Category LPP by Recall Outcome

Compare LPP amplitudes for recalled versus unrecalled items within a category.

This analysis splits items by recall outcome within a reference category, comparing LPP amplitudes at each study position for items that were subsequently recalled versus those that were not. A subsequent memory effect appears when recalled items show higher LPP than unrecalled items.

Workflow

Code 
import os
import matplotlib.pyplot as plt
import warnings
from jaxcmr.analyses.cat_lpp_by_recall import plot_cat_lpp_by_recall
from jaxcmr.helpers import find_project_root, generate_trial_mask, load_data, save_figure

warnings.filterwarnings("ignore")
Code 
data_path = "data/TalmiEEG.h5"
figure_dir = "results/figures"
figure_str = ""
ylim = None
trial_query = "data['subject'] > 0"
category_field = "condition"
category_value = 1
lpp_field = "EarlyLPP"
labels = ["Recalled", "Unrecalled"]
contrast_name = "Negative Items"
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_cat_lpp_by_recall(
    data, trial_mask,
    category_field=category_field,
    category_value=category_value,
    lpp_field=lpp_field,
    labels=labels,
    contrast_name=contrast_name,
)
if ylim is not None:
    for ax in plt.gcf().axes:
        ax.set_ylim(ylim)
save_figure(figure_dir, figure_str)

Interpretation

Two curves show mean LPP amplitude across study positions for recalled and unrecalled items.

  • Recalled > Unrecalled: subsequent memory effect — higher encoding-related neural activity predicts recall.
  • No separation: LPP does not predict recall for this category.

API Details

Notebook parameters

  • data_path — path to an HDF5 file containing a RecallDataset with EEG fields.
  • figure_dir — directory for saving figures.
  • figure_str — base filename for the saved figure. Leave empty to display without saving.
  • ylim — y-axis limits as a tuple, or None for automatic scaling.
  • trial_query — a Python expression evaluated against the dataset to select trials.
  • category_field — dataset field containing category labels.
  • category_value — the category to analyze.
  • lpp_field — dataset field containing LPP amplitudes per study position.
  • labels — legend labels for recalled and unrecalled conditions.
  • contrast_name — legend title.