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Record W4415598779 · doi:10.1115/detc2025-163043

Cognitive Demands and Individual Differences in Product Similarity Judgements: A Context-Dependent Model

2025· article· W4415598779 on OpenAlexaff
Chukwuma M. Asuzu, Peiying Jian, Alison Olechowski

Bibliographic record

Venuenot available
Typearticle
Language
FieldComputer Science
TopicAdvanced Text Analysis Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSimilarity (geometry)CognitionContext (archaeology)Product (mathematics)Schema (genetic algorithms)Pairwise comparison

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.317
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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