The Effects of Oxytocin on Fairness Perception and Related Prosocial Behaviors

Fairness rating scores

Participants are asked to rate their perceived levels of fairness on a 9-point Likert scale (1 = highly fair, 9 = highly unfair) to the computer's allocation in each trial.

Anticipated guilt rating scores

Participants are asked to rate the anticipated guilt they would feel if they accepted the allocation given by the computer in each trial. Ratings were conducted on a 9-point Likert scale (1 = not at all, 9 = highly guilty).

The binary decision to the initial allocation

Participants are asked to makde decisions of accepting or rejecting the allocation provided by the computer in each trial.

Brain activity patterns

Brain activity patterns in response to the outcome of the allocation given by computer and the binary decision. We will examine these brain response patterns via both the conventional univariate analysis and the machine learning-based multivariate pattern analysis (MVPA) approaches.

Inequity aversion parameters derived from the Fehr-Schmidt model

Inequity aversion parameters derived from the Fehr-Schmidt model are used to elucidate whether OT would modulate inequity aversion. This model-based analysis was conducted based on choices using hierarchical Bayesian modeling. In the Fehr-Schmidt model, the subjective value of a given allocationis determined by one's own and the other's allocation using the following formula: SV(Mself, Mother) = Mself - βmax{ Mself - Mother, 0}, Mself ≠ Mother where Mself is one's payoff and Mother is the other's payoff. Parameter β represents the weight assigned to inequity. Higher parameter values indicate a stronger degree of inequity aversion in participants.