Embedded Mental States, Literariness, and the Mutual Cross-Disciplinary Benefits of Cognitive-Literary Analysis
Bibliographic record
Abstract
This article begins by reviewing the related cognitive-scientific concepts of theory of mind (ToM), embedded mental states, intentionality, and recursive mindreading. The mental processes involved in discerning others’ unstated thoughts and beliefs are essential not only to interacting with other humans in most situations but also to reading and understanding narratives. Literature models real-life situations and prompts us to practise our mindreading skills, generally with no social consequences. Through its simulation properties, literature also facilitates the scientific study of cognitive processes that are difficult to examine in real-life situations. An investigation into the creative use of embedded mental states by two prominent East German writers, Wolfgang Hilbig and Christa Wolf, illustrates both how cognitive studies can support literary analyses and how those analyses can, in turn, further the scientific understanding of the human brain’s processing of intentionality and the mental states of others.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.005 | 0.050 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".