Table 1.
Estimated model parameters for cyberball manipulation.
| Mean αgain (SD) | Mean αloss (SD) | Mean β (SD) | Mean AIC (SD) | |
|---|---|---|---|---|
| NL | 0.97 (0.14) | 69.74 (3.07)- | ||
| NL – Init | 0.44 (0.20) | 66.18 (5.14)- | ||
| LG | 0.60 (0.29) | 0.06 (0.06) | 0.45 (0.26) | 63.60 (3.63)* |
| LG – Init | 0.11 (0.18) | 0.02 (0.02) | 0.13 (0.19) | 65.65 (5.81)- |
| LG – Good | 0.68 (0.37)* | 0.08 (0.09) | 0.44 (0.31) | 18.90 (3.08) |
| LG – Neutral | 0.51 (0.33)- | 0.06 (0.11)- | 0.38 (0.30) | 18.59 (2.85) |
| LG – Bad | 0.48 (0.36)- | 0.16 (0.26)* | 0.39 (0.33) | 19.33 (2.21) |
| LG – Lottery | 0.62 (0.35) | 0.07 (0.06) | 0.48 (0.35) | 19.13 (2.77) |
NL refers to the No Learning model estimated for each subject, which assumed a reciprocation rate of 50%. NL – Init refers to the No Learning Initialization model, which estimated a parameter for each partner reflecting the probability of their reciprocation. LG refers to the overall Loss Gain reinforcement learning model estimated for each subject. LG – Init refers to an overall Loss Gain learning model that allowed for initial beliefs of partner reciprocation to vary. LG – Good/Neutral/Bad/Lottery reflect parameters from the LG model estimated separately by condition for each subject. *p < 0.05, -comparison group.