The influence of expertise and individual differences on psychological embeddings
Bibliographic record
Abstract
How does knowledge and expertise affect our perceptions and representations? How do individuals differ in the ways in which they represent and judge the similarity of concepts and percepts? And how best to measure psychological representations? In two experiments, we attempted to provide insight into these questions using PsiZ, a novel method for obtaining psychological embeddings–rich multi-dimensional representations of psychological similarity spaces inferred from behavioural similarity judgments. Specifically, we investigated whether psychological embeddings could be used to measure individual differences in the use of conceptual versus perceptual judgement strategies in domain experts and novices. In the first experiment, we presented two basketball experts, 12 basketball fans, and 16 novices unfamiliar with basketball with arrays of faces of famous basketball players from four NBA teams and asked them to make similarity judgments. We predicted that experts, and fans would show embeddings characteristic of a conceptual strategy (i.e., organising faces by team), whereas novices would show embeddings characteristic of a perceptual strategy (i.e., organising faces by featural similarity). As predicted, expert embeddings were more compatible with a conceptual judgement strategy, although fans and novices had embeddings more compatible with a perceptual judgement strategy. Importantly, embeddings aligned with participants’ self-reported strategies. In the second experiment, we presented 13 native Japanese speakers and 24 non-Japanese speakers with arrays of Japanese kanji characters from four semantic categories. We predicted that Japanese speakers (experts) would show more conceptually-structured embeddings while non-Japanese speakers (novices) would show more perceptually-structured embeddings. While novice embeddings and self-reported strategies were consistent with perceptual judgments, the embeddings and self-reported strategies of Japanese speakers were consistent with both conceptual and perceptual strategies. Crucially, self-reported strategy use was highly related with embedding structure. Overall, we provide evidence for the viability of using psychological embeddings to measure individual differences in perception and representation.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".