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A note on modeling accumulation of information when the rate of accumulation changes over time

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    Unlike the MAS models, all attended attributes are assumed to be processed concurrently. Drift diffusion models with constantly changing drift rates have also been developed (Heath, 1981; McClelland, 1979; Ratcliff, 1980; Smith & Ratcliff, 2009) although they tend to be computationally challenging. Addition of a process tracing component to a sequential sampling model significantly improved the model’s ability to account for human data.

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    Although the models referenced above have differences in the how preferences and choices are formed, all are focused on detailing how the quantitative relationship between various attributes and their values impact decision-making. A second class of relevant work consists of multi-stage sequential sampling models, some of which also model multi-attribute decision-making (Diederich, 1995, 1997; Diederich, 2015; Diederich & Oswald, 2014; Diederich & Oswald, 2016; Holmes, Trueblood, & Heathcote, 2016; Ratcliff, 1980). Multi-stage models explicitly represent evidence for different processing stages of a decision rather than combining all information into one source of evidence, which had previously described the majority of sequential sampling models found in the literature.

  • Gaussian counter models for visual identification of briefly presented, mutually confusable single stimuli in pure accuracy tasks

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    The present analyses have focused on accounting for data from non-forced pure accuracy identification tasks where the processing time of the stimuli is controlled by backward masking. This type of paradigms has previously been termed time controlled paradigms in contrast to information controlled paradigms such as response terminated choice reaction time tasks (see Ratcliff, 1980). Given the success of drift diffusion models that base decisions on FPTs it is natural to ask how the present models would have fared if reaction times had been recorded and included in the data analyses, see e.g. Jones, Hawkins, and Brown (2015).

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This research was partially supported by NSF Grant BNS 78-17471 to W. K. Estes and by a Faculty Research Grant from Dartmouth College. This paper was prepared while the author was a visiting assistant professor at Rockefeller University.

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