Hi everyone,

I really appreciate the authors for creating the wonderful decoding toolbox, TDT.

Let me ask about how to perform GLM with representation similarity analysis (RSA-GLM) in TDT.

I would be very happy if you could give me some tips.

I would like to obtain beta values of three factors (behavior dissimilar matrix) against neural (dis)similarity matrix across each trial for each participant.

I believe that what I want to do is very similar to this previous post by Phil: RSA on individual behavioral ratings

But I could not find how to correctly perform RSA-GLM in TDT.

My current settings are like this (under version 3.994):

cfg.decoding.software = āsimilarityā;

cfg.decoding.method = āclassificationā;

cfg.decoding.train.classification.model_parameters = āzcorrā;

cfg.results.output = {ārsa_betaā, āotherā}; % I believe that āotherā output values here correspond to Z-correlations across each trial

cfg.design = make_design_rsa(cfg);

This script successfully runs and I could obtain rsa_beta values of each dissimilar matrix as well as a neural similarity matrix for each participant.

However, I am not sure whether cfg.decoding.method = āsimilarityā is correct, instead of āregression.ā

If it should be āregressionā, what kind of parameters should be chosen for cfg.decoding.software?

I hope you will be able to provide the information.

Thank you!

Ryuhei