Do Human and Computational Evaluations of Similarity Align? An Empirical Study of Product Function

Abstract

Function drives many early design considerations in product development, highlighting the importance of finding functionally similar examples when searching for sources of inspiration or evaluating designs against existing technology. However, it is difficult to capture what people consider to be functionally similar and therefore, if measures that compare function directly from the products themselves are meaningful. In this work, human evaluations of similarity are compared to computationally determined values, shedding light on how quantitative measures align with human perceptions of functional similarity. Human perception of functional similarity is considered at two levels of abstraction: (1) the high-level purpose of a product, and (2) a detailed view of how the product works. These human evaluations of similarity are quantified by crowdsourcing 1360 triplet ratings at each functional abstraction and creating low-dimensional embeddings from the triplets. The triplets and embeddings are then compared to similarity that is computed between functional models using six representative measures, including matching measures like cosine similarity and network-based measures like spectral distance. The outcomes demonstrate how different levels of abstraction and the fuzzy line between what is considered “highly similar” and “somewhat similar” may impact functional similarity representations by humans and subsequently affect how computed similarity aligns with these representations. The results inform how functional similarity can be leveraged by designers and therefore, applications lie in creativity support tools, such as those used for design-by-analogy, or future computational methods in design that incorporate product function.

Artificial Neural Networks as a tool for ergonomic evaluations of vehicle control panels

“…However, many researchers emphasize the importance of incorporating behavioral data into engineering design. (nandy et al 2022(nandy et al , rahMan et al 2019(nandy et al , sha et al 2015(nandy et al , tanG et al 2020(nandy et al , yu et al 2016 In recent years, one of the most interesting studies into dashboard design was conducted by . The authors surveyed 35 drivers who were asked to evaluate various aspects of dashboard ergonomics in a questionnaire containing 50 questions.…”

“…The usability of car dashboard displays for elderly drivers has also been researched (Baldwin 2002, KiM et al 2011, yanG, couGhlin 2013. Other researchers argue that the aesthetic forms and functional features of a product are certainly important, the decision to buy or not to buy a product often depends on more, namely, the experience or feel of use (nandy, Goucher-laMBert 2022, ParK et al 2019, tovares et al 2014.…”