1_individual_differences.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "from bambi import Model\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "%matplotlib inline\n",
    "plt.rc(\"axes.spines\", top=False, right=False)\n",
    "\n",
    "from analysis_functions import make_sure_path_exists\n",
    "from analysis_functions import q1, q3, iqr, std, se, ind_sample_comp\n",
    "from analysis_functions import compute_gaze_influence_score, add_best_chosen, aggregate_subject_level, aggregate_group_level\n",
    "from plotting_functions import cm2inch, show_img, plot_correlation, add_regression_line\n",
    "\n",
    "from pymc3 import plot_posterior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "make_sure_path_exists('results/descriptives')\n",
    "make_sure_path_exists('results/figures')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data (full data set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/armin/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:26: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# load data\n",
    "krajbich2010 = pd.read_csv('data/krajbich_2010_natneuro/krajbich2010_prep_all.csv')\n",
    "krajbich2010['dataset'] = 'krajbich2010'\n",
    "krajbich2010['n_items'] = 2\n",
    "n_subjects = krajbich2010['subject'].unique().size\n",
    "\n",
    "krajbich2011 = pd.read_csv('data/krajbich_2011_pnas/krajbich2011_prep_all.csv')\n",
    "krajbich2011['dataset'] = 'krajbich2011'\n",
    "krajbich2011['n_items'] = 3\n",
    "krajbich2011['subject'] +=  n_subjects\n",
    "n_subjects += krajbich2011['subject'].unique().size \n",
    "\n",
    "folke2016 = pd.read_csv('data/folke_2016_nhb/folke2016_prep_all.csv')\n",
    "folke2016['dataset'] = 'folke2016'\n",
    "folke2016['n_items'] = 3\n",
    "folke2016['subject'] +=  n_subjects\n",
    "n_subjects += folke2016['subject'].unique().size\n",
    "\n",
    "tavares2017 = pd.read_csv('data/tavares_2017_fns/tavares2017_prep_all.csv')\n",
    "tavares2017['dataset'] = 'tavares2017'\n",
    "tavares2017['n_items'] = 2\n",
    "tavares2017['subject'] += n_subjects\n",
    "\n",
    "variables = ['subject', 'trial', 'rt', 'choice', 'item_value_0', 'item_value_1', 'item_value_2', 'gaze_0', 'gaze_1', 'gaze_2', 'n_items', 'dataset']\n",
    "\n",
    "data = pd.concat([krajbich2010, krajbich2011, folke2016, tavares2017])[variables]\n",
    "data.head()\n",
    "\n",
    "output_file = 'data/data_aggregate.csv'\n",
    "if not os.path.isfile(output_file):\n",
    "    data.to_csv(output_file, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>subject</th>\n",
       "      <th>trial</th>\n",
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       "      <th>choice</th>\n",
       "      <th>item_value_0</th>\n",
       "      <th>item_value_1</th>\n",
       "      <th>item_value_2</th>\n",
       "      <th>gaze_0</th>\n",
       "      <th>gaze_1</th>\n",
       "      <th>gaze_2</th>\n",
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       "      <th>dataset</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3808.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.663725</td>\n",
       "      <td>0.336275</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>krajbich2010</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.466578</td>\n",
       "      <td>0.533422</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1121.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.673317</td>\n",
       "      <td>0.326683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>krajbich2010</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2051.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.464375</td>\n",
       "      <td>0.535625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1655.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.425759</td>\n",
       "      <td>0.574241</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject  trial      rt  choice  item_value_0  item_value_1  item_value_2  \\\n",
       "0        0      0  3808.0     0.0           4.0           2.0           NaN   \n",
       "1        0      1  1913.0     0.0           3.0           2.0           NaN   \n",
       "2        0      2  1121.0     1.0           5.0          10.0           NaN   \n",
       "3        0      3  2051.0     1.0           6.0           6.0           NaN   \n",
       "4        0      4  1655.0     1.0           6.0           7.0           NaN   \n",
       "\n",
       "     gaze_0    gaze_1  gaze_2  n_items       dataset  \n",
       "0  0.663725  0.336275     NaN        2  krajbich2010  \n",
       "1  0.466578  0.533422     NaN        2  krajbich2010  \n",
       "2  0.673317  0.326683     NaN        2  krajbich2010  \n",
       "3  0.464375  0.535625     NaN        2  krajbich2010  \n",
       "4  0.425759  0.574241     NaN        2  krajbich2010  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/data_aggregate.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summarise data to subject level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th colspan=\"8\" halign=\"left\">rt</th>\n",
       "      <th>best_chosen</th>\n",
       "      <th>gaze_influence</th>\n",
       "      <th>dataset</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>se</th>\n",
       "      <th>q1</th>\n",
       "      <th>q3</th>\n",
       "      <th>iqr</th>\n",
       "      <th>mean</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1762.538462</td>\n",
       "      <td>1221.112889</td>\n",
       "      <td>682.0</td>\n",
       "      <td>7866.0</td>\n",
       "      <td>128.716600</td>\n",
       "      <td>1107.00</td>\n",
       "      <td>1970.50</td>\n",
       "      <td>863.50</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.040579</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1672.050505</td>\n",
       "      <td>868.963929</td>\n",
       "      <td>667.0</td>\n",
       "      <td>4826.0</td>\n",
       "      <td>87.778612</td>\n",
       "      <td>1108.50</td>\n",
       "      <td>1862.00</td>\n",
       "      <td>753.50</td>\n",
       "      <td>0.737374</td>\n",
       "      <td>0.202390</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4477.180000</td>\n",
       "      <td>3088.070399</td>\n",
       "      <td>943.0</td>\n",
       "      <td>15825.0</td>\n",
       "      <td>310.362753</td>\n",
       "      <td>2146.25</td>\n",
       "      <td>5728.50</td>\n",
       "      <td>3582.25</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.032181</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1882.613636</td>\n",
       "      <td>541.973214</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>4742.0</td>\n",
       "      <td>58.105628</td>\n",
       "      <td>1519.50</td>\n",
       "      <td>2180.25</td>\n",
       "      <td>660.75</td>\n",
       "      <td>0.693182</td>\n",
       "      <td>0.071577</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1727.370000</td>\n",
       "      <td>1007.248844</td>\n",
       "      <td>673.0</td>\n",
       "      <td>5891.0</td>\n",
       "      <td>101.232318</td>\n",
       "      <td>1160.50</td>\n",
       "      <td>1868.00</td>\n",
       "      <td>707.50</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.141787</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1270.490000</td>\n",
       "      <td>380.411790</td>\n",
       "      <td>756.0</td>\n",
       "      <td>3768.0</td>\n",
       "      <td>38.232823</td>\n",
       "      <td>1039.25</td>\n",
       "      <td>1399.25</td>\n",
       "      <td>360.00</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>-0.074187</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>3268.080000</td>\n",
       "      <td>2042.339574</td>\n",
       "      <td>857.0</td>\n",
       "      <td>13456.0</td>\n",
       "      <td>205.262850</td>\n",
       "      <td>1777.75</td>\n",
       "      <td>4336.25</td>\n",
       "      <td>2558.50</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.038518</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1799.590000</td>\n",
       "      <td>562.488224</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>3612.0</td>\n",
       "      <td>56.532194</td>\n",
       "      <td>1366.00</td>\n",
       "      <td>2098.25</td>\n",
       "      <td>732.25</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.324500</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1410.580000</td>\n",
       "      <td>537.722218</td>\n",
       "      <td>759.0</td>\n",
       "      <td>4295.0</td>\n",
       "      <td>54.043116</td>\n",
       "      <td>1046.00</td>\n",
       "      <td>1553.25</td>\n",
       "      <td>507.25</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>0.171252</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>2040.200000</td>\n",
       "      <td>935.245476</td>\n",
       "      <td>653.0</td>\n",
       "      <td>4746.0</td>\n",
       "      <td>93.995707</td>\n",
       "      <td>1285.75</td>\n",
       "      <td>2544.00</td>\n",
       "      <td>1258.25</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.313629</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2478.989899</td>\n",
       "      <td>2011.173217</td>\n",
       "      <td>955.0</td>\n",
       "      <td>13278.0</td>\n",
       "      <td>203.159174</td>\n",
       "      <td>1419.50</td>\n",
       "      <td>2546.00</td>\n",
       "      <td>1126.50</td>\n",
       "      <td>0.858586</td>\n",
       "      <td>0.070405</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>3215.939394</td>\n",
       "      <td>3201.643744</td>\n",
       "      <td>881.0</td>\n",
       "      <td>18102.0</td>\n",
       "      <td>323.414857</td>\n",
       "      <td>1514.00</td>\n",
       "      <td>3230.00</td>\n",
       "      <td>1716.00</td>\n",
       "      <td>0.848485</td>\n",
       "      <td>0.045888</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>1759.876712</td>\n",
       "      <td>1196.144010</td>\n",
       "      <td>774.0</td>\n",
       "      <td>7261.0</td>\n",
       "      <td>140.966923</td>\n",
       "      <td>1107.00</td>\n",
       "      <td>1867.00</td>\n",
       "      <td>760.00</td>\n",
       "      <td>0.808219</td>\n",
       "      <td>0.042574</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>3184.570000</td>\n",
       "      <td>2011.579823</td>\n",
       "      <td>831.0</td>\n",
       "      <td>9073.0</td>\n",
       "      <td>202.171379</td>\n",
       "      <td>1549.50</td>\n",
       "      <td>4236.75</td>\n",
       "      <td>2687.25</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>-0.105159</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>1609.257732</td>\n",
       "      <td>705.154590</td>\n",
       "      <td>653.0</td>\n",
       "      <td>4378.0</td>\n",
       "      <td>71.969539</td>\n",
       "      <td>1164.00</td>\n",
       "      <td>1833.00</td>\n",
       "      <td>669.00</td>\n",
       "      <td>0.876289</td>\n",
       "      <td>0.091820</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>4243.430000</td>\n",
       "      <td>3248.966283</td>\n",
       "      <td>1172.0</td>\n",
       "      <td>17782.0</td>\n",
       "      <td>326.533397</td>\n",
       "      <td>2117.00</td>\n",
       "      <td>5066.75</td>\n",
       "      <td>2949.75</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>0.055573</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>3795.180000</td>\n",
       "      <td>1596.530547</td>\n",
       "      <td>1546.0</td>\n",
       "      <td>8326.0</td>\n",
       "      <td>160.457357</td>\n",
       "      <td>2501.50</td>\n",
       "      <td>4780.00</td>\n",
       "      <td>2278.50</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.112425</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>3254.505051</td>\n",
       "      <td>3439.749070</td>\n",
       "      <td>804.0</td>\n",
       "      <td>23572.0</td>\n",
       "      <td>347.467128</td>\n",
       "      <td>1354.00</td>\n",
       "      <td>3594.50</td>\n",
       "      <td>2240.50</td>\n",
       "      <td>0.878788</td>\n",
       "      <td>0.003515</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>2570.464646</td>\n",
       "      <td>1425.243139</td>\n",
       "      <td>874.0</td>\n",
       "      <td>10804.0</td>\n",
       "      <td>143.971298</td>\n",
       "      <td>1612.00</td>\n",
       "      <td>3066.00</td>\n",
       "      <td>1454.00</td>\n",
       "      <td>0.858586</td>\n",
       "      <td>0.139128</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>1506.122449</td>\n",
       "      <td>978.752885</td>\n",
       "      <td>679.0</td>\n",
       "      <td>6454.0</td>\n",
       "      <td>141.270810</td>\n",
       "      <td>930.00</td>\n",
       "      <td>1680.00</td>\n",
       "      <td>750.00</td>\n",
       "      <td>0.795918</td>\n",
       "      <td>0.418745</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>2358.450000</td>\n",
       "      <td>874.598301</td>\n",
       "      <td>1159.0</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>87.900437</td>\n",
       "      <td>1740.00</td>\n",
       "      <td>2811.25</td>\n",
       "      <td>1071.25</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>0.055889</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>1432.600000</td>\n",
       "      <td>595.066383</td>\n",
       "      <td>791.0</td>\n",
       "      <td>4642.0</td>\n",
       "      <td>59.806422</td>\n",
       "      <td>1075.00</td>\n",
       "      <td>1509.75</td>\n",
       "      <td>434.75</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>0.324290</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>1217.370000</td>\n",
       "      <td>312.202103</td>\n",
       "      <td>577.0</td>\n",
       "      <td>2234.0</td>\n",
       "      <td>31.377492</td>\n",
       "      <td>980.75</td>\n",
       "      <td>1387.25</td>\n",
       "      <td>406.50</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.243457</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>1947.670000</td>\n",
       "      <td>591.617005</td>\n",
       "      <td>993.0</td>\n",
       "      <td>4151.0</td>\n",
       "      <td>59.459746</td>\n",
       "      <td>1535.75</td>\n",
       "      <td>2194.50</td>\n",
       "      <td>658.75</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>0.485560</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>1578.960000</td>\n",
       "      <td>516.160652</td>\n",
       "      <td>859.0</td>\n",
       "      <td>3690.0</td>\n",
       "      <td>51.876097</td>\n",
       "      <td>1226.25</td>\n",
       "      <td>1725.00</td>\n",
       "      <td>498.75</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.308846</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>2051.420000</td>\n",
       "      <td>1408.702000</td>\n",
       "      <td>836.0</td>\n",
       "      <td>7963.0</td>\n",
       "      <td>141.579878</td>\n",
       "      <td>1162.75</td>\n",
       "      <td>2318.50</td>\n",
       "      <td>1155.75</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.099257</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>957.640000</td>\n",
       "      <td>328.413444</td>\n",
       "      <td>524.0</td>\n",
       "      <td>2395.0</td>\n",
       "      <td>33.006793</td>\n",
       "      <td>705.00</td>\n",
       "      <td>1102.50</td>\n",
       "      <td>397.50</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>0.157025</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>2438.680000</td>\n",
       "      <td>1244.401727</td>\n",
       "      <td>1222.0</td>\n",
       "      <td>7803.0</td>\n",
       "      <td>125.067079</td>\n",
       "      <td>1670.75</td>\n",
       "      <td>2598.50</td>\n",
       "      <td>927.75</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.723403</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>3244.380000</td>\n",
       "      <td>2544.494035</td>\n",
       "      <td>942.0</td>\n",
       "      <td>14560.0</td>\n",
       "      <td>255.731273</td>\n",
       "      <td>1652.50</td>\n",
       "      <td>3882.75</td>\n",
       "      <td>2230.25</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.216426</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>2372.295918</td>\n",
       "      <td>1342.598905</td>\n",
       "      <td>584.0</td>\n",
       "      <td>7391.0</td>\n",
       "      <td>136.320265</td>\n",
       "      <td>1408.50</td>\n",
       "      <td>2763.25</td>\n",
       "      <td>1354.75</td>\n",
       "      <td>0.877551</td>\n",
       "      <td>0.396730</td>\n",
       "      <td>krajbich2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>88</td>\n",
       "      <td>6375.986111</td>\n",
       "      <td>3654.834967</td>\n",
       "      <td>1881.0</td>\n",
       "      <td>22931.0</td>\n",
       "      <td>305.632654</td>\n",
       "      <td>3469.25</td>\n",
       "      <td>8608.50</td>\n",
       "      <td>5139.25</td>\n",
       "      <td>0.805556</td>\n",
       "      <td>0.183851</td>\n",
       "      <td>folke2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>89</td>\n",
       "      <td>5883.777778</td>\n",
       "      <td>3244.441271</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>17575.0</td>\n",
       "      <td>271.313809</td>\n",
       "      <td>3483.75</td>\n",
       "      <td>7171.00</td>\n",
       "      <td>3687.25</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.561869</td>\n",
       "      <td>folke2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>90</td>\n",
       "      <td>6077.854167</td>\n",
       "      <td>2904.761622</td>\n",
       "      <td>2327.0</td>\n",
       "      <td>16918.0</td>\n",
       "      <td>242.908370</td>\n",
       "      <td>3729.75</td>\n",
       "      <td>7458.25</td>\n",
       "      <td>3728.50</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.416414</td>\n",
       "      <td>folke2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>91</td>\n",
       "      <td>4758.713287</td>\n",
       "      <td>2010.355407</td>\n",
       "      <td>1927.0</td>\n",
       "      <td>13707.0</td>\n",
       "      <td>168.705278</td>\n",
       "      <td>3324.00</td>\n",
       "      <td>5713.00</td>\n",
       "      <td>2389.00</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.426449</td>\n",
       "      <td>folke2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>92</td>\n",
       "      <td>6909.250000</td>\n",
       "      <td>5651.814479</td>\n",
       "      <td>2343.0</td>\n",
       "      <td>58867.0</td>\n",
       "      <td>472.628470</td>\n",
       "      <td>3910.00</td>\n",
       "      <td>7985.75</td>\n",
       "      <td>4075.75</td>\n",
       "      <td>0.763889</td>\n",
       "      <td>0.301613</td>\n",
       "      <td>folke2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>93</td>\n",
       "      <td>1320.787810</td>\n",
       "      <td>460.070945</td>\n",
       "      <td>631.0</td>\n",
       "      <td>3913.0</td>\n",
       "      <td>12.624837</td>\n",
       "      <td>1025.00</td>\n",
       "      <td>1447.00</td>\n",
       "      <td>422.00</td>\n",
       "      <td>0.854026</td>\n",
       "      <td>0.251911</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>94</td>\n",
       "      <td>1383.833084</td>\n",
       "      <td>681.875110</td>\n",
       "      <td>561.0</td>\n",
       "      <td>6429.0</td>\n",
       "      <td>18.662259</td>\n",
       "      <td>961.00</td>\n",
       "      <td>1567.50</td>\n",
       "      <td>606.50</td>\n",
       "      <td>0.901198</td>\n",
       "      <td>0.098323</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>95</td>\n",
       "      <td>2117.974301</td>\n",
       "      <td>1506.748846</td>\n",
       "      <td>652.0</td>\n",
       "      <td>12482.0</td>\n",
       "      <td>41.440515</td>\n",
       "      <td>1178.00</td>\n",
       "      <td>2512.00</td>\n",
       "      <td>1334.00</td>\n",
       "      <td>0.855631</td>\n",
       "      <td>0.163789</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>96</td>\n",
       "      <td>1769.960873</td>\n",
       "      <td>1385.619822</td>\n",
       "      <td>433.0</td>\n",
       "      <td>12374.0</td>\n",
       "      <td>38.022884</td>\n",
       "      <td>904.00</td>\n",
       "      <td>2134.00</td>\n",
       "      <td>1230.00</td>\n",
       "      <td>0.969150</td>\n",
       "      <td>0.089337</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>97</td>\n",
       "      <td>1494.228807</td>\n",
       "      <td>959.032252</td>\n",
       "      <td>519.0</td>\n",
       "      <td>9458.0</td>\n",
       "      <td>26.277322</td>\n",
       "      <td>944.00</td>\n",
       "      <td>1650.00</td>\n",
       "      <td>706.00</td>\n",
       "      <td>0.848462</td>\n",
       "      <td>0.374458</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>98</td>\n",
       "      <td>1627.551476</td>\n",
       "      <td>1292.443303</td>\n",
       "      <td>567.0</td>\n",
       "      <td>15136.0</td>\n",
       "      <td>36.526607</td>\n",
       "      <td>905.00</td>\n",
       "      <td>1825.00</td>\n",
       "      <td>920.00</td>\n",
       "      <td>0.847566</td>\n",
       "      <td>0.306694</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99</td>\n",
       "      <td>2820.881279</td>\n",
       "      <td>2404.841073</td>\n",
       "      <td>679.0</td>\n",
       "      <td>45894.0</td>\n",
       "      <td>66.367280</td>\n",
       "      <td>1548.25</td>\n",
       "      <td>3354.00</td>\n",
       "      <td>1805.75</td>\n",
       "      <td>0.806697</td>\n",
       "      <td>0.280012</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>100</td>\n",
       "      <td>2119.401387</td>\n",
       "      <td>2123.753475</td>\n",
       "      <td>445.0</td>\n",
       "      <td>22376.0</td>\n",
       "      <td>58.970406</td>\n",
       "      <td>969.50</td>\n",
       "      <td>2440.50</td>\n",
       "      <td>1471.00</td>\n",
       "      <td>0.861325</td>\n",
       "      <td>0.227844</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>101</td>\n",
       "      <td>1396.961356</td>\n",
       "      <td>777.420389</td>\n",
       "      <td>498.0</td>\n",
       "      <td>9638.0</td>\n",
       "      <td>21.840754</td>\n",
       "      <td>941.75</td>\n",
       "      <td>1596.25</td>\n",
       "      <td>654.50</td>\n",
       "      <td>0.884069</td>\n",
       "      <td>0.067097</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>102</td>\n",
       "      <td>1448.094913</td>\n",
       "      <td>1503.143594</td>\n",
       "      <td>437.0</td>\n",
       "      <td>17439.0</td>\n",
       "      <td>41.435494</td>\n",
       "      <td>691.00</td>\n",
       "      <td>1557.00</td>\n",
       "      <td>866.00</td>\n",
       "      <td>0.924829</td>\n",
       "      <td>0.041967</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>103</td>\n",
       "      <td>1074.570008</td>\n",
       "      <td>561.085157</td>\n",
       "      <td>400.0</td>\n",
       "      <td>6602.0</td>\n",
       "      <td>16.156792</td>\n",
       "      <td>712.50</td>\n",
       "      <td>1253.00</td>\n",
       "      <td>540.50</td>\n",
       "      <td>0.793703</td>\n",
       "      <td>0.260093</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>104</td>\n",
       "      <td>1393.203593</td>\n",
       "      <td>805.176321</td>\n",
       "      <td>552.0</td>\n",
       "      <td>13382.0</td>\n",
       "      <td>22.036893</td>\n",
       "      <td>917.75</td>\n",
       "      <td>1568.00</td>\n",
       "      <td>650.25</td>\n",
       "      <td>0.902695</td>\n",
       "      <td>0.098859</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>105</td>\n",
       "      <td>1840.792895</td>\n",
       "      <td>1191.440031</td>\n",
       "      <td>565.0</td>\n",
       "      <td>19666.0</td>\n",
       "      <td>32.768492</td>\n",
       "      <td>1117.50</td>\n",
       "      <td>2161.00</td>\n",
       "      <td>1043.50</td>\n",
       "      <td>0.899471</td>\n",
       "      <td>0.117386</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>106</td>\n",
       "      <td>2450.797329</td>\n",
       "      <td>2471.671382</td>\n",
       "      <td>593.0</td>\n",
       "      <td>32616.0</td>\n",
       "      <td>69.302224</td>\n",
       "      <td>1211.00</td>\n",
       "      <td>2641.00</td>\n",
       "      <td>1430.00</td>\n",
       "      <td>0.865672</td>\n",
       "      <td>0.197240</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>107</td>\n",
       "      <td>2684.351562</td>\n",
       "      <td>2842.379112</td>\n",
       "      <td>471.0</td>\n",
       "      <td>26955.0</td>\n",
       "      <td>79.477964</td>\n",
       "      <td>1100.75</td>\n",
       "      <td>3131.25</td>\n",
       "      <td>2030.50</td>\n",
       "      <td>0.871094</td>\n",
       "      <td>0.167228</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>108</td>\n",
       "      <td>1393.135754</td>\n",
       "      <td>1451.060684</td>\n",
       "      <td>373.0</td>\n",
       "      <td>21416.0</td>\n",
       "      <td>40.782019</td>\n",
       "      <td>737.00</td>\n",
       "      <td>1448.00</td>\n",
       "      <td>711.00</td>\n",
       "      <td>0.853986</td>\n",
       "      <td>0.167425</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>109</td>\n",
       "      <td>1132.529586</td>\n",
       "      <td>570.220704</td>\n",
       "      <td>476.0</td>\n",
       "      <td>4804.0</td>\n",
       "      <td>17.915885</td>\n",
       "      <td>762.25</td>\n",
       "      <td>1293.00</td>\n",
       "      <td>530.75</td>\n",
       "      <td>0.856016</td>\n",
       "      <td>0.226989</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>110</td>\n",
       "      <td>2799.972456</td>\n",
       "      <td>2313.287779</td>\n",
       "      <td>635.0</td>\n",
       "      <td>22941.0</td>\n",
       "      <td>64.011510</td>\n",
       "      <td>1319.00</td>\n",
       "      <td>3393.00</td>\n",
       "      <td>2074.00</td>\n",
       "      <td>0.911247</td>\n",
       "      <td>0.132609</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>111</td>\n",
       "      <td>1438.686186</td>\n",
       "      <td>1074.088963</td>\n",
       "      <td>506.0</td>\n",
       "      <td>17604.0</td>\n",
       "      <td>29.440910</td>\n",
       "      <td>878.75</td>\n",
       "      <td>1576.50</td>\n",
       "      <td>697.75</td>\n",
       "      <td>0.864114</td>\n",
       "      <td>0.233232</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>112</td>\n",
       "      <td>1708.739940</td>\n",
       "      <td>1190.125952</td>\n",
       "      <td>652.0</td>\n",
       "      <td>13163.0</td>\n",
       "      <td>32.499639</td>\n",
       "      <td>1130.25</td>\n",
       "      <td>1751.75</td>\n",
       "      <td>621.50</td>\n",
       "      <td>0.703428</td>\n",
       "      <td>0.150128</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>113</td>\n",
       "      <td>2446.282007</td>\n",
       "      <td>2368.807950</td>\n",
       "      <td>554.0</td>\n",
       "      <td>18550.0</td>\n",
       "      <td>69.700976</td>\n",
       "      <td>1067.00</td>\n",
       "      <td>2739.50</td>\n",
       "      <td>1672.50</td>\n",
       "      <td>0.898789</td>\n",
       "      <td>0.240880</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>114</td>\n",
       "      <td>3057.671550</td>\n",
       "      <td>3253.649677</td>\n",
       "      <td>585.0</td>\n",
       "      <td>40290.0</td>\n",
       "      <td>90.379158</td>\n",
       "      <td>1223.00</td>\n",
       "      <td>3751.00</td>\n",
       "      <td>2528.00</td>\n",
       "      <td>0.882035</td>\n",
       "      <td>0.135307</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>115</td>\n",
       "      <td>1187.276830</td>\n",
       "      <td>892.191464</td>\n",
       "      <td>469.0</td>\n",
       "      <td>10029.0</td>\n",
       "      <td>28.270168</td>\n",
       "      <td>753.00</td>\n",
       "      <td>1274.00</td>\n",
       "      <td>521.00</td>\n",
       "      <td>0.867603</td>\n",
       "      <td>0.389899</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>116</td>\n",
       "      <td>2717.587386</td>\n",
       "      <td>2839.887213</td>\n",
       "      <td>540.0</td>\n",
       "      <td>25083.0</td>\n",
       "      <td>78.313785</td>\n",
       "      <td>1166.00</td>\n",
       "      <td>3022.00</td>\n",
       "      <td>1856.00</td>\n",
       "      <td>0.870821</td>\n",
       "      <td>0.334805</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>117</td>\n",
       "      <td>1405.591431</td>\n",
       "      <td>757.727628</td>\n",
       "      <td>519.0</td>\n",
       "      <td>6273.0</td>\n",
       "      <td>20.967253</td>\n",
       "      <td>917.00</td>\n",
       "      <td>1630.00</td>\n",
       "      <td>713.00</td>\n",
       "      <td>0.791125</td>\n",
       "      <td>0.121407</td>\n",
       "      <td>tavares2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject           rt                                                     \\\n",
       "                    mean          std     min      max          se       q1   \n",
       "0         0  1762.538462  1221.112889   682.0   7866.0  128.716600  1107.00   \n",
       "1         1  1672.050505   868.963929   667.0   4826.0   87.778612  1108.50   \n",
       "2         2  4477.180000  3088.070399   943.0  15825.0  310.362753  2146.25   \n",
       "3         3  1882.613636   541.973214  1003.0   4742.0   58.105628  1519.50   \n",
       "4         4  1727.370000  1007.248844   673.0   5891.0  101.232318  1160.50   \n",
       "5         5  1270.490000   380.411790   756.0   3768.0   38.232823  1039.25   \n",
       "6         6  3268.080000  2042.339574   857.0  13456.0  205.262850  1777.75   \n",
       "7         7  1799.590000   562.488224  1020.0   3612.0   56.532194  1366.00   \n",
       "8         8  1410.580000   537.722218   759.0   4295.0   54.043116  1046.00   \n",
       "9         9  2040.200000   935.245476   653.0   4746.0   93.995707  1285.75   \n",
       "10       10  2478.989899  2011.173217   955.0  13278.0  203.159174  1419.50   \n",
       "11       11  3215.939394  3201.643744   881.0  18102.0  323.414857  1514.00   \n",
       "12       12  1759.876712  1196.144010   774.0   7261.0  140.966923  1107.00   \n",
       "13       13  3184.570000  2011.579823   831.0   9073.0  202.171379  1549.50   \n",
       "14       14  1609.257732   705.154590   653.0   4378.0   71.969539  1164.00   \n",
       "15       15  4243.430000  3248.966283  1172.0  17782.0  326.533397  2117.00   \n",
       "16       16  3795.180000  1596.530547  1546.0   8326.0  160.457357  2501.50   \n",
       "17       17  3254.505051  3439.749070   804.0  23572.0  347.467128  1354.00   \n",
       "18       18  2570.464646  1425.243139   874.0  10804.0  143.971298  1612.00   \n",
       "19       19  1506.122449   978.752885   679.0   6454.0  141.270810   930.00   \n",
       "20       20  2358.450000   874.598301  1159.0   5760.0   87.900437  1740.00   \n",
       "21       21  1432.600000   595.066383   791.0   4642.0   59.806422  1075.00   \n",
       "22       22  1217.370000   312.202103   577.0   2234.0   31.377492   980.75   \n",
       "23       23  1947.670000   591.617005   993.0   4151.0   59.459746  1535.75   \n",
       "24       24  1578.960000   516.160652   859.0   3690.0   51.876097  1226.25   \n",
       "25       25  2051.420000  1408.702000   836.0   7963.0  141.579878  1162.75   \n",
       "26       26   957.640000   328.413444   524.0   2395.0   33.006793   705.00   \n",
       "27       27  2438.680000  1244.401727  1222.0   7803.0  125.067079  1670.75   \n",
       "28       28  3244.380000  2544.494035   942.0  14560.0  255.731273  1652.50   \n",
       "29       29  2372.295918  1342.598905   584.0   7391.0  136.320265  1408.50   \n",
       "..      ...          ...          ...     ...      ...         ...      ...   \n",
       "88       88  6375.986111  3654.834967  1881.0  22931.0  305.632654  3469.25   \n",
       "89       89  5883.777778  3244.441271  1965.0  17575.0  271.313809  3483.75   \n",
       "90       90  6077.854167  2904.761622  2327.0  16918.0  242.908370  3729.75   \n",
       "91       91  4758.713287  2010.355407  1927.0  13707.0  168.705278  3324.00   \n",
       "92       92  6909.250000  5651.814479  2343.0  58867.0  472.628470  3910.00   \n",
       "93       93  1320.787810   460.070945   631.0   3913.0   12.624837  1025.00   \n",
       "94       94  1383.833084   681.875110   561.0   6429.0   18.662259   961.00   \n",
       "95       95  2117.974301  1506.748846   652.0  12482.0   41.440515  1178.00   \n",
       "96       96  1769.960873  1385.619822   433.0  12374.0   38.022884   904.00   \n",
       "97       97  1494.228807   959.032252   519.0   9458.0   26.277322   944.00   \n",
       "98       98  1627.551476  1292.443303   567.0  15136.0   36.526607   905.00   \n",
       "99       99  2820.881279  2404.841073   679.0  45894.0   66.367280  1548.25   \n",
       "100     100  2119.401387  2123.753475   445.0  22376.0   58.970406   969.50   \n",
       "101     101  1396.961356   777.420389   498.0   9638.0   21.840754   941.75   \n",
       "102     102  1448.094913  1503.143594   437.0  17439.0   41.435494   691.00   \n",
       "103     103  1074.570008   561.085157   400.0   6602.0   16.156792   712.50   \n",
       "104     104  1393.203593   805.176321   552.0  13382.0   22.036893   917.75   \n",
       "105     105  1840.792895  1191.440031   565.0  19666.0   32.768492  1117.50   \n",
       "106     106  2450.797329  2471.671382   593.0  32616.0   69.302224  1211.00   \n",
       "107     107  2684.351562  2842.379112   471.0  26955.0   79.477964  1100.75   \n",
       "108     108  1393.135754  1451.060684   373.0  21416.0   40.782019   737.00   \n",
       "109     109  1132.529586   570.220704   476.0   4804.0   17.915885   762.25   \n",
       "110     110  2799.972456  2313.287779   635.0  22941.0   64.011510  1319.00   \n",
       "111     111  1438.686186  1074.088963   506.0  17604.0   29.440910   878.75   \n",
       "112     112  1708.739940  1190.125952   652.0  13163.0   32.499639  1130.25   \n",
       "113     113  2446.282007  2368.807950   554.0  18550.0   69.700976  1067.00   \n",
       "114     114  3057.671550  3253.649677   585.0  40290.0   90.379158  1223.00   \n",
       "115     115  1187.276830   892.191464   469.0  10029.0   28.270168   753.00   \n",
       "116     116  2717.587386  2839.887213   540.0  25083.0   78.313785  1166.00   \n",
       "117     117  1405.591431   757.727628   519.0   6273.0   20.967253   917.00   \n",
       "\n",
       "                      best_chosen gaze_influence       dataset  \n",
       "          q3      iqr        mean                               \n",
       "0    1970.50   863.50    0.857143       0.040579  krajbich2010  \n",
       "1    1862.00   753.50    0.737374       0.202390  krajbich2010  \n",
       "2    5728.50  3582.25    0.850000       0.032181  krajbich2010  \n",
       "3    2180.25   660.75    0.693182       0.071577  krajbich2010  \n",
       "4    1868.00   707.50    0.840000       0.141787  krajbich2010  \n",
       "5    1399.25   360.00    0.770000      -0.074187  krajbich2010  \n",
       "6    4336.25  2558.50    0.790000       0.038518  krajbich2010  \n",
       "7    2098.25   732.25    0.850000       0.324500  krajbich2010  \n",
       "8    1553.25   507.25    0.870000       0.171252  krajbich2010  \n",
       "9    2544.00  1258.25    0.820000       0.313629  krajbich2010  \n",
       "10   2546.00  1126.50    0.858586       0.070405  krajbich2010  \n",
       "11   3230.00  1716.00    0.848485       0.045888  krajbich2010  \n",
       "12   1867.00   760.00    0.808219       0.042574  krajbich2010  \n",
       "13   4236.75  2687.25    0.640000      -0.105159  krajbich2010  \n",
       "14   1833.00   669.00    0.876289       0.091820  krajbich2010  \n",
       "15   5066.75  2949.75    0.870000       0.055573  krajbich2010  \n",
       "16   4780.00  2278.50    0.820000       0.112425  krajbich2010  \n",
       "17   3594.50  2240.50    0.878788       0.003515  krajbich2010  \n",
       "18   3066.00  1454.00    0.858586       0.139128  krajbich2010  \n",
       "19   1680.00   750.00    0.795918       0.418745  krajbich2010  \n",
       "20   2811.25  1071.25    0.910000       0.055889  krajbich2010  \n",
       "21   1509.75   434.75    0.880000       0.324290  krajbich2010  \n",
       "22   1387.25   406.50    0.840000       0.243457  krajbich2010  \n",
       "23   2194.50   658.75    0.770000       0.485560  krajbich2010  \n",
       "24   1725.00   498.75    0.790000       0.308846  krajbich2010  \n",
       "25   2318.50  1155.75    0.820000       0.099257  krajbich2010  \n",
       "26   1102.50   397.50    0.730000       0.157025  krajbich2010  \n",
       "27   2598.50   927.75    0.620000       0.723403  krajbich2010  \n",
       "28   3882.75  2230.25    0.820000       0.216426  krajbich2010  \n",
       "29   2763.25  1354.75    0.877551       0.396730  krajbich2010  \n",
       "..       ...      ...         ...            ...           ...  \n",
       "88   8608.50  5139.25    0.805556       0.183851     folke2016  \n",
       "89   7171.00  3687.25    0.555556       0.561869     folke2016  \n",
       "90   7458.25  3728.50    0.562500       0.416414     folke2016  \n",
       "91   5713.00  2389.00    0.454545       0.426449     folke2016  \n",
       "92   7985.75  4075.75    0.763889       0.301613     folke2016  \n",
       "93   1447.00   422.00    0.854026       0.251911   tavares2017  \n",
       "94   1567.50   606.50    0.901198       0.098323   tavares2017  \n",
       "95   2512.00  1334.00    0.855631       0.163789   tavares2017  \n",
       "96   2134.00  1230.00    0.969150       0.089337   tavares2017  \n",
       "97   1650.00   706.00    0.848462       0.374458   tavares2017  \n",
       "98   1825.00   920.00    0.847566       0.306694   tavares2017  \n",
       "99   3354.00  1805.75    0.806697       0.280012   tavares2017  \n",
       "100  2440.50  1471.00    0.861325       0.227844   tavares2017  \n",
       "101  1596.25   654.50    0.884069       0.067097   tavares2017  \n",
       "102  1557.00   866.00    0.924829       0.041967   tavares2017  \n",
       "103  1253.00   540.50    0.793703       0.260093   tavares2017  \n",
       "104  1568.00   650.25    0.902695       0.098859   tavares2017  \n",
       "105  2161.00  1043.50    0.899471       0.117386   tavares2017  \n",
       "106  2641.00  1430.00    0.865672       0.197240   tavares2017  \n",
       "107  3131.25  2030.50    0.871094       0.167228   tavares2017  \n",
       "108  1448.00   711.00    0.853986       0.167425   tavares2017  \n",
       "109  1293.00   530.75    0.856016       0.226989   tavares2017  \n",
       "110  3393.00  2074.00    0.911247       0.132609   tavares2017  \n",
       "111  1576.50   697.75    0.864114       0.233232   tavares2017  \n",
       "112  1751.75   621.50    0.703428       0.150128   tavares2017  \n",
       "113  2739.50  1672.50    0.898789       0.240880   tavares2017  \n",
       "114  3751.00  2528.00    0.882035       0.135307   tavares2017  \n",
       "115  1274.00   521.00    0.867603       0.389899   tavares2017  \n",
       "116  3022.00  1856.00    0.870821       0.334805   tavares2017  \n",
       "117  1630.00   713.00    0.791125       0.121407   tavares2017  \n",
       "\n",
       "[118 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subject_summary_all_datasets = pd.DataFrame()\n",
    "for dataset in data['dataset'].unique():\n",
    "\n",
    "    n_items = data[data['dataset'] == dataset]['n_items'].values[0]\n",
    "    subject_summary_single_dataset = aggregate_subject_level(data[data['dataset'] == dataset], n_items=n_items)\n",
    "    subject_summary_all_datasets = pd.concat([subject_summary_all_datasets, subject_summary_single_dataset])\n",
    "subject_summary_all_datasets.reset_index(inplace=True)\n",
    "output_file = 'results/descriptives/subject_summary.csv'\n",
    "if not os.path.isfile(output_file):\n",
    "    subject_summary_all_datasets.to_csv()\n",
    "subject_summary_all_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summarise data to group level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>se</th>\n",
       "      <th>iqr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean RT</th>\n",
       "      <td>2843.540074</td>\n",
       "      <td>1676.206504</td>\n",
       "      <td>957.640000</td>\n",
       "      <td>7577.430657</td>\n",
       "      <td>154.965346</td>\n",
       "      <td>1766.377053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P(choose best)</th>\n",
       "      <td>0.766288</td>\n",
       "      <td>0.115938</td>\n",
       "      <td>0.450704</td>\n",
       "      <td>0.969150</td>\n",
       "      <td>0.010719</td>\n",
       "      <td>0.155063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gaze Influence</th>\n",
       "      <td>0.236350</td>\n",
       "      <td>0.150307</td>\n",
       "      <td>-0.105159</td>\n",
       "      <td>0.723403</td>\n",
       "      <td>0.013896</td>\n",
       "      <td>0.230601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       mean          std         min          max          se  \\\n",
       "Mean RT         2843.540074  1676.206504  957.640000  7577.430657  154.965346   \n",
       "P(choose best)     0.766288     0.115938    0.450704     0.969150    0.010719   \n",
       "Gaze Influence     0.236350     0.150307   -0.105159     0.723403    0.013896   \n",
       "\n",
       "                        iqr  \n",
       "Mean RT         1766.377053  \n",
       "P(choose best)     0.155063  \n",
       "Gaze Influence     0.230601  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_summary = aggregate_group_level(subject_summary_all_datasets)\n",
    "output_file = 'results/descriptives/group_summary.csv'\n",
    "if not os.path.isfile(output_file):\n",
    "    group_summary.to_csv(output_file)\n",
    "group_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summarize behavioral metrics to data set level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">rt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>folke2016</th>\n",
       "      <td>5413.906542</td>\n",
       "      <td>1283.875106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>krajbich2010</th>\n",
       "      <td>2192.310626</td>\n",
       "      <td>850.526243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>krajbich2011</th>\n",
       "      <td>2462.433116</td>\n",
       "      <td>1298.372118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tavares2017</th>\n",
       "      <td>1849.234552</td>\n",
       "      <td>600.640845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       rt             \n",
       "                     mean             \n",
       "                     mean          std\n",
       "dataset                               \n",
       "folke2016     5413.906542  1283.875106\n",
       "krajbich2010  2192.310626   850.526243\n",
       "krajbich2011  2462.433116  1298.372118\n",
       "tavares2017   1849.234552   600.640845"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Response time\n",
    "subject_summary_all_datasets.groupby(['dataset']).agg({('rt', 'mean'): ['mean', std]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">best_chosen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>folke2016</th>\n",
       "      <td>0.646445</td>\n",
       "      <td>0.117248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>krajbich2010</th>\n",
       "      <td>0.813849</td>\n",
       "      <td>0.063641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>krajbich2011</th>\n",
       "      <td>0.719416</td>\n",
       "      <td>0.100058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tavares2017</th>\n",
       "      <td>0.863390</td>\n",
       "      <td>0.050335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             best_chosen          \n",
       "                    mean          \n",
       "                    mean       std\n",
       "dataset                           \n",
       "folke2016       0.646445  0.117248\n",
       "krajbich2010    0.813849  0.063641\n",
       "krajbich2011    0.719416  0.100058\n",
       "tavares2017     0.863390  0.050335"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P(choose best)\n",
    "subject_summary_all_datasets.groupby(['dataset']).agg({('best_chosen', 'mean'): ['mean', std]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">gaze_influence</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\"></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>folke2016</th>\n",
       "      <td>0.349465</td>\n",
       "      <td>0.106694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>krajbich2010</th>\n",
       "      <td>0.185281</td>\n",
       "      <td>0.171258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>krajbich2011</th>\n",
       "      <td>0.246708</td>\n",
       "      <td>0.138868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tavares2017</th>\n",
       "      <td>0.194997</td>\n",
       "      <td>0.092852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             gaze_influence          \n",
       "                                     \n",
       "                       mean       std\n",
       "dataset                              \n",
       "folke2016          0.349465  0.106694\n",
       "krajbich2010       0.185281  0.171258\n",
       "krajbich2011       0.246708  0.138868\n",
       "tavares2017        0.194997  0.092852"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gaze influence on P(choice | value)\n",
    "subject_summary_all_datasets.groupby(['dataset']).agg({('gaze_influence', ''): ['mean', std]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare datasets on behavioral metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two- vs Three-item Choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "two_item_subject_summary = subject_summary_all_datasets[subject_summary_all_datasets['dataset'].isin(['krajbich2010', 'tavares2017'])]\n",
    "three_item_subject_summary = subject_summary_all_datasets[subject_summary_all_datasets['dataset'].isin(['krajbich2011', 'folke2016'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equal variance assumption violated: True\n",
      "Normality assumption violated: True\n",
      "X1 - X2: U = 821, p = 4.874485758058148e-07, d = 0.5248842592592593\n"
     ]
    },
    {
     "data": {
      "image/png": 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n8q+BJyLiP6ocVrulecFujohhFdjXscDHIuKcEvT1K+AXEXFPxyOzzsojDOtM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\n5ZKhaqoo8c4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1145334e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Response times\n",
    "plt.hist(two_item_subject_summary['rt']['mean'], alpha=0.5, label='two items')\n",
    "plt.hist(three_item_subject_summary['rt']['mean'], alpha=0.5, label='three items')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('Response Time (ms)')\n",
    "plt.legend()\n",
    "ind_sample_comp(two_item_subject_summary['rt']['mean'],\n",
    "                three_item_subject_summary['rt']['mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equal variance assumption violated: True\n",
      "Normality assumption violated: True\n",
      "X1 - X2: U = 444, p = 2.044925097318628e-12, d = 0.7430555555555556\n"
     ]
    },
    {
     "data": {
      "image/png": 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cBPw8b9cdImIIcAFwWVY2FoiIGEBuhtabJHXeSvk5wC8johKoAmqzuZZOBaqz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12dadeda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# P(choose best)\n",
    "plt.hist(two_item_subject_summary['best_chosen']['mean'], alpha=0.5, label='two items')\n",
    "plt.hist(three_item_subject_summary['best_chosen']['mean'], alpha=0.5, label='three item')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('P(choose best)')\n",
    "plt.legend()\n",
    "ind_sample_comp(two_item_subject_summary['best_chosen']['mean'],\n",
    "                three_item_subject_summary['best_chosen']['mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equal variance assumption violated: False\n",
      "Normality assumption violated: True\n",
      "X1 - X2: U = 1001, p = 4.3485114826871794e-05, d = 0.42071759259259256\n"
     ]
    },
    {
     "data": {
      "image/png": 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8L0gKyN1J8iL1ynSEciNQHRF/UlLGurm/W+O6Ro23txZTX5JzZQXnEYHlaT5w\noqSd0ssV49N122oFsBdARLwB1Es6EUDSDg31elrwe6BK0l7p8ueBh5vs8yQwuuEyjqQPpuvvB77a\n6F7DCNohIlaRJKR/4++jgYYv9deUVBtt6SmhOpJqqrD1ZbTWYhoMLMUKz4nAchMRi0muxy8EFgA/\niYhnOqHp37D15ZzPA19Lawo9DvxDKzG9RXLfYmZ6KWYzMLXJPmuACcCdaY2fhi/tfycphf07ScvS\n5fb6BXAGyWUiImI9yf2IpSRf6k+1cNz/BX4gaRFJMmnQWkxHAvd0IEbrYVxryHokSXcBl0TEyrxj\naUpSXURU5RzDDiQjncMbnq6y4vKIwHqqy+jkaqI9TCVwmZOAgUcEZmUn6YKImJx3HGYNnAjMzArO\nl4bMzArOicDMrOCcCMzMCs6JwMys4P4XEX43eHrNghEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e5279e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gaze Influence\n",
    "plt.hist(two_item_subject_summary['gaze_influence'], alpha=0.5, label='two items')\n",
    "plt.hist(three_item_subject_summary['gaze_influence'], alpha=0.5, label='three item')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('Gaze Influence\\non P(choice | value)')\n",
    "plt.legend()\n",
    "ind_sample_comp(two_item_subject_summary['gaze_influence'],\n",
    "                three_item_subject_summary['gaze_influence'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Value-based vs Perceptual Choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "value_item_subject_summary = subject_summary_all_datasets[subject_summary_all_datasets['dataset'].isin(['krajbich2010', 'krajbich2011', 'folke2016'])]\n",
    "perceptual_item_subject_summary = subject_summary_all_datasets[subject_summary_all_datasets['dataset'].isin(['tavares2017'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equal variance assumption violated: True\n",
      "Normality assumption violated: True\n",
      "X1 - X2: U = 632, p = 0.00024110310475353996, d = 0.45634408602150534\n"
     ]
    },
    {
     "data": {
      "image/png": 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fTc937+D7zcyswsoNgl+mR9kk3QGMAPaXtBS4iiwApkk6D3gR39zGzKzmthkEkvpExEsR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\nfwNhB80jOBgPnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e586e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Response times\n",
    "plt.hist(value_item_subject_summary['rt']['mean'], alpha=0.5, label='value')\n",
    "plt.hist(perceptual_item_subject_summary['rt']['mean'], alpha=0.5, label='perceptual')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('Response Time (ms)')\n",
    "plt.legend()\n",
    "ind_sample_comp(value_item_subject_summary['rt']['mean'],\n",
    "                perceptual_item_subject_summary['rt']['mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equal variance assumption violated: True\n",
      "Normality assumption violated: True\n",
      "X1 - X2: U = 342, p = 3.312230922378368e-08, d = 0.7058064516129032\n"
     ]
    },
    {
     "data": {
      "image/png": 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eBb4IPCDpKUqh1buJfQHsmfX5Z6DhpvVFQG12E/1Z/vr018XZI8RPARuAnwNP\nAZuyG/oN648FfpbwezLbioc3tw5F0khgckT8Q9G1VJukdwG/Bo7I3v9gtl18hmEdSkQsBOZs64t7\n7VgNcKnDwnaUzzDMzCyJzzDMzCyJA8PMzJI4MMzMLIkDw8zMkjgwzMwsyf8C+ENFqS0voccAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e56e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# P(choose best)\n",
    "plt.hist(value_item_subject_summary['best_chosen']['mean'], alpha=0.5, label='value')\n",
    "plt.hist(perceptual_item_subject_summary['best_chosen']['mean'], alpha=0.5, label='perceptual')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('P(choose best)')\n",
    "plt.legend()\n",
    "ind_sample_comp(value_item_subject_summary['best_chosen']['mean'],\n",
    "                perceptual_item_subject_summary['best_chosen']['mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equal variance assumption violated: True\n",
      "Normality assumption violated: True\n",
      "X1 - X2: U = 419, p = 0.17471166042105185, d = 0.14051282051282055\n"
     ]
    },
    {
     "data": {
      "image/png": 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YnW3/hbzFY4Czs1gXUPh1q/nbeoBcme4aSfPIXa9o8XbMCnG1WjMzS+IRhpmZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e5ac2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gaze Influence (only 2-item choice datasets)\n",
    "plt.hist(value_item_subject_summary['gaze_influence'], alpha=0.5, label='value')\n",
    "plt.hist(perceptual_item_subject_summary['gaze_influence'], alpha=0.5, label='perceptual')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('Gaze Influence\\non P{choice | value)')\n",
    "plt.legend()\n",
    "ind_sample_comp(value_item_subject_summary[value_item_subject_summary['dataset']=='krajbich2010']['gaze_influence'],\n",
    "                perceptual_item_subject_summary['gaze_influence'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run mixed effects regressions, accounting for dataset level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_samples = 20000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prepare_df_for_bambi(df):\n",
    "    output = pd.DataFrame(dict(subject=df['subject'],\n",
    "                               dataset=df['dataset'],\n",
    "                               mean_rt=df['rt']['mean'],\n",
    "                               best_chosen=100 * df['best_chosen']['mean'],\n",
    "                               gaze_influence=100 * df['gaze_influence']))\n",
    "    return output.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mfx_df = prepare_df_for_bambi(subject_summary_all_datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previous results loaded from \"results/mixed_effects_models/1_individual_differences_a_best_chosen.csv\"...\n"
     ]
    },
    {
     "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>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hpd0.95_lower</th>\n",
       "      <th>hpd0.95_upper</th>\n",
       "      <th>effective_n</th>\n",
       "      <th>gelman_rubin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1|dataset[folke2016]</th>\n",
       "      <td>-7.139899</td>\n",
       "      <td>6.326962</td>\n",
       "      <td>-2.008172e+01</td>\n",
       "      <td>3.852120</td>\n",
       "      <td>13079</td>\n",
       "      <td>1.000141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|dataset[krajbich2010]</th>\n",
       "      <td>2.886228</td>\n",
       "      <td>5.095185</td>\n",
       "      <td>-6.848847e+00</td>\n",
       "      <td>13.687079</td>\n",
       "      <td>7702</td>\n",
       "      <td>1.000202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|dataset[krajbich2011]</th>\n",
       "      <td>-2.357201</td>\n",
       "      <td>5.030960</td>\n",
       "      <td>-1.291075e+01</td>\n",
       "      <td>7.720976</td>\n",
       "      <td>7602</td>\n",
       "      <td>1.000223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|dataset[tavares2017]</th>\n",
       "      <td>6.814064</td>\n",
       "      <td>5.673799</td>\n",
       "      <td>-3.156338e+00</td>\n",
       "      <td>18.370497</td>\n",
       "      <td>7856</td>\n",
       "      <td>1.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|dataset_sd</th>\n",
       "      <td>8.216208</td>\n",
       "      <td>4.491867</td>\n",
       "      <td>1.425486e-02</td>\n",
       "      <td>16.510914</td>\n",
       "      <td>10595</td>\n",
       "      <td>1.000037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>77.453097</td>\n",
       "      <td>4.887069</td>\n",
       "      <td>6.750464e+01</td>\n",
       "      <td>87.528821</td>\n",
       "      <td>6288</td>\n",
       "      <td>1.000249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>best_chosen_sd</th>\n",
       "      <td>8.791152</td>\n",
       "      <td>0.592931</td>\n",
       "      <td>7.651002e+00</td>\n",
       "      <td>9.949146</td>\n",
       "      <td>27572</td>\n",
       "      <td>0.999978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_rt</th>\n",
       "      <td>-0.000194</td>\n",
       "      <td>0.001492</td>\n",
       "      <td>-3.078861e-03</td>\n",
       "      <td>0.002869</td>\n",
       "      <td>2490</td>\n",
       "      <td>1.000150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_rt|dataset[folke2016]</th>\n",
       "      <td>-0.000766</td>\n",
       "      <td>0.001608</td>\n",
       "      <td>-4.433845e-03</td>\n",
       "      <td>0.002202</td>\n",
       "      <td>3159</td>\n",
       "      <td>1.000092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_rt|dataset[krajbich2010]</th>\n",
       "      <td>0.000613</td>\n",
       "      <td>0.001570</td>\n",
       "      <td>-2.287971e-03</td>\n",
       "      <td>0.004230</td>\n",
       "      <td>3290</td>\n",
       "      <td>1.000125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_rt|dataset[krajbich2011]</th>\n",
       "      <td>-0.001013</td>\n",
       "      <td>0.001627</td>\n",
       "      <td>-4.509295e-03</td>\n",
       "      <td>0.001882</td>\n",
       "      <td>2483</td>\n",
       "      <td>1.000086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_rt|dataset[tavares2017]</th>\n",
       "      <td>0.001056</td>\n",
       "      <td>0.001949</td>\n",
       "      <td>-2.346375e-03</td>\n",
       "      <td>0.005481</td>\n",
       "      <td>4887</td>\n",
       "      <td>1.000367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_rt|dataset_sd</th>\n",
       "      <td>0.002066</td>\n",
       "      <td>0.001765</td>\n",
       "      <td>3.751880e-08</td>\n",
       "      <td>0.005429</td>\n",
       "      <td>2108</td>\n",
       "      <td>1.000603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    mean        sd  hpd0.95_lower  \\\n",
       "1|dataset[folke2016]           -7.139899  6.326962  -2.008172e+01   \n",
       "1|dataset[krajbich2010]         2.886228  5.095185  -6.848847e+00   \n",
       "1|dataset[krajbich2011]        -2.357201  5.030960  -1.291075e+01   \n",
       "1|dataset[tavares2017]          6.814064  5.673799  -3.156338e+00   \n",
       "1|dataset_sd                    8.216208  4.491867   1.425486e-02   \n",
       "Intercept                      77.453097  4.887069   6.750464e+01   \n",
       "best_chosen_sd                  8.791152  0.592931   7.651002e+00   \n",
       "mean_rt                        -0.000194  0.001492  -3.078861e-03   \n",
       "mean_rt|dataset[folke2016]     -0.000766  0.001608  -4.433845e-03   \n",
       "mean_rt|dataset[krajbich2010]   0.000613  0.001570  -2.287971e-03   \n",
       "mean_rt|dataset[krajbich2011]  -0.001013  0.001627  -4.509295e-03   \n",
       "mean_rt|dataset[tavares2017]    0.001056  0.001949  -2.346375e-03   \n",
       "mean_rt|dataset_sd              0.002066  0.001765   3.751880e-08   \n",
       "\n",
       "                               hpd0.95_upper  effective_n  gelman_rubin  \n",
       "1|dataset[folke2016]                3.852120        13079      1.000141  \n",
       "1|dataset[krajbich2010]            13.687079         7702      1.000202  \n",
       "1|dataset[krajbich2011]             7.720976         7602      1.000223  \n",
       "1|dataset[tavares2017]             18.370497         7856      1.000011  \n",
       "1|dataset_sd                       16.510914        10595      1.000037  \n",
       "Intercept                          87.528821         6288      1.000249  \n",
       "best_chosen_sd                      9.949146        27572      0.999978  \n",
       "mean_rt                             0.002869         2490      1.000150  \n",
       "mean_rt|dataset[folke2016]          0.002202         3159      1.000092  \n",
       "mean_rt|dataset[krajbich2010]       0.004230         3290      1.000125  \n",
       "mean_rt|dataset[krajbich2011]       0.001882         2483      1.000086  \n",
       "mean_rt|dataset[tavares2017]        0.005481         4887      1.000367  \n",
       "mean_rt|dataset_sd                  0.005429         2108      1.000603  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_path = os.path.join('results', 'mixed_effects_models', '1_individual_differences_a_best_chosen.csv')\n",
    "if not os.path.isfile(result_path):\n",
    "    m = Model(mfx_df)\n",
    "    a_best_chosen_fit = m.fit('best_chosen ~ mean_rt',\n",
    "                              random=['mean_rt|dataset'],\n",
    "                              categorical=['dataset'],\n",
    "                              samples=n_samples,\n",
    "                              chains=2)\n",
    "    a_summary = a_best_chosen_fit.summary(ranefs=True)\n",
    "    a_summary.to_csv(result_path)\n",
    "else:\n",
    "    print('Previous results loaded from \"{}\"...'.format(result_path))\n",
    "    a_summary = pd.read_csv(result_path, index_col=0)\n",
    "a_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previous results loaded from \"results/mixed_effects_models/1_individual_differences_b_mean_rt.csv\"...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hpd0.95_lower</th>\n",
       "      <th>hpd0.95_upper</th>\n",
       "      <th>effective_n</th>\n",
       "      <th>gelman_rubin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1|dataset[folke2016]</th>\n",
       "      <td>2129.437453</td>\n",
       "      <td>912.135667</td>\n",
       "      <td>324.066332</td>\n",
       "      <td>3931.016968</td>\n",
       "      <td>526</td>\n",
       "      <td>1.001663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|dataset[krajbich2010]</th>\n",
       "      <td>-457.660999</td>\n",
       "      <td>761.999741</td>\n",
       "      <td>-1999.313913</td>\n",
       "      <td>1046.588591</td>\n",
       "      <td>376</td>\n",
       "      <td>1.002002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|dataset[krajbich2011]</th>\n",
       "      <td>-407.529644</td>\n",
       "      <td>770.939608</td>\n",
       "      <td>-1949.590637</td>\n",
       "      <td>1146.845312</td>\n",
       "      <td>399</td>\n",
       "      <td>1.001897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|dataset[tavares2017]</th>\n",
       "      <td>-926.833714</td>\n",
       "      <td>783.657684</td>\n",
       "      <td>-2470.851413</td>\n",
       "      <td>658.430345</td>\n",
       "      <td>415</td>\n",
       "      <td>1.001699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|dataset_sd</th>\n",
       "      <td>1582.589271</td>\n",
       "      <td>627.778133</td>\n",
       "      <td>546.112016</td>\n",
       "      <td>2927.859848</td>\n",
       "      <td>2756</td>\n",
       "      <td>1.000479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>2870.957389</td>\n",
       "      <td>743.635587</td>\n",
       "      <td>1416.949404</td>\n",
       "      <td>4379.078815</td>\n",
       "      <td>343</td>\n",
       "      <td>1.002047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaze_influence</th>\n",
       "      <td>-1.222489</td>\n",
       "      <td>16.110247</td>\n",
       "      <td>-32.808685</td>\n",
       "      <td>33.397977</td>\n",
       "      <td>8439</td>\n",
       "      <td>1.000073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaze_influence|dataset[folke2016]</th>\n",
       "      <td>11.429370</td>\n",
       "      <td>19.975956</td>\n",
       "      <td>-22.974756</td>\n",
       "      <td>57.034223</td>\n",
       "      <td>11553</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaze_influence|dataset[krajbich2010]</th>\n",
       "      <td>-10.237777</td>\n",
       "      <td>17.066176</td>\n",
       "      <td>-48.516123</td>\n",
       "      <td>20.310349</td>\n",
       "      <td>9547</td>\n",
       "      <td>1.000053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaze_influence|dataset[krajbich2011]</th>\n",
       "      <td>1.521008</td>\n",
       "      <td>16.630513</td>\n",
       "      <td>-33.184177</td>\n",
       "      <td>36.911348</td>\n",
       "      <td>11620</td>\n",
       "      <td>1.000033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaze_influence|dataset[tavares2017]</th>\n",
       "      <td>-2.328866</td>\n",
       "      <td>18.527824</td>\n",
       "      <td>-44.283122</td>\n",
       "      <td>34.368991</td>\n",
       "      <td>14510</td>\n",
       "      <td>1.000091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaze_influence|dataset_sd</th>\n",
       "      <td>22.345930</td>\n",
       "      <td>20.239510</td>\n",
       "      <td>0.008699</td>\n",
       "      <td>61.804907</td>\n",
       "      <td>8310</td>\n",
       "      <td>1.000125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_rt_sd</th>\n",
       "      <td>1070.795640</td>\n",
       "      <td>73.067007</td>\n",
       "      <td>932.998540</td>\n",
       "      <td>1217.141471</td>\n",
       "      <td>35102</td>\n",
       "      <td>0.999989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             mean          sd  hpd0.95_lower  \\\n",
       "1|dataset[folke2016]                  2129.437453  912.135667     324.066332   \n",
       "1|dataset[krajbich2010]               -457.660999  761.999741   -1999.313913   \n",
       "1|dataset[krajbich2011]               -407.529644  770.939608   -1949.590637   \n",
       "1|dataset[tavares2017]                -926.833714  783.657684   -2470.851413   \n",
       "1|dataset_sd                          1582.589271  627.778133     546.112016   \n",
       "Intercept                             2870.957389  743.635587    1416.949404   \n",
       "gaze_influence                          -1.222489   16.110247     -32.808685   \n",
       "gaze_influence|dataset[folke2016]       11.429370   19.975956     -22.974756   \n",
       "gaze_influence|dataset[krajbich2010]   -10.237777   17.066176     -48.516123   \n",
       "gaze_influence|dataset[krajbich2011]     1.521008   16.630513     -33.184177   \n",
       "gaze_influence|dataset[tavares2017]     -2.328866   18.527824     -44.283122   \n",
       "gaze_influence|dataset_sd               22.345930   20.239510       0.008699   \n",
       "mean_rt_sd                            1070.795640   73.067007     932.998540   \n",
       "\n",
       "                                      hpd0.95_upper  effective_n  gelman_rubin  \n",
       "1|dataset[folke2016]                    3931.016968          526      1.001663  \n",
       "1|dataset[krajbich2010]                 1046.588591          376      1.002002  \n",
       "1|dataset[krajbich2011]                 1146.845312          399      1.001897  \n",
       "1|dataset[tavares2017]                   658.430345          415      1.001699  \n",
       "1|dataset_sd                            2927.859848         2756      1.000479  \n",
       "Intercept                               4379.078815          343      1.002047  \n",
       "gaze_influence                            33.397977         8439      1.000073  \n",
       "gaze_influence|dataset[folke2016]         57.034223        11553      0.999979  \n",
       "gaze_influence|dataset[krajbich2010]      20.310349         9547      1.000053  \n",
       "gaze_influence|dataset[krajbich2011]      36.911348        11620      1.000033  \n",
       "gaze_influence|dataset[tavares2017]       34.368991        14510      1.000091  \n",
       "gaze_influence|dataset_sd                 61.804907         8310      1.000125  \n",
       "mean_rt_sd                              1217.141471        35102      0.999989  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_path = os.path.join('results', 'mixed_effects_models', '1_individual_differences_b_mean_rt.csv')\n",
    "if not os.path.isfile(result_path):\n",
    "    m = Model(mfx_df)\n",
    "    b_mean_rt_fit = m.fit('mean_rt ~ gaze_influence',\n",
    "                          random=['gaze_influence|dataset'],\n",
    "                          categorical=['dataset'],\n",
    "                          samples=n_samples,\n",
    "                          chains=2)\n",
    "    b_summary = b_mean_rt_fit.summary(ranefs=True)\n",
    "    b_summary.to_csv(result_path)\n",
    "else:\n",
    "    print('Previous results loaded from \"{}\"...'.format(result_path))\n",
    "    b_summary = pd.read_csv(result_path, index_col=0)\n",
    "b_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previous results loaded from \"results/mixed_effects_models/1_individual_differences_c_gaze_influence.csv\"...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hpd0.95_lower</th>\n",
       "      <th>hpd0.95_upper</th>\n",
       "      <th>effective_n</th>\n",
       "      <th>gelman_rubin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1|dataset[folke2016]</th>\n",
       "      <td>2.101048</td>\n",
       "      <td>3.729072</td>\n",
       "      <td>-4.543900</td>\n",
       "      <td>10.346268</td>\n",
       "      <td>11928</td>\n",
       "      <td>1.000226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|dataset[krajbich2010]</th>\n",
       "      <td>-2.697848</td>\n",
       "      <td>3.100629</td>\n",
       "      <td>-9.301312</td>\n",
       "      <td>2.862050</td>\n",
       "      <td>8186</td>\n",
       "      <td>1.000213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|dataset[krajbich2011]</th>\n",
       "      <td>-0.192391</td>\n",
       "      <td>3.075974</td>\n",
       "      <td>-6.573680</td>\n",
       "      <td>6.126166</td>\n",
       "      <td>9287</td>\n",
       "      <td>1.000167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|dataset[tavares2017]</th>\n",
       "      <td>1.961047</td>\n",
       "      <td>3.315126</td>\n",
       "      <td>-4.152488</td>\n",
       "      <td>9.090853</td>\n",
       "      <td>10651</td>\n",
       "      <td>1.000152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|dataset_sd</th>\n",
       "      <td>4.397901</td>\n",
       "      <td>3.124490</td>\n",
       "      <td>0.001478</td>\n",
       "      <td>10.344635</td>\n",
       "      <td>6704</td>\n",
       "      <td>1.000028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>86.659644</td>\n",
       "      <td>2.914812</td>\n",
       "      <td>80.946027</td>\n",
       "      <td>92.582767</td>\n",
       "      <td>7825</td>\n",
       "      <td>1.000257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>best_chosen_sd</th>\n",
       "      <td>6.391847</td>\n",
       "      <td>0.437133</td>\n",
       "      <td>5.559786</td>\n",
       "      <td>7.254483</td>\n",
       "      <td>31198</td>\n",
       "      <td>1.000090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaze_influence</th>\n",
       "      <td>-0.337018</td>\n",
       "      <td>0.191604</td>\n",
       "      <td>-0.706243</td>\n",
       "      <td>0.078898</td>\n",
       "      <td>5633</td>\n",
       "      <td>0.999989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaze_influence|dataset[folke2016]</th>\n",
       "      <td>-0.360610</td>\n",
       "      <td>0.203737</td>\n",
       "      <td>-0.783403</td>\n",
       "      <td>0.032407</td>\n",
       "      <td>6175</td>\n",
       "      <td>0.999986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaze_influence|dataset[krajbich2010]</th>\n",
       "      <td>0.215951</td>\n",
       "      <td>0.196880</td>\n",
       "      <td>-0.202033</td>\n",
       "      <td>0.597611</td>\n",
       "      <td>5885</td>\n",
       "      <td>0.999977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaze_influence|dataset[krajbich2011]</th>\n",
       "      <td>-0.251991</td>\n",
       "      <td>0.197499</td>\n",
       "      <td>-0.672998</td>\n",
       "      <td>0.123591</td>\n",
       "      <td>6090</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaze_influence|dataset[tavares2017]</th>\n",
       "      <td>0.203347</td>\n",
       "      <td>0.208476</td>\n",
       "      <td>-0.222889</td>\n",
       "      <td>0.616142</td>\n",
       "      <td>6624</td>\n",
       "      <td>0.999995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaze_influence|dataset_sd</th>\n",
       "      <td>0.392243</td>\n",
       "      <td>0.163615</td>\n",
       "      <td>0.144077</td>\n",
       "      <td>0.727346</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.999998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           mean        sd  hpd0.95_lower  \\\n",
       "1|dataset[folke2016]                   2.101048  3.729072      -4.543900   \n",
       "1|dataset[krajbich2010]               -2.697848  3.100629      -9.301312   \n",
       "1|dataset[krajbich2011]               -0.192391  3.075974      -6.573680   \n",
       "1|dataset[tavares2017]                 1.961047  3.315126      -4.152488   \n",
       "1|dataset_sd                           4.397901  3.124490       0.001478   \n",
       "Intercept                             86.659644  2.914812      80.946027   \n",
       "best_chosen_sd                         6.391847  0.437133       5.559786   \n",
       "gaze_influence                        -0.337018  0.191604      -0.706243   \n",
       "gaze_influence|dataset[folke2016]     -0.360610  0.203737      -0.783403   \n",
       "gaze_influence|dataset[krajbich2010]   0.215951  0.196880      -0.202033   \n",
       "gaze_influence|dataset[krajbich2011]  -0.251991  0.197499      -0.672998   \n",
       "gaze_influence|dataset[tavares2017]    0.203347  0.208476      -0.222889   \n",
       "gaze_influence|dataset_sd              0.392243  0.163615       0.144077   \n",
       "\n",
       "                                      hpd0.95_upper  effective_n  gelman_rubin  \n",
       "1|dataset[folke2016]                      10.346268        11928      1.000226  \n",
       "1|dataset[krajbich2010]                    2.862050         8186      1.000213  \n",
       "1|dataset[krajbich2011]                    6.126166         9287      1.000167  \n",
       "1|dataset[tavares2017]                     9.090853        10651      1.000152  \n",
       "1|dataset_sd                              10.344635         6704      1.000028  \n",
       "Intercept                                 92.582767         7825      1.000257  \n",
       "best_chosen_sd                             7.254483        31198      1.000090  \n",
       "gaze_influence                             0.078898         5633      0.999989  \n",
       "gaze_influence|dataset[folke2016]          0.032407         6175      0.999986  \n",
       "gaze_influence|dataset[krajbich2010]       0.597611         5885      0.999977  \n",
       "gaze_influence|dataset[krajbich2011]       0.123591         6090      0.999999  \n",
       "gaze_influence|dataset[tavares2017]        0.616142         6624      0.999995  \n",
       "gaze_influence|dataset_sd                  0.727346        10000      0.999998  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_path = os.path.join('results', 'mixed_effects_models', '1_individual_differences_c_gaze_influence.csv')\n",
    "if not os.path.isfile(result_path):\n",
    "    m = Model(mfx_df)\n",
    "    c_gaze_influence_fit = m.fit('best_chosen ~ gaze_influence', \n",
    "                                 random=['gaze_influence|dataset'],\n",
    "                                 categorical=['dataset'],\n",
    "                                 samples=n_samples,\n",
    "                                 chains=2)\n",
    "    c_summary = c_gaze_influence_fit.summary(ranefs=True)\n",
    "    c_summary.to_csv(result_path)\n",
    "else:\n",
    "    print('Previous results loaded from \"{}\"...'.format(result_path))\n",
    "    c_summary = pd.read_csv(result_path, index_col=0)\n",
    "c_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previous results loaded from \"results/mixed_effects_models/1_individual_differences_c_gaze_influence_posteriorplot.png\"...\n"
     ]
    },
    {
     "data": {
      "image/png": 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+2TVR7aeOP19H9+2niqoa/ffkUV57RVWNpt9dqfJQUIFAQA9cPknjZ96kp8qvjLn+iqoa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\nSxPqb/GeiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e2cfcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_path = os.path.join('results', 'mixed_effects_models', '1_individual_differences_c_gaze_influence_posteriorplot.png')\n",
    "if not os.path.isfile(plot_path):\n",
    "    plot_posterior(m.backend.trace, varnames=['gaze_influence'], ref_val=0)\n",
    "    plt.savefig(plot_path, dpi=300)\n",
    "else:\n",
    "    print('Previous results loaded from \"{}\"...'.format(plot_path))\n",
    "    show_img(plot_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previous results loaded from \"results/mixed_effects_models/1_individual_differences_c_gaze_influence_plotting.csv\"...\n"
     ]
    }
   ],
   "source": [
    "# we also have to estimate gaze-influence ~ P(best chosen) for regression line in Figure 2:\n",
    "result_path = os.path.join('results', 'mixed_effects_models', '1_individual_differences_c_gaze_influence_plotting.csv')\n",
    "if not os.path.isfile(result_path):\n",
    "    m = Model(mfx_df)\n",
    "    c_gaze_influence_plotting_fit = m.fit('gaze_influence ~ best_chosen', \n",
    "                                 random=['best_chosen|dataset'],\n",
    "                                 categorical=['dataset'],\n",
    "                                 samples=n_samples,\n",
    "                                 chains=2)\n",
    "    c_summary_plotting = c_gaze_influence_plotting_fit.summary(ranefs=True)\n",
    "    c_summary_plotting.to_csv(result_path)\n",
    "else:\n",
    "    print('Previous results loaded from \"{}\"...'.format(result_path))\n",
    "    c_summary_plotting = pd.read_csv(result_path, index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Figure 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def figure_2(subject_summary, n_bins=20, figsize=cm2inch(18, 10)):\n",
    "    \n",
    "    fig = plt.figure(figsize=figsize)\n",
    "\n",
    "    ax00 = plt.subplot2grid((11, 3), (0, 0))\n",
    "    ax10 = plt.subplot2grid((11, 3), (1, 0))\n",
    "    ax20 = plt.subplot2grid((11, 3), (2, 0))\n",
    "    ax30 = plt.subplot2grid((11, 3), (3, 0))\n",
    "    ax40 = plt.subplot2grid((11, 3), (5, 0), rowspan=6)\n",
    "    axs0 = np.array([ax00, ax10, ax20, ax30, ax40])\n",
    "\n",
    "    ax01 = plt.subplot2grid((11, 3), (0, 1))\n",
    "    ax11 = plt.subplot2grid((11, 3), (1, 1))\n",
    "    ax21 = plt.subplot2grid((11, 3), (2, 1))\n",
    "    ax31 = plt.subplot2grid((11, 3), (3, 1))\n",
    "    ax41 = plt.subplot2grid((11, 3), (5, 1), rowspan=6)\n",
    "    axs1 = np.array([ax01, ax11, ax21, ax31, ax41])\n",
    "\n",
    "    ax02 = plt.subplot2grid((11, 3), (0, 2))\n",
    "    ax12 = plt.subplot2grid((11, 3), (1, 2))\n",
    "    ax22 = plt.subplot2grid((11, 3), (2, 2))\n",
    "    ax32 = plt.subplot2grid((11, 3), (3, 2))\n",
    "    ax42 = plt.subplot2grid((11, 3), (5, 2), rowspan=6)\n",
    "    axs2 = np.array([ax02, ax12, ax22, ax32, ax42])\n",
    "\n",
    "    datasets = ['krajbich2010', 'krajbich2011', 'folke2016', 'tavares2017']\n",
    "\n",
    "    # Correlation plots\n",
    "    for d, (dataset, marker, markercolor) in enumerate(zip(datasets, ['+', '+', '+', '+'], ['C0', 'C1', 'C2', 'C3'])):\n",
    "        dataset_summary = subject_summary[subject_summary['dataset'] == dataset].reset_index(drop=True)\n",
    "        plot_correlation(dataset_summary['rt']['mean'],\n",
    "                         dataset_summary['best_chosen']['mean'],\n",
    "                         marker=marker,\n",
    "                         markercolor=markercolor,\n",
    "                         regression=False,\n",
    "                         xlabel='Mean RT (ms)',\n",
    "                         ylabel='P(choose best)',\n",
    "                         xlim=(0, 8500),\n",
    "                         xticks=np.arange(0, 9000, 2000),\n",
    "                         ylim=(0.20, 1.0),\n",
    "                         yticks=np.arange(0.4, 1.1, 0.2),\n",
    "                         ax=ax40)\n",
    "\n",
    "        plot_correlation(dataset_summary['gaze_influence'],\n",
    "                         dataset_summary['rt']['mean'],\n",
    "                         marker=marker,\n",
    "                         markercolor=markercolor,\n",
    "                         regression=False,\n",
    "                         ylabel='Mean RT (ms)',\n",
    "                         xlabel='Gaze influence\\non P(choice | value)',\n",
    "                         ylim=(0, 8500),\n",
    "                         yticks=np.arange(0, 9000, 2000),\n",
    "                         xlim=(-0.25, 0.85),\n",
    "                         xticks=np.arange(-0.2, 0.9, 0.2),\n",
    "                         ax=ax41)\n",
    "\n",
    "        plot_correlation(dataset_summary['best_chosen']['mean'],\n",
    "                         dataset_summary['gaze_influence'],\n",
    "                         marker=marker,\n",
    "                         markercolor=markercolor,\n",
    "                         regression=False,\n",
    "                             ylabel='Gaze influence\\non P(choice | value)',\n",
    "                         xlabel='P(choose best)',\n",
    "                         ylim=(-0.25, 0.85),\n",
    "                         yticks=np.arange(-0.2, 0.9, 0.2),\n",
    "                         xlim=(0.2, 1),\n",
    "                         xticks=np.arange(0.4, 1.1, 0.2),\n",
    "                         ax=ax42)\n",
    "\n",
    "        # Marginal histograms\n",
    "        axs0[d].hist(dataset_summary['rt']['mean'],\n",
    "                     bins=np.linspace(0, 8500, n_bins + 1),\n",
    "                     color=markercolor)\n",
    "\n",
    "        axs1[d].hist(dataset_summary['gaze_influence'],\n",
    "                     bins=np.linspace(-0.2, 0.8, n_bins + 1),\n",
    "                     color=markercolor)\n",
    "\n",
    "        axs2[d].hist(dataset_summary['best_chosen']['mean'],\n",
    "                     bins=np.linspace(0.3, 1, n_bins + 1),\n",
    "                     color=markercolor)\n",
    "\n",
    "        for axs in [axs0, axs1, axs2]:\n",
    "            axs[d].set_xlim(axs[-1].get_xlim())\n",
    "\n",
    "    patches = [mpatches.Rectangle((0, 0), 0.5, 1, fc=\"C{}\".format(i))\n",
    "               for i in range(len(datasets))]\n",
    "\n",
    "    ax40.legend(patches,\n",
    "                ['Krajbich 2010', 'Krajbich 2011', 'Folke 2016', 'Tavares 2017'],\n",
    "                ncol=2,\n",
    "                loc='lower left',\n",
    "                handlelength=1, handleheight=1,\n",
    "                fontsize=6, columnspacing=1,\n",
    "                frameon=False)\n",
    "\n",
    "    # Labels\n",
    "    for label, ax in zip(list('abc'), [ax00, ax01, ax02]):\n",
    "        ax.text(-0.35, 1.15, label, transform=ax.transAxes,\n",
    "                fontsize=7, fontweight='bold', va='top')\n",
    "    for label, ax in zip(list('def'), [ax40, ax41, ax42]):\n",
    "        ax.text(-0.35, 1.05, label, transform=ax.transAxes,\n",
    "                fontsize=7, fontweight='bold', va='top') \n",
    "        \n",
    "    # Fine-tune marginal histograms\n",
    "    for ax in np.array([ax00, ax10, ax20, ax30,\n",
    "                        ax01, ax11, ax21, ax31,\n",
    "                        ax02, ax12, ax22, ax32]):\n",
    "        ax.set_xticks([])\n",
    "        ax.set_ylim([0,10])\n",
    "        ax.set_yticks([0,10])\n",
    "        ax.set_yticklabels([0,10], fontsize=6)\n",
    "    ax20.set_ylabel('          Frequency', fontsize=6)\n",
    "    ax21.set_ylabel('          Frequency', fontsize=6)\n",
    "    ax22.set_ylabel('          Frequency', fontsize=6)\n",
    "    for ax in [ax00, ax10, ax20, ax30]:\n",
    "        ax.set_xticks(np.arange(0, 9000, 2000))\n",
    "        ax.set_xticklabels([])\n",
    "    ax30.set_xticklabels(np.arange(0, 9000, 2000).astype(np.int), fontsize=6)\n",
    "    ax30.set_xlabel('Mean RT (ms)', fontsize=6)\n",
    "    for ax in [ax01, ax11, ax21, ax31]:\n",
    "        ax.set_xticks(np.arange(-0.2, 0.9, 0.2))\n",
    "        ax.set_xticklabels([])\n",
    "    ax31.set_xticklabels(np.round(np.arange(-0.2, 0.9, 0.2), 1), fontsize=6)\n",
    "    ax31.set_xlabel('Gaze Influence\\non P(choice | value)', fontsize=6)\n",
    "    for ax in [ax02, ax12, ax22, ax32]:\n",
    "        ax.set_xticks(np.arange(0.4, 1.1, 0.2))\n",
    "        ax.set_xticklabels([])\n",
    "    ax32.set_xticklabels(np.round(np.arange(0.4, 1.1, 0.2), 1), fontsize=6)\n",
    "    ax32.set_xlabel('P(choose best)', fontsize=6)\n",
    "    \n",
    "    fig.subplots_adjust(wspace=0.5, hspace=1.05)\n",
    "    \n",
    "    return fig, ax40, ax41, ax42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E8vNJwCeBZ5LXc3knVf8GUJ38227ZAC1AHpah5qRAfn/ls1idnvuwFmIFQFUfVdU7sDoG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e436748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axa, axb, axc = figure_2(subject_summary_all_datasets)\n",
    "\n",
    "add_regression_line(axa, a_summary.loc['Intercept', 'mean'] / 100, a_summary.loc['mean_rt', 'mean'] / 100, color='darkgray', linewidth=1)\n",
    "add_regression_line(axb, b_summary.loc['Intercept', 'mean'], b_summary.loc['gaze_influence', 'mean'] * 100, color='darkgray', linewidth=1)\n",
    "add_regression_line(axc, c_summary.loc['Intercept', 'mean'] / 100, c_summary_plotting.loc['best_chosen', 'mean'], color='darkgray', linewidth=1)\n",
    "\n",
    "plt.savefig('results/figures/figure_2_individual_differences.png', dpi=330, bbox_inches='tight')\n",
    "plt.savefig('results/figures/figure_2_individual_differences.pdf', dpi=330, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}