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DATASHEET.md

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+# PolyCAT Datasheet
+
+Following the "Datasheets for Datasets" framework (Gebru et al., 2021).
+
+## Motivation
+
+**For what purpose was the dataset created?**
+PolyCAT was created to study how geometric viewing constraints (polygon-shaped apertures) affect visual attention allocation on natural images. It enables saliency prediction research under non-rectangular viewing conditions.
+
+**Who created the dataset and on behalf of which entity?**
+Created by researchers at [institution name]. Funded by [funding source].
+
+**Who funded the creation of the dataset?**
+[To be filled]
+
+## Composition
+
+**What do the instances that comprise the dataset represent?**
+Each instance is one eye-tracking trial: a participant's gaze behavior while viewing a CAT2000 image through a polygon aperture for 4 seconds.
+
+**How many instances are there in total?**
+~21,000 trials across 30 participants (702 trials per fully included participant, 2 parts x 351 trials). All 30 participants are fully included with both parts.
+
+**Does the dataset contain all possible instances or is it a sample?**
+It is a complete recording of all trials for all included participants. 600 of the ~2000 CAT2000 images were used.
+
+**What data does each instance consist of?**
+- Sample-level gaze coordinates at 500 Hz (binocular)
+- Fixation events with position, duration, and timing
+- Trial metadata (stimulus, polygon, cue position)
+- Session metadata (calibration, validation)
+
+**Is there a label or target associated with each instance?**
+No explicit labels. The gaze data itself can be used to derive saliency maps.
+
+**Is any information missing from individual instances?**
+Some trials have tracking loss (blinks, look-away). Quality metrics are provided per-trial.
+
+**Are there any errors, sources of noise, or redundancies?**
+- EyeLink tracking noise (~0.25-0.5 deg typical accuracy)
+- Blinks cause data gaps
+- Some edge fixations may be at aperture boundaries
+
+## Collection Process
+
+**How was the data associated with each instance acquired?**
+Eye movements were recorded using an EyeLink 1000+ eye tracker while participants viewed stimuli on a 27" 4K monitor.
+
+**What mechanisms or procedures were used to collect the data?**
+Custom PsychoPy experiment with EyeLink integration. 13-point calibration before each block, drift check before each trial.
+
+**Who was involved in the data collection process?**
+Trained experimenters supervised each session. Participants were university students.
+
+**Over what timeframe was the data collected?**
+January 15 - February 25, 2026.
+
+**Were any ethical review processes conducted?**
+Yes. The study was approved by the institutional ethics review board. All participants provided written informed consent.
+
+## Preprocessing
+
+**Was any preprocessing/cleaning/labeling of the data done?**
+- Raw EDF files were converted to CSV/TSV format
+- Fixations were extracted using EyeLink's built-in detection algorithm
+- Split sessions were concatenated using trial UIDs
+- Quality metrics were computed per trial and participant
+
+**Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data?**
+Yes. The original session logs and EDF files are preserved in `data/raw_output_data/`.
+
+## Uses
+
+**Has the dataset been used for any tasks already?**
+Initial analyses are presented in the accompanying ETRA 2026 paper.
+
+**What (other) tasks could the dataset be used for?**
+- Saliency prediction with aperture constraints
+- Scanpath modeling and prediction
+- Center bias analysis under geometric constraints
+- Polygon geometry effects on visual exploration
+
+**Is there anything about the composition of the dataset or the way it was collected that might impact future uses?**
+The polygon apertures create a unique viewing constraint not present in standard saliency datasets. Models should account for the masked regions.
+
+## Distribution
+
+**How will the dataset be distributed?**
+Via the university lab's SharePoint repository. A permanent link will be provided in the published paper.
+
+**When was the dataset first released?**
+2026 (ETRA 2026 conference).
+
+**What license is the dataset distributed under?**
+CC BY 4.0. Note that the CAT2000 images are subject to their original license.
+
+## Maintenance
+
+**Who is supporting/hosting/maintaining the dataset?**
+[Institution name and contact information]
+
+**Will the dataset be updated?**
+Version updates will be documented with changelogs.
+
+**How can the owner/curator/manager of the dataset be contacted?**
+[Contact email]

DATA_CARD.md

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+# PolyCAT Data Card
+
+## Dataset Summary
+
+| Property | Value |
+|----------|-------|
+| **Name** | PolyCAT (Polygon-Aperture Eye-Tracking Dataset) |
+| **Version** | 1.0 |
+| **License** | CC BY 4.0 |
+| **Modality** | Eye tracking (gaze position, fixations, pupil size) |
+| **Task** | Free viewing with polygon aperture constraint |
+| **Size** | ~3 GB (raw) + ~12 GB (processed gaze) |
+| **Participants** | 30 included |
+| **Trials** | 21,026 total |
+| **Stimuli** | 600 CAT2000 images x 27 polygon apertures |
+
+## Intended Use
+
+- Saliency prediction under geometric viewing constraints
+- Gaze modeling and scanpath prediction
+- Study of visual attention allocation in aperture-constrained viewing
+- Benchmarking saliency models with non-rectangular viewing regions
+
+## Data Collection
+
+### Hardware
+- EyeLink 1000+ (SR Research), head-mounted, binocular at 500 Hz per eye
+- 27" 4K monitor (3840 x 2160) at 144 Hz, 70 cm viewing distance
+
+### Participants
+- 30 participants, all included
+- Ages 24-29, 14 female / 16 male
+- Normal or corrected-to-normal vision
+- University students
+
+### Task
+Participants viewed CAT2000 images through irregular polygon apertures for 4 seconds each, with a secondary old/new memory task after each block.
+
+### Stimuli
+- 600 images from 6 CAT2000 categories: Fractal, Object, OutdoorNatural, Random, Satelite, Sketch
+- 27 polygon aperture shapes with systematically varied geometric properties
+- 9 fixation cue positions (3x3 grid)
+
+## Ethical Considerations
+
+- Study approved by institutional ethics board
+- All participants provided informed consent
+- Data is de-identified: no names, no identifying images, university IDs removed
+- Demographic data limited to age and gender
+
+## Known Limitations
+
+- P05 Part B has 317/351 trials (34 trials missing due to technical issues)
+- Some sessions required restart (split sessions), handled by concatenation
+- Stimulus images are from CAT2000 and subject to its original licensing
+- Polygon apertures may introduce edge artifacts at aperture boundaries
+
+## Citation
+
+See README.md for citation information.

LICENSE

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+Creative Commons Attribution 4.0 International License (CC BY 4.0)
+
+Copyright (c) 2026 PolyCAT Authors
+
+You are free to:
+
+  Share — copy and redistribute the material in any medium or format for any
+  purpose, even commercially.
+
+  Adapt — remix, transform, and build upon the material for any purpose, even
+  commercially.
+
+Under the following terms:
+
+  Attribution — You must give appropriate credit, provide a link to the
+  license, and indicate if changes were made. You may do so in any reasonable
+  manner, but not in any way that suggests the licensor endorses you or your
+  use.
+
+  No additional restrictions — You may not apply legal terms or technological
+  measures that legally restrict others from doing anything the license
+  permits.
+
+Full license text: https://creativecommons.org/licenses/by/4.0/legalcode

README.md

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+# PolyCAT: Polygon-Aperture Eye-Tracking Dataset
+
+PolyCAT is a public eye-tracking dataset for studying visual attention on natural images viewed through irregular polygon apertures. The dataset is designed to support saliency prediction, gaze modeling, and research on how geometric viewing constraints affect visual exploration strategies.
+
+## Recording Details
+
+- **Eye tracker:** EyeLink 1000+ (SR Research), head-mounted, binocular, 500 Hz per eye
+- **Display:** 27" 4K monitor (3840 x 2160 pixels) at 144 Hz
+- **Viewing distance:** 70 cm
+- **Participants:** 30 included (ages 24-29, 14 female / 16 male)
+- **Stimuli:** 600 images from CAT2000 (6 categories) viewed through 27 polygon aperture shapes
+- **Trials per participant:** 702 (351 per part x 2 parts)
+- **Stimulus duration:** 4.0 seconds per trial
+- **Secondary task:** Old/new memory probe after each block
+
+## Folder Overview
+
+```
+PolyCAT/
+├── data/
+│   ├── raw_output_data/   # Original experiment logs (per-participant session data)
+│   ├── manifests/         # Stimulus manifest, polygon geometry, demographics
+│   ├── gaze/              # Sample-level gaze streams (TSV, one per session)
+│   ├── fixations/         # Fixation events (CSV, global + per-participant)
+│   ├── saliency_maps/     # Empirical fixation density maps per polygon-image
+│   ├── scanpaths/         # Per-trial fixation sequences
+│   ├── stimuli/           # CAT2000 images and polygon definitions
+│   ├── metadata/          # Consolidated tables: participants, sessions, trials, quality
+│   └── derivatives/       # Summary tables and computed features
+├── code/
+│   ├── preprocessing/     # EDF conversion, metadata normalization, quality metrics
+│   ├── evaluation/        # Saliency metrics (AUC, NSS, CC, KL, SIM, IG)
+│   ├── visualization/     # Plotting utilities
+│   └── examples/          # Quickstart scripts and notebooks
+├── benchmarks/            # Baseline model results
+├── docs/                  # Data dictionary, file formats, acquisition protocol
+└── paper/                 # ETRA 2026 submission
+```
+
+## Getting Started
+
+```python
+import pandas as pd
+
+# Load metadata
+participants = pd.read_csv("data/metadata/participants.csv")
+trials = pd.read_csv("data/metadata/trials.csv")
+fixations = pd.read_csv("data/fixations/fixations_all.csv")
+
+# Filter to one participant
+p01_fix = fixations[fixations["participant_id"] == "P01"]
+print(f"P01 has {len(p01_fix)} fixations across {p01_fix['trial_uid'].nunique()} trials")
+
+# Fixation heatmap for a specific polygon-image combination
+import numpy as np
+polygon = "polygon_25"
+subset = fixations[(fixations["polygon_id"] == polygon) & (fixations["eye"] == "R")]
+heatmap = np.zeros((2160, 3840))
+for _, row in subset.iterrows():
+    x, y = int(row["x_px"]), int(row["y_px"])
+    if 0 <= x < 3840 and 0 <= y < 2160:
+        heatmap[y, x] += 1
+```
+
+## Polygon Apertures
+
+The 27 polygon shapes are organized into three groups:
+- **Reference** (3): regular geometric shapes (rectangle, symmetric, asymmetric)
+- **Convexity-varied** (3): shapes with varying convexity (convex, concave, intermediate)
+- **Irregular** (21): asymmetric polygons with diverse geometric properties
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@inproceedings{polycat2026,
+  title     = {PolyCAT: A Polygon-Aperture Eye-Tracking Dataset},
+  author    = {TBD},
+  booktitle = {Proceedings of the ACM Symposium on Eye Tracking Research \& Applications (ETRA)},
+  year      = {2026},
+  doi       = {TBD}
+}
+```
+
+## License
+
+This dataset is released under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](LICENSE).

code/examples/quickstart.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PolyCAT Dataset — Quickstart\n",
+    "\n",
+    "This notebook demonstrates how to load and visualize the PolyCAT dataset.\n",
+    "\n",
+    "**Display setup:** 27\" 4K monitor (3840×2160) at 70 cm viewing distance ≈ 78.5 px/deg."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "from collections import defaultdict\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Adjust to your project root\n",
+    "PROJECT_ROOT = Path(\"../..\").resolve()\n",
+    "\n",
+    "# Display constants (27\" 4K at 70 cm)\n",
+    "SCREEN_W, SCREEN_H = 3840, 2160\n",
+    "PPD = 78.5  # pixels per degree\n",
+    "\n",
+    "def load_csv(path):\n",
+    "    with open(path) as f:\n",
+    "        return list(csv.DictReader(f))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Load Metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "participants = load_csv(PROJECT_ROOT / \"data/metadata/participants.csv\")\n",
+    "trials = load_csv(PROJECT_ROOT / \"data/metadata/trials.csv\")\n",
+    "quality = load_csv(PROJECT_ROOT / \"data/metadata/quality_metrics.csv\")\n",
+    "\n",
+    "print(f\"Participants: {len(participants)}\")\n",
+    "print(f\"Trials: {len(trials)}\")\n",
+    "print(f\"\\nQuality metrics columns: {list(quality[0].keys())}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Quality Overview"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pids = [q[\"participant_id\"] for q in quality]\n",
+    "tracking_ratios = [float(q[\"tracking_ratio_mean\"]) for q in quality]\n",
+    "fix_per_trial = [float(q[\"fixations_per_trial_mean\"]) for q in quality]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "\n",
+    "axes[0].bar(range(len(pids)), tracking_ratios, color=\"steelblue\")\n",
+    "axes[0].set_xticks(range(len(pids)))\n",
+    "axes[0].set_xticklabels(pids, rotation=45, ha=\"right\", fontsize=7)\n",
+    "axes[0].set_ylabel(\"Fixation tracking ratio\")\n",
+    "axes[0].set_title(\"Per-eye fixation tracking ratio by participant\")\n",
+    "axes[0].axhline(y=0.6, color=\"red\", linestyle=\"--\", alpha=0.5, label=\"Exclusion threshold\")\n",
+    "axes[0].legend(fontsize=8)\n",
+    "\n",
+    "axes[1].bar(range(len(pids)), fix_per_trial, color=\"darkorange\")\n",
+    "axes[1].set_xticks(range(len(pids)))\n",
+    "axes[1].set_xticklabels(pids, rotation=45, ha=\"right\", fontsize=7)\n",
+    "axes[1].set_ylabel(\"Mean fixations per trial (both eyes)\")\n",
+    "axes[1].set_title(\"Fixation count by participant\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Load Fixations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fixations = load_csv(PROJECT_ROOT / \"data/fixations/fixations_all.csv\")\n",
+    "print(f\"Total fixations: {len(fixations):,}\")\n",
+    "\n",
+    "# Quick summary\n",
+    "durations = [float(f[\"duration_ms\"]) for f in fixations]\n",
+    "print(f\"Duration: mean={np.mean(durations):.0f} ms, median={np.median(durations):.0f} ms\")\n",
+    "print(f\"Columns: {list(fixations[0].keys())}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Fixation Duration Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 4))\n",
+    "ax.hist(durations, bins=np.arange(0, 2000, 25), color=\"steelblue\", edgecolor=\"white\")\n",
+    "ax.set_xlabel(\"Fixation duration (ms)\")\n",
+    "ax.set_ylabel(\"Count\")\n",
+    "ax.set_title(\"Fixation Duration Distribution\")\n",
+    "ax.axvline(np.median(durations), color=\"red\", linestyle=\"--\",\n",
+    "           label=f\"Median: {np.median(durations):.0f} ms\")\n",
+    "ax.legend()\n",
+    "ax.set_xlim(0, 1500)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Spatial Distribution of Fixations\n",
+    "\n",
+    "Fixation positions on the 3840×2160 px display (27\" 4K at 70 cm ≈ 78.5 px/deg)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Right eye fixations only\n",
+    "r_fix = [(float(f[\"x_px\"]), float(f[\"y_px\"])) for f in fixations if f[\"eye\"] == \"R\"]\n",
+    "xs = np.array([p[0] for p in r_fix])\n",
+    "ys = np.array([p[1] for p in r_fix])\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "# Heatmap in pixels\n",
+    "h, xedges, yedges = np.histogram2d(xs, ys, bins=[96, 54],\n",
+    "                                    range=[[0, SCREEN_W], [0, SCREEN_H]])\n",
+    "axes[0].imshow(h.T, extent=[0, SCREEN_W, SCREEN_H, 0],\n",
+    "               aspect=\"equal\", cmap=\"hot\", interpolation=\"gaussian\")\n",
+    "axes[0].set_xlabel(\"X (pixels)\")\n",
+    "axes[0].set_ylabel(\"Y (pixels)\")\n",
+    "axes[0].set_title(f\"Fixation density — all participants (N={len(r_fix):,})\")\n",
+    "\n",
+    "# Heatmap in degrees\n",
+    "xs_deg = (xs - SCREEN_W / 2) / PPD\n",
+    "ys_deg = (ys - SCREEN_H / 2) / PPD\n",
+    "h2, _, _ = np.histogram2d(xs_deg, ys_deg, bins=[96, 54],\n",
+    "                           range=[[-SCREEN_W/2/PPD, SCREEN_W/2/PPD],\n",
+    "                                  [-SCREEN_H/2/PPD, SCREEN_H/2/PPD]])\n",
+    "extent_deg = [-SCREEN_W/2/PPD, SCREEN_W/2/PPD, SCREEN_H/2/PPD, -SCREEN_H/2/PPD]\n",
+    "axes[1].imshow(h2.T, extent=extent_deg, aspect=\"equal\",\n",
+    "               cmap=\"hot\", interpolation=\"gaussian\")\n",
+    "axes[1].set_xlabel(\"Horizontal (deg)\")\n",
+    "axes[1].set_ylabel(\"Vertical (deg)\")\n",
+    "axes[1].set_title(\"Fixation density in degrees of visual angle\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Single Trial Scanpath"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pick a trial from the first participant\n",
+    "scanpath_dir = PROJECT_ROOT / \"data/scanpaths\"\n",
+    "pid_dirs = sorted(d for d in scanpath_dir.iterdir() if d.is_dir())\n",
+    "pid_dir = pid_dirs[0]\n",
+    "trial_file = sorted(pid_dir.glob(\"*.csv\"))[0]\n",
+    "\n",
+    "scanpath = load_csv(trial_file)\n",
+    "# Filter to right eye\n",
+    "sp_r = [f for f in scanpath if f[\"eye\"] == \"R\"]\n",
+    "\n",
+    "sp_x = [float(f[\"x_px\"]) for f in sp_r]\n",
+    "sp_y = [float(f[\"y_px\"]) for f in sp_r]\n",
+    "sp_dur = [float(f[\"duration_ms\"]) for f in sp_r]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 5.625))  # 16:9 aspect\n",
+    "ax.set_xlim(0, SCREEN_W)\n",
+    "ax.set_ylim(SCREEN_H, 0)  # Invert Y\n",
+    "ax.set_aspect(\"equal\")\n",
+    "\n",
+    "# Draw scanpath\n",
+    "ax.plot(sp_x, sp_y, \"b-\", alpha=0.3, linewidth=1)\n",
+    "scatter = ax.scatter(sp_x, sp_y, s=[d/5 for d in sp_dur],\n",
+    "                     c=range(len(sp_x)), cmap=\"viridis\", alpha=0.7,\n",
+    "                     edgecolors=\"black\", linewidths=0.5)\n",
+    "\n",
+    "# Number the fixations\n",
+    "for i, (x, y) in enumerate(zip(sp_x, sp_y)):\n",
+    "    ax.annotate(str(i+1), (x, y), fontsize=7, ha=\"center\", va=\"center\",\n",
+    "                color=\"white\", fontweight=\"bold\")\n",
+    "\n",
+    "ax.set_xlabel(\"X (pixels)\")\n",
+    "ax.set_ylabel(\"Y (pixels)\")\n",
+    "ax.set_title(f\"Scanpath: {trial_file.stem} (right eye, {len(sp_r)} fixations)\\n\"\n",
+    "             f\"Display: 3840×2160 px, 27\\\" 4K at 70 cm\")\n",
+    "plt.colorbar(scatter, label=\"Fixation order\", ax=ax)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Saliency Map Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "saliency_dir = PROJECT_ROOT / \"data/saliency_maps/by_polygon\"\n",
+    "if saliency_dir.exists():\n",
+    "    # Find first available saliency map\n",
+    "    poly_dirs = sorted(d for d in saliency_dir.iterdir() if d.is_dir())\n",
+    "    if poly_dirs:\n",
+    "        npy_files = sorted(poly_dirs[0].glob(\"*_fixmap.npy\"))\n",
+    "        if npy_files:\n",
+    "            smap = np.load(npy_files[0])\n",
+    "            fig, ax = plt.subplots(figsize=(10, 5.625))\n",
+    "            im = ax.imshow(smap, cmap=\"hot\", aspect=\"equal\")\n",
+    "            ax.set_xlabel(\"X (pixels)\")\n",
+    "            ax.set_ylabel(\"Y (pixels)\")\n",
+    "            ax.set_title(f\"Saliency map: {poly_dirs[0].name}/{npy_files[0].stem}\\n\"\n",
+    "                         f\"Resolution: {smap.shape[1]}×{smap.shape[0]} px, \"\n",
+    "                         f\"σ = 1.0 deg = {PPD:.1f} px\")\n",
+    "            plt.colorbar(im, label=\"Fixation density\", ax=ax)\n",
+    "            plt.tight_layout()\n",
+    "            plt.show()\n",
+    "        else:\n",
+    "            print(\"No .npy saliency maps found.\")\n",
+    "    else:\n",
+    "        print(\"No polygon directories found.\")\n",
+    "else:\n",
+    "    print(\"Saliency map directory not found. Run generate_saliency_maps.py first.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Category Comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fixation stats by stimulus category\n",
+    "by_category = defaultdict(list)\n",
+    "for f in fixations:\n",
+    "    cat = f.get(\"category\", \"\")\n",
+    "    if cat and f[\"eye\"] == \"R\":\n",
+    "        by_category[cat].append(float(f[\"duration_ms\"]))\n",
+    "\n",
+    "cats = sorted(by_category.keys())\n",
+    "means = [np.mean(by_category[c]) for c in cats]\n",
+    "stds = [np.std(by_category[c]) / np.sqrt(len(by_category[c])) for c in cats]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 4))\n",
+    "ax.bar(cats, means, yerr=stds, color=\"steelblue\", edgecolor=\"white\", capsize=3)\n",
+    "ax.set_ylabel(\"Mean fixation duration (ms)\")\n",
+    "ax.set_title(\"Fixation Duration by Stimulus Category (right eye)\")\n",
+    "plt.xticks(rotation=30, ha=\"right\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "**Notes:**\n",
+    "- All pixel coordinates are on a 3840×2160 display\n",
+    "- Conversion to degrees: `x_deg = (x_px - 1920) / 78.5`, `y_deg = (y_px - 1080) / 78.5`\n",
+    "- See `docs/data_dictionary.md` for full field descriptions\n",
+    "- See `docs/acquisition_protocol.md` for equipment and procedure details"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

code/examples/quickstart.py

@@ -0,0 +1,234 @@
+#!/usr/bin/env python3
+"""PolyCAT Dataset — Quickstart Example
+
+Demonstrates basic data loading and visualization for the PolyCAT dataset.
+
+Display setup: 27" 4K monitor (3840x2160) at 70 cm viewing distance (~78.5 px/deg).
+
+Requirements:
+    pip install numpy pandas matplotlib
+
+Usage:
+    python code/examples/quickstart.py
+"""
+
+import csv
+from collections import defaultdict
+from pathlib import Path
+
+import numpy as np
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+
+
+def load_csv(path):
+    """Load a CSV file as a list of dicts."""
+    with open(path, "r") as f:
+        return list(csv.DictReader(f))
+
+
+def example_1_load_metadata():
+    """Example 1: Load and explore dataset metadata."""
+    print("=" * 60)
+    print("Example 1: Dataset Metadata")
+    print("=" * 60)
+
+    # Load participants
+    participants = load_csv(PROJECT_ROOT / "data" / "metadata" / "participants.csv")
+    print(f"\nParticipants: {len(participants)}")
+    for p in participants[:3]:
+        print(f"  {p['participant_id']}: "
+              f"{p.get('total_trials_A', '?')} trials (Part A), "
+              f"{p.get('total_trials_B', '?')} trials (Part B)")
+    print(f"  ... and {len(participants) - 3} more")
+
+    # Load trials
+    trials = load_csv(PROJECT_ROOT / "data" / "metadata" / "trials.csv")
+    print(f"\nTotal trials: {len(trials)}")
+
+    # Count by category
+    categories = defaultdict(int)
+    for t in trials:
+        cat = t.get("category", "unknown")
+        categories[cat] += 1
+    print("  By category:")
+    for cat, count in sorted(categories.items()):
+        print(f"    {cat}: {count}")
+
+    return participants, trials
+
+
+def example_2_load_fixations():
+    """Example 2: Load and summarize fixation data."""
+    print("\n" + "=" * 60)
+    print("Example 2: Fixation Data")
+    print("=" * 60)
+
+    fixations = load_csv(PROJECT_ROOT / "data" / "fixations" / "fixations_all.csv")
+    print(f"\nTotal fixations: {len(fixations):,}")
+
+    # Group by participant
+    by_pid = defaultdict(list)
+    for f in fixations:
+        by_pid[f["participant_id"]].append(f)
+
+    print(f"Participants: {len(by_pid)}")
+
+    # Summary per participant
+    print("\nPer-participant fixation counts:")
+    for pid in sorted(by_pid.keys())[:5]:
+        fix_list = by_pid[pid]
+        durations = [float(f["duration_ms"]) for f in fix_list]
+        print(f"  {pid}: {len(fix_list):,} fixations, "
+              f"mean duration = {np.mean(durations):.0f} ms")
+    print(f"  ... and {len(by_pid) - 5} more")
+
+    return fixations
+
+
+def example_3_single_trial_scanpath():
+    """Example 3: Load a single trial's scanpath."""
+    print("\n" + "=" * 60)
+    print("Example 3: Single Trial Scanpath")
+    print("=" * 60)
+
+    # Find a participant's scanpath directory
+    scanpath_dir = PROJECT_ROOT / "data" / "scanpaths"
+    pid_dirs = sorted(d for d in scanpath_dir.iterdir() if d.is_dir())
+
+    if not pid_dirs:
+        print("No scanpath files found. Run export_scanpaths.py first.")
+        return
+
+    # Pick first participant, first trial
+    pid_dir = pid_dirs[0]
+    scanpath_files = sorted(pid_dir.glob("*.csv"))
+    if not scanpath_files:
+        print(f"No scanpath files in {pid_dir}")
+        return
+
+    scanpath_file = scanpath_files[0]
+    scanpath = load_csv(scanpath_file)
+
+    print(f"\nParticipant: {pid_dir.name}")
+    print(f"Trial: {scanpath_file.stem}")
+    print(f"Fixations in scanpath: {len(scanpath)}")
+
+    # Display note about coordinate interpretation
+    print(f"\nNote: Coordinates are in pixels on a 3840x2160 display at 70 cm.")
+    print(f"      At ~78.5 px/deg, a 100 px distance = ~1.27 degrees of visual angle.")
+
+    print("\nScanpath (right eye):")
+    print(f"  {'#':>3} {'X (px)':>8} {'Y (px)':>8} {'Duration':>10} {'Eye':>4}")
+    print(f"  {'-'*3:>3} {'-'*8:>8} {'-'*8:>8} {'-'*10:>10} {'-'*4:>4}")
+    for fix in scanpath[:10]:
+        print(f"  {fix['fixation_index']:>3} "
+              f"{float(fix['x_px']):>8.1f} "
+              f"{float(fix['y_px']):>8.1f} "
+              f"{float(fix['duration_ms']):>8.1f} ms "
+              f"{fix['eye']:>4}")
+    if len(scanpath) > 10:
+        print(f"  ... and {len(scanpath) - 10} more fixations")
+
+
+def example_4_fixation_heatmap():
+    """Example 4: Create a simple fixation heatmap (text-based)."""
+    print("\n" + "=" * 60)
+    print("Example 4: Fixation Spatial Distribution")
+    print("=" * 60)
+
+    # Load fixations for one participant
+    fixations = load_csv(PROJECT_ROOT / "data" / "fixations" / "fixations_all.csv")
+
+    # Get first participant's right-eye fixations
+    first_pid = fixations[0]["participant_id"]
+    pid_fix = [(float(f["x_px"]), float(f["y_px"]))
+               for f in fixations
+               if f["participant_id"] == first_pid and f["eye"] == "R"]
+
+    print(f"\n{first_pid}: {len(pid_fix)} right-eye fixations")
+
+    # Compute spatial statistics
+    xs = np.array([p[0] for p in pid_fix])
+    ys = np.array([p[1] for p in pid_fix])
+
+    ppd = 78.5  # px/deg for 27" 4K at 70 cm
+    cx, cy = 3840 / 2, 2160 / 2  # screen center
+
+    # Distance from center in degrees
+    dist_deg = np.sqrt(((xs - cx) / ppd)**2 + ((ys - cy) / ppd)**2)
+
+    print(f"\nSpatial distribution:")
+    print(f"  X range: {xs.min():.0f} - {xs.max():.0f} px "
+          f"({(xs.min() - cx) / ppd:.1f} to {(xs.max() - cx) / ppd:.1f} deg)")
+    print(f"  Y range: {ys.min():.0f} - {ys.max():.0f} px "
+          f"({(ys.min() - cy) / ppd:.1f} to {(ys.max() - cy) / ppd:.1f} deg)")
+    print(f"  Mean eccentricity: {dist_deg.mean():.1f} deg")
+    print(f"  Median eccentricity: {np.median(dist_deg):.1f} deg")
+
+    # Histogram of eccentricity
+    bins = [0, 2, 4, 6, 8, 10, 15, 20, 25]
+    counts, _ = np.histogram(dist_deg, bins=bins)
+    print(f"\n  Eccentricity distribution:")
+    for i in range(len(counts)):
+        bar = "#" * int(counts[i] / max(counts) * 30)
+        print(f"    {bins[i]:>4}-{bins[i+1]:>2} deg: {counts[i]:>5} {bar}")
+
+
+def example_5_polygon_info():
+    """Example 5: Explore polygon geometry."""
+    print("\n" + "=" * 60)
+    print("Example 5: Polygon Apertures")
+    print("=" * 60)
+
+    poly_csv = PROJECT_ROOT / "data" / "manifests" / "polygon_geometry.csv"
+    if not poly_csv.exists():
+        print(f"  Polygon geometry file not found at {poly_csv}")
+        return
+
+    polygons = load_csv(poly_csv)
+    print(f"\n{len(polygons)} unique polygon shapes")
+    print(f"\nFirst 5 polygons:")
+    for p in polygons[:5]:
+        pid = p.get("polygon_id", "?")
+        cx = p.get("center_x", "?")
+        cy = p.get("center_y", "?")
+        print(f"  {pid}: center = ({cx}, {cy})")
+
+    # Check for polygon JSON definitions
+    poly_dir = PROJECT_ROOT / "data" / "stimuli" / "polygons"
+    if poly_dir.exists():
+        json_files = list(poly_dir.glob("*.json"))
+        print(f"\n{len(json_files)} polygon JSON vertex definition files")
+        if json_files:
+            import json
+            with open(json_files[0]) as f:
+                sample = json.load(f)
+            print(f"  Sample ({json_files[0].name}):")
+            if isinstance(sample, list):
+                print(f"    {len(sample)} vertices")
+                if sample:
+                    print(f"    First vertex: {sample[0]}")
+            elif isinstance(sample, dict):
+                print(f"    Keys: {list(sample.keys())}")
+
+
+def main():
+    print("PolyCAT Dataset — Quickstart Examples")
+    print("Display: 27\" 4K (3840x2160) at 70 cm (~78.5 px/deg)")
+    print()
+
+    example_1_load_metadata()
+    example_2_load_fixations()
+    example_3_single_trial_scanpath()
+    example_4_fixation_heatmap()
+    example_5_polygon_info()
+
+    print("\n" + "=" * 60)
+    print("Quickstart complete!")
+    print("=" * 60)
+    print("\nFor visualization examples, see quickstart.ipynb")
+
+
+if __name__ == "__main__":
+    main()

croissant.json

@@ -0,0 +1,586 @@
+{
+  "@context": {
+    "@language": "en",
+    "@vocab": "https://schema.org/",
+    "citeAs": "cr:citeAs",
+    "column": "cr:column",
+    "conformsTo": "dct:conformsTo",
+    "cr": "http://mlcommons.org/croissant/",
+    "rai": "http://mlcommons.org/croissant/RAI/",
+    "data": {"@id": "cr:data", "@type": "@json"},
+    "dataType": {"@id": "cr:dataType", "@type": "@vocab"},
+    "dct": "http://purl.org/dc/terms/",
+    "examples": {"@id": "cr:examples", "@type": "@json"},
+    "extract": "cr:extract",
+    "field": "cr:field",
+    "fileProperty": "cr:fileProperty",
+    "fileObject": "cr:fileObject",
+    "fileSet": "cr:fileSet",
+    "format": "cr:format",
+    "includes": "cr:includes",
+    "isLiveDataset": "cr:isLiveDataset",
+    "jsonPath": "cr:jsonPath",
+    "key": "cr:key",
+    "md5": "cr:md5",
+    "parentField": "cr:parentField",
+    "path": "cr:path",
+    "recordSet": "cr:recordSet",
+    "references": "cr:references",
+    "regex": "cr:regex",
+    "repeated": "cr:repeated",
+    "replace": "cr:replace",
+    "sc": "https://schema.org/",
+    "separator": "cr:separator",
+    "source": "cr:source",
+    "subField": "cr:subField",
+    "transform": "cr:transform"
+  },
+  "@type": "sc:Dataset",
+  "name": "PolyCAT",
+  "description": "PolyCAT (Polygon-Aperture Eye-Tracking Dataset) is a public eye-tracking dataset for studying visual attention on natural images viewed through irregular polygon apertures. 30 participants viewed 600 CAT2000 images through 27 polygon shapes while their eye movements were recorded with an EyeLink 1000+ at 500 Hz binocular. The dataset includes sample-level gaze streams, fixation events, empirical saliency maps, scanpath sequences, and comprehensive metadata.",
+  "conformsTo": "http://mlcommons.org/croissant/1.0",
+  "license": "https://creativecommons.org/licenses/by/4.0/",
+  "url": "https://github.com/TBD/PolyCAT",
+  "version": "1.0.0",
+  "datePublished": "2026-03-05",
+  "keywords": [
+    "eye tracking",
+    "saliency",
+    "visual attention",
+    "gaze prediction",
+    "fixation density",
+    "polygon aperture",
+    "CAT2000"
+  ],
+  "creator": {
+    "@type": "sc:Organization",
+    "name": "TBD"
+  },
+  "citeAs": "TBD (2026). PolyCAT: A Polygon-Aperture Eye-Tracking Dataset. In Proceedings of ETRA 2026.",
+  "distribution": [
+    {
+      "@type": "cr:FileObject",
+      "@id": "participants-csv",
+      "name": "participants.csv",
+      "description": "Per-participant metadata including demographics, dominant eye, trial counts, and inclusion status.",
+      "contentUrl": "data/metadata/participants.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "TBD"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "sessions-csv",
+      "name": "sessions.csv",
+      "description": "Per-session metadata including session labels (primary, split_primary, extra), trial counts, and inclusion flags.",
+      "contentUrl": "data/metadata/sessions.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "TBD"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "trials-csv",
+      "name": "trials.csv",
+      "description": "Source-of-truth trial table with one row per trial. Contains stimulus assignments, polygon definitions, cue positions, and drift-check metrics.",
+      "contentUrl": "data/metadata/trials.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "TBD"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "quality-metrics-csv",
+      "name": "quality_metrics.csv",
+      "description": "Per-participant quality metrics: tracking ratio, fixation statistics, calibration error, data loss.",
+      "contentUrl": "data/metadata/quality_metrics.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "TBD"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "fixations-all-csv",
+      "name": "fixations_all.csv",
+      "description": "All fixation events across all participants and trials. Each row is one fixation with position (pixels and degrees), duration, pupil size, and stimulus metadata.",
+      "contentUrl": "data/fixations/fixations_all.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "TBD"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "fixations-mat",
+      "name": "fixations.mat",
+      "description": "Fixation data in CAT2000-compatible MATLAB format with fields: x, y, dur, subj, img, polygon.",
+      "contentUrl": "data/fixations/fixations.mat",
+      "encodingFormat": "application/x-matlab-data",
+      "sha256": "TBD"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "polygon-geometry-csv",
+      "name": "polygon_geometry.csv",
+      "description": "Polygon shape definitions with polygon ID, group, and JSON filename.",
+      "contentUrl": "data/manifests/polygon_geometry.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "TBD"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "stimulus-manifest-csv",
+      "name": "stimulus_manifest.csv",
+      "description": "Fixed stimulus-polygon-cue assignment table (702 trial slots shared by all participants).",
+      "contentUrl": "data/manifests/stimulus_manifest.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "TBD"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "baseline-results-csv",
+      "name": "baseline_results.csv",
+      "description": "Saliency benchmark results for baseline models (Uniform, Center Bias, Gold Standard LOO).",
+      "contentUrl": "benchmarks/baseline_results.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "TBD"
+    },
+    {
+      "@type": "cr:FileSet",
+      "@id": "gaze-tsv-files",
+      "name": "Gaze TSV files",
+      "description": "Sample-level gaze streams. One TSV file per session with timestamp, eye, gaze position, pupil size, and validity.",
+      "containedIn": {"@id": "gaze-directory"},
+      "includes": "*.tsv",
+      "encodingFormat": "text/tab-separated-values"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "gaze-directory",
+      "name": "gaze/",
+      "description": "Directory containing per-session gaze TSV files.",
+      "contentUrl": "data/gaze/",
+      "encodingFormat": "application/x-directory"
+    },
+    {
+      "@type": "cr:FileSet",
+      "@id": "saliency-map-files",
+      "name": "Saliency map files",
+      "description": "Empirical fixation density maps per polygon-image combination. Gaussian-smoothed (sigma=1 deg) at native 3840x2160 resolution.",
+      "containedIn": {"@id": "saliency-maps-directory"},
+      "includes": "**/*_fixmap.npy",
+      "encodingFormat": "application/octet-stream"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "saliency-maps-directory",
+      "name": "saliency_maps/by_polygon/",
+      "description": "Directory containing saliency maps organized by polygon ID.",
+      "contentUrl": "data/saliency_maps/by_polygon/",
+      "encodingFormat": "application/x-directory"
+    },
+    {
+      "@type": "cr:FileSet",
+      "@id": "scanpath-files",
+      "name": "Scanpath files",
+      "description": "Per-trial fixation sequences. One CSV per trial with fixation index, timing, position, and duration.",
+      "containedIn": {"@id": "scanpaths-directory"},
+      "includes": "**/*.csv",
+      "encodingFormat": "text/csv"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "scanpaths-directory",
+      "name": "scanpaths/",
+      "description": "Directory containing per-trial scanpath CSVs organized by participant.",
+      "contentUrl": "data/scanpaths/",
+      "encodingFormat": "application/x-directory"
+    }
+  ],
+  "recordSet": [
+    {
+      "@type": "cr:RecordSet",
+      "@id": "participants",
+      "name": "participants",
+      "description": "One record per participant with demographics, eye dominance, trial counts, and inclusion status.",
+      "key": {"@id": "participants/participant_id"},
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "participants/participant_id",
+          "name": "participant_id",
+          "description": "Participant identifier (e.g., P01).",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {"@id": "participants-csv"},
+            "extract": {"column": "participant_id"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "participants/age",
+          "name": "age",
+          "description": "Age at time of recording (years).",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileObject": {"@id": "participants-csv"},
+            "extract": {"column": "age"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "participants/gender",
+          "name": "gender",
+          "description": "Self-reported gender (Male/Female).",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {"@id": "participants-csv"},
+            "extract": {"column": "gender"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "participants/dominant_eye",
+          "name": "dominant_eye",
+          "description": "Dominant eye (left/right).",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {"@id": "participants-csv"},
+            "extract": {"column": "dominant_eye"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "participants/included_in_release",
+          "name": "included_in_release",
+          "description": "Whether participant is included in the released dataset.",
+          "dataType": "sc:Boolean",
+          "source": {
+            "fileObject": {"@id": "participants-csv"},
+            "extract": {"column": "included_in_release"}
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "trials",
+      "name": "trials",
+      "description": "One record per trial across all participants. Source-of-truth trial table linking participants, stimuli, and polygon apertures.",
+      "key": {"@id": "trials/trial_uid"},
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "trials/participant_id",
+          "name": "participant_id",
+          "description": "Participant identifier.",
+          "dataType": "sc:Text",
+          "references": {"field": {"@id": "participants/participant_id"}},
+          "source": {
+            "fileObject": {"@id": "trials-csv"},
+            "extract": {"column": "participant_id"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "trials/trial_uid",
+          "name": "trial_uid",
+          "description": "Unique trial ID: {pid}_{part}_b{block:02d}_t{trial:02d}.",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {"@id": "trials-csv"},
+            "extract": {"column": "trial_uid"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "trials/part",
+          "name": "part",
+          "description": "Experiment part (A or B).",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {"@id": "trials-csv"},
+            "extract": {"column": "part"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "trials/image_id",
+          "name": "image_id",
+          "description": "CAT2000 image identifier (e.g., A_Random_169).",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {"@id": "trials-csv"},
+            "extract": {"column": "image_id"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "trials/category",
+          "name": "category",
+          "description": "CAT2000 image category.",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {"@id": "trials-csv"},
+            "extract": {"column": "category"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "trials/polygon_id",
+          "name": "polygon_id",
+          "description": "Polygon aperture identifier (e.g., polygon_25).",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {"@id": "trials-csv"},
+            "extract": {"column": "polygon_id"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "trials/trial_type",
+          "name": "trial_type",
+          "description": "Trial type: image or empty.",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {"@id": "trials-csv"},
+            "extract": {"column": "trial_type"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "trials/stimulus_duration_s",
+          "name": "stimulus_duration_s",
+          "description": "Stimulus presentation duration in seconds (4.0).",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {"@id": "trials-csv"},
+            "extract": {"column": "stimulus_duration_s"}
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "fixations",
+      "name": "fixations",
+      "description": "One record per fixation event. Fixations detected by EyeLink's built-in algorithm (velocity 30 deg/s, acceleration 8000 deg/s²). Positions computed from raw gaze samples.",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "fixations/participant_id",
+          "name": "participant_id",
+          "description": "Participant identifier.",
+          "dataType": "sc:Text",
+          "references": {"field": {"@id": "participants/participant_id"}},
+          "source": {
+            "fileObject": {"@id": "fixations-all-csv"},
+            "extract": {"column": "participant_id"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "fixations/trial_uid",
+          "name": "trial_uid",
+          "description": "Unique trial ID linking to trials table.",
+          "dataType": "sc:Text",
+          "references": {"field": {"@id": "trials/trial_uid"}},
+          "source": {
+            "fileObject": {"@id": "fixations-all-csv"},
+            "extract": {"column": "trial_uid"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "fixations/fixation_index",
+          "name": "fixation_index",
+          "description": "Fixation order within trial and eye (1-indexed).",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileObject": {"@id": "fixations-all-csv"},
+            "extract": {"column": "fixation_index"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "fixations/eye",
+          "name": "eye",
+          "description": "Eye (L or R).",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {"@id": "fixations-all-csv"},
+            "extract": {"column": "eye"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "fixations/start_time_ms",
+          "name": "start_time_ms",
+          "description": "Fixation start time relative to stimulus onset (ms).",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {"@id": "fixations-all-csv"},
+            "extract": {"column": "start_time_ms"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "fixations/duration_ms",
+          "name": "duration_ms",
+          "description": "Fixation duration (ms).",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {"@id": "fixations-all-csv"},
+            "extract": {"column": "duration_ms"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "fixations/x_px",
+          "name": "x_px",
+          "description": "Fixation X position in screen pixels (top-left origin, 3840x2160 display).",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {"@id": "fixations-all-csv"},
+            "extract": {"column": "x_px"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "fixations/y_px",
+          "name": "y_px",
+          "description": "Fixation Y position in screen pixels (top-left origin, 3840x2160 display).",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {"@id": "fixations-all-csv"},
+            "extract": {"column": "y_px"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "fixations/x_deg",
+          "name": "x_deg",
+          "description": "Fixation X position in degrees from screen center.",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {"@id": "fixations-all-csv"},
+            "extract": {"column": "x_deg"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "fixations/y_deg",
+          "name": "y_deg",
+          "description": "Fixation Y position in degrees from screen center.",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {"@id": "fixations-all-csv"},
+            "extract": {"column": "y_deg"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "fixations/pupil_size",
+          "name": "pupil_size",
+          "description": "Mean pupil size during fixation (EyeLink arbitrary area units).",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {"@id": "fixations-all-csv"},
+            "extract": {"column": "pupil_size"}
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "gaze_samples",
+      "name": "gaze_samples",
+      "description": "Sample-level gaze data at 500 Hz per eye. One record per sample across all sessions. Data is distributed across per-session TSV files.",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "gaze_samples/participant_id",
+          "name": "participant_id",
+          "description": "Participant identifier.",
+          "dataType": "sc:Text",
+          "references": {"field": {"@id": "participants/participant_id"}},
+          "source": {
+            "fileSet": {"@id": "gaze-tsv-files"},
+            "extract": {"column": "participant_id"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "gaze_samples/trial_uid_raw",
+          "name": "trial_uid_raw",
+          "description": "Raw trial UID from EDF messages (e.g., B_MB01_T001).",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {"@id": "gaze-tsv-files"},
+            "extract": {"column": "trial_uid_raw"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "gaze_samples/timestamp_rel_ms",
+          "name": "timestamp_rel_ms",
+          "description": "Time relative to stimulus onset (ms).",
+          "dataType": "sc:Float",
+          "source": {
+            "fileSet": {"@id": "gaze-tsv-files"},
+            "extract": {"column": "timestamp_rel_ms"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "gaze_samples/eye",
+          "name": "eye",
+          "description": "Eye (L or R).",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {"@id": "gaze-tsv-files"},
+            "extract": {"column": "eye"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "gaze_samples/x_px",
+          "name": "x_px",
+          "description": "Gaze X position in screen pixels.",
+          "dataType": "sc:Float",
+          "source": {
+            "fileSet": {"@id": "gaze-tsv-files"},
+            "extract": {"column": "x_px"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "gaze_samples/y_px",
+          "name": "y_px",
+          "description": "Gaze Y position in screen pixels.",
+          "dataType": "sc:Float",
+          "source": {
+            "fileSet": {"@id": "gaze-tsv-files"},
+            "extract": {"column": "y_px"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "gaze_samples/pupil_size",
+          "name": "pupil_size",
+          "description": "Pupil size (EyeLink arbitrary area units).",
+          "dataType": "sc:Float",
+          "source": {
+            "fileSet": {"@id": "gaze-tsv-files"},
+            "extract": {"column": "pupil_size"}
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "gaze_samples/validity",
+          "name": "validity",
+          "description": "Sample validity (0 = valid gaze, 1 = missing/out-of-range).",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileSet": {"@id": "gaze-tsv-files"},
+            "extract": {"column": "validity"}
+          }
+        }
+      ]
+    }
+  ]
+}

data/scanpaths/P19/P19_A_b01_t36.csv

@@ -0,0 +1,11 @@
+fixation_index,eye,start_time_ms,end_time_ms,duration_ms,x_px,y_px
+1,L,258.0,642.0,384.0,3025.9,1665.3
+2,L,658.0,1130.0,472.0,2921.9,1632.5
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+4,L,1470.0,2106.0,636.0,3049.8,1693.4
+5,L,2126.0,4018.0,1892.0,2890.5,1734.4
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+2,R,658.0,1130.0,472.0,2847.0,1659.0
+3,R,1198.0,1386.0,188.0,1796.3,1517.6
+4,R,1468.0,2106.0,638.0,3008.2,1709.9
+5,R,2122.0,4018.0,1896.0,2845.6,1738.9

data/scanpaths/P19/P19_A_b01_t37.csv

@@ -0,0 +1,5 @@
+fixation_index,eye,start_time_ms,end_time_ms,duration_ms,x_px,y_px
+1,L,230.0,506.0,276.0,1004.0,550.0
+2,L,522.0,4017.0,3495.0,949.6,559.1
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+2,R,522.0,4017.0,3495.0,856.6,547.3

data/scanpaths/P19/P19_A_b04_t06.csv

@@ -0,0 +1,25 @@
+fixation_index,eye,start_time_ms,end_time_ms,duration_ms,x_px,y_px
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+9,L,3038.0,3306.0,268.0,2869.9,1499.7
+10,L,3334.0,3554.0,220.0,2842.5,1794.1
+11,L,3578.0,3790.0,212.0,2884.7,1580.3
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+7,R,2606.0,2788.0,182.0,1670.1,576.2
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+9,R,3038.0,3302.0,264.0,2819.1,1481.3
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+11,R,3572.0,3790.0,218.0,2830.6,1579.6
+12,R,3866.0,4017.0,151.0,1885.2,788.6

data/scanpaths/P19/P19_A_b04_t07.csv

@@ -0,0 +1,27 @@
+fixation_index,eye,start_time_ms,end_time_ms,duration_ms,x_px,y_px
+1,L,220.0,354.0,134.0,1867.8,1397.1
+2,L,400.0,594.0,194.0,1982.1,820.9
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+13,L,3610.0,4017.0,407.0,1967.8,500.1
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+13,R,3610.0,4017.0,407.0,1901.6,509.7

data/scanpaths/P19/P19_A_b04_t12.csv

@@ -0,0 +1,20 @@
+fixation_index,eye,start_time_ms,end_time_ms,duration_ms,x_px,y_px
+1,L,225.0,577.0,352.0,2209.3,730.4
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+9,L,3321.0,4016.0,695.0,1965.8,857.2
+1,R,217.0,577.0,360.0,2130.7,756.2
+2,R,601.0,853.0,252.0,1935.2,727.9
+3,R,863.0,1123.0,260.0,1890.4,739.8
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+9,R,2871.0,3279.0,408.0,2152.2,757.1
+10,R,3319.0,4016.0,697.0,1894.3,886.6

data/scanpaths/P19/P19_A_b04_t13.csv

@@ -0,0 +1,23 @@
+fixation_index,eye,start_time_ms,end_time_ms,duration_ms,x_px,y_px
+1,L,215.0,285.0,70.0,1799.9,1069.8
+2,L,333.0,515.0,182.0,2140.0,1540.1
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+6,L,1243.0,1577.0,334.0,2298.7,1662.7
+7,L,1599.0,2667.0,1068.0,2111.6,1444.6
+8,L,2693.0,3081.0,388.0,2302.1,1417.2
+9,L,3133.0,3421.0,288.0,1735.0,1046.4
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data/scanpaths/P19/P19_A_b05_t16.csv

@@ -0,0 +1,26 @@
+fixation_index,eye,start_time_ms,end_time_ms,duration_ms,x_px,y_px
+1,L,226.0,258.0,32.0,2077.2,1159.5
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data/scanpaths/P19/P19_A_b08_t08.csv

@@ -0,0 +1,23 @@
+fixation_index,eye,start_time_ms,end_time_ms,duration_ms,x_px,y_px
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data/scanpaths/P19/P19_A_b09_t18.csv

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data/scanpaths/P19/P19_A_b09_t19.csv

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data/scanpaths/P19/P19_A_b09_t24.csv

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data/scanpaths/P19/P19_A_b09_t30.csv

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data/scanpaths/P19/P19_B_b02_t24.csv

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data/scanpaths/P19/P19_B_b02_t25.csv

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data/scanpaths/P19/P19_B_b03_t08.csv

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data/scanpaths/P19/P19_B_b03_t21.csv

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+8,L,2382.0,2636.0,254.0,1284.6,1617.0
+9,L,2898.0,3134.0,236.0,2224.2,1109.5
+10,L,3154.0,3492.0,338.0,2023.6,1079.4
+11,L,3654.0,4019.0,365.0,2060.9,955.8
+1,R,282.0,554.0,272.0,2510.3,1013.3
+2,R,608.0,856.0,248.0,2127.5,1115.0
+3,R,902.0,1080.0,178.0,1652.5,1202.4
+4,R,1132.0,1282.0,150.0,1241.7,1514.6
+5,R,1514.0,1888.0,374.0,1876.2,743.2
+6,R,1912.0,2102.0,190.0,1986.5,892.6
+7,R,2136.0,2320.0,184.0,2009.3,1136.4
+8,R,2380.0,2634.0,254.0,1226.9,1627.8
+9,R,2928.0,3132.0,204.0,2159.3,1088.1
+10,R,3154.0,3488.0,334.0,1956.1,1057.5
+11,R,3664.0,4019.0,355.0,1963.4,935.6

data/scanpaths/P19/P19_B_b07_t28.csv

@@ -0,0 +1,19 @@
+fixation_index,eye,start_time_ms,end_time_ms,duration_ms,x_px,y_px
+1,L,202.0,234.0,32.0,1436.6,707.4
+2,L,296.0,378.0,82.0,1941.2,1007.5
+3,L,432.0,506.0,74.0,2161.8,1402.7
+4,L,532.0,722.0,190.0,1965.3,1340.6
+5,L,746.0,1302.0,556.0,1807.6,1316.1
+6,L,1324.0,1538.0,214.0,1970.9,1253.5
+7,L,1564.0,2172.0,608.0,1784.5,1014.7
+8,L,2198.0,3438.0,1240.0,1971.5,1248.2
+9,L,3454.0,4017.0,563.0,1915.3,1273.2
+1,R,204.0,234.0,30.0,1387.1,690.9
+2,R,296.0,378.0,82.0,1887.5,996.5
+3,R,432.0,508.0,76.0,2110.7,1376.7
+4,R,532.0,722.0,190.0,1903.0,1317.4
+5,R,746.0,1300.0,554.0,1760.9,1306.1
+6,R,1324.0,1536.0,212.0,1916.9,1253.0
+7,R,1564.0,2172.0,608.0,1702.6,1015.0
+8,R,2200.0,3438.0,1238.0,1914.3,1221.6
+9,R,3456.0,4017.0,561.0,1860.3,1219.6

data/scanpaths/P19/P19_B_b07_t29.csv

@@ -0,0 +1,16 @@
+fixation_index,eye,start_time_ms,end_time_ms,duration_ms,x_px,y_px
+1,L,218.0,342.0,124.0,1981.7,897.1
+2,L,380.0,622.0,242.0,2076.4,1169.3
+3,L,672.0,950.0,278.0,2255.7,1606.8
+4,L,990.0,1300.0,310.0,2021.5,1000.7
+5,L,1320.0,1930.0,610.0,1860.5,841.3
+6,L,1988.0,2782.0,794.0,2140.2,1538.3
+7,L,2802.0,4018.0,1216.0,2059.1,890.1
+1,R,220.0,342.0,122.0,1900.7,859.1
+2,R,368.0,622.0,254.0,2003.5,1115.5
+3,R,674.0,950.0,276.0,2205.8,1555.4
+4,R,990.0,1302.0,312.0,1971.8,977.3
+5,R,1320.0,1656.0,336.0,1825.3,844.1
+6,R,1666.0,1930.0,264.0,1812.9,793.9
+7,R,1990.0,2780.0,790.0,2124.1,1507.0
+8,R,2802.0,4018.0,1216.0,2040.0,871.6

docs/acquisition_protocol.md

@@ -0,0 +1,114 @@
+# PolyCAT Acquisition Protocol
+
+## Equipment
+
+| Component | Specification |
+|-----------|--------------|
+| Eye tracker | EyeLink 1000+ (SR Research) |
+| Mounting | Head-mounted (desktop mount) |
+| Recording mode | Binocular |
+| Sampling rate | 500 Hz per eye (1000 Hz hardware, split binocular) |
+| Display | 27" 4K LCD monitor |
+| Resolution | 3840 x 2160 pixels |
+| Refresh rate | 144 Hz |
+| Viewing distance | 70 cm |
+| Background luminance | Gray (0.3 normalized) |
+
+## Display Geometry
+
+The pixels-per-degree (PPD) value is computed from the physical display properties:
+
+```
+Screen diagonal = 27" = 68.58 cm
+Aspect ratio = 16:9
+Screen width  = 68.58 * (16/sqrt(16^2 + 9^2)) = 59.77 cm
+Screen height = 68.58 * (9/sqrt(16^2 + 9^2))  = 33.62 cm
+Pixel pitch = 59.77 / 3840 = 0.01557 cm/px
+
+PPD = viewing_distance * tan(1 deg) / pixel_pitch
+    = 70 * tan(1 deg) / 0.01557
+    = 70 * 0.017455 / 0.01557
+    ≈ 78.5 px/deg
+```
+
+Note: This gives the conversion for uniform pixel density. Since pixels are square on modern displays, horizontal and vertical PPD are equal.
+
+## Calibration
+
+- **Calibration type:** HV13 (13-point horizontal-vertical)
+- **Validation:** Performed before each block
+- **Drift check:** Before each trial (fixation on cue position)
+- **Drift retry limit:** 1 attempt, max 10 seconds
+- **Re-calibration:** Triggered if drift check fails
+
+## Saccade Detection Parameters
+
+The EyeLink built-in saccade detection was configured with:
+
+| Parameter | Value |
+|-----------|-------|
+| Velocity threshold | 30 deg/s |
+| Acceleration threshold | 8000 deg/s^2 |
+
+Fixations are defined as periods between saccades (EyeLink cognitive configuration).
+
+## Experiment Design
+
+### Structure
+
+Each participant completed 2 parts (A and B), typically on separate days.
+
+Each part consists of:
+- 9 mini-blocks of 39 trials each = 351 trials
+- 36 image trials + 3 empty trials per block
+- 1 memory probe after each block
+
+### Trial Sequence
+
+1. **Drift check** — Participant fixates on a cue at one of 9 grid positions (3x3 grid at 960/1920/2880 x 540/1080/1620 px). Max 10 s, 1 retry.
+2. **Stimulus presentation** — Image displayed through polygon aperture for 4.0 seconds.
+3. **Inter-trial interval** — Blank screen for 0.5 seconds.
+
+### Stimuli
+
+- **Images:** 600 images from the CAT2000 dataset across 6 categories:
+  - Fractal, Object, OutdoorNatural, Random, Satelite, Sketch
+  - 100 images per category (102 available, 100 used per part assignment)
+- **Polygon apertures:** 27 shapes with systematically varied geometric properties
+  - See `data/manifests/polygon_geometry.csv` for center coordinates
+  - See `data/stimuli/polygons/*.json` for vertex definitions
+- **Aperture scale factor:** 1987 (applied uniformly)
+
+### Memory Task
+
+After each block, a memory probe image was shown for 3 seconds:
+- Participants judged whether the image was "old" (seen in that block) or "new"
+- Response time and accuracy recorded
+- Purpose: ensure attentive viewing
+
+### Fixation Cue Positions
+
+A 3x3 grid of possible fixation cue locations:
+
+| Grid ID | X (px) | Y (px) |
+|---------|--------|--------|
+| grid_11 | 960    | 540    |
+| grid_12 | 1920   | 540    |
+| grid_13 | 2880   | 540    |
+| grid_21 | 960    | 1080   |
+| grid_22 | 1920   | 1080   |
+| grid_23 | 2880   | 1080   |
+| grid_31 | 960    | 1620   |
+| grid_32 | 1920   | 1620   |
+| grid_33 | 2880   | 1620   |
+
+## Data Collection Timeline
+
+Data was collected between January 15, 2026 and February 25, 2026. Most participants completed Part A and Part B on separate days.
+
+## Split Sessions
+
+Some participants required multiple session folders to complete a single part (due to experiment restarts or technical issues). In these cases:
+- Trials are concatenated across sessions using `trial_uid` as the unique key
+- For overlapping trials (recorded in multiple sessions), the later session's data takes precedence
+- The `sessions.csv` metadata file labels these as `split_primary`

docs/data_dictionary.md

@@ -0,0 +1,198 @@
+# PolyCAT Data Dictionary
+
+This document describes every field in the shared data files.
+
+---
+
+## `data/metadata/participants.csv`
+
+One row per recorded participant.
+
+| Field | Type | Units | Description |
+|-------|------|-------|-------------|
+| `participant_id` | string | — | Participant identifier (e.g., `P01`) |
+| `age` | int | years | Age at time of recording |
+| `gender` | string | — | Self-reported gender (`Male` / `Female`) |
+| `dominant_eye` | string | — | Dominant eye (`left` / `right`) |
+| `hardware_sampling_rate_hz` | int | Hz | EyeLink hardware sampling rate (1000 Hz for all participants). Effective per-eye rate depends on recording mode: 1000 Hz for monocular sessions, 500 Hz for binocular sessions. See `recording_mode` in `sessions.csv` for per-session details. |
+| `has_part_a` | bool | — | Whether Part A data exists |
+| `has_part_b` | bool | — | Whether Part B data exists |
+| `n_trials_total` | int | — | Total trials across both parts |
+| `n_image_trials` | int | — | Number of image (non-empty) trials |
+| `n_empty_trials` | int | — | Number of empty-aperture trials |
+| `included_in_release` | bool | — | True if participant is included in the released dataset |
+| `exclusion_reason` | string | — | Reason for exclusion (empty if included) |
+
+---
+
+## `data/metadata/sessions.csv`
+
+One row per session folder in the raw data.
+
+| Field | Type | Units | Description |
+|-------|------|-------|-------------|
+| `participant_id` | string | — | Participant identifier |
+| `part` | string | — | Experiment part (`A` or `B`) |
+| `session_id` | string | — | Session folder name (e.g., `session_20260118_100426`) |
+| `session_label` | string | — | `primary`, `split_primary`, or `extra` |
+| `session_timestamp` | ISO 8601 | — | Parsed from session folder name |
+| `n_trials` | int | — | Number of data rows in that session's `trials.csv` |
+| `included_in_release` | bool | — | True if session contributes to the released dataset |
+| `exclusion_reason` | string | — | Reason for exclusion (empty if included) |
+| `recording_mode` | string | — | Eye recording mode: `binocular` (both eyes, 500 Hz effective per eye), `monocular_R` (right eye only, 1000 Hz), or `no_gaze_file` (excluded sessions) |
+
+---
+
+## `data/metadata/trials.csv`
+
+One row per trial across all included participants and parts. This is the **source-of-truth trial table**.
+
+| Field | Type | Units | Description |
+|-------|------|-------|-------------|
+| `participant_id` | string | — | Participant identifier |
+| `part` | string | — | `A` or `B` |
+| `session_id` | string | — | Session folder this trial came from |
+| `trial_uid` | string | — | Unique trial ID: `{pid}_{part}_b{block:02d}_t{trial:02d}` |
+| `mini_block` | int | — | Block number (1–9) |
+| `trial_in_block` | int | — | Trial number within block (1–39) |
+| `trial_type` | string | — | `image` or `empty` |
+| `image_id` | string | — | CAT2000 image identifier (e.g., `A_Random_169`) |
+| `image_path` | string | — | Relative path to source image |
+| `category` | string | — | CAT2000 category (Fractal, Object, OutdoorNatural, Random, Satelite, Sketch) |
+| `polygon_id` | string | — | Polygon identifier (e.g., `polygon_25`) |
+| `polygon_case` | string | — | Polygon group (`reference`, `convexity_varied`, or `irregular`) |
+| `polygon_json_path` | string | — | Relative path to polygon JSON definition |
+| `aperture_scale_factor` | int | — | Polygon scaling factor applied during experiment |
+| `cue_pos_id` | string | — | Fixation cue grid position (e.g., `grid_11`) |
+| `cue_x_px`, `cue_y_px` | float | pixels | Cue position in screen coordinates |
+| `cue_x_deg`, `cue_y_deg` | float | degrees | Cue position in degrees from screen center |
+| `stimulus_duration_s` | float | seconds | Stimulus presentation time (4.0) |
+| `iti_s` | float | seconds | Inter-trial interval (0.5) |
+| `center_screen_x_px`, `center_screen_y_px` | float | pixels | Screen center (1920, 1080) |
+| `center_screen_x_deg`, `center_screen_y_deg` | float | degrees | Screen center in degrees (0, 0) |
+| `fixation_achieved` | bool | — | Whether drift-check fixation was achieved |
+| `fixation_attempts` | int | — | Number of drift-check attempts |
+| `fixation_total_time_s` | float | seconds | Total time spent on drift check |
+| `ts_trial_start` ... `ts_trial_end` | float | seconds | Key event timestamps (experiment clock) |
+| `aborted` | bool | — | Whether the trial was aborted |
+
+---
+
+## `data/fixations/fixations_all.csv`
+
+One row per fixation event. Fixations detected by EyeLink's built-in algorithm (velocity threshold 30 deg/s, acceleration 8000 deg/s²).
+
+| Field | Type | Units | Description |
+|-------|------|-------|-------------|
+| `participant_id` | string | — | Participant identifier |
+| `part` | string | — | `A` or `B` |
+| `session_id` | string | — | Session folder |
+| `trial_uid` | string | — | Unique trial ID |
+| `fixation_index` | int | — | Fixation order within trial and eye (1-indexed) |
+| `eye` | string | — | `L` (left) or `R` (right) |
+| `image_id` | string | — | CAT2000 image identifier |
+| `category` | string | — | CAT2000 category |
+| `polygon_id` | string | — | Polygon identifier |
+| `start_time_ms` | float | ms | Fixation start relative to stimulus onset |
+| `end_time_ms` | float | ms | Fixation end relative to stimulus onset |
+| `duration_ms` | float | ms | Fixation duration |
+| `x_px`, `y_px` | float | pixels | Average fixation position (screen coordinates, top-left origin) |
+| `x_deg`, `y_deg` | float | degrees | Fixation position relative to screen center |
+| `pupil_size` | float | area | Mean pupil size (EyeLink arbitrary units) |
+
+---
+
+## `data/gaze/*.tsv`
+
+Sample-level gaze data. One TSV file per session. Tab-separated.
+
+| Field | Type | Units | Description |
+|-------|------|-------|-------------|
+| `participant_id` | string | — | Participant identifier |
+| `part` | string | — | `A` or `B` |
+| `session_id` | string | — | Session folder |
+| `trial_uid_raw` | string | — | Raw trial UID from EDF (e.g., `B_MB01_T001`) |
+| `timestamp_rel_ms` | float | ms | Time relative to stimulus onset |
+| `eye` | string | — | `L` or `R` |
+| `x_px`, `y_px` | float | pixels | Gaze position (screen coordinates) |
+| `pupil_size` | float | area | Pupil size (EyeLink units) |
+| `validity` | int | — | 0 = valid gaze, 1 = missing/out-of-range |
+
+---
+
+## `data/metadata/quality_metrics.csv`
+
+One row per participant. Aggregated quality statistics.
+
+| Field | Type | Units | Description |
+|-------|------|-------|-------------|
+| `participant_id` | string | — | Participant identifier |
+| `total_trials` | int | — | Total trials for this participant |
+| `valid_trials` | int | — | Trials with tracking ratio >= 0.8 |
+| `excluded_trials` | int | — | Trials below tracking threshold |
+| `tracking_ratio_mean` | float | — | Mean tracking ratio (0–1) |
+| `tracking_ratio_std` | float | — | Std of per-trial tracking ratio |
+| `calibration_error_deg_mean` | float | degrees | Mean validation error (if available) |
+| `fixations_per_trial_mean` | float | — | Mean fixation count per trial |
+| `fixations_per_trial_std` | float | — | Std of fixation count per trial |
+| `fixation_duration_mean_ms` | float | ms | Mean fixation duration |
+| `fixation_duration_std_ms` | float | ms | Std of fixation duration |
+| `data_loss_percent` | float | % | Percentage of trials with no fixations |
+
+---
+
+## `data/manifests/polygon_geometry.csv`
+
+One row per polygon shape (27 total). Canonical center coordinates.
+
+| Field | Type | Units | Description |
+|-------|------|-------|-------------|
+| `polygon_id` | string | — | Polygon identifier |
+| `polygon_case` | string | — | Polygon condition type |
+| `json_filename` | string | — | Filename of polygon JSON definition |
+
+---
+
+## `data/manifests/stimulus_manifest.csv`
+
+One row per trial slot (702 total: 351 per part). Defines the fixed stimulus-polygon-cue assignment shared by all participants.
+
+| Field | Type | Units | Description |
+|-------|------|-------|-------------|
+| `aperture_scale_factor` | int | — | Polygon scaling factor |
+| `category` | string | — | CAT2000 category |
+| `cue_pos_id` | string | — | Fixation cue grid position |
+| `cue_x_px`, `cue_y_px` | float | pixels | Cue position |
+| `image_id` | string | — | Image identifier |
+| `image_path` | string | — | Path to image file |
+| `mini_block` | int | — | Block number (1–9) |
+| `part` | string | — | `A` or `B` |
+| `polygon_case`, `polygon_id` | string | — | Polygon condition and ID |
+| `polygon_json_path` | string | — | Path to polygon JSON |
+| `stimulus_duration_s` | float | seconds | Presentation duration |
+| `trial_in_block` | int | — | Trial position within block |
+| `trial_type` | string | — | `image` or `empty` |
+| `trial_uid` | string | — | Trial identifier |
+
+---
+
+## Coordinate Systems
+
+**Screen coordinates:** Origin at top-left corner of the 3840x2160 display. X increases rightward, Y increases downward.
+
+**Degree coordinates:** Origin at screen center (1920, 1080 px). Computed using the display geometry:
+- 27" diagonal, 3840x2160 resolution, 70 cm viewing distance
+- Pixels-per-degree: ~78.5 (computed from `ppd = distance_cm * tan(1 deg) / cm_per_px`; see `docs/acquisition_protocol.md` for full derivation)
+
+**Time reference:**
+- `timestamp_rel_ms` and `start_time_ms` / `end_time_ms`: relative to stimulus onset (ms)
+- `ts_*` fields in trials.csv: experiment clock (seconds from session start)
+
+---
+
+## Known Data Issues
+
+| Issue | Scope | Details |
+|-------|-------|---------|
+| P05 missing trials | Part B only | P05 completed only 317 of 351 Part B trials (34 missing from block 9) due to a session interruption. Total: 668 trials instead of 702. |
+| Aborted trials | 48 trials across 9 participants | Trials that were aborted before stimulus presentation. Marked with `aborted=True`; all timestamp and stimulus fields are NaN. Participants affected: P05 (7), P07 (1), P10 (2), P13 (9), P18 (9), P20 (2), P24 (13), P25 (1), P28 (4). |

docs/file_formats.md

@@ -0,0 +1,103 @@
+# PolyCAT File Formats
+
+## Gaze TSV (`data/gaze/*.tsv`)
+
+Tab-separated sample-level gaze data. One file per session, named `{participant_id}_part{part}_{session_id}.tsv`.
+
+- **Sampling rate:** 500 Hz per eye (binocular recording; hardware rate 1000 Hz split between eyes)
+- **Scope:** Only samples during stimulus presentation (4.0 s per trial) are included
+- **Eyes:** One row per sample per eye (binocular sessions produce 2 rows per timestamp)
+
+### Validity codes
+
+| Code | Meaning |
+|------|---------|
+| 0 | Valid gaze sample (within screen bounds 0-3840 x 0-2160) |
+| 1 | Missing or out-of-range (blink, track loss, or coordinates outside screen) |
+
+---
+
+## Fixations CSV (`data/fixations/fixations_all.csv`)
+
+Comma-separated fixation events. Fixations detected by EyeLink's built-in algorithm with:
+- Saccade velocity threshold: 30 deg/s
+- Saccade acceleration threshold: 8000 deg/s^2
+
+Only fixations starting during stimulus presentation are included. Fixation `end_time_ms` is clamped to stimulus offset.
+
+### Per-participant files
+
+Individual files at `data/fixations/per_participant/{participant_id}_fixations.csv` with the same schema.
+
+---
+
+## Saliency Maps
+
+### NumPy format (`data/saliency_maps/by_polygon/{polygon_id}/{image_id}_fixmap.npy`)
+
+- 2D float32 array at stimulus resolution (3840 x 2160)
+- Gaussian kernel sigma = 1.0 degrees visual angle (~78.5 pixels)
+- Values outside polygon aperture set to 0
+- Normalized to sum to 1 (probability distribution)
+- Only generated for polygon-image combinations with >= 3 observers
+
+### PNG format (`data/saliency_maps/by_polygon/{polygon_id}/{image_id}_fixmap.png`)
+
+- Grayscale visualization of the saliency map
+- 8-bit, linearly scaled from 0 (min) to 255 (max)
+
+---
+
+## Scanpaths (`data/scanpaths/{participant_id}/{trial_uid}.csv`)
+
+One CSV per trial, containing the ordered fixation sequence.
+
+Columns: `fixation_index`, `start_time_ms`, `end_time_ms`, `x_px`, `y_px`, `duration_ms`
+
+---
+
+## MATLAB Export (`data/fixations/fixations.mat`)
+
+CAT2000-compatible MATLAB struct:
+
+```matlab
+fixations.x       % double vector: x positions (pixels)
+fixations.y       % double vector: y positions (pixels)
+fixations.dur     % double vector: durations (ms)
+fixations.subj    % cell array: participant IDs
+fixations.img     % cell array: image IDs
+fixations.polygon % cell array: polygon IDs (PolyCAT extension)
+```
+
+---
+
+## Coordinate System
+
+**Screen pixels:** 3840 x 2160, origin top-left. `x_px` increases rightward, `y_px` increases downward.
+
+**Degrees:** Relative to screen center (1920, 1080 px).
+
+Conversion formula:
+```
+x_deg = (x_px - 1920) / ppd
+y_deg = (y_px - 1080) / ppd
+```
+
+Where `ppd` (pixels per degree) is computed from:
+- Screen diagonal: 27 inches = 68.58 cm
+- Resolution: 3840 x 2160 px
+- Screen width: 59.77 cm, height: 33.62 cm (from diagonal + 16:9 aspect ratio)
+- Viewing distance: 70 cm
+- Pixel pitch = screen_width / horizontal_pixels = 59.77 cm / 3840 ≈ 0.01557 cm/px
+- 1° at 70 cm = 70 × tan(1°) ≈ 1.2228 cm
+- `ppd = 1.2228 / 0.01557` ≈ **78.5 px/deg**
+
+---
+
+## Time Reference
+
+| Context | Reference point | Units |
+|---------|----------------|-------|
+| `timestamp_rel_ms` (gaze TSV) | Stimulus onset for that trial | milliseconds |
+| `start_time_ms` / `end_time_ms` (fixations) | Stimulus onset for that trial | milliseconds |
+| `ts_*` fields (trials.csv) | Session start (experiment clock) | seconds |

manifests/polygon_geometry.csv

@@ -0,0 +1,28 @@
+polygon_id,polygon_case,json_filename
+polygon_04,convexity_varied,polygon_04.json
+polygon_05,convexity_varied,polygon_05.json
+polygon_06,convexity_varied,polygon_06.json
+polygon_01,reference,polygon_01.json
+polygon_02,reference,polygon_02.json
+polygon_03,reference,polygon_03.json
+polygon_25,irregular,polygon_25.json
+polygon_19,irregular,polygon_19.json
+polygon_21,irregular,polygon_21.json
+polygon_15,irregular,polygon_15.json
+polygon_23,irregular,polygon_23.json
+polygon_24,irregular,polygon_24.json
+polygon_26,irregular,polygon_26.json
+polygon_10,irregular,polygon_10.json
+polygon_08,irregular,polygon_08.json
+polygon_11,irregular,polygon_11.json
+polygon_18,irregular,polygon_18.json
+polygon_20,irregular,polygon_20.json
+polygon_16,irregular,polygon_16.json
+polygon_12,irregular,polygon_12.json
+polygon_09,irregular,polygon_09.json
+polygon_13,irregular,polygon_13.json
+polygon_22,irregular,polygon_22.json
+polygon_17,irregular,polygon_17.json
+polygon_14,irregular,polygon_14.json
+polygon_07,irregular,polygon_07.json
+polygon_27,irregular,polygon_27.json