{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Murdock (1962) Free Recall Data\n",
    "\n",
    "This tutorial demonstrates analyzing the classic Murdock (1962) free recall dataset, which established the serial position effect as a fundamental phenomenon in memory research.\n",
    "\n",
    "The dataset contains 90 subjects (15 per condition) across 6 experimental conditions varying in list length (10, 15, 20, 30, or 40 items) and presentation rate (1 or 2 sec/item). Each subject completed 80 lists.\n",
    "\n",
    "**Conditions:**\n",
    "- LL10-2s: 10 items, 2 sec/item (15 subjects, 80 lists each)\n",
    "- LL15-2s: 15 items, 2 sec/item (15 subjects, 80 lists each)\n",
    "- LL20-1s: 20 items, 1 sec/item (15 subjects, 80 lists each)\n",
    "- LL20-2s: 20 items, 2 sec/item (15 subjects, 80 lists each)\n",
    "- LL30-1s: 30 items, 1 sec/item (15 subjects, 80 lists each)\n",
    "- LL40-1s: 40 items, 1 sec/item (15 subjects, 80 lists each)\n",
    "\n",
    "We'll analyze recall performance using:\n",
    "1. Probability of First Recall (PFR) - probability of recalling each position first\n",
    "2. Lag-CRP - conditional recall probability by temporal lag\n",
    "3. Serial Position Curve (SPC) - recall probability by encoding position\n",
    "\n",
    "**Reference:**\n",
    "Murdock, B. B. (1962). The serial position effect of free recall. *Journal of Experimental Psychology*, 64(5), 482-488. https://doi.org/10.1037/h0045106"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import quail\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "\n",
    "# Suppress RuntimeWarnings about empty slices\n",
    "warnings.filterwarnings('ignore', category=RuntimeWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataset\n",
    "\n",
    "The Murdock 1962 dataset is included with quail and can be loaded using `load_example_data()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the Murdock 1962 dataset\n",
    "egg = quail.load_example_data('murd62')\n",
    "\n",
    "print(f\"Loaded Murdock 1962 data: {egg.n_subjects} subjects, {egg.n_lists} lists per subject\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up subject groupings\n",
    "\n",
    "Since each subject belongs to a single condition, we create a `subjgroup` list that maps each subject to their condition. This allows us to plot separate curves for each condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build subjgroup: map each subject index to its condition name\n",
    "subjgroup = []\n",
    "for subj_idx in range(egg.n_subjects):\n",
    "    try:\n",
    "        sample = egg.pres.loc[(subj_idx, 0)][0]\n",
    "        if sample and 'condition' in sample:\n",
    "            subjgroup.append(sample['condition'])\n",
    "        else:\n",
    "            subjgroup.append('Unknown')\n",
    "    except (KeyError, IndexError, TypeError):\n",
    "        subjgroup.append('Unknown')\n",
    "\n",
    "print(f\"Subject conditions: {len(set(subjgroup))} unique conditions\")\n",
    "\n",
    "# Create listgroup for averaging across lists within each subject\n",
    "listgroup = ['average'] * egg.n_lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze and plot\n",
    "\n",
    "We'll create three plots showing key memory phenomena:\n",
    "- **PFR**: Shows the primacy effect - items at the beginning of a list are more likely to be recalled first\n",
    "- **Lag-CRP**: Shows temporal contiguity - items studied close together are likely to be recalled together\n",
    "- **SPC**: Shows the classic serial position curve with primacy and recency effects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create figure with 3 subplots in order: PFR, Lag-CRP, SPC\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "\n",
    "# 1. Probability of First Recall - use quail's built-in plot with error bars\n",
    "pfr = egg.analyze('pfr', listgroup=listgroup)\n",
    "pfr.plot(ax=axes[0], subjgroup=subjgroup, plot_type='subject', legend=True)\n",
    "\n",
    "# 2. Lag-CRP - use quail's built-in plot with error bars (legend=False)\n",
    "lagcrp = egg.analyze('lagcrp', listgroup=listgroup)\n",
    "lagcrp.plot(ax=axes[1], subjgroup=subjgroup, plot_type='subject', legend=False)\n",
    "\n",
    "# 3. Serial Position Curve - use quail's built-in plot with error bars (legend=False)\n",
    "spc = egg.analyze('spc', listgroup=listgroup)\n",
    "spc.plot(ax=axes[2], subjgroup=subjgroup, plot_type='subject', legend=False)\n",
    "\n",
    "# Configure PFR plot\n",
    "axes[0].set_title('Probability of First Recall')\n",
    "axes[0].set_xlabel('Serial Position')\n",
    "axes[0].set_ylabel('Probability')\n",
    "axes[0].set_ylim([0, 0.6])\n",
    "\n",
    "# Configure Lag-CRP plot\n",
    "axes[1].set_title('Lag-CRP')\n",
    "axes[1].set_xlabel('Lag')\n",
    "axes[1].set_ylabel('Conditional Recall Probability')\n",
    "axes[1].set_xlim([-10, 10])\n",
    "axes[1].set_ylim([0, 0.5])\n",
    "axes[1].axvline(x=0, color='gray', linestyle='--', alpha=0.5)\n",
    "\n",
    "# Configure SPC plot\n",
    "axes[2].set_title('Serial Position Curve')\n",
    "axes[2].set_xlabel('Serial Position')\n",
    "axes[2].set_ylabel('Recall Probability')\n",
    "axes[2].set_ylim([0, 1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.suptitle('Murdock (1962) Free Recall Data', y=1.02, fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Key findings\n",
    "\n",
    "The plots reveal several classic memory phenomena:\n",
    "\n",
    "1. **Primacy Effect**: Items at the beginning of the list are recalled more often (visible in SPC)\n",
    "2. **Recency Effect**: Items at the end of the list are recalled more often (visible in SPC)\n",
    "3. **Temporal Contiguity**: Items studied nearby in time tend to be recalled together (visible in Lag-CRP asymmetry toward +1 lag)\n",
    "4. **List Length Effects**: Longer lists show lower overall recall probability but similar curve shapes"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}