The Attentional Drift-Diffusion Model Extends to Simple Purchasing Decisions

By Ian Krajbich, Dingchao Lu, Colin F. Camerer, Antonio Rangel. Front. Psychology, 2012

330 citations recorded by the source corpus.

How do we make simple purchasing decisions (e.g., whether or not to buy a product at a given price)? Previous work has shown that the attentional drift-diffusion model (aDDM) can provide accurate quantitative descriptions of the psychometric data for binary and trinary value-based choices, and of how the choice process is guided by visual attention. Here we extend the aDDM to the case of purchasing decisions, and test it using an eye-tracking experiment. We find that the model also provides a reasonably accurate quantitative description of the relationship between choice, reaction time, and visual fixations using parameters that are very similar to those that best fit the previous data. The only critical difference is that the choice biases induced by the fixations are about half as big in purchasing decisions as in binary choices. This suggests that a similar computational process is used to make binary choices, trinary choices, and simple purchasing decisions.

Subfields: Cognitive Neuroscience, Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/242f6851ade4762f8f225e153eea88370fbaa75b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Antonio Rangel
2. Scott D. Brown
3. John A. Clithero
4. Roger Ratcliff
5. Gail McKoon
6. Philip L. Smith
7. Ian Krajbich
8. Jacob L. Orquin

In Management Science and Operations Research

1. Jacob L. Orquin
2. Simone Mueller Loose
3. John A. Clithero
4. Antonio Rangel
5. Ernst Fehr
6. Philip L. Smith

In Statistical and Nonlinear Physics

1. John A. Clithero
2. Jacob L. Orquin
3. Simone Mueller Loose
4. Antonio Rangel
5. Ernst Fehr

Citations in the corpus

• The Computation of Stimulus Values in Simple Choice (2014, methodology): “…description of the choice and reaction time curves generated by simple choice Common psychometric choice curveCommon choice environment(A) (B) NEUROECONOMICS tasks (Basten et al., 2010; Gluth et al., 2012; Krajbich and Rangel, 2011; Krajbich et al., 2010, 2012; Milosavljevic et al., 2010).”
• Benefits of neuroeconomic modeling: New policy interventions and predictors of preference (2014, background): “2), consumer purchasing decisions, and temporal discounting decisions (Krajbich, Armel and Rangel 2010; Krajbich and Rangel 2011; Krajbich et al. 2012).”
• Diffusion Decision Model: Current Issues and History (2016, methodology): “This and other diffusion models have been used to account for, among other things, value-based decision, social choice, and purchasing decisions [150,152,153,157,207–210].”
• The speed-accuracy tradeoff: history, physiology, methodology, and behavior (2014, background): “Likewise, these models can be shown to account for high-level behaviors such as visual fixations and purchasing decisions (Krajbich et al., 2010, 2012; Milosavljevic et al., 2010; Towal et al., 2013).”
• Attention and choice: a review on eye movements in decision making. (2013, methodology): “Overall, the model does not adequately describe eye movements in decision making, but fails to recognize important aspects of eye movement control, such as bottom up and top down processes, and the authors have also commented on this issue (Krajbich et al., 2012; Rangel, 2009).”