Mixed-effects model for response data (fitlme)

I have preference ratings (integers from 1-to-7) for k=80 stimuli, obtained from N=30 subjects. I want to use a mixed-effects model to test how well the following variables - as fixed effects - can predict the responses(ratings)

Predictor 1, called "Parncutt" below = value describing each stimulus

Predictor 2, called "GMSI" below = score describing each subject

Subject and stimulus number are to be considered random effects.

I used the command:

lme = fitlme(tbl,formula)

where the data is in the tbl attachment, and where:

 formula = 'ratings_pls ~ Parncutt + GMSI + (1|subjectNr) + (1|stimulusNr)';

I am, however, not sure which output of the LME model to interpret that would give me information above&beyond the correlations that I computed between the DV (rating) and each of the two factors (GMSI and Parncutt). Also, I am not sure whether to also add a term in the LME model for:

A) random slopes: if allowing for differences between subjects and stimuli (random intercepts in the model), then why not also allow the slopes for both factors to be random?

B) interaction terms between any of the subject-wise factors (SubjectNr and GMDI) and any of the stimulus-wise factors (StimulusNr and Parncutt)

Many thanks for any help - very much appreciated!

[z8080]

Any thoughts anyone? :)