Datasets:

osteele
/

mental-spaces

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).

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; the generated files are deterministic:

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

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