Seek, and ye shall find: Differences between spontaneous and voluntary analogical retrieval

2016 ◽  
Vol 69 (4) ◽  
pp. 698-712 ◽  
Author(s):  
Máximo Trench ◽  
Valeria Olguín ◽  
Ricardo Minervino
Keyword(s):  
1991 ◽  
pp. 265-276
Author(s):  
Trent E. Lange ◽  
Eric R. Melz ◽  
Charles M. Wharton ◽  
Keith J. Holyoak

2017 ◽  
Vol 17 (3-4) ◽  
pp. 281-305
Author(s):  
Saba Torabian ◽  
Zhe Chen ◽  
Beth A. Ober ◽  
Gregory K. Shenaut

Abstract This cross-cultural study addressed how individuals retrieve and transfer naturally learned information (i.e., folktales) from long-term memory by analogy with a previously unencountered story, concept, or problem. American and Iranian participants read target stories constructed to be analogous to folktales either familiar or unfamiliar to their culture, all having high structural familiarity and either high or low surface similarity to the source folktales. Participants reported whether targets (analogues) reminded them of any specific folktale they had learned in the past; positive responses plus additional justification (i.e., the folktale’s name or its gist) were interpreted as successful analogical retrievals. The current experiment demonstrated a high overall rate of analogical retrieval for familiar folktales and essentially no retrieval for unfamiliar folktales. There was also reliably more retrieval for analogue stories having higher versus lower surface similarity to target folktales. The high salience of surface similarity was also revealed when participants rated retrieved folktales for similarity to the target. Personal familiarity with folktales increased the retrieval rate, but presenting the folktale’s name as a cue produced mixed effects on retrieval. In summary, individuals readily retrieved culturally familiar folktales from long-term memory when they encountered structurally similar analogues, but retrieval was modulated by surface similarity.


Author(s):  
Hyeonik Song ◽  
Katherine Fu

Design-by-analogy (DbA) is an important method for innovation that has gained much attention due to its history of leading to successful and novel design solutions. The method uses a repository of existing design solutions where designers can recognize and retrieve analogical inspirations. Yet, exploring for analogical inspiration has been a laborious task for designers. This work presents a computational methodology that is driven by a topic modeling technique called non-negative matrix factorization (NMF). NMF is widely used in the text mining field for its ability to discover topics within documents based on their semantic content. In the proposed methodology, NMF is performed iteratively to build hierarchical repositories of design solutions, with which designers can explore clusters of analogical stimuli. This methodology has been applied to a repository of mechanical design-related patents, processed to contain only component-, behavior-, or material-based content to test if unique and valuable attribute-based analogical inspiration can be discovered from the different representations of patent data. The hierarchical repositories have been visualized, and a case study has been conducted to test the effectiveness of the analogical retrieval process of the proposed methodology. Overall, this paper demonstrates that the exploration-based computational methodology may provide designers an enhanced control over design repositories to retrieve analogical inspiration for DbA practice.


Author(s):  
Hyeonik Song ◽  
Jacob Evans ◽  
Katherine Fu

AbstractComputational support for design-by-analogy (DbA) is a growing field, as it aids the process for designers looking to draw inspiration from external sources by harnessing the power of data mining and data visualization. This study presents a unique exploration-based approach for the analogical retrieval process using a computational tool called VISION (Visual Interaction tool for Seeking Inspiration based On Nonnegative Matrix Factorization). Leveraging the U.S. patent database as a source of inspiration, VISION enables designers to visualize a patent repository and explore for analogical inspiration in a user-driven manner. To achieve this, we perform hierarchical Nonnegative Matrix Factorization to generate a clustered structure of patent data and employ D3.js to visualize the patent structure in a node-link network, in which user interaction capabilities are enabled for data exploration. In this study, we also analyze the effect of data size (ranging from 100 to 3000 patents) on two performance aspects of VISION – the clustering quality of topic modeling results and the frame rate of interactive data visualization. The findings show that the tool exhibits more randomized and inconsistent topic modeling results when the database size is too small. But, increasing the database size lowers the frame rate to the point that it could diminish designers’ ability to retrieve and recall information. The scope of the work here is to present the creation of the DbA visualization tool called VISION and to evaluate its data scale limitations in order to provide a basis for developing a visual interaction tool for the analogical retrieval process during DbA.


2007 ◽  
Vol 14 (6) ◽  
pp. 1101-1106 ◽  
Author(s):  
Arthur B. Markman ◽  
Eric Taylor ◽  
Dedre Gentner

AI Magazine ◽  
2014 ◽  
Vol 35 (1) ◽  
pp. 76 ◽  
Author(s):  
Maria D. Chang ◽  
Kenneth D. Forbus

One of the major challenges to building intelligent educational software is determining what kinds of feedback to give learners. Useful feedback makes use of models of domain-specific knowledge, especially models that are commonly held by potential students. To empirically determine what these models are, student data can be clustered to reveal common misconceptions or common problem-solving strategies. This article describes how analogical retrieval and generalization can be used to cluster automatically analyzed hand-drawn sketches incorporating both spatial and conceptual information. We use this approach to cluster a corpus of hand-drawn student sketches to discover common answers. Common answer clusters can be used for the design of targeted feedback and for assessment.


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