Learning to reason about desires: An infant training study
Bibliographic record
Abstract
A key aspect of theory of mind is the ability to reason\nabout other people's desires. As adults, we know that desires\nand preferences are subjective and specific to the individual.\nHowever, research in cognitive development suggests that a\nsignificant conceptual shift occurs in desire-based reasoning\nbetween 14 and 18 months of age, allowing 18- but not 14-\nmonth-olds to understand that different people can have\ndifferent preferences (Lucas et al., 2014; Ma & Xu 2011;\nRepacholi & Gopnik, 1997). The present research investigates\nthe kind of evidence that is relevant for inducing this shift and\nwhether younger infants can be trained to learn about the\ndiversity of preferences. In Experiment 1, infants younger\nthan 18 months of age were shown demonstrations in which\ntwo experimenters either liked the same objects as each other\n(in one training condition) or different objects (in another\ntraining condition). Following training, all infants were asked\nto share one of two foods with one of the experimenters –\nthey could either share a food that the experimenter showed\ndisgust towards (and the infants themselves liked) or a food\nthat the experimenter showed happiness towards (and the\ninfants themselves did not like). We found that infants who\nobserved two different experimenters liking different objects\nduring training later provided the experimenter with the food\nshe liked, even if it was something they disliked themselves.\nHowever, when infants observed two experimenters liking the\nsame objects, they later incorrectly shared the food that they\nthemselves liked with the experimenter. Experiment 2\ncontrolled for an alternative interpretation of these findings.\nOur results suggest that training allows infants to overturn an\ninitial theory in the domain of Theory of Mind for a more\nadvanced one.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".