{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CCN Lab 3 -- Drift Diffusion Process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initial Simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
    "    \"\"\" Simulate a Wiener diffusion process (cf. Ratcliff & Rouder, 1994), where evidence accumulates until it reaches one of two decision boundaries. \n",
    "\n",
    "    Parameters:\n",
    "    v: drift rate (mean amount of information accumulated per time step)\n",
    "    a: boundary separation (distance between the two decision boundaries; we assume the boundaries are at 0 and a)\n",
    "    s: variability of the noise (the standard deviation of the noise drawn over each second. Exercise: Prove this.)\n",
    "    z: starting point (initial evidence level. z = a /2 means an unbiased starting point)\n",
    "    dt: length of each time step (in seconds)\n",
    "    timeout: maximum number of time steps to simulate (default: 2000)\n",
    "    \"\"\"\n",
    "\n",
    "    step = 0 # Step counter\n",
    "    W = z # Current level of the evidence\n",
    "\n",
    "    # Keep track of (x, y) values for plotting the trajectory of the evidence accumulation process\n",
    "    times = [0] # x values (time points; in seconds)\n",
    "    vals = [W] # y values (evidence levels)\n",
    "\n",
    "    # While none of the stopping conditions are met (i.e. we have not reached the timeout, and we have not reached either boundary)...\n",
    "    while step < timeout and W > 0 and W < a:\n",
    "        # ... simulate the next time step.\n",
    "        \n",
    "        eta = np.random.normal(0, np.sqrt(dt)) # Draw the random noise.\n",
    "        W += v * dt + s * eta # Update the evidence level by adding the drift (v * dt) and the noise (s * eta)\n",
    "\n",
    "        W = np.clip(W, 0, a) # This ensures that W is never below 0 or above a -- for plotting purposes.\n",
    "\n",
    "        step += 1\n",
    "        times.append(step * dt) # Add the current time point to the list of times (in seconds; hence multiplying the step number by dt)\n",
    "        vals.append(W) # Add the current evidence level to the list of values\n",
    "\n",
    "    # Once we exit the loop, we have either reached the timeout, or we have reached one of the boundaries. \n",
    "    # We return the times and values for plotting, and a code denoting the stopping condition / outcome.\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": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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2NI1edZwW77xGyemPxHVTNpyj134+RSsPRmrdhjuHp2VpB71B3s5UuXIlsXa7ayNX+vhZTcO8x8Hl4m3q15QvF1beHZFQeDafH/9TSw5Q/8X7C933yl1NplsqMzenG/GaAwV3dEruAQAAAADg8SDohmLZczVOdPhWWnUoknJzVZnri3eSRdn5m2tP06HriWLbwh1h1PKjbSIYP3BNlVVdsS9C6z6iC8j4crO0H19uQ3bWxltv/PWLwdS/hQetG9eO3NVrvCW8nYP8zuoM+J2kDIOZ7T9ORYvznL1nfOCAS+QLahK38cwd+fLJm/fNOh/8dtJD2nIxRr78t+KxAQAAAADA40MjNSiSg9cSROb6noGAMuFBJtV2tNfKgitxTM7BuCRHJ9CMvv9QfPWp5SAC1xn9m5ApcdOwpS8Ei/PKoNerRhV5+09HbtL+8AQRmOornefMNrsSkyoCcMny/RE0sacf2dvkP0gw7qcTWpdvJKTRV7uu0Zs9/cTj4Odeza503pbZObk08OtDWtt4rf78QS2oUqVKJbpPPviSkZ0jOrKnZ2XT3qvx1KGhC1W3sxZVCwAAAAAAFQ0y3VAks/6+mC/gHtVB07l89eGblJaZTaeiNOu9C8IZ34eKcvIjEaqs+NA2den4+6GiUVppUQaYgV7O8vm66vXjYbHaBxIOX0+kkT8cky8v3xdBh9WPn32z5zq9sOIITf/zPC3fd12Unkvd109HaZq1vd2rkfj65+nb4uvPR25S81lb6V9FIzlTikhIoxh1IzcO+iVxqZklvs/3N56nVh/tED+zkSuP0eu/nKLAOdso+OPtFIPSdQAAAACogBB0Q5Eos6+1qtrSpQ/70Oynm1FwXVWQumT3NWo2ayv9fCRKXH6zR8NC73N/eLz4yhne45H3xPnujVWdxEvbL6+0pb7N3OnDZ5pprSnnePxmYjrFqoPTD/+5RMNWHKELtzXrsiW2im7qp6KSaM3RKPpk8xUK/WKvOCDBTeQkU5/wpyGtvcX5m4lp9NPhSPpAXQkwfs2pAh9rTm4eLdkVTnM3X6YMPc3fiooz9Ixfw8m9GpGvi2oueXgJ15lHxD+gtcduiYZ0Ty4+QMcjNQdgktIf0e6r2ssSAAAAAAAqAgTdUCQ2Vpps8LMtPUX5MGvs7qi3O/jE0Ea0Y3IXcq2uGs/l4WRPTTwcyc3Rjhq7VRfbxv10UqyJfmv9Gcp4lKuVXS5tHRu60LLhraiWYpyYUxUbOfP93/m7Inu78uANg/exYmSIwet4jbtUps7B7WtdG4ifjbODjSi9lwJuSUFZ4b1hcTR/Wxh9uy9CZNlL6rS6KoFfF9awtqqre3ic/iUCBfnzdDT1WLBXvpyVo3o9dfsBAAAAAABUNAi6ocjl4HJWtLeqLJoNCKqTb9+FQ4LIqnIlali7Om1+szMNDvGilaNa01/jO9Kut7vRky085H0n/3pWq7FYFVvjNUszhieau4uvRyLuUYSiy7e+CoCODWoZvJ+dV+LohRVHxfk6zlXksnbOputzOEL/mLJrcan08irNuvAvtoeJLHpxcbZ8ywVVA7VODVUN4xqpD4aUpKP6W+vPFroPj4KLS0WJOQAAAABULAi6oVDhsakUqZ7f/MXgIDnLzdr61hKBdYcGtahjw1oiu92sjpN8PWdz5z0fKLKpttaVqaqdNY3pXJ9Gtq9HZYH0XLjD92s/n5Qz1e19a9F3I0Lo2Ps96blgT/q/Z5uTtVVlekOnrH7WU03z3aefYk54T3/95fT6ytczs3Mo9It9+R/jrK2UmqEayVYUcSkZ1GD6ZrqrzqYHqgN/6XFxB/riBvDWBpqk8UEZfo58NWf0L98tfhYdAAAAAKAsQ9ANheq1cJ9Wd29dTes40pqx7eiXV9qJ7HZhOGif80xz+mF0a7J0jdw1AbKkdzN3WjuuHYU2dRPPhQ9EvNhWdRDh7d6NaWzn+vK+7XxriQMSklc61ad3ejeWL7/Qtp4YXzbn6WZ05aO+NH9QoNjOZffc/VtpX5j+7DcLmL2NLtwuWrA8dPkRrcvSyDR+rOz87eQCx57pupP0kLJz88Sa9uuf9JM7z7tUs6UZTzal70e1pieaq6obwgx0twcAAAAAKK8wMgyKhbO5xsJlzd41q9Cte6q1zlP6aIJRS+GqWOMtrXvmcvmCcODdpZGrCEI5w88HJH48FCnWb+t2Zecy/H4BmnL7/gEeNOefi3Q//ZHICreqV0O+7tgNTYf0TW90oie/Uo0sk3D3808Htig0K81dy5WkUV5ujvZUr5aDaBx3NTZVDsILcvLmPRr4zWFxnm/Lz2dMp/oU4lOT6jjZi9FsrIGrqklbZGL+En0AAAAAgPIMQTcUKCldk/H8SNHZ2xhsrCqL0vTcXKLq9pY5x1k5TowDyh2TuxZ6G57P3dnPVWvbSMV4tYLwmvbmdZzECLJjN+5pBd0nb6oan30xOJCaezrRxvEdacDSg/L1HChLuCP8rydukYdTFXEAQMKd0pU4y67kU6uqCLp5v7b1a9KyvRHUxKM6ddPTVf5RTi6N+VGzvlx6zvrWqnurG+T9cjSKPh7QvMRzwAEAAAAAyhqUl0OB3v3tnJzFHN6+aIFjcVS3tyEnBxuLDLglXPLt6VxFNIIrDT4uqgD1sy1XKJcXQhOJ0WDSOm8pEOfA9urHfem9vv7iMs8AX3lA1V19f3gCvff7eRqx8hgNW35ErAdXjgljQ1t7a2XZxfeupfrePFv8zXVnxGMY9cNxvY9z6u/nxSgwyfAC1ukru9JvPq9q4AYAAAAAUBEg6AaDUjIe0bZLseL8p88VXLZcnj3fyosOTu0hssulYYCiBP3Zbw7RwywOuJPFGC4u11YGsHbWVvRaV1/yd1etpZdGmm27pAlsOWu+41KcGEN25a4qcOcSeX2l6PVqqcrAV+y/Qf+cvaNVlq5LOXd76QvBVF8951sf5c/u77O3i/RzAAAAAAAoDxB0g0HR6rXWNRxsqH0B47DAuLgjfHNP1ezss7eS6J0NZ+nQddV67lb1nPOVZvPltWPbifPR9x+KgyW376teO8n4Naeo3dydtHjXNYPz1ZVZdl33FcsMpPJ1qdFbZz8XebSaIdy1fsukzuI8H8jh5msAAAAAABUBgm4w6LY6MPKqoT8QA9PpqliH/e/5u2Iet2q7/hFjNarakpujqmnZv+fu0p0k1TgwQ03feOSZPlKmm/mqm58p57RLEtOyKONRLnH8//3I1kVaHuDv7kgBnk6Ul6dZnw4AAAAAUN4h6AbZN3uu0/Q/z9ODTFUG89Y91WxuXs8MpeuNHn6iCzg3mJM0cqtGgwronC51Cp/2x3m5qdq4Lg1olE4TN+4Sz2Pe9OEZ5FxOz3PUd07uKpetf/zvJa2mejfUHdA9HO3F/PWiCvBSlZnP23pFlM0DAAAAAJR3CLpBzmRy06w1R6Oo+ayt9Oba0/ThpkvymCwoXdwB/YMnm9LUJ1RN0hh3EOeO74YkP9Q0NZPwSDbunG5vU5l6+tem/yZ2pv91a2DwPrhUnRvH8Rx1Pi+t0z54LZHmbb0q73fpjmptOI9EK47RHXyomp21GBPHGXxz4fL4LRfu0sU7RZttDgAAAABQUgi6K7AD4QkiKzplw1nao2iKxf5WNNFC0G0+yqZpPM6rIL6u2q9TO9+aotEaB84nZ/SiZcNbiSC5OOO6lFUOfEBGIv1/aWYgY26In1t1ejqojjjPTd34fsIUo84KqsIYufIYJSu6pT+ODSei6bWfT9HYH0+IABwAAAAAwFQwp7uCupv8kF76/qh8ebM662hduRJlKzpV82xqNFEzn5B6NamOkz3dSc7IN/taF89R//jfy6LxXVxqJr0V2kirkVlJPB/iRd+px5BJSw5G/XCMrsenifXczwUbLnc3xKuGKpDn+5Xue9MbnQx2h+d54FyFwV5ZfZy+eamVXEpfUlsuqrq78881PO4BNXJTldEDAAAAABgbMt3lWFxqBn21MzxfEyx2VTGvmaWp19d+8lwAOdhaifPrx7Wjqx/1JTdH+1J6xKCriq0VbX2rC21+s7PBddjKJmgrRoTQvOcDadXoNhRYSJBeFNz8LOKTfmJOO+s8b7cIuNkzgXXIp4AxYYbo6xFw8FqCwf0vq8ecseOR92m0gbnhxRF9X9WvgC3be/2x7w8AAAAAwBAE3eXY17uv04LtYdR/8f58c5YnrT+j9zZt69ekjeM70s9j2orRVdYFrCGG0lHd3qbQgNuUuDN5Q53SdZ9aDnrnfBdFq3o1qLqdNdkq/m+dv61aW82zwaWqC8b/b59eclDr9rzvR5u0G7sVB99nZIIm6D4dlVSi+wEAAAAAKApEVGVYeGwq9Zi/h34/Ga33+v8uqIIXLjX+YrumCRZnvpP0rI3t1dRNZEu51LaTn4sJHzmUNb2buWldHtqmrmj2VhI8gu7UzF4U9n9P0PLhrcS2qHvpovrijbWn6X+/nKLD1xPpZmIa7Q+Pl2/3Zo+GYuQY+/7ADfqmhBlqznJn5eTKl/l7Z2ajkzoAAAAAmAbWdJdhb/16hiIS0ujtDWdpYCvN2tqI+Af0yeYrFJuiKSvfcSmOpvRRdcLeF6YJZGb0b0IvtatHlStVIhurojfYgorl+VbelJqRLdZ0W1WuTMNa132s+5O6sHuq13ffvv+QNp65LV+/8fRtWn/iltZtXmpfTxxAkrLiWy/E0LQnmhT7e5+NVt2ex6FF338oRuRFJaaLJm8AAAAAAMaGoLsMu6ge28S4AzRnqBMeZFKPBXvz7RubmiGf36sOuid0b0ivdPYtpUcLZRk31DPF/xUvZ9Va8cS0LNqqbm7GdAPu8d0bUO3q9jS6Y31ad1x1XcYjTba6OH4+clN8berhSHbWlUUQfj3+AYJuAAAAADAJlJeXUbeTHpJy0tHsvy/Ka2L14XLyjEeqEtpL6sZUberXLI2HCmCQYxVrqm6vOvYXoW7Qps+bPf3E18bu1en87N7ifExKhhghdiLynig3L8ror+ycXDqjXsPNmfMG6rXqu65oj8wDAAAAADAWBN1l1A2dAOVwRCIlP3xEF25rst9s3bh2ZG+jepnjUjIpNzdPrGFlPL8ZwJx4Znhh88d59BnPG1c2lpM6oAd+uI2eX3ZYNFZTZsoN+XzbVbGemzv0B3k5UyN3VXb71xPRYh05AAAAAIDZgu4bNzSzesH8UjJUjdBa+9QQnaQ5yTdhzSm6eEe1XpWtHduO2vnWono1VUHN8ch7NHrVccrKzhXzuD2cMAoMzK+5p6YzO5d8S/q38KDVL7ehN3s2LPA2ko82XaZxq0/QumNR4v3BvQsGfnOIbiSkiYNNPD7v270RYt+WdZ1FV/ZAL+d8ZecAAAAAAGYJuhs0aED169enl19+mX766SeKjtbfMbu4li5dSj4+PmRvb09t27alY8eOFbj/hg0byN/fX+wfEBBAmzdvzrfP5cuX6emnnyYnJyeqWrUqtW7dmqKioqg8SVUH3Y72NvJa1P3hCXQlJlWsvz3+fii1b1BLbA/xqSG+csM1aT03b8M4MLAEvE5bwnPiJZyJ7tLIVWTDdfVq6q53ycW2S7E09Y/zNGrlMRqx8hidvHmfxv9yitYcixLj8ySzn2omvgbX0wTd0vILAAAAAABjKnLUtWvXLho5ciRFRETQuHHjqF69euTn50evvvoqrVu3jmJjY4v9zdevX0+TJ0+mWbNm0alTpygwMJD69OlDcXH611ceOnSIhg0bRmPGjKHTp0/TgAEDxOnChQvyPtevX6dOnTqJwHzPnj107tw5+uCDD0SQXp6kPMwWX3k9bP8Aj3yztl2r28mX9a3d/mJwUCk8SoDCcQPAyE/70425/SjI25mea+lJ1eys6clA7f/XSk+28KC6NR3I1roybXqjE/nqLJU4pZi9zT0M/u/fy/Lljwc0lw9Ucdk6z6RnkYmG15QDAAAAAJRUpbyidB/SkZGRIQJgDmr5xNnpR48eiUD34kVVQ6+i4Mw2Z6GXLFkiLufm5pK3tze98cYbNHXq1Hz7DxkyhNLS0mjTpk3ytnbt2lFQUBAtW7ZMXB46dCjZ2NiIbHxJpaSkiCx5cnIyOTrmL2O1BF9su0qLd12jEe3r0Zynm1H3+XsoMlG1Vvt/3RrQu31V48HY3eSH1H7uLvnyiRmh5FJNE5QDWJKc3DzRFK2wSoz7aVlijFndWg703f4I+lgRWOvj7GBDB97rIQJ6pWtxDyj0i71i+4U5fYzyHAAAAACg/EspYtxYovpizhr36NGDZsyYQXPmzKE333yTqlWrRleuXCnyfWRlZdHJkycpNDRU82AqVxaXDx8+rPc2vF25P+PMuLQ/B+3//vsvNWrUSGyvXbu2COw3btxI5U1KhibTzeW3y4a3kq/r2aS21r4eTlXo6xeDafnwViKjiIAbLBkvjyjK0ocaVW1FwC3OO9jK21/ton+0Gc/l1g24mbu6twHP6151EL0rAAAAAMC4ihV0c6C8b98+EWh3796dnJ2d6bXXXqP79++LbHVxmq0lJCRQTk4Oubm5aW3nyzEx+rsQ8/aC9uey9AcPHtCnn35Kffv2pW3bttGzzz5Lzz33HO3dm392tSQzM1McpVCeykojNV7TzfzdHenLoUEisG5VL385eb8AD+rdLP86WIDyoG9zd/KrXY2GtvYW68D1NWar46TqeK5LGYjP/ucSPczKEZl2br4GAAAAAPC48qd9DODM9tGjR0Uzta5du4q13GvWrCEPD8PrLksbZ7rZM888Q2+99ZY4z6XnXArP5ef8uPWZO3euOJBQllhVqkRVba3IsYoq6GbPBHma9TEBmEtVO2vaPln1/uZg+dPnAig9K4c6+7lQr4X7Ch2RNzjES4wNY01mbhFf+bY/jm4jupz/dea26Po/KMS7VJ4PAAAAAFTAoHv//v0iwObgu1u3biKArVVL1R27JFxcXMjKyipfAza+7O6uPyPL2wvan+/T2tqamjZtqrVPkyZN6MCBAwYfy7Rp00RDNwlnunltuSX7fFCgOJVgST5AucZB8tA2deXLXAFy6146vdxJ0yVd17znA+nWvYdi3r2EpwHsuhJHnfxcaOK6M2Jbt8a1tZoUAgAAAAAYrbw8KSmJli9fTg4ODvTZZ59RnTp1xMiuCRMm0G+//Ubx8apRVEVla2tLrVq1op07d2plqvly+/bt9d6Gtyv3Z9u3b5f35/vkxmxXr17V2icsLEx0WzfEzs5OLHxXnsoKfeOUAIC0KkAm9PAjB9uCjzH66MmEv7L6BO24rDnQF5uSYZLHCAAAAADlV5Ez3TzvmtdJ84mlpqaK7PHu3btp3rx59OKLL4oRYsrxXYXh7DKPIQsJCaE2bdrQokWLRHfy0aNHi+tHjBhBnp6eovybTZw4UWTYFyxYQP379xejyk6cOCEOBkimTJkiupx36dJFrDvfsmUL/fPPP6LLOgCAIdz1/78LdykpXdUvQTJhzWn5fExyBjX3dDLDowMAAACAch906wvCa9asKU41atQQZd2XLxc8skcXB8ecIZ85c6ZohsbrrzlIlpqlRUVFiY7mkg4dOoh15Nw1ffr06SLI587kzZs3l/fhxmm8fpsDde6q3rhxY/r999/F7G4AAEO8azrQ0ek9yaZyZUp/lEPt5+4UI8mUvt5zTUwHQIUJAAAAABh9TjeXfnNWmTPGnN0+ePCgyEpzJpozytKpoDLusqIszOkGANNKz8qmpjO35tu+cEggPdvSyyyPCQAAAADKXtxY5KCb74SDbG5aJgXY3FCtQYMGVN4g6AYAdi8ti95ce5rO3EoSc7wlf43vSIHezrTjUiztvBJLw9rUpRZezmZ9rAAAAABQxoPub7/9VgTajRo1ovIOQTcA6Ep8kEnt5+6irJxcquNkT9+NbE39Fu8X13Vq6EI/v9LWKN8nMzuH7KytjHJfAAAAAGD+uLHI3ct5LndFCLgBAPSpVc2OvnkpWJy/k5whB9zswLUEmvlX0ZtIGvLn6WhR0v7P2TuPfV8AABXB8n3XKeTjHfTr8VvmfigAAI8fdAMAVHQ9m7hRaJPaeq9bffgm3U56WOL7zsnNo7fWnxVfp/5+7jEeJQBAxbH22C1KeJBJ7/5+jlIztKdPAABYCgTdAADFMLGn4Yqf4zfuleg+Mx7lUIPpm+XLaVk59DArp0T3BQBQUfAKydiUDPlyWOwDsz4eAABDEHQDABRDgJcTzXu+BQ0J8aalLwTTjsld5OtiFB/+iuNIRGK+bW+s1cwHBwAAbWGxqfT7qduUrjhAeTe55NVGAAAWOacbAKCiGhziLU6S17o2oGV7r2tlXIrj8t3UfNt2XI6li3eSqVkdp8d6rAAA5THg7r1wX77tdx5jiQ8AgCkh0w0A8JjcHe3E16jE9GLflm/z/YEb4vz/ujWg6vaaY6HnopON+CgBAMqH7ZditS53aeQqvt5JKtmBTwAAU0PQDQDwmHxdq4mve8LixWgxXdwczdB0xqeXHhBNgFjr+jVp/7vdaWT7euIyZ7oBAEDjQHgCfb71qta2XuoGl8h0A4ClQtANAPCYeE53zaq2Iri+Hp+mdd2NhDQKnLON3tPTkZxnciela7rtNvNwJGcHWwquV0Nc/vlIFJ28eb8UngEAQNnw8b+XtC57ONmTh1MVcf5OAWu6+fft0YhENKkEALNA0A0A8JgqV65EDVyrivNxqRkiq80BOPvz9G16kJlNv56IpoPXErRupyxH79/Cg2o72ovzynXc/6fzAXPp7ms0dPlhfHAEgArJqYqNfJ5HOC59MZjqOKuC7gu3U2jVwRt6K4vG/3KKhiw/Qm+tP1OqjxcAgKGRGgCAEUgB8+Kd4TRlwzlyrW5Hw9rUFZcli3aEUceGLvJlKStexcaKFg9tKW/3dalK9V2qiiz5xTsplJaZTVXtrMUHSams8r8Ld+m5YC/5NhzYW1WqRFVsrR7reWRl51J2bi452OLPAwBYHqlb+Tu9G9GEHn7iPP+OtLWqTFk5uTT7n0vUxMORfFyq0q/Hb9G99Czyd69OOy7HiX23XIyh6Pvp5FXDwazPAwAqFnyqAgAwgtrV7bTmxEbdS6fPtlzR2ud45H3acSmWQpu6icvX41X79mnmRlaVK2llzne93ZW6z99DkYnpFPrFXhoU4k2X7qTI+2Rm5yrO51C3z/dQFdvKtPed7uL2xRWXkkHhcQ/oy53hdPlOCu18u6t8IAEAwFLcVq/b7uGv+j3K+KDk6E4+9O3eCHF526VYuUGlPoeuJdLg1gi6AaD0oLwcAMAIuvi5ko2V/mDXpZoqIGe/nYym1AzVOu7wWNWosAbqRmxKlSpVoomhqizO3eQMkTHnMWKShFRNwzbOiHMztlv3HlKcYruES95v3Su4s/rQFUfoxe+O0rEb9yg1M1t8aC2u3VfjqNvnu2nrxZhi3xYAoDDpWdl0Ly1LnPesoSopl0x7ogmN7ugjzhcUcLNLdzUHMPXhZTyF3QcAQHEg0w0AYATd/WvT6Zm9ReAtlTlykzR7aytR8j37n4u05miUKG3k0/juDeTANsSnpt77HBDkSW+tP6v3ulWHImlwa29yc7SnmGTNmJzbSenk7qTJUPMHx482adaF//G/DhRcV9Woje0Pj6f41EyK0GkAx7dpWsdRa9/CLN8bITLzr/50km7M7ScOHAAAGIvUnZxHKyrXdkta1q1BPxyMNHj79r616HBEIkUkpMlZ85ycPKpbS5P1jkxIk5fxvNi2LtnbPN6SHQAAhkw3AICRVLOzJjtrKxFs8lcOiJ0cbMjWujJN7tVIa9+lu6+LtYnujvbUzld/0M3388srbfVel5iWRWuPRWmVW7Ll+yIoKT2LjkfeE+uzF24P07rdqJXH5POccR/+/TGa/Gv+wJ7L15/7+hD9euJWkZ776sOR4sOsJPo+RvcAgHHdVDef9FQ3TtNVR3HAsbFbdZrRvwkNCfGmoa29afGwljRJXT20LyyefKb+Sx0/3UU9v9ijVQm08qAmw80HJAEAjAFBNwBAKVCWmCsF13MuMCPcsq6zCNoZNwM6Or0nDVQ3UAuPfSCaq+1SNwiSuvc+veQgDVp2mJbsviYarPH3lrJCKRnZ4jZ3kx9SwOxtWt/LwdaKRnVQlWdK/jgVXehz4/v7coemYRzbGxZf6O0AAIrjmz3XxdcAT82EByVllU8Tj+r0Smdf+uz5FvTpwBb0dGAdCvR2Jpdqtlq3eZSTRxfvJMu/y7giSbk0BwDAGBB0AwCUkt9ea59v29OBngXehruIH3i3O60YEUL/TewssudPBXqI685GJ4mOvDuvaIJuznpzEzcmdU7v4e9Ke97pppWF/vvMnXzfa3q/JjT76WZa22JTCs/08Jpyzryzph6O4uuMjRfoB0XGCADgcfCIxRM374vzPZtomqgpceWQxF/9u0iJS8VXjmqdb/trP5+i7Jxc0T8jWz3ukXGfDAAAY0DQDQBQSnjtNq915nJHab0gdy4vDHcR79XUTc6IB9erQdXtrEXwPHb1CbHt5Y71Dd7+mSBPqlHVVg6IuYmQ9OFVqWsjV/G1bzN3eRuvF+fsz8mb92niutNyEyOlMHVDuBZeTvTRgOby9jn/XBKd1QEAHtet+6qDiVVtrQz+3rS2qiwmL0zv508vtK2rd58WXs6i1FxXw/f/ow6f7tLaJi3hAQB4XAi6AQBKEQfOHJhymfj/PRtQomZjjvY21MJbU17JdzG2i/6gu3tjV3k2ODdGYzx67GaiqpGQ1O1XuU5y/uBA+nZ4K3H+4aMcke0e+M0h+uvMHVqwTdVgSEmaN87zxRu4VtW6bnsJuqADAOiSelfwQceCfm/yNIhxXRqI35OGcKn5mZm96IfRrQ3ch+r32OmoJNEbAwDgcSHoBgAoZbxGm8vEH0cTd03p5KIhQeThVIXmDwrMt1+3xrXl883UQTc3PZPmiY9o70NvhTair18Mlud7c0O4Ps3cyUO9PvKdDZpGa78cjaIfD2l3B5Y6n/u6ViNnB+31kjyCDADgcd1NyiiwiVpx8e+q7o1r07H3e2ptt65ciVaPaUs1HGzEFIoL6vXe0sgyHif2woojtA5ZcAAoBowMAwAogyb1akRpWdn0XLAXtVaPHHu+lRc929JTZLKfWnJAK7stzqvLy++nq+aEM68aVeR54Lp4f17jeOBagtb2WX9fpKFtvEWHdhaRoArgfdXZoS2TOosu6n+cuk1HIhJFJ/WW3s6i9BMA4HHGhdUxUtAtqV3dnlaOChG/r7i7OWfKOZPeoYEL/Xv+Lm25ECNGJ3LAHbpgL91Rj2g8dD2RBod4ywcrAQAKgk9AAABlEGej5z7XQg64JVaVK1GAlxO92aOhCMA52JVw5966NTXzaIe3q0c2BQTC3DndkJUHIrUaqTH+sMr83R3p7d6NxXnOqHMndeWscACA4rqTrAq6pQocY+rh70ZLXgimhrWry6XrzwTVEV9/OnxTTIHgruZSwK1vvBgAQEEQdAMAlEOTezemhUOCtLLL3Ll3zjOa7uSDQlSjxwx5IkDVJZ1Vt7emWlVtRVDPvtoVThHxD8RInaT0R2JdeX2XqlrzcpWjeX48fFNrfTevk+QydeV83KLgD79XY1SN2wCgYrgW94D2hycYtby8MNy80rW6nehrMXjZYVp9+KbYPqJ9Perhr1q28/G/l+l0VP6mlAAAuhB0AwBUILyGccGgQLGO29CsWwlnrrmhWv8AD9Ht98SMULr8YV8KrutM6Vk5tGB7GF28nSLvy0G9hLNFnw1sofUB+e1fz4ixPG+sPU2NZvwnytRDv9hb5GZrc/+7TM1nbaU+i/bRrito0AZQETzMyhG/JyTeimodU+LfYYFezvLEBx7F6GhvTVOf8KdPnwuQ91u4QzWaEQCM68ytJDF6NFcxxq8sQ9ANAFDBDGzlJdZxF6VzOjdUW/pisAjWeX9uAvfJc9x1nejfc3dpvrqbub4Anmfp/jeJZ4vbicspGdn0f5sv0z9nNTPCM7Nzxdgzn6n/0v/9e0mMKNPnevwD+nZvhHz5vd/Pl+i5A0DZsvn8Xfm8vU3lUgu62aynmmpdHhTiTQ621mKM474p3cW2fWHxFK0eZwYAxZPxKIe+P3BDnk6g9Pw3h8To0R90mreWVQi6AQCgWHjNdscGqjFkF++oMt2GZuLy2J6j00NFZp39cFDzx1N3vNiK/Tfoxe+O5LsPHm/Wc4Em08XiUzMpKrH0P+hyY7g/T0fT32fvlKky9/DYVDoReY82nr5NF24ni9nrnD3g5QEAlmynoqrlhTb1SvV76wb4XFouqVvLQZ4I8f6fF0r1cQGUFXeTH9JfZ25TjoFs9Vvrz4ieLx9s1LyHpv95nhpO30zZ6tusLSeTAtC9HAAAim1UBx+trubKhm36dGvsSgt3hGl9eJ31VDOR5d51JU5r5ndaZjZVtVP9eeIA9631mpFlShfvJIsPvoXZHx5Pr/10kj57vgU92ULVHKkkuCv80OXaBwWuftxX7uJuSfigAC+/v3I3lZ4N9qReC/fl24fX3Cc8yKKlLwRT/xaa9fsApYnfV1y63be5e77ruGP45vMx8uV6RXi/GxtPZeCxiHzgsF4t7QOFM/o3pWErjtDesHjRn6I0s/AAZcEHGy/SjsuxtPLADVo7rp2oFJFwIP7fBdX7W/ocwAeFuWmh0ju9VQftyzpkugEAoNhCm7pRw9qqbuW+LlULHQfWwsuJfNQfmDkYfL9/E9GUjdeK6+I5uIyPfisD7k+eDaDRHX3kJka8zrIoZv99kdKycmjCmtOPtRZ8X3h8vm2NZ2wR5XGW5Fx0Er259rR4vkt2X8tXJSDhgFvK3gOYy8urjtNrP58UVRi6Lqh7RkgjEXlUYWn75ZW2tOylVvRmz4b5rmvfoJa8tIazc1xBAgAqXE3FATc7G51MX+4Mz3fgXOnjTZfoya9U404lr3VtQH2bl4+Dwgi6AQCgRBYNCRJjxxYMDix0X14PvvudbiKryl+l7DCPPtMdAbTnarzIdvM6L8kbPRqKEnbOjvdp5ia28RHygj7kTlp3mkb9cIysK2v+1L286gTdS1MFm4V1SR+3+oRWWRsfgTe03txS8M/jnQ36KwMktjoHSKQ56wClhTPYX+0Mp2l/nKeYFNUSh2/3qXo2JDzIFO9/dkn9oZw7ic8fFGiWqhIPpyoiC2+oB4ZjFVXmjrurbytiU0iAiiD6vvY67W8VfVnYZZ0D598p/uaziE/6icaF5QWCbgAAKJHmnk700YDm1LJujSLtzx9auYxZt0Rz75Tu9HLH+jSmU305g91s1lb5+tUvt6HJvTTlZf0CPERDJR4jxN1N9YlLyaCNZ+6IAP5qrPba6+CPtlNmdsHZ6Q0nbokP0BwUcICgXL/+dGAdei7YU973mz3XyVLwgQiejW7I+O4NaEof1Qx1yblbyeIgA0Bp4SoWnn6gPKjFH8B/OnKTun++h15YcUSrGoNHEFqql9pq1nlzc8nSwGW5XNHyKCe3VL4fQHHxQeo5/1zMt6TpQHgC3Ul6SE99daDAhqg8OaWyekRpeWERQffSpUvJx8eH7O3tqW3btnTs2LEC99+wYQP5+/uL/QMCAmjz5s1a18+ePVtcX7VqVapRowaFhobS0aNHTfwsAACgJLgj+synmtIHTzYVpepKk0L9qEsjV60sU3V7G+qnLjf79cQtvfd5sZDSc90j8JKUjEdirBkH9JLTUUmU/PAR3UhIE5dnP92MvhgcRC+1UzWP23TurlZW3lI6Pa8f104+7+5oL8pkJ/dqTF0bu4ptXO7vXbMKpWZmiw9CjL8O//4oRaqfK4Cx8QGvv89oJhgocTMl/v/Ipah84Exq9OfsYEuW6okAD/p+ZIg4z2u7DTWMMqZle6/T00sO0oJtmj4ZAJbk2a8P0k71Om2pmi3hQRa99P1R6vDpLjqvqByraqtdwcJ/m3hySnlj9qB7/fr1NHnyZJo1axadOnWKAgMDqU+fPhQXp2mso3To0CEaNmwYjRkzhk6fPk0DBgwQpwsXNF3vGjVqREuWLKHz58/TgQMHREDfu3dvio/Pvx4PAAAsBwfYSh3UXdJ1PRfsJb7uvBynt8T89M37+bZ19tPc1209QTevL+s4dxf1XrRPlIoqy8f/PqNab+pXuxrVrKoKAN7o4Uc2VpXkzB03UjI36WABl+K29a1F855vQbWq2tLXLwVTx4YuYh19I7fqtHVSF9o4viO1UlcpSAcUeG0tP/eXfzxu1ucB5RP3V+A+CNxjQWlISP612r+diqZfT0SL8zUcbMiSdW3kSk5VbMTBOUPLUIqKl7/wwURlFptL7p9ZepA+2XxZ/L77fOtVOfgGsDT8f/dRjubvcqt6hqvh2tSvST++3EaMIbWuXEk0LPzt9Q5UHpk96P7iiy9o7NixNHr0aGratCktW7aMHBwcaOXKlXr3//LLL6lv3740ZcoUatKkCX300UcUHBwsgmzJCy+8ILLbvr6+1KxZM/E9UlJS6Ny5c6X4zAAAoLi4WZLk1a6+FGLgjzX/Eec/0HGpmXrnex66npivGcvKUa31Zro588aNx/ovPiCybNypmLspS8JiU+mfs3e1gn3m5mgvAleJVH5uTjxKjUmjjAaHeNOJGaEUrLMEoLF7dZE99FFXFnBmmzP8Upk5/wz4gz6AsXCwuEoxbzfQy4maezpSaJPa9OnAgHwjBOdtUQWWlp7pZtxIkt9TTPrdwe8frpwprtd/Pknv/naOXv/5lDzuL+TjHXT2VhIt3xdBPx+NEr0wwPy4L8HQ5YfzNQQrSEn+T5Q1/PdD9+D5yx1Vy8eU+GDaiuEhFOJTkza82l70e5kY6kcu1eyoPDJr0J2VlUUnT54UAbL8gCpXFpcPHz6s9za8Xbk/48y4of35eyxfvpycnJxEFl2fzMxMEZQrTwAAYJ514n9P6EgHp/agaU80Mbimq4qtFTVVB5YnFVltDhz7fbmfTqi37X+3u9yMxcaqMo1Uz9mNvp8ubscfaLnUlUdsGfLzkSg6FnlP7xH7ZnWc6LmWnnJwbsqAhR+jNJucO7xzdl2Z5efsQqK6SZxrdc2HFkMNoFh9ddB9IzGN1ujMQkVXczAm3SUdDVyr0aY3OtN3I1uL/6Nzn2shsl1cjaErK9vy1y5LJbQxyRm0Lyye2n6ykwYsPVis++D389Ebqt813PX5akwq/e8XVfAt2XEpVuv9zetjofTxa8V9CY5E3FMdsC1CMM3zqlvM3kY/H7lJ5dmVGFUcxZ39+W/wsDbeNL2fP+2b0p1+GtNGLCPjfjAnZ/QiJ3UVCwfe5X3knlmD7oSEBMrJySE3N1UnWglfjonRzGVU4u1F2X/Tpk1UrVo1se574cKFtH37dnJx0V+mOHfuXBGUSydv79IfSQEAACotvJzJ07lKoftJ2Vtecy3hQFoaJcYzffmPuDJw96rhIH+gHfjNIer75X5REmoIN2zTfmyq8UBKUoZLt2GbMecYB8zeJrLxXT7fTXeTH4ryUl5HrvyenAljHLTULGJm0KeWJtPNWbSCOssCPM7/4c7zdmtt89L5gM1lpmc+6E2rRmsqUqTmSz2bqMYEWjJ3ddB9NzmDvt13Xazt5oyf1IjRUMPG1YcjxUHAKRvO0qko7WUxf56+TeGK/hLSunFpOQjDwTHzWHlQU7XBvtyhPQ5LSTo4OnHdGfF1xkbNktjy+n5ngd5O4m8wH1SztqpMdWs5UGc/V9r1Tjcx+aS8NUqz+PJyU+nevTudOXNGrAHncvTBgwcbXCc+bdo0Sk5Olk+3bulvzAMAAJYjWJ11Vma6D17TrMX++sXgfLfxqqEK5qUO3/zBWHcNJq+D5hnka8e2E+u2JZ8+F0D2NvlHFvEaaXYmKolW7IvQW+7+OPot3q/VXXz14Zv5yvg42/X8MlXFF5eeFvXDjBR0c5m+lImUuptfvmu6zD1ULP/75aTW5Seau9PgEM1SDQlnvfhD+c9j2tIPo1pT5Kf96ej0UKpVBspNPRxVQTcH0DwRQBKXkpkvAOOKHK5cmfLbOZr510Xq9Nlu2nAymgZ+o121qVyzzRlDfSb/epbuF2EMIhgXVxrpjrtS9vXg6gw+cJmUnkUvfneU+i7aZ4ZHqfobx70+TDlDnu97y4W74v8+P19p/F9IvZom+55lkVkXhXDm2crKimJjteca8mV3d/1d63h7UfbnzuUNGzYUp3bt2pGfnx99//33IsDWZWdnJ04AAFB2BNd1Fl+5CyofWecROot3XRPbPnk2QJR+6/JTB8hKPFpMarQ2IMiTBrbyEmuhpXJybqR2PjqZehjItkmZbg62/2/zZXHa8Fp7au3z+B84DuusTdcdUbbp3B0xQk05mkXf2jlDOMipW9NBXofKByU448iQ6QZjuJmYRpHqZRHSQZ3x3RsWeJtOiqaH+srNLZG7k+qAnu6s7tiUDLl3Ar+nXvruqLwMpDjqOFchN0c7ik3JpH4B7mKdrJQxbfnRdrGMRjrYxr8P7WwqixJ+PiDHzR/1HTDMzc0TJf0FLUEBVVDJwSsfrOWKBjtr/TlLrlSQSqRf/ekE7b5quIEz319p/N/mSoo5/6gOEOyd0i3fyE5j4NGcr6l7ECiFNtWuTK7ozJrptrW1pVatWtHOnTvlbbm5ueJy+/bt9d6Gtyv3Z1w6bmh/5f3y2m0AACgfuASdO3NL2eCpf5wvtFsqZ7D5g6s+nBnngFt3nBmPB9s+uSvVrm5v8MOwrl+PG6di6kOdbIq+udzcsGm74oP+qA4+JW5e18O/NvmrDyJwmWxyevlv+gOmdfCa5sAR/9/iOfflkbSmW9dN9QEtzgI++dWBEgXcP4xuLQK0zW92pmPv96SvX2xFL7WrR0HeqgOPLEJdcs7vWV5L3nPBXrGEhsczDfn2sNYoMz6/7lgUBX64Ld+acciPu8Y3fP8/8Xem/dyd8lIetvPtrvLfFC4f5ww3B+mHCyn7P6Zeu29qS3drDtIu26u9hMhYzkZrfh5KaPhnYeXlPC5sxYoV9OOPP9Lly5fp9ddfp7S0NNHNnI0YMUIrOz1x4kTasmULLViwgK5cuSJmcp84cYImTJggrufbTp8+nY4cOUI3b94Ujdpefvllun37Ng0aNMhszxMAAIyLszMvd9Kf1eXRXoa0962Vbxtn33j+d0m936+J1uVdV+JE05zj6gZsxcEf2Dg7xR/SDWWbuzV2FQcduGKQOxtLn6d/e6293JimqBq5aX5WnOHnn4N0MCM6yfxj0KBsO3hdteSD13BumdSl3DZL0g26pSwmdyLnDtdcQl7UGd51FPfFv1u6N1ZV2XCZvfLg3+oxbeTzXOkjNbHKUo8bk5be8Nzz/eGarOs/Z++Ig5SpGdniwF1JSo8TH2SatHmkpeBmdiv235AvZ2bn0gvfHZUP7nI1waBWml5QvRbuFevuMx7lav1f4N4EjKdusKM3Smctfm1F0z1TrP/n/zvnovN3b5/YU7M0Cywk6B4yZAjNnz+fZs6cSUFBQWIdNgfVUrO0qKgountXNaqFdejQgdasWSM6knM38t9++402btxIzZs3F9dzuToH4wMHDhTzup966ilKTEyk/fv3i/FhAABQfnCZqqO99tH0gcFeBa5p5gwRc7DVlFs29VB1Qi+psV18tS5zNouzHoOWHS7yiBj+AMuB9pBvj4jsVNfP98gdxnUPIvAItKGtNR/0+GfAa2C5A2xxKcsNpfXpHs7qplBJGcW+PwBlafm/51Sf4XgZQ3mmu+589lNN5fPc4VoXL+XgsYh/je9Ik0L96LsRIdTYrTr1b+FBK0e3Jl/XqqLjs+7vFiVHext5OclR9fc4pWgsqcQNJtMys8XylLU6kwoKaibJTdu+2B4mbqv08qrj1HvhPpq/9arWTHElzvry+vWyjGemG8Jl+0x58PdmYjqN+uG41n58wOnvCZ1o6QvBNE19gLY0Rkxyg0ypsaj0Whp7ZNkTX+4XB5l1D2y/iaA7n0p5plxZX0bxyDDuYs5N1RwdH++DGAAAmBavex624ogIole/3IZa1q1R6Fo57gDOs0B5TfSF2ykim/S4nVS5WQ0HGb+djBbZI6XujV1pxYgQ0cFVH143zuWg+jzb0pMWDgkSGQXuWs6Zkrd6NRKZfl6vt+FEtMjUd2nkWuLH/tPhSDGGqG9zD3F57OoTomT9o2ea0fD2xStXB8jMzqGwmAe0/VKM3GeBqzBKclCoLDlzK0mUdnMZPf8uavOJ9nJIxhUq99Ky6MNnmtEgde+Ix8EVNVJX7Dd6NKS1x26JJSe6Qpu4iakN+myd1EXuTaHEmfkG0zeL884ONjRvYAvq3cxdHBzkzL1yScvsp5uJho+/HLlJzwZ7knXlytR74V5q4uFIP41pS2UNN7E7EJ5ABxTNOXW927cx/a9bQzkj/sKKI/mWD3w8oLmoIOKlSuzkzXtywzw+8NK1kSu929dfHDjlAJkPpBirGmT23xdp1aFI8X8u41GOeGx8kCdQsSzhcfDr3XzWVnF+dEcfMeaTu/XzSE876/w9BCp63IhiewAAKNPaN6hFN+b2o+zcPDGLuyg81E2Pnm3pRc+2NM7j4PXifPpL3ZhNiRvqcCDe1rcmVbGxylfKXlA2hWeXMw6y+cOZ0oj2PuL0uHQDa6m8lT8ELtweRi+0rUtu6u7MAIWZu/mK+LCvzPSV94Cb8Rpr7jLOa1k5SNX1xeBA0fiQf08Zq4lWx4aapnNfqQ9wVLezplR1Zrqnf23aeSXOYMDN4lIz9Abd3+3XrAFOSn9E4346Sb++2l4cYFTi15o70u8JixeZ9HXHb9Hr3RpQwoMs2h+eQBHxD8jX1fCSH0tz6HoCffrfFa1tf/yvgxhTyeu5uXFYHWd78VpK+Od3Ykao6FR+SN0Ak39vSpVVkiDvGuJvwMNHOWJixC9Ho8RYOF7m8/ORKHHw89DUHkX+W1YQqZz8gyebiLXdHHTrOyCjr8Fe+qMcMZHj7Q1n6InmHjSjf5N8B41vqydesJlPNhV/o2ytizausiIye3k5AADA4+I/9sb4kGIMXPqtz5YLMdT98z00+NsjBuea6tO3uf5pHqbkoW4Ox1mzL3eGizJ5U+BM2ms/naT3/9Q0wYOyjUuNlQE3692s4nQx5ixljaq24nfSLHWJua1VZRrTqT49F+wluogbs2s1V+wsGBSote2l9vW0RityplMXl/tzQM4M9Y6YqxN4ssHfHqbp6vcrj317rqWnOL9ge5jcQJLLmHktu75RjpaGK4j+OBUtfj9Ll5WPXXcJEmeJJ4b6iSqFqjqNwvg15+UA/PIGeDqJKRq6+LXXnUXPTdU44GbxqZlao8ceBzfDZLzuXFpTXpSgmxt4cgb7pe+Pim75/H7u+NkusY5fiXuPSD8bdMAvHDLdAAAARtS0jiNd+agv7bwcR0kPs2jRjnDxQerf83flD7jKzA9/yOOxZ4bo+8Bc2k2heKRYTHKGGJdjTLyOfctF1Ydd/iBrqEM8lA1LdoWL5ly6+EN/RTS6Y31xMjVurKgM6l5sW1cEkhww8UQCPqjHIw2VhrT2FqOvOAu+6mAkjepQXy6BZg+zcuTzNRxsaEBLT/rhoPbBlJ5N3ETAxb/bCurGfeLmfYtdpsLd26XlQK90qk+eNaqIDLQS98/QN3JNH256d+qDXgUeBG7rW4tOzgilVh/v0Ht9jwV7xQGbgv7v8Dr8xTvDxWvjVcOBngmqIwJfXuLwzZ5rdD/9kbxWn39v88EZac15QTgo1z1oxvj/Ej/eLZM6k7+7o7wsSiqTh8Ih6AYAADAy/oDGDZFYS+8aYtSMEnd75aCbA25ek82lm0o8FuirnddEeaI56BuDxtkqHqnGHdknrTsjf4hfOSqEeviXLJPJgbzk1M0ks2T1wTjCY1Np/rYwvRlVff+fwHi4oZevS1UxNoyDNQ7CeJ02B0pc9tzCy0k+6CcJqVeD/Nyq08f/XqY7yRliTXKAlxNtPH2bNp27Sy+0Va0353FYR6b1FAEdN1P79US02M7l5L2buontHPRvvWi4fJ3nWytN2XBW/P5YOap1kYNZU7hwO1mr/8Z3BzRdypX0ZawL4uxgW6TGe/MHBdI7G87qvZ5na3drXFs00tRn5l8XtJYycZ8AbujGPQWUeKmDmEihDrq/3nOd3u7d2GC1BTfNK8gvR6LoowHNxd8uqTqA+6hA4SyjFg8AAKCc8q6ZP+CQAtafj0bRu7+fkzPa3PX1s4EBIuPLH2y4CZE5NNSTmZSyP0t3X9PKmr32U8nn/EYr7ud0lGq8EZRNcan5y1aD6zrT3xM6muXxVCQc+G6c0JH2Teku93jgwE9ap81l7ZwRVf4ualLHUQTr3PSN3UtXNQCbtP6MWP/98qoT4jKvY5ZKhz8b2ELu2P1On8by9lcVS2o46/n58y20Hh83mJT6NnPgvuFktFj3XNA6c1Pjdctf7QrXO1qLicaXlStRG5+aj91k05DnW3nRspdayZf/m9hZ6/ru8/eI9fZSRlnCTdF0e4dwSTgH3roCvVU9QfoFuBdpOdPJSNXvYel1frtXI63rfzpyk4Z/f1SsRecKBv4v0N2/5E08KxJkugEAAExI3/xv7kL+ahdf+vec5oPTyA71aFwX/evBSxuvSX0qsI54fNyoiZshxaSoAmTd0UE8E3jHpVhRNj+yo4/ovltUN+LT5POnEHSXaV/vUTXxkpyb3btY/xfg8fDP2tDPm5tz7Xy7mygf5/FOvMZc2reWeq3vyJXH9DZ/46BbwkE2d7/msVPKJQPKfTijyuudH+Xk0b20TJE5Tc/KEU3V+HEol9Lw+Sdb1CFzWHnwhpyd5z4cU5/wF5MweB1zDQdbkb0/PK0nVdcZSWlsPZvUFhMquBcAH2TlCoU+i/bJ17f5P1UHfF4HzplvxpUIEl5jzs3dWPBH27Xumw/kLh8eIs638HIWUzS4qefB6wligkfXxq50PjpZVDhk5+TRc18foqvq2evb3uoinrvUhZzX7Ev47wFXCbCpff3lcnMoGIJuAAAAE+My8TVHo6hjw1p08Jqqo+zlu6nyrNZ149pRO99aZEk4W8UfRPeHxYsPWdxU7flW3nRcnQlp51tTnj/8ympVVuxBVrYYG2MIrzPl7sYjO/iIrr48PkdyNjpZzPXlwICzUKbKLoHxceZN+n/Ntr/VBQG3hZEyl08Hage5NatqMry6y1wYrxVWMjTOigPXlQdu0MfPNBeXpaUx/HuDK2P2hsVT/wAPkSWV3NeTmS0tXFYvaVO/hjwJg4NNPijBa7L5IIGp8ffhkZASfV3k2YJtYSLoTkrPEgcHpDXoM55sSh9tukTfK0rjOfvcsYGLyNbz+C6JD5eqX42XO7PzftLgaG60xgdGJNIacDauqy91969NT351QN7Ga8a5J4C5lkCVRSgvBwAAMDFeExjxST8568B+PBwpmk7xeB9lpshS8FpLzpTwmlzJwG8Oyeen9NEeX8b2hRnuUsydbyf/elaMx/lsyxXxQZwDbQkH3LuuxIq5w34z/qNt6gZrUDZGLEnWjG0r1gpD2cBl5/pM7+cvGkLWLuKowPf6NhbVDZw1VZLWDvPa5SYzt4gMeEFBfmnhgJHx+K7OfpryaM7gG2tOtjHxwUg+SBn04Xb6/ZRqXT0Hwow740scbK3EGvyfX2mrFXDra5ApBdxMGXBP1ikp52w3j678drimFJ71aeaut5IL9EPQDQAAUAo4c8sjZqROw7+dVH1wei7YU6trsKXRl4HnjsW8Xlc3C3Y1JkVkPfX5+6ymlJ4PNoQu2CtffqmdKlvCo3omrjsjRonN2HjB4GPi6/kDqKHvZUq8bpK7roOmGZ60/ndYm7rUoYFmbjRYPr/a+fs38Pual7oUp8kZl57r2z9XGdnpMFfQnZrxiDKzc8V5nq1tKeMmJSMUI98kJ2/epw83abLznIXnHiCMGxWGffwEnZ3Zm3a93Y3cDBwoMbRdaXi7evRmTz+913GQPfc5TVO5D55UjcSDorGs/2UAAADlnO54lWZ1tDNDlniwYM7TzbS2rR7TRnzI/nJoSzEjmLP1LDdPM0ZG176weK3LD9UBM39wfKd3Y3E+RTFuihtzJSs+lPNBijGrjovyyrXHomjgN5p5wcbG45L0NSViTy85QL0X7hNj34Doxe+OFBgsgGXjZR4/jWkjRouZYsTbvIHaTdWYNFecRyqaA0+PYNwoTXfWtiWY0b8p/fm/DvLvVYm0dlv6O6JcgsMHbp0cbAoc62iodF3St5k7vdtX9bu4oOZvE3v60fpx7Uql/L48QdANAABQikZ18CHpsxKvqWvlY3ml5fpmj0s2v9lZa70fjxHjstK29WuKy9yYRxd3LuZOt6xeLe3Sza9fDBadlmup15wqBX64jeb+d1l08OXyVJ4p/PORm2I+Lfvj1G0yhbGrT1CXebsp+r72TFtuHiR1cT9wzXApfUXBlQbXFc3w9GVNwbLxwTMur/5yqGZdMfeeMJYODbUrH7jDuhT88brg0sZVMtwsTV7jbIE4gOYxXGvHtRON0vStmx4Q5Fns++WGZzwdg7PZS15oKaqV+HXfMbkLXfqwDy0b3qrQcnGuCnirVyMxaxyKx/IO7wAAAJRjDWtXp4i5/UX3WC7NNWZWyVSksULMS88INP7gzkH30Rv3xCibIa29xTb+gMulnFxOziduUMQf+JRNjKSOyYkGMsvf7o3QyvjozoLeHx6vtSazJPigAAfTXKY5etVxOaDmsTic/UnLyqb41ExR+i65n2a+9aiWIuGBZkzYlD6NydrCynSh6DjY4rJiPpBi7B4Ti4YE0Zc7w8XIsbq1HEQFC6/15vcUTz0ozdGIPKZMqnh/oY1lNwHjddTcLZ5dvJ0s98B4vVsDUaFQEkNa16UhrVXnzdU5vqJC0A0AAGAGHk5VxKmsfCD/YXRrMVbGUFfqfi086Os91+lwRKJYj/1/zwbQ5F/P0H/nY6hlXWexj59bNfFhcc/VeDobnSQazMmzfrv40rf7Imhoa2+a3r8JLd8bQUt2X9MbaCtxJ9/HCbr/O3+XXv9FNWuc5xjfuqeZHf7d/gjRbV2fGwkoL5eaL9Vxsqfx3Rua++HAY9JtoGUsA1p6ipOEy6B7+tembZdiadGOMPpW0WDSlPgg4Ks/nZQP9r2saEBm6d7u3ZhG/XBMLOHhNfdSgzooOxB0AwAAQKG6q2fEFlS6yAH1qkORIkPMJemc9WacAWcdGtQSzZa4s66uiaF+Yl1pm/o1RSD+Tp/GovsuzzQvCJeYv9s3fyf1ouJxOxJlwM2UnZZ1nVasr6yoOFPJsLYTimtY27oi6L4WV3oHr347eUs+b6U+2FdWdGnkSgen9hAzz8tCdRTkhzogAAAAMAouMZa8/2f+7uNcdm6Ig621WCcoZb7Z6I4+8nkO7Lo2UmW0eY2j1EU3JiWDjqmD+uLiUto7yRl6r+Oycl2O9tZ0dlZvcf5mYjqlZFTMEnP+uXEzOynbr1zjD1AUPI6woGUlpsAHAyVfKGZjlxVcGYWAu+xCphsAAACMgjsBv9i2rtaHW0m/AHexnr04OBD/eEBzWrr7mih9HRTiLdZfc2DOI8Mkm8/fFRny4opM1DQB013DPqVvY9p9NU4eLSS2eziSUxUbscY8NTObElIzDZbbl2df7Qqnpbs1ZfcezkWb5QwgkQ7U8NiwRzm5Jh/bxb83rquz6pve6CTWSwOUJmS6AQAAwGgMfXhu4l6yZklcsn54Wk8RcDMpE87NnqQGb+dv5++YXhQ31J23uUPwmlfa0tjO9cX5+YMCRUZp/3vd5UZvqueg+n41q9mWepbOkqzYd0Prsk8ty+wCDZbLuYrmfbXzcqzJvx+PIEzLyhFroRu5Fe/gH4AxIOgGAAAAo1GO/grydqZnW3rSwGAvGtvF16jfh4Pv5eoGTDy/lmdrF1dEgirobuBSVYw2er9/U9EtWMqC1a5uL2aRS17v1lDrOSYqundXFFGJ6ZSVo8n+MwTdUFw8Y1oqMX/t51M05NvDoqu5qWy5ECOPteORXAClDeXlAAAAYDQjO/rQ/msJ1D/Ag0Z20KzJNgXuNs4BMGecr8c/kIPlE5H3RPZ7cAiPLiPacCKa2vrWFM3elKQmTvULmNfL68gjP+2vta2WujSWG4kZszRWKp23ZFzqr6t9A8zsheJbPKwlDfzmkNxsMfDDbbThtfbU2qf4S0XYgfAE2hsWR691bSDeo9ytnN+f3LyRR4Wx51t5GfU5ABQVgm4AAAAwGl7j/Our7Uvle3GA6utaVQTd3E2cg+7kh4/oxe+OirXYYbGplPIwm/49f1fsf2FOH6qmnvnNa8L/PH1bnG/hrRppVlRujqqg+4O/LoqRYlvf6iLGqnHQ/MqPJyghLYvWj2snPuwX1e4rcfS/X06JtemrRrcuVvDN3/fTLVfE+LTvR7Y2WTfxuNQM2qDoAM1Cm7iJtfwAxdWqXv554JPWnRFdupVORd2n1IxsuZEi44BaOTbrQWY2vfT9UXm9+NjOviJ7fi3+Ac15uhkdj1T1gOjY0MWEzwjAMNRXAAAAQJklZak/2HhBzLHlub9S8zMeJyYF3Oy9387J538/pQq4WTvf4mXWOjTQfHDn7uct5mwT59cfv0U7r8SJcve9YfHFus+v91yjh49yxO0WFDCXXPLn6WjxXDng5qz+t3sj6Fx0sgiKOYDnjDSX3N9NfkjR99PJGC7fTRVzghvWrkZrxrYVHd650R1ASekeW7qd9JD+98tJuhaXKi7n5ubRc18fopErj8nvqZM371OL2Vtp5QFNb4HLd1Pk81H30unQ9UQ6cfO+aNQ2cd0ZyniUS83qOFJjrOcGM0HQDQAAAGXWkNZ15fN7rsbTDwcj5cvKzuOMA3DOiLHwWNWH+hn9m5CdddEz0oybrSnl5RH9ffYOTf3jvLxNN+jOzDa85jwmOYOOR96XLy/ZfY2Gf39UBBz6cKD91vqztGhHuHjOHGxL5m25SqNXHRfzzWf+dYH6LtpP/RcfoNRijjfjTOK6Y1EigJdI3Z8buFYVBx6WDW9F7k7oXA4l9/OYttStsStte6uLvG3z+Rh69aeT4nyCom8CB97nopPoo02XRFO0Dzddkq+7dCdFK3C/qn5/K73fr4lYSw5gDqgHAgAAgDJdosol5hHqTuT62FlXlgNwzpIFeTnLHc87+RW/3NTdMX+g+eba01qX917VBN0/HblJc/6+SNP7NaGXO9XPd1tpXavS/vAEOn3rPgV4Oudr/LTjsiYQ3hceTwevJeh9nBtORsvnL9xOKdba61+O3qSZf10UJbzfvBhMPi5V6WqMKpDhTDeAMXC5t1TyzQdzrqvfx9LXW/cfau3/6wnt5Q2z/75I7/ZtrBV0H79xTxzIYqM6+IiGjtz/oVW9kq0VBzAGZLoBAACgTHtVT2d0ZdnztCf85QZKHPy++vMJUW7KpaYlKTdVriXVVVPd2ZyzbVJH9Q0nblF2bp5WZk7qBN530T6xL+vQoJZ8ezbwm8PUbu5O+uuMphT+1r10Grv6hHyZM/thsQ/E7PAvBgcafFwXijhWLTYlgwYtOyQCbinjPe6nk9R74T46FKEK7lt4FW8NPEBRNKujPTv7i+1hotpCaePpO2SraFy46lAkDf/+mFjWIeEs+BX1ASI/t2o0oKUnAm4wOwTdAAAAUKZxl/JlL7Wi9/r6i1LVA+91F2PKJAFeTtTOt5Y8JuxIhKqpEmd+S9ot/MW2dUUGesfkriJYltaXH5nWU27WdiVGlX27q866sWzFuK3Jv56RgwMHWytRauuhU659Ly2Lpv1xXpSUs3D1WlddcwcG0HPBXrT/3e5UW08jtXNFCLq5nL3tJzu1St2Vbt1THRxoWRdBNxjf1Cf8KUTRXG3xznC5WkNqusbLQ46pm6JJuHpFKkP//fX2ZGNVqcBmbQDmgKAbAAAAyjQOnPs2d6fXuzWgVaPbkFcNB6pia0ULBgXS270aUXDdGnqbpXHZaUlxJv3szN6i1HrlqNb03YgQ+uP1DiIQ96qhmj/8woqjopFZxiPNem4pAI9MSBONniQ8Xo3Xm342sAX5ulQVWWtpnWt6Vg7FpmTS51uv0If/aGfLJf2ae4iv3jUd6Nj7oWIesdI/Z+/QnH9U2WtdfHDgxe+O0PsbNWvSC8LzywGMrY5zFfrt9Q56D+qM794g33bux1C3poN8uYd/bZHRnvVUM3F5QFCdfGMCAcwFa7oBAACgXBqomMnLgbiuJ1uoAtWSBvoc2DMeDRba1E2+roWXk8hgczfyzp/tFqXlkglrT9O6se2o2/w9Wvc3JMRbfOWxZ7ve6SZv57WonGHm7uarD9/UyuC19Ham7w/eoDWvtMvXIOqXV9qKtd9PBnrQE4v2ixJ2LkXn2z3Zoo68H2fQ3/v9vOi4fpASxTbOFF6c05duJKRRn0X7tO53dEfTzl4HmNjTj0b9cFxrW/fGtcm7hgP1Wqj5/zimU32qVc1WNBXs1dSNlg9vJVeh9A/wIGcHm1J/7ACGVMqT6pVAlpKSQk5OTpScnEyOjjhCBgAAUB4EzN4q5v2yc7N7i5nippCUnkVBH24v8v7/TOgkSuD1eem7o3TgWgLVcbIX48kk8wa2oGeDPel+elahmWfuXM4zwLk5G8fmPMOYZ3nvvhpHI9r7yJ2iJbw+/PycPnIJ/KZzd+nNHg2pV1N3UUKv29gNwNj4IFBaVrao0JjSx1/udeAz9d98/0fvp2WJALukS0UASiNuRNCtB4JuAACA8mfTuTs0Yc1p0dF49tOqElRT6TJvt5gXLOG1qspycsnc5wJoWBvN2DNdS3aF03ydud2LhgTRU4F1CmzopisrO5fG/HhcBN5KUjDf2c9F67rIT/vL5/mjIgIasATL910XY/HWjG1HbeqjORqUnbgRhyoBAACgQuCyam58xg2bTG3tuHbUST0KiXGQoGwSJa1BlcrKDemrXqst2Ti+o+jGXJyAm3F2eoGe7uZS9pzHoHk6q9ai60LADZZiXJcGdOWjvgi4ocyxiKB76dKl5OPjQ/b29tS2bVs6duxYgftv2LCB/P39xf4BAQG0efNmrev5iOzMmTPJw8ODqlSpQqGhoRQeHm7iZwEAAACWjhuf8RpsU+MAdskLLalLI1ea/VRTEfSO7qg9o3v2U83yrcXWpZyJzYH24zR/4zJ0zmjrU6uaHX07vJXonv758y1K/D0ATM1aMTIMoKww+//a9evX0+TJk2nWrFl06tQpCgwMpD59+lBcnGbentKhQ4do2LBhNGbMGDp9+jQNGDBAnC5cuCDvM2/ePFq8eDEtW7aMjh49SlWrVhX3mZGhWQsFAAAAYErODra0+uU2NEodbPcLcKfq9tby7PC6tfI3d9Nnzdi25FLNjhYOCXrsx/T9yNZ6t/Pj4iZuh6f1pEGFZN8BAKB4zL6mmzPbrVu3piVLlojLubm55O3tTW+88QZNnTo13/5DhgyhtLQ02rRpk7ytXbt2FBQUJIJsfjp16tSht99+m9555x1xPdfYu7m50apVq2jo0KGFPias6QYAAABTiE/NpEPXE+ipFnUKzXKbyvcHbtBHm7RHj/F4NWW3dwAAKCdrurOysujkyZOi/Ft+QJUri8uHDx/WexvertyfcRZb2v/GjRsUExOjtQ//IDi4N3SfAAAAAKWBu4Y/E+RptoCbvdzRR6yLHdram1yq2dLYzvXFaDEAACiHc7oTEhIoJydHZKGV+PKVK1f03oYDan3783bpemmboX10ZWZmipOEj1RIRy4AAAAAyqPpvXzEiWWmp5HmkxAAABSFFC8WVjxu1qDbUsydO5fmzJmTbzuXuQMAAAAAAAAYkpqaKqqrLTLodnFxISsrK4qNjdXazpfd3d313oa3F7S/9JW3cfdy5T687lufadOmiWZuEl5Xfu/ePapVq5bFjsngoyp8UODWrVtYd26B8PpYLrw2lg2vj+XCa2O58NpYNrw+lguvjWVLKQOvD2e4OeDmnmIFMWvQbWtrS61ataKdO3eKDuRSwMuXJ0yYoPc27du3F9dPmjRJ3rZ9+3axndWvX18E3ryPFGTzC8ZdzF9//XW992lnZydOSs7OJR/JUZr4P6Cl/icEvD6WDK+NZcPrY7nw2lguvDaWDa+P5cJrY9kcLfz1KSjDbTHl5ZxhHjlyJIWEhFCbNm1o0aJFojv56NGjxfUjRowgT09PUQLOJk6cSF27dqUFCxZQ//79ad26dXTixAlavny5uJ4z0xyQf/zxx+Tn5yeC8A8++EAcfZACewAAAAAAAIDSYPagm0eAxcfH08yZM0WjM85Ob9myRW6EFhUVJTqaSzp06EBr1qyhGTNm0PTp00VgvXHjRmrevLm8z7vvvisC93HjxlFSUhJ16tRJ3Ke9vb1ZniMAAAAAAABUTGYPuhmXkhsqJ9+zZ0++bYMGDRInQzjb/eGHH4pTecXl8LNmzcpXFg+WAa+P5cJrY9nw+lguvDaWC6+NZcPrY7nw2lg2u3L0+lTKK6y/OQAAAAAAAACUiKZuGwAAAAAAAACMCkE3AAAAAAAAgIkg6AYAAAAAAAAwEQTdFmTp0qXk4+Mjuqy3bduWjh07VuD+GzZsIH9/f7F/QEAAbd68Wet6Xq7PXeE9PDyoSpUqFBoaSuHh4SZ+FuVTcV6bFStWUOfOnalGjRrixD933f1HjRolGv4pT3379i2FZ1I+Fef1WbVqVb6fve5kA7x3zPPadOvWLd9rwyceDynBe8c49u3bR0899ZQYp8k/Q54CUhhubBocHCwa2jRs2FC8lx737xgY5/X5448/qFevXuTq6ipm2bZv3562bt2qtc/s2bPzvXf4MwSY9rXh942+32s8sUcJ753Sf230/T3hU7NmzeR98L4xjrlz51Lr1q2pevXqVLt2bTHG+erVq4XerjzFOgi6LcT69evFzHLu0Hfq1CkKDAykPn36UFxcnN79Dx06RMOGDaMxY8bQ6dOnxX9ePl24cEHeZ968ebR48WJatmwZHT16lKpWrSruMyMjoxSfWcV7bfgPLL82u3fvpsOHD5O3tzf17t2bbt++rbUfBwp3796VT2vXri2lZ1SxXx/GH0qVP/ubN29qXY/3jnleGw4clK8L/z6zsrLKN60C753Hx2M1+fXgD/pFcePGDXHwo3v37nTmzBmaNGkSvfLKK1qBXUnei2Cc14eDDQ66+QPpyZMnxevEwQd/PlDiYEL53jlw4ICJnkH5VdzXRsIBhvJnz4GHBO8d87w2X375pdZrcuvWLapZs2a+vzl43zy+vXv30vjx4+nIkSO0fft2evTokfhszK+ZIeUu1uHu5WB+bdq0yRs/frx8OScnJ69OnTp5c+fO1bv/4MGD8/r376+1rW3btnmvvvqqOJ+bm5vn7u6e9/nnn8vXJyUl5dnZ2eWtXbvWZM+jPCrua6MrOzs7r3r16nk//vijvG3kyJF5zzzzjEkeb0VT3Nfnhx9+yHNycjJ4f3jvWM57Z+HCheK98+DBA3kb3jvGxx8F/vzzzwL3effdd/OaNWumtW3IkCF5ffr0MdrrDSV/ffRp2rRp3pw5c+TLs2bNygsMDDTyo6vYivLa7N69W+x3//59g/vgvWMZ7xvev1KlSnmRkZHyNrxvTCMuLk68Rnv37jW4T3mLdZDptgBZWVniyDSXREgqV64sLnOmVB/ertyf8ZEdaX/OSnDpknIfJycnUbJk6D7BOK+NrvT0dHFEj4+e6mbE+Uh348aN6fXXX6fExESjP/7yrqSvz4MHD6hevXqiCuGZZ56hixcvytfhvWM5753vv/+ehg4dKo5cK+G9U/oK+5tjjNcbjCc3N5dSU1Pz/d3hsksuvfX19aUXX3yRoqKizPYYK5qgoCBRAssVCQcPHpS3471jOfhvDv/c+fOBEt43xpecnCy+6v6OKs+xDoJuC5CQkEA5OTnk5uamtZ0v6675kfD2gvaXvhbnPsE4r42u9957T/yyVv5S4PLY1atX086dO+mzzz4TZTdPPPGE+F5g2teHA7WVK1fSX3/9RT///LP4cNq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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\" # String representing the outcome of the most recent simulation\n",
    "    axes[i].set_title(\"Outcome: {}\".format(outcome_str)) # Set the title of the subplot to show the outcome\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: 0.66\n",
      "Mean response time for Answer 1: 1.04s\n",
      "Mean response time for Answer 0: 1.05s\n"
     ]
    }
   ],
   "source": [
    "# Run 1000 simulations and store the outcomes and response times for each simulation\n",
    "outcomes = []\n",
    "response_times = []\n",
    "for _ in range(1000):\n",
    "    times, _, outcome = drift_diffusion_model(v, a, s, z, dt) # Collect time points (for the response time) and the outcome of the simulation\n",
    "    outcomes.append(outcome)\n",
    "    response_times.append(times[-1]) # The response time is the last time point in the list returned by the function\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",
    "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 ($66.0\\%$). A timeout is the second most common outcome ($26.5\\%$), followed by a false response in distant third place ($7.5\\%$). 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": [
    "#### Exploring parameter settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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: 0.22s, Mean response time for Answer 0: 0.23s\n",
      "v = 0.01, a = 0.05, Accuracy: 0.57, Mean response time for Answer 1: 0.43s, Mean response time for Answer 0: 0.44s\n",
      "v = 0.01, a = 0.07, Accuracy: 0.57, Mean response time for Answer 1: 0.64s, Mean response time for Answer 0: 0.61s\n",
      "v = 0.01, a = 0.08, Accuracy: 0.55, Mean response time for Answer 1: 0.76s, Mean response time for Answer 0: 0.78s\n",
      "v = 0.01, a = 0.10, Accuracy: 0.48, Mean response time for Answer 1: 0.93s, Mean response time for Answer 0: 0.98s\n",
      "v = 0.01, a = 0.11, Accuracy: 0.43, Mean response time for Answer 1: 1.01s, Mean response time for Answer 0: 1.07s\n",
      "v = 0.01, a = 0.13, Accuracy: 0.32, Mean response time for Answer 1: 1.16s, Mean response time for Answer 0: 1.13s\n",
      "v = 0.01, a = 0.14, Accuracy: 0.28, Mean response time for Answer 1: 1.27s, Mean response time for Answer 0: 1.23s\n",
      "v = 0.01, a = 0.16, Accuracy: 0.26, Mean response time for Answer 1: 1.30s, Mean response time for Answer 0: 1.41s\n",
      "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: 0.58, Mean response time for Answer 1: 0.21s, Mean response time for Answer 0: 0.21s\n",
      "v = 0.01, a = 0.05, Accuracy: 0.61, Mean response time for Answer 1: 0.41s, Mean response time for Answer 0: 0.42s\n",
      "v = 0.01, a = 0.07, Accuracy: 0.62, Mean response time for Answer 1: 0.64s, Mean response time for Answer 0: 0.67s\n",
      "v = 0.01, a = 0.08, Accuracy: 0.60, Mean response time for Answer 1: 0.80s, Mean response time for Answer 0: 0.80s\n",
      "v = 0.01, a = 0.10, Accuracy: 0.54, Mean response time for Answer 1: 0.91s, Mean response time for Answer 0: 0.87s\n",
      "v = 0.01, a = 0.11, Accuracy: 0.48, Mean response time for Answer 1: 1.00s, Mean response time for Answer 0: 1.00s\n",
      "v = 0.01, a = 0.13, Accuracy: 0.44, Mean response time for Answer 1: 1.12s, Mean response time for Answer 0: 1.10s\n",
      "v = 0.01, a = 0.14, Accuracy: 0.34, Mean response time for Answer 1: 1.24s, Mean response time for Answer 0: 1.17s\n",
      "v = 0.01, a = 0.16, Accuracy: 0.31, Mean response time for Answer 1: 1.24s, Mean response time for Answer 0: 1.28s\n",
      "v = 0.02, a = 0.02, Accuracy: 0.58, Mean response time for Answer 1: 0.08s, Mean response time for Answer 0: 0.07s\n",
      "v = 0.02, a = 0.04, Accuracy: 0.63, Mean response time for Answer 1: 0.21s, Mean response time for Answer 0: 0.21s\n",
      "v = 0.02, a = 0.05, Accuracy: 0.64, Mean response time for Answer 1: 0.41s, Mean response time for Answer 0: 0.40s\n",
      "v = 0.02, a = 0.07, Accuracy: 0.66, Mean response time for Answer 1: 0.61s, Mean response time for Answer 0: 0.61s\n",
      "v = 0.02, a = 0.08, Accuracy: 0.65, Mean response time for Answer 1: 0.80s, Mean response time for Answer 0: 0.81s\n",
      "v = 0.02, a = 0.10, Accuracy: 0.61, Mean response time for Answer 1: 0.91s, Mean response time for Answer 0: 0.93s\n",
      "v = 0.02, a = 0.11, Accuracy: 0.54, Mean response time for Answer 1: 1.03s, Mean response time for Answer 0: 1.09s\n",
      "v = 0.02, a = 0.13, Accuracy: 0.45, Mean response time for Answer 1: 1.11s, Mean response time for Answer 0: 1.13s\n",
      "v = 0.02, a = 0.14, Accuracy: 0.43, Mean response time for Answer 1: 1.20s, Mean response time for Answer 0: 1.17s\n",
      "v = 0.02, a = 0.16, Accuracy: 0.34, Mean response time for Answer 1: 1.27s, 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: 0.07s\n",
      "v = 0.02, a = 0.04, Accuracy: 0.64, Mean response time for Answer 1: 0.21s, Mean response time for Answer 0: 0.20s\n",
      "v = 0.02, a = 0.05, Accuracy: 0.69, Mean response time for Answer 1: 0.41s, Mean response time for Answer 0: 0.41s\n",
      "v = 0.02, a = 0.07, Accuracy: 0.70, Mean response time for Answer 1: 0.62s, Mean response time for Answer 0: 0.63s\n",
      "v = 0.02, a = 0.08, Accuracy: 0.70, Mean response time for Answer 1: 0.79s, Mean response time for Answer 0: 0.80s\n",
      "v = 0.02, a = 0.10, Accuracy: 0.65, Mean response time for Answer 1: 0.90s, Mean response time for Answer 0: 0.86s\n",
      "v = 0.02, a = 0.11, Accuracy: 0.58, Mean response time for Answer 1: 1.04s, Mean response time for Answer 0: 1.04s\n",
      "v = 0.02, a = 0.13, Accuracy: 0.54, Mean response time for Answer 1: 1.12s, Mean response time for Answer 0: 1.15s\n",
      "v = 0.02, a = 0.14, Accuracy: 0.47, Mean response time for Answer 1: 1.19s, Mean response time for Answer 0: 1.13s\n",
      "v = 0.02, a = 0.16, Accuracy: 0.41, Mean response time for Answer 1: 1.23s, Mean response time for Answer 0: 1.17s\n",
      "v = 0.03, a = 0.02, Accuracy: 0.60, Mean response time for Answer 1: 0.07s, Mean response time for Answer 0: 0.08s\n",
      "v = 0.03, a = 0.04, Accuracy: 0.65, Mean response time for Answer 1: 0.22s, Mean response time for Answer 0: 0.21s\n",
      "v = 0.03, a = 0.05, Accuracy: 0.72, Mean response time for Answer 1: 0.41s, Mean response time for Answer 0: 0.39s\n",
      "v = 0.03, a = 0.07, Accuracy: 0.75, Mean response time for Answer 1: 0.63s, Mean response time for Answer 0: 0.61s\n",
      "v = 0.03, a = 0.08, Accuracy: 0.75, Mean response time for Answer 1: 0.78s, Mean response time for Answer 0: 0.75s\n",
      "v = 0.03, a = 0.10, Accuracy: 0.73, Mean response time for Answer 1: 0.91s, Mean response time for Answer 0: 0.93s\n",
      "v = 0.03, a = 0.11, Accuracy: 0.64, Mean response time for Answer 1: 1.02s, Mean response time for Answer 0: 0.96s\n",
      "v = 0.03, a = 0.13, Accuracy: 0.58, Mean response time for Answer 1: 1.11s, Mean response time for Answer 0: 1.13s\n",
      "v = 0.03, a = 0.14, Accuracy: 0.52, Mean response time for Answer 1: 1.17s, Mean response time for Answer 0: 1.22s\n",
      "v = 0.03, a = 0.16, Accuracy: 0.48, Mean response time for Answer 1: 1.22s, Mean response time for Answer 0: 1.19s\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: 0.20s, Mean response time for Answer 0: 0.20s\n",
      "v = 0.03, a = 0.05, Accuracy: 0.74, Mean response time for Answer 1: 0.40s, Mean response time for Answer 0: 0.37s\n",
      "v = 0.03, a = 0.07, Accuracy: 0.80, Mean response time for Answer 1: 0.59s, Mean response time for Answer 0: 0.60s\n",
      "v = 0.03, a = 0.08, Accuracy: 0.81, Mean response time for Answer 1: 0.75s, Mean response time for Answer 0: 0.78s\n",
      "v = 0.03, a = 0.10, Accuracy: 0.75, Mean response time for Answer 1: 0.87s, Mean response time for Answer 0: 0.87s\n",
      "v = 0.03, a = 0.11, Accuracy: 0.71, Mean response time for Answer 1: 0.96s, Mean response time for Answer 0: 0.93s\n",
      "v = 0.03, a = 0.13, Accuracy: 0.66, Mean response time for Answer 1: 1.06s, Mean response time for Answer 0: 1.14s\n",
      "v = 0.03, a = 0.14, Accuracy: 0.58, 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: 1.25s, Mean response time for Answer 0: 1.26s\n",
      "v = 0.04, a = 0.02, Accuracy: 0.60, Mean response time for Answer 1: 0.07s, Mean response time for Answer 0: 0.07s\n",
      "v = 0.04, a = 0.04, Accuracy: 0.70, Mean response time for Answer 1: 0.19s, Mean response time for Answer 0: 0.19s\n",
      "v = 0.04, a = 0.05, Accuracy: 0.78, Mean response time for Answer 1: 0.37s, Mean response time for Answer 0: 0.36s\n",
      "v = 0.04, a = 0.07, Accuracy: 0.81, Mean response time for Answer 1: 0.56s, Mean response time for Answer 0: 0.55s\n",
      "v = 0.04, a = 0.08, Accuracy: 0.82, Mean response time for Answer 1: 0.74s, Mean response time for Answer 0: 0.68s\n",
      "v = 0.04, a = 0.10, Accuracy: 0.82, Mean response time for Answer 1: 0.88s, Mean response time for Answer 0: 0.85s\n",
      "v = 0.04, a = 0.11, Accuracy: 0.73, Mean response time for Answer 1: 0.94s, Mean response time for Answer 0: 0.96s\n",
      "v = 0.04, a = 0.13, Accuracy: 0.71, Mean response time for Answer 1: 1.04s, Mean response time for Answer 0: 1.09s\n",
      "v = 0.04, a = 0.14, Accuracy: 0.67, Mean response time for Answer 1: 1.15s, Mean response time for Answer 0: 1.20s\n",
      "v = 0.04, a = 0.16, Accuracy: 0.57, Mean response time for Answer 1: 1.21s, Mean response time for Answer 0: 1.24s\n",
      "v = 0.04, a = 0.02, Accuracy: 0.63, Mean response time for Answer 1: 0.07s, Mean response time for Answer 0: 0.06s\n",
      "v = 0.04, a = 0.04, Accuracy: 0.73, Mean response time for Answer 1: 0.20s, Mean response time for Answer 0: 0.19s\n",
      "v = 0.04, a = 0.05, Accuracy: 0.78, Mean response time for Answer 1: 0.36s, Mean response time for Answer 0: 0.36s\n",
      "v = 0.04, a = 0.07, Accuracy: 0.84, Mean response time for Answer 1: 0.56s, Mean response time for Answer 0: 0.57s\n",
      "v = 0.04, a = 0.08, Accuracy: 0.86, Mean response time for Answer 1: 0.69s, Mean response time for Answer 0: 0.69s\n",
      "v = 0.04, a = 0.10, Accuracy: 0.82, Mean response time for Answer 1: 0.87s, Mean response time for Answer 0: 0.79s\n",
      "v = 0.04, a = 0.11, Accuracy: 0.81, Mean response time for Answer 1: 0.93s, Mean response time for Answer 0: 0.89s\n",
      "v = 0.04, a = 0.13, Accuracy: 0.76, Mean response time for Answer 1: 1.06s, Mean response time for Answer 0: 1.06s\n",
      "v = 0.04, a = 0.14, Accuracy: 0.68, Mean response time for Answer 1: 1.11s, Mean response time for Answer 0: 1.10s\n",
      "v = 0.04, a = 0.16, Accuracy: 0.66, Mean response time for Answer 1: 1.18s, Mean response time for Answer 0: 0.95s\n",
      "v = 0.05, a = 0.02, Accuracy: 0.67, 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: 0.20s, Mean response time for Answer 0: 0.20s\n",
      "v = 0.05, a = 0.05, Accuracy: 0.82, Mean response time for Answer 1: 0.36s, Mean response time for Answer 0: 0.35s\n",
      "v = 0.05, a = 0.07, Accuracy: 0.88, Mean response time for Answer 1: 0.52s, Mean response time for Answer 0: 0.50s\n",
      "v = 0.05, a = 0.08, Accuracy: 0.89, Mean response time for Answer 1: 0.70s, Mean response time for Answer 0: 0.56s\n",
      "v = 0.05, a = 0.10, Accuracy: 0.87, Mean response time for Answer 1: 0.82s, Mean response time for Answer 0: 0.88s\n",
      "v = 0.05, a = 0.11, Accuracy: 0.83, Mean response time for Answer 1: 0.91s, Mean response time for Answer 0: 0.96s\n",
      "v = 0.05, a = 0.13, Accuracy: 0.79, Mean response time for Answer 1: 0.98s, Mean response time for Answer 0: 1.08s\n",
      "v = 0.05, a = 0.14, Accuracy: 0.74, Mean response time for Answer 1: 1.09s, Mean response time for Answer 0: 1.30s\n",
      "v = 0.05, a = 0.16, Accuracy: 0.70, Mean response time for Answer 1: 1.12s, Mean response time for Answer 0: 0.95s\n",
      "v = 0.05, a = 0.02, Accuracy: 0.67, Mean response time for Answer 1: 0.07s, Mean response time for Answer 0: 0.07s\n",
      "v = 0.05, a = 0.04, Accuracy: 0.77, Mean response time for Answer 1: 0.19s, Mean response time for Answer 0: 0.19s\n",
      "v = 0.05, a = 0.05, Accuracy: 0.84, Mean response time for Answer 1: 0.36s, Mean response time for Answer 0: 0.36s\n",
      "v = 0.05, a = 0.07, Accuracy: 0.89, Mean response time for Answer 1: 0.50s, Mean response time for Answer 0: 0.45s\n",
      "v = 0.05, a = 0.08, Accuracy: 0.90, Mean response time for Answer 1: 0.65s, Mean response time for Answer 0: 0.71s\n",
      "v = 0.05, a = 0.10, Accuracy: 0.89, Mean response time for Answer 1: 0.79s, Mean response time for Answer 0: 0.88s\n",
      "v = 0.05, a = 0.11, Accuracy: 0.88, Mean response time for Answer 1: 0.89s, Mean response time for Answer 0: 0.80s\n",
      "v = 0.05, a = 0.13, Accuracy: 0.83, Mean response time for Answer 1: 1.01s, Mean response time for Answer 0: 1.13s\n",
      "v = 0.05, a = 0.14, Accuracy: 0.80, Mean response time for Answer 1: 1.07s, Mean response time for Answer 0: 0.89s\n",
      "v = 0.05, a = 0.16, Accuracy: 0.73, Mean response time for Answer 1: 1.15s, Mean response time for Answer 0: 1.51s\n"
     ]
    },
    {
     "data": {
      "image/png": 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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(times[-1])\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. 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": [
    "#### 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": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z = 0.010, Accuracy: 0.09\n",
      "z = 0.013, Accuracy: 0.12\n",
      "z = 0.017, Accuracy: 0.14\n",
      "z = 0.020, Accuracy: 0.17\n",
      "z = 0.024, Accuracy: 0.21\n",
      "z = 0.027, Accuracy: 0.23\n",
      "z = 0.031, Accuracy: 0.26\n",
      "z = 0.034, Accuracy: 0.28\n",
      "z = 0.038, Accuracy: 0.31\n",
      "z = 0.041, Accuracy: 0.33\n",
      "z = 0.044, Accuracy: 0.38\n",
      "z = 0.048, Accuracy: 0.41\n",
      "z = 0.051, Accuracy: 0.45\n",
      "z = 0.055, Accuracy: 0.44\n",
      "z = 0.058, Accuracy: 0.51\n",
      "z = 0.062, Accuracy: 0.51\n",
      "z = 0.065, Accuracy: 0.54\n",
      "z = 0.069, Accuracy: 0.56\n",
      "z = 0.072, Accuracy: 0.60\n",
      "z = 0.076, Accuracy: 0.62\n",
      "z = 0.079, Accuracy: 0.68\n",
      "z = 0.082, Accuracy: 0.71\n",
      "z = 0.086, Accuracy: 0.71\n",
      "z = 0.089, Accuracy: 0.75\n",
      "z = 0.093, Accuracy: 0.79\n",
      "z = 0.096, Accuracy: 0.78\n",
      "z = 0.100, Accuracy: 0.81\n",
      "z = 0.103, Accuracy: 0.84\n",
      "z = 0.107, Accuracy: 0.87\n",
      "z = 0.110, Accuracy: 0.90\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 = 0.1\n",
    "zs = np.linspace(0.01, 0.11, 30)\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(times[-1])\n",
    "    accuracy, _, _ = show_results(outcomes, response_times, plot=False)\n",
    "    print(\"z = {:.3f}, 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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