Evaluate model performance for feature RSA

Computes condition-pattern metrics (trial x trial correlation matrix), voxel-level encoding metrics, global reconstruction metrics (MSE, R-squared), and optionally performs permutation tests.

Usage

evaluate_model.feature_rsa_model(
  object,
  predicted,
  observed,
  nperm = 0,
  save_distributions = FALSE,
  compute_rdm_vectors = isTRUE(object$return_rdm_vectors),
  ...
)

Arguments

object

The feature RSA model

predicted

Matrix of predicted values (observations x voxels)

observed

Matrix of observed values (observations x voxels)

nperm

Number of permutations for statistical testing (default: 0)

save_distributions

Logical indicating whether to save full permutation distributions

compute_rdm_vectors

Logical; when TRUE, also return compact predicted and observed RDM vectors for reuse by downstream code.

...

Additional arguments

Value

A list containing:

pattern_correlation

Mean diagonal of the trial x trial correlation matrix – how well the predicted spatial pattern for each trial matches the correct observed pattern.

pattern_discrimination

Diagonal minus off-diagonal of the trial x trial correlation matrix – how much better the correct trial is matched than incorrect trials.

pattern_rank_percentile

For each trial, percentile rank of the correct pattern among all candidates. 0.5 = chance, 1 = perfect.

voxel_correlation

Correlation of the flattened predicted and observed matrices (global reconstruction quality).

mse

Mean squared error.

r_squared

1 - RSS/TSS.

mean_voxelwise_temporal_cor

Average per-voxel temporal correlation (encoding fidelity).

permutation_results

If nperm > 0, a list with p-values and z-scores for each metric.

Examples

if (FALSE) { # \dontrun{
  # Internal S3 method called after cross-validation
  # perf <- evaluate_model(feature_rsa_model, newdata, observed)
} # }