A martingale analysis of first passage times of time-dependent Wiener diffusion models

Vaibhav Srivastava, Samuel F. Feng, Jonathan D. Cohen, Naomi Ehrich Leonard, Amitai Shenhav

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Research in psychology and neuroscience has successfully modeled decision making as a process of noisy evidence accumulation to a decision bound. While there are several variants and implementations of this idea, the majority of these models make use of a noisy accumulation between two absorbing boundaries. A common assumption of these models is that decision parameters, e.g., the rate of accumulation (drift rate), remain fixed over the course of a decision, allowing the derivation of analytic formulas for the probabilities of hitting the upper or lower decision threshold, and the mean decision time. There is reason to believe, however, that many types of behavior would be better described by a model in which the parameters were allowed to vary over the course of the decision process. In this paper, we use martingale theory to derive formulas for the mean decision time, hitting probabilities, and first passage time (FPT) densities of a Wiener process with time-varying drift between two time-varying absorbing boundaries. This model was first studied by Ratcliff (1980) in the two-stage form, and here we consider the same model for an arbitrary number of stages (i.e. intervals of time during which parameters are constant). Our calculations enable direct computation of mean decision times and hitting probabilities for the associated multistage process. We also provide a review of how martingale theory may be used to analyze similar models employing Wiener processes by re-deriving some classical results. In concert with a variety of numerical tools already available, the current derivations should encourage mathematical analysis of more complex models of decision making with time-varying evidence.

Original languageEnglish (US)
Pages (from-to)94-110
Number of pages17
JournalJournal of Mathematical Psychology
StatePublished - Apr 1 2017

All Science Journal Classification (ASJC) codes

  • Applied Mathematics
  • General Psychology


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