Schematic depiction of the drift diffusion process.
Accumulation of evidence starts at point Most variants of reward learning models that use the drift diffusion process as a choice rule replace the constant drift rate by an individually scaled difference of expected values for the both options (As an alternative scaling method, we implemented a non-linear function as suggested by where With that, ± In summary, we combined different parameterizations of the outcome sensitivity (static or scaled) and the mapping of expected values to the drift rate (linear or sigmoidal) into different models (