Inquiry Regarding Nested Equations from HDDM Tutorial

[Jiawen]

Dear professor,

I am writing this email to inquire about the application of the Hierarchical Drift Diffusion Model (HDDM). I am currently engaged in a study that uses HDDM to analyze behavioral responses within a belief-updating paradigm. In our design, we aim to model the drift rate parameter v using two nested equations:

1. v = β0+β1×(probability−E2)
2. E2 = r×(Feedback−E1)+E1

Here, r represents a fixed learning rate, and  β0, β1, and r are parameters to be estimated. In our dataset, Feedback is a continuous variable with a fixed value for each event, rather than a binary reinforcement signal or subjective response. Our experimental setup does not conform to the classic reinforcement learning paradigms, and we treat the learning rate as a trait-like, non-time-varying element. The learning rate is a parameter we wish to estimate—assumed to be stable for each participant—and is used to dynamically compute 2 on each trial. The derived 2 value then serves as a regressor in the drift rate equation.

I was wondering if you might have suggestions on how we could implement these nested equations within the HDDM framework to accommodate our unique experimental conditions. Would it be possible to modify the existing HDDM regressor toolkit, or would an alternative approach be more suitable?

Thank you very much for your time and any guidance you could provide. I look forward to potentially collaborating or discussing these topics further.

Warm regards,

Jiawen Li

[Michael J Frank]

This flexible use of different RL formulations will be easier within HSSM (we no longer support HDDM).

I am cc'ing Krishn from my lab who is working on the RL integration in HSSM and some point soon may be able to share some code that would be helpful for building off of

Michael

Michael J Frank, PhD | Edgar L. Marston Professor

Director, Carney Center for Computational Brain Science 

Laboratory of Neural Computation and Cognition  

Brown University
website 

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[Jiawen]

Thank you very much, professor! We'll try it.