Decomposing categorical and item-specific preferences using sequential sampling models

Value-based decision making can be complex because many factors influence choices. For example, one might choose Item A over Item B because As are generally better, because As come with a bonus, or because you get more of A than of B. These factors would all manifest as increased choice probabilities for As over Bs despite arising from different considerations. Here, we use the diffusion model (HDDM) to identify patterns associated with these different kinds of biases, namely response, stimulus, and magnitude biases.In two experiments, subjects (n_1=67, n_2=89) first rated how much they would like to eat various snack foods, then made binary choices between those foods. We randomly designated options on one side of the screen as the targets. Our conditions were as follows:1) Proportion – the target side had higher value foods a majority of the time2) Quarter – the target side came with a 25-cent bonus3) Double – the target side had twice as much food4) Demand – we told subjects that we were testing if people favor options on the target sideOur results revealed an overall response bias in the Proportion condition, but not in the Demand condition; a stimulus bias in the Quarter condition; and a magnitude bias in the Double condition. We also investigated how response biases develop and disappear over time. In conditions where the target side was consistently better, starting-point biases increased over time; in conditions where the target and non-target side were equally attractive, starting-point biases decreased over time.