To present cue and target location signals, four red LED lights attached to the foam board. The LED lights were at the same distance from the front and back edges of the board as the targets locations. The LEDs were 12 cm from the left and right edges of the foam board so that they were not blocked by the limbs of the individuals performing the task. The object that was moved to these targets was a wooden dowel (2.2 cm in diameter and 8 cm in height). To capture the position of the wooden dowel, an infrared light-emitting diode (IRED) from an active motion tracking system (Optotrak Certus) was attached to the center of one tip of the wooden dowel. The position of the IRED marker was recorded at 200 Hz. The tip of the wooden dowel was cut on a 45◦ angle so that the IRED could be easily seen by the motion tracking system. Two Dell speakers were used to present auditory signals. A Dell Optiplex 780 computer was used to run custom Matlab software and experimental output was displayed on a 19 ′′ LCD monitor. The custom Matlab software sent signals to the speakers and the red LED lights, recorded and analyzed the dowel position, performed block and trial randomizations and organized the structure of experimental session.

#### Design and Procedure

Participants performed individual and joint versions of the task on separate days. The order of the sessions was counterbalanced across participants. The experimental sessions were performed 1–2 days apart.

In the joint version of the task, the participant performed the task with a confederate (a 23 year old male who was unknown to the participants). Once the partners had been introduced and the informed consent was read and signed, verbal instructions were given. The participant and confederate were told that they were teammates and their goal was to move a wooden dowel as quickly and accurately as possible onto a target. In addition, they were told that the task was to be divided up between them and they each had very specific roles for completing the task. The participant was told that they would be the initiator and the confederate was told that they would be the finisher. The participant was told that the role assignment was random; however, the participant always performed the initiator role. The initiator was responsible for moving the wooden dowel from the home position and placing it anywhere they wanted on the black poster board sheet that contained the targets (but not on one of the actual targets). In addition, they were told that the finisher would have to make their movement to the target from wherever the initiator had placed it down and that, although the finisher had to move as fast as possible, there was no time constraint for the initiator's portion of the task.

The team was told that prior to the beginning each trial, there would be a cue that indicated the potential target location and that, depending on the block, the cue validity was either random/non-predictive (25% valid) or predictive (50 or 75% valid). The cue was signaled via a flash of light from a red LED that spatially corresponded to one of the target locations. The teammates were told that the cue validity remained constant during a block and that they would be told the cue validity at the beginning of each block. The initiator performed their portion of the task following the cue presentation. Once the initiator placed the dowel on the black square, they released the dowel and then the finisher grasped the dowel and waited for one the four red LEDs to flash and signal the actual target location.

The participants were told that there were three blocks; one block for each cue validity. The block order was counterbalanced across participants. In each block there were 48 trials. Each target location was cued 12 times per block. In the 25% cue validity block, the target appeared in the same location as the cue approximately one out of every four trials (three out of the 12 trials for that cued location). The target locations for the remaining nine trials were divided evenly between the other three target locations (three trials per location). Thus, target location was random with respect to the cue. In the 50% cue validity block, the target was at the same location as the cue on six out of 12 trials; on the remaining six trials the location of the target was divided evenly between the remaining three locations (two trials per location). For the 75% cue validity block, the target was in the same location as the cue for nine out of the 12 trials for a particular cued location. For the remaining three trials, the actual target location was divided evenly between the remaining target locations (one trial per location). The cue and target locations were chosen, according to the parameters mentioned above, via a randomization procedure using custom Matlab software.

Each trial began with the teammates sitting across from each other and the dowel in the home position in front of the initiator. Following the cue presentation, which occurred after a variable foreperiod (range of 100–1,000 ms), the initiator placed the dowel on the black sheet and the dowel location was recorded. The dowel position was displayed to the experimenter who verified that the recording procedure had worked. If there were any recording issues, then the experimenter was prompted to record another sample of the dowel position. Sample recordings were repeated until a valid dowel position was recorded for each trial. Once the location of the dowel was recorded, the finisher grasped the dowel and waited for the target location to be signaled. Following a variable foreperiod (range of 100–1,000 ms) that was determined by a randomization procedure in Matlab, the target location was signaled by a flash of light from one of the LEDs. The finisher moved the dowel as quickly and accurately as possible to the target location. Once the movement was finished, an auditory beep was presented for 50 ms to signal to the initiator that they could bring the dowel back to the home position for the next trial.

The trial procedure for the individual version of the task was similar to the joint version of the task except that the individual performed both the initiator and finisher roles. Therefore, the key difference was that after the cue presentation and dowel placement, the participant continued holding on to the dowel until the target location was signaled. Once the dowel position was recorded, the target location was signaled and then the dowel was moved as quickly and accurately as possible to the target.

#### Data Analysis

The current experiment was designed to determine if dowel placement, following the presentation of a cue, was influenced by cue validity, the side of space effect and/or the proximityto-body effect. The main dependent measure for this study was the distance, in millimeters, that the dowel was placed relative to the cued location. The dowel position was recorded in absolute X and Y coordinates; therefore, each data point had to be transformed to a relative distance, in the X and Y coordinates, to the cued location. Separate statistical analyses were performed on the X and Y coordinates. The analysis in the X coordinate would reveal differences in ipsilateral and contralateral space, whereas the Y coordinate analysis would reveal differences in near and far space relative to the position of the participant. The relative distance to the cued location was calculated by taking the absolute position of the dowel and subtracting the distance to the center of the cued location. The cued locations were coded relative to their position to the initiator. Because the initiator used their right hand to perform the task, the cued locations on the right side of space were coded as ipsilateral and the cued locations on the left side of space were coded as contralateral. In addition, the two cued locations on the bottom row, relative to the initiator (always the participant), were coded as near locations and the two cued locations closer to the finisher were coded as far locations. Because the confederate was in the opposite side of space and used their right hand all of the spatial coding is reversed; therefore, ipsilateral space is the finisher's contralateral space and near space is the finisher's far space. The data were also coded based on the cue validity (25, 50, and 75%) and task (individual vs. joint).

# Results

#### Absolute Data

**Figure 2** provides a pictorial representation of the average placement of the dowel, in absolute coordinates for each cue validity and task condition. The dotted lines represent the midline for both the X axis and the Y axis.

### Differences in the Distances to Cued Locations Along the Y Axis (Near to Far)

The Y coordinate data was analyzed with a mixed model ANOVA with 2 (task context: Individual, Joint) x 3 (cue validity: 25, 50, 75) x 2 (side of space: Ipsilateral, Contralateral) x 2 (proximity-tobody: Near, Far) as repeated measures factors and Order (Joint task first, Individual task first) as the between-subjects factor. This analysis revealed statistically significant main effects for task context, F(1, 19) = 7.81, p = 0.012, cue validity, F(2, 38) = 46.72, p < 0.001, and proximity-to-body, F(1, 19) = 4.90, p = 0.039. The main effects for side of space, F(1, 19) = 0.24, p = 0.632 and order, F(1, 19) = 0.690, p = 0.797, were not statistically significant. The full ANOVA table is presented in Appendix A.

Post-hoctesting of effects with 3 or more means was completed using the Bonferroni (Dunn's test) correction. The task context analysis revealed that, overall, when participants performed the individual task (M = 72.5 mm, SD = 29.8) they placed the dowel closer to the cued location in comparison to when they performed the task with a partner (M = 89.8 mm, SD = 19.1). The cue validity analysis (see **Figure 3**) showed that the dowel was placed significantly closer to the cued location when the cue

was 75% valid (M = 53.3 mm, SD = 25.0) than when the cue was 50% valid (M = 75.4 mm, SD = 28.4), t(20) = 4.47, p < 0.001, and 25% valid (M = 114.6 mm, SD = 26.7), t(20) = 8.35, p < 0.001. In addition, the dowel was placed significantly closer to the 50% cue in comparison to the 25% cue, t(20) = 5.94, p < 0.001. The proximity-to-body analysis revealed that the dowel was placed closer to the cued location when the cue was in Near space (M = 75.6 mm, SD = 28) in comparison to when the cue was in Far space (M = 86.6 mm, SD = 17.2). Taken together, these results show that the dowel was placed closer to the cued location, along the Y axis during the individual task. In addition, when the cued location was in the Near space, relative to the initiator's body, the dowel was placed closer to the cued location in comparison to when the cued location was in Far space. Lastly, this pattern of effects also shows that as cue validity increased the dowel was placed closer to the cued location.

The results of the ANOVA analysis also showed that there was a significant interaction between task context and proximity-tobody, F(1, 19) = 19.46, p < 0.001 (see **Figure 4**). Post-hoc analysis revealed that when the cue was presented in Near space (to the initiator), the dowel was placed closer to the cued location along the Y axis in the Individual task (M = 56.8 mm, SD= 45.0) in comparison to the Joint task (M = 94.5 mm, SD = 25.3), t(20) = 3.78, p = 0.001. In contrast, when the cue was in Far space (relative to the initiator) there was no significant difference in the distance that the dowel was placed between the Individual task (M = 88.2 mm, SD = 21.4) and the Joint task (M = 85.1 mm, SD = 18.3), t(20) = 0.82, p = 0.442. To determine how the dowel placement in Near and Far space varied as a function of task context additional post-hoc testing was performed. That analysis showed that during the Individual task the dowel was placed closer to cued locations in Near Space (M = 56.8 mm, SD = 45.0) in comparison to the Far Space (M = 88.2, SD = 21.4), t(20) = 3.87, p = 0.001. In contrast, the analysis of the Joint task did not show a statistically significant difference between the Near Space (M = 94.5 mm, SD= 25.3) and Far space (M = 85.1,

SD = 18.3), t(20) = 1.97, p = 0.063. Overall, this pattern of effects reveals that in the individual task the placement of the dowel was clearly biased toward the initiator's body. In contrast, during the joint task the dowel placement was biased toward the finisher's body in Near space but not Far space.

Finally, the analysis also revealed an interaction between proximity to the initiator's body and side of space, F(1, 19) = 22.62, p < 0.001. In ipsilateral space, the dowel was placed closer to the cued location when it was in Near space (M = 71.3 mm, SD = 30.7 mm) in comparison to when it was in Far space (M = 90.3 mm, SD = 17.7), t(20) = 3.54, p = 0.002. There were no significant differences between Near space (M = 79.9 mm, SD = 27.2) and Far space (M = 83.2 mm, SD = 18.2), t(20) = 0.66, p = 0.51, in the contralateral side of space relative to the initiator. This interaction reveals that the dowel was placed closer to the initiator's body in ipsilateral space in comparison to contralateral space. Note that the three-way interaction between task context, proximity-to-the body and side of space was not statistically significant, F(1, 19) = 4.00, p = 0.060, indicating that task context did not influence the interaction between body and side of space in dowel placements.

#### Differences in the Distances to Cued Locations Along the X Axis (Left to Right)

To determine what factors influenced dowel position placement in the X coordinate, a 2 (task context: Individual, Joint) x 3 (cue validity: 25, 50, 75) x 2 (side of space: Ipsilateral, Contralateral) x 2 (proximity-to-body: Near, Far) repeated measures mixed model ANOVA with Order (Joint task first, Individual task first) as the between-subjects factor was conducted. There were statistically significant main effects for cue validity, F(2, 38) = 49.65, p < 0.001, and side of space, F(1, 19) = 32.62, p < 0.001. In contrast, the main effects for proximity-to-body, F(1, 19) = 2.17, p = 0.157, task, F(1, 19) = 0.47, p = 0.501, and order, F(1, 19) = 0.52, p = 0.481, did not reach statistical significance. The full ANOVA table is presented in Appendix B.

Post-hoc testing, using Bonferroni's adjustment, was completed to determine differences in the levels of the main effects. The cue validity analysis (see **Figure 5**) showed that the dowel was placed significantly closer to the cued location when the cue was 75% valid (M = 59.0 mm, SD = 27.5) than when then when the cue was 50% (M = 84.0 mm, SD = 29.0), t(20) = 5.48, p < 0.001, and 25% (M = 120.9 mm, SD = 29.0), t(20) = 8.17, p < 0.001. In addition, the dowel was placed significantly closer to the cue when the cue was 50% valid (M = 84 mm, SD = 29.0) in comparison to the 25% valid cue (M = 120.9 mm, SD = 29.0), t(20) = 5.34, p < 0.001. The side of space analysis revealed that the dowel was placed closest to the cue when the cue was in contralateral space relative to the initiator's body (M = 73.7 mm, SD = 23.9 mm) than when the cue was in ipsilateral space (M = 102.6 mm, SD = 27.3), t(20) = 5.54, p < 0.001. Overall, these results for cue validity indicate that the dowel was placed closer to the cued location as cue validity increased. In addition, when the cued location was in contralateral space (relative to the initiator), the dowel was placed closer to the cued location than when the cued location was in ipsilateral space.

For additional results, please see Appendix C.

FIGURE 4 | Experiment 1: Mean distance of the dowel to the cued location in Near and Far space during the Individual and Joint tasks. Error bars represent the standard error of the mean. The \* indicates a statistically significant difference.

FIGURE 5 | Experiment 1: The mean distance that the dowel was placed from the cued location along the X axis (left and right space) for each cue validity condition. Error bars represent the standard error of the mean. The \* indicates a statistically significant difference.

## Discussion

Experiment 1 was designed to test if: (1) the initiator of a sequential joint action adopted the finisher's body-centered-frame of reference, even though there was a high angular disparity between them, and (2) the initiator adopted multiple frames of reference during the anticipation of the finisher's action, and hence, planned an action that accommodated multiple action features that are represented in different frames of reference. The following sections will address these hypotheses and findings.

The first hypothesis was that an allocentric frame of reference would be adopted to utilize the cue validity information during response planning. The prediction was that the dowel would be placed, by the initiator, closer to the cued location as cue validity increased while simultaneously minimizing the distance to the other potential target locations. The results were congruent with the use of an allocentric frame of reference during the response selection and planning of the initiator. Specifically, the dowel was placed closer to the cued location as the cue validity increased and when the cue validity was low the dowel was placed in a location that was a similar distance to all the other potential actions. This finding builds on previous research by showing that when critical environment-related information which can be used to facilitate the achievement of the shared goal is available, individuals can use an allocentric frame of reference during the anticipation of a co-actor's action. The following section will explore whether individuals were able to adopt their co-actor's body-centered frame of reference.

In certain joint action contexts, if individuals adopted their co-actor's body-centered frame of reference, then they might be able to anticipate and accommodate action features that are body-based during response planning (e.g., posture). Therefore, an additional hypothesis was that the finisher's body-centered frame of reference would be adopted by the initiator when they anticipated the finisher's potential actions, even though there was a large angular disparity between the co-actors, and hence, bodyrelated action features (side of space effect, proximity-to-body effect) would be integrated into the response planning of the initiator. There were two results that helped to elucidate whether individuals adopted their co-actor's body-centered frame of reference.

The first result, based on the Y axis (near and far from the initiator) analysis, showed that the proximity-to-body effect was modulated by both task context (Individual, Joint) and proximity to the initiators body (Near, Far). In the Near space condition (relative to the initiator's body), the dowel was placed closer to the cued location (along the Y axis) in the individual task in comparison to the joint task. Secondly, in the individual task the dowel was placed closer to the cued location in Near space and farther from the cue in Far space. Taken together, this pattern of effects indicates that in the individual task the dowel was biased toward the initiator's body in both near and far space (i.e., consistent with the proximity to body effect: Brown et al., 1948; Reed and Smith, 1961). Therefore, this finding indicates that a body-centered frame of reference was used in the individual task because the dowel was placed closer to the more difficult near targets (relative to the initiator's body) requiring flexor movements than to the far movements requiring extensor movements (see Augustyn and Rosenbaum, 2005; Ray et al., 2017 for evidence of similar biasing in a Fitts' Law task). In contrast, it remains unclear to what degree the initiator adopted the finisher's body-centered frame of reference because the dowel was only biased toward the finisher's body in Near space (far from the finisher's body) but not Far space (near to the finisher's body). If the initiator had adopted the finisher's body-centered-frame of reference during all of the trials, then the dowel should have been biased toward the finisher's body in both Near and Far space. However, the bias toward the finisher's body, in the initiator's Near space, does provide some evidence that body-related information was considered during response planning.

The second result, derived from the analysis of the X axis, showed a main effect for side of space (Ipsilateral, Contralateral), but no task context interaction. The X axis (left to right) dowel placement analysis showed that the dowel placement was biased toward the contralateral space (relative to the initiator), but not the finisher's contralateral space. The contralateral bias of the dowel placement in the individual task is consistent with the findings of Ray et al. (2017) and likely emerged because movements into contralateral space are less efficiently executed than those into ipsilateral space (Fisk and Goodale, 1985). Hence, the contralateral bias would help to equate the difficulty of the movements into each direction should the cue prove to be invalid (see also Ray et al., 2017). If the initiator had fully adopted their co-actors body-centered frame of reference, then there should have been a side of space by task context interaction due to the fact that contralateral space was the opposite side of space in the Individual and Joint tasks [i.e., a contralateral bias in the individual task and an ipsilateral bias (from the participants' perspective) in the joint task]. Such was not the case. Overall, the lack of a side of space effect and the presence of a partial proximity-to-body effect is congruent with the pattern of effects from the Frischen et al. (2009) study. In their study, the negative priming was strongest when the distracting stimuli was placed closest to their co-actor's hand. In contrast, the negative priming was stronger in the ipsilateral space of the observer and not their co-actor's ipsilateral space. Taken together, the pattern of effects from this experiment and the Frischen et al. (2009) study are consistent with the idea that when a task is complex and the co-actors are sitting opposite to one another, individuals might not completely adopt their co-actor's body-centered frame of reference (Pezzulo et al., 2013).

An additional purpose of this study was to investigate if the initiator adopted multiple frames of reference (i.e., both allocentric and other person body-centered) when they anticipated the finisher's potential actions and integrated that anticipated information into their response planning. In the joint task, the initiator appeared to select and plan their action based on information derived from an allocentric frame of reference (cue validity) and partially based on the body-centered frame of reference of the finisher (partial proximity-to-body effect), and hence, provides tentative support for the use of multiple frames of reference during the selection and planning of a joint action. This finding is consistent with the suggestion of Pezzulo et al. (2013) that during complex joint actions multiple frames of reference might be used simultaneously. This finding potentially goes one step further than previous work that has shown that individuals can represent a joint task from both their own egocentric frame of reference and their co-actor's body-centered frame of reference during joint tasks (e.g., Frischen et al., 2009; Böckler et al., 2011; Meyer et al., 2013; Vesper et al., 2014; Dötsch and Schubö, 2015), by showing that individuals may be able to represent the task from their own egocentric frame of reference, an allocentric frame of reference and a partially adopted body-centered frame of reference of their co-actor.

There is one potential design issue that potentially makes it difficult to interpret the different pattern of effects in Near and Far space in the Individual and Joint tasks. One reason why the dowel might not have been biased toward the finisher's body in Far space (their co-actors Near space) might be the size of the action space. Although the initiator could clearly reach into Far space, because they were able to move the dowel to those targets during the individual task, it may have been undesirable to make such large amplitude reaches due to the effort required. Curiously, the dowel placement was almost identical, in both the individual and joint tasks, in Far space. This finding might be an indication that the initiator was using their own bodycentered frame of reference for response planning in Far space (and not fully accounting for the proximity-to-body effect), or it might indicate that the response planning was influenced by the distance required to reach into Far space. Therefore, to test between these competing hypotheses an additional experiment was performed (Experiment 2). The task was identical except that the action space was reduced by half to limit the distance that the initiator would have to reach into Far space during both initiator and finisher roles.

# EXPERIMENT 2

This experiment was designed to test if a smaller action space would influence how the initiator placed the dowel for the finisher in both Near and Far space. If the dowel placement in Far space was shaped by the reaching distance and not the partial adoption of the finisher's body-centered frame of reference, then now that the action space is smaller the dowel should be biased toward the finisher's body in both Near and Far space. In contrast, if the dowel placement is due to partially adopting the finisher's bodycentered frame of reference, then the dowel placement would be biased toward the finisher's body in Near space but not in Far space.

# Methods

#### Participants

Nineteen new participants (right-handed; mean age = 20.7, SD = 3.18; 6 males, 13 females) were recruited from the student population at the University of Toronto. All participants were naïve to the purpose of the study. Handedness was selfreported and all participants reported normal or corrected-tonormal vision. There were two separate experimental sessions, each lasting approximately 45 min, and participants were compensated \$15 for their time. Written informed consent was given by all participants and this research complied with the Declaration of Helsinki and the procedures were approved by the University of Toronto Health Sciences Research Ethics Board.

### Experimental Set Up and Apparatus

The experimental set up and apparatus were identical to Experiment 1 except for two features. In Experiment 1, the targets (6 cm diameter circles) were arranged in a square around the center of the black poster board and each target was 20 cm from the center of the board. In Experiment 2, each target was 10 cm from the center of the board. To maintain the same index of difficulty (Fitts, 1954) as the reaching movements in Experiment 1, the targets were reduced from 6 to 3 cm in diameter.

### Design, Procedure, and Data Analysis

The design, procedure, and data reduction and analysis was identical to that of Experiment 1.

# Results

Absolute Data

**Figure 6** provides a pictorial representation of the average locations of where the dowel was placed, in absolute coordinates for the different cue validity and task conditions. The dotted lines represent the midline for both the X axis and the Y axis.

### Differences in the Distances to Cued Locations Along the Y Axis (Near to Far)

To determine what factors influenced where the initiators placed the dowel the Y coordinate data was analyzed with a 2 (task context: Individual, Joint) x 3 (cue validity: 25%, 50%, 75%) x 2 (side of space: Ipsilateral, Contralateral) x 2 (proximity-to-body: Near, Far) repeated measures mixed model ANOVA with Order (joint task first, individual task first) as the between-subjects factor. There was a statistically significant main effect for cue validity, F(2, 34) = 69.17, p < 0.001. However, the main effects for side of space, F(1, 17) = 0.22, p = 0.645, order, F(1, 17) = 0.71, p = 0.412, task context, F(1, 17) = 0.86, p = 0.366, proximityto-body, F(1, 17) = 4.27, p = 0.056, did not reach statistical significance. The full ANOVA table is presented in Appendix D.

Post-hoctesting, using the Bonferroni correction, on the dowel position data as a function of cue validity (see **Figure 7**), showed that participants placed the dowel significantly closer to the cued location when the cue was 75% valid (M = 28.3 mm, SD = 13.4) in comparison to when the cue was 50% valid (M = 46.4 mm, SD = 10.7), t(18) = 8.47, p < 0.001, and 25% valid (M = 60.8 mm, SD = 10.1), t(18) = 9.69, p < 0.001. In addition, the dowel was placed significantly closer the cued location when the cue was 50% valid (M = 46.4 mm, SD = 10.7) in comparison to when

the cue was 25% valid (M = 60.8 mm, SD = 10.1), t(18) = 5.29, p < 0.001. Similar to Experiment 1, these results demonstrate that as cue validity increased the dowel was placed closer to the cued location.

The task context and proximity to the initiators body interaction did not reach conventional levels of statistical significance, F(1, 17) = 3.68, p = 0.072 (see **Figure 8**); however, planned comparisons were performed based on a priori predictions (and the results of Exp. 1). Comparisons were made between the dowel placement from the Individual and Joint tasks in both Near and Far space. In addition, separate comparisons were made between Near and Far space for the Individual task and the Joint task. Because there were four comparisons the alpha level was set at 0.013 following the Bonferroni t correction. The paired sample t-test on the dowel placement data in Near space revealed that there was not a statistically significant difference between the Individual (M = 40.8 mm, SD = 22.4) and Joint tasks (M = 54.2 mm, SD = 9.2), t(18) = 2.35, p = 0.030. Similarly, the analysis of the dowel placement in Far space revealed that there was not a statistically significant difference between the Individual (M = 46.9 mm, SD = 18.8) and Joint tasks (M = 38.3 mm, SD = 17.1), t(18) = 1.29, p = 0.213. The analysis of the dowel placement in the Individual task revealed that there was not a statistically significant difference between Near Space (M = 40.8 mm, SD = 22.4) and Far space (M = 46.9 mm, SD = 18.8), t(18) = 0.80, p = 0.435. Lastly, the analysis of the dowel placement from the Joint Task revealed that there was a statistically significant difference with the dowel being placed closer to the cued location in Far Space (M = 38.27 mm, SD = 17.06 mm) than in Near space (M = 54.22 mm, SD = 9.15 mm), t(18) = 3.61, p = 0.002. Overall, this pattern of effects demonstrates that the dowel was biased toward the finisher's body in the joint task, but not toward the initiator's body in the individual task. The proximity-to-body

effect in the joint condition in Near and Far space is partially consistent with the finding from Experiment 1. However, the lack of a proximity-to-body effect in the Individual task is not fully consistent with the findings of Experiment 1.

For additional results please see Appendix E.

#### Differences in the Distances to Cued Locations Along the X Axis (Left to Right)

To determine what factors influenced where the dowel was placed during the planning of a sequential action, the X coordinate data was analyzed with a 2 (task context: Individual, Joint) x 3 (cue validity: 25%, 50%, 75%) x 2 (side of space: Ipsilateral, Contralateral) x 2 (proximity-to-body: Near, Far) repeated measures mixed model ANOVA with Order (Individual task first, Joint task first) as the between-subjects factor. There were statistically significant main effects for cue validity, F(2, 34) = 49.58, p < 0.001, and side of space, F(1, 17) = 126.21, p < 0.001. The main effects for order, F(1, 19) = 0.12, p = 0.732, task context, F(1, 17) = 1.34, p = 0.263, and proximity-to-body, F(1, 17) = 0.02, p = 0.965, did not reach statistical significance. The full ANOVA table is presented in Appendix F.

All post-hoc testing was completed using the Bonferroni's t (Dunn's test) correction based on the number of comparisons. The cue validity analysis (see **Figure 9**), showed that participants placed the dowel closer to the cued location when the cue was 75% valid (M = 27.0 mm, SD = 18.6) in comparison to both 50% valid (M = 42.3 mm, SD = 17.6), t(18) = 7.09, p < 0.001, and 25% valid cues (M = 59.0 mm, SD = 13.7), t(18) = 8.18, p < 0.001. In addition, there was a significant difference in the dowel placement between the 50% valid cue condition (M = 42.3 mm, SD = 17.6) and the 25% valid cue condition (M = 59.0 mm, SD = 13.7), t(18) = 4.31, p < 0.001. These results demonstrate that as cue validity increased the dowel was placed closer to the cued location.

The side of space analysis revealed that the dowel was placed closer to the cued location when the cued location was in contralateral space (M = 27.78 mm, SD = 12.30), relative to the

initiator, than in ipsilateral space (M = 57.7 mm, SD = 18.3), t(18) = 11.37, p < 0.001. Again, there was no interaction between task conditions and side-of-space, F(1, 17) = 2.34, p = 0.145, indicating that participants demonstrated a similar contralateral bias in dowel placement from their own perspective and did not adapt to the contralateral space perspective of the partner.

For additional results please see Appendix G.

# Discussion

The main purpose of Experiment 2 was to determine if the size of the action space influenced how the dowel was placed in Near and Far space. Therefore, the tasks from Experiment 1 were performed in a smaller action space. The key finding from Experiment 2 was that the dowel was biased toward the finisher's body in both Near and far Space. This finding is in contrast to Experiment 1 where the proximity-to-body effect was only seen in Near space. Therefore, the data indicates that the size of the action space did influence the dowel placement in Far space in Experiment 1. Experiment 2 also replicated the finding that cue validity influenced where the dowel was placed. Lastly, the dowel placement was only consistent with the side of space effect in the Individuals task. These findings are discussed in greater detail in the General Discussion.

# GENERAL DISCUSSION

The main purpose of the present work was to further our understanding of the frames of reference that are used to anticipate a co-actor's potential action, and hence, what environment and/or body-centered information can be integrated into the selection and planning of actions that facilitate the achievement of shared goals. To build on previous literature, we investigated if individuals could adopt their co-actors bodycentered frame of reference, even though they had a large angular disparity between them and they were performing a joint task that had high action requirements. In addition, because individuals in our task could facilitate their co-actor's task using either (or both) environment and body-centered information, we went one step further than previous research and investigated if individuals would use multiple frames of reference during the anticipation of their co-actor's potential actions, so that they could accommodate both environment- and body-centered information during their response selection and planning. The following sections discuss the findings and the implications of this work in detail.

The results of both experiments provided evidence that the individuals represented their co-actors portion of the task from an allocentric frame of reference. Specifically, when the cue validity was 25% the initiator placed the dowel close to the center of the board, and therefore, a similar distance would be required to reach to any of the other four target locations. In addition, even as the cue validity increased and the dowel was placed closer to the cued location, the dowel was still placed in a location that minimized the distance to the other three targets. Because the initiator adopted an allocentric frame of reference, they were able to facilitate the finisher's task by reducing the impact of incorrectly anticipating the future location of a response. This finding builds on previous research that has shown that individuals will adopt their co-actor's body-centered frame of reference (e.g., Meyer et al., 2013) during the selection and planning of sequential joint actions by showing that individuals will adopt an allocentric frame of reference when they can facilitate the achievement of the shared goal based on the spatial relationship of targets, objects, and people in the shared environment.

In terms of whether the initiator fully adopted the finisher's body-centered frame of reference, during a joint action task that had demanding action requirements, the results from Experiments 1 and 2 demonstrated that the initiator partially adopted the body-centered frame of reference of the finisher. The reason for stating that the initiator only partially adopted the finisher's body-centered frame of reference is because the initiator planned actions that accommodated the distance of the dowel to the finisher's body (consistent with the proximityto-body effect; Brown et al., 1948; Reed and Smith, 1961), but did not accommodate the increased difficulty of moving to targets in the finisher's contralateral space (which would have been consistent with the side of space effect; Fisk and Goodale, 1985; also Ray et al., 2017). If the initiator had fully adopted the body-centered frame of reference of the finisher, then the dowel placement should have been biased toward ipsilateral space (which is actually the finisher's contralateral space) in the Joint task. Instead, the results showed that the dowel was placed closer to the initiator's contralateral space (the finisher's ipsilateral space) in both the individual and joint contexts. To the best of our knowledge, the existing literature on the frames of reference used to anticipate a co-actor's action, during tasks that have lower action requirements and a smaller angular disparity between them, consistently shows that individuals are able to fully adopt their co-actor's body-centered frame of reference to plan actions that facilitate the comfort of their co-actor (e.g., Gonzalez et al., 2011; Ray and Welsh, 2011; Meyer et al., 2013; Dötsch and Schubö, 2015; Scharoun et al., 2016) or synchronize the timing of imagined movements (Vesper et al., 2014). Therefore, we present a novel finding by providing evidence that during more complex joint actions, where co-actors have a high angular disparity between them, individuals did not fully adopt their co-actor's body centered frame of reference.

The presence of a proximity-to-body effect, but not a side of space effect, during the Joint task, could be based on a number of factors. First, it may be that the initiator never anticipated the differences in difficulty of moving into different sides of space, and hence, did not plan their actions to accommodate the difficulty of moving into contralateral space. Secondly, the initiator may have anticipated the differences in difficulty but simply chose not to integrate this information into their response planning. Neither of these explanations seems likely given that in the individual task the response planning was influenced by the increased difficulty of moving into contralateral space (i.e., the dowel was biased toward the targets located in contralateral space). A more likely explanation is that due to the increased cognitive effort required to fully adopt the finisher's body centered frame of reference the initiator only partially adopted their co-actor's body-centered frame of reference. According to Pezzulo et al. (2013), when co-actors have a high angular disparity between them (>60–90◦ ) and actions need to be coded based on origin dependent spatial information (e.g., laterality information), individuals need to undergo effortful spatial transformations to align opposing egocentric frames of reference. Although both the proximity-to-body effect (which is based on the spatial relationship to the body) and the side of space effect (which is based on the spatial relationship for both the hand and body) would be based on origin dependent spatial information, the coding for side of space would be more complex because both the hand and body need to be considered. For example, side of space can be coded based on laterality (i.e., left or right side of the body, left or right hand) and the side of space of the effector relative to the midline of the body (i.e., contralateral or ipsilateral), whereas, coding Near and Far space is only based on the distance to the body. Therefore, based on the pattern of effects in the present study, it appears that individuals might anticipate actions that originate from the body-centered frame of reference of a co-actor but that not all the spatial coding will be represented and simulated from their co-actor's body-centered frame of reference.

The lack of a side of space effect is somewhat congruent with a study by Ray et al. (2017) that showed that, in the first phase of the study, individuals did not plan actions to accommodate the difficulty of their co-actor's actions based on the side of space effect. However, after first-hand motor experience the dowel was biased toward targets in contralateral space. There are two variables from that study that might highlight factors that affect whether co-actors fully adopt each other's body-centered frame of reference. First, co-actors used mirror effectors (i.e., the initiator used the right hand while the partner in the finisher role used their left hand), and therefore this arrangement likely reduced (if not completely obviated) the need to fully align egocentric frames of reference. Secondly, the individual and joint task were performed in the same session and that design might have aided in the transfer of the response planning strategy from the firsthand motor experience to the joint task. In contrast, in the present study the individual and joint tasks were not performed in the same day (note also that, unlike in Ray et al., 2017 there were no statistically significant or theoretically-relevant effects of order). The interpretation that the spatial alignment (mirrored vs. opposite orientation) and learning will affect the frames of reference adopted are both consistent with the Pezzulo et al. (2013) shared space framework, which suggests that learning may be necessary to form complex shared spatial representations and that alignment is one factor that will modulate what type of frame of reference is used.

An additional purpose of the present studies was to determine if individuals represented their co-actor's task using both environment and body-centered frames of reference during complex joint actions where co-actors had a high angular disparity between them. Previous research has shown that individuals can represent a joint task from their own bodycentered frame of reference and their co-actor's body-centered frame of reference (e.g., Frischen et al., 2009; Böckler et al., 2011; Ray and Welsh, 2011; Meyer et al., 2013; Vesper et al., 2014; Dötsch and Schubö, 2015; Ray et al., 2017); however, there is no evidence regarding whether or not individuals represented their co-actor's task from multiple frames of reference. In addition, because previous research had not explicitly tested if individuals could represent their co-actor's task from both environment centered and body-centered frames of reference, the tasks were not designed in such a way that individuals could facilitate their co-actor's task using either, or both, environment centered or body-centered information. Consistent with our hypothesis and the framework of Pezzulo et al. (2013), our results provide novel evidence that the initiator represented the finisher's portion of the task using multiple frames of reference during the anticipation of their potential actions. Specifically, the initiator's response planning was influenced by the distance to the finisher's body (finisher's body-centered frame of reference), cue validity and the spatial relationship between targets (allocentric frame of reference), and the side of space of the initiator (initiators own egocentric frame of reference). Evidence that the initiator adopted both the finisher's body-centered frame of reference and an allocentric frame of reference has already been discussed, therefore the use of an egocentric frame of reference will be discussed next.

The conclusion that the initiator's response planning was also based on their own egocentric frame of reference is based on the finding that the dowel was placed closer to targets in the initiator's contralateral space, consistent with the side of space effect, in both the individual and joint conditions. Based on this result, it would appear that action codes that concerned side of space were origin dependent on the initiator's body and hands. As previously mentioned, Pezzulo et al. (2013) have suggested that the most complex and effortful spatial transformations would occur when co-actors have completely opposite spatial orientations to each other (180◦ angular disparity) and the task requires origin dependent spatial information. Therefore, due to the difficulty and effort required to completely adopt the finisher's body-centered frame of reference the initiator may have defaulted to coding certain aspects of their task to their own egocentric frame of reference.

In addition to cognitive effort influencing the frames of reference used during joint actions, the present experiments also demonstrate that physical effort can modulate how co-actor's select and plan joint actions. Experiment 2 was conducted to investigate if the size of the action space, and hence, the amplitude of reaching movements, influenced the dowel placement in the finisher's near space. The results of Experiment 2 did show that when the action space was reduced the proximity-to-body-effect was observed in the finisher's near space. Given that the only variable that changed between Experiment 1 and 2 was the size of the action space, it seems reasonable to suggest that the lack of proximity-to-body-effect in finisher's near space was due to effort and not due to an inability adopt their co-actor's bodycentered frame of reference. This explanation is also congruent with previous joint action research that has shown that physical effort modulates decision making in joint actions (Santamaria and Rosenbaum, 2011). The effect of effort on joint actions is clearly a topic that requires further research.

# CONCLUSION

The present research has revealed that during joint actions individuals will use multiple frames of reference to anticipate their co-actor's task and integrate information from those different frames of reference into their response planning. The finding that co-actors can represent their co-actor's task from multiple frames of reference is an important contribution to the joint action literature because it provides a potential mechanism for how individuals can represent both environment- and bodyrelated factors that need to be considered during response selection and planning. In addition, our research shows that there are limitations in a person's ability to fully adopt their co-actor's body centered frame of reference. Although, previous sequential joint action research has shown that individuals fully adopted their co-actor's body-centered frame of reference and planned actions to facilitate the use of particular postures when

# REFERENCES

manipulating objects (Gonzalez et al., 2011; Ray and Welsh, 2011; Meyer et al., 2013; Dötsch and Schubö, 2015; Constable et al., 2016; Scharoun et al., 2016), the present study builds on that literature by showing that when co-actors have a large angular disparity between them and they are anticipating a complex action (one that can be facilitated based on numerous response features), they might not fully adopt their co-actor's body-centered frame of reference. Instead, this study shows that when a co-actor's task can be accommodated based on a number of action features then multiple frames of reference can be used. Although the exact underlying mechanisms that support the adoption of multiple frames of reference are still unclear and beyond the scope of this paper, the present results demonstrate that when individuals are planning joint actions to accommodate aspects of a co-actor's task, they consider multiple action features based on multiple frames of reference. Future research should investigate how modulations in task complexity impact the frames of reference used during joint actions and what learning experiences are required to fully adopt a co-actor's body-centered frame of reference.

# AUTHOR CONTRIBUTIONS

MR was the primary contributor to the design, data collection and analysis, and writing while TW contributed to the design, analysis and writing.

# FUNDING

Funding for this study was provided by an NSERC grant to TW.

# ACKNOWLEDGMENTS

The authors would like to thank all the participants and the confederate for their time and effort in completing the study.

# SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg. 2018.00542/full#supplementary-material

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Ray and Welsh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Response Coordination Emerges in Cooperative but Not Competitive Joint Task

Francesca Ciardo<sup>1</sup> \* and Agnieszka Wykowska1,2

1 Istituto Italiano di Tecnologia, Genoa, Italy, <sup>2</sup> Luleå University of Technology, Luleå, Sweden

Effective social interactions rely on humans' ability to attune to others within social contexts. Recently, it has been proposed that the emergence of shared representations, as indexed by the Joint Simon effect (JSE), might result from interpersonal coordination (Malone et al., 2014). The present study aimed at examining interpersonal coordination in cooperative and competitive joint tasks. To this end, in two experiments we investigated response coordination, as reflected in instantaneous cross-correlation, when co-agents cooperate (Experiment 1) or compete against each other (Experiment 2). In both experiments, participants performed a go/no-go Simon task alone and together with another agent in two consecutive sessions. In line with previous studies, we found that social presence differently affected the JSE under cooperative and competitive instructions. Similarly, cooperation and competition were reflected in coagents response coordination. For the cooperative session (Experiment 1), results showed higher percentage of interpersonal coordination for the joint condition, relative to when participants performed the task alone. No difference in the coordination of responses occurred between the individual and the joint conditions when coagents were in competition (Experiment 2). Finally, results showed that interpersonal coordination between co-agents implies the emergence of the JSE. Taken together, our results suggest that shared representations seem to be a necessary, but not sufficient, condition for interpersonal coordination.

#### Keywords: response coordination, shared representations, joint Simon effect, cooperation, competition

# INTRODUCTION

As social species, humans are skillful in attuning to others in social contexts. Several studies showed that performing a task individually could be affected by social presence. Indeed, when embedded in the social environment, we dynamically coordinate our actions with those of others in time and space (Sebanz et al., 2006; Knoblich and Sebanz, 2008). Such coordination during joint actions is supported by a complex plethora of mechanisms, such as shared representations, sensorimotor coordination, and goal sharing (see Vesper et al., 2017 for a review). However, since these mechanisms have been mostly investigated independently, it is still unclear how they are orchestrated in order to support efficient joint tasks.

#### Edited by:

Kerstin Dittrich, Albert-Ludwigs-Universität Freiburg, Germany

#### Reviewed by:

Thomas Dolk, University of Regensburg, Germany Kerry Marsh, University of Connecticut, United States

> \*Correspondence: Francesca Ciardo<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Cognition, a section of the journal Frontiers in Psychology

Received: 13 April 2018 Accepted: 18 September 2018 Published: 09 October 2018

#### Citation:

Ciardo F and Wykowska A (2018) Response Coordination Emerges in Cooperative but Not Competitive Joint Task. Front. Psychol. 9:1919. doi: 10.3389/fpsyg.2018.01919

# Shared Representations and the Joint Simon Effect

According to the shared representations account, joint action is based on the ability to share task representations, i.e., the ability to represent the task as shared, and to create a representation of the task that includes both our and co-agents' actions (Sebanz et al., 2006). In the recent years, researchers investigated joint action by means of the "Joint Simon" task (Sebanz et al., 2003). In the standard Simon task, participants respond to a non-spatial feature (e.g., color or shape) of stimuli presented to the left or to the right of fixation with assigned right and left key presses. The Simon effect (SE) refers to the finding that performance is faster and more accurate when stimulus and response location spatially correspond, as compared to when they do not (Simon and Rudell, 1967; see also Proctor and Vu, 2006 for a review). The SE is absent when participants perform a go/no-go version of the task, responding only to one feature while withholding the response for the other feature, which indicates that the SE is due to the activation of automatic links between stimulus location and the corresponding response position (Tagliabue et al., 2000). Sebanz et al. (2003) showed that SE occurs even when the Simon task is shared between two participants, i.e., when two participants perform the go/no-go task in a joint context, each one responding to one color only. The spatial compatibility effect emerging in the joint go/no-go task is known as the Joint Simon Effect (JSE)<sup>1</sup> . According to Sebanz et al. (2003, 2006), the JSE has been interpreted as an indication that when people perform together complementary parts of a task, they tend to represent the whole task and to integrate both their and other's action options into a shared representation, as if they were performing the standard Simon task alone, i.e., performing the task with two hands. In the absence of such a representation, no alternative action is represented and thus no conflict between alternative responses would arise, as is the case of the individually performed go/no-go Simon task. Thus, the JSE has been considered as an index of emergence of shared representations (e.g., Sebanz et al., 2006, 2003; Knoblich and Sebanz, 2008). However, studies that systematically investigated how social presence influences individual performance suggested that when we perform a task along with another person the representations guiding joint performance might differ from representations guiding performance in the individual task (e.g., Ferraro et al., 2011; Ciardo et al., 2016). Several alternative accounts have been proposed to explain the emergence of the JSE (see Prinz, 2015 for a review), including the referential coding account (Dolk et al., 2011, 2013; Dittrich et al., 2013; see Dolk et al., 2014 for a review). The referential coding account proposes that during a joint Go/Nogo Simon task, the presence of any salient action event, generated by a biological or non-biological agent (Stenzel and Liepelt, 2016; Miss and Burkhart, 2018), is represented by an action event code. Given the high similarity of the two action events in the Joint Simon task (i.e., pressing a button), participants need to discriminate between internally (one's own) and externally (the other agent's) activated events. In order to solve the conflict arising from this discrimination, the differences between the two action events (i.e., the left-right location of the response), are strengthened and automatically interfere with the task-irrelevant stimulus spatial code, which generates the JSE (Dolk et al., 2014).

# Interpersonal Coordination

Sharing the context with another agent does not always require intentional representation of one's own and others' actions. Indeed, the presence of another person can interfere with our performance at a lower level, as in the case of sensorimotor timing (e.g., Schmidt and Turvey, 1994; Richardson et al., 2007). For example, during a conversation, we tend to nod at a same rhythm as the speaker. Similarly, when we walk with someone, we reciprocally adapt our gait to each other. This tendency to unintentionally adapt the timing of our movements to others is called entrainment and it seems necessary in order to be temporally coupled with others (Marsh et al., 2009). Entrainment also underlies joint action, according to the dynamic account (see Marsh et al., 2009 for a review). For instance, it has been shown that pairs of participants performing rhythmic movements (i.e., swinging a pendulum or rocking chairs) tend to become temporally correlated by adopting the same movement rate (Schmidt and Turvey, 1994; Richardson et al., 2007). Vesper et al. (2011) showed that when pairs of participants perform two independent Simon tasks at the same moment, their responses tend to be coordinated (Vesper et al., 2011). Specifically, response variability positively correlated with asynchrony in reaction times across the two members of the pairs, suggesting that reducing response variability may represent an implicit strategy to facilitate cooperation. Similarly, other results show that coordination supports access to others' mental states and spontaneous cooperation (Semin and Cacioppo, 2008; Koehne et al., 2016). In a recent study, Malone et al. (2014) investigated the dynamic structure of reaction times (RTs) in a Joint Simon task. The authors compared the response variability structure of participants performing a go/no-go Simon task. Two groups of participants performed the same go/no-go Simon task individually or together with another person having the complementary go/no-go assignment. Results showed that variability structure was whiter<sup>2</sup> in the individual than in the joint condition (Malone et al., 2014); indicating that when participants performed the task sideby-side of another person, responses were characterized by nested patterns of variability which were not due to random fluctuations (Malone et al., 2014). In line with the idea

<sup>1</sup>According to the classification of spatial compatibility effects proposed by Donders (1969), the SE typically reported in the Joint Go/Nogo Simon task belongs to group "c," including spatial-compatibility effects emerging in tasks in which the spatial nature of the stimulus is task-irrelevant.

<sup>2</sup>Note that when decomposing reaction times variability, it is possible to identify three types of temporal structures. White noise indicates that the temporal variability in an action sequence is generated by unsystematic or unrelated changes from trial to trial. Brown noise corresponds to a stochastic function, indicating that each subsequent action is a function of the previous action to which a random increment is added. Pink noise is a mixture of randomness and rigidity, and it is typical of interaction dominant (complex) systems across multiple time scales. For example, it has been suggested that pink noise reflects emergent coordination between cognitive processes and behavior (e.g., Van Orden et al., 2003).

of decreasing fractal structure of RT variability, the authors reported that RTs of pairs in the joint condition were more correlated across time scales than RTs of pseudo-pairs of participants who performed the task individually. These latter results suggested that responses of co-agents during the joint Simon task were coupled and that the dynamics of co-agents' responses might be mutually constrained. In sum, the authors proposed that "dynamic processes of constraints may decouple behavior over time" (cf. p. 6 Malone et al., 2014) and may underlie the JSE instead of any form of shared or integrated representation of the task. Alternatively, it is also plausible that the emergence of shared representations, or the integration of "self " and "other" action events, may actually promote emergent temporally evolving coupling and modulate the inter-agent response dynamics.

# Social Context and Shared Representations: The Case of Cooperation and Competition

Cooperation and competition are social relations that rely on opposite goal interdependency (Deutsch, 2011), and differently affect social cognitive processes, as joint attention (e.g., Ciardo et al., 2015), sensorimotor synchronization (e.g., Fairhurst et al., 2012), and reach-to-grasp kinematics (e.g., Ciardo et al., 2017). When we cooperate with someone, our goals are positively related. In contrast, when we compete, reaching our personal goal is negatively related to others' achievement of the goal: if our competitor reaches his/her goal, then we cannot reach our goal anymore (Deutsch, 2011). Positive and negative goals interdependency between co-agents differently affects the emergence of shared representations (e.g., Ruys and Aarts, 2010; Iani et al., 2011, 2014) and self-other integration (Hommel et al., 2009; Ruissen and de Bruijn, 2016). For instance, Hommel et al. (2009), manipulated the valence of the interaction between two co-agents during a Joint Simon Task. Participants performed the task with a friendly and cooperative, or with an intimidating and competitive confederate. Results showed that the JSE occurred only for participants involved in a positive relationship, whereas the negative relationship led to a reduction of the JSE. Similarly, Iani et al. (2011, 2014) showed that when pairs of participants performed a joint Simon task, the JSE emerged only when the two co-agents were required to cooperate but not when they were in competition against each other (Iani et al., 2011, 2014). Under the cooperative condition, participants were told that the pair with the fastest and most accurate responses would receive a reward. This condition elicited a positive interdependence, as the success of one individual rendered the success of the other more likely. Under the competitive condition, they were told that the participant of the pair with the fastest and most accurate responses would receive a monetary reward. Such a design indicated that by manipulating goals interdependency, it is possible to promote or inhibit the emergence of shared representations without manipulating the physical and dynamical features of the social environment and its task constraints. According to the referential coding account (Dolk et al., 2013, 2014), the lack of JSE during competitive tasks can be explained by the fact that negative interpersonal relationships do not promote selfother integration. Thus, during competitive tasks participants do not need to discriminate between internally and externally activated action events, and they do not need to strength task-relevant information (i.e., left and right response location) resulting in the lack of the JSE. Results from a recent study by Ruissen and de Bruijn (2016) are in line with the selfother integration account (Dolk et al., 2014) showing that the JSE is reduced following a competitive game play. According to Ruissen and de Bruijn (2016), motivation and contextual factors might affect self-other integration during the Joint Simon task by exerting different effect on attentional processes. In a cooperative situation we might be motivated to attend to our co-agents performance even if, as in the Joint Simon task (Ferraro et al., 2011), it actually interferes with our own performace, – in order to monitor potential co-agent's mistakes, and to better adapt our internal action model. On the contrary, during competitive interactions, co-agents might be focused on stabilizing their own performance and do not attend the co-agent's behavior, which results in attenuation of self-other integration (Hommel et al., 2009; Ruissen and de Bruijn, 2016).

Recently, Keller et al. (2016) proposed a model of joint action, which connects shared representation of goals and interpersonal coordination. The authors proposed that during joint action, distinct self and other internal models are maintained in order to ensure that each co-agent controls their action planning and execution. When shared representations of goals are established, self and other models work together allowing co-agents to anticipate, attend, and adapt to each other in real time (Keller et al., 2014). The coupling of self and other models into a joint model facilitates interpersonal coordination. Thus, the emergence of shared representations of goals guide joint action by supporting the interaction between cognitive and online sensorimotor processes. Previous studies investigating how cooperation and competition affect self-other integration or shared representations used a monetary reward to manipulate cooperation and competition between co-agents (Ruys and Aarts, 2010; Iani et al., 2011). However, individual and contextual differences can shape the actual perception and experience of a monetary reward as a motivational cue (e.g., Kahneman and Tversky, 1979; see Schultz, 2006 for a review). This would explain the controversial nature of the results reported by previous studies on how competition affects JSE (Ruys and Aarts, 2010; Iani et al., 2011). In order to minimize the effect of individual and contextual differences in the motivation to cooperate or compete, in the present study, we manipulated positive and negative interdependency between co-agents through punishment avoidance. Indeed it has been shown that reward and punishment avoidance emerge from different learning mechanisms rely on distinct neural circuits (e.g., Palminteri et al., 2012). Thus, by using punishment avoidance instead of reward, we aimed at testing whether previous findings showing that the JSE can be modulated by cooperative vs. competitive instructions generalize to different types of experimental manipulation.

# Aim of Study

The present study aimed at examining the relationship between interpersonal coordination and the JSE, with JSE being taken as an index of shared representations. To this end, in two experiments we asked participants to perform a go-no/go Simon task alone or side-by-side of another person. In Experiment 1, we investigated the response coordination when co-agents were required to cooperate, with the assumption that cooperation promotes self-other integration or the emergence of shared representations. In Experiment 2, we examined response coordination when co-agents' goals were mutually exclusive, like in competition, assuming that in this case, self-other integration would be attenuated, or shared representation would not be activated.

# EXPERIMENT 1

The present experiment aimed at assessing interpersonal response coordination during joint action. To this end, we compared the coordination between RTs when participants performed a go/no-go Simon task alone or together with another person. We focused on cooperative joint actions, i.e., when the goals of two co-agents are positively related to each other. In line with previous studies, we expected a non-significant SE (i.e., no difference between corresponding and non-corresponding trials) when participants perform the task alone, and a JSE when they are required to cooperate (Hommel et al., 2009; Ruys and Aarts, 2010; Iani et al., 2011, 2014; Ruissen and de Bruijn, 2016). Regarding response coordination, to explore interpersonal coordination in the context of JSE, we examined if RTs of coagents were correlated (i.e., coordinated) with each other over time. We hypothesized that if shared representations or self-other integration are reflected in the dynamics of the behavior then the response coordination should be greater between the RT timeseries of individuals in the joint condition, as compared to RT time-series of pseudo–pairs created using RT time-series from the two individual conditions. Specifically, a higher percentage of response coordination in RT times-series is expected for the Joint compare to the Individual condition (Malone et al., 2014).

# Materials and Methods

#### Participants

Twenty participants (11 males; 4 left-handed; Mean age: 24 ± 3.9 years) took part in the study. All participants had normal or corrected-to-normal vision and were not informed with respect to the purpose of the experiment. Participants received a reimbursement of 15€ for their participation. All gave their written informed consent before participating. Both Experiment 1 and Experiment 2 were conducted in accordance with the ethical standards laid down in the 2013 Declaration of Helsinki and were approved by the local ethical committee (Comitato Etico Regione Liguria). Sample size was defined according to previous experiments (Ruys and Aarts, 2010; Iani et al., 2011), and by an a priori power analysis indicating a sample N = 18 to detect a medium effect size [Cohen's d for repeated measures (Dz) = 0.60, alpha (one-tailed) = 0.05 and power = 0.95] for within-subjects comparisons. Participants were recruited individually from the subject database of the Italian Institute of Technology. They were paired according to the time slots in which they were available to take part in the experiment.

#### Apparatus and Stimuli

Stimuli presentation, response timing, and data collection were controlled by the E-Prime version 3 software (Psychology Software Tools, Inc.). Stimuli were red and green solid squares (2.3◦× 2.3◦ ), which were randomly presented on the left or on the right of a central white fixation cross (0.6◦× 0.6◦ ) on a black background. Responses were executed by pressing with the index finger the "z" or "-" key of a standard Italian QWERTY keyboard. Response keys were highlighted with two white circular stickers. The experiment was carried out in a dimly lit and noiseless room. Participants were seated facing a 27<sup>00</sup> LCD screen driven by a 2.4 GHz processor computer. Viewing distance was about 60 cm.

#### Procedure

Pairs of participants performed two consecutive sessions, separated by a 5-min interval and lasting about 60 min in total. To avoid transfer of learning effects typical of spatial compatibility tasks (Ansorge and Wühr, 2009; Dittrich et al., 2012; Lugli et al., 2013), the order of the two sessions was fixed: an Individual session was followed by a Joint session (for a similar procedure see Dittrich et al., 2017). In the Individual session, each participant performed the task alone, sitting to the right or on the left from of the center of the screen, with an empty chair next to him/her. Left-handed participants seated always on the left side of the screen, in order to let them perform the task with their dominant hand. At their arrival to the lab, participants were told that they were going to perform two different experiments. The two members of the pair participated in the Individual session in parallel (i.e., at the same time), sitting in two different rooms without any possibility to see or talk to each other. Instructions for the individual session were provided separately to each participant by the same experimenter. In the Joint session, participants seated side-by-side, one to the left and one to the right of the center of the screen. Pairs of participants were instructed to cooperate in order to be the bestperforming pair, in terms of both speed and accuracy. They were told that, at the end of the experiment, if they did not perform as the best couple they would receive a punishment, consisting in performing an additional task (i.e., performing the first session again). In both sessions (i.e., Individual and Joint), the experimental procedure was as follows: A trial began with the presentation of the fixation cross at the center of the screen. After 1 s, the stimulus appeared to the right or to the left of the fixation and remained visible until a response was collected, or for 800 ms. Maximum time allowed for response was 1 s after stimulus presentation. Immediately after a response was collected, or the stimulus elapsed, a black screen was presented for 1 s. In the Individual session, Nogo stimulus was presented for 800 ms and followed by a 1 s black screen before the next trial started. For both sessions, the task consisted of 16 practice trials and 384 experimental trials divided into four blocks of 96 trials each. For half of the trials, stimulus and response location

corresponded (corresponding trials), for the other half, they did not correspond (non-corresponding trials). A fictional partial score was displayed at the end of each block. The score was computed as the difference between corresponding and notcorresponding trials. Participants were told that the score was computed by an algorithm based on their speed in responding, corrected by the overall percentage of correct answers. For half of the pairs, the participant sitting on the right chair pressed the right key to the red stimulus whereas the participant sitting on the left chair pressed the left key to the green stimulus. The other half was assigned opposite stimulus–response mapping. Response-, seat- and stimulus assignment to each participant was identical across the two sessions.

# Data Analysis

First, we analyzed correct responses to check the JSE. Mean correct RTs were submitted to a repeated-measures analysis of variance (ANOVA) with Condition (Individual vs. Joint), and Correspondence (non-corresponding vs. corresponding) as within-subjects factors.

In order to evaluate whether JSE requires time to emerge, we conducted a distributional analysis of RTs (Ratcliff, 1979). To have enough observations in each bin, we chose to divide the RT distribution in quartiles (Liepelt et al., 2011). Thus, individual correct RTs for each condition were rank ordered and divided into four bins. Mean RTs for each bin were then entered into a repeated-measures ANOVA with Condition (Individual vs. Joint), and Correspondence (noncorresponding vs. corresponding) and Bin (1–4) as withinparticipant factors. To investigate trial-by-trial modulations (Liepelt et al., 2011; Yamaguchi et al., 2018), mean RTs were submitted to an ANOVA with Condition (Individual vs. Joint), Trial Transition (n−1 Go/ n go vs. n−1 Nogo/ n go), Trial n−1 Correspondence (non-corresponding vs. corresponding), and Trial n Correspondence (non-corresponding vs. corresponding) as within-participant factors. When necessary, comparisons were performed using paired samples t-tests. Significance thresholds were corrected for the number of comparisons (Bonferroni correction).

To quantify the degree of coordination between the agents, following study Malone et al.'s (2014), we applied instantaneous cross-correlation on RTs series (Barbosa et al., 2008); which allows determining the correlation between time-series across multiple time-scales. This is done by computing correspondence between two time-series recursively and generating a time-series of how past and future samples are correlated at all points in time. This method has been applied to determine objective coordination between non-synchronous behaviors occurring at different time lags, like in articulatory coordination of two vocals tracts (Vatikiotis-Bateson et al., 2014). Subsequently, an index of response coordination was estimated as the proportion of correlated activity (i.e., the proportion of r > 0.25, see Malone et al., 2014, 2013) between the RT time-series of the two members of a pair. RT time-series were computed by ordering for each participant RTs in the order they were collected, and then by subtracting from each data point the mean of respective condition for each participant. RTs for missing and incorrect responses were substituted by the mean of RTs for the respective condition. We ran the instantaneous correlation analysis for offsets of −9 to + 9 trials with a conservative (η = 0.1) non-causal filter (Barbosa et al., 2008). Thus, the offset range was chosen by reducing the interval size applied in Malone et al.'s (2014) study, in order to consider delays proportional to the lower number of trials.

Finally, a paired samples t-test was applied to compare if the proportion of correlated response activity (i.e., index of response coordination) within each pseudo-pair in the individual condition differed from the percent of coupling observed for pairs in the joint condition.

# Results

### Reaction Times

Errors were 0.4 and 0.5% of the total amount of trials, for the Individual and Joint conditions, respectively, and were not further analyzed. Tukey outlier thresholds (1977) were used for each condition to identify outliers in the number of erroneous trials. No participants were excluded. Mean RTs are summarized in **Table 1**. The ANOVA revealed a main effect of Correspondence, F1,<sup>19</sup> = 8.70, p = 0.008, η 2 <sup>p</sup> = 0.31, together with a significant two-way interaction with Condition, F1,<sup>19</sup> = 15.09, p = 0.001, η 2 <sup>p</sup> = 0.44. Pairwise comparisons showed that the difference between corresponding (M = 340 ms) and non-corresponding trials (M = 350 ms) was significant for the Joint condition only, t<sup>19</sup> = 4.11, pBonferroni–corrected = 0.001, d = 0.92. In the Individual session no effect of correspondence was evident (M = 345 and M = 347 ms for corresponding and non-corresponding trials, respectively), t<sup>19</sup> < 1 (**Figure 1**).

#### RTs Distribution

Besides the main effect of Correspondence and its interaction with Condition already reported in the previous analysis, the ANOVA revealed a main effect of Bin, F1,<sup>19</sup> = 175.98, p < 0.001, η 2 <sup>p</sup> = 0.90, indicating that RTs increase across quartiles. No other main effects or interactions were significant, all ps > 0.77 (**Figure 2**).

#### Trial-by-Trial Modulation and Transition Effects

The first trial of each block, errors and responses that were preceded by an incorrect response were discarded from the analysis (1.15 and 1.41% of the total trials in the Individual and Joint condition, respectively). The results are summarized in **Table 2**. The ANOVA showed that responses were faster in corresponding (M = 342 ms, SE = 8.74 ms) than noncorresponding (M = 348 ms, SE = 8.71 ms) trials, as indicated

TABLE 1 | Experiment 1: Mean correct reaction times (and standard deviation) in ms as a function of Condition (individual vs. joint) and Correspondence (non-corresponding vs. corresponding).

TABLE 2 | Experiment 1: Mean correct reaction times (and standard deviation) in ms as a function of Trial Transition (Nogo/go, Go/go), Trial n−1 (corresponding, C vs. non-corresponding, NC), and Trial n (corresponding, C vs. non-corresponding, NC).

panel) and Joint condition (Right panel). Error bars show standard errors of the means.

The Simon effect (SE) is computed as the difference in RTs between noncorresponding and corresponding trials.

by the main effect of Trial n Correspondence, F1,<sup>19</sup> = 9.66, p = 0.006, η 2 <sup>p</sup> = 0.34. As in the previous analysis, the twoway interaction between Condition and Correspondence was significant, F1,<sup>19</sup> = 8.73, p = 0.008, η 2 <sup>p</sup> = 0.32. Pairwise comparisons showed a significant 9-ms JSE for the Joint condition only, t<sup>19</sup> = 3.98, pBonferroni–corrected < 0.001, d = 0.89, and a non-significant 3-ms JSE in the Individual session, t<sup>19</sup> = 1.37, pBonferroni–corrected = 0.187, d = 0.31. The interaction between Trial n Correspondence and Trial n−1 Correspondence was also significant, F1,<sup>19</sup> = 58.88, p < 0.001, η 2 <sup>p</sup> = 0.32. Post hoc comparisons showed a 15-ms effect after a corresponding n−1 trial, t<sup>19</sup> = 6.4, pBonferroni−corrected < 0.001, d = 1.43, and a non-significant 3-ms effect after non-corresponding n−1 trials, t<sup>19</sup> = 1.11, pBonferroni−corrected > 0.05, d = 0.25. The three-way interaction between Trial Transition, Trial n−1 Correspondence, and Trial n Correspondence was significant, F1,<sup>19</sup> = 18.49, p < 0.001, η 2 <sup>p</sup> = 0.49. Planned comparison showed that trialby-trial modulation occurred always for Nogo/go transitions with a significant 24-ms effect following a corresponding n−1 trial, t<sup>19</sup> = 5.63, pBonferroni−corrected < 0.001, d = 1.26, and a reversed 9 ms effect following a non-corresponding n−1 trial, t<sup>19</sup> = 3.01, pBonferroni−corrected = 0.007, d = 0.67. On the contrary, no trial-by-trial modulations occurred for Go/go transitions, all ps > 0.06. No other main effects or interaction were significant, all ps > 0.14.

#### Response Coordination

As mentioned above, we compared the proportion of response coordination (i.e., the proportion of correlation between the times series higher than 0.25) within each pseudo-pair (N = 10) in the individual condition with the percent of response coordination observed for pairs in the joint condition. Results

showed higher response coordination for the Joint (15.3%) than for the Individual session (12.7%), t<sup>9</sup> = 3.44, p = 0.007, d = 1.09.

# Discussion

In Experiment 1, we examined coordination of RTs when participants performed a go/no-go Simon task alone or sideby-side of another person, in a cooperative context. In line with previous studies, results from mean RTs showed a nonsignificant SE when participants performed the task alone, and a significant JSE when they were performing the task sideby-side and were instructed to cooperate (Iani et al., 2011, 2014, see Karlinsky et al., 2017b for a meta-analysis on the magnitude of the JSE). The comparison of the distributional trends showed that response speed did not affect the magnitude of the SE neither in the Individual task nor in the Joint task. The similarity in the distributional patterns between the Joint and the Individual tasks replicates previous results reported by study Liepelt et al.'s (2011), suggesting that the emergence of JSE cannot be attributed to different temporal dynamics underlying the two conditions. Trial-by-trial modulations occurred both in the Individual and Joint conditions, as indicated by the lack of significant interaction involving trial sequence (n−1/n) and Condition. As reported by previous studies (Liepelt et al., 2011; Yamaguchi et al., 2018), trial-by-trial modulations occurred when Go trials were proceeded by a Nogo trial (Nogo-Go Transition), probably reflecting response inhibition during Nogo trials. Trial-by-trial modulations mimic the pattern typically reported in standard two-choice Simon tasks (Iani et al., 2009; Ciardo et al., 2018), with a reversed effect following non-corresponding n−1 trial and a positive effect following corresponding n−1 trial. These trial-by-trial modulations have been taken as evidence that the conflict experienced in a trial is accompanied by changes aiming at preventing the reocurrence of the conflict in the next trial by means of enhanced processing of task-relevant information (e.g., Egner and Hirsch, 2005) or inhibition of task-irrelevant features (e.g., Ridderinkhof, 2002). Alternatively, it has been proposed that trial-by-trial modulation may reflect binding effects (e.g., Hommel et al., 2004). Indeed in the Simon task, sequences of two corresponding trials (C–C) and sequences of two noncorresponding trials (NC–NC) are either complete repetitions or complete changes of stimulus position and response or complete changes of both stimulus position and response. In contrast, mixed sequences (C–NC or NC–C) are always partial repetitions in which either stimulus position or response repeats. Thus, the SE may be reduced following a noncorresponding trial because responses are faster for complete repetitions and alternations compared to partial repetitions (Hommel et al., 2004). The results of response coordination showed that when co-agents were instructed to cooperate, their RT time-series were more coordinated (i.e., a higher percentage of correlation) with each other over time, as compared to when they were performing the task individually. Such result confirms and extends Malone et al. (2014) evidence for the idea that coordination of behavior is observed together with the JSE.

# EXPERIMENT 2

Experiment 1 suggested that when co-agents' goals are positively related, the correlation between co-agents' responses across time scales increases. However, it could be that co-agents' coordination reflects their adaptation in space and time related to any dynamic event occurring during the task, like another agent (human or not) acting in the same environment, independently from positive goal. Thus, it is possible that the increase in response coordination reported in Experiment 1 results from the natural tendency to adapt the timing of our movements to external events (e.g., Marsh et al., 2009), rather than resulting from integration of self and other action events, or from the emergence of a shared representation. In line with this hypothesis, there are several results showing that the JSE can occur even when no shared representation is necessary, like when the co-agent is not present physically (e.g., Sellaro et al., 2013) or when an object is performing the complementary go/no-go task (Stenzel and Liepelt, 2016). For example, a recent evidence showed that JSE emerges even when the alternative response is executed by a non-human agent (e.g., a Japanese cat, a metronome, or a wooden hand, Dolk et al., 2013; Stenzel and Liepelt, 2016). Interestingly, the JSE was larger when the external event (i.e., the non-human agent) was acting in a turn-taking way with respect to the participant, as compared to a condition when it was acting in a continuous way, i.e., not task-related (Stenzel and Liepelt, 2016). To test this alternative explanation, in Experiment 2 we examined the effect of mutually exclusive goals (assuming no shared representation) on response coordination. As in Experiment 1, we compared coordination between RT timeseries when participants performed a go/no-go Simon task alone or side-by-side of another person. However, during the Joint session participants were instructed to compete against each other. Note that competition is a particular case of joint task in a shared environment, where individuals work to reach an individual goal that – in order to be reached – excludes the goal of the other co-agent. In line with previous studies showing that competition disrupts the emergence of shared representation in joint tasks (Ruys and Aarts, 2010; Iani et al., 2011, 2014) or affect the integration of self and other action events (Hommel et al., 2009; Ruissen and de Bruijn, 2016); we expected no difference in the SE between the Individual and Joint condition. Regarding response coordination, we hypothesized that if findings of Experiment 1 are merely due to environmental perturbations produced by dynamic events, i.e., the co-agent acting in the shared environment, then results should replicate the pattern reported in Experiment 1, with greater response coordination (i.e., higher percentage of correlation) in the joint condition compared to the individual condition. This result would speak against the idea that shared representations, are the consequence of response coordination. On the contrary, if the percentage of response coordination reflects the emergence of shared representations, or the integration of self-other action events, then the social presence should not modulate coordination between RT times-series across the individual and joint conditions, similarly to the standard SE. This would speak in favor of the hypothesis that interpersonal coordination yields shared representation.

# Materials and Methods Participants

fpsyg-09-01919 October 6, 2018 Time: 16:59 # 8

Twenty-six new participants (8 males; 4 left-handed; Mean age: 24 ± 2.9 years), selected as in the previous experiment, took part in Experiment 2. All participants gave their written informed consent and the study was conducted in accordance with the ethical protocol applied also in Experiment 1. Three pairs, six participants in total, were excluded from the data analysis, given the number of errors made by at least one member of the pair.

#### Apparatus, Stimuli, and Procedure

The apparatus, stimuli, and procedure were the same as in Experiment 1. With the only exception that in the Joint session, pairs of participants received the instructions to compete against each another. They were told that at the end of the experiment, the worst performer of the pair would receive a punishment, i.e., s-/he had to perform an additional task. Apart from the instructions, all other aspects of the experimental design were as in Experiment 1.

# Results and Discussion

#### Reaction Times

Errors were 0.6 and 1.1% of the total amount of trials for the Individual and Joint conditions, respectively, and were not further analyzed. Tukey outlier thresholds (1977) were used for each condition to identify outliers in the number of erroneous trials. These thresholds removed 1 participant from the Individual condition and 2 participants from the Joint condition. In total 3 pairs were excluded from the analyses, thus data analysis was run on a sample size including 10 pairs (N = 20). Mean correct reaction times (RTs) were analyzed as in Experiment 1. The results are summarized in **Table 3**. The analysis revealed a main effect of Condition, F1,<sup>19</sup> = 34.82, p < 0.001, η 2 <sup>p</sup> = 0.65, indicating that participants performed faster in the Joint condition (M = 305 ms) than the Individual condition (M = 339 ms). Main effect of Correspondence, F1,<sup>19</sup> = 7.50, p = 0.013, η 2 <sup>p</sup> = 0.28, indicated faster responses for corresponding (M = 320 ms) than noncorresponding trials (M = 325 ms), however, this effect did not differ across the Joint and the Individual conditions, as indicated by the lack of significance for the two-way interaction, F < 1.

TABLE 3 | Experiment 2: Mean correct reaction times (and standard deviation) in ms as a function of Condition (individual vs. joint) and Correspondence (non-corresponding vs. corresponding).

### RTs Distribution

Besides the main effect of Correspondence and Condition already discussed in the previous analysis, the ANOVA revealed a main effect of Bin, F1,<sup>19</sup> = 528.90, p < 0.001, η 2 <sup>p</sup> = 0.97, indicating that RTs increase across quartiles. Two-way interaction between Condition and Bins was significant, F1,<sup>19</sup> = 20.90, p < 0.001, η 2 <sup>p</sup> = 0.52. Pairwise comparisons showed that for all the quartiles responses were faster in the Joint compared to the Individual condition, all ps < 0.001. No other main effects or interactions were significant, all ps > 0.55.

#### Trial-by-Trial Modulation and Transition Effects

The first trial of each block, errors and responses that were preceded by an incorrect response were discarded from the analysis (1.20 and 1.04% of the total trials in the Individual and Joint condition, respectively). The results are summarized in **Table 4**. The ANOVA showed that responses were faster in corresponding (M = 319 ms, SE = 7.31 ms) than noncorresponding (M = 324 ms, SE = 7.61 ms) trials, as indicated by the main effect of Trial n Correspondence, F1,<sup>19</sup> = 6.78, p = 0.017, η 2 <sup>p</sup> = 0.26. As in the previous analysis, the main effect of Condition was significant, F1,<sup>19</sup> = 34.24, p < 0.001, η 2 <sup>p</sup> = 0.64, as well its interaction with Trial n−1 Correspondence, F1,<sup>19</sup> = 6.40, p < 0.001, η 2 <sup>p</sup> = 0.64. The interaction between Trial n Correspondence and Trial n−1 Correspondence was also significant, F1,<sup>19</sup> = 91.40, p = 0.020, η 2 <sup>p</sup> = 0.25. Post hoc comparisons showed a 14-ms effect after a corresponding n−1 trial, t<sup>19</sup> = 7.04, pBonferroni−corrected < 0.001, d = 1.58, and a reversed 5-ms effect after non-corresponding n−1 trials, t<sup>19</sup> = 2.55, pBonferroni−corrected = 0.02, d = 0.57. The three-way interaction between Trial Transition, Trial n−1 Correspondence, and Trial n Correspondence was significant, F1,<sup>19</sup> = 54.42, p < 0.001, η 2 <sup>p</sup> = 0.74. Planned comparison showed that trialby-trial modulations for Nogo/go transitions with a significant 22-ms effect following a corresponding n−1 trial, t<sup>19</sup> = 8.64, pBonferroni−corrected < 0.001, d = 1.93, and a reversed 11-ms effect following a non-corresponding n−1 trial, t<sup>19</sup> = 4.94, pBonferroni−corrected < 0.001, d = 1.11. When trial transition was Go/go a significant 6-ms effect occurred following a corresponding n−1 trial, t<sup>19</sup> = 2.65, pBonferroni−corrected = 0.02, d = 0.59, and a 2-ms non-significant effect following a

TABLE 4 | Experiment 2: Mean correct reaction times (and standard deviation) in ms as a function of Trial Transition (Nogo/go, Go/go), Trial n−1 (corresponding, C vs. non-corresponding, NC), and Trial n (corresponding, C vs. non-corresponding, NC).

The Simon effect (SE) is computed as the difference in RTs between noncorresponding and corresponding trials.

non-corresponding n−1 trial, t<sup>19</sup> < 1. No other main effects or interaction were significant, all ps > 0.36 (**Figure 3**).

#### Response Coordination

Reaction time-series were computed and analyzed as in Experiment 1. A paired samples t-test was applied to compare if the proportion of correlated activity between the two members of the pair (N = 10) in the individual condition differed from the percent of response coordination observed in the joint condition. Results showed no difference in the proportion of correlated activity between the Individual (12.7%) and the Joint session (13.1%), t<sup>9</sup> < 1.

# Discussion

Experiment 2 aimed at testing if the increased response coordination reported during the joint task in Experiment 1 can be interpreted as the consequence of the mere temporal coupling with external events. To this end, we compared response coordination of participants performing a go/no-go Simon task alone or in competition with another person. Results from mean RTs showed that participants were faster in the Joint condition, as compared to the Individual condition. This result is not surprising since participants were instructed to be the best performer in the couple, in order to avoid punishment. A similar increase in speed of responses has been reported in a recent study investigating the role of turn-taking in the emergence of JSE (Karlinsky et al., 2017a). Specifically, the authors reported faster RTs when the structure of the task did not require to alternate own actions with those of the co-agent. Thus, it is possible that in our experiment the competitive framework affected the perception of turn-taking during the task. In line with our prediction, results from mean RTs indicated no difference in the SE (5 ms) between the Individual and the Joint condition. In line with results from Experiment 1, no difference emerged from the analysis of distributional trends across the Individual and the Joint condition. Similarly, trial-by-trial modulations occurred both in the Individual and Joint conditions. Again, trial-by-trial modulations were stronger for the Nogo/go transitions compared to the Go/go transitions.

Response coordination analysis showed that the percentage of coordination between RT time-series was similar for the Individual and the Joint condition. Results of Experiment 2 suggest that the increase in response coordination reported in Experiment 1 cannot be interpreted as merely the consequence of the perturbation produced by a dynamic event in the task environment. Indeed, if this was the case, a similar pattern should have emerged in Experiment 2. On the contrary, the present experiment shows that, despite the presence of the co-agent acting in a shared environment, participants did not coordinate their responses with those of a competitor.

## Comparisons Between Experiments Linear Mixed-Effects Analysis

To examine the contribution of response coordination in the JSE, we used a linear mixed-effects model analysis on mean RTs to re-analyze data from both Experiment 1 and Experiment 2. We compared our Model 1, which, as fixed factors, included Condition (Individual, Joint), Correspondence (corresponding, non-corresponding) and their interaction, with Model 2, which included coordination as a random effect. We began with a maximal random effects structure (Barr et al., 2013; Bates et al., 2015). Then, we redefined the model by including coordination as a random effect to check whether the goodness of fit was significantly increased or reduced after removing variance accounted by the random effect of coordination. In other words, by using the percentage of correlation between co-agents' RT time-series as the random effect, we controlled if it influenced main effects. The significance of the effects and parameters was evaluated using Chisquare test. Analyses were carried out using the package lme4 (version 1.0−5; Bates et al., 2015) available for the

TABLE 5 | Model comparisons for the random effect of correlated response coordination on mean RTs.

statistical software R (version 3.0.1, freely available at http:// www.rproject.org). Results of the two models are displayed in **Table 5**.

#### Experiment 1

Results showed that including the percentage of response coordination as random effect significantly improved model fit. Then, we re-estimated the mean differences in mean RTs for Experiment 1 using Model 2. Results showed that both the main effect of Correspondence and its interaction with Condition were still significant, χ <sup>2</sup> = 19.58, p < 0.001 and χ <sup>2</sup> = 8.22, p = 0.001.

### Experiment 2

Results showed that including the percentage of response coordination as random effect significantly improved model fit. We then re-estimated the mean differences in mean RTs for Experiment 2 using Model 2. Results showed that only the main effect of Condition was still significant, χ <sup>2</sup> = 10.50, p = 0.001, while the main effect of Correspondence did not reach the significance, χ <sup>2</sup> < 1.

Results from the linear mixed models analysis indicate that in both experiments introducing the percentage of response coordination as random effect increased the goodness of fit. In Experiment 1, by removing the variance explained by response coordination, the main effect of Correspondence survived, and so did the two-way interaction with Condition. Such a result suggests that the significant JSE reported in the joint condition is not fully explained by correlation between coagents' responses. In contrast, no main effect of Correspondence emerged in Experiment 2 when the percentage of response coordination is introduced as random effect. Thus, it is possible that the main effect of Correspondence in Experiment 2 could be a false positive (i.e., a type I error). Summing up, results from the linear mixed models analysis suggest that by using mixed linear models it is possible to account for random effects produced by response coordination. In both experiments, including response coordination as a random effect significantly improved model fit, suggesting that accounting for random effects at the pair level allows to reduce substantial biases in analyses (Dittrich et al., 2017). However, since in Experiment 1 response coordination did not mediate the interaction between Correspondence and Condition, the JSE under cooperative instructions cannot be interpreted as the mere consequence of the perturbation produced by a dynamic event in the task environment.

### RTs Distribution

In order to evaluate the time course of the JSE across experiments, we performed an ANOVA with Condition (Individual vs. Joint), and Correspondence (non-corresponding vs. corresponding) and Bin (1–4) as within-participants factors. In addition, we included Experiment (Exp. 1 vs. Exp. 2) as between-subjects factor. Results are reported in the **Supplementary Material (SM-1)**. In these analyses we observed significant three-way interactions Bin x Condition x Experiment, F1,<sup>38</sup> = 6.07, p = 0.001, η 2 <sup>p</sup> = 0.14, and Experiment x Condition x Correspondence interaction, F1,<sup>38</sup> = 6.66, p = 0.014, η 2 <sup>p</sup> = 0.15. In order to explore in more detail the three-way interactions we performed two separate ANOVAs for Individual and Joint condition, including Correspondence (non-corresponding vs. corresponding) and Bin (1–4) as within-participant factors, and Experiment (Exp. 1 vs. Exp. 2) as between-subjects factor.

Individual condition. The ANOVA revealed a main effect of Bin, F1,<sup>38</sup> = 452.35, p < 0.001, η 2 <sup>p</sup> = 0.92, and a main effect of Correspondence, F1,<sup>38</sup> = 4.86, p = 0.034, η 2 <sup>p</sup> = 0.11. No main effect or significant interaction with Experiment were found, all Fs < 1.

Joint condition. The ANOVA revealed a main effect of Bin, F1,<sup>38</sup> = 269.29, p < 0.001, η 2 <sup>p</sup> = 0.88, a main effect of Correspondence, F1,<sup>38</sup> = 21.26, p < 0.001, η 2 <sup>p</sup> = 0.36. The main effect of Experiment was significant, F1,<sup>38</sup> = 12.71, p = 0.001, η 2 <sup>p</sup> = 0.25, indicating that participants performed faster under competitive (M = 305 ms, SE = 7.84 ms) than cooperative instructions (M = 344 ms, SE = 7.84 ms). No main effect or significant interaction with Experiment were found, all ps > 0.08.

#### Trial-by-Trial Modulation and Transition Effects

In order to compare trial-by-trial modulations across experiments, we performed an ANOVA with Condition (Individual vs. Joint), Trial Transition (n−1 Go/ n go vs. n−1 Nogo/ n go), Trial n−1 Correspondence (noncorresponding vs. corresponding), and Trial n Correspondence (non-corresponding vs. corresponding) as within-participant factors. Also for this analysis, Experiment (Exp. 1 vs. Exp. 2) was included as between-subject factor. Results are reported in **Supplementary Material (SM-2)**. In these analyses we observed a marginally significant Condition x Trial n Correspondence x Experiment interaction, F1,<sup>38</sup> = 4.21, p = 0.047, η 2 <sup>p</sup> = 0.10. In order to explore in more detail the three-way interaction, we performed two separate ANOVAs for Individual and Joint condition, including Trial Transition (n−1 Go/ n go vs. n−1 Nogo/ n go), Trial n−1 Correspondence (noncorresponding vs. corresponding), and Trial n Correspondence (non-corresponding vs. corresponding) as within-participant factors, and Experiment (Exp. 1 vs. Exp. 2) as between-subjects factor.

Individual condition. The ANOVA showed a main effect of Trial n−1 Correspondence, F1,<sup>38</sup> = 5.26, p = 0.027, η 2 <sup>p</sup> = 0.12, and a main effect of Trial n Correspondence, F1,<sup>38</sup> = 6.07, p = 0.018, η 2 <sup>p</sup> = 0.14. The interaction between Trial n Correspondence and Trial n−1 Correspondence was

also significant, F1,<sup>38</sup> = 45.68, p < 0.001, η 2 <sup>p</sup> = 0.55, as well the three way interaction with Trial Transition, F1,<sup>38</sup> = 49.70, p < 0.001, η 2 <sup>p</sup> = 0.57. Planned comparison showed that trial-by-trial modulations for Nogo/go transitions with a significant 22-ms effect following a corresponding n−1 trial, t<sup>39</sup> = 7.77, pBonferroni−corrected < 0.001, d = 1.23, and a reversed 13-ms effect following a non-corresponding n−1 trial, t<sup>39</sup> = 4.57, pBonferroni−corrected < 0.001, d = 0.72. No trial-by-trial modulations occurred for Go/go transitions, all ps > 0.08. No other main effects or interaction were significant, all ps > 0.14. No main effect or significant interaction with Experiment were found, all ps > 0.17.

Joint Condition. The ANOVA showed a main effect of Trial n Correspondence, F1,<sup>38</sup> = 18.61, p < 0.001, η 2 <sup>p</sup> = 0.33. The interaction between Trial n Correspondence and Trial n−1 Correspondence was also significant, F1,<sup>38</sup> = 115.96, p < 0.001, η 2 <sup>p</sup> = 0.75, as well the three way interaction with Trial Transition, F1,<sup>38</sup> = 14.86, p < 0.001, η 2 <sup>p</sup> = 0.28. Planned comparison showed that trial-by-trial modulations for Nogo/go transitions with a significant 23-ms effect following a corresponding n−1 trial, t<sup>39</sup> = 7.49, pBonferroni−corrected < 0.001, d = 1.18, and a reversed 9-ms effect following a noncorresponding n−1 trial, t<sup>39</sup> = 3.49, pBonferroni−corrected = 0.001, d = 0.55. When trial transition was Go/go, a significant 10-ms effect occurred following a corresponding n−1 trial, t<sup>39</sup> = 3.87, pBonferroni−corrected = 0.02, d = 0.61, and a 2-ms null effect following a non-corresponding n−1 trial, t<sup>19</sup> < 1. The main effect of Experiment was significant, F1,<sup>38</sup> = 12.65, p = 0.001, η 2 <sup>p</sup> = 0.25, indicating that participants performed faster under competitive (M = 304 ms, SE = 7.80 ms) than cooperative instructions (M = 344 ms, SE = 7.80 ms). No significant interactions with Experiment were found, all ps > 0.10.

#### Response Coordination

In order to assess the effect of goal interdependency (and thus shared representation) in modulating the percentage of response coordination across the two experiments, we conducted an additional analysis to compare data pattern from the two experiments. The proportion of correlated activity was entered into an ANOVA with Condition (Individual vs. Joint) as within-subjects factor and Experiment as between-subjects factor. The analysis revealed a main effect of Condition, F1,<sup>18</sup> = 11.23, p = 0.004, η 2 <sup>p</sup> = 0.38, and a significant twoway interaction with Experiment, F1,<sup>18</sup> = 6.43, p = 0.021, η 2 <sup>p</sup> = 0.26. Two separate one-way ANOVAs indicated that for the Joint condition the percentage of response coordination was higher in Experiment 1 (15.3%) than in Experiment 2 (13.1%), F = 8.74, p = 0.008, d = 1.32. No difference emerged for the Individual condition between the two experiments, F < 1 (**Figure 4**).

The comparison of the two experiments showed that when participants performed the task alone, a comparable response coordination between pseudo pairs occurred, independently from the experiment at which they were assigned. In contrast, when they were performing the task side-by-side of another person, response coordination in joint

condition increased (relatively to the individual condition) only under cooperation. This latter result indicates that the increased response coordination in the joint Simon Task cannot be explained by the perturbation of temporal and spatial features induced by presence of a second agent performing a task. Our results suggest that positive goal interdependency (shared representation) may be a necessary condition for response coordination and inter-agent response dynamics.

# GENERAL DISCUSSION

The present study aimed at examining the role of temporally evolving coupling and inter-agent response dynamics during joint tasks. In two experiments, we investigated the contribution of interpersonal coordination to the emergence of JSE, as an index of shared representations. In both experiments, participants performed a go/no-go Simon task alone and together with another agent in two consecutive sessions. Across experiments, we manipulated goal interdependency by administering cooperative or competitive instructions. In Experiment 1, we instructed participants to cooperate during the social task, while in Experiment 2 participants were required to compete against each other. We examined JSE and response coordination between co-agents as a function of instructed competition or cooperation.

# Shared Representations and Joint Simon Task

Results from the analysis of mean RTs confirmed that when participants performed the go/no-go Simon task alongside another agent, social presence modulated the SE only for the group instructed to cooperate (Experiment 1). On the contrary, no influence of the presence of the co-agent was observed in the group who received competitive instructions (Experiment 2), as indicated by the lack of significant interaction between correspondence and condition (Individual vs. Joint). Our results replicate previous studies showing that when coagents' goals are mutually exclusive, the presence of another agent does not affect performance (Iani et al., 2011, 2014). Our results extend previous studies in different ways. First,

we directly compared social and individual context within participants. Indeed, previous studies investigating cooperation and competition in the joint Simon task did not include an individual condition as a baseline (Ruys and Aarts, 2010; Iani et al., 2011; Ruissen and de Bruijn, 2016). This allowed us to compare the effect of goal interdependency in the social environment with a baseline performance for each participant in the individual go/no-go task. Sellaro et al. (2013) showed that when the SE emerges in a go/no-go task due to nonsocial factors (i.e., the extended practice with spatially compatible actions, as typing on a keyboard), then the JSE vanishes in the subsequent task. However, this was not our case: although in the individual condition participants had an empty chair next to them and were aware of the presence of another person in the neighboring room, the magnitude of the SE in the individual condition was comparable across experiments. Results from the distributional analysis do not show any differences in the time course of the SE in the Joint and Individual conditions across the two experiments, as indicated by the lack of four-way interaction. This result is in line with evidence reported by Liepelt et al. (2011) showing that the time course of the JSE is stable across conditions and despite the faster performance under competitive instructions. Moreover, it should be noted that the lack of differences across experiments in the time course suggests that the non-significant JSE under competitive or negative relationships cannot be attributed to faster RTs (Hommel et al., 2009). However, further research is needed to address the relationship between the JSE and response speed. Thus, we can argue that the differential effect of social presence on the JSE between the two social conditions (competitive vs. cooperative) can be explained mainly by the difference in goal interdependency. Second, we investigated the relationship between response coordination and the JSE under cooperative and competitive instructions. Our results showed that response coordination in joint Simon task increased (relative to the individual condition) only under cooperative instructions. Interestingly, when participants were in competition, response coordination of the co-agents was equal to when they performed the task alone. This result suggests that the positive or negative interdependency between co-agents' goals is reflected not only in the representation of the task (i.e., Iani et al., 2011, 2014) but it also at the sensorimotor timing level. In line with results reported by Liepelt et al. (2011) RTs distribution was comparable between conditions (Individual vs. Joint) and across experiments, suggesting that cooperative and competitive instructions do not affect the temporal dynamics of the JSE. Trial-by-trial effects were comparable across experiments with a significant effect following a corresponding trial, and a non-significant or even reversed after a not corresponding one (Liepelt et al., 2011; Yamaguchi et al., 2018; but see also Ciardo et al., 2018 for results using social cues). Sequential modulations are thought to represent reactive adjustments of control settings (an increase of attention weights on relevant information after experiencing conflict in a non-corresponding trial; e.g., Iani et al., 2009), priming of an earlier stimulus episode (Hommel et al., 2004), or both. Interestingly, we found comparable trial-by-trial modulation in the Individual and Joint condition when the preceding

trial was a Nogo trial, i.e., when no response occurred in the Individual condition or the trial required a response of the co-agent in the Joint task. Again, no differences emerged across experiments suggesting that during both cooperative and competitive joint tasks participants represented the coagents' S-R associations. Finally, we used punishment avoidance instead of reward to manipulate goal interdependency between co-agents. Our results generalized previous findings showing that the JSE in cooperative vs. competitive condition can be modulated by the need to avoid punishment. Taken together, our results confirm that JSE is elicited by goal sharing between coagents (Iani et al., 2011) and not by the mere fact that during social task co-agents attend to each other (Ruys and Aarts, 2010).

### Interpersonal Coordination

In line with Malone et al. (2014), work the analysis of temporally emergent response coordination revealed that when participants were instructed to cooperate, the percentage of coordination between co-agents' responses was higher relative to the individual condition (Experiment 1). Interestingly, this was not true when participants were in competition, as indicated by the lack of difference in the percentage of response coordination across conditions in Experiment 2. The lack of increase of coordination occurred despite the overall speeding up of responses in the joint condition, which is in line with data showing that when performing a Simon task alongside another person, response speed does not correlate with asynchrony between co-agents' responses (Vesper et al., 2011).

It can be argued that the percentage of response coordination in our study is smaller in size than those reported in previous studies. Indeed, in two different studies, Malone et al. (2013, 2014) reported on average 24 and 33% of response coordination between co-agents for the individual and joint condition, respectively. In our study, we found on average 13% of response coordination for the individual condition and the 15% of correlation between co-agent's responses in the cooperative condition (Experiment 1, joint condition). The discrepancy between our results and those reported by Malone et al.'s (2013, 2014) could be explained by the fact that in their work, the joint task included 1100 trials. Our joint task comprised only 384 trials. Thus, it is possible that in our study participants did not reach the same amount of response correlation given the lower number of trials, which consisted in a lower number of samples available to create an accurate model of the co-agent's behavior. However, the choice to include a lower number of trials was motivated by the need to avoid transfer of learning effects typical in spatial compatibility tasks (e.g., Lugli et al., 2013), since we manipulated the social presence (Individual vs. Joint) within participants. In addition, in computing RT time-series, we considered all the data points collected during the task, while Malone and colleagues analyzed only the last 512 responses. By analyzing all the collected responses, we also considered the initial phase of the task during which participants could not have yet coordinated. Future studies should explore in more

depth the lack of response coordination in the Joint Simon task under competitive instructions. For instance, by analyzing the structure of RT variability in order to test if during a competitive task responses of co-agents are characterized by nested patterns of variability or by random fluctuations (Malone et al., 2014).

Interestingly, results of Experiment 2 showed that response coordination was not modulated by the competitive social context. By comparing the two experiments, we showed that the lack of difference across conditions in Experiment 2 could not be explained by a difference in the two groups. Indeed, we reported that the percentage of response correlation did not differ across experiments in the Individual condition (12.7% for both experiments). Our results extend Malone et al. (2013, 2014) evidence by showing that when co-agents are in competition, response coordination in the joint Simon Task is comparable to when they perform the go/no-go task alone, despite the dynamic nature of the tasks and their constraints are different. The increase of correlation between co-agents' responses reported in Experiment 1 cannot be interpreted only as the consequence of the natural tendency to adapt the timing of movements to timing of external events (e.g., Marsh et al., 2009). Indeed, if this was the case, the same pattern should have emerged in Experiment 2. On the contrary, the comparison between the two experiments indicates that in Experiment 2 response coordination was not affected by the mere presence of a competitor acting in the shared setting (i.e., both the screen and the keyboard were shared), suggesting that response coordination emerges when the framework of the task allows co-agents to become an integrated perception-action system.

# Social Context and Coordination – The Case of Cooperation and Competition

Our results suggest that emergence of shared representations is a necessary condition for temporally evolving response coordination but not necessarily a sufficient one. Accordingly, by introducing response coordination as a random effect in mean RTs models, we showed that response coordination did not affect the JSE in the cooperative joint task. Such result is in line with a recent study by Dittrich et al. (2017), showing that including random effect at the pair level increases model fitting and reduces potential biases driven by differences across pairs. The current findings give hints about the relation between shared representations or self-other integration indexed by JSE, sensorimotor coordination, and goal sharing and how these mechanisms are orchestrated to reach efficient joint action. Specifically, our results suggest that response coordination between co-agents does not account for JSE. However, they also highlight the importance of taking into account response coupling between co-agents when investigating the nature of the JSE (Dittrich et al., 2017). The percentage of coupling and the JSE might be considered as two independent elements supporting effective joint action. Keller et al. (2016) proposed that joint action outcome results from the integration and segregation of internal models of the self and of others. The authors proposed that during joint action, although goals are represented as shared, in order to guide the joint performance, a distinction between self and other internal model is preserved to allow each co-agent to keep control over their action planning and execution. This facilitates co-agents to anticipate, attend and adapt to each other in real time, resulting into a precise and flexible interpersonal coordination (c.f. Keller et al., 2016). It is plausible that in our study, when co-agents' goals were positively related, shared goal representation may have promoted the integration of self and other models. As a result, in the joint action model both alternative of response were represented. Selfother integration allowed co-agents to attend and adapt their performance to each other's sensorimotor timing, resulting in response coordination. In contrast, when the co-agents were in competition, the lack of shared representation may have favored self-other segregation. Thus, the resulting joint model did not include co-agent's alternative of response. Therefore co-agents did not consider other's behavior, but focused on stabilizing their own performance (i.e., increasing the response speed) in order to suppress the sensorimotor interference generated by co-agent's action timing. Then, as a final result no response coordination emerged.

Our results are limited to joint tasks based on discrete nonrhythmic actions. However, they highlight the importance of competitive interactions in the context of understanding how different mechanisms support joint action. If we suppose that goal interdependency between co-agents may vary on a dipole between positive and negative relation, then we can assume that when cooperation is not explicitly requested, the shared nature of the task (i.e., context, setting, and space) prompts the integration of self and other models into a joint model. As a result, when others do not explicitly interfere with our goals, by default we perceive positive interdependence with them (Ruys and Aarts, 2010; Iani et al., 2011), and coordinate with them at the sensorimotor level (Malone et al., 2013, 2014) even if their actions have no direct consequences on our next action, as in the case of the joint Go/nogo Simon task. Future studies should address the relation between coordination and the emergence of the JSE by de-personalizing the goal of the task. For instance, synchronization with external and variable events generated by human or artificial agents can be examined (e.g., a Humanoid robot, see Wykowska et al., 2016; Wiese et al., 2017), as perceived natural/intentional vs. artificial agency may moderate self-other integration in the Joint Simon task. In addition, future studies need to address the role of visual access to co-agent's action for the emergence of response coordination during joint tasks.

To conclude, the present study was designed to investigate the contribution of interpersonal entrainment to the emergence of shared representations by comparing response coordination in a joint Simon task during cooperation and competition. Our results show that emerging coordination increases during joint action only if co-agents' goals are shared, but not when co-agents' goals are mutually exclusive. The results show that interpersonal coordination requires the emergence of shared representations or self-other integration, indexed by JSE. Therefore, in joint action shared representations seem to be

a necessary condition for interpersonal coordination, but not sufficient one.

# AUTHOR CONTRIBUTIONS

FC conceived, designed and performed the study, analyzed the data, discussed and interpreted the results, and wrote the manuscript. AW conceived and designed the study, discussed and interpreted the results, and wrote the manuscript. All authors reviewed the manuscript.

# FUNDING

This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020

# REFERENCES

research and innovation program (Grant Awarded to AW, Titled "InStance: Intentional Stance for Social Attunement." Grant Agreement No. 715058).

# ACKNOWLEDGMENTS

We thank Claudio Campus and Kyveli Kompatsiari for helping with data analysis.

# SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg. 2018.01919/full#supplementary-material

The data related to this study can be accessed online at https://doi.org/10.5281/zenodo.1421280.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Ciardo and Wykowska. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# The Social Situation Affects How We Process Feedback About Our Actions

Artur Czeszumski <sup>1</sup> \*, Benedikt V. Ehinger <sup>1</sup> , Basil Wahn1,2 and Peter König1,3

1 Institute of Cognitive Science, Universität Osnabrück, Osnabrück, Germany, <sup>2</sup> Department of Psychology, University of British Columbia, Vancouver, BC, Canada, <sup>3</sup> Institut für Neurophysiologie und Pathophysiologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany

Humans achieve their goals in joint action tasks either by cooperation or competition. In the present study, we investigated the neural processes underpinning error and monetary rewards processing in such cooperative and competitive situations. We used electroencephalography (EEG) and analyzed event-related potentials (ERPs) triggered by feedback in both social situations. 26 dyads performed a joint four-alternative forced choice (4AFC) visual task either cooperatively or competitively. At the end of each trial, participants received performance feedback about their individual and joint errors and accompanying monetary rewards. Furthermore, the outcome, i.e., resulting positive, negative, or neutral rewards, was dependent on the pay-off matrix, defining the social situation either as cooperative or competitive. We used linear mixed effects models to analyze the feedback-related-negativity (FRN) and used the Threshold-free cluster enhancement (TFCE) method to explore activations of all electrodes and times. We found main effects of the outcome and social situation, but no interaction at mid-line frontal electrodes. The FRN was more negative for losses than wins in both social situations. However, the FRN amplitudes differed between social situations. Moreover, we compared monetary with neutral outcomes in both social situations. Our exploratory TFCE analysis revealed that processing of feedback differs between cooperative and competitive situations at right temporo-parietal electrodes where the cooperative situation elicited more positive amplitudes. Further, the differences induced by the social situations were stronger in participants with higher scores on a perspective taking test. In sum, our results replicate previous studies about the FRN and extend them by comparing neurophysiological responses to positive and negative outcomes in a task that simultaneously engages two participants in competitive and cooperative situations.

Keywords: social cognition, joint action, EEG, feedback related negativity, cooperation, competition

# 1. INTRODUCTION