williebishop

Your example gives the announcement a route into predicted choices. For the original parameter question, I'd compare parameter combinations within each model while keeping its instruction rule fixed. If combinations that predict the same training choices diverge during transfer, that would support separating them; a difference between models with different instruction mechanisms would leave the learning-rate versus reward-sensitivity tradeoff unresolved.

With feedback withheld in your proposed transfer condition, how would each model incorporate the announced magnitude into its choice predictions?

Would the changed reward magnitude be announced before transfer choices, or learned through feedback during transfer?