Social competition shapes motor response execution and inhibition in a Go/NoGo task: Behavioral, signal-detection, and ERP evidence

Social competition shapes motor response execution and inhibition in a Go/NoGo task: Behavioral, signal-detection, and ERP evidence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Social competition shapes motor response execution and inhibition in a Go/NoGo task: Behavioral, signal-detection, and ERP evidence Yansong Li, Cuihong Liu, Guoliang Chen, Bing Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8892090/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The present work examined how social competition modulates motor response execution and inhibition by integrating behavioral, signal-detection, and ERP evidence. Forty-three participants completed a Go/NoGo (GNG) task under social-competition and control conditions while EEG was recorded. Behaviorally, relative to control, social competition shortened Go-trial reaction times (RTs) without affecting Go-trial accuracy. Moreover, such Go-RT speeding correlated negatively with competition-related increases in the rated importance of response speed. Social competition also increased NoGo commission errors. Signal-detection analyses indicated reduced Go/NoGo discriminability (lower d′ ) and a shift toward a more liberal criterion ( C ). Neurally, in addition to the NoGo effects on the N2/P3, social competition enhanced both Go-P3 and NoGo-P3 amplitudes. Competition-related increases in Go-P3 amplitude, maximal over centro-parietal/parietal sites, correlated negatively with competition-related reductions in Go RTs and statistically mediated the effect of social competition on Go RTs. Competition-related increases in NoGo-P3 amplitude, maximal over fronto-central/central sites, correlated positively with competition-related increases in commission errors and negatively correlated with competition-related reductions in d′ , and statistically mediated the effects of social competition on both outcomes. These findings shed light on how social competition modulates motor response execution and inhibition, preferentially affecting P3-indexed, time-resolved control processes supporting speeded response implementation and diminished withholding. Biological sciences/Neuroscience/Cognitive neuroscience/Cognitive control Biological sciences/Psychology/Human behaviour Biological sciences/Neuroscience/Social behaviour Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction One’s ability to flexibly adjust ongoing motor behavior in response to changing environmental demands is essential for adaptive functioning in daily life 1 – 4 . For example, when driving, a green light typically elicits a habitual tendency to press the accelerator. Yet if a pedestrian suddenly steps into the road, this prepotent impulse must be suppressed and replaced with braking. This everyday need to execute a dominant response while occasionally withholding it when circumstances require restraint is commonly studied using the Go/NoGo (GNG) task. In this paradigm, participants respond to frequent Go stimuli (e.g., via a button press), thereby establishing a prepotent motor response tendency, while withholding responses to infrequent NoGo stimuli that require inhibition of this prepotent motor response. Two event-related potential (ERP) components are most commonly examined in the GNG task: the N2 and the P3. The N2 is a negative-going deflection peaking around 200–300 ms post-stimulus, maximal over fronto-central regions, and typically larger on NoGo than on Go trials. Although early accounts interpreted the enhanced NoGo-N2 as reflecting inhibitory processing 5 – 7 , converging evidence increasingly supports an alternative view that it primarily indexes conflict monitoring 3 , 8 – 11 . The P3 is a positive-going deflection peaking around 300–600 ms post-stimulus that is also enhanced on NoGo compared with Go trials. The NoGo-P3 typically shows a fronto-central/central maximum and has traditionally been interpreted as reflecting either inhibitory processing 12 , 13 or outcome evaluation 14 , 15 . By contrast, the Go-P3 typically shows a centro-parietal/parietal maximum and has been associated with stimulus–response mapping—that is, the translation of perceptual analysis into response preparation 16 , 17 —highlighting its relevance to response-execution stages. An expanding body of work on GNG-related electrophysiological signatures provides a foundation for examining how demands imposed by diverse contexts modulate motor response execution and inhibition at psychophysiological levels. In particular, given that humans are inherently social beings and that social interactions are a key determinant of everyday behavior, an important topic of investigation is how social contexts shape the cognitive and neural mechanisms supporting motor response execution and inhibition as indexed by the GNG task 18 . Much of the existing evidence comes from joint-action studies of co-acting, in which complementary task demands are distributed across two co-actors—effectively, each co-actor implements a complementary GNG task 19 , 20 . Across joint versions of the Simon task 21 – 24 , the Flanker task 25 , and the Stroop task 26 , 27 , Go-trial performance is broadly comparable between joint-action and individual GNG settings when compatibility is not taken into account, suggesting that motor response execution is largely unchanged when acting alongside a co-actor. At the neural level, Go-P3 amplitudes are likewise comparable 23 , 28 , with the few studies assessing Go-N2 also reporting no reliable differences 26 . By contrast, NoGo-P3 amplitudes—on trials where the participant withholds a response while the co-actor responds—are larger in joint-action than in individual settings 23 , 26 , 28 , 29 , a pattern commonly interpreted as reflecting increased inhibitory demands; However, available evidence has not revealed reliable NoGo-N2 differences 26 , 28 . Because joint-action tasks instantiate coordination-based interdependence via complementary role division, the joint-action evidence summarized above primarily speaks to how coordination demands shape the cognitive and neural mechanisms supporting action execution and inhibition. Yet this coordination-based interdependence—i.e., mutually engaged interaction through complementary role division—captures only one facet of social interdependence. Social interdependence can also arise when two individuals are embedded in the same task context and pursue the same objective, but their outcome interests are antagonistic rather than complementary. A prototypical example is social competition, in which individuals perform the same task under a goal structure where one person’s gain is contingently linked to the other’s loss 30 . Because competitive demands differ from those in complementary joint action, competition may be expected to modulate the cognitive and neural mechanisms supporting motor response execution and inhibition in a qualitatively different manner. Consistent with this possibility, our recent behavioral study began to address this question 31 . Participants were randomly assigned to a competition or control group and completed a standard GNG task. Relative to controls, the competition group responded faster on Go trials without a reliable cost to Go accuracy, but committed more errors on NoGo trials. Signal detection analyses further showed a more liberal response criterion (i.e., a stronger overall tendency to respond) and reduced perceptual sensitivity in discriminating NoGo from Go stimuli. Together, these findings suggest that social competition induces a response mode characterized by speeded prepotent responding without a cost to accuracy accompanied by less efficient response inhibition, consistent with an overall bias toward responding and diminished Go/NoGo discriminability. Importantly, our prior behavioral work went beyond aggregate performance measures by adopting a signal-detection approach. Building on this behavioral characterization, further work is needed to obtain a more comprehensive understanding of competition-related modulation of action control. One useful approach is therefore to complement behavioral and signal-detection measures with indices that offer higher temporal resolution. In this regard, ERPs are well suited to track the time course of competition effects and, when interpreted in light of the established functional significance of GNG-related components, to help pinpoint the processes that underlie these effects. A related issue concerns the behavioral strategy adopted under social competition, which also warrants further clarification. In our previous work, the pattern of speeded prepotent responding without a reliable accuracy cost suggested that participants may strategically shift toward prioritizing response speed while maintaining accuracy to outperform an opponent. However, more direct evidence is needed to substantiate this strategic account. The present study was designed to address these issues by integrating behavioral, signal-detection, and ERP analyses. Using a within-subject design, participants completed the GNG task both under social competition and in a control (alone) condition while EEG was recorded continuously (Fig. 1 ). Following each condition, they rated perceived competition as a manipulation check and separately rated the importance of response speed and response accuracy to index strategic priorities. Behaviorally, we expected a pattern similar to our recent study 31 . Specifically, relative to the control condition, we expected social competition to speed Go-trial responding (i.e., faster prepotent responding) without a reliable cost to Go-trial accuracy. This competition-related shift toward prioritizing speed without sacrificing accuracy should be further supported by participants’ self-reported speed/accuracy importance ratings. At the same time, this speed-prioritization shift was expected to be accompanied by increased commission errors on NoGo trials, potentially reflecting reduced perceptual sensitivity in discriminating Go from NoGo stimuli and/or a shift toward a more liberal response criterion. At the neural level, our predictions were informed by prior ERP studies using a cued GNG task that manipulated speed–accuracy settings either by contrasting fast versus slow responders across participants 12 or by using explicit task instructions that that varied the emphasis on response speed and accuracy 16 , 32 . Across this literature, the N2 component showed reduced sensitivity when speed and accuracy were relatively balanced compared to when speed was prioritized at the expense of accuracy 32 , and it showed no reliable modulation when speed was emhasized without an explicit accuracy trade-off 12 . By contrast, the P3 component is more reliably modulated by speed–accuracy manipulations 12 , 16 . Based on our expected behavioral pattern under social competition, we predicted weak—if any—competition effects on the N2, but robust competition-related modulation of the P3 component. Results Behavioral results Manipulation check and other self-report measures Paired-samples t tests showed that participants reported significantly higher perceived competition in the social competition condition (M = 5.30, SE = 0.18) than in the control condition (M = 2.88, SE = 0.25) ( t (42) = 9.21, p < .001, d = 1.41), confirming the effectiveness of the manipulation. Participants also rated response speed as more important under social competition (M = 6.07, SE = 0.21) than under control (M = 5.44, SE = 0.23) ( t (42) = 2.34, p = .02, d = 0.36) ( Figure 2A ). By contrast, perceived importance of response accuracy did not differ between conditions (social competition: M = 6.56, SE = 0.11; control: M = 6.49, SE = 0.11) (t(42) = 0.68, p = .50, d = 0.10) ( Figure 2A ). Effects of social competition on GNG task performance Go trials The ACC on Go trials did not differ between the social competition (M = 0.98, SE = 0.003) and control conditions (M = 0.99, SE = 0.002) ( t (42) = −1.64, p = .11, d = −0.25) ( Figure 2C ). In contrast, RTs on Go trials were significantly shorter under social competition (M = 360.37 ms, SE = 5.12) than under control (M = 367.98 ms, SE = 5.92) ( t (42) = −2.80, p = .008, d = −0.43) ( Figure 2C ). Moreover, competition-related changes in Go RTs (computed as the difference between the social competition and control conditions) were negatively correlated with changes in the rated importance of response speed ( r = −0.47, p = .002) ( Figure 2D ). NoGo trials A significant difference in commission errors on NoGo trials was observed between the social competition and control conditions ( t (42) = 2.34, p = .02, d = 0.36), with the social competition condition exhibiting a higher error rate (M = 0.02, SE = 0.003) compared to the control condition (M = 0.01, SE = 0.002) ( Figure 2B ). Effects of social competition on signal detection indices With respect to perceptual sensitivity ( d′ ), a paired-samples t test showed a significant effect of social context ( t (42) = −2.38, p = .02, d = −0.36), with lower d′ in the social competition condition (M = 4.99, SE = 0.06) than in the control condition (M = 5.10, SE = 0.04) ( Figure 2E ). For response bias ( C ), the social-context difference was marginal ( t (42) = −1.86, p = .07, d = −0.28), with a more negative C under social competition (M = −0.32, SE = 0.02) than under control condition (M = −0.27, SE = 0.02) ( Figure 2E ). ERP Results N2 Analysis of N2 peak amplitudes revealed a significant main effect of trial type ( F (1, 41) = 49.65, p < .001, η 2 p = 0.55), with more negative N2 amplitudes for NoGo trials (M = −5.93, SE = 0.74) than for Go trials (M = −2.74, SE = 0.70) ( Figure 3A,B ). The main effect of social context was not significant ( F (1, 41) = 0.37, p = .55, η 2 p = 0.01) ( Figure 3A,C ). A significant interaction between social context and electrode site was observed ( F (1, 41) = 5.49, p = .02, η 2 p = .12). However, resolving this interaction showed that social context did not reliably modulate N2 amplitudes at either Fz (social competition: M= -4.10, SE = 0.68; control: M = -4.38, SE = 0.69; p = 0.29) or FCz (social competition: M = -4.41, SE = 0.72; control: M = -4.46, SE = 0.72; p = .88). Likewise, electrode site did not significantly affect N2 amplitudes within either the social competition condition (Fz: M= -4.10, SE = 0.68; FCz: M = -4.41, SE = 0.72; p = .10) or the control condition (Fz: M = -4.38, SE = 0.69; FCz: M = -4.46, SE = 0.72; p = .69). No other significant effects were observed (all ps > .28). Analysis of N2 latency showed a significant main effect of trial type ( F (1, 41) = 27.58, p < .001, η 2 p = .40), with shorter latencies on NoGo trials (M = 248.57 ms, SE = 1.73) than on Go trials (M = 255.99 ms, SE = 1.79). Neither the main effect of social context ( F (1, 41) = 0.71, p = .41, η 2 p = 0.02), nor the main effect of electrode site ( F (1, 41) = 0.72, p = .40, η 2 p = 0.02), was significant. No interactions were significant (all ps > .07). P3 Our ANOVA snalysis of P3 peak amplitudes revealed a significant main effect of trial type ( F (1, 41) = 48.03, p < .001, η 2 p = 0.54), with larger amplitudes for NoGo trials (M = 13.68, SE = 0.73) than for Go trials (M = 10.11, SE = 0.68) ( Figure 4A,C ). The main effect of social context was also significant ( F (1, 41) = 17.95, p < .001, η 2 p = 0.30), with larger P3 amplitudes in the social competition condition (M = 12.54, SE = 0.68) than in the control condition (M = 11.26, SE = 0.67) ( Figure 4A,B ). In addition, a significant interaction between trial type and electrode site was observed ( F (3, 123) = 86.42, p < .001, η 2 p = 0.68). Follow-up simple-effects analyses showed that on Go trials, P3 amplitudes were larger at CPz (M = 10.85, SE = 0.71) and Pz (M = 11.12, SE = 0.69) than at Cz (M = 9.67, SE = 0.72; ps 0.31) and FCz (M = 8.81, SE = 0.70; ps 0.55), and Cz was larger than FCz ( p < .001, d = 0.24). By contrast, on NoGo trials, P3 amplitudes were larger at FCz (M = 14.51, SE = 0.85) and Cz (M = 14.39, SE = 0.81) than at CPz (M = 13.47, SE = 0.72; ps 0.24) and Pz (M = 12.37, SE = 0.68; ps 0.54). In addition, CPz amplitudes exceeded Pz ( p .18). Given the significant social-context effect on P3 amplitudes, follow-up correlations indicated that social competition-related changes in Go RTs (computed as social competition minus control) were negatively associated with changes in Go-P3 amplitude ( r = −0.36, p = .02) ( Figure 4D ). Meanwhile, social competition-related changes in NoGo-P3 amplitude were positively correlated with changes in NoGo commission errors ( r = 0.39, p = .012) and negatively correlated with changes in sensitivity ( d′ ) ( r = −0.37, p = .015) (all computed as social competition minus control) ( Figure 4F,H ). Analysis of P3 latency revealed a significant main effect of trial type ( F (1, 41) = 27.76, p < .001, η 2 p = 0.40), with longer latencies on NoGo trials (M = 392.25 ms, SE = 4.92) than on Go trials (M = 365.72 ms, SE = 4.27). The main effect of social context was not significant ( F (1, 41) = 0.54, p = .47, η 2 p = 0.01). A main effect of electrode site emerged ( F (3, 123) = 19.84, p < .001, η 2 p = 0.33), qualified by a significant interaction between trial type × electrode site ( F (3, 123) = 13.36, p < .001, η 2 p = 0.25). Follow-up simple-effects analyses showed that on Go trials, P3 latency was longer at FCz (M = 390.98 ms, SE = 6.40) than at Cz (M = 370.74 ms, SE = 5.76; p < .001, d = 0.44), CPz (M = 351.71 ms, SE = 4.74; p < .001, d = 0.85), and Pz (M = 349.44 ms, SE = 4.39; p < .001, d = 0.90); Cz was also longer than CPz ( p < .001, d = 0.41) and Pz ( p .22). No other effects were significant (all ps > .37). Mediation of social competition effects on behavioral outcomes via ERP indices Given that social competition significantly modulated both Go-P3 amplitude and Go RTs, we conducted a within-subject mediation analysis to test whether Go-P3 amplitude (averaged across FCz, Cz, CPz, and Pz) mediated the competition effect on Go RTs. Social competition (vs. control) predicted larger Go-P3 amplitudes (β = 1.00, SE = 0.27, p < .001), which in turn predicted shorter Go RTs (β = −3.51, SE = 1.49, p = .02), yielding a significant indirect effect (β = −3.50, SE = 2.12, 95% CI [−8.84, −0.42]) ( Figure 4E ). Similarly, because social competition also modulated NoGo-P3 amplitude as well as NoGo commission errors and d′ , we ran two within-subject mediation models testing whether NoGo-P3 amplitude (averaged across FCz, Cz, CPz, and Pz) mediated competition effects on these outcomes. Social competition predicted larger NoGo-P3 amplitudes (β = 1.55, SE = 0.44, p = .001), which predicted higher commission error rates (β = 0.002, SE = 0.001, p = .01); the indirect effect was significant (β = 0.003, SE = 0.001, 95% CI [0.001, 0.007]) ( Figure 4G ). Social competition again predicted larger NoGo-P3 amplitudes (β = 1.55, SE = 0.44, p = .001), which in turn predicted reduced perceptual sensitivity ( d′ ) (β = −0.04, SE = 0.02, p = .02), yielding a significant indirect effect on d′ (β = −0.06, SE = 0.04, 95% CI [−0.15, −0.02]) ( Figure 4I ). Discussion Extending our prior work, we combined ERP recording with a GNG task to characterize how social competition modulates prepotent motor response execution and inhibition at both behavioral and neural levels. At the behavioral level, we replicated the key pattern observed in our prior study 31 . At the neural level, we observed the well-established NoGo-N2 effect, maximal over frontal and fronto-central sites, such that NoGo-N2 amplitude was larger than Go-N2 amplitude. We also observed the widely reported NoGo-P3 effect: NoGo-P3 amplitude was larger than Go-P3 amplitude, and the NoGo-P3 exhibited a fronto-central/central maximum (i.e., a more anterior scalp distribution), whereas the Go-P3 exhibited a centro-parietal/parietal maximum. As expected, we did not observe any significant modulation by social competition of the NoGo (relative to Go) effect on the N2 component. By contrast, social competition reliably modulated P3 activity, suggesting that social competition primarily influences later, implementation-related processing stages rather than early N2-indexed conflict monitoring. In what follows, we integrate the behavioral, signal-detection, and ERP evidence to discuss how these time-resolved neural findings sharpen our understanding of competition-related modulation of prepotent motor response execution and inhibition. Effects of social competition on prepotent motor response execution Behaviorally, we replicated our previous findings: relative to the control condition, social competition shortened Go-trial RTs without affecting Go-trial accuracy. The absence of an accuracy cost alongside faster Go responding is consistent with our proposition that social competition promotes a strategic shift toward prioritizing response speed without a cost to accuracy, as we argued in our prior work 31 . The present study provides more direct support for this account by incorporating self-report measures showing that participants rated response speed as more important under social competition than under control, whereas the perceived importance of response accuracy did not differ between social contexts. Moreover, individuals who placed greater emphasis on response speed under social competition also exhibited greater speeding of the prepotent Go response, as indicated by the negative correlation between competition-related increases in the rated importance of response speed and competition-related reductions in Go RTs. Taken together, the self-report and correlational evidence lends additional support to our proposition that social competition shifts participants’ response strategy toward speed prioritization without an observable decrement in Go-trial accuracy. Beyond the behavioral-level evidence that broadens our understanding of how social competition modulates motor response execution, our ERP results elucidate the neural manifestations of competition-related speeding of the prepotent Go response, thereby sharpening the interpretation derived from behavioral findings. We found that the centro-parietal/parietal–maximal Go-P3 was larger under social competition than under control. Moreover, participants showing a larger Go-P3 enhancement also exhibited greater speeding of the prepotent Go response, as indicated by the negative correlation between competition-related increases in Go-P3 amplitude and competition-related reductions in Go RTs. Importantly, the mediation analysis further indicated that the effect of social competition (vs. control) on Go RTs was statistically accounted for by competition-related increases in Go-P3 amplitude: social competition predicted larger Go-P3 amplitudes, which in turn predicted shorter Go RTs. This pattern can be interpreted within a framework that integrates the stimulus–response (S–R) mapping account of the centro-parietal/parietal–maximal Go-P3 17,33-36 with a broader executive-control model 37,38 , thereby helping to specify the processes through which social competition speeds execution of the prepotent Go response. Accordingly, Go performance depends on how efficiently task-relevant features of Go stimuli (e.g., a left- vs. right-pointing white arrow) are translated into the appropriate response representation (e.g., a left- vs. right-hand keypress), within a broader control configuration that involves energization and task-setting (e.g., selecting task-relevant criteria and operations). In light of the enhanced Go-P3 under social competition, its association with faster Go responding, and its mediating role in the effect of social competition on Go RTs, these results suggest that competition-related speeding of the prepotent Go response, without an observable decrement in accuracy, may arise from greater energization of this S–R control configuration—thereby enabling more rapid translation of task-relevant stimulus features into the appropriate Go response representation and supporting more efficient motor execution. Effects of social competition on prepotent motor response inhibition Consistent with our recent study 31 , social competition not only sped the prepotent Go response but also impaired response withholding on NoGo trials, as reflected in higher commission error rates relative to the control condition on NoGo trials. Our signal-detection analyses further revealed that diminished withholding of the prepotent Go response under social competition could be attributable to poorer differentiation between Go and NoGo stimuli, as evidenced by decreased d′ , together with a trend toward a more liberal response criterion ( C )—a pattern that replicates the direction of our prior findings. Extending this behavioral and signal-detection evidence, our ERP results further deepen our understanding by revealing the neural manifestations of competition-related changes in prepotent Go-response inhibition. Specifically, we observed that the fronto-central/central–maximal NoGo-P3 component was larger under social competition than under control. This competition-related enhancement of the NoGo-P3 is not only consistent with previous findings showing larger NoGo-P3 amplitudes in fast responders than in slow responders 12,39 , but also extends this literature by demonstrating that larger NoGo-P3 amplitudes can emerge within individuals when response speed is selectively prioritized, whether induced by competitive task demands (as in the present study) or by task instructions that manipulate speed–accuracy settings in prior studies 16 . Notably, because the NoGo-P3 peaked after the mean Go RTs (M = 392.25 ms vs. M = 364.18 ms), this timing is difficult to reconcile with an interpretation of the NoGo-P3 as an index of inhibition 40,41 . Instead, it aligns with the alternative view that the fronto-central/central–maximal NoGo-P3 component is associated with reactive executive-control processes, primarily involving implementation of the alternative non-response set and monitoring, which are reflected behaviorally in NoGo commission errors and perceptual sensitivity ( d′ ), respectively 42 . In line with this account, the competition-related changes in the NoGo-P3 should be linked to competition-related changes in NoGo commission errors and d′ . Our correlational results indeed support this prediction. Specifically, participants showing a larger competition-related enhancement in NoGo-P3 amplitude also exhibited greater increases in commission errors and larger reductions in perceptual sensitivity ( d′ ). Together, these associations further suggest that the NoGo-P3 may serve as a mediating neural index through which social competition relates to these reactive executive-control operations in response to NoGo stimuli, a possibility further supported by our mediation analyses. Mediation analyses indicated that competition-related increases in NoGo-P3 amplitude statistically accounted for the effects of social competition (vs. control) on commission errors and d′ : social competition predicted larger NoGo-P3 amplitudes, which in turn predicted higher commission error rates and lower perceptual sensitivity ( d′ ). Taken together, these ERP findings suggest that under social competition, prioritizing speed degrades Go/NoGo discrimination (lower d′ ) and raises the cost of switching from the prepotent Go response tendency with the alternative non-response set on NoGo trials, thereby increasing uncertainty and the likelihood of inhibitory failures. This heightened demand on monitoring and on implementing the alternative non-response set would be expected to upregulate the NoGo-P3, consistent with its statistical linkage to competition effects on d′ and commission errors. Potential limitations We acknowledge several limitations. First, the sample size was moderate. Future research with larger samples would help increase generalizability of our findings. Second,our manipulation focused on symmetrically engaged interaction, where two individuals pursue the same goal while their outcomes are opposed. Yet many real-world social contexts are asymmetrically engaged, such as social presence or evaluative observation, where one person observes/evaluates and the other is behaviorally passive 43 . Future work should test how such asymmetric interactions influence prepotent motor response execution and inhibition, and whether their effects converge with or diverge from those observed under social competition. Third, competitive level (e.g., amateur vs. professional contexts) may modulate how executive control is deployed 44 . Thus, it will be important to examine whether competition effects on neurocognitive indices of motor response execution and inhibition vary across levels of competition. Conclusion By integrating behavioral, signal-detection, and ERP evidence, the present study clarifies how social competition shapes prepotent response execution and inhibition in the GNG task. Social competition facilitated Go responding (shorter Go RTs) without an accuracy cost, consistent with speed prioritization while maintaining accuracy. Self-reports corroborated this account: social competition increased the perceived importance of speed (not accuracy), and this shift was associated with greater Go-RT speeding. Neurally, social competition enhanced the centro-parietal/parietal–maximal Go-P3, which correlated with Go-RT speeding and statistically mediated the competition effect on Go RTs. For inhibition, social competition increased NoGo commission errors, accompanied by reduced Go/NoGo discriminability and a trend toward a more liberal criterion. Social competition also enhanced the fronto-central/central–maximal NoGo-P3; its increase correlated with higher commission errors and lower perceptual sensitivity and statistically mediated competition effects on both outcomes. Together, these findings indicate that social competition preferentially modulates P3-indexed, time-resolved control processes underlying speeded response implementation and diminished withholding, without altering early N2-indexed conflict monitoring, thereby underscoring the utility of established electrophysiological signatures for refining our interpretation of social-competition effects on action control. Methods Participants An a priori power analysis was conducted to determine the required sample size using G*Power 3.1 45 . The analysis indicated that a minimum of 34 participants would be sufficient to detect a medium effect ( d = 0.50) with α = .05 and power = .80. The assumption of a medium effect size was informed by our recent research examining the effects of social competition on prepotent motor response execution and inhibition 31 . As in our prior studies 31,46,47 , we recruited a larger sample than that required by the power analysis to enhance the robustness of the findings. A total of 43 participants (M = 20.58 years, SD = 1.71; 22 males) were recruited, with one participant excluded from the ERP analyses due to excessive artifacts (final EEG sample: N = 42). All participants were right-handed and had normal or corrected-to-normal vision. Written informed consent was obtained prior to participation, and participants received monetary compensation. The experimental protocol was approved by the Ethics Committee of Nanjing University. Task Procedure Interpersonal competition manipulation We used a within-subject design to manipulate interpersonal competition, in which each participant completed the GNG task in both the competition and control conditions ( Figure 1 ), following a procedure similar to that used in our recent study 31 . Specifically, in the competition condition, participants were told that they would be competing, via two linked computers, against another individual in an adjacent room. They were informed that if their overall performance surpassed that of their opponent, they would earn an additional ¥25 bonus; otherwise, they would receive only the standard participation payment of ¥25. In the control condition, participants completed the task alone. They were informed that if their performance exceeded a predefined criterion, they would receive an additional bonus of ¥25. The order of the two conditions was counterbalanced across participants, and a 10-min break was provided between them. To assess the effectiveness of the competition manipulation, participants completed a self-report rating of perceived competition after completing each condition on a 7-point Likert scale (1 = not at all, 7 = extremely). In addition, participants provided ratings of the perceived importance of response speed and response accuracy after each condition. The Go/NoGo (GNG) task The GNG task was closely matched to the one used in our recent study 31 . Specifically, the task consisted of 320 trials divided into three blocks ( Figure 1 ). Go trials accounted for 75% of all trials (240 trials) and NoGo trials for 25% (80 trials). A white arrow served as the Go stimulus and a red arrow as the NoGo stimulus. Each trial began with a fixation cross presented for 300-700 ms, followed by a white or red arrow displayed for 250 ms, and then a blank screen lasting 800 ms. Participants responded to Go stimuli by pressing “F” with the left index finger for left-pointing white arrows and “J” with the right index finger for right-pointing white arrows, and withheld responses to NoGo stimuli (red arrows). Prior to the formal task, participants completed 16 practice trials and were required to achieve at least 80% accuracy before proceeding. A 1.5-min rest period was provided between consecutive blocks. EEG data recording and processing EEG data were collected and preprocessed following our prior work 48 . EEG was recorded from 64 channels (Brain Products GmbH, Germany; 10–20 system), referenced online to a fronto-central midline electrode (impedances < 10 kΩ), amplified (0.05–100 Hz), and sampled at 1000 Hz. Offline preprocessing in EEGLAB/ERPLAB 49,50 included re-referencing to the average mastoids, filtering (0.01–30 Hz) with a 50-Hz notch, ICA-based removal of ocular and cardiac artifacts 51 , epoching (−200 to 800 ms) with baseline correction (−200 to 0 ms), and rejection of trials exceeding ±80 μV at any non-EOG channel. ERPs were averaged separately for correct Go and correct NoGo trials, and grand averages were computed across participants. Statistical analysis Behavioral analysis Two paired-samples t tests on Go trials examined the effects of social competition on accuracy and reaction times (RTs). A paired-samples t test on NoGo trials assessed competition effects on commission errors. For self-report measures, paired-samples t tests assessed competition effects on perceived competition and the perceived importance of response speed and response accuracy. As a follow-up to significant competition effects, Pearson’s correlation analyses examined whether social competition-related changes in task performance (Go accuracy, Go RTs, and NoGo commission errors) covaried with changes in self-report measures (the rated importance of response speed and response accuracy) using difference scores (social competition − control) for each participant. Furthermore, following our recent study 31 , we probed the psychological mechanisms underlying social competition-related changes in GNG performance using the signal detection model by computing perceptual sensitivity ( d′ ) and response criterion ( C ). Sensitivity was calculated as d′ = z(H) − z(FA), and criterion as C = −0.5 × [z(H) + z(FA)], where z(H) and z(FA) are the z-transformed hit rate (responses on Go trials) and false-alarm rate (commission errors on NoGo trials), respectively. Higher d′ values indicate better discrimination between Go and NoGo stimuli. For C , 0 indicates no bias; more negative values reflect a more liberal response tendency (greater overall responding), whereas more positive values indicate greater response caution. The electrophysiological data analysis For electrophysiological data, statistical analyses were guided by the topographical distribution of the grand-average ERPs and by analytical approaches commonly adopted in previous ERP research 5,13,52-57 . We focused on two GNG-related ERP components: N2 and P3. N2 peak amplitude was quantified in a 225–275 ms window at Fz and FCz, and P3 peak amplitude in a 300–500 ms window at FCz, Cz, CPz, and Pz. Component latencies were defined as the time point of the maximal peak within the corresponding window. To test social competition effects on N2 and P3 peak amplitudes and latencies, four repeated-measures analyses of variance (ANOVAs) were conducted, each including social context (social competition vs. control), trial type (Go vs. NoGo), and electrode site (Fz/FCz for N2; FCz/Cz/CPz/Pz for P3) as within-subject factors. When significant social-context effects were observed for N2 or P3, Pearson’s correlations examined whether social competition-related changes in ERP measures covaried with changes in behavioral performance (Go ACC, Go RTs, NoGo commission errors, d′ , or C ), using difference scores computed as social competition minus control. Building on these analyses, we conducted within-subject mediation analyses to test whether social competition-related changes in ERP indices accounted for social competition-related changes in GNG performance. In each model, the independent variable (X) was the within-subject social-context contrast (social competition vs. control). The mediator (M) was the Go- or NoGo-related N2/P3 amplitude or latency for which competition-related effects were found, and the dependent variable (Y) was behavioral performance for which competition-related effects were found (Go ACC, Go RTs, NoGo commission errors, d′ , or C ). Indirect effects were evaluated using bias-corrected bootstrapping with 5,000 resamples; significance was inferred when the 95% bootstrap confidence interval excluded zero. Mediation analyses used the MEMORE macro for SPSS 58 ; all other analyses were conducted in R (v4.3.3). The alpha level was set at .05, with false discovery rate (FDR) correction for multiple comparisons. Effect sizes are reported as partial eta squared ( η ²ₚ) for ANOVAs and Cohen’s d for t tests. Declarations Funding This study was supported by Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project (20227D0205100). Conflicts of interest All authors have no conflicts of interest to declare. Ethics approval Ethics approval was obtained from the ethics committee of the university. Author contributions Conceptualization: Y.L and C.L.; Methodology: C.L., G.C., and Y.L.; Resources: Y.L.; Data Curation: C.L. and Y.L.; Visualization: C.L. and Y.L.; Validation: C.L., and Y.L.; Writing—Original draft: C.L. and Y.L.; Writing—review and editing: G.L. and B.Z. Consent for publication Not applicable. Data availability The data and materials of this study are available from the corresponding author upon reasonable request. Code availability Not applicable. References Musslick, S. & Cohen, J. D. Rationalizing constraints on the capacity for cognitive control. Trends in cognitive sciences 25 , 757–775 (2021). Diamond, A. Executive functions. Annual review of psychology 64 , 135–168 (2013). Donkers, F. C. & Van Boxtel, G. J. The N2 in go/no-go tasks reflects conflict monitoring not response inhibition. Brain and cognition 56 , 165–176 (2004). Aron, A. R., Robbins, T. W. & Poldrack, R. A. 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Event-related potentials and cognition: A critique of the context updating hypothesis and an alternative interpretation of P3. Behavioral and brain sciences 11 , 343–356 (1988). Verleger, R., Baur, N., Metzner, M. F. & Śmigasiewicz, K. The hard oddball: Effects of difficult response selection on stimulus‐related P 3 and on response‐related negative potentials. Psychophysiology 51 , 1089–1100 (2014). Verleger, R., Metzner, M. F., Ouyang, G., Śmigasiewicz, K. & Zhou, C. Testing the stimulus-to-response bridging function of the oddball-P3 by delayed response signals and residue iteration decomposition (RIDE). NeuroImage 100 , 271–280 (2014). Verleger, R. Effects of relevance and response frequency on P3b amplitudes: Review of findings and comparison of hypotheses about the process reflected by P3b. Psychophysiology 57 , e13542 (2020). Stuss, D. T., Shallice, T., Alexander, M. P. & Picton, T. W. A multidisciplinary approach to anterior attentional functions. (1995). Stuss, D. T. & Alexander, M. P. Is there a dysexecutive syndrome? Philosophical Transactions of the Royal Society B: Biological Sciences 362 , 901–915 (2007). Dimoska, A., Johnstone, S. J. & Barry, R. J. The auditory-evoked N2 and P3 components in the stop-signal task: indices of inhibition, response-conflict or error-detection? Brain and cognition 62 , 98–112 (2006). Gajewski, P. D. & Falkenstein, M. Effects of task complexity on ERP components in Go/Nogo tasks. International Journal of Psychophysiology 87 , 273–278 (2013). Fallgatter, A. J. & Strik, W. K. The NoGo-anteriorization as a neurophysiological standard-index for cognitive response control. International Journal of Psychophysiology 32 , 233–238 (1999). Brunner, J. F. et al. Neuropsychological parameters indexing executive processes are associated with independent components of ERPs. Neuropsychologia 66 , 144–156 (2015). Garcia-Marques, T. & Fernandes, A. C. Meta-analysis of social presence effects on Stroop task performance. Psychological Reports , 00332941241227150 (2024). Hermalin, B. E. The effects of competition on executive behavior. The RAND Journal of Economics , 350–365 (1992). Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior research methods 39 , 175–191 (2007). Liu, Z., Liu, T. & Li, Y. How does social competition affect true and false recognition? Psychonomic Bulletin & Review 28 , 292–303 (2021). Li, Y. et al. Spillover effects of competition outcome on future risky cooperation. Scientific Reports 13 , 5535 (2023). Liu, C. et al. Pathophysiological changes in incentive processing in episodic migraine: a preliminary event-related potential study. Social cognitive and affective neuroscience 20 , nsaf039 (2025). Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods 134 , 9–21 (2004). Lopez-Calderon, J. & Luck, S. J. ERPLAB: an open-source toolbox for the analysis of event-related potentials. Frontiers in human neuroscience 8 , 213 (2014). Jung, T.-P. et al. Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clinical neurophysiology 111 , 1745–1758 (2000). Dierolf, A. M., Fechtner, J., Böhnke, R., Wolf, O. T. & Naumann, E. Influence of acute stress on response inhibition in healthy men: An ERP study. Psychophysiology 54 , 684–695 (2017). Dierolf, A. M. et al. Good to be stressed? Improved response inhibition and error processing after acute stress in young and older men. Neuropsychologia 119 , 434–447 (2018). Gao, H., Wang, X., Huang, M. & Qi, M. Chronic academic stress facilitates response inhibition: Behavioral and electrophysiological evidence. Cognitive, Affective, & Behavioral Neuroscience 22 , 533–541 (2022). Huster, R. J., Enriquez-Geppert, S., Lavallee, C. F., Falkenstein, M. & Herrmann, C. S. Electroencephalography of response inhibition tasks: functional networks and cognitive contributions. International journal of psychophysiology 87 , 217–233 (2013). Maruo, Y. & Masaki, H. Monetary reward enhances response inhibition processes manifested in No-go P3. International Journal of Psychophysiology 203 , 112410 (2024). Nieuwenhuis, S., Yeung, N., Van Den Wildenberg, W. & Ridderinkhof, K. R. Electrophysiological correlates of anterior cingulate function in a go/no-go task: effects of response conflict and trial type frequency. Cognitive, affective, & behavioral neuroscience 3 , 17–26 (2003). Hayes, A. F., Montoya, A. K. & Rockwood, N. J. The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australasian Marketing Journal 25 , 76–81 (2017). Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8892090","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":595837926,"identity":"707dbc1a-14f8-4f2c-922e-5e75a3cc8b72","order_by":0,"name":"Yansong Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3RvQrCMBDA8UghLqezwc9HOAnURXwWRegUXFwcVApC3dwLPkRHx0DBLhVXR6dOFRQfQNM8QKqbYP7D5Yb7TSHEZvvhWrTqE1RLxf+UAAX5LSGNsV7KCSan+P44rKDObnIOZNiOpJNdjSSdeSxME6DN2ZgD8Xgk6QCNRArXqQVHRQQqEk8iCbRhJOecPzVhaUFeH5CLwGYtWKozKIgsJ+ySuywM1BkI7O9xysOYukZSPwt+fwTrTnebupgvRu1dssmMpCf1ExeDov5Mx3Sv6vr6WRfDuZYc22w225/2Bh11Q+5ZMiA1AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-9080-507X","institution":"Nanjing University","correspondingAuthor":true,"prefix":"","firstName":"Yansong","middleName":"","lastName":"Li","suffix":""},{"id":595837927,"identity":"eac60e66-ca5c-4815-8496-b03e5f7fbcc5","order_by":1,"name":"Cuihong Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Cuihong","middleName":"","lastName":"Liu","suffix":""},{"id":595837928,"identity":"48b9cccc-a0bb-47e2-a60b-395c1b1c36a7","order_by":2,"name":"Guoliang Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Guoliang","middleName":"","lastName":"Chen","suffix":""},{"id":595837929,"identity":"e49e7405-fd25-4a81-9042-b90809ffaf94","order_by":3,"name":"Bing Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-02-16 10:11:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8892090/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8892090/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103345352,"identity":"3853142b-b2fb-4167-b80e-391ff29a95ac","added_by":"auto","created_at":"2026-02-24 16:11:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1251007,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIllustration of the study design and experimental procedure.\u003c/strong\u003e Each participant completed the Go/NoGo (GNG) task under both social competition and control conditions, with the order counterbalanced across participants. In the social competition condition, participants competed against another individual (a confederate) in an adjacent room, whereas in the control condition they performed the task alone. A 10-min break was included between the two conditions.In the GNG task, participants were instructed to respond to white arrows (Go stimuli) by pressing the “F” key for left-pointing arrows and the “J” key for right-pointing arrows, and to withhold any response to red arrows (NoGo stimuli). After each condition, participants provided self-report ratings of perceived competition, the importance of response speed, and the importance of response accuracy on a 7-point Likert scale (1 = not at all, 7 = extremely).\u003c/p\u003e","description":"","filename":"FIG1.png","url":"https://assets-eu.researchsquare.com/files/rs-8892090/v1/430084927a449a7e2b4fb46c.png"},{"id":103345322,"identity":"c76cd084-4e8d-4441-a92e-8685d5757ba0","added_by":"auto","created_at":"2026-02-24 16:11:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1005155,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of social competition on self-report ratings and motor response execution and inhibition. (\u003cstrong\u003eA\u003c/strong\u003e) Self-report ratings of the importance of response accuracy and response speed as a function of social context (social competition vs. control). (\u003cstrong\u003eB\u003c/strong\u003e) NoGo commission-error rates as a function of social context. (\u003cstrong\u003eC\u003c/strong\u003e) Go-trial accuracy and reaction times (RTs) as a function of social context. (\u003cstrong\u003eD\u003c/strong\u003e) The competition–control difference in rated importance of response speed (Δ) was negatively correlated with the corresponding competition–control difference in Go RTs. (\u003cstrong\u003eE\u003c/strong\u003e) Signal-detection indices (sensitivity (\u003cem\u003ed′\u003c/em\u003e) and response bias (\u003cem\u003eC\u003c/em\u003e)) by social context. Error bars represent the standard errors of the mean. “n.s.” indicates not significant; † \u003cem\u003ep\u003c/em\u003e = .07, *\u003cem\u003ep\u003c/em\u003e \u0026lt; .05, **\u003cem\u003ep \u003c/em\u003e\u0026lt; .01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt; .001.\u003c/p\u003e","description":"","filename":"FIG2.png","url":"https://assets-eu.researchsquare.com/files/rs-8892090/v1/e84c59714418a3d12a4403c3.png"},{"id":103345309,"identity":"17106668-e048-4db5-a6ed-bd1e2ab5dafc","added_by":"auto","created_at":"2026-02-24 16:11:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1007140,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of social competition on the N2 component.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Grand-average ERPs at Fz and FCz for Go (dashed lines) and NoGo (solid lines) trials in the social-competition (red) and control (black) conditions. The gray shading marks the N2 time window (225–275 ms) used for amplitude quantification. Scalp topographies depict mean voltage in the N2 window for each condition and trial type (red = positive, blue = negative). (\u003cstrong\u003eB\u003c/strong\u003e) Mean N2 peak amplitude (µV) as a function of social context (social competition vs. control), shown separately for Go and NoGo trials. (\u003cstrong\u003eC\u003c/strong\u003e) Mean N2 peak amplitude (µV) as a function of trial type (Go vs. NoGo), collapsed across social contexts. Error bars represent the standard errors of the mean. “n.s.” indicates not significant; ***\u003cem\u003e p\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e","description":"","filename":"FIG3.png","url":"https://assets-eu.researchsquare.com/files/rs-8892090/v1/e072d06f04fde25a6ad076e5.png"},{"id":103506583,"identity":"0f582c50-d627-40c7-bc62-43e5de22358a","added_by":"auto","created_at":"2026-02-26 13:37:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1045750,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of social competition on the P3 component.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Grand-average ERP waveforms elicited by Go (dashed) and NoGo (solid) stimuli at FCz, Cz, CPz, and Pz in the social-competition (red) and control (black) conditions. The gray shading indicates the P3 time window (300–500 ms) used for amplitude quantification. Scalp topographies depict the mean voltage in this window for each trial type and condition. (\u003cstrong\u003eB\u003c/strong\u003e) Mean P3 peak amplitude as a function of social context (social competition vs. control), shown separately for Go-P3 and NoGo-P3. (\u003cstrong\u003eC\u003c/strong\u003e) Mean P3 peak amplitude as a function of trial type (Go vs. NoGo), collapsed across social contexts. (\u003cstrong\u003eD\u003c/strong\u003e) The competition–control difference in Go-P3 amplitude (Δ) was negatively correlated with the corresponding difference in Go reaction times (RTs). (\u003cstrong\u003eE\u003c/strong\u003e) Mediation model showing that Go-P3 amplitude statistically mediated the effect of social competition on Go RTs.\u003cbr\u003e\n(\u003cstrong\u003eF–H\u003c/strong\u003e) The competition–control difference in NoGo-P3 amplitude (Δ) was positively correlated with the corresponding difference in NoGo commission-error rate and negatively correlated with the corresponding difference in sensitivity (\u003cem\u003ed′\u003c/em\u003e). (\u003cstrong\u003eG, I\u003c/strong\u003e) Mediation models showing that NoGo-P3 amplitude statistically mediated the effects of social competition on commission-error rate and \u003cem\u003ed′\u003c/em\u003e. Error bars represent the standard errors of the mean; *\u003cem\u003ep\u003c/em\u003e \u0026lt; .05,**\u003cem\u003ep \u003c/em\u003e\u0026lt; .01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e","description":"","filename":"FIG4.png","url":"https://assets-eu.researchsquare.com/files/rs-8892090/v1/4a8a8498ad1451386bf79ceb.png"},{"id":104835101,"identity":"ffc5e9bd-1bb8-468b-9c4e-4749d87ec006","added_by":"auto","created_at":"2026-03-17 17:40:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5885484,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8892090/v1/aa033543-6d49-40a8-9d5a-f2a2a4f5aab9.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Social competition shapes motor response execution and inhibition in a Go/NoGo task: Behavioral, signal-detection, and ERP evidence","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOne\u0026rsquo;s ability to flexibly adjust ongoing motor behavior in response to changing environmental demands is essential for adaptive functioning in daily life \u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. For example, when driving, a green light typically elicits a habitual tendency to press the accelerator. Yet if a pedestrian suddenly steps into the road, this prepotent impulse must be suppressed and replaced with braking. This everyday need to execute a dominant response while occasionally withholding it when circumstances require restraint is commonly studied using the Go/NoGo (GNG) task. In this paradigm, participants respond to frequent Go stimuli (e.g., via a button press), thereby establishing a prepotent motor response tendency, while withholding responses to infrequent NoGo stimuli that require inhibition of this prepotent motor response. Two event-related potential (ERP) components are most commonly examined in the GNG task: the N2 and the P3. The N2 is a negative-going deflection peaking around 200\u0026ndash;300 ms post-stimulus, maximal over fronto-central regions, and typically larger on NoGo than on Go trials. Although early accounts interpreted the enhanced NoGo-N2 as reflecting inhibitory processing \u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, converging evidence increasingly supports an alternative view that it primarily indexes conflict monitoring \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The P3 is a positive-going deflection peaking around 300\u0026ndash;600 ms post-stimulus that is also enhanced on NoGo compared with Go trials. The NoGo-P3 typically shows a fronto-central/central maximum and has traditionally been interpreted as reflecting either inhibitory processing \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e or outcome evaluation \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. By contrast, the Go-P3 typically shows a centro-parietal/parietal maximum and has been associated with stimulus\u0026ndash;response mapping\u0026mdash;that is, the translation of perceptual analysis into response preparation \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u0026mdash;highlighting its relevance to response-execution stages.\u003c/p\u003e \u003cp\u003eAn expanding body of work on GNG-related electrophysiological signatures provides a foundation for examining how demands imposed by diverse contexts modulate motor response execution and inhibition at psychophysiological levels. In particular, given that humans are inherently social beings and that social interactions are a key determinant of everyday behavior, an important topic of investigation is how social contexts shape the cognitive and neural mechanisms supporting motor response execution and inhibition as indexed by the GNG task \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Much of the existing evidence comes from joint-action studies of co-acting, in which complementary task demands are distributed across two co-actors\u0026mdash;effectively, each co-actor implements a complementary GNG task \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Across joint versions of the Simon task \u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, the Flanker task \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and the Stroop task \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, Go-trial performance is broadly comparable between joint-action and individual GNG settings when compatibility is not taken into account, suggesting that motor response execution is largely unchanged when acting alongside a co-actor. At the neural level, Go-P3 amplitudes are likewise comparable \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, with the few studies assessing Go-N2 also reporting no reliable differences \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. By contrast, NoGo-P3 amplitudes\u0026mdash;on trials where the participant withholds a response while the co-actor responds\u0026mdash;are larger in joint-action than in individual settings \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, a pattern commonly interpreted as reflecting increased inhibitory demands; However, available evidence has not revealed reliable NoGo-N2 differences \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Because joint-action tasks instantiate coordination-based interdependence via complementary role division, the joint-action evidence summarized above primarily speaks to how coordination demands shape the cognitive and neural mechanisms supporting action execution and inhibition.\u003c/p\u003e \u003cp\u003eYet this coordination-based interdependence\u0026mdash;i.e., mutually engaged interaction through complementary role division\u0026mdash;captures only one facet of social interdependence. Social interdependence can also arise when two individuals are embedded in the same task context and pursue the same objective, but their outcome interests are antagonistic rather than complementary. A prototypical example is social competition, in which individuals perform the same task under a goal structure where one person\u0026rsquo;s gain is contingently linked to the other\u0026rsquo;s loss \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Because competitive demands differ from those in complementary joint action, competition may be expected to modulate the cognitive and neural mechanisms supporting motor response execution and inhibition in a qualitatively different manner. Consistent with this possibility, our recent behavioral study began to address this question \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Participants were randomly assigned to a competition or control group and completed a standard GNG task. Relative to controls, the competition group responded faster on Go trials without a reliable cost to Go accuracy, but committed more errors on NoGo trials. Signal detection analyses further showed a more liberal response criterion (i.e., a stronger overall tendency to respond) and reduced perceptual sensitivity in discriminating NoGo from Go stimuli. Together, these findings suggest that social competition induces a response mode characterized by speeded prepotent responding without a cost to accuracy accompanied by less efficient response inhibition, consistent with an overall bias toward responding and diminished Go/NoGo discriminability. Importantly, our prior behavioral work went beyond aggregate performance measures by adopting a signal-detection approach. Building on this behavioral characterization, further work is needed to obtain a more comprehensive understanding of competition-related modulation of action control. One useful approach is therefore to complement behavioral and signal-detection measures with indices that offer higher temporal resolution. In this regard, ERPs are well suited to track the time course of competition effects and, when interpreted in light of the established functional significance of GNG-related components, to help pinpoint the processes that underlie these effects. A related issue concerns the behavioral strategy adopted under social competition, which also warrants further clarification. In our previous work, the pattern of speeded prepotent responding without a reliable accuracy cost suggested that participants may strategically shift toward prioritizing response speed while maintaining accuracy to outperform an opponent. However, more direct evidence is needed to substantiate this strategic account.\u003c/p\u003e \u003cp\u003eThe present study was designed to address these issues by integrating behavioral, signal-detection, and ERP analyses. Using a within-subject design, participants completed the GNG task both under social competition and in a control (alone) condition while EEG was recorded continuously (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Following each condition, they rated perceived competition as a manipulation check and separately rated the importance of response speed and response accuracy to index strategic priorities. Behaviorally, we expected a pattern similar to our recent study \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Specifically, relative to the control condition, we expected social competition to speed Go-trial responding (i.e., faster prepotent responding) without a reliable cost to Go-trial accuracy. This competition-related shift toward prioritizing speed without sacrificing accuracy should be further supported by participants\u0026rsquo; self-reported speed/accuracy importance ratings. At the same time, this speed-prioritization shift was expected to be accompanied by increased commission errors on NoGo trials, potentially reflecting reduced perceptual sensitivity in discriminating Go from NoGo stimuli and/or a shift toward a more liberal response criterion. At the neural level, our predictions were informed by prior ERP studies using a cued GNG task that manipulated speed\u0026ndash;accuracy settings either by contrasting fast versus slow responders across participants \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e or by using explicit task instructions that that varied the emphasis on response speed and accuracy \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Across this literature, the N2 component showed reduced sensitivity when speed and accuracy were relatively balanced compared to when speed was prioritized at the expense of accuracy \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, and it showed no reliable modulation when speed was emhasized without an explicit accuracy trade-off \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. By contrast, the P3 component is more reliably modulated by speed\u0026ndash;accuracy manipulations \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Based on our expected behavioral pattern under social competition, we predicted weak\u0026mdash;if any\u0026mdash;competition effects on the N2, but robust competition-related modulation of the P3 component.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBehavioral results\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eManipulation check\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and other self-report measures\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePaired-samples \u003cem\u003et\u003c/em\u003e tests showed that participants reported significantly higher perceived competition in the social competition condition (M = 5.30, SE = 0.18) than in the control condition (M = 2.88, SE = 0.25) (\u003cem\u003et\u003c/em\u003e(42) = 9.21, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003ed\u003c/em\u003e = 1.41), confirming the effectiveness of the manipulation. Participants also rated response speed as more important under social competition (M = 6.07, SE = 0.21) than under control (M = 5.44, SE = 0.23) (\u003cem\u003et\u003c/em\u003e(42) = 2.34, \u003cem\u003ep\u003c/em\u003e = .02, \u003cem\u003ed\u003c/em\u003e = 0.36) (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). By contrast, perceived importance of response accuracy did not differ between conditions (social competition: M = 6.56, SE = 0.11; control: M = 6.49, SE = 0.11) (t(42) = 0.68, \u003cem\u003ep\u003c/em\u003e = .50, \u003cem\u003ed\u003c/em\u003e = 0.10) (\u003cstrong\u003eFigure 2A\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffects of\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003esocial competition on\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGNG task performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGo trials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ACC on Go trials did not differ between the social competition (M = 0.98, SE = 0.003) and control conditions (M = 0.99, SE = 0.002) (\u003cem\u003et\u003c/em\u003e(42) = \u0026minus;1.64, \u003cem\u003ep\u003c/em\u003e = .11, \u003cem\u003ed\u003c/em\u003e = \u0026minus;0.25) (\u003cstrong\u003eFigure 2C\u003c/strong\u003e). In contrast, RTs on Go trials were significantly shorter under social competition (M = 360.37 ms, SE = 5.12) than under control (M = 367.98 ms, SE = 5.92) (\u003cem\u003et\u003c/em\u003e(42) = \u0026minus;2.80, \u003cem\u003ep\u003c/em\u003e = .008, \u003cem\u003ed\u003c/em\u003e = \u0026minus;0.43) (\u003cstrong\u003eFigure 2C\u003c/strong\u003e). Moreover, competition-related changes in Go RTs (computed as the difference between the social competition and control conditions) were negatively correlated with changes in the rated importance of response speed (\u003cem\u003er\u003c/em\u003e = \u0026minus;0.47, \u003cem\u003ep\u003c/em\u003e = .002) (\u003cstrong\u003eFigure 2D\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNoGo trials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA significant difference in commission errors on NoGo trials was observed between the social competition and control conditions (\u003cem\u003et\u003c/em\u003e(42) = 2.34, \u003cem\u003ep\u003c/em\u003e = .02, \u003cem\u003ed\u003c/em\u003e = 0.36), with the social competition condition exhibiting a higher error rate (M = 0.02, SE = 0.003) compared to the control condition (M = 0.01, SE = 0.002) (\u003cstrong\u003eFigure 2B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffects of\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003esocial competition on\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003esignal detection indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith respect to perceptual sensitivity (\u003cem\u003ed\u0026prime;\u003c/em\u003e), a paired-samples t test showed a significant effect of social context (\u003cem\u003et\u003c/em\u003e(42) = \u0026minus;2.38, \u003cem\u003ep\u003c/em\u003e = .02, \u003cem\u003ed\u003c/em\u003e = \u0026minus;0.36), with lower \u003cem\u003ed\u0026prime;\u003c/em\u003e in the social competition condition (M = 4.99, SE = 0.06) than in the control condition (M = 5.10, SE = 0.04) (\u003cstrong\u003eFigure 2E\u003c/strong\u003e). For response bias (\u003cem\u003eC\u003c/em\u003e), the social-context difference was marginal (\u003cem\u003et\u003c/em\u003e(42) = \u0026minus;1.86, \u003cem\u003ep\u003c/em\u003e = .07, \u003cem\u003ed\u003c/em\u003e = \u0026minus;0.28), with a more negative \u003cem\u003eC\u003c/em\u003e under social competition (M = \u0026minus;0.32, SE = 0.02) than under control condition (M = \u0026minus;0.27, SE = 0.02) (\u003cstrong\u003eFigure 2E\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eERP Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eN2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of N2 peak amplitudes revealed a significant main effect of trial type (\u003cem\u003eF\u003c/em\u003e(1, 41) = 49.65, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001,\u0026nbsp;\u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e = 0.55), with more negative N2 amplitudes for NoGo trials (M = \u0026minus;5.93, SE = 0.74) than for Go trials (M = \u0026minus;2.74, SE = 0.70) (\u003cstrong\u003eFigure 3A,B\u003c/strong\u003e). The main effect of social context was not significant (\u003cem\u003eF\u003c/em\u003e(1, 41) = 0.37, \u003cem\u003ep\u003c/em\u003e = .55,\u0026nbsp;\u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e = 0.01) (\u003cstrong\u003eFigure 3A,C\u003c/strong\u003e). A significant interaction between social context and electrode site was observed (\u003cem\u003eF\u003c/em\u003e(1, 41) = 5.49, \u003cem\u003ep\u003c/em\u003e = .02,\u0026nbsp;\u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e = .12). However, resolving this interaction showed that social context did not reliably modulate N2 amplitudes at either Fz (social competition: M= -4.10, SE = 0.68; control: M = -4.38, SE = 0.69; \u003cem\u003ep\u003c/em\u003e = 0.29) or FCz (social competition: M = -4.41, SE = 0.72; control: M = -4.46, SE = 0.72; \u003cem\u003ep\u003c/em\u003e = .88). Likewise, electrode site did not significantly affect N2 amplitudes within either the social competition condition (Fz: M= -4.10, SE = 0.68; FCz: M = -4.41, SE = 0.72; \u003cem\u003ep\u003c/em\u003e = .10) or the control condition (Fz: M = -4.38, SE = 0.69; FCz: M = -4.46, SE = 0.72; \u003cem\u003ep\u003c/em\u003e = .69). No other significant effects were observed (all \u003cem\u003eps\u0026nbsp;\u003c/em\u003e\u0026gt; .28).\u003c/p\u003e\n\u003cp\u003eAnalysis of N2 latency showed a significant main effect of trial type (\u003cem\u003eF\u003c/em\u003e(1, 41) = 27.58, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001,\u0026nbsp;\u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e = .40), with shorter latencies on NoGo trials (M = 248.57 ms, SE = 1.73) than on Go trials (M = 255.99 ms, SE = 1.79). Neither the main effect of social context (\u003cem\u003eF\u003c/em\u003e(1, 41) = 0.71, \u003cem\u003ep\u003c/em\u003e = .41,\u0026nbsp;\u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e = 0.02), nor the main effect of electrode site (\u003cem\u003eF\u003c/em\u003e(1, 41) = 0.72, \u003cem\u003ep\u003c/em\u003e = .40,\u0026nbsp;\u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e = 0.02), was significant. No interactions were significant (all \u003cem\u003eps\u003c/em\u003e \u0026gt; .07).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eP3\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur ANOVA snalysis of P3 peak amplitudes revealed a significant main effect of trial type (\u003cem\u003eF\u003c/em\u003e(1, 41) = 48.03, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001,\u0026nbsp;\u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e = 0.54), with larger amplitudes for NoGo trials (M = 13.68, SE = 0.73) than for Go trials (M = 10.11, SE = 0.68) (\u003cstrong\u003eFigure 4A,C\u003c/strong\u003e). The main effect of social context was also significant (\u003cem\u003eF\u003c/em\u003e(1, 41) = 17.95, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001,\u0026nbsp;\u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e = 0.30), with larger P3 amplitudes in the social competition condition (M = 12.54, SE = 0.68) than in the control condition (M = 11.26, SE = 0.67) (\u003cstrong\u003eFigure 4A,B\u003c/strong\u003e). In addition, a significant interaction between trial type and electrode site was observed (\u003cem\u003eF\u003c/em\u003e(3, 123) = 86.42, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001,\u0026nbsp;\u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e = 0.68). Follow-up simple-effects analyses showed that on Go trials, P3 amplitudes were larger at CPz (M = 10.85, SE = 0.71) and Pz (M = 11.12, SE = 0.69) than at Cz (M = 9.67, SE = 0.72; \u003cem\u003eps\u003c/em\u003e \u0026lt; .001, \u003cem\u003eds\u003c/em\u003e \u0026gt; 0.31) and FCz (M = 8.81, SE = 0.70; \u003cem\u003eps\u003c/em\u003e \u0026lt; .001, \u003cem\u003eds\u0026nbsp;\u003c/em\u003e\u0026gt; 0.55), and Cz was larger than FCz (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003ed\u003c/em\u003e = 0.24). By contrast, on NoGo trials, P3 amplitudes were larger at FCz (M = 14.51, SE = 0.85) and Cz (M = 14.39, SE = 0.81) than at CPz (M = 13.47, SE = 0.72; \u003cem\u003eps\u003c/em\u003e \u0026lt; .03, \u003cem\u003eds\u003c/em\u003e \u0026gt; 0.24) and Pz (M = 12.37, SE = 0.68; \u003cem\u003eps\u003c/em\u003e \u0026lt; .001, \u003cem\u003eds\u003c/em\u003e \u0026gt; 0.54). In addition, CPz amplitudes exceeded Pz (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003ed\u003c/em\u003e = 0.30). No other effects were significant (all \u003cem\u003eps\u003c/em\u003e \u0026gt; .18). Given the significant social-context effect on P3 amplitudes, follow-up correlations indicated that social competition-related changes in Go RTs (computed as social competition minus control) were negatively associated with changes in Go-P3 amplitude (\u003cem\u003er\u003c/em\u003e = \u0026minus;0.36, \u003cem\u003ep\u003c/em\u003e = .02) (\u003cstrong\u003eFigure 4D\u003c/strong\u003e). Meanwhile, social competition-related changes in NoGo-P3 amplitude were positively correlated with changes in NoGo commission errors (\u003cem\u003er\u003c/em\u003e = 0.39, \u003cem\u003ep\u003c/em\u003e = .012) and negatively correlated with changes in sensitivity (\u003cem\u003ed\u0026prime;\u003c/em\u003e) (\u003cem\u003er\u003c/em\u003e = \u0026minus;0.37, \u003cem\u003ep\u003c/em\u003e = .015) (all computed as social competition minus control) (\u003cstrong\u003eFigure 4F,H\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAnalysis of P3 latency revealed a significant main effect of trial type (\u003cem\u003eF\u003c/em\u003e(1, 41) = 27.76, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001,\u0026nbsp;\u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e= 0.40), with longer latencies on NoGo trials (M = 392.25 ms, SE = 4.92) than on Go trials (M = 365.72 ms, SE = 4.27). The main effect of social context was not significant (\u003cem\u003eF\u003c/em\u003e(1, 41) = 0.54, \u003cem\u003ep\u003c/em\u003e = .47,\u003cem\u003e\u0026nbsp;\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e = 0.01). A main effect of electrode site emerged (\u003cem\u003eF\u003c/em\u003e(3, 123) = 19.84, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001,\u0026nbsp;\u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e = 0.33), qualified by a significant interaction between trial type \u0026times; electrode site (\u003cem\u003eF\u003c/em\u003e(3, 123) = 13.36, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001,\u0026nbsp;\u003cem\u003e\u0026eta;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e= 0.25). Follow-up simple-effects analyses showed that on Go trials, P3 latency was longer at FCz (M = 390.98 ms, SE = 6.40) than at Cz (M = 370.74 ms, SE = 5.76; \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003ed\u003c/em\u003e = 0.44), CPz (M = 351.71 ms, SE = 4.74; \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003ed\u003c/em\u003e = 0.85), and Pz (M = 349.44 ms, SE = 4.39; \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003ed\u003c/em\u003e = 0.90); Cz was also longer than CPz (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003ed\u003c/em\u003e = 0.41) and Pz (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003ed\u003c/em\u003e = 0.46). No electrode-site differences in P3 latencies were observed on NoGo trials (all \u003cem\u003eps\u003c/em\u003e \u0026gt; .22). No other effects were significant (all \u003cem\u003eps\u003c/em\u003e \u0026gt; .37).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMediation of social competition effects on behavioral outcomes via ERP indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven that social competition significantly modulated both Go-P3 amplitude and Go RTs, we conducted a within-subject mediation analysis to test whether Go-P3 amplitude (averaged across FCz, Cz, CPz, and Pz) mediated the competition effect on Go RTs. Social competition (vs. control) predicted larger Go-P3 amplitudes (\u0026beta; = 1.00, SE = 0.27, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), which in turn predicted shorter Go RTs (\u0026beta; = \u0026minus;3.51, SE = 1.49, \u003cem\u003ep\u003c/em\u003e = .02), yielding a significant indirect effect (\u0026beta; = \u0026minus;3.50, SE = 2.12, 95% CI [\u0026minus;8.84, \u0026minus;0.42]) (\u003cstrong\u003eFigure 4E\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eSimilarly, because social competition also modulated NoGo-P3 amplitude as well as NoGo commission errors and \u003cem\u003ed\u0026prime;\u003c/em\u003e, we ran two within-subject mediation models testing whether NoGo-P3 amplitude (averaged across FCz, Cz, CPz, and Pz) mediated competition effects on these outcomes. Social competition predicted larger NoGo-P3 amplitudes (\u0026beta; = 1.55, SE = 0.44, \u003cem\u003ep\u003c/em\u003e = .001), which predicted higher commission error rates (\u0026beta; = 0.002, SE = 0.001, \u003cem\u003ep\u003c/em\u003e = .01); the indirect effect was significant (\u0026beta; = 0.003, SE = 0.001, 95% CI [0.001, 0.007]) (\u003cstrong\u003eFigure 4G\u003c/strong\u003e). Social competition again predicted larger NoGo-P3 amplitudes (\u0026beta; = 1.55, SE = 0.44, \u003cem\u003ep\u003c/em\u003e = .001), which in turn predicted reduced perceptual sensitivity (\u003cem\u003ed\u0026prime;\u003c/em\u003e) (\u0026beta; = \u0026minus;0.04, SE = 0.02, \u003cem\u003ep\u003c/em\u003e = .02), yielding a significant indirect effect on \u003cem\u003ed\u0026prime;\u003c/em\u003e (\u0026beta; = \u0026minus;0.06, SE = 0.04, 95% CI [\u0026minus;0.15, \u0026minus;0.02]) (\u003cstrong\u003eFigure 4I\u003c/strong\u003e). \u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eExtending our prior work, we combined ERP recording with a GNG task to characterize how social competition modulates prepotent motor response execution and inhibition at both behavioral and neural levels. At the behavioral level, we replicated the key pattern observed in our prior study \u003csup\u003e31\u003c/sup\u003e. At the neural level, we observed the well-established NoGo-N2 effect, maximal over frontal and fronto-central sites, such that NoGo-N2 amplitude was larger than Go-N2 amplitude. We also observed the widely reported NoGo-P3 effect: NoGo-P3 amplitude was larger than Go-P3 amplitude, and the NoGo-P3 exhibited\u0026nbsp;a\u0026nbsp;fronto-central/central maximum (i.e., a more anterior scalp distribution), whereas the Go-P3 exhibited a\u0026nbsp;centro-parietal/parietal maximum. As expected, we did not observe any significant modulation by social competition of the NoGo (relative to Go) effect on the N2 component.\u0026nbsp;By contrast, social competition reliably modulated P3 activity, suggesting that social competition primarily influences later, implementation-related processing stages rather than early N2-indexed conflict monitoring. In what follows, we integrate the behavioral, signal-detection, and ERP evidence to discuss how these time-resolved neural findings sharpen our understanding of competition-related modulation of prepotent motor response execution and inhibition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffects of social competition on\u0026nbsp;prepotent\u0026nbsp;motor\u0026nbsp;response execution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBehaviorally,\u0026nbsp;we replicated our previous findings: relative to the control condition, social competition shortened Go-trial RTs without affecting Go-trial accuracy. The absence of an accuracy cost alongside faster Go responding is consistent with our proposition that social competition promotes a strategic shift toward prioritizing response speed without a cost to accuracy, as we argued in our prior work \u003csup\u003e31\u003c/sup\u003e. The present study provides more direct support for this account by incorporating self-report measures showing that participants rated response speed as more important under social competition than under control, whereas the perceived importance of response accuracy did not differ between social contexts.\u0026nbsp;Moreover, individuals who placed greater emphasis on response speed under social competition also exhibited greater speeding of the prepotent Go response, as indicated by the negative correlation between competition-related increases in the rated importance of response speed and competition-related reductions in Go RTs. Taken together, the self-report and correlational evidence\u0026nbsp;lends additional support\u0026nbsp;to our proposition that social competition shifts participants\u0026rsquo; response strategy toward speed prioritization without an observable\u0026nbsp;decrement in Go-trial accuracy.\u003c/p\u003e\n\u003cp\u003eBeyond the behavioral-level evidence that broadens our understanding of how social competition modulates motor response execution, our ERP results elucidate the neural manifestations of competition-related speeding of the prepotent Go response, thereby sharpening the interpretation derived from behavioral findings. We found that the centro-parietal/parietal\u0026ndash;maximal Go-P3 was larger under social competition than under control. Moreover, participants showing a larger Go-P3 enhancement also exhibited greater speeding of the prepotent Go response, as indicated by the negative correlation between competition-related increases in Go-P3 amplitude and competition-related reductions in Go RTs. Importantly, the mediation analysis further indicated that the effect of social competition (vs. control) on Go RTs was statistically accounted for by competition-related increases in Go-P3 amplitude: social competition predicted larger Go-P3 amplitudes, which in turn predicted shorter Go RTs. This pattern can be interpreted within a framework that integrates the stimulus\u0026ndash;response (S\u0026ndash;R) mapping account of the centro-parietal/parietal\u0026ndash;maximal Go-P3 \u003csup\u003e17,33-36\u003c/sup\u003e with a broader executive-control model \u003csup\u003e37,38\u003c/sup\u003e, thereby helping to specify\u0026nbsp;the processes through which social competition speeds execution of the prepotent Go response. Accordingly, Go performance depends on how efficiently task-relevant features of Go stimuli (e.g., a left- vs. right-pointing white arrow) are translated into the appropriate response representation (e.g., a left- vs. right-hand keypress), within a broader control configuration that involves energization and task-setting (e.g., selecting task-relevant criteria and operations). In light of the enhanced Go-P3 under social competition, its association with faster Go responding, and its mediating role in the effect of social competition on Go RTs, these results\u0026nbsp;suggest\u0026nbsp;that competition-related speeding of the prepotent Go response, without an observable decrement in accuracy, may arise from\u0026nbsp;greater energization of this S\u0026ndash;R control configuration\u0026mdash;thereby\u0026nbsp;enabling\u0026nbsp;more rapid translation of task-relevant stimulus features into the appropriate Go response representation and\u0026nbsp;supporting\u0026nbsp;more efficient motor execution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffects of social competition on prepotent\u0026nbsp;motor response inhibition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsistent with our recent study \u003csup\u003e31\u003c/sup\u003e, social competition not only sped the prepotent Go response but also impaired response withholding on NoGo trials, as reflected in higher commission error rates relative to the control condition on NoGo trials. Our signal-detection analyses further revealed that diminished withholding of the prepotent Go response under social competition could be attributable to poorer differentiation between Go and NoGo stimuli, as evidenced by decreased \u003cem\u003ed\u0026prime;\u003c/em\u003e,\u0026nbsp;together with a trend toward a more liberal response criterion (\u003cem\u003eC\u003c/em\u003e)\u0026mdash;a pattern that replicates the direction of our prior findings.\u003c/p\u003e\n\u003cp\u003eExtending this behavioral and signal-detection evidence, our ERP results further deepen our understanding by revealing the neural manifestations of competition-related changes in prepotent Go-response inhibition. Specifically, we observed that the fronto-central/central\u0026ndash;maximal NoGo-P3 component was larger under social competition than under control. This competition-related enhancement of the NoGo-P3 is not only consistent with previous findings showing larger NoGo-P3 amplitudes in fast responders than in slow responders \u003csup\u003e12,39\u003c/sup\u003e, but also extends this literature by demonstrating that larger NoGo-P3 amplitudes can emerge within individuals when response speed is selectively prioritized, whether induced by competitive task demands (as in the present study) or by task instructions that manipulate speed\u0026ndash;accuracy settings in prior studies \u003csup\u003e16\u003c/sup\u003e. Notably, because the NoGo-P3 peaked after the mean Go RTs (M = 392.25 ms vs. M = 364.18 ms),\u0026nbsp;this timing is difficult to reconcile with an interpretation of the NoGo-P3 as an index of inhibition\u0026nbsp;\u003csup\u003e40,41\u003c/sup\u003e. Instead, it aligns with the alternative view that the fronto-central/central\u0026ndash;maximal NoGo-P3 component is associated with reactive executive-control processes, primarily involving implementation of the alternative non-response set and monitoring, which are reflected behaviorally in NoGo commission errors and perceptual sensitivity (\u003cem\u003ed\u0026prime;\u003c/em\u003e), respectively\u0026nbsp;\u003csup\u003e42\u003c/sup\u003e. In line with this account, the competition-related changes in the NoGo-P3 should be linked to competition-related changes in NoGo commission errors and \u003cem\u003ed\u0026prime;\u003c/em\u003e. Our correlational results indeed support this\u0026nbsp;prediction.\u0026nbsp;Specifically, participants showing a larger competition-related enhancement in NoGo-P3 amplitude also exhibited greater increases in commission errors and larger reductions in perceptual sensitivity (\u003cem\u003ed\u0026prime;\u003c/em\u003e). Together, these associations further suggest that the NoGo-P3 may serve as a mediating neural index through which social competition relates to these reactive executive-control operations in response to NoGo stimuli, a possibility further supported by our mediation analyses. Mediation analyses indicated that competition-related increases in NoGo-P3 amplitude statistically accounted for the effects of social competition (vs. control) on commission errors and \u003cem\u003ed\u0026prime;\u003c/em\u003e: social competition predicted larger NoGo-P3 amplitudes, which in turn predicted higher commission error rates and lower perceptual sensitivity (\u003cem\u003ed\u0026prime;\u003c/em\u003e). Taken together, these ERP findings suggest that under social competition, prioritizing speed degrades Go/NoGo discrimination (lower \u003cem\u003ed\u0026prime;\u003c/em\u003e) and raises\u0026nbsp;the cost of\u0026nbsp;switching from\u0026nbsp;the\u0026nbsp;prepotent Go response tendency with the alternative non-response set on NoGo trials, thereby increasing uncertainty and the likelihood of inhibitory failures. This heightened demand on monitoring and on implementing the alternative non-response set would be expected to upregulate the NoGo-P3, consistent with its statistical linkage to competition effects on \u003cem\u003ed\u0026prime;\u003c/em\u003e and commission errors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge several limitations. First, the sample size was moderate. Future research with larger samples would help increase generalizability of our findings. Second,our manipulation focused on symmetrically engaged interaction, where two individuals pursue the same goal while their outcomes are opposed. Yet many real-world social contexts are asymmetrically engaged, such as social presence or evaluative observation, where one person observes/evaluates and the other is behaviorally passive \u003csup\u003e43\u003c/sup\u003e. Future work should test how such asymmetric interactions influence prepotent motor response execution and inhibition, and whether their effects converge with or diverge from those observed under social competition. Third, competitive level (e.g., amateur vs. professional contexts) may modulate how executive control is deployed \u003csup\u003e44\u003c/sup\u003e. Thus, it will be important to examine whether competition effects on neurocognitive indices of motor response execution and inhibition vary across levels of competition.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBy integrating behavioral, signal-detection, and ERP evidence, the present study clarifies how social competition shapes prepotent response execution and inhibition in the GNG task. Social competition facilitated Go responding (shorter Go RTs) without an accuracy cost, consistent with speed prioritization while maintaining accuracy. Self-reports corroborated this account: social competition increased the perceived importance of speed (not accuracy), and this shift was associated with greater Go-RT speeding. Neurally, social competition enhanced the centro-parietal/parietal\u0026ndash;maximal Go-P3, which correlated with Go-RT speeding and statistically mediated the competition effect on Go RTs. For inhibition, social competition increased NoGo commission errors, accompanied by reduced Go/NoGo discriminability and a trend toward a more liberal criterion. Social competition also enhanced the fronto-central/central\u0026ndash;maximal NoGo-P3; its increase correlated with higher commission errors and lower perceptual sensitivity and statistically mediated competition effects on both outcomes. Together, these findings indicate that social competition preferentially modulates P3-indexed, time-resolved control processes underlying speeded response implementation and diminished withholding, without altering early N2-indexed conflict monitoring, thereby underscoring the utility of established electrophysiological signatures for refining our interpretation of social-competition effects on action control.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn a priori power analysis was conducted to determine the required sample size using G*Power 3.1 \u003csup\u003e45\u003c/sup\u003e. The analysis indicated that a minimum of 34 participants would be sufficient to detect a medium effect (\u003cem\u003ed\u003c/em\u003e = 0.50) with \u0026alpha; = .05 and power = .80. The assumption of a medium effect size was informed by our recent research examining the effects of social competition on prepotent motor response execution and inhibition \u003csup\u003e31\u003c/sup\u003e. As in our prior studies \u003csup\u003e31,46,47\u003c/sup\u003e, we recruited a larger sample than that required by the power analysis to enhance the robustness of the findings. A total of 43 participants (M = 20.58 years, SD = 1.71; 22 males) were recruited, with one participant excluded from the ERP analyses due to excessive artifacts (final EEG sample: N = 42).\u0026nbsp;All participants were right-handed and had normal or corrected-to-normal vision. Written informed consent was obtained prior to participation, and participants received monetary compensation. The experimental protocol was approved by the Ethics Committee of Nanjing University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTask Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpersonal competition manipulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used a within-subject design to manipulate interpersonal competition, in which each participant completed the GNG task in both the competition and control conditions (\u003cstrong\u003eFigure 1\u003c/strong\u003e), following a procedure similar to that used in our recent study \u003csup\u003e31\u003c/sup\u003e. Specifically, in the competition condition, participants were told that they would be competing, via two linked computers, against another individual in an adjacent room. They were informed that if their overall performance surpassed that of their opponent, they would earn an additional \u0026yen;25 bonus; otherwise, they would receive only the standard participation payment of \u0026yen;25. In the control condition, participants completed the task alone. They were informed that if their performance exceeded a predefined criterion, they would receive an additional bonus of \u0026yen;25. The order of the two conditions was counterbalanced across participants, and a 10-min break was provided between them.\u003c/p\u003e\n\u003cp\u003eTo assess the effectiveness of the competition manipulation, participants completed a self-report rating of perceived competition after completing each condition on a 7-point Likert scale (1 = not at all, 7 = extremely). In addition, participants provided ratings of the perceived importance of response speed and response accuracy after each condition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Go/NoGo (GNG)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;task\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GNG task was closely matched to the one used in our recent study \u003csup\u003e31\u003c/sup\u003e. Specifically, the task consisted of 320 trials divided into three blocks (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Go trials accounted for 75% of all trials (240 trials) and NoGo trials for 25% (80 trials). A white arrow served as the Go stimulus and a red arrow as the NoGo stimulus. Each trial began with a fixation cross presented for 300-700 ms, followed by a white or red arrow displayed for 250 ms, and then a blank screen lasting 800 ms. Participants responded to Go stimuli by pressing \u0026ldquo;F\u0026rdquo; with the left index finger for left-pointing white arrows and \u0026ldquo;J\u0026rdquo; with the right index finger for right-pointing white arrows, and withheld responses to NoGo stimuli (red arrows). Prior to the formal task, participants completed 16 practice trials and were required to achieve at least 80% accuracy before proceeding. A 1.5-min rest period was provided between consecutive blocks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEEG data recording and processing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEEG data were collected and preprocessed following our prior work \u003csup\u003e48\u003c/sup\u003e. EEG was recorded from 64 channels (Brain Products GmbH, Germany; 10\u0026ndash;20 system), referenced online to a fronto-central midline electrode (impedances \u0026lt; 10 k\u0026Omega;), amplified (0.05\u0026ndash;100 Hz), and sampled at 1000 Hz. Offline preprocessing in EEGLAB/ERPLAB \u003csup\u003e49,50\u003c/sup\u003e included re-referencing to the average mastoids, filtering (0.01\u0026ndash;30 Hz) with a 50-Hz notch, ICA-based removal of ocular and cardiac artifacts \u003csup\u003e51\u003c/sup\u003e, epoching (\u0026minus;200 to 800 ms) with baseline correction (\u0026minus;200 to 0 ms), and rejection of trials exceeding \u0026plusmn;80 \u0026mu;V at any non-EOG channel. ERPs were averaged separately for correct Go and correct NoGo trials, and grand averages were computed across participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBehavioral analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo paired-samples t tests on Go trials examined the effects of social competition on accuracy and reaction times (RTs). A paired-samples t test on NoGo trials assessed competition effects on commission errors. For self-report measures, paired-samples t tests assessed competition effects on perceived competition and the perceived importance of response speed and response accuracy. As a follow-up to significant competition effects, Pearson\u0026rsquo;s correlation analyses examined whether social competition-related changes in task performance (Go accuracy, Go RTs, and NoGo commission errors) covaried with changes in self-report measures (the rated importance of response speed and response accuracy) using difference scores (social competition \u0026minus; control) for each participant.\u003c/p\u003e\n\u003cp\u003eFurthermore, following our recent study \u003csup\u003e31\u003c/sup\u003e, we probed the psychological mechanisms underlying social competition-related changes in GNG performance using the signal detection model by computing perceptual sensitivity (\u003cem\u003ed\u0026prime;\u003c/em\u003e) and response criterion (\u003cem\u003eC\u003c/em\u003e). Sensitivity was calculated as \u003cem\u003ed\u0026prime;\u003c/em\u003e = z(H) \u0026minus; z(FA), and criterion as \u003cem\u003eC\u003c/em\u003e = \u0026minus;0.5 \u0026times; [z(H) + z(FA)], where z(H) and z(FA) are the z-transformed hit rate (responses on Go trials) and false-alarm rate (commission errors on NoGo trials), respectively. Higher \u003cem\u003ed\u0026prime;\u003c/em\u003e values indicate better discrimination between Go and NoGo stimuli. For \u003cem\u003eC\u003c/em\u003e, 0 indicates no bias; more negative values reflect a more liberal response tendency (greater overall responding), whereas more positive values indicate greater response caution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe electrophysiological data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor electrophysiological data, statistical analyses were guided by the topographical distribution of the grand-average ERPs and by analytical approaches commonly adopted in previous ERP research \u003csup\u003e5,13,52-57\u003c/sup\u003e. We focused on two GNG-related ERP components: N2 and P3. N2 peak amplitude was quantified in a 225\u0026ndash;275 ms window at Fz and FCz, and P3 peak amplitude in a 300\u0026ndash;500 ms window at FCz, Cz, CPz, and Pz. Component latencies were defined as the time point of the maximal peak within the corresponding window. To test social competition effects on N2 and P3 peak amplitudes and latencies, four repeated-measures analyses of variance (ANOVAs) were conducted, each including social context (social competition vs. control), trial type (Go vs. NoGo), and electrode site (Fz/FCz for N2; FCz/Cz/CPz/Pz for P3) as within-subject factors. When significant social-context effects were observed for N2 or P3, Pearson\u0026rsquo;s correlations examined whether social competition-related changes in ERP measures covaried with changes in behavioral performance (Go ACC, Go RTs, NoGo commission errors, \u003cem\u003ed\u0026prime;\u003c/em\u003e, or \u003cem\u003eC\u003c/em\u003e), using difference scores computed as social competition minus control.\u003c/p\u003e\n\u003cp\u003eBuilding on these analyses, we conducted within-subject mediation analyses to test whether social competition-related changes in ERP indices accounted for social competition-related changes in GNG performance. In each model, the independent variable (X) was the within-subject social-context contrast (social competition vs. control). The mediator (M) was the Go- or NoGo-related N2/P3 amplitude or latency for which competition-related effects were found, and the dependent variable (Y) was behavioral performance for which competition-related effects were found (Go ACC, Go RTs, NoGo commission errors, \u003cem\u003ed\u0026prime;\u003c/em\u003e, or \u003cem\u003eC\u003c/em\u003e). Indirect effects were evaluated using bias-corrected bootstrapping with 5,000 resamples; significance was inferred when the 95% bootstrap confidence interval excluded zero.\u003c/p\u003e\n\u003cp\u003eMediation analyses used the MEMORE macro for SPSS \u003csup\u003e58\u003c/sup\u003e; all other analyses were conducted in R (v4.3.3). The alpha level was set at .05, with false discovery rate (FDR) correction for multiple comparisons. Effect sizes are reported as partial eta squared (\u003cem\u003e\u0026eta;\u003c/em\u003e\u0026sup2;ₚ) for ANOVAs and Cohen\u0026rsquo;s \u003cem\u003ed\u0026nbsp;\u003c/em\u003efor \u003cem\u003et\u003c/em\u003e tests.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project (20227D0205100).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflicts of interest to declare.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was obtained from the ethics committee of the university.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Y.L and C.L.; Methodology: C.L., G.C., and Y.L.; Resources: Y.L.; Data Curation: C.L. and Y.L.; Visualization: C.L. and Y.L.; Validation: C.L., and Y.L.; Writing\u0026mdash;Original draft: C.L. and Y.L.; Writing\u0026mdash;review and editing: G.L. and B.Z.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent\u0026nbsp;for\u0026nbsp;publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and materials of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMusslick, S. \u0026amp; Cohen, J. 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The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. \u003cem\u003eAustralasian Marketing Journal\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 76\u0026ndash;81 (2017). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8892090/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8892090/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present work examined how social competition modulates motor response execution and inhibition by integrating behavioral, signal-detection, and ERP evidence. Forty-three participants completed a Go/NoGo (GNG) task under social-competition and control conditions while EEG was recorded. Behaviorally, relative to control, social competition shortened Go-trial reaction times (RTs) without affecting Go-trial accuracy. Moreover, such Go-RT speeding correlated negatively with competition-related increases in the rated importance of response speed. Social competition also increased NoGo commission errors. Signal-detection analyses indicated reduced Go/NoGo discriminability (lower \u003cem\u003ed\u0026prime;\u003c/em\u003e) and a shift toward a more liberal criterion (\u003cem\u003eC\u003c/em\u003e). Neurally, in addition to the NoGo effects on the N2/P3, social competition enhanced both Go-P3 and NoGo-P3 amplitudes. Competition-related increases in Go-P3 amplitude, maximal over centro-parietal/parietal sites, correlated negatively with competition-related reductions in Go RTs and statistically mediated the effect of social competition on Go RTs. Competition-related increases in NoGo-P3 amplitude, maximal over fronto-central/central sites, correlated positively with competition-related increases in commission errors and negatively correlated with competition-related reductions in \u003cem\u003ed\u0026prime;\u003c/em\u003e, and statistically mediated the effects of social competition on both outcomes. These findings shed light on how social competition modulates motor response execution and inhibition, preferentially affecting P3-indexed, time-resolved control processes supporting speeded response implementation and diminished withholding.\u003c/p\u003e","manuscriptTitle":"Social competition shapes motor response execution and inhibition in a Go/NoGo task: Behavioral, signal-detection, and ERP evidence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 16:11:14","doi":"10.21203/rs.3.rs-8892090/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e59e26b9-5516-491b-85ca-cd46bb02a7a3","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63401528,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Cognitive control"},{"id":63401529,"name":"Biological sciences/Psychology/Human behaviour"},{"id":63401530,"name":"Biological sciences/Neuroscience/Social behaviour"}],"tags":[],"updatedAt":"2026-02-24T16:11:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 16:11:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8892090","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8892090","identity":"rs-8892090","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}