Does brms automatically standardise data and/or coefficients? I'm running some regression models in R using brms and lme4.
When I run a Bayesian model:
priors.mod.1 <- c(set_prior("normal(0, 1)", class = "Intercept"),
                  set_prior("normal(0, 1)", class = "b"),
                  set_prior("cauchy(0, 1)", class = "sd"),
                  set_prior("cauchy(0, 1)", class = "sigma"), 
                  set_prior("dirichlet(1, 1)", class = "simo", coef = "motest.time.num1"),
                  set_prior("dirichlet(1, 1)", class = "simo", coef = "motest.time.num:training.dum1"),
                  set_prior("dirichlet(1, 1)", class = "simo", coef = "motest.time.num:Condition.dum1"),
                  set_prior("dirichlet(1, 1)", class = "simo", coef = "motest.time.num:training.dum:Condition.dum1"),
                  set_prior("lkj(2)", class = "cor"))


maximal.mod.ln <- brm(RT ~  mo(test.time.num) * training.dum * Condition.dum + (mo(test.time.num) * training.dum | Ppt.No),
                      family = gaussian(),
                      chain = 4,
                      iter = 2000,
                      warmup = 1000,
                      seed = 1234, 
                      prior = priors.mod.1,
                      save_pars = save_pars(all = TRUE),
                      data = dat)

My population level estimates look like this:

However when I run a frequentist model
mod <- lmer(RT ~  test.time.num * training.dum * Condition.dum + (test.time.num * training.dum | Ppt.No),
                      data = dat)

they look like this:

Does anybody know why the estimates are so different (my random effect estimates are also wildly different in terms of scale)? My outcome variable (RT) is measured in milliseconds and so the the output from the frequentist models looks as if the coefficients are in the outcome variable units. I have tried a variety of prior distributions but the Bayesian results seem robust.
It looks as if the Bayesian coefficient estimates are standardised. And if I standardise my frequentist model coefficients, I get some similar looking values:

but I've looked online I don't think this is a default for brms models. Perhaps it is because I am modelling one of my variables as a monotonic effect within the Bayesian model? Could this cause my coefficient estimates to be standardised?
Any help or advance would be most appreciated!

These two models have different design matrices, so it's not surprising you get different results.
Model A:
RT ~ mo(test.time.num) * training.dum * Condition.dum + (mo(test.time.num) * training.dum | Ppt.No)

Model B:
RT ~ test.time.num * training.dum * Condition.dum + (test.time.num * training.dum | Ppt.No)

So in Model A you specify time as a monotonic predictor. brms::mo is intended for ordinal categorical variables, so why apply it to time? Or maybe that's the appropriate way to handle the time variable but then you don't do it in Model B.
PS. And yes, brms centers continuous predictors as this improves convergence. However, it reports coefficient estimates on the original scale. And brms is not a black box; you can easily see the Stan code behind the brms model with the functions make_stancode (without fitting) or stancode (after fitting).
