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.

Highlights

  • Sequential sampling models are powerful models to account simultaneously for choice probabilities and choice response times

  • They have become the dominant approach to modeling decision processes in cognitive science. Their application includes a variety of psychological tasks from basic perceptual decision to complex preferential choice tasks. On they have been applied to identification and discrimination tasks (e.g., Edwards, 1965; Laming, 1968; Pike, 1973; Link and Heath, 1975; Heath, 1981; Ashby, 1983); memory retrieval (e.g., Stone, 1960; Ratcliff, 1978; Van Zandt et al, 2000); and classification to account for speed-accuracy data

  • They have been used for preferential decision tasks (e.g., decision field theory (DFT), Busemeyer and Townsend, 1993; multiattribute dynamic decision model, Diederich, 1997; Diederich and Busemeyer, 1999) to account for choice response times and choice probabilities interpreted as preference strength; judgment and confidence ratings (Pleskac and Busemeyer, 2010); to account for selling prices, certainty equivalents, and preference reversal phenomena (Busemeyer and Goldstein, 1992; Johnson and Busemeyer, 2005)

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Summary

METHODS

Reviewed by: Chris Donkin, University of New South Wales, Australia José Antonio Díaz, Universidad de Granada, Spain. 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. 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.

INTRODUCTION
PRELIMINARIES
MATRIX APPROACH
TIME AND ORDER SCHEDULE
CHOICE PROBABILITIES AND MEAN CHOICE RESPONSE TIMES
DETERMINISTIC TIME AND ORDER SCHEDULE
Constructing random time and order schedules
SIMULATIONS
IMPACT OF ATTENTION TIME DISTRIBUTIONS
CONCLUDING REMARKS
Full Text
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