From monkeys to infants: the empirical challenges facing mental fictionalism
Bibliographic record
Abstract
In recent decades, a novel theoretical account of folk psychology has emerged that challenges traditional assumptions: mental fictionalism. Rather than treating mental states as things in creatures’ minds that we are really tracking, mental fictionalists argue that folk psychology is best understood as a collective fiction that we partake in. On this account, it is this fiction rather than an ability to track mental states that enables us to predict/explain one another’s behavior, or to coordinate our affective states to grow and maintain social relationships. I provide an overview of the evidence from comparative and developmental psychology that is inconsistent with mental fictionalism, arguing that both views considered here are unable to account for the mindreading abilities of non-linguistic primates and children. I then offer an ontogenetic challenge, arguing that mental fictionalism faces the difficult task of explaining how mindreading could emerge from what are thought to be more sophisticated cognitive mechanisms, such as those underlying participation in either pretense or collective narratives. I conclude by highlighting the specific body of evidence that a more robust version of mental fictionalism should account for, especially as it continues to be refined and emerges as a serious competitor amongst interpretations of folk psychology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".