Outcome contest model (using multimembership grouping terms)

I have an experimental paradigm with n stimuli, where pairs of stimuli are presented to participants (one on the left, one on the right) and participants are asked to indicate which seems most likely to be intentionally produced. Each stimulus is presented with each other stimulus to each participant.
My hypothesis is that a given property of the stimuli is predictive of which stimuli are going to be more likely to be indicated as intentional. I also want to control for “random effects” of stimuli, and participant variability in assessing the property.

The difficulty is that each trial has two stimuli, and the same stimuli can sometimes happen in the left and sometimes in the right.

With much help from Richard McElreath, I put this together in Stan: https://gist.github.com/fusaroli/6d4a406549c3750be3b786bd5790c041
which works just fine.

However, I was wondering whether this was possible to create in brms as well

A null model (with no role of the hypothesized feature) could be:

fit0 <- brm(Outcome ~ 1 +
                                   (1 | mm(StimulusRight, StimulusLeft)), 
                 data = d, 
                 family=bernoulli)

This works.

A model including the feature (coded as numeric -1,0,1) could be (intuitively):

fit1 <- brm(Outcome ~ 1 + mm(FeatureRight,FeatureLeft) + 
                                    (1 | mm(StimulusRight, StimulusLeft)) + 
                                    (1 + mm(FeatureRight,FeatureLeft) | ID), 
                 data = d, 
                 family=bernoulli)

However, this generates the error

Error in model.frame.default(bterms$allvars, data, na.action = na.action, :
invalid type (list) for variable ‘mm(PeriodR, PeriodL)’

The mm() help page indicates this could work:

d$Feature = (d$FeatureR + d$FeatureL) / 2
fit1a <- brm(Outcome ~ 1 + Feature + 
                                    (1 | mm(StimulusRight, StimulusLeft)) + 
                                    (1 + mmc(FeatureRight,FeatureLeft) | ID), 
                 data = d, 
                 family=bernoulli)

This also gives an error

Error: ‘mmc’ is only supported in multi-membership terms.

which suggests this can only be applied to “random slopes” on the mm random intercept.
Any better way of implementing what I am trying to in brms?

Currently, the brms syntax does not allow that kind of “interaction” between the varying effects of persons and items you are coding in your Stan model, as items are expressed as a multimembership term. The multimembership structure that brms supports is not expressive enough for this purpose. I will think of this a little bit more. Maybe I can come up with a nice generalization at some point that also includes your model.

that was my suspicion from looking at the code, but I wasn’t 100% sure. Thanks in any case!