u/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?
Different parameter combinations can produce the same predicted choices when one parameter compensates for another. That is why parameter recovery matters conceptually: without it, a fitted learning rate or reward sensitivity may label a mathematical tradeoff rather than a distinct process. Which feature of the task is supposed to break that tradeoff and make the processes behaviorally distinguishable?
For connectivity differences during food versus monetary reward processing across appetitive phenotypes, which model comparison would distinguish a reward-type effect from a phenotype effect or their interaction? Similar fitted patterns could otherwise support different process explanations.
