--- license: cc0-1.0 tags: - cognitive-science - benchmark - machine-learning - executive-functions - wisconsin-card-sorting-test - rule-shifting - cognitive-flexibility - set-shifting task_categories: - question-answering language: - en size_categories: - n<1K configs: - config_name: default data_files: - split: train path: ruleshift_train.jsonl - split: test path: ruleshift_test.jsonl dataset_info: features: - name: scenario_id dtype: string - name: task dtype: int32 - name: task_name dtype: string - name: domain dtype: string - name: phase1_rule dtype: string - name: phase2_rule dtype: string - name: shift_type dtype: string - name: cv_split dtype: string - name: difficulty dtype: string - name: created_at dtype: string - name: human_baseline_RAL dtype: float32 - name: content_hash dtype: string - name: rule_sequence sequence: string - name: distractor_dim dtype: string - name: n_items_per_trial dtype: int32 - name: generation_seed dtype: int32 - name: notes dtype: string - name: total_trials dtype: int32 - name: phase1_answer_entropy dtype: int32 - name: phase2_answer_entropy dtype: int32 - name: estimated_difficulty_score dtype: float32 - name: phase1_system_prompt dtype: string - name: target_category dtype: string - name: target_function dtype: string - name: target_color dtype: string - name: target_shape dtype: string - name: phase1_trials sequence: - name: trial_num dtype: int32 - name: rule dtype: string - name: prompt dtype: string - name: item_description sequence: string - name: correct_answer dtype: string - name: phase2_trials sequence: - name: trial_num dtype: int32 - name: rule dtype: string - name: prompt dtype: string - name: item_description sequence: string - name: correct_answer dtype: string - name: phase3_trials sequence: - name: trial_num dtype: int32 - name: rule dtype: string - name: prompt dtype: string - name: item_description sequence: string - name: correct_answer dtype: string splits: - name: train num_examples: 123 - name: test num_examples: 27 citation: | @dataset{Barma2026RuleShift, title={RuleShift: A Zero-Feedback Cognitive Set-Shifting Benchmark Dataset}, author={Barma, Niloy Deb}, year={2026}, url={https://huggingface.co/datasets/niloydebbarma/ruleshift-dataset}, doi={10.57967/hf/8246} } --- # RuleShift: Zero-Feedback Set-Shifting ## Overview The **RuleShift Dataset** is a procedurally generated benchmark comprising **150 classification scenarios** across three task domains designed to measure cognitive set-shifting abilities in language models and other AI systems. Each scenario tests a model's capacity to **self-detect a silent rule change** through internal consistency monitoring alone, without corrective feedback. This dataset directly operationalizes the Wisconsin Card Sorting Test (WCST) — a foundational cognitive psychology instrument — into machine learning benchmark tasks. ## Dataset Description ### Core Statistics - **Total Scenarios**: 150 (50 per task) - **Total Trials**: 2,700 - **Phase Structure**: - Phase 1 (Rule Learning): 5 trials - Phase 2 (Silent Rule Change): up to 10 trials - Phase 3 (Confirmation): 3 trials - **Train/Test Split**: 123/27 (82/18% — stratified by task and difficulty) - **Difficulty Tiers**: Easy (17), Medium (17), Hard (16) per task ### Three Task Domains #### Task 1: Perceptual (Shape → Color) - **Domain**: Visual object descriptions - **Phase 1 Rule**: Classify by shape (dominant visual property) - **Phase 2 Rule**: Classify by color (silent rule shift) - **Distractor Dimension**: Texture (varies but never correct) - **Human Baseline (RAL)**: 1.8 trials #### Task 2: Semantic (Category → Function) - **Domain**: Word classification - **Phase 1 Rule**: Classify by semantic category - **Phase 2 Rule**: Classify by functional use (silent rule shift) - **Distractor Dimension**: Etymology/origin (varies but never correct) - **Human Baseline (RAL)**: 2.1 trials #### Task 3: Procedural (Alphabetical → Frequency) - **Domain**: List operations - **Phase 1 Rule**: Sort alphabetically (first word of list) - **Phase 2 Rule**: Sort by word frequency (most common word) - **Distractor Dimension**: Rarity (varies but never correct) - **Human Baseline (RAL)**: 1.5 trials ## Key Features ### Rule Adaptation Latency (RAL) The primary metric: **number of incorrect trials after a silent rule change before two consecutive correct responses under the new rule**. **Range**: 0 (immediate detection) to 10 (rule change never detected, DNF) ### Distractor Dimensions Each task includes a **third dimension** (texture, origin, rarity) that varies across trials but is never the correct answer. This prevents solving by elimination and requires genuine cross-trial rule tracking. ### Zero-Feedback Design Models receive **no corrective feedback** at any point. Success requires: 1. Learning the Phase 1 rule from examples alone 2. Detecting the silent Phase 2 rule change through internal consistency failure 3. Adapting to the new rule without external signals ### Contamination Prevention - Vocabulary drawn from **low-frequency registers** not common in standard pre-training corpora - Rare color/shape names, specialised technical nouns - **3-level deduplication** (scenario ID, content hash, answer sequence) - Fixed random seed (42) for reproducibility ## Data Schema **Nested JSONL format** — each line is a complete scenario with embedded phase/trial structure: ```json { "scenario_id": "T1_S001", "task": 1, "task_name": "perceptual", "domain": "visual_objects", "phase1_rule": "shape", "phase2_rule": "color", "shift_type": "perceptual_shift", "cv_split": "A", "difficulty": "medium", "created_at": "2026-04-03", "human_baseline_RAL": 1.8, "content_hash": "f02d0851305d", "phase1_trials": [ { "trial_num": 1, "rule": "shape", "prompt": "Items: coral rhombus, crimson rhombus, amber tetrahedron, teal cuboid. Which one appears most?", "item_description": ["coral rhombus", "crimson rhombus", "amber tetrahedron", "teal cuboid"], "correct_answer": "rhombus" }, ... ], "phase2_trials": [ { "trial_num": 6, "rule": "color", "prompt": "Items: jade prism, ivory octagon, jade tetrahedron, jade ellipse. Which one appears most?", "item_description": ["jade prism", "ivory octagon", "jade tetrahedron", "jade ellipse"], "correct_answer": "jade" }, ... ], "phase3_trials": [...] } ``` ### Top-Level Scenario Fields - **scenario_id**: Unique identifier (e.g., "T1_S001" = Task 1, Scenario 1) - **task**: 1 (Perceptual), 2 (Semantic), or 3 (Procedural) - **task_name**: "perceptual", "semantic", or "procedural" - **domain**: "visual_objects", "word_classification", or "list_operations" - **phase1_rule**: Rule active in Phase 1 - **phase2_rule**: Rule active in Phase 2 (silent change) - **shift_type**: Type of rule shift (e.g., "perceptual_shift", "semantic_shift") - **cv_split**: "A" or "B" for cross-validation (separate from train/test) - **difficulty**: "easy", "medium", or "hard" - **created_at**: Generation date - **human_baseline_RAL**: Expected human Rule Adaptation Latency - **content_hash**: Unique hash identifying scenario content ### Trial Fields (within phase1_trials, phase2_trials, phase3_trials) - **trial_num**: Absolute trial number across all phases (1–18) - **rule**: Correct sorting rule for this trial - **prompt**: Human-readable question presented to model - **item_description**: List of item descriptions (length = n_items_per_trial) - **correct_answer**: Expected response (single word) ## Usage ### Load from Hugging Face ```python from datasets import load_dataset import json # Load full dataset dataset = load_dataset("niloydebbarma/ruleshift-dataset") # Access a single scenario scenario = dataset["train"][0] # Access Phase 1 trials for first scenario phase1 = scenario["phase1_trials"] print(f"Phase 1: {len(phase1)} trials") # Access first trial first_trial = phase1[0] print(f"Trial prompt: {first_trial['prompt']}") print(f"Correct answer: {first_trial['correct_answer']}") # Filter by task task1_only = dataset["train"].filter(lambda x: x["task"] == 1) # Filter by difficulty hard_only = dataset["train"].filter(lambda x: x["difficulty"] == "hard") ``` ### Compute RAL (Rule Adaptation Latency) ```python def compute_ral(scenario): """ Compute RAL: trials to two consecutive correct responses in Phase 2 after the silent rule change. Returns: RAL (int): Number of incorrect predictions in Phase 2 before two consecutive correct responses. Max value 10 if rule never detected (DNF). """ phase2_trials = scenario["phase2_trials"] correct_count = 0 for trial in phase2_trials: # In practice: compare model response to correct_answer # For now, assume comparison logic here is_correct = (model_response == trial["correct_answer"]) if is_correct: correct_count += 1 if correct_count >= 2: return trial["trial_num"] - phase2_trials[0]["trial_num"] else: correct_count = 0 return 10 # DNF (Did Not Find rule) # Usage: from datasets import load_dataset dataset = load_dataset("niloydebbarma/ruleshift-dataset") scenario = dataset["train"][0] ral = compute_ral(scenario) print(f"Rule Adaptation Latency: {ral} trials") ``` ## Difficulty Calibration Difficulty is assigned based on expected adaptation latency: | Difficulty | Characteristics | |---|---| | **Easy** | Clear Phase 1 rule, obvious Phase 2 shift, high contrast features | | **Medium** | Moderate Phase 1 ambiguity, subtle Phase 2 shift, mixed feature clarity | | **Hard** | Ambiguous Phase 1, minimal Phase 2 signal, confusable features across dimensions | ## Baseline Performance **Human Performance** (from Wisconsin Card Sorting Test literature): - Task 1 (Perceptual): RAL = 1.8 - Task 2 (Semantic): RAL = 2.1 - Task 3 (Procedural): RAL = 1.5 Human baseline represents educated adult performance without prior training. ## Generation Method - **Procedural Python generation** with fixed random seed (42) - **No neural models used** in dataset creation - **Fully reproducible** — same seed produces identical dataset - **RAM usage**: < 100 MB - **GPU**: Not required ## Cross-Validation Structure **Two-split cross-validation (CV A/B)**: - 75 scenarios in split A → train on B, test on A - 75 scenarios in split B → train on A, test on B *Note: CV A/B is separate from train/test split. CV enables cross-validation within the training partition; train/test is for final evaluation.* ## Citation **If you use this dataset, please cite:** ```bibtex @dataset{Barma2026RuleShift, title={RuleShift: A Zero-Feedback Cognitive Set-Shifting Benchmark Dataset}, author={Barma, Niloy Deb}, year={2026}, url={https://huggingface.co/datasets/niloydebbarma/ruleshift-dataset}, doi={10.57967/hf/8246} } ``` **Foundational references:** ```bibtex @article{Berg1948, author = {Esta A. Berg}, title = {A Simple Objective Technique for Measuring Flexibility in Thinking}, journal = {The Journal of General Psychology}, volume = {39}, number = {1}, pages = {15--22}, year = {1948}, publisher = {Routledge}, doi = {10.1080/00221309.1948.9918159} } @inproceedings{Heaton1993WisconsinCS, title={Wisconsin Card Sorting Test Manual -- Revised and Expanded}, author={Robert K. Heaton and C. Chelune and J. D. Talley and Gary G. Kay and Glenn Curtiss}, year={1993}, publisher={Psychological Assessment Resources}, url={https://api.semanticscholar.org/CorpusID:65194761} } ``` ## Dataset Versions | Version | Date | Changes | |---|---|---| | 1.0.0 | 2026-04-03 | Initial release: 150 scenarios, 3 tasks, 2,700 trials | ## License ### Data License **CC0 1.0 Universal (Public Domain)** — No rights reserved. Reusable without restriction. This dataset is released into the public domain. You are free to use, modify, and distribute it for any purpose, commercial or non-commercial, without seeking permission. ### Code License **Apache License 2.0** The code in this repository is licensed under the Apache License, Version 2.0. You may use, modify, and distribute the code, provided that you include the required license notice and comply with the terms of the license. ## Repository **Kaggle Dataset**: [niloydebbarma/ruleshift-dataset](https://www.kaggle.com/datasets/niloydebbarma/ruleshift-dataset) **Generation Code**: [RuleShift Dataset Generation Notebook](https://www.kaggle.com/code/niloydebbarma/ruleshift-dataset-generation-notebook) ## Contact For questions, issues, or feedback: - **Author**: Niloy Deb Barma - Please open an **issue** in this repository for bug reports, suggestions, or discussions. --- *RuleShift is designed as a benchmark for measuring progress toward AGI in executive function capabilities, specifically cognitive flexibility measured through silent rule adaptation tasks.*