ltd., Tokyo, Japan), or one or two drops of chloramphenicol (Chloromycetin® Succinate, Sankyo Co., Ltd., Tokyo, Japan) solution (0.1 g/ml) were dropped in the hole. The hole was covered with a sterile Teflon sheet, and sealed with an epoxy glue. In the recording sessions, after the U-frame of the monkey head was placed in the stereotaxic frame, the Teflon sheet and ointment were removed. Then, a glassinsulated microelectrode (0.5–1.0 M at 1000 Hz) was stereotaxically inserted with a 15◦ angle inclination stepwise into the anterior cingulate bank by a pulse motordriven manipulator (SM-21, Narishige, Tokyo, Japan). Neuronal extracellular activity was monitored on an oscilloscope (TDS 1002, Tektronix Inc.) and recorded via a Multichannel Acquisition Processor (MAP, Plexon Inc., Dallas, TX, USA). Another two computers controlled the task events (one for each touch panel) and sent outputs corresponding to each event (event flags) to the MAP system to be time-stamped along with neuronal data. The outputs from the MAP System (neuronal activity and event flags) were digitized and stored on the computer hard disk for off-line spike sorting. The monkeys’ behaviors were also recorded by a CCD camera from a top view and stored on the hard disk. The digitized neuronal activities were isolated into single units by their waveform components using the Offline Sorter program (Plexon). Superimposed waveforms of the isolated units were drawn to check the variability throughout the recording sessions and only then they were transferred to the NeuroExplorer software (Nex Technologies, MA, USA) for further analysis. 2.5. Data analysis Each trial was divided into four phases: (1) baseline period of 1.0 s before the beginning of the trial, (2) observation phase related to the robot actions and outcomes, (3) inter-phase period of 2.5 s between the observation and the following execution phase and (4) execution phase related to the monkeys’ own actions and outcomes (Fig. 1B). The observation phase was subdivided into four periods: fixation (0.5 s after fixation onset), stimulus presentation (0.5 s after stimulus onset), robot movement (from 1.5 s before to 0.5 s after robot touching the screen) and feedback signals (0.7 s after feedback onset). The robot movement period was further subdivided into four intervals: between 1.5 and 1.0 s, between 1.0 and 0.5 s, between 0.5 and 0 s before touching the screen and between 0 and 0.5 s after touching the screen. Feedback signals were also subdivided into the correct and incorrect feedback signals. These subdivisions yielded a total of eight analyzed periods in the observation phase. The execution phase was subdivided into five periods in a similar way as those in the observation phase: fixation (0.5 s after fixation onset), stimulus presentation (0.5 s after stimulus onset), monkey’s movement (from 0.5 s before to 0.5 s after

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Fig. 1. Schematic representation of the task. (A) Experimental set up. The monkeys sat on a chair attached to a touch panel. From there, they could also see, inside a cage, a robot placed in front of another touch panel. (B) Sequence of the events in the task. The periods in parentheses indicate the intervals used for analyses.

monkeys’ touching the screen), feedback signals (0.7 s after feedback onset) and reward (1.0 s after the beginning of reward delivery). The monkeys’ movement period was further subdivided into two intervals: 0.5 s before and 0.5 s after the touch. Again, feedback signals were further subdivided into the correct and incorrect feedback signals. These subdivisions yielded a total of seven analyzed periods in the execution phase. Mean firing rates (spikes/s) during the 17 intervals described above were computed in each trial and neuronal activity during these intervals was analyzed by one-way ANOVA. Responsive neurons were defined as those with a main significant difference (p ≤ 0.05) in the mean firing rates (spikes/s) between the periods, more specifically between the baseline and, at least, one of the remaining 16 intervals by post hoc comparison (p ≤ 0.05, Bonferroni test). Since the first three events of the execution phase (fixation onset, stimuli onset and touch) are very close in time, some neurons responded during the both stimuli presentation and monkeys’ movement. In such cases, when response peaks were more closely aligned to the stimuli onset, those neurons were classified as “stimuli”, whereas the neurons were assigned as “execution-related” when response peaks were more closely aligned to touch onset.

The correlation between the neuronal activities during action observation and execution was also analyzed. Analyses by the CCD camera indicated that it took 1.4 s for the robot arm to touch the screen from the initial position, and around 0.7 s for the monkey to touch the screen. Therefore, we analyzed the period of 1.4 s before and that of 0.7 s after the touch in the observation phase, and the period of 0.7 s before and that of 0.7 s after the touch in the execution phase. The mean firing rate (spikes/s) at every 0.2 s during 1.4 s before the robot touched the screen and at every 0.1 s during 0.7 s after the robot touched the screen was computed. Neuronal activity during action execution was also computed as the mean firing rate (spikes/s) at every 0.1 s during the interval between 0.7 s before and 0.7 s after the monkey touched the screen. Correlation between the data of 14 bins in the observation phase and those of 14 bins in the execution phase was analyzed by Pearson’s correlation coefficients.

3. Results A total of 264 neurons were recorded from both the superior and inferior banks of the anterior part of the cingulate sulcus, at

M.F.P. de Araujo et al. / Behavioural Brain Research 230 (2012) 48–61 Table 1 Response types of the recorded neurons in the monkey cingulate sulcus. Response type

3.3. Execution interval-related

Number of cells Monkey T

Monkey B

Total

Observation phase-related Robot’s movement (before touch) Robot’s movement and feedback Feedback Other categories

2 4 3 2

3 0 1 1

Inter-phase-related

1

4

5 1

1 1

10 12 6

3 1 1

8 2 5 6

6 1 3 0

24 14 1 25 48

2 4 1 0 52

18 2 25 100

179

85

264

Execution phase-related Fixation Stimuli Execution-related Before touch During touch After touch Outcome-related Feedback Error Reward Feedback and Reward Multiple intervals Both phases-related Whole trials Unclassified Unresponsive Total

51

16

5 98

stereotaxic AP levels ranging from ±17 to ±25 in the two monkeys. From these, 164 (62.12%) responded during one or more intervals of the task. Based on their responsiveness to the event-related intervals described in the experimental procedures, these neurons were grouped into six major categories: observation phase-related, inter-phase-related, execution phase-related, both phases related, whole trials, and unclassified (Table 1). The monkeys kept their performance above 90% of correct choices in all recording sessions. 3.1. Observation phase-related The activity of 16 neurons increased during at least one period related to the observation phase (Table 1). Among them, five were excited only during robot movements towards the screen (before touching). Fig. 2 illustrates an example of this neuron type. The activity of the neuron increased at the beginning of robot’s movements (Ab). Another four neurons were excited by both the robot movements and by the feedback following the robot’s correct choices. In addition, four neurons were excited only by feedback onsets (one by incorrect feedback, one by correct feedback and two by both signals). The remaining three neurons did not fall into any of the categories described above. Two of them responded to the stimuli onset at the robot’s screen, but only at its first presentation in each trial. The remaining neuron responded to the fixation cross on the robot’s screen, but only after the robot’s errors. 3.2. Inter-phase-related The activity of the five neurons included in this category increased (n = 3) or decreased (n = 2) after the correct feedback signal following the robot’s correct choices. This activity changes persisted during the whole interval between the observation and the execution phases. Fig. 3 shows an example of this neuron type with excitatory responses. Activity of the neuron increased after the robot’s touch (Ab), during the correct feedback signals (Ac), and during stimuli presentation to the monkeys (Ba), but returned to the baseline level after monkey’s touch on the screen (Bb).

This group includes 98 neurons that responded during at least one of the intervals of the execution phase. These neurons were further divided into four categories, according to their specific responses during this phase (Table 1); fixation or stimuli, execution-related, outcome-related and multiple intervals. Presentation of the fixation cross excited six neurons. These responses were usually sharp and reached their peaks within 0.5 s (fixation). The activity returned to the baseline levels after stimuli onset. Presentation of the figures excited 2 neurons (stimuli). Responses of the 2 neurons reached their peaks and returned to the baseline levels before the monkey touched the screen. The remaining three execution interval-related categories are further explained below. 3.3.1. Execution-related Action execution elicited responses in 33 neurons. This group was further divided into three subgroups according to the periods during which activity of the neurons increased (see Section 2.5); before the monkey touched the screen (n = 11), after the monkey touched the screen (n = 7) and during both periods (n = 13). The activity of the neurons grouped in the category of both periods started to increase after the fixation or stimuli onset, reaching the peak around the time the monkey touched the screen. An example of an execution-interval related neuron with differential activity during both before and after action execution is shown in Fig. 4. Activity of the neuron increased in alignment with monkey’s touch on the screen (Bb). 3.3.2. Outcome-related Outcome-related neurons responded to the feedback signals and/or the reward associated with the monkeys’ choices. This category, comprising 31 neurons, was subdivided into four groups: feedback neurons (n = 14) responded to the feedback signals; error neurons (n = 3) specifically responded to the incorrect feedback signals; reward neurons (n = 8) responded to the reward delivery; feedback and reward neurons responded to both feedback signals and reward (n = 6). An example of a feedback neuron is shown in Fig. 5. The activity of this neuron started to increase around 0.4 s before the feedback onset and reached its maximum just after the feedback onset (Bc). Fig. 6 shows an example of a reward neuron. This neuron increased its activity 0.15 s before the reward delivery and kept this increased activity during the reward delivery (1.0 s) (Bc). 3.3.3. Multiple intervals-related This category includes 26 neurons that responded to at least one of the intervals related to action execution and to at least one of the outcome periods. The activity of these neurons started to increase (or decrease) just after the fixation onset and usually returned to the baseline levels after the feedback signals or reward onset. 3.4. Both phases-related The 18 neurons included in this group responded to at least one of the intervals in the observation phase and to at least one of the intervals in the execution phase. An example of such neuron is shown in Fig. 7. Activity of this neuron increased during the first interval of the robot movements (Ab). It was also excited during action execution (Bb). 3.5. Whole trial-related Activity of two neurons was inhibited just after the tone indicating the beginning of a trial. The inhibition continued during the

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Fig. 2. A typical example of an observation phase-related neuron. Raster displays of neuronal activity and averaged peri-event histograms during the observation (A) and execution phases (B). The events in each phase are shown in sequence and are stimuli onset (Aa and Ba), touch (Ab and Bb) and feedback onset (Ac and Bc). Activity of this neuron increased before the robot touched the screen (Ab). A vertical line in each of the raster displays and histograms indicates the event onset. Black arrows below the abscissas indicate fixation onset, the horizontal bar above the raster display in Bc indicates reward delivery period and the dotted horizontal line indicates the mean firing rate. Calibration at the right bottom of each histogram: number of spikes per second/trial. Bin width: 0.1 s.

whole trial, returning to baseline levels after around 2 s after the trial ended. 3.6. Unclassified The 25 neurons that belong to this group could not be placed in any of the previously described categories, although they exhibited differential activity during the task. 3.7. Correlated activity between the observation and execution phases Activity of 69 neurons (26.14%) during action observation was significantly correlated with activity during real action (execution) (Table 2). Of these 69 neurons, 49 (71%) presented a task-related Table 2 Number of neurons with a correlation between the activity during touch observation and execution. Positive correlation

Negative correlation

0.7 < r < 1

0.4 < r < 0.7

−0.7 > r > −1

−0.4 > r > −0.7

8

38

4

19

Total

69

response and 20 (29%) did not. The 49 task related neurons belonged to one of those categories: observation phase (7 neurons), execution phase (29 neurons), both phases (6 neurons) and unclassified (7 neurons). Of the 69 neurons, 46 showed positive correlation (r > 0.4), 8 of them with high positive correlation coefficients (r > 0.7). The remaining 23 neurons showed negative correlation (r < −0.4), 4 being highly negatively correlated (r < −0.7). Fig. 8 shows two neurons in which activity during action observation and execution was correlated. Fig. 8A shows an example of a neuron with positive correlation; the activity of this neuron temporally decreased before and after robot’s touch (Aa). It also showed similar time course of activity before and after monkey’s touch (Ab). Correlation coefficient for this neuron’s activity was 0.84. Fig. 8B shows an example of a neuron with negative correlation. Activity of the neuron continuously decreased before and after robot’s touch (Ba). However, the neuron showed opposite excitatory activity before and after monkey’s touch (Bb). Correlation coefficient for this neuron’s activity was −0.48. Furthermore, in 40 of the 69 neurons (57.97%), the correlation between action observation and action execution was specific to the target location/movement direction. When the arms of the demonstrator and monkeys were moved to the right, 18 neurons showed positive correlation (r > 0.4) and 2 neurons showed negative correlation (r < −0.4). When the arms were moved to

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Fig. 3. A typical example of an inter-phase related neuron. Activity of this neuron started to increase after the robot’s touch, remained increased during the interval between observation and execution phases, and returned to baseline levels after the monkey touched the screen. Other descriptions as for Fig. 2.

the left, 8 neurons showed positive correlation (r > 0.4) and 12 neurons showed negative correlation (r < −0.4). The correlation of the remaining 29 neurons, was not specific to target location/movement direction. Among them, 20 neurons showed positive correlation (r > 0.4), and 9, negative correlation (r < −0.4) in both the sides. 3.8. Specificity of the correlated activity Since the duration of robot and monkey arm movements were different (1.4 s and 0.7 s, respectively), the compared periods of observation and execution were likewise different. When same length periods were used to compare neuronal activity, correlation between the two phases reduced significantly (Fig. 9). We compared correlation coefficients in three conditions: (1) the analyzed period in the execution phase before pressing was expanded to 1.4 s (‘1.4 vs 1.4’ condition), (2) the asymmetrical analyzed periods that matched the actual duration of the robot’s and monkeys’ movements (‘1.4 vs 0.7’ condition) and (3) the analyzed period in the observation phase before pressing was reduced to 0.7 s (‘0.7 vs 0.7’ condition). For the neurons with positive correlation coefficients larger than 0.4, a statistical comparison by repeated measures one-way ANOVA indicated that there was a significant main effect of condition [F(2, 90) = 35.50, p < 0.0001]. Post hoc multiple comparisons indicated that the mean correlation coefficients in the ‘1.4 vs 0.7’ condition was significantly larger than those in the ‘1.4 vs 1.4’ condition

(Newman–Keuls multiple comparison test, p < 0.001) and 0.7 vs 0.7 (Newman–Keuls multiple comparison test, p < 0.01) (upper panel in Fig. 9A). The same results were found for the comparison using the neurons with negative correlation coefficients smaller than −0.4. A statistical comparison by repeated measures one-way ANOVA indicated that there was a significant main effect of condition [F(2, 48) = 10.88, p < 0.0001]. Post hoc multiple comparisons indicated that the mean correlation coefficients in the ‘1.4 vs 0.7’ condition was significantly smaller than those in the ‘1.4 vs 1.4’ (Newman–Keuls multiple comparison test, p < 0.001) and ‘0.7 vs 0.7’ conditions (Newman–Keuls multiple comparison test, p < 0.01) (lower panel in Fig. 9A). Notwithstanding, even when the correlation coefficients of the neurons without correlation (i.e., −0.4 < r < 0.4) were analyzed, similar results were obtained (Fig. 9B). For the neurons with positive correlation coefficients smaller than 0.4, the mean correlation coefficients in the ‘1.4 vs 0.7’ condition was significantly larger than those in the ‘1.4 vs 1.4’ condition (Newman–Keuls multiple comparison test, p < 0.001 after repeated measures one-way ANOVA) and ‘0.7 vs 0.7’ (Newman–Keuls multiple comparison test, p < 0.05 after repeated measures one-way ANOVA) (upper panel in Fig. 9B). For the neurons with negative correlation coefficients larger than −0.4, the mean correlation coefficients in the ‘1.4 vs 0.7’ condition was significantly smaller than those in the ‘1.4 vs 1.4’ (Newman–Keuls multiple comparison test, p < 0.05 after repeated measures oneway ANOVA) and ‘0.7 vs 0.7’ (Newman–Keuls multiple comparison

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Fig. 4. A typical example of an execution-interval related neuron. Activity of this neuron increased in alignment with monkey’s touch on the screen (Bb). Other descriptions as for Fig. 2.

test, p < 0.05 after repeated measures one-way ANOVA) (lower panel in Fig. 9B). Neuronal activities in different phases of the trials, however, could be correlated due to many different factors. Therefore, we also calculated the correlation coefficients between phases not directly involved in action observation or execution: the baseline and the interphase periods (‘BL vs IP’) and the observed and received correct feedbacks (‘OC vs RC’). We then compared these coefficients with those obtained for action observation and execution periods (‘1.4 vs 0.7’). For the neurons with positive correlation, a statistical comparison indicated that the mean correlation coefficients in the ‘1.4 vs 0.7’ were significantly larger than those in the ‘BL vs IP’ condition (unpaired t-test, p < 0.0001) and than those in the ‘OC vs RC’ condition (unpaired t-test, p < 0.001). Similar results were found for the neurons with negative correlation: the mean correlation coefficients in the ‘1.4 vs 0.7’ were significantly smaller than those in the ‘BL vs IP’ condition (unpaired t-test, p < 0.0001) and than those in the ‘OC vs RC’ condition (unpaired t-test, p < 0.0001). These results indicated that the correlated activity was specifically observed during the action observation and execution periods. 3.9. Recording sites The recording sites of all neurons are depicted in Fig. 10. Fig. 10A shows the location of the responsive neurons. The data is shown in three planes, from more anterior (AP 24) to more posterior coordinates (AP18). In general, all major categories of neurons were found

on all three planes. The more posterior regions, however, tended to concentrate a higher number of responsive neurons (chi-square test, p = 0.081): 41.45% of the responsive neurons are located in AP 18, 30.49% are in AP 21 and 28.06% are in AP 24. The exception was the inter-phase neurons, which were observed only in more anterior portions of the anterior cingulate sulcus. Fig. 10B shows locations of the neurons with correlated activity during action observation and execution. The number of correlated neurons also tended to decrease in more anterior coordinates (16, 22 and 31 correlated neurons in AP 24, AP 21 and AP 18, respectively) (chi-square test, p = 0.084). 4. Discussion In the present study, the neuronal activity of the ACC of two monkeys was recorded while they performed an observationbased decision making task. The responsive neurons were divided into five major categories: observation phase-related, inter-phaserelated, execution-phase related, both phases-related and whole trials. These results are discussed in terms of the pmPFC roles in decision-making and social cognition, highlighting its involvement in the mirror neuron circuitry. 4.1. Cognitive abilities required in the task Neural mechanisms involved in non-social factors might play a role in the present results. Neuronal activities in this study

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Fig. 5. A typical example of a feedback neuron. Activity of this neuron started to increase just before the feedback onset in the execution phase (Bc) and reached its maximum just after it. Other descriptions as for Fig. 2.

could reflect some sorts of mental rehearsal of the actions necessary to complete the task or some aspects of short-term memory necessary to complete the task. Both mental rehearsal of the action steps and short-term memory of observed actions are particularly important for cognitive representation and behavioral reproduction in observational learning [26]. The neurons that responded during the interphase period might reflect such mental rehearsal or short-term memory activities (see Section 4.3). Furthermore, the present results could be explained in terms of cue-guided behavior, instead of socially guided behavior. It is noted that the present task design is based on observational learning [27], in which humans could learn from observed actions and outcomes based on reinforcement learning. The mirror neuron system is also involved in stimulus-reward mappings [27]. In the current task design, the observers had to use the information given by the robotic movements and its associated outcomes to guide their choices. The monkeys had no other previous information of which target could be correct in each particular trial, since it was randomly assigned. The cues in this task included observation of actions (a robotic arm reaching a target) and outcomes related to the actions of an agent, which are basic components of observational learning based on reinforcement learning [27]. These inferences suggest that, although the present task was not purely social, it represented basic components of observational learning. Additional experiments, however, are necessary to better understand how social and non-social

cues influence the activity of the neurons in this region and how the processing of social cues leads to social-based decision making. 4.2. Observation phase-related neurons Among the 16 observation phase-related neurons recorded, 13 responded to either action observation (5 cells), feedback observation (4 cells) or both (4 cells). A previous study reported that the monkey premotor cortex has neurons that are activated during the observation of hand actions but not during the execution of a correspondent action [11]. Some of those neurons showed a very similar course of activity to those of the observation phase-related neurons: they were excited by observation of human actions (movements of one hand holding a piece of food towards the other hand) before they touched each other. Thus, the neuronal activities in the ACC during the action observation in the present study seem to correspond to the ones previously reported in the premotor cortex, a part of the core MNS with bidirectional connections with the pmPFC [12,20]. Those activities are also correspondent to the ones recently reported by Yoshida et al. [28] in the mPFC. Furthermore, seven of the observation phase-related neurons displayed correlated activity between action observation and execution although the responses of those neurons in the execution phase did not reach a statistically significant level. These results suggest that the observation phase-related neurons are involved in cognition of other’s action.

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Fig. 6. A typical example of a reward neuron. Activity of this neuron started to increase just before the reward onset and kept this increased activity during the whole period in which reward was delivered (Bc, horizontal bar). Other descriptions as for Fig. 2.

In the present paradigm, however, an artificial device (robot arm) performed the action during the observation phase. It raises the question whether responses elicited by observation of movements performed by a non-biological moving object are similar to those elicited by observation of actions performed by biological agents. A brain imaging study using monkeys recently addressed this question. Nelissen et al. [29] found that observation of a robot arm performing grasping actions activated some of the same premotor areas that human grasping actions did, albeit less robustly [29]. Similar findings were described for humans, in which observation of actions performed by both humans and robots activated areas involved in motor execution of similar actions, with no significant differences in brain activation between these two agents [30]. The above previous studies reported activations in the premotor cortex. However, action observation has also been reported to activate other areas, including the dorsal and rostral parts of the ACC [21]. Therefore, these findings suggest that, in the present study, robot arm movements elicited pmPFC activation during action observation in similar ways as hand movements would. The use of a biological agent as a demonstrator, however, might elicit stronger responses to action observation. In addition to action observation, four neurons responded to the feedback observation. These results suggest that the pmPFC is involved not only in processing information of kinematics of action movements, but also in that of goals and outcomes associated with observed actions. Such activities have been previously reported in the core MNS [31,32].

4.3. Inter-phase-related neurons Five of the recorded neurons responded during the inter-phase period. Buccino et al. [33] described a correspondent activation of the anterior mesial wall (including the ACC) during a pause period between hand action observation and imitation. According to the authors, the activation of those areas during the pause period reflects their role in control of action initiation, particularly the inhibition of action execution until it is allowed. This activity can, however, be related to motor preparation. Indeed, several studies suggest an ACC involvement in the process of selecting and preparing motor responses [34,35]. Similar neuronal activity during a preparatory (or premovement) interval was also found in the anterior mesial wall in tasks involving movement sequences [36]. The authors suggested that such activity was important for planning the sequence of movements and preparing forthcoming movements based on memorized information about the sequence of movements. These findings suggest that the activity of the inter-phase related neurons might be related not only to action planning but also to some kind of retrieval of the information related to the observation phase (robot’s choices and associated outcomes). 4.4. Execution phase-related neurons A majority of responsive neurons were active during the execution-phase (98 out of 164 responsive neurons). The neuronal

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Fig. 7. A typical example of a both phases-related neuron. Activity of this neuron increased during observation of the robot’s movement initiation (Ab). It was also excited during action execution (Bb). Other descriptions as for Fig. 2.

responses during this phase were divided in three main subcategories: execution-related, outcome-related and multiphaserelated. A previous study also reported ACC activation during response execution and feedback [37]. These data are consistent with the idea that this region is involved not only in action selection [2] but also in conflict monitoring [3] and error processing [4,5]. These findings further support the idea that the ACC has a major role in monitoring the consequences of internally generated actions and in guiding subsequent decisions [38,39]. 4.5. Both phases-related neurons This group included 18 neurons that responded to at least one of the intervals in the observation phase (robot’s movement and/or feedbacks) and to at least one of the intervals in the execution phase (own movements and/or outcomes). Such responses always involved at least one of the intervals related to the robot’s movements. The time course and amplitude of the responses of the both phases neurons were very different between the observation and execution phases. In general, the response amplitudes during the observation phase were smaller than those during the execution phase. Furthermore, five both phases neurons were excited in one of the phases and inhibited in the other. Such differences could reflect some kinds of differential processing of the observed and executed actions, which could contribute to self-other differentiation mechanisms. The time course of the responses in the

observation and execution phases also varied considerably. All of the both phases neurons responded to robotic movements towards the screen during the observation phase, while the motion execution responses of the same neurons usually lasted after the touch event and the responses sometimes continued to the outcome events. These neurons might, then, process not only observed and executed motions but also aspects of action outcome. These results indicate that, although the activity of the both phases neurons was more complex than those of classical mirror neurons [11], they are suggested to code the goals and outcomes associated with observed and executed actions, just like classical mirror neurons do [31,32]. Therefore, the activity of the bothphases related neurons in the pmPFC might have mirroring-like functions. These results are consistent with the previously discussed role of the ACC in monitoring the consequences of the individuals’ own choices and in guiding subsequent decisions [38,39]. The present results extend this role of the ACC to situations where actions performed by others have a critical importance in decision processing. Further studies, however, are necessary to better clarify the specific properties of those cells. 4.6. Correlated activity between the observation and execution phases Activities of about one fourth of the recorded cells during action observation were positively or negatively correlated with those during the real action (execution). These results suggest that such

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Fig. 8. Typical examples of two neurons in which the activity during action observation was correlated with activity during action execution. (A) An example of positive correlation (r = 0.84). Activity of this neuron temporally decreased before and after robot’s touch (a); similar pattern of activity was also observed before and after the monkey’s touch (b). (B) An example of negative correlation (r = −0.48). Activity of this neuron decreased before and after robot’s touch (a) but increased before and after monkey’s touch (b). Black squares indicate action observation and action execution periods. Other descriptions as for Fig. 2.

activity corresponds to the one usually recorded in mirror neurons [11], although no previous studies analyzed correlation between action observation and execution. In addition, the activity during action observation and execution of 40 neurons was correlated only when the arms were moved towards the stimulus positioned in a specific side of the screen (either left or right). These results indicate that these cells encode the location of the targets or the direction of the movements. Accordingly, a recent study also reported neurons in the mPFC that were selective for target position [28]. Taken together, these results corroborate the role of mPFC in goal-based action selection [2]. Furthermore, when the neuronal activities in the same period length for observation and execution phases were analyzed, correlation between the two phases was significantly reduced. This finding suggests that neuronal activities during the observation phase were replayed in a time-compressed way during the execution phase in the ACC so that the time course of neuronal activities match the actual action of the animals. This further suggests that these cells code not the action alone but also its temporal aspects. It is possible that these responses would be more clearly observed if a biological agent was used as the demonstrator, since responses to observation of actions done by robots were smaller [29] and decreased during observation of repetitive actions [30]. However, in these previous studies, the subjects were only required to observe actions, whereas in the present study the subjects had to choose an action based on what they had previously observed in order to receive a reward. Further studies are required to investigate effects of observation-based choices and reward association on pmPFC neuronal activity.

4.7. ACC responses and the MNS In classical mirror neuron studies, goal coding is defined as the processing of movements directed towards an object that is considered to be the final goal (see [40] for a revision). The mirror neurons code such object-goal information, independently of the kinematics of the movement. These neurons are thought to be strong evidence in favor of the direct-matching hypothesis [41], which states that we understand actions when we map the visual representation of the observed action onto our motor representation of the same action. Such motor resonance does not require a biological agent: similar activations in motor and premotor areas can be achieved by the observation of both robotic and human actions [29,30]. In the present study, we defined goal-directed motion as the sequence of movements towards the touch screen to acquire rewards. In fact, all of the motion observation sensitive neurons responded to the movements of the robot directed to the screen, but not to its returning movements to the initial position. Furthermore, eight observation-phase neurons responded also to the outcome (feedback) of the robotic action. These results suggest that these activities are related to goal-reaching movements and processing of observed action outcome, which we defined as mirror-like activity. Such activity has previously been reported in the classical mirror neuron system [42], for both biological and inanimate demonstrators [11,29,30] and for an action monitoring task involving a pair of monkeys [28]. There are also evidences that the classical mirror neuron system encodes the physical outcomes of actions, regardless of the kinematics parameters [31,43].

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Fig. 9. Comparison of Pearson’s correlation coefficients between the action observation and execution phases among the three different conditions. Comparison of the correlation coefficients of the pmPFC neurons with absolute values of the coefficients larger than 0.4 (A) and less than 0.4 (B). The data of the positive and negative coefficients are indicated in the upper and lower panels, respectively. The numbers before and after “vs” indicate the analyzed periods before pressing in the action observation and execution phases, respectively. ***, **, *, significant difference at p < 0.0001, 0.001, 0.05, respectively.

It is important to note that none of the above definitions involves the concept of intention coding or attribution, which is usually defined as the processing of the reason why an agent is performing a determined act. The activities described in the present study could be interpreted as an initial step for inferring the intentions underlying actions and decisions made in a social context, but are too far from intention reading. Further studies are required to better clarify the firing properties of these neurons and whether these activities can support intention reading. 4.8. Role of the pmPFC Recently, some works focused on possible contributions of the mPFC and MNS to social cognition, since both are involved in representing self and the other. Uddin et al. [14] proposed that, although those two systems interact, they have relatively segregated functions: the mPFC, especially its anterior part [19], is responsible for mental state attributions and evaluative simulation, whereas the MNS supports a physical other-to-self mapping which could be essential for action understanding. Ferrari et al. [44] proposed that

two distinct pathways contribute to social cognition: a direct and an indirect mirror pathway. The direct mirror pathway, formed by the core MNS, would be responsible for an uninhibited motor resonance in neonatal and automatic imitation. On the other hand, the indirect mirror pathway, formed by the connections between the MNS and prefrontal cortex, would be responsible for parsing, sorting and organizing motor representations. According to this view, the prefrontal cortical regions are not only responsible for mentalizing but also for selection and organization of actions that match the observed [44]. The present findings suggest that the posterior part of the ACC processes not only information about the internally generated actions to guide future decisions, as previously reported [38,39], but also information about observed actions and their associated outcomes. This area might be a critical area for the indirect mirror pathway, since it has bidirectional connections with the amPFC [18], involved in social cognition, and the premotor cortex [12,20], a part of the core MNS. Furthermore, about one fourth of the ACC neurons showed correlated activity between action observation and execution, most of which also showed activity changes during observation and/or execution phases.

Fig. 10. Recording sites of the all pmPFC neurons. (A) Neurons with differential activity. Filled triangles, filled stars, open stars and open squares indicate observation phase related neurons (robot’s movement, robot’s movement and feedback, feedback and other categories, respectively); filled squares, inter-phase related neurons. Open circles, filled circles, crosses and open inverted triangles indicate execution phase related neurons (fixation/stimuli, execution, outcome and multiple intervals, respectively). Filled inverted triangles and crosses indicate both phases related and whole trial neurons, respectively. Open triangles and dots indicate unclassified and unresponsive neurons, respectively. (B) Neurons with correlated activity. Filled diamonds, high positive correlation (correlation coefficient higher than 0.7); open diamonds, positive correlation (correlation coefficient between 0.4 and 0.7); filled stars, high negative correlation (correlation coefficient smaller than −0.7); open stars, negative correlation (correlation coefficient between −0.4 and −0.7); dots, no correlation.

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Recently, the neuronal activity in the mPFC sulcus and convexity was recorded while two monkeys monitored each other’s action. The authors reported neuronal activities during observation of others’ action and during observation and execution of actions on both mPFC sulcus and convexity [28]. Such data provide evidence to support the findings we describe and discuss in the present study and extend our findings to the convexity of the pmPFC, indicating that not only the pmPFC sulcus, but also its convexity, is involved in action observation processing. The authors, however, analyzed only the action period (defined as −200 to 0 ms before the monkeys pressed the target button) [28]. Therefore, the present results extend the role of the mPFC to processing of other events that are related to social cognition and decision making. Furthermore, we report here that there were two types of mirror-like neurons; one with positive correlation between observation and execution, and the other with negative correlation. These results suggest that these neurons code not only the action alone but also its temporal aspects (see Section 4.6). Taken together, these findings suggest that not only the posterior ACC, but the entire pmPFC, might play a key role in decision-making in an observed social context, and that this social decision processing might rely on mirroring-like mechanisms. However, the specific firing properties of those pmPFC neurons and the potential differences in processing among the convexity and the sulcus of this region need to be further investigated. Acknowledgments This work was supported partly by JSPS Asian Core Program, and the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Scientific Research (A) (22240051). References [1] Paulus M. Decision-making dysfunctions in psychiatry – altered homeostatic processing. Science 2007;318:602–6. [2] Matsumoto K, Suzuki W, Tanaka K. Neuronal correlates of goal-based motor selection in the prefrontal cortex. Science 2003;301:229–32. [3] Botvinick MM, Cohen JD, Carter CS. Conflict monitoring and anterior cingulate cortex: an update. Trends Cogn Sci 2004;8:539–46. [4] Holroyd CB, Coles MGH. The neural basis of human error processing: reinforcement learning, dopamine and error-related negativity. Psychol Rev 2002;109:679–709. [5] Holroyd CB, Nieuwenhuis S, Yeung N, Nystrom L, Mars RB, Coles MGH, et al. Dorsal anterior cingulate cortex shows fMRI response to internal and external error signals. Nat Neurosci 2004;7:497–8. [6] Gallagher HL, Happ F, Brunswick N, Fletcher PC, Frith U, Frith CD. Reading the mind in cartoons and stories: an fMRI study of ‘theory of mind’ in verbal and nonverbal tasks. Neuropsychology 2000;38:11–21. [7] Ciaramidaro A, Adenzato M, Enrici I, Erk S, Pia L, Bara BG, et al. The intentional network: how the brain reads varieties of intentions. Neuropsychology 2007;45:3105–13. [8] Steinbeis N, Koelsh S. Understanding the intentions behind man-made products elicits neural activity in areas dedicated to mental state attribution. Cereb Cortex 2009;19:619–23. [9] Hadland KA, Rushworth MFS, Gaffan D, Passingham RE. The effect of cingulate lesions on social behaviour and emotion. Neuropsychology 2003;41:919–31. [10] Rudebeck PH, Buckley MJ, Walton ME, Rushworth MFS. A role for the macaque anterior cingulate gyrus in social valuation. Science 2006;313:1310–2. [11] Gallese V, Fadiga L, Fogassi L, Rizzolatti G. Action recognition in the premotor cortex. Brain 1996;119:593–609. [12] Rizzolatti G, Luppino G. The cortical motor system. Neuron 2001;31:889–901. [13] Fogassi L, Ferrari PF, Gesierich B, Rozzi S, Chersi F, Rizzolatti G. Parietal lobe: from action organization to intention understanding. Science 2005;308: 662–7.

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