Cue-Centered Lag-Rank

Condense cue-driven temporal organization into a single scalar per subject.

The cue-centered lag-rank parallels the cue-centered CRP but produces a scalar factor per subject instead of a CRP curve. A score of 0.5 indicates chance; scores above 0.5 reflect temporal contiguity between the retrieval cue and the recalled item. The dataset must contain a cue_clips field.

Workflow

Code 
import os
import warnings
from jaxcmr.analyses.cue_centered_lagrank import plot_cue_centered_lagrank
from jaxcmr.helpers import find_project_root, generate_trial_mask, load_data, save_figure

warnings.filterwarnings("ignore")
Code 
data_path = "data/VRT_clips.h5"
figure_dir = "results/figures"
figure_str = ""
trial_query = None
size = 1
Code 
project_root = find_project_root()
figure_dir = os.path.join(project_root, figure_dir)
data_path = os.path.join(project_root, data_path)
data = load_data(data_path)
trial_mask = generate_trial_mask(data, trial_query) if trial_query else None
Code 
plot_cue_centered_lagrank(data, trial_mask, size=size)
save_figure(figure_dir, figure_str)

Interpretation

The plot shows per-subject cue-centered temporal factor scores.

  • Scores above 0.5: cues reinstate temporal context, biasing recall toward nearby study positions.
  • Scores near 0.5: cues do not drive temporally organized retrieval.

API Details

Notebook parameters

  • data_path — path to an HDF5 file containing a RecallDataset with a cue_clips field.
  • figure_dir — directory for saving figures.
  • figure_str — base filename for the saved figure. Leave empty to display without saving.
  • trial_query — a Python expression to select trials, or None for all trials.
  • size — maximum number of study positions a single item can occupy.