Neuro-Behavioral Dynamic Prediction of Interpersonal Cooperation and Aggression

Wang, Wei; Fu, Chao; Kong, Xiangzeng; Osinsky, Roman; Hewig, Johannes; Wang, Yiwen

doi:10.1007/s12264-021-00777-7

Cited by 7 publications

(5 citation statements)

References 77 publications

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“…This suggests that this activity goes beyond joint action and is relevant when evaluating others’ intentions and expectations. Aligned with this idea, Wang et al 57 found that frontal activity in the beta band during option evaluation predicted cooperative behavior in a computerized version of the Chicken Game 58 , where two players independently choose if they want to cooperate. Incidentally, Betti et al 59 presented evidence showing beta might have a role in integrating prior inferences with incoming information, which applies to the activity the mental activity of the participants at this moment.…”

Section: Discussionmentioning

confidence: 99%

Intra- and inter-brain synchrony oscillations underlying social adjustment

Vicente

Ara

Marco-Pallarés

2023

Sci Rep

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Humans naturally synchronize their behavior with other people. However, although it happens almost automatically, adjusting behavior and conformity to others is a complex phenomenon whose neural mechanisms are still yet to be understood entirely. The present experiment aimed to study the oscillatory synchronization mechanisms underlying automatic dyadic convergence in an EEG hyperscanning experiment. Thirty-six people performed a cooperative decision-making task where dyads had to guess the correct position of a point on a line. A reinforcement learning algorithm was used to model different aspects of the participants’ behavior and their expectations of their peers. Intra- and inter-connectivity among electrode sites were assessed using inter-site phase clustering in three main frequency bands (theta, alpha, beta) using a two-level Bayesian mixed-effects modeling approach. The results showed two oscillatory synchronization dynamics related to attention and executive functions in alpha and reinforcement learning in theta. In addition, inter-brain synchrony was mainly driven by beta oscillations. This study contributes preliminary evidence on the phase-coherence mechanism underlying inter-personal behavioral adjustment.

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Section: Discussionmentioning

confidence: 99%

Intra- and inter-brain synchrony oscillations underlying social adjustment

Vicente

Ara

Marco-Pallarés

2023

Sci Rep

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“…However, this study focused only on differences in the amount of cheating and did not consider differences in the moral motivations that might underlie each behavior. Even if the frequency of prosocial rule-breaking is similar to that of antisocial rule-breaking, these two constructs may still be driven by completely different internal processes (Wang et al, 2022).…”

Section: Measuring Prosocial Rule-breakingmentioning

confidence: 99%

“…However, recent research indicates that this is likely untrue. For example, rather than demonstrating differences in activation of the same neural markers, prosocial and antisocial behaviors activate very different neural markers, indicating that prosocial and antisocial behaviors are driven by unrelated neural processes (Wang et al, 2022). Furthermore, recent studies investigating real-world behavior have shown that prosocial and antisocial behaviors often coexist in the same individual and sometimes work in tandem to achieve a singular goal (Basurto et al, 2016; Bodin et al, 2020).…”

Section: Two Types Of Rule-breakingmentioning

confidence: 99%

Breaking rules for moral reasons: Development and validation of the Prosocial and Antisocial Rule-Breaking (PARB) scale.

Hennigan¹,

Cohn²

2022

Law and Human Behavior

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Objectives: To determine whether prosocial rule-breaking exists as a separate construct from antisocial rule-breaking and to develop a valid rule-breaking scale with prosocial and antisocial subscales. Hypotheses: We hypothesized that (a) rule-breaking would have prosocial and antisocial subfactors; (b) the prosocial rule-breaking subscale would positively associate with prosocial intentions, empathy, moral identity, and guilt proneness, whereas the antisocial rule-breaking subscale would negatively associate with these same factors; and (c) the two subscales would predict prosocial and antisocial cheating behaviors, respectively. Method: We developed the Prosocial and Antisocial Rule-Breaking (PARB) scale using a sample of 497 undergraduates (Study 1) and 257 Amazon Mechanical Turk workers (Study 2). Participants completed all surveys (Studies 1 and 2) and took part in a between-subjects experiment (Study 2) in which cheating behavior was measured in two conditions—when cheating helps others (prosocial) or oneself (antisocial). Results: The final PARB scale demonstrated the expected factor structure (comparative fit index = .96, Tucker–Lewis index = .93, root-mean-square error of approximation = .064; χ² = 177, df = 88, p < .001), with the prosocial (α = .81) and antisocial (α = .93) subscales showing good reliability. Prosocial rule-breaking was positively associated with prosocial intentions, empathy, and guilt proneness, whereas antisocial rule-breaking was negatively associated with these same factors. Each additional point in prosocial rule-breaking PARB score predicted a 37% increased likelihood of participating in protest behavior in an exploratory investigation (p = .025) and predicted a 268% increase in actual prosocial cheating behavior (p < .001) but did not predict antisocial cheating behavior (p = .293). Conversely, each additional point in antisocial rule-breaking PARB score did not predict protest participation (p = .410) but did predict a 69% increase in actual antisocial cheating behavior (p = .025). Conclusions: These findings suggest that our current understanding of rule-breaking is limited, as many types of rule-breaking are prosocially motivated and are not necessarily antisocial.

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“…However, recent research indicates that this is likely untrue. For example, rather than demonstrating differences in activation of the same neural markers, prosocial and antisocial behavior activate very different neural markers, indicating that prosocial and antisocial behavior are driven by unrelated neural processes (Wang et al, 2022). Furthermore, recent studies investigating real-world behavior have shown that prosocial and antisocial behavior often coexist in the same individual and sometimes work in tandem to achieve a singular goal (Basurto et al, 2016;Bodin et al, 2020).…”

Section: Two Types Of Rule-breakingmentioning

confidence: 99%

“…However, this study focused only on differences in the amount of cheating and did not consider differences in the relation between cheating and the moral motivations that might underlie such behavior. Even if the frequency of prosocial rule-breaking is similar to that of antisocial rule-breaking, these two constructs may still be driven by completely different internal processes (Wang et al, 2022).…”

Section: Measuring Prosocial Rule-breakingmentioning

confidence: 99%

Breaking Rules for Moral Reasons: Development and Validation of the Prosocial and Antisocial Rule-Breaking (PARB) Scale

Hennigan¹,

Cohn²

2022

Preprint

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Objective: To determine whether prosocial rule-breaking exists as a separate construct from antisocial rule-breaking and to develop a valid rule-breaking scale with prosocial and antisocial subscales. Hypotheses: We hypothesized that (a) rule-breaking would have prosocial and antisocial subfactors; (b) the prosocial rule-breaking subscale would positively associate with prosocial intentions, empathy, moral identity, and guilt proneness, whereas the antisocial rule-breaking subscale would negatively associate with these same factors; and (c) the two subscales would respectively predict prosocial and antisocial cheating behavior. Method: We developed the Prosocial and Antisocial Rule-Breaking (PARB) Scale using a sample of 497 undergraduates (Study 1) and 257 Amazon Mechanical Turk (MTurk) workers (Study 2). Participants completed all surveys (Studies 1 and 2) and took part in a between-subjects experiment (Study 2) in which cheating behavior was measured in two conditions – when cheating helps others (prosocial) or oneself (antisocial). Results: The final PARB Scale demonstrated the expected factor structure (CFI = .96, TLI = .93, RMSEA = .064; χ² = 177, df = 88, p < .001), with the prosocial (α = .81) and antisocial (α = .93) subscales showing good reliability. Prosocial rule-breaking was positively associated with prosocial intentions, empathy, and guilt proneness, whereas antisocial rule-breaking negatively associated with these same factors. Each additional point in prosocial rule-breaking PARB score predicted a 37% increased likelihood of participating in protest behavior in an exploratory investigation (p = .025) and predicted a 268% increase in actual prosocial cheating behavior (p < .001) but did not predict antisocial cheating behavior (p = .293). Conversely, each additional point in antisocial rule-breaking PARB score did not predict protest participation (p = .410) but did predict a 69% increase in actual antisocial cheating behavior (p = .025). Conclusion: These findings suggest that our current understanding of rule-breaking is limited, as many types of rule-breaking are prosocially motivated and are not necessarily antisocial.

show abstract

Neuro-Behavioral Dynamic Prediction of Interpersonal Cooperation and Aggression

Cited by 7 publications

References 77 publications

Intra- and inter-brain synchrony oscillations underlying social adjustment

Intra- and inter-brain synchrony oscillations underlying social adjustment

Breaking rules for moral reasons: Development and validation of the Prosocial and Antisocial Rule-Breaking (PARB) scale.

Breaking Rules for Moral Reasons: Development and Validation of the Prosocial and Antisocial Rule-Breaking (PARB) Scale

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