Fig. 5
Neural effects of interaction between choice and frame. (A) ROI-based analysis of the contrast ‘Loss (rej-acc) − Gain (rej-acc)’. SVC revealed an activation cluster in the left DS, whose rejection-induced activation was higher in the loss compared with gain domain. (B) Activation timecourse extracted from a 6 mm sphere around the maximum coordinates indicates that this interaction effect was driven by the amplified activation difference in the loss relative to the gain domain. (C) The differences in beta estimates extracted from the activation maximum (Loss − Gain) predicted the increases in rejection rate in the loss relative to the gain domain (r = 0.67, P < 0.05). Note, the white and grey dots are outliers identified by robust regression and they are down-weighted in computing the correlation coefficients (Wager et al., 2005).

Neural effects of interaction between choice and frame. (A) ROI-based analysis of the contrast ‘Loss (rej-acc) − Gain (rej-acc)’. SVC revealed an activation cluster in the left DS, whose rejection-induced activation was higher in the loss compared with gain domain. (B) Activation timecourse extracted from a 6 mm sphere around the maximum coordinates indicates that this interaction effect was driven by the amplified activation difference in the loss relative to the gain domain. (C) The differences in beta estimates extracted from the activation maximum (Loss − Gain) predicted the increases in rejection rate in the loss relative to the gain domain (r = 0.67, P < 0.05). Note, the white and grey dots are outliers identified by robust regression and they are down-weighted in computing the correlation coefficients (Wager et al., 2005).

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