Datasets:
license: cc-by-4.0
language:
- en
task_categories:
- visual-question-answering
pretty_name: Perception, Not Reasoning, Limits Visual Theory of Mind
size_categories:
- 1K<n<10K
tags:
- theory-of-mind
- visual-reasoning
- synthetic
- neurips-2026
configs:
- config_name: default
data_files:
- split: test
path: images/**
Perception, Not Reasoning, Limits Visual Theory of Mind
Accepted to NeurIPS (E&D) 2026. Oral presentation at the BabyVLM Workshop, NeurIPS 2026.
Mohamed Rayan Barhdadi, Syed Talal Wasim, Juergen Gall, Erchin Serpedin, and Hasan Kurban.
This release contains the paper's original abstract 2D stimuli, annotations, and saved evaluation results. It includes 1,000 probes across 20 templates, with 50 variants per template. All probes belong to the test split.
Dataset
| Stage | Task | Templates | Probes |
|---|---|---|---|
| 1 | Object tracking | 6 | 300 |
| 2 | Goal attribution | 4 | 200 |
| 3 | False belief | 10 | 500 |
Load
from datasets import load_dataset
data = load_dataset("bmrayan/perception-not-reasoning", split="test")
print(data[0]["question"], data[0]["answer"])
Files
| Path | Contents |
|---|---|
images/ |
PNG sequences and ImageFolder annotations |
probes.json |
Prompts, answers, seeds, scene metadata, and scaffolds |
metadata.csv |
Flat annotations |
images.zip |
Image archive referenced by Croissant |
croissant.json |
Dataset schema, download URLs, and responsible-use metadata |
checksums.json |
SHA-256 file hashes |
results/reference/ |
Saved paper responses and scores |
results/followup/ |
Additional control results |
results/provenance.json |
Original result-file hashes |
The default loader reads the image dataset. Result bundles are separate files.
Croissant metadata:
The naturalistic metadata describes saved evaluation outcomes, with source-file and row references. It does not redistribute the original videos or simulator assets. Obtain the original benchmarks from MuMA-ToM and MindPower.
Fields and construction
probe_id, stage, template, and panel_count identify each task. question, answer, and answer_kind specify the prompt and grading key. scaffold_percept, scaffold_full, and text_narration contain the text conditions.
The deterministic generator is evaluation/abstract2d/evaluate.py in the code repository. Variants are indexed 0-49. Seeds follow 10000 * (template_index + 1) + variant_index. Questions, answers, and descriptions derive from the same scene state.
The 1,000 images match the original experiment files byte for byte. Prompts and answer keys match all 35,000 saved seven-model phase-1 rows. Packaging changes image paths only.
Use and limitations
The dataset supports comparisons of input conditions in controlled visual theory-of-mind tasks. It is synthetic, English-only, and template-based. Repeated layouts and answer conventions can support shortcuts; template variants are not independent concepts.
The permanence and color-change Stage 1 prompts explicitly include their expected answers. Those items do not independently measure visual understanding. Full scaffolds contain more information than perceptual scaffolds. Report conditions, model panels, and scoring rules separately.
Scenes contain fictional geometric characters. No participant data or personal information were collected. Simplified scenes and English-only prompts limit population and cultural coverage. The scores do not establish general social competence and are not validated for clinical assessment or decisions about people.
License
Original abstract images and annotations: CC BY 4.0, as specified in LICENSE. Code: MIT. Naturalistic source media are not included. MuMA-ToM benchmark text retains MIT terms; MindPower reference text retains Apache-2.0 terms. See third-party attribution and license notices. Simulator software and assets are not included.
Citation
@inproceedings{barhdadi2026perception,
title = {Perception, Not Reasoning, Limits Visual Theory of Mind},
author = {Barhdadi, Mohamed Rayan and Wasim, Syed Talal and Gall, Juergen
and Serpedin, Erchin and Kurban, Hasan},
booktitle = {Advances in Neural Information Processing Systems},
year = {2026}
}