03_michotte_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pyreadr\n",
    "from scipy.stats import pearsonr, spearmanr, kendalltau, fisher_exact, ttest_ind, sem, binom\n",
    "from helpers import plot_params\n",
    "from natsort import natsorted, natsort_keygen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condition</th>\n",
       "      <th>participant</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>time</th>\n",
       "      <th>trial</th>\n",
       "      <th>clip</th>\n",
       "      <th>index</th>\n",
       "      <th>outcome</th>\n",
       "      <th>outcome.actual</th>\n",
       "      <th>outcome.counterfactual</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>outcome</td>\n",
       "      <td>12.0</td>\n",
       "      <td>male</td>\n",
       "      <td>53.0</td>\n",
       "      <td>13.977862</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>miss</td>\n",
       "      <td>miss</td>\n",
       "      <td>99.634766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>outcome</td>\n",
       "      <td>12.0</td>\n",
       "      <td>male</td>\n",
       "      <td>53.0</td>\n",
       "      <td>13.977862</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>miss</td>\n",
       "      <td>miss</td>\n",
       "      <td>99.232422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>outcome</td>\n",
       "      <td>12.0</td>\n",
       "      <td>male</td>\n",
       "      <td>53.0</td>\n",
       "      <td>13.977862</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>miss</td>\n",
       "      <td>close</td>\n",
       "      <td>99.232422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>outcome</td>\n",
       "      <td>12.0</td>\n",
       "      <td>male</td>\n",
       "      <td>53.0</td>\n",
       "      <td>13.977862</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>miss</td>\n",
       "      <td>close</td>\n",
       "      <td>99.968099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>outcome</td>\n",
       "      <td>12.0</td>\n",
       "      <td>male</td>\n",
       "      <td>53.0</td>\n",
       "      <td>13.977862</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>miss</td>\n",
       "      <td>hit</td>\n",
       "      <td>99.266927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    condition  participant   sex   age       time  trial  clip  index  \\\n",
       "520   outcome         12.0  male  53.0  13.977862   13.0   1.0      1   \n",
       "521   outcome         12.0  male  53.0  13.977862   14.0   2.0      2   \n",
       "522   outcome         12.0  male  53.0  13.977862    4.0   3.0      1   \n",
       "523   outcome         12.0  male  53.0  13.977862   15.0   4.0      2   \n",
       "524   outcome         12.0  male  53.0  13.977862   11.0   5.0      1   \n",
       "\n",
       "     outcome outcome.actual outcome.counterfactual     rating  \n",
       "520      0.0           miss                   miss  99.634766  \n",
       "521      0.0           miss                   miss  99.232422  \n",
       "522      0.0           miss                  close  99.232422  \n",
       "523      0.0           miss                  close  99.968099  \n",
       "524      0.0           miss                    hit  99.266927  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Human data from Gerstenberg\n",
    "humans = pyreadr.read_r('data/trackingDataFrames.RData')['df.judgments']\n",
    "counterfactual = humans[humans.condition == \"counterfactual\"]\n",
    "outcome = humans[humans.condition == \"outcome\"]\n",
    "outcome.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>SEQ</th>\n",
       "      <th>COND</th>\n",
       "      <th>FUYU</th>\n",
       "      <th>OTTER</th>\n",
       "      <th>ADAPTER</th>\n",
       "      <th>GPT-4V</th>\n",
       "      <th>CLAUDE-3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>color</td>\n",
       "      <td>pink</td>\n",
       "      <td>NaN</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>color</td>\n",
       "      <td>pink</td>\n",
       "      <td>white</td>\n",
       "      <td>blue</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>color</td>\n",
       "      <td>pink</td>\n",
       "      <td>black</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>color</td>\n",
       "      <td>pink</td>\n",
       "      <td>white</td>\n",
       "      <td>blue</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>color</td>\n",
       "      <td>pink</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   SEQ   COND  FUYU  OTTER ADAPTER GPT-4V CLAUDE-3\n",
       "0    1  color  pink    NaN   white  white    white\n",
       "1    2  color  pink  white    blue  white    white\n",
       "2    3  color  pink  black   white  white    white\n",
       "3    4  color  pink  white    blue  white    white\n",
       "4    5  color  pink  white   white  white    white"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model data we collected\n",
    "models = pd.read_csv(\"data/MICHOTTE_RESULTS_REV2.csv\")\n",
    "models.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Analyze match to ground truth for color test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Color, FUYU: 0.0000 %\n",
      "Color, OTTER: 55.5556 %\n",
      "Color, ADAPTER: 77.7778 %\n",
      "Color, GPT-4V: 100.0000 %\n",
      "Color, CLAUDE-3: 100.0000 %\n"
     ]
    }
   ],
   "source": [
    "# Define subjects\n",
    "subjects = [\"FUYU\", \"OTTER\", \"ADAPTER\", \"GPT-4V\", \"CLAUDE-3\"]\n",
    "colors = np.zeros(len(subjects))\n",
    "colors_errs = np.zeros(len(subjects))\n",
    "\n",
    "# Loop through subjects\n",
    "for ind, subject in enumerate(subjects):  \n",
    "\n",
    "        # Absolute distance to ground truth and absolute distance to human average\n",
    "        correct = sum(models[models.COND == \"color\"][subject] == \"white\")\n",
    "        colors[ind] = (correct / 18)*100\n",
    "        colors_errs[ind] = binom.std(100, correct / 18)\n",
    "        print(f\"Color, {subject}: {colors[ind]:.4f} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Analyze visual understanding test with trajectories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trajectory, FUYU: 0.0000 %\n",
      "Trajectory, OTTER: 72.2222 %\n",
      "Trajectory, ADAPTER: 0.0000 %\n",
      "Trajectory, GPT-4V: 50.0000 %\n",
      "Trajectory, CLAUDE-3: 11.1111 %\n"
     ]
    }
   ],
   "source": [
    "trajectory = np.zeros(len(subjects))\n",
    "trajectory_errs = np.zeros(len(subjects))\n",
    "\n",
    "# Loop through subjects\n",
    "for ind, subject in enumerate(subjects):  \n",
    "\n",
    "        # Absolute distance to ground truth and absolute distance to human average\n",
    "        correct = sum(models[models.COND == \"trajectory\"][subject] == \"right to left\")\n",
    "        trajectory[ind] = (correct / 18)*100\n",
    "        trajectory_errs[ind] = binom.std(100, correct / 18)\n",
    "        print(f\"Trajectory, {subject}: {trajectory[ind]:.4f} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Analyze correlation with human data for outcome condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FUYU, 0.2727\n",
      "OTTER, 0.0163\n",
      "ADAPTER, 0.0000\n",
      "GPT-4V, 0.0520\n",
      "CLAUDE-3, -0.1933\n",
      "HUMAN, 0.5917\n"
     ]
    }
   ],
   "source": [
    "# Save human answers next to model answers for all models\n",
    "# Output shape here is: (Number of humans * Number of test images, 3) \n",
    "# With 3 being: Model answers repeated number of humans times, All human answers, Human index\n",
    "\n",
    "num_humans = len(outcome.participant.unique())\n",
    "num_seqs = 18\n",
    "human_mean = outcome[outcome[\"clip\"] < 19].groupby('clip').mean('rating')['rating'].values\n",
    "\n",
    "for model in (subjects + [\"HUMAN\"]):\n",
    "\n",
    "    # Loop through all human subjects\n",
    "    temp = np.zeros((num_humans*num_seqs, 3))\n",
    "    temp[:] = np.nan\n",
    "    for ind, human in enumerate(outcome.participant.unique()):\n",
    "\n",
    "        # If array for human to human comparison, save human mean. Otherwise, save model answers in first column.\n",
    "        if model == \"HUMAN\":\n",
    "            temp[ind*num_seqs:(ind+1)*num_seqs, 0] = human_mean\n",
    "        else:\n",
    "            temp[ind*num_seqs:(ind+1)*num_seqs, 0] = models[models.COND == \"outcome\"][model].values.astype(float)\n",
    "\n",
    "        # Save individual human answers and individual human id\n",
    "        ind_human = outcome[(outcome[\"participant\"] == human) & (outcome[\"clip\"] < 19)].sort_values(\"clip\", key=natsort_keygen())\n",
    "        temp[ind*num_seqs:(ind+1)*num_seqs, 1] = ind_human.rating.values.astype(float)\n",
    "        temp[ind*num_seqs:(ind+1)*num_seqs, 2] = human\n",
    "        \n",
    "    print(f\"{model}, {spearmanr(temp[:, 0], temp[:, 1], nan_policy=\"omit\").statistic:.4f}\")\n",
    "\n",
    "    # Save to read in R after\n",
    "    temp = pd.DataFrame(temp, columns=[\"Model\", \"Human\", \"Ind\"])\n",
    "    temp.to_csv(f\"dataframes/03_michotte/{model}_outcome.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load\n",
    "outcome_lmer = pd.read_csv('dataframes/03_michotte/03_outcome_lmer.csv')\n",
    "\n",
    "# Separate coefficient and re-format CI\n",
    "outcome_lmer_coef = outcome_lmer.iloc[:,0].values\n",
    "outcome_lmer_conf = outcome_lmer.iloc[:,1:].values.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Analyze correlation with human data for counterfactual condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FUYU, 0.3978\n",
      "OTTER, -0.2197\n",
      "ADAPTER, 0.0000\n",
      "GPT-4V, nan\n",
      "CLAUDE-3, -0.0730\n",
      "HUMAN, 0.7668\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1547187/1232106037.py:27: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
      "  print(f\"{model}, {spearmanr(temp[:, 0], temp[:, 1], nan_policy=\"omit\").statistic:.4f}\")\n"
     ]
    }
   ],
   "source": [
    "# Save human answers next to model answers for all models\n",
    "# Output shape here is: (Number of humans * Number of test images, 3) \n",
    "# With 3 being: Model answers repeated number of humans times, All human answers, Human index\n",
    "\n",
    "num_humans = len(counterfactual.participant.unique())\n",
    "num_seqs = 18\n",
    "human_mean = counterfactual[counterfactual[\"clip\"] < 19].groupby('clip').mean('rating')['rating'].values\n",
    "\n",
    "for model in (subjects + [\"HUMAN\"]):\n",
    "\n",
    "    # Loop through all human subjects\n",
    "    temp = np.zeros((num_humans*num_seqs, 3))\n",
    "    temp[:] = np.nan\n",
    "    for ind, human in enumerate(counterfactual.participant.unique()):\n",
    "\n",
    "        # If array for human to human comparison, save human mean. Otherwise, save model answers in first column.\n",
    "        if model == \"HUMAN\":\n",
    "            temp[ind*num_seqs:(ind+1)*num_seqs, 0] = human_mean\n",
    "        else:\n",
    "            temp[ind*num_seqs:(ind+1)*num_seqs, 0] = models[models.COND == \"counterfactual\"][model].values.astype(float)\n",
    "\n",
    "        # Save individual human answers and individual human id\n",
    "        ind_human = counterfactual[(counterfactual[\"participant\"] == human) & (counterfactual[\"clip\"] < 19)].sort_values(\"clip\", key=natsort_keygen())\n",
    "        temp[ind*num_seqs:(ind+1)*num_seqs, 1] = ind_human.rating.values.astype(float)\n",
    "        temp[ind*num_seqs:(ind+1)*num_seqs, 2] = human\n",
    "        \n",
    "    print(f\"{model}, {spearmanr(temp[:, 0], temp[:, 1], nan_policy=\"omit\").statistic:.4f}\")\n",
    "\n",
    "    # Save to read in R after\n",
    "    temp = pd.DataFrame(temp, columns=[\"Model\", \"Human\", \"Ind\"])\n",
    "    temp.to_csv(f\"dataframes/03_michotte/{model}_counterfactual.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load\n",
    "counterfactual_lmer = pd.read_csv('dataframes/03_michotte/03_counterfactual_lmer.csv')\n",
    "\n",
    "# Separate coefficient and re-format CI\n",
    "counterfactual_lmer_coef = counterfactual_lmer.iloc[:,0].values\n",
    "counterfactual_lmer_conf = counterfactual_lmer.iloc[:,1:].values.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make main plot\n",
    "I imagine a plot with 4 panels, single row with four columns,  for every study\n",
    "- A: Barplots model comparison for background color task. Bars show percentage correct.\n",
    "- B: Counting model comparison as in A\n",
    "- C: Reasoning ground truth comparison as in A\n",
    "- D: Match to humans,  probably via regression coefficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'counting_errs_subj' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 21\u001b[0m\n\u001b[1;32m     19\u001b[0m axs[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mbar(np\u001b[38;5;241m.\u001b[39marange(num_subjects), trajectory, color\u001b[38;5;241m=\u001b[39mmodel_colors)\n\u001b[1;32m     20\u001b[0m axs[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39merrorbar(np\u001b[38;5;241m.\u001b[39marange(num_subjects), trajectory, trajectory_errs, ls\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m\"\u001b[39m, ecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgray\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 21\u001b[0m sns\u001b[38;5;241m.\u001b[39mswarmplot(\u001b[43mcounting_errs_subj\u001b[49m)\n\u001b[1;32m     22\u001b[0m axs[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTrajectory\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     23\u001b[0m axs[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mB\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, fontweight\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbold\u001b[39m\u001b[38;5;124m'\u001b[39m, loc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mleft\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedium\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'counting_errs_subj' is not defined"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Init plotting params\n",
    "model_colors = plot_params()\n",
    "\n",
    "# Prepare plot\n",
    "num_subjects = len(subjects)\n",
    "fig, axs = plt.subplots(1, 4, figsize=(8,3), sharey=False, tight_layout=True)  \n",
    "\n",
    "# Plot A\n",
    "axs[0].bar(np.arange(num_subjects), colors, color=model_colors)\n",
    "axs[0].errorbar(np.arange(num_subjects), colors, colors_errs, ls=\"none\", ecolor=\"gray\")\n",
    "axs[0].set_title(\"Color\\n\")\n",
    "axs[0].set_title(\"A\\n\", fontweight='bold', loc='left', fontsize='medium')\n",
    "axs[0].set_ylabel(\"Percentage correct\")\n",
    "axs[0].set_ylim(0, 100)\n",
    "#axs[0].axhline(y=random_dists[0])\n",
    "axs[0].set_xticks(np.arange(num_subjects))\n",
    "\n",
    "# Plot B\n",
    "axs[1].bar(np.arange(num_subjects), trajectory, color=model_colors)\n",
    "axs[1].errorbar(np.arange(num_subjects), trajectory, trajectory_errs, ls=\"none\", ecolor=\"gray\")\n",
    "axs[1].set_title(\"Trajectory\\n\")\n",
    "axs[1].set_title(\"B\\n\", fontweight='bold', loc='left', fontsize='medium')\n",
    "axs[1].set_ylabel(\"Percentage correct\")\n",
    "axs[1].set_ylim(0, 100)\n",
    "axs[1].axhline(50, linewidth=1, zorder=0)\n",
    "axs[1].set_xticks(np.arange(num_subjects))\n",
    "\n",
    "# Plot C\n",
    "axs[2].bar(np.arange(num_subjects+1), outcome_lmer_coef, color=model_colors)\n",
    "axs[2].errorbar(np.arange(num_subjects+1), outcome_lmer_coef, yerr=outcome_lmer_conf, ls=\"none\", ecolor=\"gray\")\n",
    "axs[2].set_title(\"Outcome\\n\", loc=\"center\")\n",
    "axs[2].set_title(\"C\\n\", fontweight='bold', loc='left', fontsize='medium')\n",
    "axs[2].set_ylabel(f\"Pseudo $r$ to humans\")\n",
    "axs[2].set_ylim(-1, 1)\n",
    "axs[2].set_xticks(np.arange(num_subjects+1))\n",
    "\n",
    "# Plot D\n",
    "axs[3].bar(np.arange(num_subjects+1), counterfactual_lmer_coef, color=model_colors)\n",
    "axs[3].errorbar(np.arange(num_subjects+1), counterfactual_lmer_coef, yerr=counterfactual_lmer_conf, ls=\"none\", ecolor=\"gray\")\n",
    "axs[3].set_title(\"Counterfactual\\n\", loc=\"center\")\n",
    "axs[3].set_title(\"D\\n\", fontweight='bold', loc='left', fontsize='medium')\n",
    "axs[3].set_ylabel(f\"Pseudo $r$ to humans\")\n",
    "axs[3].set_ylim(-1, 1)\n",
    "axs[3].set_xticks(np.arange(num_subjects+1))\n",
    "\n",
    "# Despine and set x tick labels afterwards\n",
    "sns.despine(offset=5, trim=True)\n",
    "axs[0].set_xticklabels(subjects, rotation=90)\n",
    "axs[1].set_xticklabels(subjects, rotation=90)\n",
    "axs[2].set_xticklabels(subjects + [\"Humans\"], rotation=90)\n",
    "axs[3].set_xticklabels(subjects + [\"Humans\"], rotation=90)\n",
    "plt.savefig(\"figures/03_michotte_main.pdf\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Additional: example plot for counterfactual condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13  8  2 14  7  1 10 16  3  4 15  9  6 11 12  5 17 18]\n"
     ]
    }
   ],
   "source": [
    "# Print images in order from easy to hard for humans \n",
    "human_mean = counterfactual[counterfactual[\"clip\"] < 19].groupby('clip').mean('rating').rating\n",
    "gpt4v = models[models.COND == \"counterfactual\"][\"GPT-4V\"]\n",
    "\n",
    "# Sort ratings from low high (kind of like a confidence rating)\n",
    "human_lowhigh = np.argsort(human_mean.values)\n",
    "gpt4v_lowhigh = np.argsort(gpt4v.values)\n",
    "print(human_lowhigh+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare plot\n",
    "fig, axs = plt.subplots(1, 4, sharey=False, tight_layout=True, figsize=(6,4))  \n",
    "\n",
    "# First column: Humans say it doesnt go in, GPT says it goes in (13)\n",
    "axs[0].imshow(plt.imread(\"eval/images/michotte_edit/clip_13.png\"))\n",
    "axs[0].axis(\"off\")\n",
    "\n",
    "# Second column: Humans say it doesnt go in, GPT says it goes in (8)\n",
    "axs[1].imshow(plt.imread(\"eval/images/michotte_edit/clip_8.png\"))\n",
    "axs[1].axis(\"off\")\n",
    "\n",
    "# Third column: Humans say it goes in, GPT says it goes in (17)\n",
    "axs[2].imshow(plt.imread(\"eval/images/michotte_edit/clip_17.png\"))\n",
    "axs[2].axis(\"off\")\n",
    "\n",
    "# Fourth column: Humans say it goes in, GPT says it goes in (18)\n",
    "axs[3].imshow(plt.imread(\"eval/images/michotte_edit/clip_18.png\"))\n",
    "axs[3].axis(\"off\")\n",
    "\n",
    "# Set column titles\n",
    "axs[0].set_title(\"Humans: out\\nGPT4-V: in\", fontweight=\"bold\")\n",
    "axs[1].set_title(\"Humans: out\\nGPT4-V: in\", fontweight=\"bold\")\n",
    "axs[2].set_title(\"Humans: in\\nGPT4-V: in\", fontweight=\"bold\")\n",
    "axs[3].set_title(\"Humans: in\\nGPT4-V: in\", fontweight=\"bold\")\n",
    "plt.savefig(\"figures/03_michotte_examples.pdf\", dpi=300, bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}