Learning Children’s Conceptual Spaces using Deep Metric Learning.
Bibliographic record
Abstract
Children learn to represent the world around them in meaningful categories that allow them to generalize past experiences. Understanding how these categorical representations develop is fundamental to cognitive science. However, capturing the structure of human conceptual knowledge is a challenging experimental task. The most prominent approach, Multidimensional Scaling (MDS), usually requires participants to produce many similarity judgments, leading to long experiments. Moreover, the representations found by MDS are limited to the fixed set of experimental stimuli and have to be reconstructed for every new item. In contrast, we present a more flexible machine-learning method that can generalize to novel stimuli. This method uses a child-friendly task that allows researchers to uncover the development of categories with fewer participant judgments. We evaluate our approach on simulated data and find that it can accurately reveal representations even when trained on data generated by groups that categorize differently. We then analyze data from the World Color Survey and find that we can recover language-specific color organization when aggregating languages that only share the same number of basic color terms. Finally, we use the method in a developmental experiment and find age-dependent differences in how complex fruit stimuli are organized. These differences were consistent with participants' reasoning and additional experimental measures. Our results suggest that our approach is applicable in psychological tasks and opens the possibility of examining children's developing psychological spaces in new detail.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".