Model Wishlist

[DominiqueMakowski]

Was just wondering about the possibility of implementing models with time-varying boundaries (or collapsing boundaries). That said, it might make sense to not have that as a priority, given some of their critiques.

• https://www.frontiersin.org/articles/10.3389/fpsyg.2014.01364/full (implementation)
• https://www.jneurosci.org/content/35/6/2476 (a critique of these models)
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5450920/ (another critique)

[itsdfish]

@DominiqueMakowski, I agree with your assessment. There is some controversy regarding the validity of time varying boundaries. I'm definitely not opposed to adding support at some point.

I think this is a good opportunity to create a working wishlist of models we could add. Let me know if there is anything else you want to add.

Model Wishlist

model	assigned	priority	references	finished
time varying boundary	NA	low	1,2,3	No
mlba	itsdfish	high	4	No
MTLNR	itsdfish	medium	5	No
trial varying wald	kiante-fernandez	medium	6	No
ex-gaussian	itsdfish	medium	7	Yes
RDEX	itsdfish	low	RDEX	8

References

1. Zhang, S., Lee, M. D., Vandekerckhove, J., Maris, G., & Wagenmakers, E. J. (2014). Time-varying boundaries for diffusion models of decision making and response time. Frontiers in psychology, 5, 1364.
2. Hawkins, G. E., Forstmann, B. U., Wagenmakers, E. J., Ratcliff, R., & Brown, S. D. (2015). Revisiting the evidence for collapsing boundaries and urgency signals in perceptual decision-making. Journal of Neuroscience, 35(6), 2476-2484.
3. Voskuilen, C., Ratcliff, R., & Smith, P. L. (2016). Comparing fixed and collapsing boundary versions of the diffusion model. Journal of mathematical psychology, 73, 59-79.
4. Trueblood, J. S., Brown, S. D., & Heathcote, A. (2014). The multiattribute linear ballistic accumulator model of context effects in multialternative choice. Psychological review, 121(2), 179.
5. Reynolds, A., Kvam, P. D., Osth, A. F., & Heathcote, A. (2020). Correlated racing evidence accumulator models. Journal of Mathematical Psychology, 96, 102331.
6. Howard et al., in press
7. Marmolejo-Ramos et al., 2022
8. hybrid racing-diffusion ex-Gaussian(RDEX) stop-signal model:

[DominiqueMakowski]

rtdists seems to implement LBA with varying drift SDs. Is this a feature that we could add?

[itsdfish]

Yeah. I pushed a new release which uses a vector for the drift SDs. The default is a vector of ones.

[DominiqueMakowski]

I suspect the same could be done for LNR, but I don't know if that's common

[itsdfish]

I was wondering the same thing myself. I need to verify its identifiable when the SDs differ. I believe it is.

[DominiqueMakowski]

Also, to add to the wishlist above:

multiple-threshold log-normal race (MTLNR): Reynolds et al. (2020) :)

[DominiqueMakowski]

• Time Varying Shifted Wald Model: Howard et al., in press
  • R code
• Generalised exponential-Gaussian distribution (GEG models): Marmolejo-Ramos et al., 2022
• Exgaussian

[kiante-fernandez]

I am also interested in the time varying shifted wald. Let me read that paper this weekend and give a shot at the translation.

[itsdfish]

Sounds good. I will take a look at the Generalised exponential-Gaussian model. I updated the model list accordingly.

[itsdfish]

I decided against adding the generalized Ex-Gaussian because it does not appear to be theoretically motivated. I added the Ex-Gaussian instead. The psychological interpretation of the Ex-Gaussian is dubious too, but I added it because it was simple and it has a historical role in RT modeling.

[DominiqueMakowski]

but I added it because it was simple and it has a historical role in RT modelling

Yes, totally agree, very cool!

because it does not appear to be theoretically motivated

Out of curiosity, can you elaborate a bit on your thoughts?

[itsdfish]

I did not go through the papers in fine detail, but my tentative impression is that the validity of the ex-gaussian distribution has not been strongly established, and the new shape parameter of the generalized ex-gaussian model has no psychological interpretation. If the $\alpha$ changes, what does that mean in terms of cognitive processes or mechanisms? Probably nothing.

I recommend checking out the paper below. The authors show through simulation and experimentation that the ex-Gaussian parameters do not change selectively with experimental manipulations (or varying DDM parameters). In many cases, all parameters respond to the manipulations, which seems to imply they do not map onto cognitive processes in a clear fashion if at all.

Matzke, D., & Wagenmakers, E. J. (2009). Psychological interpretation of the ex-Gaussian and shifted Wald parameters: A diffusion model analysis. Psychonomic bulletin & review, 16, 798-817.

[kiante-fernandez]

• Time Varying Shifted Wald Model: Howard et al., in press

  • R code

After reviewing the specific paper you mentioned, @DominiqueMakowski, it appears to primarily focus on exploiting a particular aspect of the Wald distribution's relationship with another distribution (e.g., ex-Gaussian). This approach is employed to facilitate on-the-fly computation of the posterior through conjugacy. Such trial-varying estimation permits variation in parameters across different trials. Initially, however, I had the impression that you were interested in exploring dynamics within trials, such as fluctuations in drift in the ADDM model, for example. If our focus remains on the former aspect, I believe only minor adjustments (if any) are necessary for the current Wald implementation. Subsequently, it would simply be a matter of demonstrating the procedure as an example of how trial-varying estimation could be implemented in Julia. However, this seems more like an illustrative application rather than the development of an entirely new model for the package. What are your guys thoughts on this?

[itsdfish]

on-the-fly computation of the posterior through conjugacy

I only gave the paper a cursory read, but arrived at a similar conclusion and wanted to wait for your evaluation. There is no need to create a new model type because the Wald is applied to a moving window of data. In my opinion, the biggest innovation was deriving a conjugate prior, which is difficult except in very simple cases. I think it would be sufficient to add an example to the parameter estimation examples in the docs showing how to use the conjugate prior without the moving window of data. The only thing that was a bit unclear to me was how non-decision time was treated.