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Record W4381167918 · doi:10.1017/pds.2023.358

TOWARDS A BETTER UNDERSTANDING OF THE INFLUENCE OF VISUAL REFERENCES ON CONSUMER AESTHETIC PERCEPTION

2023· article· en· W4381167918 on OpenAlexaff
Chukwuma M. Asuzu, Alison Olechowski

Bibliographic record

VenueProceedings of the Design Society · 2023
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFluencyProcessing fluencyPerceptionProduct (mathematics)Computer scienceProcess (computing)CognitionCognitive psychologySimilarity (geometry)Visual perceptionAffect (linguistics)Function (biology)PsychologyVisual processingHuman–computer interactionImage (mathematics)Artificial intelligenceCommunication

Abstract

fetched live from OpenAlex

Abstract When viewing a product for the first time, a consumer's aesthetic perception is based on their knowledge of other products, artefacts, and concepts. These mental images function as visual references for consumers and affect the processing fluency of the new product. Designers frequently use visual references as inspiration during the research stage of the design process. It has been documented, however, that there is a gap between designer intent and consumer response; Consumers do not always realize the intent of designers nor draw on the same visual references when perceiving a product, which can reduce their processing fluency of new products. Visual references differ from one consumer to the other which make them difficult to study. In this paper, we argue for a new way of studying visual references: by analyzing the cognitive process that occurs when consumers view a new product and recognize aspects of that product that are similar to visual references in their memory. We present a framework of three approaches for recognizing this similarity and implications for design practice.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.062
GPT teacher head0.287
Teacher spread0.225 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2023
Admission routes1
Has abstractyes

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