Abstract
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.
Highlights
A basic goal of decision neuroscience and neuroeconomics is to characterize the computations carried out by the brain to make different types of decisions (Busemeyer and Johnson, 2004; Smith and Ratcliff, 2004; Bogacz, 2007; Gold and Shadlen, 2007; Rangel et al, 2008; Kable and Glimcher, 2009; Hare and Rangel, 2010; Rushworth et al, 2011)
In previous work we have shown that a variant of the DDM, which we refer to as the attentional drift-diffusion model, provides quantitatively accurate predictions of the relationship between choices, reaction times, and visual fixations in experiments where subjects make either binary or trinary snack food choices (Krajbich et al, 2010; Krajbich and Rangel, 2011)
We have described the results of an eye-tracking experiment of purchasing decisions designed to investigate if the attentional drift-diffusion model (aDDM) is able to provide a reasonable quantitative description of the relationship between the fixation, choice, and reaction time data in the case of simple purchasing decisions
Summary
A basic goal of decision neuroscience and neuroeconomics is to characterize the computations carried out by the brain to make different types of decisions (Busemeyer and Johnson, 2004; Smith and Ratcliff, 2004; Bogacz, 2007; Gold and Shadlen, 2007; Rangel et al, 2008; Kable and Glimcher, 2009; Hare and Rangel, 2010; Rushworth et al, 2011). It has been shown that these models provide good accounts of value-based choice (Basten et al, 2010; Krajbich et al, 2010; Milosavljevic et al, 2010; Philiastides et al, 2010; Hare et al, 2011; Krajbich and Rangel, 2011) This class of models assumes that decisions are made by accumulating noisy evidence in favor of the different options. This introduces two sources of noise in the process: noise intrinsic to the sampling of attribute values, and noise due to random shifts in attention between the options which affect how the attribute values are sampled (Busemeyer and Townsend, 1993; Diederich, 1997; Roe et al, 2001; Busemeyer and Diederich, 2002; Usher and McClelland, 2004; Johnson and Busemeyer, 2005; Usher et al, 2008; Tsetsos et al, 2010)
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