NYU Dataset
Psychophysical Reverse Correlation Reflects Both Sensory and Decision-making Processes
Part of: Kiani Lab |
UID: 10676
- Description
- This study investigated how psychophysical reverse correlation may be used to understand the sensory mechanisms and decision-making processes underlying goal-directed behavior. The investigators used simulation modeling to build variations of a drift diffusion model (DDM) to develop a quantitative measure of psychophysical kernels on the integration of sensory input over time (non-decision time) until subjects made a choice (decision time). The psychophysical kernels in these models were compared against kernels derived from two experiments with human subjects: a reaction time version of the direction discrimination task with random dots and a face discrimination task using the MacBrain Face Stimulus Set. DDM parameters were fitted to behavioral data using a maximum-likelihood procedure to produce the experimentally-derived psychophysical kernels.
Access
- Restrictions
-
Free to All
- Instructions
- Code for psychophysical kernels for drift diffusion and accumulator (race) models can be accessed through GitHub. These models correspond to figures 3 and 6, respectively, in the associated publication.
Observational
- Grant Support
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McKnight Scholar Award/McKnight FoundationSloan Research Fellowship/Alfred P. Sloan FoundationTransition to Independence Award/Simons Collaboration on the Global BrainPostdoctoral Fellowship/Japan Society for the Promotion of ScienceSenior Fellowship in Biomedical Science/Charles H. Revson Foundation
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