README.md

---
pretty_name: Mental Spaces Corpus
language:
- en
license: mit
task_categories:
- text-generation
- text-classification
tags:
- mental-spaces
- counterfactual-reasoning
- belief-tracking
- mechanistic-interpretability
- minimal-pairs
- text
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train.jsonl
  - split: validation
    path: data/validation.jsonl
  - split: test
    path: data/test.jsonl
---

# Mental Spaces Corpus

Version: 0.1.0

The Mental Spaces Corpus is a controlled suite of natural-language stimuli for testing
whether language models keep base-space and alternative-space discourse targets
separate. It is designed for probing, causal interventions, and behavioral readouts in
mental-space constructions such as counterfactuals, belief contexts, and depictive
spaces, including nested belief and nested depictive spaces.

This release is a stimulus suite for controlled experiments, not a broad natural-language
benchmark. It provides confound control: items expose explicit base-space and
alternative-space target values, matched readout prompts, and annotations for the space
builder and control type.

Each construction is realized across several surface *frames* (distinct templates, tracked
by `template_id`) so that results do not rest on a single fixed sentence. Counterfactual
frames vary the space builder itself (painting / dream / movie / story / hypothetical);
every other construction holds the space role constant and varies only the wording.

## Dataset Structure

Files:

- `data/train.jsonl`
- `data/validation.jsonl`
- `data/test.jsonl`
- `schema.json`
- `summary.json`
- `VALIDATION.md`
- `CITATION.cff`
- `LICENSE`

Each JSONL row is one readout prompt. A single underlying scenario can yield multiple
rows, for example a reality readout and a belief readout.

Core fields:

- `context`: the full setup text.
- `prompt`: the completion prompt ending immediately before the expected answer.
- `answer`: the expected color/value continuation.
- `scenario_id`: stable ID shared by all readout rows from one scenario.
- `construction`: the construction family, such as `belief_reality` or
  `counterfactual_or_alternative_space`.
- `answer_space`: the discourse space queried by the prompt.
- `base_value` and `alternative_value`: the competing target values when present.
- `value_space_map`: all target values keyed by space, or by entity-and-space for
  multi-entity controls.
- `template_id`: stable template/frame identifier for frame-sensitive analyses.
- `answer_kind`: the answer vocabulary type, currently `color`.
- `confound_controls`: design controls active for the item.
- `source_experiment`: project experiment family the construction derives from.

See `schema.json` for the complete field list.

## Intended Use

The corpus is intended for:

- behavioral completion tests;
- linear probes over model activations;
- activation patching and low-rank steering experiments;
- controlled comparisons of base-space, alternative-space, and nested-space readouts.

Prompts are completion-style and currently use short color/value answers. Users should
verify tokenizer behavior for a target model before interpreting token-level results.

## Design Controls and Known Confounds

This corpus is built around known failure modes in counterfactual, belief-tracking, and
theory-of-mind evaluations. Each row includes a `confound_controls` list that records
which controls are active for that item.

The main controls are:

- `distinct_values`: competing spaces use different answer values, so a model must
  choose a space rather than copy the only value in the context.
- `randomized_clause_order`: base-space and alternative-space clauses appear in
  randomized order, reducing first/last-mention and recency shortcuts.
- `generic_entities`: counterfactual and depictive items use arbitrary entities and
  colors, following the generic-subject logic of Li, Yu, and Ettinger (2023), so world
  knowledge is not enough to answer.
- `copy_proof_readout`: belief and nested-belief rows include both relevant values in
  context, addressing the copying concern raised by theory-of-mind benchmark critiques
  such as Ullman (2023), Sclar et al. (2023), and Shapira et al. (2024).
- `two_entities` and `entity_bound_readout`: two-entity rows distinguish a global
  image-versus-reality feature from an entity-bound space assignment.
- `three_distinct_values` and `nested_space_builder`: nested depictive rows separate
  reality, inner-depiction, and outer-depiction values (for example a photograph and a
  painting of that photograph) so nested-space behavior cannot be reduced to a binary
  frame choice.

These controls do not make the corpus a solved benchmark. They are meant to make the
stimuli inspectable and to expose likely shortcuts. Users should still report
tokenization checks, clean behavioral accuracy, and whether results survive irrelevant
context or frame variation. `VALIDATION.md` reports a behavioral check of the released
stimuli on Qwen2.5-7B-Instruct. The context-fragility concern follows Schouten et al.
(2024), and the broader requirement that a representation be accurate, coherent,
uniform, and causally used follows Herrmann and Levinstein (2024).

## Related Papers

These constructions are studied in:

- Steele, Oliver. 2026. One mechanism for many mental spaces: a shared router over a value
  slot in language models. arXiv:2607.10248.
- Steele, Oliver. 2026. Belief-reality separation lives in routing over a shared value slot
  in language models. arXiv:2607.11945.

The corpus is a companion stimulus suite for the mental-space phenomena these papers study,
not the exact experimental data behind their reported numbers.

## References

- Herrmann, Daniel A., and Benjamin A. Levinstein. 2024. Criteria for belief-like
  representations in language models.
- Li, Jiaxuan, Lang Yu, and Allyson Ettinger. 2023. Counterfactual reasoning in
  language models.
- Sclar, Melanie, Sachin Kumar, Peter West, Alane Suhr, Yejin Choi, and Yulia
  Tsvetkov. 2023. Benchmark concerns for theory-of-mind evaluation.
- Schouten et al. 2024. Belief probes are fragile under irrelevant context.
- Shapira, Natalie, et al. 2024. Clever Hans-style shortcuts in theory-of-mind
  evaluation.
- Ullman, Tomer. 2023. Large language models fail on trivial alterations to
  theory-of-mind tasks.

## Citation

Please cite this dataset via `CITATION.cff` (Oliver Steele) and
the Hugging Face dataset DOI. The dataset lives at
https://huggingface.co/datasets/osteele/mental-spaces.

## Provenance

The templates derive from the `mental-spaces` project experiment suite; the release adds
surface-frame variants (further space builders and wordings) to broaden coverage beyond
the single frame each experiment used. The build script lives in the source repository,
[github.com/osteele/mental-spaces](https://github.com/osteele/mental-spaces); the generated
files are deterministic:

```bash
uv run python scripts/build_corpus.py --profile expanded
```

The checked-in `summary.json` states which profile generated the current files.