Sequential sampling model for multiattribute choice alternatives with random attention time and processing order

Abstract

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 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 finite and infinite decision horizons for the attribute considered last is investigated. For the former case the model predicts a probability p0 > 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.

Amount and time exert independent influences on intertemporal choice

“…First, we show that the processing of amount information and time information have uncorrelated contributions to the choice process. While our design cannot confirm complete statistical independence, the observed lack of correlation between drift slopes for amount and time stands in contrast to other models that assume a limited capacity constraint on attention such that weights on amount and time trade-off within the decision process (i.e., sum to a constant) 5,43 . Moreover, prior work suggests that although attention can constrain processes of evidence accumulation in decision making, this bias is partial rather than absolute 29 .…”

A two-stage diffusion modeling approach to the compelled-response task

“…However, it is possible that the switch is delayed and/or follows a probability distribution. Diederich and Oswald (2014) investigated several distributions for the switching time, among them geometric, Poisson, uniform. The exact shape of the distributions changes whereas, however, the overall pattern does not.…”

“…The model is implemented as a continuous Markov process approximated by a discrete Markov chain (for the matrix approach, see Diederich, 1997;Diederich & COMPELLED-RESPONSE 13 Busemeyer, 2003;Diederich & Oswald, 2014. The drift coefficient was set to σ = 1; the time unit to τ = .1 (that is, 10 sample points per 1 ms).…”

Fixation patterns in simple choice reflect optimal information sampling

“…Despite this progress in understanding the effects attention has on decision making, less is known about the factors that drive attention itself as we make decisions. Most previous models have assumed that attention influences the decision making process, but not vice versa; in this case, attention may be either completely random (e.g., 16,24), or influenced by the items' features, such as spatial location, visual saliency, and subjective value (e.g., 10,11,[25][26][27]. Alternatively, the fixation and decision processes could have reciprocal interactions [7].…”