23,215 research outputs found
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Verbal analogy problem sets: An inventory of testing materials.
Analogical reasoning is an active topic of investigation across education, artificial intelligence (AI), cognitive psychology, and related fields. In all fields of inquiry, explicit analogy problems provide useful tools for investigating the mechanisms underlying analogical reasoning. Such sets have been developed by researchers working in the fields of educational testing, AI, and cognitive psychology. However, these analogy tests have not been systematically made accessible across all the relevant fields. The present paper aims to remedy this situation by presenting a working inventory of verbal analogy problem sets, intended to capture and organize sets from diverse sources
Second order isomorphism: A reinterpretation and its implications in brain and cognitive sciences
Shepard and Chipman's second order isomorphism describes how
the brain may represent the relations in the world.
However, a common interpretation of the theory can cause difficulties.
The problem originates from the static nature
of representations. In an alternative interpretation, I propose that
we assign an active role to the internal representations and
relations. It turns out that a collection of such active units can
perform analogical tasks. The new interpretation is supported
by the existence of neural circuits that may be implementing such a function.
Within this framework, perception, cognition, and motor function
can be understood under a unifying principle of analogy
Accelerating Innovation Through Analogy Mining
The availability of large idea repositories (e.g., the U.S. patent database)
could significantly accelerate innovation and discovery by providing people
with inspiration from solutions to analogous problems. However, finding useful
analogies in these large, messy, real-world repositories remains a persistent
challenge for either human or automated methods. Previous approaches include
costly hand-created databases that have high relational structure (e.g.,
predicate calculus representations) but are very sparse. Simpler
machine-learning/information-retrieval similarity metrics can scale to large,
natural-language datasets, but struggle to account for structural similarity,
which is central to analogy. In this paper we explore the viability and value
of learning simpler structural representations, specifically, "problem
schemas", which specify the purpose of a product and the mechanisms by which it
achieves that purpose. Our approach combines crowdsourcing and recurrent neural
networks to extract purpose and mechanism vector representations from product
descriptions. We demonstrate that these learned vectors allow us to find
analogies with higher precision and recall than traditional
information-retrieval methods. In an ideation experiment, analogies retrieved
by our models significantly increased people's likelihood of generating
creative ideas compared to analogies retrieved by traditional methods. Our
results suggest a promising approach to enabling computational analogy at scale
is to learn and leverage weaker structural representations.Comment: KDD 201
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Understanding analogical reasoning : viewpoints from psychology and related disciplines
Analogy and metaphor have a long history of study in linguistics, education, philosophy and psychology. Consensus over what analogy is or how analogy functions in language and thought, however, has been elusive. This paper, the first in a two part series, examines these various research traditions, attempting to bring out major lines of agreement over the role of analogy in individual human experience. As well as being a general literature review which may be helpful for newcomers to the study of analogy, this paper attempts to extract from these literatures existing theories, models and concepts which may be interesting or useful for computational studies of analogical reasoning
Active Learning: Effects of Core Training Design Elements on Self-Regulatory Processes, Learning, and Adaptability
This research describes a comprehensive examination of the cognitive, motivational, and emotional processes underlying active learning approaches, their effects on learning and transfer, and the core training design elements (exploration, training frame, emotion-control) and individual differences (cognitive ability, trait goal orientation, trait anxiety) that shape these processes. Participants (N = 350) were trained to operate a complex computer-based simulation. Exploratory learning and error-encouragement framing had a positive effect on adaptive transfer performance and interacted with cognitive ability and dispositional goal orientation to influence trainees’ metacognition and state goal orientation. Trainees who received the emotion-control strategy had lower levels of state anxiety. Implications for developing an integrated theory of active learning, learner-centered design, and research extensions are discussed
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