Serial Repetition Lag-Rank

Measure temporal organization of transitions from repeated items during correct serial run-ups.

The serial repetition lag-rank parallels the serial repetition CRP but produces a scalar factor per presentation index instead of a CRP curve. It is restricted to the first qualifying transition during a correct serial run-up. A score of 0.5 indicates chance; scores above 0.5 reflect temporal contiguity.

Workflow

Code 
import os
import warnings
from jaxcmr.analyses.serialreplagrank import plot_serial_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_serial_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, restricted to serial run-up transitions.

  • Scores above 0.5: temporal contiguity during ordered recall sequences.
  • First vs second presentation differences: reveals which occurrence anchors serial retrieval.

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.