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Understanding how a person thinks, i.e., measuring a single individual’s cognitive characteristics, is challenging because cognition is not directly observable. Practically speaking, standardized cognitive tests (tests of IQ, memory, attention, etc.), with results interpreted by expert clinicians, represent the state of the art in measuring a person’s cognition. Three areas of AI show particular promise for improving the effectiveness of this kind of cognitive testing: 1) behavioral sensing, to more robustly quantify individual test-taker behaviors, 2) data mining, to identify and extract meaningful patterns from behavioral datasets; and 3) cognitive modeling, to help map ob- served behaviors onto hypothesized cognitive strategies. We bring these three areas of AI research together in a unified conceptual framework and provide a sampling of recent work in each area. Continued research at the nexus of AI and cognitive testing has potentially far-reaching implications for society in virtually every context in which measuring cognition is important, including research across many disciplines of cognitive science as well as applications in clinical, educational, and workforce settings.
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Similarity in Engineering Design: A Knowledge-Based Approach
Abstract Similarity assessment is a cognitive activity that pervades engineering design practice, research, and education. There has been a significant effort in understanding similarity in cognitive science, and some recent efforts on quantifying the similarity of design problems in the engineering design community. However, there is a lack of approaches for measuring similarity in engineering design that embody the characteristics identified in cognitive science, and accounts for the nature of design activities, particularly in the embodiment design phase where scientific knowledge plays a significant role. To address this gap, we present an approach for measuring the similarity among design problems. The approach consists of (i) modeling knowledge using probabilistic graphical models, (ii) modeling the functional mapping between design characteristics and the performance measures relevant in a particular context, and (iii) modeling the dissimilarity using KL-divergence in the performance space. We illustrate the approach using an example of a parametric shaft design for fatigue, which is typically a part of mechanical engineering design curricula, and test the validity of the approach using an experiment study involving 167 student subjects. The results indicate that the proposed approach can capture the well-documented characteristics of similarity, including directionality, context dependence, individual-specificity, and its dynamic nature. The approach is general enough that it can be extended further for assessing the similarity of design problems for analogical design, for assessing the similarity of experimental design tasks to real design settings, and for evaluating the similarity between design problems in educational settings.
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- Award ID(s):
- 1662230
- PAR ID:
- 10177428
- Date Published:
- Journal Name:
- ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
- Volume:
- 7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/DETC2019-98272
