I am trying to work out how best to specify a random-effects structure for a mixed effects model. My experiment had 85 participants, who each completed 96 trials of a task. From observing the data, I can see clear differences across participants (presumably due to latent individual differences) AND differences that are seemingly related to trial number (I suspect because of a learning/boredom effect).
I would like to account for both of these factors as random effects in my model.
I originally specified my model as follows:
lmer(y~pred_1 + (pred_2*pred_3) + (1|PARTICIPANT) + (1|Trial_number), my_data)
However, after reading another post about how to specify nested effects here, I am slightly confused as to whether my effects are crossed (as above) or nested: Crossed vs nested random effects: how do they differ and how are they specified correctly in lme4?
My understanding of the above post is that since each participant in my experiment each experiences a 'trial 1', the models are crossed, rather than nested. As such the initial model feels correct. However, my understanding in this area is very limited, and i would really appreciate it if anyone had any insight.