When is mindreading accurate? A commentary on Shannon Spaulding’s <i>How We Understand Others: Philosophy and Social Cognition</i>
Bibliographic record
Abstract
In How We Understand Others: Philosophy and Social Cognition, Shannon Spaulding develops a novel account of mindreading with pessimistic implications for mindreading accuracy: according to Spaulding, mistakes in mentalizing are much more common than traditional theories of mindreading commonly assume. In this commentary, I push against Spaulding’s pessimism from two directions. First, I argue that a number of the heuristic mindreading strategies that Spaulding views as especially prone to error might actually be quite reliable in practice. Second, I argue that current methods for measuring mindreading performance are not well-suited for the task of determining whether our mental-state attributions are generally accurate. I conclude that any claims about the accuracy or inaccuracy of mindreading are currently unjustified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".