Create a Feature-Based RSA Model

Creates a model for feature-based Representational Similarity Analysis (RSA) that relates neural patterns (X) to a predefined feature space (F).

Usage

feature_rsa_model(
  dataset,
  design,
  method = c("scca", "pls", "pca"),
  crossval = NULL
)

Arguments

dataset

An mvpa_dataset object containing the neural data (X).

design

A feature_rsa_design object specifying the feature space (F).

method

Character string specifying the analysis method. One of:

scca

Sparse Canonical Correlation Analysis

pls

Partial Least Squares

pca

Principal Component Analysis + linear regression on F

crossval

Optional cross-validation specification.

Value

A feature_rsa_model object (S3 class).

Details

Feature RSA models analyze the relationship between neural patterns X and a predefined feature space F. Methods: - scca: Finds canonical correlations between X and F and reconstructs F from X via these canonical directions. - pls: Uses partial least squares regression to predict F from X. - pca: PCA on X, then regress F_sc ~ PC(X_sc) for prediction.