{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CCN Lab 3 -- Drift Diffusion Process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task 1 -- Initial Simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def drift_diffusion_model(v, a, s, z, dt, timeout=2000):\n",
    "    step = 0\n",
    "    W = z\n",
    "\n",
    "    times = [0]\n",
    "    vals = [W]\n",
    "\n",
    "    while step < timeout and W > 0 and W < a:\n",
    "        eta = np.random.normal(0, np.sqrt(dt))\n",
    "        W += v * dt + s * eta\n",
    "        W = np.clip(W, 0, a) # We truncate values outside [0, a] for plotting purposes\n",
    "\n",
    "        step += 1\n",
    "        times.append(step * dt)\n",
    "        vals.append(W)\n",
    "\n",
    "    if step == timeout:\n",
    "        return times, vals, -1\n",
    "    elif W == 0:\n",
    "        return times, vals, 0\n",
    "    elif W == a:\n",
    "        return times, vals, 1\n",
    "    else:\n",
    "        raise ValueError(\"W is not 0 or a. This should not happen.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v, a, s, z, dt = 0.03, 0.12, 0.04, 0.06, 0.001\n",
    "\n",
    "# Plot 4 example paths\n",
    "fig, axes = plt.subplots(4, 1, figsize=(10, 10))\n",
    "for i in range(4):\n",
    "    times, vals, outcome = drift_diffusion_model(v, a, s, z, dt)\n",
    "    axes[i].plot(times, vals)\n",
    "    outcome_str = \"Answer 1\" if outcome == 1 else \"Answer 0\" if outcome == 0 else \"Timeout\"\n",
    "    axes[i].set_title(\"Outcome: {}\".format(outcome_str))\n",
    "    axes[i].set_xlabel(\"Time (s)\")\n",
    "    axes[i].set_ylabel(\"W\")\n",
    "    axes[i].set_ylim(0, a)\n",
    "    axes[i].set_yticks(np.linspace(0, a, 5))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.65\n",
      "Mean response time for Answer 1: 1.03s\n",
      "Mean response time for Answer 0: 1.02s\n"
     ]
    }
   ],
   "source": [
    "outcomes = []\n",
    "response_times = []\n",
    "for _ in range(50000):\n",
    "    times, _, outcome = drift_diffusion_model(v, a, s, z, dt)\n",
    "    outcomes.append(outcome)\n",
    "    response_times.append((len(times) - 1) * dt)\n",
    "\n",
    "def show_results(outcomes, response_times, plot=True):\n",
    "    response_times_1 = [response_times[i] for i in range(len(outcomes)) if outcomes[i] == 1]\n",
    "    response_times_0 = [response_times[i] for i in range(len(outcomes)) if outcomes[i] == 0]\n",
    "\n",
    "    # Plot three histograms of response times, for outcome 1, 0 and overall\n",
    "    if plot:\n",
    "        fig, axes = plt.subplots(3, 1, figsize=(10, 10))\n",
    "        axes[0].hist(response_times_1, bins=20)\n",
    "        axes[0].set_title(\"Response times for Answer 1\")\n",
    "        axes[0].set_xlabel(\"Response time (s)\")\n",
    "        axes[0].set_ylabel(\"Frequency\")\n",
    "        axes[1].hist(response_times_0, bins=20)\n",
    "        axes[1].set_title(\"Response times for Answer 0\")\n",
    "        axes[1].set_xlabel(\"Response time (s)\")\n",
    "        axes[1].set_ylabel(\"Frequency\")\n",
    "        axes[2].hist(response_times, bins=20)\n",
    "        axes[2].set_title(\"Response times overall\")\n",
    "        axes[2].set_xlabel(\"Response time (s)\")\n",
    "        axes[2].set_ylabel(\"Frequency\")\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "        # Plot the distribution of outcomes as a pie chart\n",
    "        plt.figure(figsize=(5, 5))\n",
    "        plt.pie([outcomes.count(1), outcomes.count(0), outcomes.count(-1)], labels=[\"Answer 1\", \"Answer 0\", \"Timeout\"], autopct='%1.1f%%')\n",
    "        plt.title(\"Outcome distribution\")\n",
    "        plt.show()\n",
    "\n",
    "    # Return accuracy and mean response times for correct and incorrect answers\n",
    "    accuracy = outcomes.count(1) / len(outcomes)\n",
    "    mean_response_time_1 = np.mean(response_times_1)\n",
    "    mean_response_time_0 = np.mean(response_times_0)\n",
    "    return accuracy, mean_response_time_1, mean_response_time_0\n",
    "\n",
    "accuracy, mean_response_time_1, mean_response_time_0 = show_results(outcomes, response_times)\n",
    "print(\"Accuracy: {:.2f}\".format(accuracy))\n",
    "print(\"Mean response time for Answer 1: {:.2f}s\".format(mean_response_time_1))\n",
    "\n",
    "print(\"Mean response time for Answer 0: {:.2f}s\".format(mean_response_time_0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since, above, the mean drift rate $v$ is positive, Answer 1 ($h_+$) is the correct response. It occurs in about two thirds of all simulations ($\\sim 65\\%$). A timeout is the second most common outcome ($\\sim 28\\%$), followed by a false response in distant third place ($\\sim 7\\%$). This distribution of outcomes may indicate that subjects' performance may benefit from not hesitating too long, but rather answering confidently based on first impressions. Errors due to indecisiveness appear much more common than errors due to incorrect sampling / integration of the evidence.\n",
    "\n",
    "In terms of the model, performance in the experiment would likely improve if the decision boundary $a$ was lowered, adjusting $W(0) = z = a/2$ accordingly. The same would occur if the timeout boundary was increased.\n",
    "\n",
    "This reasoning may not apply if the response time distributions for correct and incorrect responses had very different shapes; e.g., if answers given quickly had a higher chance of being incorrect than answers given close to the timeout -- this is not the case, however."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task 2 -- Exploring parameter settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v = 0.01, a = 0.02, Accuracy: 0.55, Mean response time for Answer 1: 0.07s, Mean response time for Answer 0: 0.07s\n",
      "v = 0.01, a = 0.04, Accuracy: 0.57, Mean response time for Answer 1: 0.21s, Mean response time for Answer 0: 0.21s\n",
      "v = 0.01, a = 0.05, Accuracy: 0.57, Mean response time for Answer 1: 0.40s, Mean response time for Answer 0: 0.43s\n",
      "v = 0.01, a = 0.07, Accuracy: 0.60, Mean response time for Answer 1: 0.63s, Mean response time for Answer 0: 0.63s\n",
      "v = 0.01, a = 0.08, Accuracy: 0.55, Mean response time for Answer 1: 0.83s, Mean response time for Answer 0: 0.84s\n",
      "v = 0.01, a = 0.10, Accuracy: 0.48, Mean response time for Answer 1: 0.95s, Mean response time for Answer 0: 0.94s\n",
      "v = 0.01, a = 0.11, Accuracy: 0.41, Mean response time for Answer 1: 1.03s, Mean response time for Answer 0: 1.05s\n",
      "v = 0.01, a = 0.13, Accuracy: 0.40, Mean response time for Answer 1: 1.12s, Mean response time for Answer 0: 1.17s\n",
      "v = 0.01, a = 0.14, Accuracy: 0.33, Mean response time for Answer 1: 1.22s, Mean response time for Answer 0: 1.18s\n",
      "v = 0.01, a = 0.16, Accuracy: 0.24, Mean response time for Answer 1: 1.28s, Mean response time for Answer 0: 1.24s\n",
      "v = 0.01, a = 0.02, Accuracy: 0.56, Mean response time for Answer 1: 0.07s, Mean response time for Answer 0: 0.07s\n",
      "v = 0.01, a = 0.04, Accuracy: 0.58, Mean response time for Answer 1: 0.20s, Mean response time for Answer 0: 0.20s\n",
      "v = 0.01, a = 0.05, Accuracy: 0.58, Mean response time for Answer 1: 0.40s, Mean response time for Answer 0: 0.40s\n",
      "v = 0.01, a = 0.07, Accuracy: 0.62, Mean response time for Answer 1: 0.63s, Mean response time for Answer 0: 0.62s\n",
      "v = 0.01, a = 0.08, Accuracy: 0.60, Mean response time for Answer 1: 0.78s, Mean response time for Answer 0: 0.77s\n",
      "v = 0.01, a = 0.10, Accuracy: 0.55, Mean response time for Answer 1: 0.93s, Mean response time for Answer 0: 0.91s\n",
      "v = 0.01, a = 0.11, Accuracy: 0.47, Mean response time for Answer 1: 1.05s, Mean response time for Answer 0: 1.10s\n",
      "v = 0.01, a = 0.13, Accuracy: 0.40, Mean response time for Answer 1: 1.15s, Mean response time for Answer 0: 1.18s\n",
      "v = 0.01, a = 0.14, Accuracy: 0.34, Mean response time for Answer 1: 1.21s, Mean response time for Answer 0: 1.19s\n",
      "v = 0.01, a = 0.16, Accuracy: 0.31, Mean response time for Answer 1: 1.30s, Mean response time for Answer 0: 1.35s\n",
      "v = 0.02, a = 0.02, Accuracy: 0.56, Mean response time for Answer 1: 0.07s, Mean response time for Answer 0: 0.08s\n",
      "v = 0.02, a = 0.04, Accuracy: 0.63, Mean response time for Answer 1: 0.21s, Mean response time for Answer 0: 0.20s\n",
      "v = 0.02, a = 0.05, Accuracy: 0.66, Mean response time for Answer 1: 0.42s, Mean response time for Answer 0: 0.39s\n",
      "v = 0.02, a = 0.07, Accuracy: 0.65, Mean response time for Answer 1: 0.61s, Mean response time for Answer 0: 0.65s\n",
      "v = 0.02, a = 0.08, Accuracy: 0.66, Mean response time for Answer 1: 0.76s, Mean response time for Answer 0: 0.81s\n",
      "v = 0.02, a = 0.10, Accuracy: 0.60, Mean response time for Answer 1: 0.93s, Mean response time for Answer 0: 0.94s\n",
      "v = 0.02, a = 0.11, Accuracy: 0.55, Mean response time for Answer 1: 1.04s, Mean response time for Answer 0: 1.06s\n",
      "v = 0.02, a = 0.13, Accuracy: 0.47, Mean response time for Answer 1: 1.13s, Mean response time for Answer 0: 1.15s\n",
      "v = 0.02, a = 0.14, Accuracy: 0.41, Mean response time for Answer 1: 1.18s, Mean response time for Answer 0: 1.21s\n",
      "v = 0.02, a = 0.16, Accuracy: 0.35, Mean response time for Answer 1: 1.25s, Mean response time for Answer 0: 1.25s\n",
      "v = 0.02, a = 0.02, Accuracy: 0.59, Mean response time for Answer 1: 0.07s, Mean response time for Answer 0: 0.07s\n",
      "v = 0.02, a = 0.04, Accuracy: 0.64, Mean response time for Answer 1: 0.21s, Mean response time for Answer 0: 0.22s\n",
      "v = 0.02, a = 0.05, Accuracy: 0.67, Mean response time for Answer 1: 0.41s, Mean response time for Answer 0: 0.40s\n",
      "v = 0.02, a = 0.07, Accuracy: 0.70, Mean response time for Answer 1: 0.62s, Mean response time for Answer 0: 0.67s\n",
      "v = 0.02, a = 0.08, Accuracy: 0.71, Mean response time for Answer 1: 0.78s, Mean response time for Answer 0: 0.79s\n",
      "v = 0.02, a = 0.10, Accuracy: 0.66, Mean response time for Answer 1: 0.91s, Mean response time for Answer 0: 0.86s\n",
      "v = 0.02, a = 0.11, Accuracy: 0.60, Mean response time for Answer 1: 1.02s, Mean response time for Answer 0: 1.09s\n",
      "v = 0.02, a = 0.13, Accuracy: 0.54, Mean response time for Answer 1: 1.11s, Mean response time for Answer 0: 1.22s\n",
      "v = 0.02, a = 0.14, Accuracy: 0.47, Mean response time for Answer 1: 1.20s, Mean response time for Answer 0: 1.24s\n",
      "v = 0.02, a = 0.16, Accuracy: 0.40, Mean response time for Answer 1: 1.28s, Mean response time for Answer 0: 1.13s\n",
      "v = 0.03, a = 0.02, Accuracy: 0.59, Mean response time for Answer 1: 0.07s, Mean response time for Answer 0: 0.07s\n",
      "v = 0.03, a = 0.04, Accuracy: 0.67, Mean response time for Answer 1: 0.21s, Mean response time for Answer 0: 0.21s\n",
      "v = 0.03, a = 0.05, Accuracy: 0.71, Mean response time for Answer 1: 0.40s, Mean response time for Answer 0: 0.40s\n",
      "v = 0.03, a = 0.07, Accuracy: 0.73, Mean response time for Answer 1: 0.60s, Mean response time for Answer 0: 0.57s\n",
      "v = 0.03, a = 0.08, Accuracy: 0.72, Mean response time for Answer 1: 0.77s, Mean response time for Answer 0: 0.84s\n",
      "v = 0.03, a = 0.10, Accuracy: 0.74, Mean response time for Answer 1: 0.87s, Mean response time for Answer 0: 0.87s\n",
      "v = 0.03, a = 0.11, Accuracy: 0.66, Mean response time for Answer 1: 1.02s, Mean response time for Answer 0: 1.05s\n",
      "v = 0.03, a = 0.13, Accuracy: 0.57, Mean response time for Answer 1: 1.12s, Mean response time for Answer 0: 1.00s\n",
      "v = 0.03, a = 0.14, Accuracy: 0.52, Mean response time for Answer 1: 1.14s, Mean response time for Answer 0: 1.04s\n",
      "v = 0.03, a = 0.16, Accuracy: 0.49, Mean response time for Answer 1: 1.26s, Mean response time for Answer 0: 1.35s\n",
      "v = 0.03, a = 0.02, Accuracy: 0.62, Mean response time for Answer 1: 0.07s, Mean response time for Answer 0: 0.07s\n",
      "v = 0.03, a = 0.04, Accuracy: 0.70, Mean response time for Answer 1: 0.21s, Mean response time for Answer 0: 0.22s\n",
      "v = 0.03, a = 0.05, Accuracy: 0.74, Mean response time for Answer 1: 0.38s, Mean response time for Answer 0: 0.39s\n",
      "v = 0.03, a = 0.07, Accuracy: 0.76, Mean response time for Answer 1: 0.58s, Mean response time for Answer 0: 0.61s\n",
      "v = 0.03, a = 0.08, Accuracy: 0.78, Mean response time for Answer 1: 0.74s, Mean response time for Answer 0: 0.76s\n",
      "v = 0.03, a = 0.10, Accuracy: 0.74, Mean response time for Answer 1: 0.89s, Mean response time for Answer 0: 0.85s\n",
      "v = 0.03, a = 0.11, Accuracy: 0.71, Mean response time for Answer 1: 0.98s, Mean response time for Answer 0: 0.93s\n",
      "v = 0.03, a = 0.13, Accuracy: 0.62, Mean response time for Answer 1: 1.07s, Mean response time for Answer 0: 0.98s\n",
      "v = 0.03, a = 0.14, Accuracy: 0.61, Mean response time for Answer 1: 1.15s, Mean response time for Answer 0: 1.14s\n",
      "v = 0.03, a = 0.16, Accuracy: 0.54, Mean response time for Answer 1: 1.23s, Mean response time for Answer 0: 1.32s\n",
      "v = 0.04, a = 0.02, Accuracy: 0.61, Mean response time for Answer 1: 0.07s, Mean response time for Answer 0: 0.07s\n",
      "v = 0.04, a = 0.04, Accuracy: 0.72, Mean response time for Answer 1: 0.21s, Mean response time for Answer 0: 0.21s\n",
      "v = 0.04, a = 0.05, Accuracy: 0.74, Mean response time for Answer 1: 0.37s, Mean response time for Answer 0: 0.38s\n",
      "v = 0.04, a = 0.07, Accuracy: 0.82, Mean response time for Answer 1: 0.56s, Mean response time for Answer 0: 0.60s\n",
      "v = 0.04, a = 0.08, Accuracy: 0.83, Mean response time for Answer 1: 0.75s, Mean response time for Answer 0: 0.76s\n",
      "v = 0.04, a = 0.10, Accuracy: 0.82, Mean response time for Answer 1: 0.84s, Mean response time for Answer 0: 0.86s\n",
      "v = 0.04, a = 0.11, Accuracy: 0.74, Mean response time for Answer 1: 0.98s, Mean response time for Answer 0: 0.97s\n",
      "v = 0.04, a = 0.13, Accuracy: 0.71, Mean response time for Answer 1: 1.07s, Mean response time for Answer 0: 0.94s\n",
      "v = 0.04, a = 0.14, Accuracy: 0.63, Mean response time for Answer 1: 1.12s, Mean response time for Answer 0: 1.17s\n",
      "v = 0.04, a = 0.16, Accuracy: 0.57, Mean response time for Answer 1: 1.20s, Mean response time for Answer 0: 1.37s\n",
      "v = 0.04, a = 0.02, Accuracy: 0.63, Mean response time for Answer 1: 0.07s, Mean response time for Answer 0: 0.07s\n",
      "v = 0.04, a = 0.04, Accuracy: 0.71, Mean response time for Answer 1: 0.19s, Mean response time for Answer 0: 0.21s\n",
      "v = 0.04, a = 0.05, Accuracy: 0.77, Mean response time for Answer 1: 0.37s, Mean response time for Answer 0: 0.34s\n",
      "v = 0.04, a = 0.07, Accuracy: 0.83, Mean response time for Answer 1: 0.55s, Mean response time for Answer 0: 0.53s\n",
      "v = 0.04, a = 0.08, Accuracy: 0.85, Mean response time for Answer 1: 0.69s, Mean response time for Answer 0: 0.64s\n",
      "v = 0.04, a = 0.10, Accuracy: 0.83, Mean response time for Answer 1: 0.85s, Mean response time for Answer 0: 0.86s\n",
      "v = 0.04, a = 0.11, Accuracy: 0.81, Mean response time for Answer 1: 0.97s, Mean response time for Answer 0: 0.94s\n",
      "v = 0.04, a = 0.13, Accuracy: 0.73, Mean response time for Answer 1: 1.01s, Mean response time for Answer 0: 1.10s\n",
      "v = 0.04, a = 0.14, Accuracy: 0.71, Mean response time for Answer 1: 1.10s, Mean response time for Answer 0: 1.16s\n",
      "v = 0.04, a = 0.16, Accuracy: 0.63, Mean response time for Answer 1: 1.20s, Mean response time for Answer 0: 1.20s\n",
      "v = 0.05, a = 0.02, Accuracy: 0.65, Mean response time for Answer 1: 0.07s, Mean response time for Answer 0: 0.07s\n",
      "v = 0.05, a = 0.04, Accuracy: 0.75, Mean response time for Answer 1: 0.21s, Mean response time for Answer 0: 0.21s\n",
      "v = 0.05, a = 0.05, Accuracy: 0.81, Mean response time for Answer 1: 0.37s, Mean response time for Answer 0: 0.39s\n",
      "v = 0.05, a = 0.07, Accuracy: 0.86, Mean response time for Answer 1: 0.53s, Mean response time for Answer 0: 0.47s\n",
      "v = 0.05, a = 0.08, Accuracy: 0.88, Mean response time for Answer 1: 0.69s, Mean response time for Answer 0: 0.73s\n",
      "v = 0.05, a = 0.10, Accuracy: 0.87, Mean response time for Answer 1: 0.78s, Mean response time for Answer 0: 0.83s\n",
      "v = 0.05, a = 0.11, Accuracy: 0.84, Mean response time for Answer 1: 0.90s, Mean response time for Answer 0: 0.91s\n",
      "v = 0.05, a = 0.13, Accuracy: 0.81, Mean response time for Answer 1: 1.03s, Mean response time for Answer 0: 1.00s\n",
      "v = 0.05, a = 0.14, Accuracy: 0.76, Mean response time for Answer 1: 1.08s, Mean response time for Answer 0: 0.94s\n",
      "v = 0.05, a = 0.16, Accuracy: 0.68, Mean response time for Answer 1: 1.16s, Mean response time for Answer 0: 1.11s\n",
      "v = 0.05, a = 0.02, Accuracy: 0.64, Mean response time for Answer 1: 0.07s, Mean response time for Answer 0: 0.07s\n",
      "v = 0.05, a = 0.04, Accuracy: 0.74, Mean response time for Answer 1: 0.19s, Mean response time for Answer 0: 0.20s\n",
      "v = 0.05, a = 0.05, Accuracy: 0.83, Mean response time for Answer 1: 0.36s, Mean response time for Answer 0: 0.34s\n",
      "v = 0.05, a = 0.07, Accuracy: 0.87, Mean response time for Answer 1: 0.52s, Mean response time for Answer 0: 0.48s\n",
      "v = 0.05, a = 0.08, Accuracy: 0.90, Mean response time for Answer 1: 0.67s, Mean response time for Answer 0: 0.68s\n",
      "v = 0.05, a = 0.10, Accuracy: 0.90, Mean response time for Answer 1: 0.78s, Mean response time for Answer 0: 0.68s\n",
      "v = 0.05, a = 0.11, Accuracy: 0.88, Mean response time for Answer 1: 0.89s, Mean response time for Answer 0: 0.94s\n",
      "v = 0.05, a = 0.13, Accuracy: 0.82, Mean response time for Answer 1: 0.96s, Mean response time for Answer 0: 1.11s\n",
      "v = 0.05, a = 0.14, Accuracy: 0.80, Mean response time for Answer 1: 1.03s, Mean response time for Answer 0: 1.05s\n",
      "v = 0.05, a = 0.16, Accuracy: 0.75, Mean response time for Answer 1: 1.11s, Mean response time for Answer 0: 1.35s\n"
     ]
    },
    {
     "data": {
      "image/png": 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XyIsvvigzZ86ULVu2yKJFi0yrcl0fqihPyBArAwcOlP/7v/8zBS5Tpkx4/WWXXSYTJ06Md3cAAHjCrFmzTPZNnjzZVI6PGzdO2rdvLxs3bpSKFSvGnIH7iiuuMM/99a9/lWrVqsmPP/4oZcuWzfeyk90AAHgL2Q0A8KOuXbvKnj17ZOjQobJz505p1qyZLFiwIDxx59atW6NajGtPq6SkJPP/7du3y+mnn24qx5966qm4jht3BfnXX38tM2bMOGG9VgDorKEAABTEib7Gjh0rffr0Cd+51oryefPmydSpU81FbFa6ft++fbJ06VIpWrSoWaetz20guwEA1jBJZ56Q3QAAv2Z33759zZLdpJyRihQpYnpWnWrvqriHWNGWbzt27Dhh/Zo1a0zrOAAAvCQ9PT1qOXr0aMzW4KtWrZK0tLTwOr1rrY+XLVsWc7/vvfeeGSdNh1jRu92NGjUys2sHAvl/dU92AwDgLWQ3AAD5J+4K8m7dusljjz1mmrlrE3YdLP1f//qXPPzww2YcGAAAEibT4UVEqlevLqmpqeFl1KhRJxxWW2ppxXaoW1eIPtY8jOX77783Q6vo63TccR0HbcyYMaa7dH4juwEABSm7/YDsBgBYk+m/7I57iBVt/aat4bRCQS/6GzZsaP5/yy23mPFeAADwUlevbdu2SUpKSni1TqbpBL2Q1W7QU6ZMMZOD6GzcOiba6NGj831yLbIbAGANQ6zkCdkNALAm4L/sjruCPDk52cwmqoOl67hoOpv2ueeeK3Xr1k1MCQEASCCtHI+sII+lQoUKppJ7165dUev1ceXKlWO+pkqVKmbs8ciZsxs0aGBagumQLZqn+YXsBgDAW8huAABcPMTKyJEj5fDhw+ZOdseOHaVLly4mpH/77TfzHAAACb+T7dQSx0WqtgBfvHhxVAtxfazjjMdy4YUXyqZNm8x2Id9++62pOM/PynFFdgMA/JbdXkd2AwCsCfgvu+OuIB8xYoS5e52Vhrc+BwBAQRwLbcCAAaYl1+uvvy7r16+X++67Tw4dOiS9evUyz+t4oIMGDQpvr8/v27dP+vXrZyrG582bF+4und/IbgCANT4cx9QJZDcAwJpM/2V33EOsBINBM0lIVl9++aWUK1fOqXIBAOAqXbt2lT179piuzjpMSrNmzWTBggXhiTu3bt0qhQr9776ztvhauHCh9O/fX5o0aSLVqlUzleU64VZ+I7sBAPAWshsAABdWkJ922mkmoHU5++yzo8JaJwvRu9v33ntvosoJAID1yUL69u1rlliWLFlywjodfuXzzz8XW8huAIB1Ppzo61SQ3QAA6wL+y+5cV5CPGzfO3MW+8847TZeu1NTU8HM6lmrNmjWzHYcVAABHZDoYsB7p6nUqyG4AgHVkd1zIbgCAdZn+y+5cV5D37NnT/L9WrVrSpk0bKVq0aCLLBQAAThHZDQCAt5DdAAB4YAzytm3bhv995MgROXbsWNTzKSkpzpQMAICsnJzkwyN3sp1AdgMArCG784TsBgBYk+m/7P7fbGK5pLNm6/irFStWlFKlSpkx0iIXAADgLmQ3AADeQnYDAODiCvJHHnlEPvzwQ3nxxRelWLFi8sorr5ix0apWrSpvvPFGYkoJAEDkZCFOLT5BdgMArCG784TsBgBYE/Bfdsc9xMrf//53E8iXXnqp9OrVSy6++GI566yzpEaNGjJ9+nS59dZbE1NSAAB82NXLCWQ3AMAasjtPyG4AgDWZ/svuuFuQ79u3T2rXrh0e90wfq4suukg++eQT50sIAABOCdkNAIC3kN0AALi4glxDesuWLebf9evXl7fffjt8h7ts2bJ5KsSkSZOkZs2aUrx4cWnVqpWsWLEix+1nz55tjq3bN27cWObPnx/1/PDhw83zobHa0tLSZPny5VHb6BcMveuuXza03L1795aDBw/mqfwAgHziw65eTnA6u8ltAECukd15QnYDAKwJ+C+7464g1+5dX375pfn3wIEDTdBqaPbv39+MkxavWbNmyYABA2TYsGGyevVqadq0qbRv3152794dc/ulS5dK9+7dTbiuWbNGOnfubJZ169aFtzn77LNl4sSJ8vXXX8tnn31mvghceeWVsmfPnvA2GtTffPONLFq0SN5//31zF/7uu++Ou/wAgHzkw6B2gpPZTW4DAOJCducJ2Q0AsCbgv+xOCgaDwVPZwY8//iirVq0y46E1adIk7tfr3evzzz/fhKvKzMyU6tWry4MPPmi+CGTVtWtXOXTokAnYkAsuuECaNWsmkydPjnmM9PR0SU1NlQ8++EAuv/xyWb9+vTRs2FC++OILadGihdlmwYIF0rFjR/npp5/MxCcnE9rngeEiKcXFnu5i35nlbZdARGpYPn5Nse9j2wUQ2fmL7RKIzLJdABE5bLsAIhLdyCffpWeIpH4ucuDAAdNq6JT3F/qb200kJdmRIkr6MZHUmc6V0UtOJbu9mtuR+y2tX4DiOmtntRf7elo+/tW2Y1vdYrsAInKt7QLoL7XtAugvZEfbJRCRC20XQEQut3r09PRDkpp6OdntUmS3vey+WuzrarsAInJdXdslcEluNrZdABFpaLsAIpK7X+HEqnaD7RKIyJ+sHj09/VdJTT2X7M7vFuTHjx83Yffdd9+F1+kkITfccEOeKsePHTtmQl67Y4ULVKiQebxs2bKYr9H1kdsrvfud3fZ6jClTppgfsN4pD+1Du3iFglrpPvXYWbuFAQBcOFmIU4sPOJnd5DYAIG5kd9zIbgCAVZn+y+4i8WxctGhR+eqrrxw7+N69eyUQCEilSpWi1uvjDRs2xHzNzp07Y26v6yPp3e5u3brJ4cOHpUqVKqZbV4UKFcL7qFixYtT2RYoUkXLlyp2wn5CjR4+aJfKuCgAAbudkdnsptxXZDQDwIrKb7AYAuLiCXN12223y6quvyjPPPCNu1q5dO1m7dq35QvDyyy9Lly5dzJ3qrCGdW6NGjZIRI0ac+MR2EXGo20GebBT7kl0wrMbplstwZLVYV6qY7RKIuOBtcEUZYjeuyV/bLB//lAbvykGmg2OYeeROthO8kN1O53ZO2Z1peYiVwmJfGdsFKGW7ACJSzg3NRG0XQH8ZqtkugQuGylMZtgugVZCWj/9rYnZLducJ2e2e7D4i9hUVF/jf8PL2uGGYl5K2CyAi57vg2n///25k2fOu7QKIyOuWj18iMbvN9F92x11BnpGRIVOnTjVjizVv3tzMWh1p7Nixud6X3l0uXLiw7Nq1K2q9Pq5cuXLM1+j63Gyv5dLx2XTR8dLq1q1rvmAMGjTIbJt1QhI9L51lO7vj6ut0YpPIO9k6bhsAIB8F8jK9dA778gmnsttLua3IbgBwAbI7T8ju35HdAGBBwH/ZHffp6szV5513npQpU0a+/fZbM6t1aNG7x/FITk42Yb948eLwOp0wRB+3bt065mt0feT2SrtyZbd95H5DXbV02/3795ux2EI+/PBDs41OYBJLsWLFzGDykQsAAF7gVHZ7KbcV2Q0A8Cqym+wGALi4BflHH33kaAH07nDPnj3N5B0tW7aUcePGmRmze/XqZZ7v0aOHVKtWzXS1Uv369ZO2bdvKmDFjpFOnTjJz5kxZuXKlmRRE6Wufeuopufbaa804aNrda9KkSbJ9+3a5+eabzTYNGjSQDh06SJ8+fcws3DoJSt++fc34abmdTRsAYIGTk3x4pKuXE5zMbnIbABAXsjtPyG4AgDWZ/svuuCvInda1a1fZs2ePDB061EzW0axZM1mwYEF4UpCtW7eama5D2rRpIzNmzJAhQ4bI4MGDTTeuuXPnSqNGjczz2n1MJxt5/fXXTVCXL19ezj//fPn000/lnHPOCe9n+vTpJqB1dnDd/4033ijPP/+8hXcAAJBrPuzq5TbkNgAgLmS3dWQ3ACAuAf9ld1IwGEzUVGoFmo6FlpqaKgf6iKTYnKTzOrGvsbhgkk7Lx3fDzC1umKRzvgsm6phluwBM0qnSgyKpR0UOHDjgSNfY8N/cq0VSHJqlKP24SOr7zpUR7hf6HJW0PEnn1WLffZaP37ah2Pd7w0m7sh9lIP9c7IZJOq+1XQAdddl2AbQdr9Wjp6f/KqmpTchuuIobsvtKse8u2wUQkY5lbZdARNwwZ60bJvm+2QXX/m6YpLNsYXHBG+GCv5HVyO6C0IIcAIBc82FXLwAAPI3sBgDAWzL9l91UkAMAvMOHXb0AAPA0shsAAG8J+C+74z7dTz75RDIyMk5Yr+v0OQAA4C5kNwAA3kJ2AwDg4grydu3ayb59+05Yr2PJ6HMAACRMwOHFJ8huAIA1ZHeekN0AAGsC/svuuCvIdU7PpKQTp8f45ZdfpFSpUk6VCwAAOITsBgDAW8huAABcOAb5DTfcYP6vIX3HHXdIsWL/mzU3EAjIV199JW3atElMKQEAUEEHJ/nQfRVwZDcAwDqyOy5kNwDAuqD/sjvXFeSpqanhO9llypSREiVKhJ9LTk6WCy64QPr06ZOYUgIAIP/tnpXk4L4KOLIbAGAd2R0XshsAYF3Af9mdqwryAQMGyMSJE01Xrh9++EFeeeUVKV26dOJLBwAA8oTsBgDAW8huAADsyNUY5BMmTJCDBw+af+uM2YcPH050uQAAOJEPJwvJK7IbAOAKZHeukd0AAFcI+C+7c9WCvGbNmvL888/LlVdeabp6LVu2TE477bSY215yySVOlxEAgN9lOjgWmlP7cSmyGwDgCmR3rpHdAABXyPRfdueqgnz06NFy7733yqhRo8xkIddff33M7fQ5nTgEAADYRXYDAOAtZDcAAC6uIO/cubNZtLtXSkqKbNy4USpWrJj40gEA4PPJQvKK7AYAuALZnWtkNwDAFQL+y+5cVZCH6AQhH330kdSqVUuKFInrpQAAnDofdvU6VWQ3AMAqsjtuZDcAwKpM/2V3rtI2PT3d3MFW5557bo6ThYS2AwAA9pDdAAB4C9kNAICLK8h1YpAdO3aY7l1ly5Y1Y55lpZO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      "text/plain": [
       "<Figure size 1500x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Keep s = 0.04, dt = 0.001 fixed. Always set z = a / 2.\n",
    "# Vary settings for v and a, and plot heatmaps of accuracy, mean response time for Answer 1 and mean response time for Answer 0.\n",
    "\n",
    "vs = np.linspace(0.01, 0.05, 10)\n",
    "as_ = np.linspace(0.02, 0.16, 10)\n",
    "num_simulations = 1000\n",
    "\n",
    "accuracies = np.zeros((len(vs), len(as_)))\n",
    "mean_response_times_1 = np.zeros((len(vs), len(as_)))\n",
    "mean_response_times_0 = np.zeros((len(vs), len(as_)))\n",
    "\n",
    "for i, v in enumerate(vs):\n",
    "    for j, a in enumerate(as_):\n",
    "        outcomes = []\n",
    "        response_times = []\n",
    "        for _ in range(num_simulations):\n",
    "            times, _, outcome = drift_diffusion_model(v, a, s, a / 2, dt)\n",
    "            outcomes.append(outcome)\n",
    "            response_times.append((len(times) - 1) * dt)\n",
    "        accuracy, mean_response_time_1, mean_response_time_0 = show_results(outcomes, response_times, plot=False)\n",
    "        print(\"v = {:.2f}, a = {:.2f}, Accuracy: {:.2f}, Mean response time for Answer 1: {:.2f}s, Mean response time for Answer 0: {:.2f}s\".format(v, a, accuracy, mean_response_time_1, mean_response_time_0))\n",
    "        accuracies[i, j] = accuracy\n",
    "        mean_response_times_1[i, j] = mean_response_time_1\n",
    "        mean_response_times_0[i, j] = mean_response_time_0\n",
    "\n",
    "X, Y = np.meshgrid(as_, vs)\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "# Accuracy heatmap\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.pcolormesh(X, Y, accuracies, cmap='hot', shading='auto')\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"Threshold a\")\n",
    "plt.ylabel(\"Drift rate v\")\n",
    "plt.title(\"Accuracy\")\n",
    "\n",
    "# Mean response time for Answer 1 heatmap\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.pcolormesh(X, Y, mean_response_times_1, cmap='hot', shading='auto')\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"Threshold a\")\n",
    "plt.ylabel(\"Drift rate v\")\n",
    "plt.title(\"Mean response time for Answer 1\")\n",
    "\n",
    "# Mean response time for Answer 0 heatmap\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.pcolormesh(X, Y, mean_response_times_0, cmap='hot', shading='auto')\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"Threshold a\")\n",
    "plt.ylabel(\"Drift rate v\")\n",
    "plt.title(\"Mean response time for Answer 0\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consistently, higher values of $v$ increase the accuracy and lower the mean response time for correct answers. This is intuitively clear, as raising $v$ lowers the impact of noise and makes it more likely that evidence is sampled without being misled. It is equally clear that lowering decision threshold $a$ consistently lowers the mean response times for both answers -- as both decision boundaries move further away from the starting value. The relationship between $a$ and the mean accuracy is somewhat more involved. If $a$ is too large or too small for a given $v$, errors increase due to, respectively, more timeouts or more rash, wrong decisions after sampling noisy, misleading evidence. The \"sweet spot\" -- how high $a$ should be to maximize the performance -- further depends on $v$ and the timeout constraint, in a way that's not immediately obvious. Conceptually, the trade-off at play is that the model wants to make its decision as late as possible -- reducing the coefficient of variation of the total evidence -- without running the risk of a timeout."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task 3 -- Prior information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prior information can be said to correspond to evidence sampled before the start of the diffusion process or, more concretely, to a shift in the starting condition $W(0) = z$. If hypothesis $h_+$ is deemed more likely a priori, then $W(0)$ should be larger than $a/2$.\n",
    "\n",
    "Q: *How far* above the default should $W(0)$ lie for a given prior probability?\n",
    "\n",
    "A: It is not entirely possible to say, as values of $W$ cannot be uniquely mapped to posterior probabilities without further modelling assumptions -- e.g., what posterior probability would the value $W = a$ correspond to? The true answer is not \"1\".\n",
    "\n",
    "Coming up with a full \"Bayesian treatment\" of our DDM thus lies outside the scope of this Lab, but we can propose an answer by making some simplifying assumptions. For a given decision threshold, noise level and initial condition, we might equate the prior probability of $h_+$ with the likelihood of $W$ reaching $a$ before reaching $0$, if future evidence, on average, did not favour either hypothesis. (Note: Then, $W=a$ *would* correspond to a posterior of $1$, which is why this approach is not **entirely** principled.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z = 0.01, Accuracy: 0.10\n",
      "z = 0.02, Accuracy: 0.18\n",
      "z = 0.03, Accuracy: 0.27\n",
      "z = 0.04, Accuracy: 0.34\n",
      "z = 0.05, Accuracy: 0.41\n",
      "z = 0.06, Accuracy: 0.50\n",
      "z = 0.07, Accuracy: 0.57\n",
      "z = 0.08, Accuracy: 0.67\n",
      "z = 0.09, Accuracy: 0.75\n",
      "z = 0.10, Accuracy: 0.83\n",
      "z = 0.11, Accuracy: 0.89\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracies = []\n",
    "a = 0.12\n",
    "s = 0.03\n",
    "zs = np.linspace(0.01, 0.11, 11)\n",
    "\n",
    "for z in zs:\n",
    "    outcomes = []\n",
    "    response_times = []\n",
    "    for _ in range(1000):\n",
    "        times, _, outcome = drift_diffusion_model(0.0, a, s, z, dt, timeout=1000000) # Set v = 0 to simulate a random walk. We set a high timeout to ensure the simulation always ends with one of the outcomes.\n",
    "        outcomes.append(outcome)\n",
    "        response_times.append((len(times) - 1) * dt)\n",
    "    accuracy, _, _ = show_results(outcomes, response_times, plot=False)\n",
    "    print(\"z = {:.2f}, Accuracy: {:.2f}\".format(z, accuracy))\n",
    "    accuracies.append(accuracy)\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(zs, accuracies)\n",
    "plt.xlabel(\"Starting point z\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Accuracy vs. starting point z\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prior $h_+ = 2 \\times h_-$ corresponds to an \"accuracy\" of $0.67$ in our above setup, suggesting that, for decision threshold $a = 0.12$, the prior may be appropriately represented by the initial condition $W(0) = 0.08$. \n",
    "\n",
    "You would be absolutely correct in pointing out that, effectively, all we are doing is setting $W(0) = a * p$ where $p = P(X=1)$."
   ]
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    "#### Task 4 -- Group differences"
   ]
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    "a)\n",
    "\n",
    "It is entirely possible that the parameters of a diffusion model will be more sensitive to group differences than statistical tests considering reaction times OR accuracy individually. Patterns in the reaction times and response accuracies of subjects may suggest significant differences in, e.g., drift rate between experimental groups, even if these groups do not (strongly) differ in their mean outcome measures. As discussed in class, this argument was made by [White et al. (2010)](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=97735d2bb86abbc26e54916d044fd8614608951c).\n",
    "\n",
    "\n",
    "b)   \n",
    "\n",
    "Difficulties in evidence accumulation might be paraphrased as a failure to holistically consider the total body of sampled evidence that should influence one's decision-making. One may hypothesize that this occurs by discarding previous evidence as new percepts are processed. Following this idea, one could integrate a decay rate $\\gamma$ into the DDM model. Independently of the content of new sampled evidence, the accumulated evidence should drift towards its initial value $z$ over time. This can be achieved via the formulation $$W(t+dt) = W(t) + \\gamma \\times (z - W(t)) + v \\times dt + s \\times \\eta$$ or, equivalently ,$$W(t+dt) = \\gamma z + (1 - \\gamma)W(t) + v dt + s \\eta$$.\n",
    "\n",
    "To consider a slow-down of 'non-decision processes' (perception and/or motor execution), it is helpful to realize that a subject's 'reaction time' $T$ to a stimulus, is the sum of the time it takes one's sensory cortices to process the stimulus ($T_e$), the time it takes to make a decision as to what action to take in response ($T_d$) and the time it takes to execute that response ($T_r$). Without further insight and neural recordings, separating $T_e$ and $T_r$ is tough. Therefore, these variables are usually lumped together as 'non-decision time' $T_{er}$. If a DDM is taken to be a model of the cognitive processes yielding decision time $T_d$, then parameter $T_{er}$ can be fitted simultaneously with the remainder of the model, when matching experimentally observed reaction times and accuracies (see [Myers et al.  (2022)](https://pmc.ncbi.nlm.nih.gov/articles/PMC9784241/))."
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