```
Y_O.modelrt.1 <- lmer(log(RT)~Condition*SubjType+(1+Condition|Participant)+(1+Condition|trialnum_1)+(1+SubjType|Participant)+(1+SubjType|trialnum_1),data=Y_O_B.rt.2,REML=FALSE)
summary(Y_O.modelrt.1)
Linear mixed model fit by maximum
likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula:
log(RT) ~ Condition * SubjType + (1 + Condition | Participant) +
(1 + Condition | trialnum_1) + (1 + SubjType | Participant) +
(1 + SubjType | trialnum_1)
Data: Y_O_B.rt.2
AIC BIC logLik deviance df.resid
3983.7 4080.4 -1974.8 3949.7 2167
Scaled residuals:
Min 1Q Median 3Q Max
-3.3264 -0.6232 0.0735 0.7143 3.2694
Random effects:
Groups Name Variance Std.Dev.
Participant (Intercept) 0.000e+00 0.000000
SubjType2 1.641e-01 0.405087
Participant.1 (Intercept) 6.319e-02 0.251368
Condition2 9.997e-03 0.099985
trialnum_1 (Intercept) 6.662e-05 0.008162
SubjType2 1.948e-03 0.044131
trialnum_1.1 (Intercept) 8.185e-03 0.090470
Condition2 7.871e-02 0.280557
Residual 3.251e-01 0.570183
Corr
NaN
1.00
1.00
0.04
Number of obs: 2184, groups:
Participant, 49; trialnum_1, 20
Fixed effects:
Estimate Std. Error
(Intercept) 6.95300 0.05267
Condition2 0.04204 0.06984
SubjType2 0.10131 0.09730
Condition2:SubjType2 -0.07813 0.05952
df t value
(Intercept) 53.62195 132.007
Condition2 21.53052 0.602
SubjType2 44.84822 1.041
Condition2:SubjType2 45.70474 -1.313
Pr(>|t|)
(Intercept) <2e-16 ***
Condition2 0.554
SubjType2 0.303
Condition2:SubjType2 0.196
---
Signif. codes:
0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) Cndtn2 SbjTy2
Condition2 0.156
SubjType2 0.145 0.022
Cndtn2:SbT2 0.046 0.110 0.359
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see ?isSingular
>
```

Even if I used different optimizers, the warning is still "optimizer convergence code: 0 (OK)" and "boundary (singular) fit: see ?isSingular". I am wondering how to deal with the problem.

Thank you so much!