Category Recall by LPP

Test whether higher LPP amplitudes predict better recall for different item types.

This analysis bins items by their LPP amplitude and computes recall rate within each bin, filtered by category. An upward trend indicates that higher neural encoding signals predict better subsequent memory.

Workflow

Code 
import os
import matplotlib.pyplot as plt
import warnings
from jaxcmr.analyses.cat_recall_by_lpp import plot_cat_recall_by_lpp
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_values = [1, 2]
lpp_field = "EarlyLPP"
labels = ["Negative", "Neutral"]
color_cycle = ["red", "black"]
contrast_name = "condition"
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_recall_by_lpp(
    data, trial_mask,
    category_field=category_field,
    category_values=category_values,
    lpp_field=lpp_field,
    labels=labels,
    color_cycle=color_cycle,
    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

The x-axis shows LPP amplitude bins; the y-axis shows recall probability within each bin.

  • Upward trend: higher encoding signals predict better recall (subsequent memory effect).
  • Category differences: the LPP–recall relationship may vary by item type.

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_values — list of category values to plot.
  • lpp_field — dataset field containing LPP amplitudes per study position.
  • labels — legend labels for each category.
  • color_cycle — colors for each curve.
  • contrast_name — legend title.