Cognitive Demands and Individual Differences in Product Similarity Judgements: A Context-Dependent Model
Bibliographic record
Abstract
Abstract Product similarity is employed as a key manipulation in design cognition experimental research. To understand user behaviour, design researchers utilize appearance or functional similarity to study far ranging concepts such as feature recognition, aesthetic preferences, and even multi-modal search. These studies rely on an implicit assumption: that product similarity is intuitive, proceeds without conscious elaboration, and is easily understood by research participants. However, this notion has not been empirically tested. In this study, we consider the cognitive demands and individual differences involved in making product similarity judgements. We devised a repeated-measures, pairwise similarity comparison experiment with pairs of products similar by appearance, function, and a baseline comparator. Our analysis of more than 7,000 similarity ratings found that appearance similarity judgements exert less cognitive demand than functional similarity judgements, however participants are more accurate when making functional similarity judgements. This phenomenon denotes the presence of a schema, which we term the functionality context schema, that is employed when making functional similarity judgements; applying the schema takes more cognitive effort but improves the accuracy of the judgements. We discuss the implications of our results for design research employing product similarity as a manipulation as well as for consumer behaviour studies.
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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 | 0.005 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".