A Computational Developmental Model of the Implicit False Belief Task
Bibliographic record
Abstract
Do children understand that others have mental representations, for instance, mental representations of an object's location?This understanding, known as a representational Theory of Mind (ToM) has typically been studied using false-belief (FB) tasks.Standard, verbal FB tasks test whether a child can use protagonists' beliefs to say that they will search for objects where they last saw them.Whereas children under 3.5 years typically fail the task and expect protagonists to search where objects are (expectation consistent with an omniscient ToM), older children expect protagonists to search where they last saw the objects (expectation consistent with a representational ToM).Recently, 15-month-olds were shown to succeed at a visual, implicit version of the task.We present a sibling-descendant cascade-correlation connectionist model that learns to succeed at an implicit FB task.When trained on twice as many true-as false-belief trials, our model reproduced the omniscient-torepresentational transition observed in explicit tasks.That is, networks first had expectations consistent with an omniscient ToM, and after further training had expectations consistent with a representational ToM.Thus, our model predicts that infants may also go through a transition on the implicit task, and suggests that this transition may be due in part to people holding more true than false beliefs.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".