An official website of the United States government
Abstract External sources of inspiration can promote the discovery of new ideas as designers ideate on a design task. Data-driven techniques can increasingly enable the retrieval of inspirational stimuli based on nontext-based representations, beyond semantic features of stimuli. However, there is a lack of fundamental understanding regarding how humans evaluate similarity between non-semantic design stimuli (e.g., visual). Toward this aim, this work examines human-evaluated and computationally derived representations of visual and functional similarities of 3D-model parts. A study was conducted where participants (n=36) assessed triplet ratings of parts and categorized these parts into groups. Similarity is defined by distances within embedding spaces constructed using triplet ratings and deep-learning methods, representing human and computational representations. Distances between stimuli that are grouped together (or not) are determined to understand how various methods and criteria used to define non-text-based similarity align with perceptions of 'near' and 'far'. Distinct boundaries in computed distances separating stimuli that are 'too far' were observed, which include farther stimuli when modeling visual vs. functional attributes.
more »
« less
Exploring the Effectiveness of Interactive Preference Learning for Adapting Designs to Abstract Semantic Attributes
Abstract semantic attributes of designs (e.g., comfortable, luxurious, and durable) play a significant role in the assessment of user-facing products, capturing intangible factors that people may consider aside from performance requirements. However, due to the difficulty of mapping highly subjective and varying perceptions to specific design features, it remains a challenge to quickly and accurately translate these qualities into designs using computational design tools. Seeking to align computational and human representations of subjective design information, we investigate the utility of adapting representations of semantic attributes to designers’ perceptions through interactive models. A study is conducted in which users evaluate parameterized drinking mugs, indicating their perceptions of how comfortable each is to hold. Interactive Bayesian optimization is used to adaptively arrive at a design that optimizes this subjective quantity for each participant individually. Participants (N = 31) guide the model by providing their own decisions or building off of empirical data from a prior group of participants (N = 25). The resulting designs are evaluated across different scenarios, demonstrating the extent to which outputs of noninteractive models can be used to represent a subjective, semantic attribute and how interactive models may improve perceived alignment between human intent and computionally generated outputs.
more »
« less
- Award ID(s):
- 2145432
- PAR ID:
- 10656979
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- Journal Name:
- Journal of Mechanical Design
- Volume:
- 148
- Issue:
- 4
- ISSN:
- 1050-0472
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
This paper investigates and compares people’s subjective impression of an office with a biophilic design and blue lighting. Existing studies have examined their influence on perception separately, but how they compare is unclear. Additionally, only a few studies have used an office setting as a case study. To address this research gap, this study collected people’s ratings and rankings of four simulated interior scenes of a private office using an online survey. The scenes include blue lighting, a biophilic design with daylight and view, a biophilic design with indoor plants, and a non-biophilic baseline with conventional white lighting. A total of 284 complete responses were collected and analyzed using a mixed-effect model. It was found that the two biophilic designs improved people’s perception of the office compared to the base case. The biophilic design with access to daylight and view outperformed the space with indoor plants in all the examined perceptual categories, specifically how the office space was perceived by participants as brighter, more comfortable, and spacious. On the contrary, the space with blue lighting decreased people’s ratings in most perceptual attributes in comparison to the baseline. The negative influence was notably significant in how lively, comfortable, bright, and appealing the space was perceived as being by participants. Subjects’ preference rankings of the four simulated office spaces showed a similar pattern.more » « less
-
In Human–Robot Interaction, researchers typically utilize in-person studies to collect subjective perceptions of a robot. In addition, videos of interactions and interactive simulations (where participants control an avatar that interacts with a robot in a virtual world) have been used to quickly collect human feedback at scale. How would human perceptions of robots compare between these methodologies? To investigate this question, we conducted a 2x2 between-subjects study (N=160), which evaluated the effect of the interaction environment (Real vs. Simulated environment) and participants’ interactivity during human-robot encounters (Interactive participation vs. Video observations) on perceptions about a robot (competence, discomfort, social presentation, and social information processing) for the task of navigating in concert with people. We also studied participants’ workload across the experimental conditions. Our results revealed a significant difference in the perceptions of the robot between the real environment and the simulated environment. Furthermore, our results showed differences in human perceptions when people watched a video of an encounter versus taking part in the encounter. Finally, we found that simulated interactions and videos of the simulated encounter resulted in a higher workload than real-world encounters and videos thereof. Our results suggest that findings from video and simulation methodologies may not always translate to real-world human–robot interactions. In order to allow practitioners to leverage learnings from this study and future researchers to expand our knowledge in this area, we provide guidelines for weighing the tradeoffs between different methodologies.more » « less
-
null (Ed.)Abstract Creativity research requires assessing the quality of ideas and products. In practice, conducting creativity research often involves asking several human raters to judge participants’ responses to creativity tasks, such as judging the novelty of ideas from the alternate uses task (AUT). Although such subjective scoring methods have proved useful, they have two inherent limitations—labor cost (raters typically code thousands of responses) and subjectivity (raters vary on their perceptions and preferences)—raising classic psychometric threats to reliability and validity. We sought to address the limitations of subjective scoring by capitalizing on recent developments in automated scoring of verbal creativity via semantic distance, a computational method that uses natural language processing to quantify the semantic relatedness of texts. In five studies, we compare the top performing semantic models (e.g., GloVe, continuous bag of words) previously shown to have the highest correspondence to human relatedness judgements. We assessed these semantic models in relation to human creativity ratings from a canonical verbal creativity task (AUT; Studies 1–3) and novelty/creativity ratings from two word association tasks (Studies 4–5). We find that a latent semantic distance factor—comprised of the common variance from five semantic models—reliably and strongly predicts human creativity and novelty ratings across a range of creativity tasks. We also replicate an established experimental effect in the creativity literature (i.e., the serial order effect) and show that semantic distance correlates with other creativity measures, demonstrating convergent validity. We provide an open platform to efficiently compute semantic distance, including tutorials and documentation ( https://osf.io/gz4fc/ ).more » « less
-
Users’ perceptions of fitness tracking privacy is a subject of active study, but how do various aspects of social identity inform these perceptions? We conducted an online survey (N=322) that explores the influence of identity on fitness tracking privacy perceptions and practices, considering participants’ gender, race, age, and whether or not they identify as LGTBQ*. Participants reported how comfortable they felt sharing fitness data, commented on whether they believed their identity impacted this comfort, and brainstormed several data sharing risks and a possible mitigation for each risk. For each surveyed dimension of social identity, we find one or more reliable effects on participants’ level of comfort sharing fitness data, specifically when considering institutional groups like employers, insurers, and advertisers. Further, 64% of participants indicate at least one of their identity characteristics informs their comfort. We also find evidence that the perceived risks of sharing fitness data vary by identity, but do not find evidence of difference in the strategies used to manage these risks. This work highlights a path towards reasoning about the privacy challenges of fitness tracking with respect for the lived experiences of all users.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1115/1.4069687
