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. 2015 Mar 29;10(11):1477–1483. doi: 10.1093/scan/nsv036

Table 1.

Fractional logit estimations for reinforcement error rates

Variable High incentives
Low incentives
Joint regression
β SE β SE β SE
FRN (normalized) 0.531*** 0.159 −0.346 0.493 −0.439 0.487
SVD −0.898*** 0.266 0.855*** 0.310 0.283 0.228
Incentives (1=High) 1.139 0.951
Incentives X FRN 1.151** 0.519
Incentives X SVD −0.509 0.313
Gender (1=Male) −0.362 0.588 2.110** 0.916 −0.188 0.402
Age −0.261*** 0.098 −0.155*** 0.055 −0.185*** 0.067
Mastery 0.451** 0.229 1.208*** 0.236 0.717*** 0.150
Knowledge of Probabilities −0.176 0.142 −0.282** 0.122 −0.383*** 0.100
Statistics Course (1=Yes) −1.016 0.674 3.604*** 0.650 0.934* 0.485
Effort 0.186 0.272 −1.960*** 0.401 −0.798*** 0.215
Cb 0.831 0.730 −0.754* 0.421 −0.162 0.463
Constant 4.448* 2.708 7.935*** 2.279 6.721*** 2.057
pseudo-log likelihood −7.002 −5.989 −14.731

The individual FRN is the normalized FRN after negative feedback. Cb is counterbalance (1 if the winning color was blue). SVD is the subjective (self-reported) valence difference between the winning and the losing colors. Mastery and knowledge of probabilities (self-reported) were measured in a 0–10 scale. Statcourse is a dummy indicating that the participant reported having followed a statistics course. Effort was the self-reported effort invested in the task (0–10). N = 20 for low incentives, N = 19 for high incentives (1 subject dropped due to missing questionnaire data). *P < 0.1. **P < 0.05. ***P < 0.01.