In simulation 1, the model had to learn 8 sequence prediction tasks that varied along 3 independent feature dimensions. Models with EM learned to represent the 3 dims in abstract format (Bernardi 2020)...

… this is because encoding and retrieving TRs in EM can reduce representation drift, which facilitates learning. Without EM, “good representation” for the ongoing task is often non-unique, leading to unnecessary TR drift – change in TRs after performance converges.