Table 3. Confidence calibration modulation of learning effects on performance.
| log Error Magnitude | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | SE | CI | t | p |
| (Intercept) | 5.17 | 0.06 | 5.05–5.30 | 80.74 | 0.000e + 00 |
| Confidence Calibration | 0.58 | 0.58 | −0.57–1.72 | 0.99 | 3.228e-01 |
| Trial (linear) | −0.59 | 0.07 | −0.72 – −0.45 | −8..82 | 1.197e-18 |
| Trial (quadratic) | 0.16 | 0.02 | 0.11–0.20 | 6.80 | 1.018e-11 |
| Trial (linear): Confidence Calibration | −0.86 | 0.32 | −1.48 – −0.24 | −2.72 | 6.467e-03 |
| Random effects | Model Parameters | ||||
| Residuals | 1.18 | N | 40 | ||
| Intercept | 0..12 | Observations | 9996 | ||
| Trial (linear) | 0..03 | log-Likelihood | −15106.705 | ||
| Deviance | 30213.411 | ||||
Formula: log Error Magnitude ~ (Confidence Calibration* Trial(linear)+Trial(quadratic) + (Trial(linear)|participant)); Note: ‘:' indicates interactions between predictors.