Sequential sampling model for multiattribute choice alternatives with random attention time and processing order - Diederich, Adele; Oswald, Peter (Frontiers in Human Neuroscience, 2014)
A sequential sampling model for multiattribute binary choice options, called Multiattribute attention switching (MAAS) model, assumes a separate sampling process for each attribute. During the deliberation process attention switches from one attribute consideration to the next. The order in which attributes are considered as well for how long each attribute is considered - the attention time - influences the predicted choice probabilities and choice response times. Several probability distributions for the attention time including deterministic, Poisson, binomial, geometric, and uniform with different variances are investigated. Depending on the time and order schedule the model predicts a rich choice probability/choice response time pattern including preference reversals and fast errors. Furthermore, the difference between a finite and infinite decision horizons for the attribute considered last is investigated. For the former case the model predicts a probability $p_0> 0$ of not deciding within the available time. The underlying stochastic process for each attribute is an Ornstein-Uhlenbeck process approximated by a discrete birth-death process. All predictions are also true for the widely applied Wiener process.