SDT(Signal Detection Theory) mixed modeling with brms

Modeling
1

Hi,
I am trying to do modeling based on SDT framework. Therefore, I am following guidelines in Matti Vuorre’s posting (Matti’s homepage - Bayesian Estimation of Signal Detection Models). This has been very useful, but still, I am a bit confused in applying to my data analysis purpose. What I am trying to do is to find out whether criterion would differ depending on “sceneCond” (which has three conditions) factor in the code below (actually, there was a main effect of this factor when analyzed using frequentist methods but I am trying to convert all my analyses to a bayesian approach).

## model formula
glmm2 <- bf(samediffresp ~ Phi(dprime * changeCond - c), 
  dprime ~ sceneCond + (sceneCond | subindex),
  c ~ sceneCond + (sceneCond | subindex),
  nl = TRUE
)

prior <- get_prior(data = totaldata, glmm2, family = bernoulli(link="identity"))
prior <- c(
  prior(normal(.5, 3), nlpar = "dprime"),
  prior(normal(0, 1.5), nlpar = "c")
)

evsdt_glmm2 <- brm(glmm2,
  family = bernoulli(link = "identity"),
  data = totaldata,
  prior = prior,
  control = list(adapt_delta = .99),
  cores = 4, init = 0,
  # file = "sdtmodel2-2"
)

I want to ask you guys 1) whether I wrote the formula correctly, and if it is right, 2) how I can compute bayes factor of this model that incorporates the “sceneCond”, and 3) how to do post-hoc analysis between two conditions?
Regarding the second question, I want to check whether it is appropriate to construct a null model like the below.

## model formula
glmm2 <- bf(samediffresp ~ Phi(dprime * changeCond - c), 
  dprime ~ (1 | subindex),
  c ~ (1 | subindex),
  nl = TRUE
)

prior <- get_prior(data = totaldata, glmm2, family = bernoulli(link="identity"))
prior <- c(
  prior(normal(.5, 3), nlpar = "dprime"),
  prior(normal(0, 1.5), nlpar = "c")
)

null_model <- brm(glmm2,
  family = bernoulli(link = "identity"),
  data = totaldata,
  prior = prior,
  control = list(adapt_delta = .99),
  cores = 4, init = 0,
  # file = "sdtmodel2-2"
)

This question is quite lengthy, and I would greatly appreciate receiving an answer to any of the questions I’ve posed!

Thank you!