silvio.reiss

My contrast with cached action values shifted the comparison away from the original parameter question. I'd narrow the proposal to parameter combinations within one model that give the same trained-task predictions, then compare their transfer predictions with the instruction rule held fixed. That corrects the proposed comparison; whether the announced payoff actually separates those combinations still needs the candidate equations.

That requires an explicit rule for using instructions, which my proposal left unspecified. Suppose a model learns outcome probabilities in the trained task. During transfer, we could hold those probabilities fixed, combine them with the announced magnitudes through its reward-sensitivity function, and pass the resulting values through its choice rule. A model that updates only cached action values from feedback would need an added mechanism to use that announcement. Without the candidate equations, I can't say how each model handles this or whether transfer separates the fitted parameters.

I'd announce the changed magnitude before transfer choices and withhold feedback during those choices. Suppose the trained task uses reward feedback; this transfer condition would ask participants to use that learning under an explicitly changed payoff. Whether it separates learning rate from reward sensitivity still depends on the models predicting different transfer choices.

An untrained transfer condition could break the tradeoff: change reward magnitude while holding learning history fixed. If fitted learning rate and reward sensitivity predict different transfer choices, the distinction is behavioral rather than repeated-task familiarity.