# Characterizing Reference Distribution of Key Parameters of Drift Diffusion Model of Perceptual and Value-Based Decisions

> **NIH NIH R21** · YALE UNIVERSITY · 2020 · $183,750

## Abstract

Project Summary
 Effective decision making across contexts is essential for successful navigation of a complex world. Decision
making styles vary greatly between individuals, and with context and state. Theyy are altered in a range of
psychopathologies. However, the development of a systematic understanding of variation in decision making
has been hampered by variable and limited characterization of decision-making parameters, small samples in
most individual studies, and a lack of robust normative data. Computational models of decision making, such as
the drift-diffusion model (DDM), can be fitted to behavioral data from individual participants to reveal variation in
underlying processes. Parameters of such computational models may serve as “cleaner” measures of processes
of interest than unmodeled behavioral data, or self-report measures. They can also be used as correlates of
neural activation patterns during decision making. The validation of computational models and the identification
of model parameters that correlate robustly with brain activation sets the stage for parallel studies in animals, in
which causal relations can be more readily probed.
 We propose to conduct a large-scale online data collection of two DDM-compatible tasks, which probe
perceptual and value-based decision-making processes. We will use best practices developed for Amazon
Mechanical Turk (MTurk) to generate a reference distribution of DDM parameters. Since DDM relies on precise
measurements of reaction time, it is critically important to establish validity of online instruments, which we
propose to do by collecting parallel in-lab and online data in an initial medium size sample; this will permit robust
hypothesis-driven and exploratory analyses, as well as allowing us to optimize and validate online data collection
for the collection of online-only data in a larger sample (N = 500).
 If successful, this validation will allow large-scale behavioral data collection powered to detect small to
medium effect size associations and will provide a reference distribution and cutoff levels for extreme cases of
DDM parameters. We will investigate relations between continuous measures of selected clinical tendencies in
general population and DDM parameters in a large sample. We will also investigate relations between DDM
parameters and individual approach and avoidance tendencies, which are hypothesized to underlie individual
variations in decision making styles and have been translationally validated. This will generate new hypotheses
as to the role of decision-making abnormalities in psychopathology.
 The use of computational modeling approaches like DDM and large general population samples may be
more powerful for the elucidation of such relationships than simple correlations of behavioral measures with
symptomatology. This approach is consistent with the RDoC framework and can be extended in future work to
transdiagnostic and translational studies of psychopathology.

## Key facts

- **NIH application ID:** 9999049
- **Project number:** 5R21MH120801-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Christopher John Pittenger
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $183,750
- **Award type:** 5
- **Project period:** 2019-08-19 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9999049

## Citation

> US National Institutes of Health, RePORTER application 9999049, Characterizing Reference Distribution of Key Parameters of Drift Diffusion Model of Perceptual and Value-Based Decisions (5R21MH120801-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9999049. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*