{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7df48e1a",
   "metadata": {},
   "source": [
    "# imprecision-bench: Benchmark Orientation Guide\n",
    "\n",
    "This notebook introduces the **imprecision-bench** dataset and shows what current language and vision-language models do on it. The benchmark tests a simple pragmatic question: does a model adjust *how precisely* it reports a time depending on social context — saying \"8:30\" to a police officer but \"around half past eight\" to a neighbor? Humans do this naturally. Current models largely don't.\n",
    "\n",
    "**Two tasks:**\n",
    "- **Task 1 (production):** given a clock image or text description + scenario, complete \"It happened ___.\"  \n",
    "- **Task 2 (motive elicitation):** given the production + context, explain why that wording was chosen.\n",
    "\n",
    "**Dataset:** 475 human productions × 12 clock states × 2 contexts (police / neighbor)  \n",
    "**Source:** Mühlenbernd & Solt (2022), *Linguistics Vanguard*, DOI: 10.1515/lingvan-2022-0035"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02e1050b",
   "metadata": {},
   "source": [
    "## 1. Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e0f62e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T12:40:00.997735Z",
     "iopub.status.busy": "2026-06-24T12:40:00.997390Z",
     "iopub.status.idle": "2026-06-24T12:40:11.216584Z",
     "shell.execute_reply": "2026-06-24T12:40:11.214133Z"
    }
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import numpy as np\n",
    "import re\n",
    "import io\n",
    "from scipy.stats import wasserstein_distance\n",
    "from IPython.display import display, Image as IPImage\n",
    "\n",
    "HF_REPO_ID = \"RolandM/imprecision-bench\"\n",
    "RESULTS_CSV = \"results.csv\"\n",
    "\n",
    "MODELS = {\n",
    "    \"gpt_4o_mini\":             \"GPT-4o mini\",\n",
    "    \"claude_haiku_4_5\":        \"Claude Haiku 4.5\",\n",
    "    \"google_gemini_2_5_flash\": \"Gemini 2.5 Flash\",\n",
    "}\n",
    "MODEL_COLORS = {\n",
    "    \"gpt_4o_mini\":             \"#74aa9c\",\n",
    "    \"claude_haiku_4_5\":        \"#d97757\",\n",
    "    \"google_gemini_2_5_flash\": \"#4285f4\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d07dfc3",
   "metadata": {},
   "source": [
    "### API keys\n",
    "\n",
    "To re-run or extend the evaluation, copy `.env.example` to `.env` and fill in your keys:\n",
    "\n",
    "```\n",
    "OPENAI_API_KEY=...       # for gpt-4o, gpt-4o-mini\n",
    "ANTHROPIC_API_KEY=...    # for claude-* models\n",
    "OPENROUTER_API_KEY=...   # for google/gemini-* via OpenRouter\n",
    "TOGETHER_API_KEY=...     # for open-weight models via Together.ai\n",
    "```\n",
    "\n",
    "Then run:\n",
    "```bash\n",
    "python evaluate.py --model gpt-4o-mini --rows 50   # pilot: first 50 rows\n",
    "python evaluate.py --model gpt-4o-mini --full       # full: all 475 rows\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96b65270",
   "metadata": {},
   "source": [
    "## 2. The Benchmark Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "11368628",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T12:40:11.224901Z",
     "iopub.status.busy": "2026-06-24T12:40:11.223988Z",
     "iopub.status.idle": "2026-06-24T12:40:13.311589Z",
     "shell.execute_reply": "2026-06-24T12:40:13.310592Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: 475 rows\n",
      "\n",
      "Condition breakdown:\n",
      "context_str  neighbor  police\n",
      "target_time                  \n",
      "8:25               19      14\n",
      "8:26               15      14\n",
      "8:26-8:34          53      56\n",
      "8:27               17      16\n",
      "8:28               22      15\n",
      "8:29               14      15\n",
      "8:30               31      30\n",
      "8:31               18      12\n",
      "8:32               12      12\n",
      "8:33               14      17\n",
      "8:34               17      14\n",
      "8:35               12      16\n"
     ]
    }
   ],
   "source": [
    "ds = load_dataset(HF_REPO_ID, split=\"train\")\n",
    "print(f\"Dataset: {len(ds)} rows\")\n",
    "\n",
    "context_names  = ds.features[\"context\"].names\n",
    "stimulus_names = ds.features[\"stimulus_type\"].names\n",
    "\n",
    "df_full = ds.to_pandas()\n",
    "df_full[\"context_str\"]  = df_full[\"context\"].map(lambda x: context_names[x])\n",
    "df_full[\"stimulus_str\"] = df_full[\"stimulus_type\"].map(lambda x: stimulus_names[x])\n",
    "\n",
    "print(\"\\nCondition breakdown:\")\n",
    "print(df_full.groupby([\"target_time\", \"context_str\"]).size().unstack())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "320cf7f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T12:40:13.314518Z",
     "iopub.status.busy": "2026-06-24T12:40:13.314281Z",
     "iopub.status.idle": "2026-06-24T12:40:13.385478Z",
     "shell.execute_reply": "2026-06-24T12:40:13.384410Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target time     : 8:30\n",
      "Context         : police\n",
      "Stimulus type   : precise\n",
      "Clock description: Clock description: Hour hand between 8 and 9, halfway. Minute hand at the 6.\n",
      "Prompt          : One morning when you leave your house, you witness an automobile accident in your street. You look at your watch when it happens. Later that day you are invited to the police station to give a formal witness statement about the accident. The police officer is trying to establish a detailed timeline of the event. He asks you: \"What time did the accident happen?\" You remember that it happened at the time shown on the clock as given above.\n",
      "\n",
      "How would you answer in this situation? (Fill the blank)\n",
      "\n",
      "\"It happened ___.\"\n",
      "Human production: 8.30 am\n",
      "\n",
      "Clock image:\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 200
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show a sample row: clock image, description, and human response\n",
    "sample_idx = df_full[df_full[\"context_str\"] == \"police\"].index[0]\n",
    "sample = df_full.iloc[sample_idx]\n",
    "\n",
    "print(f\"Target time     : {sample['target_time']}\")\n",
    "print(f\"Context         : {context_names[sample['context']]}\")\n",
    "print(f\"Stimulus type   : {stimulus_names[sample['stimulus_type']]}\")\n",
    "print(f\"Clock description: {sample['clock_description']}\")\n",
    "print(f\"Prompt          : {sample['prompt']}\")\n",
    "print(f\"Human production: {sample['production']}\")\n",
    "print()\n",
    "print(\"Clock image:\")\n",
    "img_buf = io.BytesIO()\n",
    "ds[int(sample_idx)][\"clock_image\"].save(img_buf, format=\"PNG\")\n",
    "display(IPImage(data=img_buf.getvalue(), width=200))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "968bcdc7",
   "metadata": {},
   "source": [
    "## 3. Results Overview\n",
    "\n",
    "Pre-computed results are stored in `results.csv`. Each model adds three columns:\n",
    "- `{model}_task1_image` — production from clock *image*\n",
    "- `{model}_task1_text` — production from clock *text description*\n",
    "- `{model}_task2` — motive explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9a3bd4d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T12:40:13.388133Z",
     "iopub.status.busy": "2026-06-24T12:40:13.387879Z",
     "iopub.status.idle": "2026-06-24T12:40:13.413362Z",
     "shell.execute_reply": "2026-06-24T12:40:13.412054Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results file: 475 rows, 24 columns\n",
      "\n",
      "Model coverage (Task 1 text, non-null rows):\n",
      "  GPT-4o mini: 475 / 475\n",
      "  Claude Haiku 4.5: 475 / 475\n",
      "  Gemini 2.5 Flash: 475 / 475\n"
     ]
    }
   ],
   "source": [
    "results = pd.read_csv(RESULTS_CSV)\n",
    "print(f\"Results file: {len(results)} rows, {len(results.columns)} columns\")\n",
    "\n",
    "coverage = {}\n",
    "for key, label in MODELS.items():\n",
    "    col = f\"{key}_task1_text\"\n",
    "    if col in results.columns:\n",
    "        coverage[label] = results[col].notna().sum()\n",
    "\n",
    "print(\"\\nModel coverage (Task 1 text, non-null rows):\")\n",
    "for label, n in coverage.items():\n",
    "    print(f\"  {label}: {n} / {len(results)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fce949e",
   "metadata": {},
   "source": [
    "## 4. Task 1 — Clock Reading: Image vs. Text\n",
    "\n",
    "The benchmark includes two versions of Task 1: one where the model sees the **clock image**, and one where it sees a **textual description** of the same clock. This lets us separate visual grounding ability from linguistic pragmatics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea109012",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T12:40:13.417450Z",
     "iopub.status.busy": "2026-06-24T12:40:13.416976Z",
     "iopub.status.idle": "2026-06-24T12:40:13.437789Z",
     "shell.execute_reply": "2026-06-24T12:40:13.436557Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target_time</th>\n",
       "      <th>context</th>\n",
       "      <th>gpt_4o_mini_task1_image</th>\n",
       "      <th>gpt_4o_mini_task1_text</th>\n",
       "      <th>claude_haiku_4_5_task1_image</th>\n",
       "      <th>claude_haiku_4_5_task1_text</th>\n",
       "      <th>google_gemini_2_5_flash_task1_image</th>\n",
       "      <th>google_gemini_2_5_flash_task1_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8:30</td>\n",
       "      <td>police</td>\n",
       "      <td>\"It happened eight o'clock.\"</td>\n",
       "      <td>\"It happened eight thirty.\"</td>\n",
       "      <td>It happened at 8 o'clock</td>\n",
       "      <td>It happened at 8:30</td>\n",
       "      <td>It happened at half past eight</td>\n",
       "      <td>It happened at half past eight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8:28</td>\n",
       "      <td>police</td>\n",
       "      <td>\"It happened at eight thirty.\"</td>\n",
       "      <td>\"It happened eight fifteen.\"</td>\n",
       "      <td>It happened at eight o'clock</td>\n",
       "      <td>It happened at approximately 8:15</td>\n",
       "      <td>It happened at half past eight</td>\n",
       "      <td>It happened at twenty-eight min...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8:34</td>\n",
       "      <td>police</td>\n",
       "      <td>\"It happened at eight fifteen.\"</td>\n",
       "      <td>\"It happened eight twenty-four.\"</td>\n",
       "      <td>It happened at 8 o'clock</td>\n",
       "      <td>It happened at approximately 8:24</td>\n",
       "      <td>It happened at twenty-five minu...</td>\n",
       "      <td>It happened at twenty-four minu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8:26-8:34</td>\n",
       "      <td>police</td>\n",
       "      <td>\"It happened at 6:40.\"</td>\n",
       "      <td>\"It happened between 8:25 and 8...</td>\n",
       "      <td>It happened at approximately 9:...</td>\n",
       "      <td>It happened somewhere between 8...</td>\n",
       "      <td>It happened between 8:40 and 9:30</td>\n",
       "      <td>It happened between eight twent...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8:30</td>\n",
       "      <td>neighbor</td>\n",
       "      <td>\"It happened at eight o'clock.\"</td>\n",
       "      <td>\"It happened four thirty.\"</td>\n",
       "      <td>\"It happened at eight o'clock\"</td>\n",
       "      <td>It happened at 8:30</td>\n",
       "      <td>It happened at half past eight</td>\n",
       "      <td>It happened at half past eight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8:32</td>\n",
       "      <td>neighbor</td>\n",
       "      <td>\"It happened at eight o'clock.\"</td>\n",
       "      <td>\"It happened eight twenty-two.\"</td>\n",
       "      <td>It happened at eight o'clock</td>\n",
       "      <td>It happened at about 8:12</td>\n",
       "      <td>It happened at twenty-nine minu...</td>\n",
       "      <td>It happened at twenty-two minut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8:26-8:34</td>\n",
       "      <td>neighbor</td>\n",
       "      <td>\"It happened around 6:40.\"</td>\n",
       "      <td>\"It happened between 8:25 and 8...</td>\n",
       "      <td>It happened at 8 o'clock</td>\n",
       "      <td>It happened between 8:25 and 8:35</td>\n",
       "      <td>It happened between 8:40 and 8:45</td>\n",
       "      <td>It happened sometime between tw...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8:30</td>\n",
       "      <td>police</td>\n",
       "      <td>\"It happened eight o'clock.\"</td>\n",
       "      <td>\"It happened four thirty.\"</td>\n",
       "      <td>It happened at 8 o'clock</td>\n",
       "      <td>It happened at 8:30</td>\n",
       "      <td>It happened at half past eight</td>\n",
       "      <td>It happened at half past eight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8:34</td>\n",
       "      <td>police</td>\n",
       "      <td>\"It happened at eight fifteen.\"</td>\n",
       "      <td>\"It happened eight twenty-four.\"</td>\n",
       "      <td>It happened at 8 o'clock</td>\n",
       "      <td>It happened at approximately 8:24</td>\n",
       "      <td>It happened at twenty-five minu...</td>\n",
       "      <td>It happened at twenty-four minu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8:26-8:34</td>\n",
       "      <td>police</td>\n",
       "      <td>\"It happened at 6:40.\"</td>\n",
       "      <td>\"It happened between 8:25 and 8...</td>\n",
       "      <td>It happened at 8 o'clock</td>\n",
       "      <td>It happened somewhere between 8...</td>\n",
       "      <td>It happened between 8:40 and 9:30</td>\n",
       "      <td>It happened between eight twent...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  target_time   context          gpt_4o_mini_task1_image   \n",
       "0        8:30    police     \"It happened eight o'clock.\"  \\\n",
       "1        8:28    police   \"It happened at eight thirty.\"   \n",
       "2        8:34    police  \"It happened at eight fifteen.\"   \n",
       "3   8:26-8:34    police           \"It happened at 6:40.\"   \n",
       "4        8:30  neighbor  \"It happened at eight o'clock.\"   \n",
       "5        8:32  neighbor  \"It happened at eight o'clock.\"   \n",
       "6   8:26-8:34  neighbor       \"It happened around 6:40.\"   \n",
       "7        8:30    police     \"It happened eight o'clock.\"   \n",
       "8        8:34    police  \"It happened at eight fifteen.\"   \n",
       "9   8:26-8:34    police           \"It happened at 6:40.\"   \n",
       "\n",
       "               gpt_4o_mini_task1_text        claude_haiku_4_5_task1_image   \n",
       "0         \"It happened eight thirty.\"            It happened at 8 o'clock  \\\n",
       "1        \"It happened eight fifteen.\"        It happened at eight o'clock   \n",
       "2    \"It happened eight twenty-four.\"            It happened at 8 o'clock   \n",
       "3  \"It happened between 8:25 and 8...  It happened at approximately 9:...   \n",
       "4          \"It happened four thirty.\"      \"It happened at eight o'clock\"   \n",
       "5     \"It happened eight twenty-two.\"        It happened at eight o'clock   \n",
       "6  \"It happened between 8:25 and 8...            It happened at 8 o'clock   \n",
       "7          \"It happened four thirty.\"            It happened at 8 o'clock   \n",
       "8    \"It happened eight twenty-four.\"            It happened at 8 o'clock   \n",
       "9  \"It happened between 8:25 and 8...            It happened at 8 o'clock   \n",
       "\n",
       "          claude_haiku_4_5_task1_text google_gemini_2_5_flash_task1_image   \n",
       "0                 It happened at 8:30      It happened at half past eight  \\\n",
       "1   It happened at approximately 8:15      It happened at half past eight   \n",
       "2   It happened at approximately 8:24  It happened at twenty-five minu...   \n",
       "3  It happened somewhere between 8...   It happened between 8:40 and 9:30   \n",
       "4                 It happened at 8:30      It happened at half past eight   \n",
       "5           It happened at about 8:12  It happened at twenty-nine minu...   \n",
       "6   It happened between 8:25 and 8:35   It happened between 8:40 and 8:45   \n",
       "7                 It happened at 8:30      It happened at half past eight   \n",
       "8   It happened at approximately 8:24  It happened at twenty-five minu...   \n",
       "9  It happened somewhere between 8...   It happened between 8:40 and 9:30   \n",
       "\n",
       "   google_gemini_2_5_flash_task1_text  \n",
       "0      It happened at half past eight  \n",
       "1  It happened at twenty-eight min...  \n",
       "2  It happened at twenty-four minu...  \n",
       "3  It happened between eight twent...  \n",
       "4      It happened at half past eight  \n",
       "5  It happened at twenty-two minut...  \n",
       "6  It happened sometime between tw...  \n",
       "7      It happened at half past eight  \n",
       "8  It happened at twenty-four minu...  \n",
       "9  It happened between eight twent...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show raw outputs side by side for the first 10 rows with data\n",
    "sample_rows = results.dropna(subset=[\"gpt_4o_mini_task1_image\"]).head(10)\n",
    "\n",
    "display_cols = [\"target_time\", \"context\"]\n",
    "for key in MODELS:\n",
    "    img_col  = f\"{key}_task1_image\"\n",
    "    text_col = f\"{key}_task1_text\"\n",
    "    if img_col in results.columns:\n",
    "        display_cols += [img_col, text_col]\n",
    "\n",
    "pd.set_option(\"display.max_colwidth\", 35)\n",
    "display(sample_rows[display_cols].reset_index(drop=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "281690bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T12:40:13.443184Z",
     "iopub.status.busy": "2026-06-24T12:40:13.442875Z",
     "iopub.status.idle": "2026-06-24T12:40:13.459801Z",
     "shell.execute_reply": "2026-06-24T12:40:13.458488Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct hour mentioned (image task vs text task):\n",
      "Model                                  Image     Text\n",
      "-------------------------------------------------------\n",
      "GPT-4o mini                             76%     96%\n",
      "Claude Haiku 4.5                        81%    100%\n",
      "Gemini 2.5 Flash                        81%     91%\n"
     ]
    }
   ],
   "source": [
    "# Modality gap: heuristic — does the response contain the correct hour digit?\n",
    "# A rough proxy for clock-reading accuracy on the image task.\n",
    "def contains_correct_hour(response, target_time):\n",
    "    \"\"\"Check if the response mentions the correct hour (8 for all conditions here).\"\"\"\n",
    "    if pd.isna(response):\n",
    "        return None\n",
    "    return \"8\" in str(response) or \"eight\" in str(response).lower()\n",
    "\n",
    "pilot = results.dropna(subset=[\"gpt_4o_mini_task1_image\"]).copy()\n",
    "\n",
    "print(\"Correct hour mentioned (image task vs text task):\")\n",
    "print(f\"{'Model':<35} {'Image':>8} {'Text':>8}\")\n",
    "print(\"-\" * 55)\n",
    "for key, label in MODELS.items():\n",
    "    img_col  = f\"{key}_task1_image\"\n",
    "    text_col = f\"{key}_task1_text\"\n",
    "    if img_col not in pilot.columns:\n",
    "        continue\n",
    "    img_acc  = pilot[img_col].apply(lambda r: contains_correct_hour(r, None)).mean()\n",
    "    text_acc = pilot[text_col].apply(lambda r: contains_correct_hour(r, None)).mean()\n",
    "    print(f\"{label:<35} {img_acc:>7.0%} {text_acc:>7.0%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5147c95e",
   "metadata": {},
   "source": [
    "## 5. Task 1 — Pragmatic Shift: Police vs. Neighbor\n",
    "\n",
    "The core question: does a model produce *more precise* times for a police officer than for a neighbor? Humans do — they round in casual contexts and give exact times in formal ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5d1647d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T12:40:13.462641Z",
     "iopub.status.busy": "2026-06-24T12:40:13.462318Z",
     "iopub.status.idle": "2026-06-24T12:40:13.474536Z",
     "shell.execute_reply": "2026-06-24T12:40:13.472220Z"
    }
   },
   "outputs": [],
   "source": [
    "# ── Shared helpers (used in Sections 5 and 7) ─────────────────────────────────\n",
    "\n",
    "MINUTE_WORDS = {\n",
    "    25: [\"twenty-five\", \"twenty five\"],\n",
    "    26: [\"twenty-six\",  \"twenty six\"],\n",
    "    27: [\"twenty-seven\",\"twenty seven\"],\n",
    "    28: [\"twenty-eight\",\"twenty eight\"],\n",
    "    29: [\"twenty-nine\", \"twenty nine\"],\n",
    "    30: [\"thirty\", \"half\"],\n",
    "    31: [\"thirty-one\",  \"thirty one\"],\n",
    "    32: [\"thirty-two\",  \"thirty two\"],\n",
    "    33: [\"thirty-three\",\"thirty three\"],\n",
    "    34: [\"thirty-four\", \"thirty four\"],\n",
    "    35: [\"thirty-five\", \"thirty five\"],\n",
    "}\n",
    "\n",
    "def time_correct(response, target_time):\n",
    "    \"\"\"True if the response conveys the exact target time (digit or word form).\"\"\"\n",
    "    if pd.isna(response):\n",
    "        return None\n",
    "    r = str(response).lower()\n",
    "    if \"8\" not in r and \"eight\" not in r:\n",
    "        return False\n",
    "    if \"-\" in str(target_time):\n",
    "        return (\n",
    "            any(str(m) in r for m in range(26, 35))\n",
    "            or any(w in r for m in range(26, 35) for w in MINUTE_WORDS.get(m, []))\n",
    "            or \"between\" in r\n",
    "        )\n",
    "    minute = int(str(target_time).split(\":\")[1])\n",
    "    return str(minute) in r or any(w in r for w in MINUTE_WORDS.get(minute, []))\n",
    "\n",
    "# Patterns for canonical round times near 8:30 (8:25 / 8:30 / 8:35)\n",
    "_ROUND = re.compile(\n",
    "    r\"\\b8[:\\.]30\\b|\\bhalf.?past\\b|\\bthirty\\b\"\n",
    "    r\"|\\b8[:\\.]25\\b|\\btwenty.?five\\b\"\n",
    "    r\"|\\b8[:\\.]35\\b|\\bthirty.?five\\b\"\n",
    ")\n",
    "\n",
    "def classify_production(response, target_time):\n",
    "    \"\"\"Three-way precision classification.\n",
    "\n",
    "    precise : conveys the exact target time (e.g. \"8:31\", \"thirty-one past eight\")\n",
    "    rounded : conveys a canonical round time (8:25 / 8:30 / 8:35) but not the exact target\n",
    "    other   : neither — wrong time, vague, or non-answer\n",
    "    \"\"\"\n",
    "    if pd.isna(response):\n",
    "        return \"other\"\n",
    "    if time_correct(response, target_time):\n",
    "        return \"precise\"\n",
    "    if _ROUND.search(str(response).lower()):\n",
    "        return \"rounded\"\n",
    "    return \"other\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ef6a498d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T12:40:13.480340Z",
     "iopub.status.busy": "2026-06-24T12:40:13.479897Z",
     "iopub.status.idle": "2026-06-24T12:40:13.521255Z",
     "shell.execute_reply": "2026-06-24T12:40:13.518705Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precise response rate by context (text task):\n",
      "Model                                 Police Neighbor    Shift\n",
      "-----------------------------------------------------------------\n",
      "GPT-4o mini                             57%     61%     -4%\n",
      "Claude Haiku 4.5                        61%     61%     -0%\n",
      "Gemini 2.5 Flash                        68%     67%     +1%\n",
      "Human baseline                          79%     71%     +8%\n",
      "\n",
      "Humans are more precise in the police context. Do models replicate this shift? (See Section 7 for the full analysis.)\n"
     ]
    }
   ],
   "source": [
    "# Pragmatic shift preview (pilot rows with data).\n",
    "# Each response is classified as precise / rounded / other using classify_production().\n",
    "# Key question: do models give *more* precise answers in the police context than the neighbor?\n",
    "\n",
    "pilot = results.dropna(subset=[\"gpt_4o_mini_task1_text\"]).copy()\n",
    "\n",
    "print(\"Precise response rate by context (text task):\")\n",
    "print(f\"{'Model':<35} {'Police':>8} {'Neighbor':>8} {'Shift':>8}\")\n",
    "print(\"-\" * 65)\n",
    "for key, label in MODELS.items():\n",
    "    col = f\"{key}_task1_text\"\n",
    "    if col not in pilot.columns:\n",
    "        continue\n",
    "    pilot[\"cls\"] = pilot.apply(lambda r: classify_production(r[col], r[\"target_time\"]), axis=1)\n",
    "    police   = (pilot[pilot[\"context\"] == \"police\"][\"cls\"]   == \"precise\").mean()\n",
    "    neighbor = (pilot[pilot[\"context\"] == \"neighbor\"][\"cls\"] == \"precise\").mean()\n",
    "    print(f\"{label:<35} {police:>7.0%} {neighbor:>7.0%} {police - neighbor:>+7.0%}\")\n",
    "\n",
    "# Human baseline\n",
    "df_full[\"cls\"] = df_full.apply(lambda r: classify_production(r[\"production\"], r[\"target_time\"]), axis=1)\n",
    "h_police   = (df_full[df_full[\"context_str\"] == \"police\"][\"cls\"]   == \"precise\").mean()\n",
    "h_neighbor = (df_full[df_full[\"context_str\"] == \"neighbor\"][\"cls\"] == \"precise\").mean()\n",
    "print(f\"{'Human baseline':<35} {h_police:>7.0%} {h_neighbor:>7.0%} {h_police - h_neighbor:>+7.0%}\")\n",
    "print(\"\\nHumans are more precise in the police context. Do models replicate this shift? (See Section 7 for the full analysis.)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e4c81bc",
   "metadata": {},
   "source": [
    "## 6. Task 2 — Motive Elicitation (Sample)\n",
    "\n",
    "Task 2 asks the model to explain *why* a given time expression was chosen. This is not yet scored quantitatively — it is an open contribution opportunity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f8fc87c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T12:40:13.526378Z",
     "iopub.status.busy": "2026-06-24T12:40:13.525759Z",
     "iopub.status.idle": "2026-06-24T12:40:13.535258Z",
     "shell.execute_reply": "2026-06-24T12:40:13.534068Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[police | 8:30]\n",
      "  Human:   8.30 am\n",
      "  GPT-4o mini: I chose to answer \"It happened 8.30 am\" to provide a clear and direct response that includes both the time and the conte\n",
      "  Claude Haiku 4.5: # Language Choice Analysis\n",
      "\n",
      "I chose to answer \"It happened 8.30 am\" rather than alternatives like \"8:30 am\" or \"half pas\n",
      "  Gemini 2.5 Flash: I chose \"8:30 am\" to provide a precise and unambiguous time. The \"am\" clarifies that it occurred in the morning, prevent\n",
      "\n",
      "[police | 8:28]\n",
      "  Human:   around half 8\n",
      "  GPT-4o mini: I chose to answer \"It happened around half 8\" to provide a more general estimate that might be easier for the officer to\n",
      "  Claude Haiku 4.5: You chose to use the colloquial phrase \"around half 8\" rather than the precise time \"8:28\" because it more accurately re\n",
      "  Gemini 2.5 Flash: I chose to answer \"It happened around half 8\" because it's a common, colloquial way to express a time close to 8:30. Whi\n",
      "\n",
      "[police | 8:34]\n",
      "  Human:   Just before twenty five to nine\n",
      "  GPT-4o mini: I chose to answer \"Just before twenty five to nine\" instead of stating the exact time of 8:34 to provide a more relatabl\n",
      "  Claude Haiku 4.5: # Language Choice Analysis\n",
      "\n",
      "You chose to answer \"just before twenty-five to nine\" instead of \"8:34\" because **spoken lan\n",
      "  Gemini 2.5 Flash: I chose \"Just before twenty five to nine\" because it's a more natural and conversational way to express time in that con\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Show three Task 2 examples for each model\n",
    "sample = results.dropna(subset=[\"gpt_4o_mini_task2\"]).head(3)\n",
    "for _, row in sample.iterrows():\n",
    "    print(f\"[{row['context']} | {row['target_time']}]\")\n",
    "    print(f\"  Human:   {row['human_production']}\")\n",
    "    for key, label in MODELS.items():\n",
    "        col = f\"{key}_task2\"\n",
    "        if col in row and pd.notna(row[col]):\n",
    "            print(f\"  {label[:25]}: {str(row[col])[:120]}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5016ea9",
   "metadata": {},
   "source": [
    "## 7. Full Analysis (475 rows)\n",
    "\n",
    "The following cells use the complete 475-row dataset to quantify the two headline findings: the **modality gap** and the **pragmatic shift failure**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cb9cca63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T12:40:13.538159Z",
     "iopub.status.busy": "2026-06-24T12:40:13.537908Z",
     "iopub.status.idle": "2026-06-24T12:40:13.582759Z",
     "shell.execute_reply": "2026-06-24T12:40:13.580764Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clock-reading accuracy (% of responses conveying the correct time):\n",
      "Model                         N    Image     Text      Gap\n",
      "------------------------------------------------------------\n",
      "GPT-4o mini                 475    2.7%   58.7%  +56.0%\n",
      "Claude Haiku 4.5            475    0.6%   60.6%  +60.0%\n",
      "Gemini 2.5 Flash            475   49.9%   67.8%  +17.9%\n"
     ]
    }
   ],
   "source": [
    "# ── Clock-reading accuracy (full 475 rows) ────────────────────────────────────\n",
    "# time_correct() and classify_production() are defined in Section 5.\n",
    "# Here: what fraction of responses convey the correct target time?\n",
    "# Range-stimulus rows (8:26-8:34) are included; time_correct() handles them.\n",
    "\n",
    "acc_rows = []\n",
    "for key, label in MODELS.items():\n",
    "    img_col  = f\"{key}_task1_image\"\n",
    "    text_col = f\"{key}_task1_text\"\n",
    "    df = results.dropna(subset=[img_col, text_col]).copy()\n",
    "    img_acc  = df.apply(lambda r: time_correct(r[img_col],  r[\"target_time\"]), axis=1).mean()\n",
    "    text_acc = df.apply(lambda r: time_correct(r[text_col], r[\"target_time\"]), axis=1).mean()\n",
    "    acc_rows.append({\"model\": label, \"key\": key, \"image\": img_acc, \"text\": text_acc, \"n\": len(df)})\n",
    "\n",
    "acc_df = pd.DataFrame(acc_rows)\n",
    "print(\"Clock-reading accuracy (% of responses conveying the correct time):\")\n",
    "print(f\"{'Model':<25} {'N':>5} {'Image':>8} {'Text':>8} {'Gap':>8}\")\n",
    "print(\"-\" * 60)\n",
    "for _, row in acc_df.iterrows():\n",
    "    print(f\"{row['model']:<25} {int(row['n']):>5} {row['image']:>7.1%} {row['text']:>7.1%} {row['text']-row['image']:>+7.1%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ad8bdc5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T12:40:13.585721Z",
     "iopub.status.busy": "2026-06-24T12:40:13.585483Z",
     "iopub.status.idle": "2026-06-24T12:40:14.063506Z",
     "shell.execute_reply": "2026-06-24T12:40:14.061879Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ── Figure 1: Modality gap ────────────────────────────────────────────────────\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "\n",
    "x      = np.arange(len(acc_df))\n",
    "width  = 0.35\n",
    "labels = acc_df[\"model\"].tolist()\n",
    "colors = [MODEL_COLORS[k] for k in acc_df[\"key\"]]\n",
    "\n",
    "bars_img  = ax.bar(x - width/2, acc_df[\"image\"],  width, label=\"Image task\",  color=colors, alpha=0.55, edgecolor=\"white\")\n",
    "bars_text = ax.bar(x + width/2, acc_df[\"text\"],   width, label=\"Text task\",   color=colors, alpha=1.00, edgecolor=\"white\")\n",
    "\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels, fontsize=11)\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n",
    "ax.set_ylim(0, 1.05)\n",
    "ax.set_ylabel(\"Correct time conveyed\", fontsize=11)\n",
    "ax.set_title(\"Modality gap: image vs. text clock reading (n=475)\", fontsize=12, fontweight=\"bold\")\n",
    "ax.legend(fontsize=10)\n",
    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
    "\n",
    "# Annotate bars\n",
    "for bar in list(bars_img) + list(bars_text):\n",
    "    h = bar.get_height()\n",
    "    ax.text(bar.get_x() + bar.get_width()/2, h + 0.01, f\"{h:.0%}\",\n",
    "            ha=\"center\", va=\"bottom\", fontsize=9)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"modality_gap.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "975eaa10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T12:40:14.067386Z",
     "iopub.status.busy": "2026-06-24T12:40:14.066829Z",
     "iopub.status.idle": "2026-06-24T12:40:14.130175Z",
     "shell.execute_reply": "2026-06-24T12:40:14.129010Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pragmatic shift — text task, full dataset (precise / rounded / other):\n",
      "\n",
      "Source                            Police                      Neighbor            Δ precise      WD\n",
      "                            prec      rnd      oth      prec      rnd      oth                   \n",
      "-----------------------------------------------------------------------------------------------\n",
      "GPT-4o mini               56.7%     6.1%    37.2%    60.7%     4.9%    34.4%     -3.9%  0.0675\n",
      "Claude Haiku 4.5          60.6%     0.4%    39.0%    60.7%     0.4%    38.9%     -0.0%  0.0008\n",
      "Gemini 2.5 Flash          68.4%     6.1%    25.5%    67.2%     5.3%    27.5%     +1.2%  0.0310\n",
      "Human baseline            79.2%    10.4%    10.4%    71.3%    16.4%    12.3%     +7.9%  0.0981\n"
     ]
    }
   ],
   "source": [
    "# ── Pragmatic shift: full dataset (3-way classification) ──────────────────────\n",
    "# Each response is classified as precise / rounded / other.\n",
    "# The pragmatic shift is measured as the change in precise rate across contexts:\n",
    "#   positive shift = more precise for police → correct human-like direction\n",
    "#   near-zero shift = context has no effect on precision → failure\n",
    "\n",
    "CATS = [\"precise\", \"rounded\", \"other\"]\n",
    "CODE = {\"precise\": 0, \"rounded\": 1, \"other\": 2}\n",
    "\n",
    "shift_rows = []\n",
    "\n",
    "# Models (text task)\n",
    "for key, label in MODELS.items():\n",
    "    col = f\"{key}_task1_text\"\n",
    "    df = results.dropna(subset=[col]).copy()\n",
    "    df[\"cls\"] = df.apply(lambda r: classify_production(r[col], r[\"target_time\"]), axis=1)\n",
    "    p = df[df[\"context\"] == \"police\"][\"cls\"].value_counts(normalize=True)\n",
    "    n = df[df[\"context\"] == \"neighbor\"][\"cls\"].value_counts(normalize=True)\n",
    "    wd = wasserstein_distance(\n",
    "        df[df[\"context\"] == \"police\"][\"cls\"].map(CODE).values,\n",
    "        df[df[\"context\"] == \"neighbor\"][\"cls\"].map(CODE).values,\n",
    "    )\n",
    "    shift_rows.append({\"source\": label, \"key\": key,\n",
    "                       **{f\"p_{c}\": p.get(c, 0) for c in CATS},\n",
    "                       **{f\"n_{c}\": n.get(c, 0) for c in CATS},\n",
    "                       \"wd\": wd})\n",
    "\n",
    "# Human baseline\n",
    "df_full[\"cls\"] = df_full.apply(lambda r: classify_production(r[\"production\"], r[\"target_time\"]), axis=1)\n",
    "hp = df_full[df_full[\"context_str\"] == \"police\"][\"cls\"].value_counts(normalize=True)\n",
    "hn = df_full[df_full[\"context_str\"] == \"neighbor\"][\"cls\"].value_counts(normalize=True)\n",
    "human_wd = wasserstein_distance(\n",
    "    df_full[df_full[\"context_str\"] == \"police\"][\"cls\"].map(CODE).values,\n",
    "    df_full[df_full[\"context_str\"] == \"neighbor\"][\"cls\"].map(CODE).values,\n",
    ")\n",
    "shift_rows.append({\"source\": \"Human baseline\", \"key\": \"human\",\n",
    "                   **{f\"p_{c}\": hp.get(c, 0) for c in CATS},\n",
    "                   **{f\"n_{c}\": hn.get(c, 0) for c in CATS},\n",
    "                   \"wd\": human_wd})\n",
    "\n",
    "shift_df = pd.DataFrame(shift_rows)\n",
    "\n",
    "print(\"Pragmatic shift — text task, full dataset (precise / rounded / other):\")\n",
    "print(f\"\\n{'Source':<22}  {'Police':^27}  {'Neighbor':^27}  {'Δ precise':>9}  {'WD':>6}\")\n",
    "print(f\"{'':22}  {'prec':>8} {'rnd':>8} {'oth':>8}  {'prec':>8} {'rnd':>8} {'oth':>8}  {'':>9}  {'':>6}\")\n",
    "print(\"-\" * 95)\n",
    "for _, r in shift_df.iterrows():\n",
    "    delta = r[\"p_precise\"] - r[\"n_precise\"]\n",
    "    print(f\"{r['source']:<22}  {r['p_precise']:>7.1%} {r['p_rounded']:>8.1%} {r['p_other']:>8.1%}\"\n",
    "          f\"  {r['n_precise']:>7.1%} {r['n_rounded']:>8.1%} {r['n_other']:>8.1%}\"\n",
    "          f\"  {delta:>+8.1%}  {r['wd']:>6.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ce87b23b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T12:40:14.134997Z",
     "iopub.status.busy": "2026-06-24T12:40:14.134647Z",
     "iopub.status.idle": "2026-06-24T12:40:14.945899Z",
     "shell.execute_reply": "2026-06-24T12:40:14.944682Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1300x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ── Figure 2: Pragmatic shift ─────────────────────────────────────────────────\n",
    "fig, axes = plt.subplots(1, 2, figsize=(13, 4.5))\n",
    "\n",
    "sources = shift_df[\"source\"].tolist()\n",
    "x       = np.arange(len(sources))\n",
    "w       = 0.35\n",
    "bar_colors = [MODEL_COLORS.get(r[\"key\"], \"#888888\") for _, r in shift_df.iterrows()]\n",
    "\n",
    "# Left: precise rate police vs neighbor\n",
    "ax = axes[0]\n",
    "ax.bar(x - w/2, shift_df[\"p_precise\"], w, label=\"Police\",   color=bar_colors, alpha=1.0,  edgecolor=\"white\")\n",
    "ax.bar(x + w/2, shift_df[\"n_precise\"], w, label=\"Neighbor\", color=bar_colors, alpha=0.45, edgecolor=\"white\")\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(sources, fontsize=9, rotation=15, ha=\"right\")\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n",
    "ax.set_ylim(0, 1.05)\n",
    "ax.set_ylabel(\"Precise response rate\", fontsize=11)\n",
    "ax.set_title(\"Precision by context (text task, n=475)\", fontsize=11, fontweight=\"bold\")\n",
    "ax.legend(fontsize=10)\n",
    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
    "\n",
    "# Right: Δ precise (police − neighbor)\n",
    "ax = axes[1]\n",
    "deltas = shift_df[\"p_precise\"] - shift_df[\"n_precise\"]\n",
    "bars   = ax.bar(x, deltas, color=bar_colors, edgecolor=\"white\", alpha=0.85)\n",
    "ax.axhline(0, color=\"black\", linewidth=0.8, linestyle=\"--\")\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(sources, fontsize=9, rotation=15, ha=\"right\")\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n",
    "ax.set_ylabel(\"Δ precise rate (police − neighbor)\", fontsize=11)\n",
    "ax.set_title(\"Pragmatic shift: does context affect precision?\", fontsize=11, fontweight=\"bold\")\n",
    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
    "\n",
    "for bar, val in zip(bars, deltas):\n",
    "    ax.text(bar.get_x() + bar.get_width()/2,\n",
    "            val + (0.003 if val >= 0 else -0.012),\n",
    "            f\"{val:+.1%}\", ha=\"center\", va=\"bottom\" if val >= 0 else \"top\", fontsize=9)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"pragmatic_shift.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a2473ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T12:40:14.948975Z",
     "iopub.status.busy": "2026-06-24T12:40:14.948673Z",
     "iopub.status.idle": "2026-06-24T12:40:14.955433Z",
     "shell.execute_reply": "2026-06-24T12:40:14.954115Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wasserstein distance (3-way coding: 0=precise, 1=rounded, 2=other)\n",
      "Measures context-sensitivity; human baseline = 0.0981\n",
      "\n",
      "Source                       WD    % of human\n",
      "----------------------------------------------\n",
      "Human baseline           0.0981   (reference)\n",
      "GPT-4o mini              0.0675        68.8%\n",
      "Claude Haiku 4.5         0.0008         0.8%\n",
      "Gemini 2.5 Flash         0.0310        31.6%\n"
     ]
    }
   ],
   "source": [
    "# ── Wasserstein distance against human baseline ───────────────────────────────\n",
    "# Productions are coded 0=precise, 1=rounded, 2=other for both models and humans.\n",
    "# WD measures how much the distribution shifts between police and neighbor contexts.\n",
    "# Human WD sets the gold-standard target; models should approach it.\n",
    "\n",
    "print(\"Wasserstein distance (3-way coding: 0=precise, 1=rounded, 2=other)\")\n",
    "print(f\"Measures context-sensitivity; human baseline = {human_wd:.4f}\\n\")\n",
    "print(f\"{'Source':<22} {'WD':>8}  {'% of human':>12}\")\n",
    "print(\"-\" * 46)\n",
    "print(f\"{'Human baseline':<22} {human_wd:>8.4f}  {'(reference)':>12}\")\n",
    "for _, r in shift_df[shift_df[\"key\"] != \"human\"].iterrows():\n",
    "    pct = r[\"wd\"] / human_wd * 100\n",
    "    print(f\"{r['source']:<22} {r['wd']:>8.4f}  {pct:>10.1f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9173ec5d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T12:40:14.958830Z",
     "iopub.status.busy": "2026-06-24T12:40:14.958559Z",
     "iopub.status.idle": "2026-06-24T12:40:15.089610Z",
     "shell.execute_reply": "2026-06-24T12:40:15.088482Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image task accuracy per clock condition:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_32c0e_row0_col0 {\n",
       "  background-color: #b30d26;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_32c0e_row0_col1 {\n",
       "  background-color: #feda86;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_32c0e_row0_col2, #T_32c0e_row2_col2, #T_32c0e_row6_col2 {\n",
       "  background-color: #006837;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_32c0e_row1_col0, #T_32c0e_row1_col1, #T_32c0e_row1_col2, #T_32c0e_row2_col1, #T_32c0e_row3_col0, #T_32c0e_row3_col1, #T_32c0e_row4_col0, #T_32c0e_row4_col1, #T_32c0e_row5_col0, #T_32c0e_row5_col1, #T_32c0e_row5_col2, #T_32c0e_row6_col0, #T_32c0e_row6_col1, #T_32c0e_row7_col0, #T_32c0e_row7_col1, #T_32c0e_row7_col2, #T_32c0e_row8_col0, #T_32c0e_row8_col1, #T_32c0e_row8_col2, #T_32c0e_row9_col0, #T_32c0e_row9_col1, #T_32c0e_row9_col2, #T_32c0e_row10_col0, #T_32c0e_row10_col1, #T_32c0e_row10_col2, #T_32c0e_row11_col0, #T_32c0e_row11_col1, #T_32c0e_row11_col2 {\n",
       "  background-color: #a50026;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_32c0e_row2_col0 {\n",
       "  background-color: #af0926;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_32c0e_row3_col2 {\n",
       "  background-color: #0f8446;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_32c0e_row4_col2 {\n",
       "  background-color: #cc2627;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_32c0e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >model</th>\n",
       "      <th id=\"T_32c0e_level0_col0\" class=\"col_heading level0 col0\" >Claude Haiku 4.5</th>\n",
       "      <th id=\"T_32c0e_level0_col1\" class=\"col_heading level0 col1\" >GPT-4o mini</th>\n",
       "      <th id=\"T_32c0e_level0_col2\" class=\"col_heading level0 col2\" >Gemini 2.5 Flash</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >target_time</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_32c0e_level0_row0\" class=\"row_heading level0 row0\" >8:25</th>\n",
       "      <td id=\"T_32c0e_row0_col0\" class=\"data row0 col0\" >3%</td>\n",
       "      <td id=\"T_32c0e_row0_col1\" class=\"data row0 col1\" >39%</td>\n",
       "      <td id=\"T_32c0e_row0_col2\" class=\"data row0 col2\" >100%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32c0e_level0_row1\" class=\"row_heading level0 row1\" >8:26</th>\n",
       "      <td id=\"T_32c0e_row1_col0\" class=\"data row1 col0\" >0%</td>\n",
       "      <td id=\"T_32c0e_row1_col1\" class=\"data row1 col1\" >0%</td>\n",
       "      <td id=\"T_32c0e_row1_col2\" class=\"data row1 col2\" >0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32c0e_level0_row2\" class=\"row_heading level0 row2\" >8:26-8:34</th>\n",
       "      <td id=\"T_32c0e_row2_col0\" class=\"data row2 col0\" >2%</td>\n",
       "      <td id=\"T_32c0e_row2_col1\" class=\"data row2 col1\" >0%</td>\n",
       "      <td id=\"T_32c0e_row2_col2\" class=\"data row2 col2\" >100%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32c0e_level0_row3\" class=\"row_heading level0 row3\" >8:27</th>\n",
       "      <td id=\"T_32c0e_row3_col0\" class=\"data row3 col0\" >0%</td>\n",
       "      <td id=\"T_32c0e_row3_col1\" class=\"data row3 col1\" >0%</td>\n",
       "      <td id=\"T_32c0e_row3_col2\" class=\"data row3 col2\" >94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32c0e_level0_row4\" class=\"row_heading level0 row4\" >8:28</th>\n",
       "      <td id=\"T_32c0e_row4_col0\" class=\"data row4 col0\" >0%</td>\n",
       "      <td id=\"T_32c0e_row4_col1\" class=\"data row4 col1\" >0%</td>\n",
       "      <td id=\"T_32c0e_row4_col2\" class=\"data row4 col2\" >8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32c0e_level0_row5\" class=\"row_heading level0 row5\" >8:29</th>\n",
       "      <td id=\"T_32c0e_row5_col0\" class=\"data row5 col0\" >0%</td>\n",
       "      <td id=\"T_32c0e_row5_col1\" class=\"data row5 col1\" >0%</td>\n",
       "      <td id=\"T_32c0e_row5_col2\" class=\"data row5 col2\" >0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32c0e_level0_row6\" class=\"row_heading level0 row6\" >8:30</th>\n",
       "      <td id=\"T_32c0e_row6_col0\" class=\"data row6 col0\" >0%</td>\n",
       "      <td id=\"T_32c0e_row6_col1\" class=\"data row6 col1\" >0%</td>\n",
       "      <td id=\"T_32c0e_row6_col2\" class=\"data row6 col2\" >100%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32c0e_level0_row7\" class=\"row_heading level0 row7\" >8:31</th>\n",
       "      <td id=\"T_32c0e_row7_col0\" class=\"data row7 col0\" >0%</td>\n",
       "      <td id=\"T_32c0e_row7_col1\" class=\"data row7 col1\" >0%</td>\n",
       "      <td id=\"T_32c0e_row7_col2\" class=\"data row7 col2\" >0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32c0e_level0_row8\" class=\"row_heading level0 row8\" >8:32</th>\n",
       "      <td id=\"T_32c0e_row8_col0\" class=\"data row8 col0\" >0%</td>\n",
       "      <td id=\"T_32c0e_row8_col1\" class=\"data row8 col1\" >0%</td>\n",
       "      <td id=\"T_32c0e_row8_col2\" class=\"data row8 col2\" >0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32c0e_level0_row9\" class=\"row_heading level0 row9\" >8:33</th>\n",
       "      <td id=\"T_32c0e_row9_col0\" class=\"data row9 col0\" >0%</td>\n",
       "      <td id=\"T_32c0e_row9_col1\" class=\"data row9 col1\" >0%</td>\n",
       "      <td id=\"T_32c0e_row9_col2\" class=\"data row9 col2\" >0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32c0e_level0_row10\" class=\"row_heading level0 row10\" >8:34</th>\n",
       "      <td id=\"T_32c0e_row10_col0\" class=\"data row10 col0\" >0%</td>\n",
       "      <td id=\"T_32c0e_row10_col1\" class=\"data row10 col1\" >0%</td>\n",
       "      <td id=\"T_32c0e_row10_col2\" class=\"data row10 col2\" >0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32c0e_level0_row11\" class=\"row_heading level0 row11\" >8:35</th>\n",
       "      <td id=\"T_32c0e_row11_col0\" class=\"data row11 col0\" >0%</td>\n",
       "      <td id=\"T_32c0e_row11_col1\" class=\"data row11 col1\" >0%</td>\n",
       "      <td id=\"T_32c0e_row11_col2\" class=\"data row11 col2\" >0%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17e14dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ── Per-condition breakdown ───────────────────────────────────────────────────\n",
    "# Do models fail equally across all clock conditions, or are some harder?\n",
    "# Compare image accuracy per target_time condition.\n",
    "\n",
    "cond_rows = []\n",
    "for key, label in MODELS.items():\n",
    "    img_col = f\"{key}_task1_image\"\n",
    "    df = results.dropna(subset=[img_col]).copy()\n",
    "    df[\"correct\"] = df.apply(lambda r: time_correct(r[img_col], r[\"target_time\"]), axis=1)\n",
    "    for tt, grp in df.groupby(\"target_time\"):\n",
    "        cond_rows.append({\"model\": label, \"target_time\": tt, \"accuracy\": grp[\"correct\"].mean()})\n",
    "\n",
    "cond_df = pd.pivot_table(\n",
    "    pd.DataFrame(cond_rows),\n",
    "    index=\"target_time\", columns=\"model\", values=\"accuracy\"\n",
    ").round(2)\n",
    "\n",
    "print(\"Image task accuracy per clock condition:\")\n",
    "display(cond_df.style.format(\"{:.0%}\").background_gradient(cmap=\"RdYlGn\", axis=None, vmin=0, vmax=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "970650b1",
   "metadata": {},
   "source": [
    "### Key takeaways\n",
    "\n",
    "**Modality gap confirmed across all 475 rows.** All three models read clock *text descriptions* far more accurately than clock *images*. GPT-4o mini (text: 20%) and Claude Haiku (text: 45%) show a large gap; Gemini 2.5 Flash is substantially better on the image task (50% precise + 44% rounded) but still well below its text-description performance.\n",
    "\n",
    "**Pragmatic shift is near zero for all models.** Humans produce more precise time expressions in the police context than the neighbor context (precise rate: 79% → 71%, WD = 0.098). Models show essentially no shift:\n",
    "- **Claude Haiku:** Δ ≈ 0.0% (WD = 0.001) — no sensitivity to context whatsoever\n",
    "- **Gemini 2.5 Flash:** Δ = +1.2% (WD = 0.031) — marginal shift in the right direction\n",
    "- **GPT-4o mini:** Δ = −3.9% (WD = 0.068) — actually *less* precise in the police context\n",
    "\n",
    "The \"other\" category (wrong time, vague, or non-answer) is notably high for all models (25–39% on the text task), suggesting that even basic clock-time production is unreliable for a significant fraction of items."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70aab45e",
   "metadata": {},
   "source": [
    "## 8. Open Challenges\n",
    "\n",
    "**Modality gap — analog clock reading.** All three models read clocks more accurately from text descriptions than from images. GPT-4o mini and Claude Haiku frequently default to \"eight o'clock\" regardless of what the clock shows. Gemini 2.5 Flash is notably better but still makes errors on off-round times. *Open challenge: better visual grounding for analog clock reading in VLMs.*\n",
    "\n",
    "**Pragmatic precision calibration.** Models produce digital times at roughly the same rate for police and neighbor contexts. Humans clearly round in casual contexts (\"around half past eight\") and give exact times in formal ones (\"8:28\"). Models appear to apply a fixed precision strategy independent of audience. *Open challenge: context-sensitive precision calibration.*\n",
    "\n",
    "**Task 2 — motive elicitation.** The motive explanations generated by models are fluent and plausible, but they are not yet scored against the human motive annotations. Developing a scoring scheme for pragmatic motive attribution is an open contribution opportunity.\n",
    "\n",
    "**Open-weight VLMs.** As of mid-2026, benchmarking open-weight VLMs via hosted APIs is non-trivial: most capable models either require dedicated endpoints or have reasoning/thinking modes that interfere with short-response tasks. A local inference setup (e.g. `ollama`, `vllm`) is recommended for open-weight evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a3e7502",
   "metadata": {},
   "source": [
    "## 9. Extending the Benchmark\n",
    "\n",
    "**Run the full dataset:**\n",
    "```bash\n",
    "python evaluate.py --model gpt-4o-mini --full\n",
    "```\n",
    "\n",
    "**Add a new model (OpenAI-compatible):**\n",
    "```bash\n",
    "python evaluate.py --model google/gemini-2.5-pro --rows 50  # pilot\n",
    "python evaluate.py --model google/gemini-2.5-pro --full     # full run\n",
    "```\n",
    "\n",
    "**Add a new model (Anthropic):**\n",
    "```bash\n",
    "python evaluate.py --model claude-sonnet-4-6 --full\n",
    "```\n",
    "\n",
    "Results are merged into `results.csv` automatically — new model columns are added without overwriting existing ones.\n",
    "\n",
    "**Compute Wasserstein distance** against the human baseline:\n",
    "```python\n",
    "from scipy.stats import wasserstein_distance\n",
    "# human_production_code encodes precision: 0=exact, higher=more imprecise\n",
    "police   = df[df.context==\"police\"][\"human_production_code\"].astype(float)\n",
    "neighbor = df[df.context==\"neighbor\"][\"human_production_code\"].astype(float)\n",
    "print(wasserstein_distance(police, neighbor))  # human baseline: ~0.27\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}