Repetition Lag-Rank

Decompose temporal organization by which presentation of a repeated item a transition originates from.

For each qualifying transition from an item studied at least twice with sufficient spacing, the absolute lag from each study position is ranked among all available lags from that position. A score of 0.5 indicates chance; scores above 0.5 reflect temporal contiguity. Comparing scores across presentation indices reveals whether transitions cluster around the first or second occurrence.

Workflow

Code

import os
import warnings
from jaxcmr.analyses.replagrank import plot_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_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 a given presentation index.

Scores above 0.5: transitions from that presentation are temporally organized.
Higher score for second presentation: transitions cluster around the more recent occurrence.
Similar scores: both presentations contribute equally to temporal contiguity.

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 (intervening items) between repeated presentations.
size — maximum number of study positions a single item can occupy.