MNLE with experimental conditions

[Jiashun97]

Hi there,

I am trying to apply MNLE in DDMs that need to allow some parameters free to vary across experimental conditions. I noticed that in the sbi/tutorials/Example_01_DecisionMakingModel.ipynb, it provides a simulator wrapper to do so. If I understand it correctly, it makes all parameters free across conditions. However, in some of the behavioral experiments, only one or two parameters are free and the others are fixed. I was wondering if it is possible to implement this in MNLE. Thanks for your help!

Best regards,
Jiashun

[Jiashun97]

I got the answer from Jan: "the emulator is trained on both the parameter and the conditioning variables. At inference time you build a wrapper that fixes the conditions via the potential function wrapper. You could use the same mechanism to fix some of the actual parameters as well at inference time."

[Jiashun97]

Thanks for your answer. I know how to fix a parameter to a specific value in the potential function wrapper. However, the key idea in my analysis is to force the parameter to be the same in two conditions. Suppose we have a five-parameter competing accumulator (CA) model: dI, I, k, a, ter are the parameters. The experiment has a difficulty manipulation that only affect dI (input difference), resulting in two exp. conditions. The behavioral data of one subject will have three columns: RTs, choices, and conditions. In the analysis, I am trying to build a CA model that has six parameters: dI1 (for condition 1), dI2 (for condition 2), I, k, a, ter. This means the dI is free to vary across the condition while the others are fixed (because the experimental manipulation was supposed to only change dI).

In the inference stage of MNLE, it needs to take the RTs, choices, and conditions into account, returning the MAP estimate (or posterior samples) for dI1, dI2, I, k, a, ter. My intuition is that we should also specify the relationship of the parameters before training the network. Maybe we need to change the underlying network structure to achieve this?

I've attached a Jupyter notebook with code for one condition and the implementation of the simulator wrapper. I would greatly appreciate any help with this issue, as fitting decision-making models to experimental data like this is critically important.

SBI with multiple experimental conditions.ipynb.zip

Best regards,
Jiashun