Table 1. Comparison of different models fitted to the animals’ choices.
Best fitting model indicated in bold.
| Model | Description | Both animals | Animal A | Animal B | |||
|---|---|---|---|---|---|---|---|
| AIC | BIC | AIC | BIC | AIC | BIC | ||
| (1) | Value from reward history1 | 2.2482 | 2.2490 | 1.5077 | 1.5084 | 7.3571 | 7.3636 |
| (2) | Value from reward history and risk2 | 2.2477 | 2.2492 | 1.5077 | 1.5092 | 7.3522 | 7.3653 |
| (3) | Value from choice history3 | 2.1614 | 2.1622 | 1.4900 | 1.4907 | 6.5043 | 6.5109 |
| (4) | Value from choice history and risk | 2.0385 | 2.0400 | 1.4023 | 1.4037 | 7.3528 | 7.3660 |
| (5) | Value from reward and choice history4 | 2.0089 | 2.0097 | 1.3914 | 1.3922 | 6.0880 | 6.0945 |
| (6) | Value from reward and choice history and risk | 2.0073 | 2.0088 | 1.3899 | 1.3914 | 6.0747 | 6.0878 |
| (7) | Objective reward probabilities5 | 2.1213 | 2.1220 | 1.4615 | 1.4622 | 6.4972 | 6.5037 |
| (8) | Objective reward probabilities and objective risk6 | 2.1210 | 2.1225 | 1.4616 | 1.4631 | 6.4982 | 6.5114 |
| (9) | Reinforcement learning (RL) model7 | 2.0763 | 2.0779 | 1.4376 | 1.4391 | 6.2161 | 6.2293 |
| (10) | RL learning, stack parameter (Huh et al., 2009)8 | 2.0810 | 2.0826 | 1.4374 | 1.4389 | 6.3198 | 6.3330 |
| (11) | RL, reversal-learning variant9 | 2.2614 | 2.2630 | 1.5330 | 1.5344 | 7.2808 | 7.2939 |
1:Value defined according to Equation 6; 2: Risk defined according to Equation 8; 3: Value defined as sum of weighted choice history derived from Equation 5; 4: Value defined according to Equation 7; 5: Objective reward probabilities defined according to Equation 1; 6: Objective reward risk defined according to Equation 2; 7: Standard Rescorla-Wagner RL model updating value of chosen option based on last outcome; 8: Modified RL model incorporating choice-dependency; 9: Modified RL model updating value of chosen and unchosen option based on last outcome.