TDT RSA with behavioral RDM

Hello everyone,

First off, thank you to the developers for the great toolbox. I’m using it to run a searchlight RSA analysis comparing neural similarity to behavioral ratings and wanted to check that my script is doing what I think it’s doing. From reading the existing posts here, I understand that using the rsa_beta output measure can be a bit tricky.

I have four conditions across three runs. I created behavioral RDMs by calculating pairwise distances between conditions. Here are relevant snippets of my script, based on the decoding_searchlight_crossnobis_filled example: = ‘similarity’;
cfg.decoding.method = ‘classification’;
cfg.decoding.train.classification.model_parameters = ‘euc’;

cfg.results.output = {‘rsa_beta’,‘other_meandist’};

cfg.scale.method = ‘cov’; % we scale by noise covariance
cfg.scale.estimation = ‘separate’;
cfg.scale.shrinkage = ‘lw2’; % Ledoit-Wolf shrinkage retaining variances

cfg.files.components.matrix = {target_dsm}; % pairwise distance matrix based on behavioral ratings
cfg.files.components.index = ones(length(target_dsm))';

The rsa_beta output looks to be on very different scales for each participant and I see from the documentation that they’re not normalized. Will this be an issue for conducting the prevalence inference analysis at the group level?

Thank you in advance for any insights!