Backward Repetition Lag-Rank

Measure temporal organization of incoming transitions to repeated items.

The backward repetition lag-rank reverses each recall sequence before computing the standard repetition lag-rank. This measures temporal organization of transitions to a repeated item (rather than from it). A score of 0.5 indicates chance; scores above 0.5 reflect temporal contiguity of incoming transitions.

Workflow

Code

import os
import warnings
from jaxcmr.analyses.backreplagrank import plot_back_rep_lagrank
from jaxcmr.helpers import find_project_root, generate_trial_mask, load_data, save_figure

warnings.filterwarnings("ignore")

Code

data_path = "data/LohnasKahana2014.h5"
figure_dir = "results/figures"
figure_str = ""
trial_query = "data['list_type'] > 2"
min_lag = 4
size = 2

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)

Code

plot_back_rep_lagrank(data, trial_mask, min_lag=min_lag, size=size)
save_figure(figure_dir, figure_str)

Interpretation

Each bar represents a temporal factor score for incoming transitions to a given presentation.

Scores above 0.5: repeated items attract transitions from temporally nearby items.
Higher score for second presentation: the more recent occurrence is a stronger attractor.

API Details

Notebook parameters

data_path — path to an HDF5 file containing a RecallDataset with repeated items.
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 evaluated against the dataset to select trials.
min_lag — minimum spacing between repeated presentations.
size — maximum number of study positions a single item can occupy.