Add "Ratcliff" log-likelihood, Wiener process with intrinsic trial-to-trial drift variability

alashworth commented 5 years ago

Issue by mdnunez Tuesday Apr 19, 2016 at 23:50 GMT Originally opened as https://github.com/stan-dev/stan/issues/1875

New Feature Suggestion:

Add a Wiener likelihood function with intrinsic trial to trial variability in the drift rate, titled the "Ratcliff" likelihood function

Description:

Adding a parameter that describes the variance in drift rate over trials to the Wiener likelihood function yields better estimates of incorrect reaction time (see Table 3 of Nunez et al, 2016 for an example deficit of incorrect-RT prediction with a hierarchical version of the simple Wiener likelihood). This version of the diffusion model and its extensions was popularized by Ratcliff (1978; see also Ratcliff and McKoon, 2008) and is used widely in the mathematical psychology and neuroscience communities. Because of sampling techniques developed by Navarro and Fuss (2009) and probability density function derivations by Tuerlinckx (2004), it is relatively straight-forward to copy and change the existing Wiener likelihood sampler in Stan to include intrinsic trial-to-trial variability. This method has already been implemented by existing hierarchical "drift-diffusion" model samplers (Wiecki, 2003).

Additional Information:

I have a version of "ratcliff_log.hpp" code mostly finished however it needs to be tested and compared to simulations. Any helpful pointers to what needs to be changed in other parts of Stan code would be appreciated.

Citations:

Navarro, D. J., & Fuss, I. G. (2009). Fast and accurate calculations for first-passage times in Wiener diffusion models. Journal of Mathematical Psychology, 53(4), 222-230.
Nunez, M. D., Vandekerckhove, J., & Srinivasan, R. (2016). How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters. Journal of Mathematical Psychology.
Ratcliff, R. (1978). A theory of memory retrieval. Psychological review, 85(2), 59.
Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural computation, 20(4), 873-922.
Tuerlinckx, F. (2004). The efficient computation of the cumulative distribution and probability density functions in the diffusion model. Behavior Research Methods, Instruments, & Computers, 36(4), 702-716.
Wiecki, T. V., Sofer, I., & Frank, M. J. (2013). HDDM: hierarchical bayesian estimation of the drift-diffusion model in python. Frontiers in neuroinformatics, 7(August), 1-10.
Current Version:

v2.9.0

alashworth commented 5 years ago

Comment by bob-carpenter Wednesday Apr 20, 2016 at 17:00 GMT

Any helpful pointers to what needs to be changed in other parts of Stan code would be appreciated.

See:

alashworth commented 5 years ago

Comment by mdnunez Wednesday Apr 20, 2016 at 18:53 GMT

This issue was moved to stan-dev/math#287

alashworth / test-issue-import