Table 1. Results of model fitting on rat behavioral data.
AIC and BIC both give model M1 as winner. All models fit the data significantly better than chance: pR2 score > 0. The table shows mean ± SEM of parameter values without outliers (|β|≥1,000).
| Model | α | β | γ | m0 | LL | AIC | BIC | pR2 |
|---|---|---|---|---|---|---|---|---|
| M1 | 0.69 ± 0.04 | 6.6 ± 8.0 | −184.3 | 560.7 | 2168.6 | 0.26 | ||
| M2 | 0.55 ± 0.04 | 16.0 ± 17.1 | 0.36 ± 0.04 | −182.4 | 652.7 | 2876.4 | 0.27 | |
| M3 | 0.59 ± 0.03 | 5.7 ± 3.5 | 0.55 ± 0.03 | 26.6 ± 14.5 | −148.9 | 681.7 | 3458.1 | 0.40 |
| M4 | 0.60 ± 0.04 | 6.0 ± 6.4 | 0.90 ± .004 | −180.4 | 648.9 | 2868.7 | 0.28 |
Abbreviations: AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; LL, log-likelihood; M, model; pR2, pseudo R2