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"$20" refers to 20 monetary units (MU). Subject payment at the end of the experiment varied between $20 and $40, depending on the number of MU the subject accumulated over 10 rounds. Payment schedule was as follows: <68 MU = $20, 68 to 133 MU = $25, 134 to 200 MU = $30, 201 to 300 MU = $35, and >300 MU = $40. Before the game, participants were informed that they would receive between $20 and $40, scaled by their performance. However, they had no knowledge of the step payoff function until the game was completed. Notice that the perfectly selfish Nash equilibrium strategy (in which the investor keeps all $20 each round) results in 200 MU; no subject adopted this strategy.
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j-1). However, it is noteworthy that reciprocity expressed by the investor (r = 0.56) was more strongly related to change in trust than reciprocity expressed by the trustee (r = 0.26). This difference is likely accounted for by an asymmetry in the structure of the exchange: In each round, the investor can accumulate money ($20 endowment) without the cooperation of the trustee, whereas the trustee is wholly dependent on the investor's cooperation. This dependency of the trustee on the investor likely results in greater responsivity by the trustee to changes in investor reciprocity.
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A description of methods is available as supporting material in Science Online.
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Each dyad contributed eight behavioral events to this analysis (48 pairs × 8 rounds = 384 rounds). Investor reciprocity cannot be calculated for the initial two rounds and was excluded. The 384 rounds had a mean ± SD of -0.01 ± 0.35, skewness of -0.19 (SE = 0.12), and kurtosis of 2.55 (SE = 0.25). Rounds were divided into approximately equal-sized categories: 125 malevolent reciprocity rounds (x < -0.025), 134 neutral reciprocity rounds (-0.025 ≤ x ≤ +0.05), and 125 benevolent reciprocity rounds (x > +0.05). For additional description of reciprocity categories, see figs. S3 and S4.
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Regions with ≥10 significant voxels were identified using t tests. Z values and statistical parametric mapping (SPM) coordinates for each region are available in table S1.
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j) grew as experience between players accrued (fig. S5).
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note
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This work was supported by the Center for Theoretical Neuroscience at Baylor College of Medicine (P.R.M.), National Institute on Drug Abuse (NIDA) grant DA11723 (P.R.M.), National Institute of Neurological Disorders and Stroke grant NS045790 (P.R.M.), National Institute of Mental Health grant MH52797 (P.R.M.), NIDA grant DA14883 (G. Berns), The Kane Family Foundation (P.R.M.), The David and Lucile Packard Foundation (S.R.Q.), and The Gordon and Betty Moore Foundation (S.R.Q.). We thank P. Dayan, J. Li, T. Lohrenz, C. Stetson, and two anonymous referees for comments on this manuscript. We thank the Hyperscan Development Team at Baylor College of Medicine for Network Experiment Management Object (NEMO) software implementation (www.hnl.bcm.tmc edu/nemo) and C. Berns for early discussions and efforts leading to the development of hyperscanning. We also thank A. Harvey, S. Flaherty, K. Pfeiffer, R. Pruitt, and S. Gleason for technical assistance.
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