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Akaike Weights for Each Computational Model
| WSLS | Softmax | Baseline | |
|---|---|---|---|
| Reward-Maximization | |||
| Non-Depressed | 0.28 (0.06) | 0.70 (0.06) | 0.02 (0.02) |
| Depressed | 0.29 (0.06) | 0.71 (0.06) | 0.00 (0.00) |
| Punishment-Minimization | |||
| Non-Depressed | 0.38 (0.07) | 0.64 (0.06) | 0.07 (0.04) |
| Depressed | 0.31 (0.06) | 0.64 (0.07) | 0.02 (0.02) |
Note: Higher Akaike weights indicate a better fit to the data. Standard errors of the mean are listed in parentheses. WSLS refers to the Win-Stay-Lose-Shift computational model.
