circles_metacog_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Circles Metacog Analysis\n",
    "\n",
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import unicode_literals\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from metacog_utils import add_sdt_utils, metacog_dfs, jointplot_group\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data\n",
    "\n",
    "The *metacog_dfs* function creates 4 dataframes with metacognitive information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []\n",
    "for f in glob('data_anto/*.csv'):\n",
    "    dfs.append(pd.read_csv(f, encoding='utf-8'))\n",
    "df = pd.concat(dfs)\n",
    "df = add_sdt_utils(df)\n",
    "means, counts, proba, mecog = metacog_dfs(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity check\n",
    "\n",
    "Let's see if the scale converge in order to keep the performance level constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sns.FacetGrid(df[~df['TrialType'].str.contains('easy|hard')], col='Name', col_wrap=5)\n",
    "g.map(plt.plot, 'Trial', 'Scale')\n",
    "\n",
    "g = sns.FacetGrid(df[~df['TrialType'].str.contains('easy|hard')], col='Name', col_wrap=5)\n",
    "g.map(plt.plot, 'trials.thisTrialN', 'Signal')\n",
    "\n",
    "g = sns.FacetGrid(df[~df['TrialType'].str.contains('easy|hard')], col='Name', col_wrap=5)\n",
    "g.map(plt.plot, 'trials.thisTrialN', 'cmax2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also important to see that no subject has a Wager value of 1 (or 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.groupby('Name')[['Response', 'Signal', 'Confidence', 'Wager']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sns.jointplot('Response', 'Wager', means, marginal_kws={'hist': False, 'kde': True}, stat_func=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot('Response', 'Confidence', means, marginal_kws={'hist': False, 'kde': True}, stat_func=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot('Wager', 'Confidence', means, marginal_kws={'hist': False, 'kde': True}, stat_func=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby('Name')[['Response RT', 'Wager RT', 'Confidence RT']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby('Name')[['Response', 'Wager', 'Confidence']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df['TrialType'].str.contains('easy|hard')].pivot_table(index='Name', columns='TrialType', values='Response')"
   ]
  }
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