LMM testing the relationship between prestimulus mu amplitude bin and SEP amplitude for each attention condition separately (model no. 1–12)
| Condition | Model No. | Lmer syntax | Likelihood | LRT |
|---|---|---|---|---|
| Subthreshold attended | (1) | P60∼1 + (1|Subject) | −177.49 | |
| (2) | P60∼1 + Bin + (1 + Bin|Subject) | −170.52 | χ2 = 13.94** | |
| (3) | P60∼1 + Bin + I(Bin∧2)+ (1 + Bin + I(Bin∧2)|Subject) | −166.37 | χ2 = 8.31* | |
| Subthreshold unattended | (4) | As (1), | −176.32 | |
| (5) | (2), | −170.67 | χ2 = 11.29** | |
| (6) | (3), respectively | −161.3 | χ2 = 18.73*** | |
| Suprathreshold attended | (7) | P50∼1 + (1|Subject) | −439.65 | |
| (8) | P50∼1 + Bin + (1 + Bin|Subject) | −434.98 | χ2 = 9.34* | |
| (9) | P50∼1 + Bin + I(Bin∧2) + (1 + Bin + I(Bin∧2)|Subject) | −426.65 | χ2 = 16.7** | |
| Suprathreshold unattended | (10) | As (7), | −458.01 | |
| (11) | (8), | −452.7 | χ2 = 10.7** | |
| (12) | (9), respectively | −435 | χ2 = 35.34*** |
↵Likelihood depicts the models' log transformed likelihood, bigger is better, i.e., the more likely the model. LRT is the likelihood ratio test comparing two models for the same dataset [Bigger models (more parameters) are compared with respective smaller ones]. This returns a χ2 value. However, p values are based on parametric bootstrapping (10,000 simulations; Halekoh and Højsgaard, 2014): * < 0.05–0.01, ** < 0.01–0.001, *** < 0.001–0.