behavioral_analyses.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Behavioral analyses\n",
    "\n",
    "This notebook shows basic behavioral figures and the statistical models for the following paper:\n",
    "\n",
    "Waskom ML, Asfour JW, Kiani R (2018). [Perceptual insensitivity to higher-order statistical moments of coherent random dot motion](http://jov.arvojournals.org/article.aspx?articleid=2685931). *Journal of Vision* 18(6):9 1-10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.regression.linear_model import OLS\n",
    "from statsmodels.discrete.discrete_model import Logit\n",
    "from statsmodels.tools import add_constant "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "sns.set(style=\"whitegrid\", font_scale=1.3, color_codes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data and define how the figures will look"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "trial_data = pd.read_csv(\"trial_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "subject_order = [\"S1\", \"S2\", \"S3\", \"S4\", \"S5\"]\n",
    "\n",
    "condition_order = [\n",
    "    \"N\", \"M5\", \"M10\", \"M15\", \"M25\",\n",
    "    \"SL8\", \"SL6\", \"SR6\", \"SR8\",\n",
    "    \"K\", \"KV\", \"V\",\n",
    "]\n",
    "\n",
    "condition_palette = dict(\n",
    "    N=\"#919191\",\n",
    "    M5=\"#7e8981\",\n",
    "    M10=\"#6c8172\",\n",
    "    M15=\"#5a7964\",\n",
    "    M25=\"#3a684a\",\n",
    "    SL8=\"#937149\",\n",
    "    SL6=\"#ab8b6a\",\n",
    "    SR6=\"#ab8b6a\",\n",
    "    SR8=\"#937149\",\n",
    "    V=\"#a94c62\",\n",
    "    KV=\"#68519e\",\n",
    "    K=\"#5478ae\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Odd patch detection accuracy in each condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x108dc3f60>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAEMCAYAAADnM+1CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"condition\", y=\"correct\",\n",
    "               data=trial_data,\n",
    "               kind=\"bar\",\n",
    "               palette=condition_palette,\n",
    "               order=condition_order,\n",
    "               size=4, aspect=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAEMCAYAAADnM+1CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBQsRERHJHgsWIiIikr3/AzWxUIxN36k5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(8, 4))\n",
    "for c, cond_data in trial_data.groupby(\"condition\"):\n",
    "    pal = sns.light_palette(condition_palette[c], 10)[-5:]\n",
    "    sns.barplot(x=\"condition\", y=\"correct\", hue=\"subject\",\n",
    "                data=cond_data,\n",
    "                palette=pal, ci=95, errwidth=1,\n",
    "                order=condition_order,\n",
    "                hue_order=subject_order,\n",
    "                ax=ax)\n",
    "ax.legend_ = None\n",
    "sns.despine()\n",
    "f.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Odd patch reaction time in each condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x109812358>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAEMCAYAAADnM+1CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYDwKCwAAMB6FBQAAGO//A93qzBwc49idAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"condition\", y=\"rt\",\n",
    "               data=trial_data,\n",
    "               kind=\"bar\",\n",
    "               palette=condition_palette,\n",
    "               order=condition_order,\n",
    "               size=4, aspect=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAEMCAYAAADnM+1CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvFvnyVgAAIABJREFUeJzt3XlcVPX+P/AXqAgYJZqaS+EAyuCC\ngIjRJVfELuEO0TXJjRLLJbteyzQDsp/ZTTMzNZeuCiWmeMWVLwpom5DStVRcEEZDcMVdliE4vz+8\nzPUIAwPMnHOGeT0fDx6POZ85y2tm4PCez/mcc6wEQRBAREREpGDWcgcgIiIiqg0LFiIiIlI8FixE\nRESkeCxYiIiISPFYsBAREZHiNZqCJTMzU+4IREREZCKNpmAhIiKixosFCxERESkeCxYiIiJSPBYs\nREREpHiyFCy///47/P399T7/3XffITAwEN7e3hgzZgyOHj0qYToiIiJSGkkLFkEQsG3bNkyaNAll\nZWXVzpOeno6lS5fi888/x9GjRzFu3DhERkbi5s2bUkYlIiIiBZG0YFm9ejU2bdqEyMhIvfNcvnwZ\nkydPhru7O6ytrTFq1Cg0adIE586dkzApERERKUlTKTc2ZswYREZG4pdfftE7z8iRI0XTmZmZuH//\nPlxcXEwdj4iIiBRK0oKlbdu2dZr/3LlzmDFjBmbMmIFWrVrVOj8vHkdERGTeevfuXW27pAVLXfz4\n44+YNWsWJk6ciNdff92gZfS9SCKyXMnJyQCAwMBAmZMQUUMosmBJSEjARx99hJiYGAQHB8sdh4jM\nWFJSEgAWLETmTnEFy+HDhxEdHY2vv/4aPj4+cschIiIiBVBEwbJgwQIAQExMDNauXYuysjK89tpr\nonk+//xz9OvXT454REREJDMrQRAEuUMYQ2ZmJsewEFEVb7/9NgBg6dKlMicxfxwPRHJSRA8LEREp\nH8cDkZx4LyEiIiJSPBYsREREpHgsWIiIiEjxWLAQEZFIcnKyboAtkVJw0C0REYlwcC0pEXtYiIiI\nSPFYsBAR1QEPlxDJg4eEiIjqgIdLiOTBHhYiIiJSPBYsRKQIDTnUUt9leXiHyHzwkJBC8B4dZOka\ncqilvsvy8I40uH8jY2DBohBS7ji58yAiKbEwJGNgwWKBuPMgIiJzY9FjWHj8moiIyDxYdA8LexqI\nSCl4qJaoZhZdsBARKQW/QBHVzKIPCREREZF5YMFiRBwTQ0RKYQ77I3PISMrBQ0JGxC5dIlIKc9gf\nmUNGUg72sBAREZHisWAhIiIixWPBQkQ14jgDIlICjmEhohpxnAERKYFF9LCY+zdEc89PRETUULIU\nLL///jv8/f31Pr97924MHjwYXl5emDJlCq5fv96g7SUlJem+JZojc89PRA/wywdR/UlasAiCgG3b\ntmHSpEkoKyurdp7Tp0/jgw8+wNKlS3H48GE8+eSTiI6OljImEZFJ8MuHeWKhqQySFiyrV6/Gpk2b\nEBkZqXeeXbt2YfDgwejVqxdsbW0xe/ZspKSkoLCwUMKkRNQQ3MGTnIz9+8dCUxkkLVjGjBmDxMRE\n9OzZU+88ubm5cHV11U07OjrCwcEBubm5UkQ0CHfGRDXjDp7kxN+/xknSs4Tatm1b6zzFxcWwtbUV\ntdnZ2aG4uLjWZTMzM6ttv3v3brXP62uvzdatWwEArVu3Nsr6alu2Ieut67aIHlWf35e6LJMa9/8A\nAH/kPFjmn7PG6J4bNO69em/vzoVjAICb1y4BANK2rwcAFN+/o5un8HIeAGBv7GcAgHbd+undxqm0\nbwEAV/LOAQDiPv07AMB94FiDMurLachz9VHX9RXmHtU9vnG1AACQvPUrAEBrZ586r/OXXat0j/PP\nP/gMVkVFAAB8h02tV0ZDyf1eUsP07t272nbFndZsa2uLkpISUVtxcTHs7e1rXVbfi3RwcKj2eX3t\ntTH2+mpbtiHrreu2iB6l7/elspexutOd6/I7lhqn/zlDf0er217afwuWuqhpe5UFS12WeZSS/86T\nHypYHlW5jrqs85dd+p+rz/rqQu73kkxDcQWLi4sLNBqNbvrGjRu4ffs2XFxcZExFRI/i9VmISEqK\nuw5LcHAwkpOTcfToUZSWlmLp0qXo168fHB0d5Y5GREQKwbGElkcRPSwLFiwAAMTExMDd3R0ffvgh\n5s2bh2vXrsHHxweLFi2SOSERkfLUdFiusWMPn+WRpWDp27cvMjIydNMxMTGi54OCghAUFCR1LCKS\ngCX/kzW2R/9pVw4EBqoODh43e4nE6YiMSxE9LERkOfjNmMzBhHn/Gw1+NfeKqG3DR+NkyWTpWLAY\nwfLlywEAFy9eFE0DwIwZM2TJVJ2oxR8CAM7/cV40HfXO+3JFIqJasEeK6AEWLERECsYeKaIHWLDI\nbFfSbgDA9RuFomkAGPZCsCyZiIhMqfLigNVdLPAfnyXIkok9WcrHgoWIzFZ68oOrTt+5cVU0Tcb1\n7/UfAwCuXfpDNA0Aoya/K0smY2NPlvIp7josRERERI9iwUJERESKx4KFiIiIFI9jWKhOODCNiJRo\nWWya7vHFyzdFbW+FD2zQulu07dKg5ck4WLBQnUg5MI3FkfnZtHQOAOByXo5o+tW3P5EtE1Gl+u5T\nWrRxNUUcqiMWLKRYHLVPjV3a9vW6xzevXRK1DRw9WZZMpqBq95jR1rVhRzoA4PL1O6JpQ3CfYt5Y\nsBCRInRq1Vzybfbo8ozk27RETu0c5I5AjQALFgOtX/+/b0KXLl2q0kZEDdPR0UbybXZ37ST5NklZ\n3vr4wYXqcv+4Jpom5WHB0khx/Ac1RPLWr3SPb1wtELUFhk6RJRMpG/c5ZGosWPQw9z8+HqslMo0u\nnVrXeRlLuCIv9znVM/f/JUrCgkUP/vERUXVcO7aSO4JB6lNYkfHxf4nxNNqCJSUlRff45s2borbB\ngwfLkomIyBgSVkUDAK7lnxdNP6whhZWHWlXvZYlMpdEWLDXZuXMnAOD69euiaQAYPny4LJmIGrNV\nURG6x/nnL1Vpo6r2xn4GACi8nCealkIPt86SbYvIUBZZsJjKM8/wFEkiIiJTYMFiRCxYiIiUJ+rL\nvQCA8/mFomkyLyxYiIjI7Li495Y7AkmMBYuCbd4WDwC4cu2qaPpvIS/Llono3+s/BgBcu/SHaJpI\nSi7uXkZdn2MHtVHXR8bHgoWIiGQhx+0Y9GHBonwsWCxQu47t5Y5ApGi82Jc0THU7hqe7eJpkvSQv\nFiwWqF2Hp+SOQKRovNiXeXva1UPuCGQCkhYsWVlZWLBgAc6dOwcnJydER0fD07NqJbxy5Ups3rwZ\nJSUl8PDwQFRUFJ5++mkpo5JM3vpgtu5xriZH1LYs+lNZMhERkfyspdpQaWkpIiMjMXr0aBw5cgTh\n4eGYNm0atFqtaL7U1FTs2LEDCQkJ+Pnnn/HMM89g3rx5UsWUTfce3dG9R/c6L5ecnKzrviYiIvnF\nrz+o+7l66RauXrqlm6b6k6xgSU9Ph7W1NcaOHYtmzZohJCQEjo6OSEtLE813/vx5VFRUoKKiAoIg\noEmTJrC1tZUqpmy69eiGbj261Xm5pKQkXfc1ERFRYyXZISGNRgMXFxdRm0qlQnZ2NoYOHapre/HF\nF7Flyxb0798fTZo0Qdu2bbF582apYhKREXR5+km5I+h4ujvLHYEUop1TD7kjUANIVrAUFRXBzs5O\n1GZra4uSkhJRm1arhbe3N7766iu0adMGixYtwqxZs7B582ZYWVnVuI3MzMwG56xcx927d/Wu09nZ\n8B2gMTLVtE59OWvKX9P6alOX9RqbHNuk+nEx0p2CjfGZe6jr/vdqqr8fOZkqZ33eM0PWZwrtnOp+\n2L06DcloLr8vcurdu/qLAkpWsNjZ2VUpTkpKSmBvby9qW7hwIYYMGYLOnTsDAObPnw9vb2+cPXsW\nbm5uNW7j4Rf58N2a66JyHQ4ODqLpY8eO6eapS8Gi742vVJC0u64RRet8NOeSFQ9ukHbrzi0AwMHD\n3zc4IwB8t2sbAKC49MFnmFOgAQC8NCzE0NgGid2pvzfNkJxkHMm5R+WOAKD2zzw9Odck23v07woA\nTqV9W+MyNTF2zvqoLWd9P3N979kvu+q1ulpzHs9Lr9+Kjai2jNnHDtZ7WdJPsjEszs7O0Gg0ojaN\nRgNXV1dRW0FBgWggrrW1NaytrdG0Kc/AJiLL06PLM+jRhfcpI5KsCvDz84NWq0VsbCxefvllJCYm\n4vr16/D39xfNN2DAAKxfvx7PP/882rVrhyVLlqBLly5QqVSS5KwcL3PlyhXRNBEpV2+PxnuV0u6u\nneSOUKNNS+cAAC7n5YimiYxNsoLFxsYGa9euRVRUFJYuXQonJyesWrUK9vb2iIiIgI+PDyIjIzF9\n+nT8+eefGDt2rG48y5dffglra8k6g4jIhFTtHjP6Or161Hy4mIjMn6THWdRqNeLj46u0r1u3TvfY\nxsYG77zzDt555x0poxGRHh5q4/ZuOrVzMOr6zElj7gkiMjUODCGiGvVw6yx3hEbD2D1BahVvs0GW\ngwULEZGZUju1kzsCKYQl3LCTBQsREZGJdHXuJcl2LOGGnSxYiIj+K2FVNADgWv550TRRfUlVsFgC\nFixERArAWwgQ1YwFCxGRAtTlFgJElogFCymWY7tWckcgood0c+4gdwSyYBZRsPTqxWOI5sixnXFu\noGdKljAyn6iSmgULycgiCpaePXvKHcGo1mxYq3tccLmgShtJxxJG5uvDb9tEJCWLKFiIyPj4bZtI\nftPHfQkAOF+YL5oGgC/i3pQlk6nwBj1ERESkeCxYiIiISPF4SKiR6uT0jOTb5ABUIiIyFRYsjVSn\nZzpJvk1LHoBKRESmxYJFDzc3495VtSHc1F3ljqCj7q6WOwIRKVCXp5+UOwI1chZdsHTv3l3vc126\ndJEwSc26uCmnYOnaTTmFHBEph0sn5V83icybRRcs7u7uckcgIiIiA1h0wUJERNQYtLRzkjuCybFg\nISIiMnMt7VmwEBFZHLXqKbkjENEjWLAQET1C7dRO7ghE9Ahe6ZaIiIgUz+CCZceOHdBqtVXai4qK\nsGHDBmNmIlK88XNex/g5r+NUzhmcyjmjmyYiItOo8ZDQlStXcPfuXQDA3Llz0alTJ7Rs2VI0z5kz\nZ7B06VJMmDDBZCFJP5WLs9wRiIiITK7GguX48eOYNm0arKysIAgCxo0bV+18o0aNMkk4qp3KRSV3\nBCIiIpOrsWAJCAhAamoqKioqEBAQgLi4OLRv3173vJWVFezt7av0uuiTlZWFBQsW4Ny5c3ByckJ0\ndDQ8PT2rzLd//34sWbIEV65cQZcuXRATEwO1mpeEJyIislS1jmHp0KEDOnXqhCeeeALt2rVDx44d\ndT8dOnQwuFgpLS1FZGQkRo8ejSNHjiA8PBzTpk2rMi4mKysL7733HhYuXIjMzEwEBARg5syZ9Xt1\nRERE1CgYfFrzhAkTMHfuXIwfPx4dOnRA8+bNRc+7urrWuHx6ejqsra0xduxYAEBISAg2btyItLQ0\nDB06VDdffHw8QkND4ePjAwCYOHEinnvuOVRUVMDamic1ERERGVNycjIAIDAwUOYkNTO4YPn8888B\nAEePHtW1VY5tsbKywqlTp2pcXqPRwMXFRdSmUqmQnZ0tKliysrIwYMAAvPrqqzhz5gy6deuGBQsW\nGFSsZGZmGvpyJKPETI9qSMaHl60coG2q19zY30uqyhzeT3PICDCnMRlrn2lqhm5r69atAIDWrZVx\nA8vevXtX225wwZKSktKgAEVFRbCzsxO12draoqSkRNR2+/ZtxMfHY9WqVXBzc8Py5csxdepU7N69\nG02b1hz34RfZ0LzGou+Nr1SQtFuiJPrVlhEAcgo0tS7r4OBg8Pr0id25We9zDVmv0W2pvllRGRsg\nOfdo7TNJoLb3Mz05V6Ik+hnymZtDTnP5zI/npUuURL/aMmYfO1jrssbq1dgA/e+HofsjY+y7pWBw\nwdKxY8cGbcjOzq5KcVJSUgJ7e3tRm42NDYYMGYKePXsCAGbOnIkNGzYgNzcXXbt2bVAGshzm0sVJ\nRJYpKSkJAPdRdSHZoBBnZ2doNOJv6RqNpsrYF5VKpTu0AACCIOh+iAyVlJSk2yEQEZH5k6xg8fPz\ng1arRWxsLMrKyrBt2zZcv34d/v7+ovlGjRqF3bt34+jRoygrK8OyZcvg5OTE3hUiIiILJlnBYmNj\ng7Vr12LPnj3w9fVFXFwcVq1aBXt7e0RERGD16tUAgMGDByMqKgrvv/8+fH198fvvv2PlypWwsrKS\nKioREREpjKR3a1ar1YiPj6/Svm7dOtH0iBEjMGLECKliEdVbiycflzsCEZFFkLRgocZn7bdf6x4X\nXLkkantt7CRZMkmpRWsHuSMQEdXLz+u+AQDcvnRFNA0Az0W8IkummvBKbERERKR4LFjIoiQnJ+tO\neSYiIvPBQ0JkUXjtAyIi88QeFiIiIlI89rAQWQBe+ZdIGb5ashcAUJBXKJoGgCl/D5Ilk7lgwUJk\nAXgojMh88AtG9ViwkKyM+Ye5Yfv/Tsm7fO2KqG3CaOWdokdEVB2pv2B4O3eRZDsNxYKFZMVv/kRE\nhjNF74uXigULERERGZElf8njWUJERESkeCxYyOR4sTYiImooHhIik7PkLkwiIjIO9rAQERGR4rGH\nhYiISGKqTj2qtH0yfysA4A/NVdH0nIWh0gVTMBYsREREEuvcqbvB886btkH3OPfC5SptloIFC1Ej\nNXDyMN1jq0slora09btkyURElq0h15FhwUJERESSaMhJGCxYqFFycTePKzcSEZFhWLCQ0Th3dTF4\n3gWfxgAANHnnRdPG4qIWFyzLNnwJALh4OV80DQBvTXjTqNsmIiLjY8FCRqPq4ix3BCIis9exrVrv\nc22fMPyLoZJ8/bepAIBLN/NE05M2rzJ4HSxYiIiIFKRjGze9z7VpaZ4FizHwwnFERESkeOxhIbIA\nggP/1IlIfi62T9R7WUl7WLKyshASEgJPT0+MGDECx44dq3H+bdu2oW/fvhKlI2rEHmv64IeISEbO\ndo/D2e7xei0r2R6stLQUkZGRiIyMRGhoKBITEzFt2jSkpqbCxsamyvx5eXn4+OOP0aRJE6kikpF9\n+tUyAEBewUXRNBERUV1J1sOSnp4Oa2trjB07Fs2aNUNISAgcHR2RlpZWZd7y8nLMmTMHL730klTx\niIiISMEkK1g0Gg1cXMSjm1UqFbKzs6vMu2bNGnTp0gX9+/eXKh4REREpmGSHhIqKimBnZydqs7W1\nRUlJiajtxIkTSExMREJCAk6cOFGnbWRmZjY4p7EpMdOjzCEjYLqcxlxvXdZVOW/v3r2Ntv26blvp\nzCGnOWQEmNOYzCEj0LCcUr3G6rajb58oWcFiZ2dXpTgpKSmBvb29aPrdd9/FwoUL0aJFizpv4+EX\nmZKSUv+wRlTbP6OCpN0SJdHPkH+YOQUak2y7XaenDJ63tpzHL5yuV4Z6FQxbGr6ub775BgDw+uuv\n1337DVRbzuTcoxIlqVltOdOTcyVKop8hn7k55DSXz/x4XrpESfSrLWP2sYPSBKlFbTl//o/+faax\nv0j9hnUN3o5kBYuzszPi4uJEbRqNBsHBwbrpEydOIC8vD5GRkQAejGUpLi6Gj48Pdu7ciQ4dOkgV\nlyTStqPhBQsREVkuyQoWPz8/aLVaxMbG4uWXX0ZiYiKuX78Of39/3Tw+Pj747bffdNMZGRmYMWMG\nMjIypIpJRERECiTZoFsbGxusXbsWe/bsga+vL+Li4rBq1SrY29sjIiICq1evlioKERERmRlJrySl\nVqsRHx9fpX3duuqPbfXt25e9K2RUT7s4yR0Bn8WtBADkXckXTc8a94ZsmYiIlI6XviSLooSChYiI\n6o43PyQiIiLFY8FCREREisdDQmRynVRPyx1BkZ7p0lnuCEREZoMFC5lcx84sWKrTyZXjaYiIDMVD\nQkRERKR4LFiIFCg5ORnJyclyxyAiC6ekfREPCREpUFJSEgAgMDBQ5iREZMmUtC9iDwtRLZT0DYOI\nyFKxh4WoFkr6hkFEZKnYw0JERESKxx4WIiIiEjnwz1UAgJt/5IumA/4xVbZM7GEhIiIixWPBQkRE\nRIrHQ0JEesxcOAcAkHshRzRNRET6VZ5VaewTFViwEBERUbU8OqnqvIypzqxkwUJERETV6tlROfc8\nY8FCpBDzVizUPc7NvyBq+2jafFkyEREZKn7quwCAq3m5omlj4aBbIiIiUjz2sBDVwrH9k3JHMIip\nBroREdVF1ydMs89kwUJUC8enWssdwSC8hQARKUHXx01TsPCQEBERESkeCxYiIiJSPB4SIjJzIX8f\nDwC4e+6qaJqIqDFhDwsREREpnqQFS1ZWFkJCQuDp6YkRI0bg2LFj1c63cuVKDBgwAD4+PggPD8fZ\ns2eljEkku7ZO7dHWqb3cMYiIFEOygqW0tBSRkZEYPXo0jhw5gvDwcEybNg1arVY03/bt25GYmIjY\n2Fikp6fDz88PU6ZMQUVFhVRRyUIlJyfrTg2WW5tnnkKbZ56q0zI2rVvApnULEyUiIhKTep8pWcGS\nnp4Oa2trjB07Fs2aNUNISAgcHR2RlpYmmu/mzZuIjIzE008/jaZNm+LVV19FQUEBLl++LFVUslBJ\nSUm6U4PNUfNWLdC8FQsWIpKG1PtMyQbdajQauLi4iNpUKhWys7MxdOhQXdvkyZNF86SmpqJly5Z4\n6qm6fdskIiKixkOygqWoqAh2dnaiNltbW5SUlOhd5siRI/jggw8QExMDa+vaO4MyMzMbnNPYlJjp\nUeaQETBdzsr13r17t8HbMXVGpa7PVMwhpzlkBJjTmMwhI2CanA+v01T7zN69e1c7r2QFi52dXZXi\npKSkBPb29tXOv2PHDkRHR+P999/HsGHDDNrGwy8yJSWl/mGNSN8bX6kgabdESfSrLSMA5BRoJEhS\ns9pyHr9wukHrdXBwEE1v2rel3uuqyfenMoy73m/rvLpacybnHq37Sk2gtpzpybkSJdHPkM/cHHKa\ny2d+PC9doiT61ZYx+9hBaYLUoracP/+n7vvM/IT/jVkpvXajSltdGfL3U0myMSzOzs7QaMT/9DQa\nDVxdXavM++WXX2LRokVYuXIlRo8eLVVEIiIiUijJelj8/Pyg1WoRGxuLl19+GYmJibh+/Tr8/f1F\n8yUkJGDjxo3YvHlzlTEvRKbwwfKPAACaixdE00REpF/3Nh0k3Z5kPSw2NjZYu3Yt9uzZA19fX8TF\nxWHVqlWwt7dHREQEVq9eDQBYs2YN7t+/j5CQEHh5eel+cnJypIpKREREtXBv0wHuEhYtkl6aX61W\nIz4+vkr7unXrdI//7//+T8pIREREZAZ4aX4iIiJSPN78kOi/2j3DS+ETESkVCxai/2LBQkSkXDwk\nRERERIrHgoWIiIgUjwULERERKR4LFiIzIvXt3ImIlIKDbonMSOWt3AMDA2VOQkQkLfawEBERkeKx\nh4XIDEz5cBYA4OL5HNE0EZGlYA8LERERKR57WIjMyOPtW8kdgYhIFixYiMzI4085yh2BiEgWPCRE\nREREiseChYiIiBSPBQsREREpHgsWIiIiUjwWLERERKR4LFiIiIhI8ViwEBERkeKxYCEiIiLFY8FC\nREREiseChYiIiBSPBQsREREpHgsWIiIiUjxJC5asrCyEhITA09MTI0aMwLFjx6qdb8OGDXj++efh\n7e2N2bNno6ioSMqYREREpDCSFSylpaWIjIzE6NGjceTIEYSHh2PatGnQarWi+dLS0rB+/Xps2rQJ\nhw4dwu3bt7F8+XKpYhIREZECSVawpKenw9raGmPHjkWzZs0QEhICR0dHpKWlieZLTExESEgIVCoV\nHBwcMHPmTGzbtg3l5eVSRSUiIiKFkaxg0Wg0cHFxEbWpVCpkZ2eL2nJzc+Hq6iqa5+7du7hy5Yok\nOYmIiEh5rARBEKTY0MqVK5GVlYUVK1bo2ubMmYO2bdti9uzZurYhQ4bg3XffxeDBgwEAFRUVcHd3\nx969e6sUPA/LzMw0XXgiIiKSTO/evau0NZVq43Z2digpKRG1lZSUwN7eXtRma2uL0tJS3XRxcTEA\noEWLFjWuv7oXR0RERI2DZIeEnJ2dodFoRG0ajUZ0+AcAXFxckJubK5rHwcEBbdu2lSQnERERKY9k\nBYufnx+0Wi1iY2NRVlaGbdu24fr16/D39xfNN3z4cGzZsgXZ2dm4d+8eli9fjmHDhsHampeMISIi\nslSSjWEBgNOnTyMqKgpnzpyBk5MToqKi4OnpiYiICPj4+CAyMhIAsGnTJmzYsAF37txB//79sXDh\nQtjZ2UkVk4iIiBRG0oKFiIiIqD54nIWIiIgUjwULERERKR4Lloe4ubnhgw8+qNI+aNCgKlfklZKb\nmxt69eqFe/fuidrLysrQt29fDBo0SNcWHR2NHj16wMvLS/dTUFAge65K+/fvx5gxY0RtFy9exPjx\n4+Hl5YWhQ4ca9b02VsZ9+/ahW7duovd1586dsuQ8cOAAhg0bBm9vb7z44ovYv3+/ZDmBB2fuTZ06\nFX369IGXlxeGDx+OrVu3AgC2b9+O0aNH6102JSUFQUFB8Pb2xogRI/DTTz8ZNZuxcmZnZ+OVV16B\nl5cXAgICsHfvXsnzubu7iz5Hb29vvPrqq6KzKM0lp5Sfe3Xc3Nxw9uxZ3bRWq8XUqVMRHBws20VJ\na8qUn58Pd3d3nDx5sspyhw8fhre3N+7fvy9lXADA+PHjsXDhwirtgiBg0KBB+Pe//23aAALpdO3a\nVXBzcxMOHjwoah84cKCQmpoqU6oHuXx9fYUdO3aI2lNSUgQvLy9h4MCBurawsDBh3759istVVlYm\nrFmzRujRo4cwatQo0fyjR48WPv30U0Gr1QoHDx4UvLy8hMLCQkVlXLp0qRAdHW2UTA3JmZubK3h5\neQk///yzUFFRIfzwww+Cp6encO7cOUlylpeXC4MGDRJWrFghFBcXC+Xl5UJGRobQp08fISkpSUhI\nSKjy3lUqLCwUPD09hf/85z+CIAjCzp07hV69egklJSWKyllUVCT0799f2Lhxo1BeXi788ssvgoeH\nh5Cfny9rvtu3bwvTp08XQkPyBrnWAAANOklEQVRDzSqnlJ+7Pl27dhXOnDkjCIIgFBcXC5MmTRJC\nQkKEmzdvSpahrpkmT54sfPjhh1WW+/vf/y4sWLBA0qyV9uzZI/Tt21fQarWi9p9++kno06ePUFxc\nbNLts4flEaGhoZg3bx5u3boldxSRoUOHYs+ePaK2Xbt2ITAwUDddUVGBM2fOwN3dXVG5ACAqKgqH\nDh3CpEmTRO05OTk4e/Ys3nzzTTRr1gz9+/eHr68vduzYoZiMwIM7jZv6fTUkZ35+Pl566SX4+fnB\nysoK/v7+UKlUOH78uCQ5b968iYsXL2L48OGwtbWFtbU1fH198Y9//ANlZWU1LltQUICSkhL8+eef\nEAQBTZo0QfPmzSGYYNx/Q3KmpqbiySefxKuvvgpra2v06dMHW7duxeOPPy5rvscffxzDhw/X3c7E\nXHJK+bnXpqioCK+//jrKy8uxYcMGtGzZUvIMhmYKCwvD7t27Re/z3bt3sX//fvztb3+TJWtAQACs\nrKxw6NAhUXtCQgJGjhwJW1tbk26fBcsjxo0bB1dX12oPDckpKCgIGRkZuHnzJgDg3r17OHLkCAYO\nHKib5/z58ygpKcHixYvx7LPPYuTIkSY/lGVILgCYPn064uLi4OTkJGrPzc1Fx44dRb/o1d1jSs6M\nAHDq1CkkJSXh+eefR0BAAL766iuj73ANyenv7493331XN52Xl4fs7Gyo1WpJcrZu3Rq+vr6YOHEi\nli9fjvT0dBQVFSE0NBTBwcE1LtutWzf0798fr7zyCrp374533nkHn376qUl2cg3JefLkSahUKsyd\nOxd9+/bFsGHDcOnSJTz22GOy5rt27RpiY2Px7LPPmlVOKT/3mty7dw+TJ0/GvXv3sGbNmlqvni53\npoEDB6Jp06ai4mDXrl1Qq9W6v3ep2djYYOTIkaJDP3fu3MGBAwcQFhZm8u2zYHmElZUVFi1ahJ9+\n+gm7du2SO45Oq1at0KdPHyQnJwN4MM5iwIABsLGx0c1z584d+Pr6IiIiAj/88AOmTZuGt956C2fO\nnJE1FwC0a9eu2uWLioqqXGPH1ta2ym0c5MxYXFyMzp07Y9iwYThw4ABWrFiBLVu2ID4+3mgZ65Kz\n0pUrV/Daa69h1KhRUKvVkuVct24dxo0bh/T0dERERMDX1xdvv/22rtDSR6vVom3bttiwYQOOHTuG\nBQsWYPbs2SYbQ1DfnLdv38bevXvRt29f/Pjjj5g5cyZmzpyJCxcuSJrv9OnT8PHxgZeXF3r06IGQ\nkBCo1Wp88sknZpVT6s9dn7fffhv29vY4e/asrkdSbjVlatq0KcaMGYPExERdW0JCAl5++WWpY4qE\nhYXh+++/133+u3btgoeHR433+jMWFizVaN++PebPn4+YmBhcvnxZ7jg6wcHB2L17N4AHvyTDhw8X\nPe/p6YmNGzfC29sbzZo1Q0BAAPz8/HDw4EFZc9XE0HtMyZ0xLi4OI0eORPPmzaFWqzFu3DgcOHDA\nqBnrkjMrKwuhoaHw9fVFVFSUpDmbN2+OCRMm4Ntvv0VmZibWrFkDjUaD9957r8blvvnmG5SUlMDP\nzw82NjYIDQ2FSqXSFWjGVt+cNjY2cHd3x8iRI3V/Rz179sQPP/wgaT61Wo2jR4/i119/xSeffIKS\nkhI899xzcHBwMKucUn/u+gwePBjr1q3DxIkTMWvWLNy4cUPS7dcnU2hoKA4dOoRbt27hzJkzyMvL\nw1//+leZ0j7QuXNneHl56Q5fJyQkSHaIigWLHiNHjsSzzz6LuXPnynKstTpDhgzBiRMncPLkSfzx\nxx/o06eP6PnDhw9X+TZdWlqK5s2by5qrJi4uLsjPz4dWq9W1VXePKTkz5uXl4dNPPxX9HpSWlurt\n+TB1zu+//x7h4eGYMGECYmJidLetkCLn3r17MWjQIN02mjdvjueeew5Tp07F6dOna1z20qVLos8Z\nePAtsmlT49+DtSE5VSoV7t69K2qrqKgw6n6gLvmsrKwQFBSEN998E7NmzUJOTo5Z5ZTyc69JWFgY\nrKysMHPmTDz11FOYPXs2KioqJM1Q10ydOnVCnz59sGfPHmzfvl2ScSKGCAsLw44dO3D69GlcvnwZ\nAQEBkmyXBUsNoqOjcfbsWZOdFlxXLVq0wIABAzBnzhwEBQXByspK9Ly1tTUWL16Mo0ePory8HLt3\n78Zvv/1m8oq8tlw1cXFxgaurK5YtWwatVotDhw4hIyMDL7zwgmIyPvHEE9iyZQs2btyIiooKnDx5\nEnFxcVVOfZYiZ3Z2NmbMmIGYmJgqg4OlyOnn54eioiJ89NFHKCwshCAIuHDhAuLj43Vjbf78809c\nvnxZ9FNcXIx+/fohNTUV33//PSoqKrBv3z6cOnUK/fv3N1o+Y+QcOnQorl69qnsfDxw4gJMnT1Z7\nCrwp8z0qPDwcPXr0wLx581BRUWE2OaX83A3RtGlTLFmyBL/99htWrlwpS4ZH1ZQpLCwMe/fuxb59\n+yQZJ2KIIUOGID8/H1999RXGjBljki9v1TLpOUhm5uHTzCqlpKQIXbt2lf205spclXmys7MFQRCE\n1NRU0am53333nTBkyBChV69ewsiRI4WMjAxF5KpU3WmQFy9eFCZNmiR4e3sLgYGBRn2vjZUxMzNT\nCA0NFTw9PYUBAwYIcXFxRstYl5zz588X3NzcBE9PT9FPfHy8JDkFQRBycnKE6dOnC88++6zg4eEh\nDBw4UFi6dKlQWloqJCQkCF27dq3y89133wmCIAhbt24VAgMDBW9vb2HMmDHCkSNHjJ7PGDl///13\nISwsTPD29hZeeOEFIS0tTfJ81Z12ff78ecHDw0P417/+ZVY5pfzcq1Pdvn3Xrl2CWq0WfvrpJ0mz\n1DVTWVmZ8Je//EUYN26c1BFr9PHHHwtubm7CH3/8Idk2eS8hIiIiUjweEiIiIiLFY8FCREREiseC\nhYiIiBSPBQsREREpHgsWIiIiUjwWLERERKR4LFiIyCxkZGTAzc0N9+/fBwAMGjQIcXFxAID79+9j\n69atunnDw8OxePFiWXISkWlIe21kIiIj2bZtm+7Gmf/617+QmpqK0NBQAMAXX3wh+aXfici0+BdN\nRGapVatWusePXv+yZcuWUschIhPjISEiapCCggK88cYb8PLywl/+8hcsXrwY5eXluHPnDqKiouDv\n7w9PT09ERkbi4sWLuuUqD+mMGzcOvXr1wvDhw3Ho0CHd84WFhbr1vvDCCzhx4oRou5XLb9++HStW\nrMDJkyfh5uYGoOohoR07diA4OBgeHh4ICgrCvn37dM998cUXmDFjBhYtWgRfX1/4+/vjo48+kv3G\neEQkxoKFiOpNq9Vi4sSJKC0txebNm7Fs2TLs3r0ba9aswfTp0/Hrr79i2bJliI+Ph1arxZQpU/Dn\nn3/qlv/8888xduxYJCQkwMnJCXPnzkVZWRkAYPr06bh16xY2b96MuXPn4uuvv642Q1BQECZNmgS1\nWo0ff/yxyvM7duzA+++/j/DwcCQmJmL06NF4++23kZmZqZsnNTUVRUVFiI+Px4wZMxAXF4fU1FQj\nv1tE1BAsWIio3n7++Wfk5+fjn//8J9RqNfr06YOYmBg89thjSE9Px6JFi+Dj4wO1Wo0lS5YgPz8f\nKSkpuuWDgoIQFBQEV1dXvPnmmygsLER+fj6ys7ORmZmJDz/8EGq1Gv3798cbb7xRbQZbW1vY29uj\nSZMmaNOmTZXnN23ahLCwMISFhUGlUiEiIgJDhw7F2rVrRet4//334ezsjJdeeglqtRrHjx83/htG\nRPXGgoWI6u3cuXPo2LGjaDzJwIED0bp1azRr1gzdunXTtTs6OkKlUiEnJ0fX1rlzZ93jxx57DABQ\nVlaGc+fOwcbGBi4uLrrne/bsWe+MHh4eojZvb29Rjvbt28PGxkaURavV1mt7RGQaLFiIqN6aNWtW\nbfvD//wfVl5eLhobUt3yDw+gffixvm3VxtbWFlZWVqK2ioqKWnMQkbKwYCGieuvcuTMKCgpw69Yt\nXVt8fDw2btyIsrIynDx5Utd+48YNXLhwAc7OzrWu183NDVqtFqdOndK1PTro9mGPFiQPc3Z2xrFj\nx0Rtv/76q0E5iEg5WLAQUb35+/vDyckJ7733HrKzs5GRkYFVq1ahX79+GDx4MN577z1kZmbi9OnT\nmD17Ntq0aYOBAwfWul5nZ2c8//zzmD9/Pk6cOIHDhw9j5cqVeue3t7dHYWEh8vLyqjwXERGB7777\nDlu2bMH58+exfv167N+/H+Hh4Q167UQkLRYsRFRvTZo0wcqVK6HVahESEoLZs2dj1KhRmDx5MhYt\nWgR3d3dMmTIFY8eORYsWLRAbG6u72FttPvvsM3Tu3Bnh4eGYN28exo8fr3fewMBANG/eHMHBwbh6\n9arouYCAAMybNw9r165FcHAwdu7cieXLl6Nfv34Neu1EJC0r4dErLhEREREpDHtYiIiISPFYsBAR\nEZHisWAhIiIixWPBQkRERIrHgoWIiIgUjwULERERKR4LFiIiIlI8FixERESkeP8fHi1onQMZjA4A\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trial_data_norm = trial_data.copy()\n",
    "for subj, subj_trial_data in trial_data.groupby(\"subject\"):\n",
    "    subj_norm_rt = subj_trial_data.rt / subj_trial_data.groupby(\"condition\").rt.mean().max()\n",
    "    trial_data_norm.loc[trial_data_norm.subject == subj, \"rt\"] = subj_norm_rt\n",
    "\n",
    "f, ax = plt.subplots(figsize=(8, 4))\n",
    "for c, cond_data in trial_data_norm.reset_index().groupby(\"condition\"):\n",
    "    pal = sns.light_palette(condition_palette[c], 10)[-5:]\n",
    "    sns.barplot(x=\"condition\", y=\"rt\", hue=\"subject\",\n",
    "                data=cond_data,\n",
    "                palette=pal,\n",
    "                ci=95, errwidth=1.5,\n",
    "                order=condition_order,\n",
    "                hue_order=subject_order,\n",
    "                ax=ax)\n",
    "ax.legend_ = None\n",
    "sns.despine()\n",
    "f.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tables of detection accuracy and reaction time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>condition</th>\n",
       "      <th>N</th>\n",
       "      <th>M5</th>\n",
       "      <th>M10</th>\n",
       "      <th>M15</th>\n",
       "      <th>M25</th>\n",
       "      <th>SL8</th>\n",
       "      <th>SL6</th>\n",
       "      <th>SR6</th>\n",
       "      <th>SR8</th>\n",
       "      <th>K</th>\n",
       "      <th>KV</th>\n",
       "      <th>V</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>correct</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rt</th>\n",
       "      <td>3.66</td>\n",
       "      <td>3.46</td>\n",
       "      <td>2.27</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.64</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.49</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.61</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "condition     N    M5   M10   M15   M25   SL8   SL6   SR6   SR8     K    KV  \\\n",
       "correct    0.21  0.30  0.56  0.78  0.89  0.20  0.23  0.18  0.18  0.17  0.62   \n",
       "rt         3.66  3.46  2.27  1.45  1.03  3.64  3.73  3.49  3.85  3.61  2.31   \n",
       "\n",
       "condition     V  \n",
       "correct    0.79  \n",
       "rt         1.25  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(trial_data\n",
    " .groupby(\"condition\")\n",
    " .mean()\n",
    " .loc[:, [\"correct\", \"rt\"]]\n",
    " .reindex(condition_order)\n",
    " .round(2)\n",
    " .T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condition</th>\n",
       "      <th>N</th>\n",
       "      <th>M5</th>\n",
       "      <th>M10</th>\n",
       "      <th>M15</th>\n",
       "      <th>M25</th>\n",
       "      <th>SL8</th>\n",
       "      <th>SL6</th>\n",
       "      <th>SR6</th>\n",
       "      <th>SR8</th>\n",
       "      <th>K</th>\n",
       "      <th>KV</th>\n",
       "      <th>V</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">correct</th>\n",
       "      <th>S1</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S5</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">rt</th>\n",
       "      <th>S1</th>\n",
       "      <td>1.45</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.49</td>\n",
       "      <td>1.57</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2</th>\n",
       "      <td>2.70</td>\n",
       "      <td>2.63</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.63</td>\n",
       "      <td>3.11</td>\n",
       "      <td>2.82</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3</th>\n",
       "      <td>5.11</td>\n",
       "      <td>4.69</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.75</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.56</td>\n",
       "      <td>5.70</td>\n",
       "      <td>5.13</td>\n",
       "      <td>5.60</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4</th>\n",
       "      <td>5.33</td>\n",
       "      <td>4.69</td>\n",
       "      <td>2.68</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.39</td>\n",
       "      <td>5.14</td>\n",
       "      <td>4.43</td>\n",
       "      <td>5.16</td>\n",
       "      <td>5.94</td>\n",
       "      <td>5.30</td>\n",
       "      <td>2.84</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S5</th>\n",
       "      <td>2.84</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.96</td>\n",
       "      <td>2.96</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "condition           N    M5   M10   M15   M25   SL8   SL6   SR6   SR8     K  \\\n",
       "        subject                                                               \n",
       "correct S1       0.17  0.29  0.54  0.76  0.86  0.21  0.39  0.09  0.07  0.20   \n",
       "        S2       0.09  0.33  0.49  0.80  0.92  0.20  0.15  0.13  0.24  0.12   \n",
       "        S3       0.24  0.33  0.64  0.83  0.95  0.24  0.13  0.19  0.14  0.16   \n",
       "        S4       0.29  0.32  0.73  0.86  0.90  0.19  0.26  0.21  0.18  0.14   \n",
       "        S5       0.20  0.24  0.43  0.66  0.83  0.15  0.22  0.24  0.30  0.22   \n",
       "rt      S1       1.45  1.59  1.32  0.87  0.62  1.49  1.57  1.48  1.67  1.51   \n",
       "        S2       2.70  2.63  1.75  1.41  0.84  2.63  3.11  2.82  2.93  2.85   \n",
       "        S3       5.11  4.69  2.73  1.21  0.75  6.00  6.56  5.70  5.13  5.60   \n",
       "        S4       5.33  4.69  2.68  1.69  1.39  5.14  4.43  5.16  5.94  5.30   \n",
       "        S5       2.84  2.84  2.43  1.89  1.40  2.96  2.96  2.87  2.81  2.85   \n",
       "\n",
       "condition          KV     V  \n",
       "        subject              \n",
       "correct S1       0.44  0.72  \n",
       "        S2       0.62  0.80  \n",
       "        S3       0.65  0.78  \n",
       "        S4       0.73  0.84  \n",
       "        S5       0.61  0.77  \n",
       "rt      S1       1.36  0.94  \n",
       "        S2       1.66  0.94  \n",
       "        S3       3.17  1.28  \n",
       "        S4       2.84  1.59  \n",
       "        S5       2.10  1.33  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(trial_data\n",
    " .groupby([\"subject\", \"condition\"])\n",
    " .mean()\n",
    " .loc[:, [\"correct\", \"rt\"]]\n",
    " .unstack(\"subject\")\n",
    " .reindex(condition_order)\n",
    " .round(2)\n",
    " .T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "## Logistic regression of accuracy on condition-wise motion statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_stats = [\"odd_m\", \"odd_v\", \"odd_s\", \"odd_k\"]\n",
    "model_data = (trial_data[sample_stats]\n",
    "              .groupby(trial_data[\"condition\"])\n",
    "              .transform(\"mean\")\n",
    "              .assign(odd_s=lambda x: x.odd_s.abs())\n",
    "              .join(trial_data[[\"condition\", \"correct\", \"rt\"]])\n",
    "              .dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>correct</td>     <th>  No. Observations:  </th>  <td>  5349</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  5344</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Fri, 11 May 2018</td> <th>  Pseudo R-squ.:     </th>  <td>0.2155</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>14:56:01</td>     <th>  Log-Likelihood:    </th> <td> -2906.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -3705.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th>  <td> 0.000</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -3.5349</td> <td>    0.198</td> <td>  -17.847</td> <td> 0.000</td> <td>   -3.923</td> <td>   -3.147</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_m</th> <td>    0.1517</td> <td>    0.009</td> <td>   16.625</td> <td> 0.000</td> <td>    0.134</td> <td>    0.170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_v</th> <td>    0.1011</td> <td>    0.005</td> <td>   22.349</td> <td> 0.000</td> <td>    0.092</td> <td>    0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_s</th> <td>   -0.0730</td> <td>    0.100</td> <td>   -0.729</td> <td> 0.466</td> <td>   -0.269</td> <td>    0.123</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_k</th> <td>    0.0307</td> <td>    0.035</td> <td>    0.880</td> <td> 0.379</td> <td>   -0.038</td> <td>    0.099</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                correct   No. Observations:                 5349\n",
       "Model:                          Logit   Df Residuals:                     5344\n",
       "Method:                           MLE   Df Model:                            4\n",
       "Date:                Fri, 11 May 2018   Pseudo R-squ.:                  0.2155\n",
       "Time:                        14:56:01   Log-Likelihood:                -2906.9\n",
       "converged:                       True   LL-Null:                       -3705.5\n",
       "                                        LLR p-value:                     0.000\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -3.5349      0.198    -17.847      0.000      -3.923      -3.147\n",
       "odd_m          0.1517      0.009     16.625      0.000       0.134       0.170\n",
       "odd_v          0.1011      0.005     22.349      0.000       0.092       0.110\n",
       "odd_s         -0.0730      0.100     -0.729      0.466      -0.269       0.123\n",
       "odd_k          0.0307      0.035      0.880      0.379      -0.038       0.099\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = add_constant(model_data[sample_stats])\n",
    "Y = model_data[\"correct\"]\n",
    "m = Logit(Y, X).fit(disp=False)\n",
    "m.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>odd_m</th>\n",
       "      <th>odd_v</th>\n",
       "      <th>odd_s</th>\n",
       "      <th>odd_k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p values</th>\n",
       "      <td>3.02712e-71</td>\n",
       "      <td>4.60223e-62</td>\n",
       "      <td>1.23658e-110</td>\n",
       "      <td>0.465984</td>\n",
       "      <td>0.378824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                const        odd_m         odd_v     odd_s     odd_k\n",
       "p values  3.02712e-71  4.60223e-62  1.23658e-110  0.465984  0.378824"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.pvalues.astype(object).to_frame(\"p values\").T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Logistic regression of accuracy on trialwise skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "trialwise_data = (trial_data[sample_stats]\n",
    "                  .assign(odd_s=lambda x: x.odd_s.abs())\n",
    "                  .join(trial_data[[\"condition\", \"correct\", \"rt\"]])\n",
    "                  .dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>correct</td>     <th>  No. Observations:  </th>  <td>   923</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   918</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Fri, 11 May 2018</td> <th>  Pseudo R-squ.:     </th>  <td>0.01447</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>14:56:01</td>     <th>  Log-Likelihood:    </th> <td> -451.61</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -458.24</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th>  <td>0.01007</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -6.9847</td> <td>    4.021</td> <td>   -1.737</td> <td> 0.082</td> <td>  -14.866</td> <td>    0.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_m</th> <td>    0.0766</td> <td>    0.110</td> <td>    0.697</td> <td> 0.486</td> <td>   -0.139</td> <td>    0.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_v</th> <td>    0.2661</td> <td>    0.183</td> <td>    1.456</td> <td> 0.145</td> <td>   -0.092</td> <td>    0.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_s</th> <td>   -0.1172</td> <td>    0.267</td> <td>   -0.439</td> <td> 0.661</td> <td>   -0.640</td> <td>    0.406</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_k</th> <td>    1.0021</td> <td>    0.305</td> <td>    3.286</td> <td> 0.001</td> <td>    0.404</td> <td>    1.600</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                correct   No. Observations:                  923\n",
       "Model:                          Logit   Df Residuals:                      918\n",
       "Method:                           MLE   Df Model:                            4\n",
       "Date:                Fri, 11 May 2018   Pseudo R-squ.:                 0.01447\n",
       "Time:                        14:56:01   Log-Likelihood:                -451.61\n",
       "converged:                       True   LL-Null:                       -458.24\n",
       "                                        LLR p-value:                   0.01007\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -6.9847      4.021     -1.737      0.082     -14.866       0.897\n",
       "odd_m          0.0766      0.110      0.697      0.486      -0.139       0.292\n",
       "odd_v          0.2661      0.183      1.456      0.145      -0.092       0.624\n",
       "odd_s         -0.1172      0.267     -0.439      0.661      -0.640       0.406\n",
       "odd_k          1.0021      0.305      3.286      0.001       0.404       1.600\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_trials = trialwise_data.condition.str.startswith(\"S\")\n",
    "X = add_constant(trialwise_data.loc[s_trials, sample_stats])\n",
    "Y = trialwise_data.loc[s_trials, \"correct\"]\n",
    "m = Logit(Y, X).fit(disp=False)\n",
    "m.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>odd_m</th>\n",
       "      <th>odd_v</th>\n",
       "      <th>odd_s</th>\n",
       "      <th>odd_k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p values</th>\n",
       "      <td>0.0823958</td>\n",
       "      <td>0.485808</td>\n",
       "      <td>0.145381</td>\n",
       "      <td>0.660546</td>\n",
       "      <td>0.00101662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              const     odd_m     odd_v     odd_s       odd_k\n",
       "p values  0.0823958  0.485808  0.145381  0.660546  0.00101662"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.pvalues.astype(object).to_frame(\"p values\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f7bfda0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEMCAYAAAA8vjqRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"odd_s\", y=\"condition\", hue=\"correct\",\n",
    "            data=trialwise_data.loc[s_trials],\n",
    "            palette=[\"r\", \".5\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic regression of accuracy on trialwise kurtosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>correct</td>     <th>  No. Observations:  </th>  <td>  2014</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  2009</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Fri, 11 May 2018</td> <th>  Pseudo R-squ.:     </th>  <td>0.1711</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>14:56:02</td>     <th>  Log-Likelihood:    </th> <td> -1130.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -1363.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>1.139e-99</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -5.1857</td> <td>    0.542</td> <td>   -9.563</td> <td> 0.000</td> <td>   -6.249</td> <td>   -4.123</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_m</th> <td>    0.0134</td> <td>    0.050</td> <td>    0.266</td> <td> 0.791</td> <td>   -0.085</td> <td>    0.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_v</th> <td>    0.1584</td> <td>    0.012</td> <td>   12.716</td> <td> 0.000</td> <td>    0.134</td> <td>    0.183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_s</th> <td>   -0.7351</td> <td>    0.329</td> <td>   -2.236</td> <td> 0.025</td> <td>   -1.379</td> <td>   -0.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_k</th> <td>    0.1031</td> <td>    0.073</td> <td>    1.404</td> <td> 0.160</td> <td>   -0.041</td> <td>    0.247</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                correct   No. Observations:                 2014\n",
       "Model:                          Logit   Df Residuals:                     2009\n",
       "Method:                           MLE   Df Model:                            4\n",
       "Date:                Fri, 11 May 2018   Pseudo R-squ.:                  0.1711\n",
       "Time:                        14:56:02   Log-Likelihood:                -1130.4\n",
       "converged:                       True   LL-Null:                       -1363.7\n",
       "                                        LLR p-value:                 1.139e-99\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -5.1857      0.542     -9.563      0.000      -6.249      -4.123\n",
       "odd_m          0.0134      0.050      0.266      0.791      -0.085       0.112\n",
       "odd_v          0.1584      0.012     12.716      0.000       0.134       0.183\n",
       "odd_s         -0.7351      0.329     -2.236      0.025      -1.379      -0.091\n",
       "odd_k          0.1031      0.073      1.404      0.160      -0.041       0.247\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_trials = trialwise_data.condition.str.startswith(\"K\")\n",
    "X = add_constant(trialwise_data.loc[k_trials, sample_stats])\n",
    "Y = trialwise_data.loc[k_trials, \"correct\"]\n",
    "m = Logit(Y, X).fit(disp=False)\n",
    "m.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>odd_m</th>\n",
       "      <th>odd_v</th>\n",
       "      <th>odd_s</th>\n",
       "      <th>odd_k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p values</th>\n",
       "      <td>1.14874e-21</td>\n",
       "      <td>0.790613</td>\n",
       "      <td>4.82173e-37</td>\n",
       "      <td>0.0253547</td>\n",
       "      <td>0.160367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                const     odd_m        odd_v      odd_s     odd_k\n",
       "p values  1.14874e-21  0.790613  4.82173e-37  0.0253547  0.160367"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.pvalues.astype(object).to_frame(\"p values\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f66d0f0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEMCAYAAAAvaXplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2j2x7W+IAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"odd_k\", y=\"condition\", hue=\"correct\",\n",
    "            data=trialwise_data.loc[k_trials],\n",
    "            palette=[\"r\", \".5\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Linear regression of detection time on conditionwise statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>rt</td>        <th>  R-squared:         </th> <td>   0.256</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.255</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   459.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 11 May 2018</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:56:02</td>     <th>  Log-Likelihood:    </th> <td> -10538.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  5349</td>      <th>  AIC:               </th> <td>2.109e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  5344</td>      <th>  BIC:               </th> <td>2.112e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    5.4407</td> <td>    0.141</td> <td>   38.619</td> <td> 0.000</td> <td>    5.165</td> <td>    5.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_m</th> <td>   -0.1143</td> <td>    0.005</td> <td>  -21.254</td> <td> 0.000</td> <td>   -0.125</td> <td>   -0.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_v</th> <td>   -0.0840</td> <td>    0.003</td> <td>  -26.402</td> <td> 0.000</td> <td>   -0.090</td> <td>   -0.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_s</th> <td>    0.0544</td> <td>    0.070</td> <td>    0.774</td> <td> 0.439</td> <td>   -0.083</td> <td>    0.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>odd_k</th> <td>   -0.0211</td> <td>    0.025</td> <td>   -0.841</td> <td> 0.400</td> <td>   -0.070</td> <td>    0.028</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>802.008</td> <th>  Durbin-Watson:     </th> <td>   1.275</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>1328.235</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.006</td>  <th>  Prob(JB):          </th> <td>3.78e-289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.383</td>  <th>  Cond. No.          </th> <td>    212.</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     rt   R-squared:                       0.256\n",
       "Model:                            OLS   Adj. R-squared:                  0.255\n",
       "Method:                 Least Squares   F-statistic:                     459.1\n",
       "Date:                Fri, 11 May 2018   Prob (F-statistic):               0.00\n",
       "Time:                        14:56:02   Log-Likelihood:                -10538.\n",
       "No. Observations:                5349   AIC:                         2.109e+04\n",
       "Df Residuals:                    5344   BIC:                         2.112e+04\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          5.4407      0.141     38.619      0.000       5.165       5.717\n",
       "odd_m         -0.1143      0.005    -21.254      0.000      -0.125      -0.104\n",
       "odd_v         -0.0840      0.003    -26.402      0.000      -0.090      -0.078\n",
       "odd_s          0.0544      0.070      0.774      0.439      -0.083       0.192\n",
       "odd_k         -0.0211      0.025     -0.841      0.400      -0.070       0.028\n",
       "==============================================================================\n",
       "Omnibus:                      802.008   Durbin-Watson:                   1.275\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1328.235\n",
       "Skew:                           1.006   Prob(JB):                    3.78e-289\n",
       "Kurtosis:                       4.383   Cond. No.                         212.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = add_constant(model_data[sample_stats])\n",
    "Y = model_data[\"rt\"]\n",
    "m = OLS(Y, X).fit(disp=False)\n",
    "m.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>odd_m</th>\n",
       "      <th>odd_v</th>\n",
       "      <th>odd_s</th>\n",
       "      <th>odd_k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p values</th>\n",
       "      <td>5.44008e-288</td>\n",
       "      <td>2.65293e-96</td>\n",
       "      <td>1.71787e-144</td>\n",
       "      <td>0.439127</td>\n",
       "      <td>0.400469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 const        odd_m         odd_v     odd_s     odd_k\n",
       "p values  5.44008e-288  2.65293e-96  1.71787e-144  0.439127  0.400469"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.pvalues.astype(object).to_frame(\"p values\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}