29,121 research outputs found
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Achieving abstraction: Generating far analogies promotes relational reasoning in children.
Analogical reasoning is essential for transfer by supporting recognition of relational similarity. However, not all analogies are created equal. The source and target can be similar (near), or quite different (far). Previous research suggests that close comparisons facilitate children's relational abstraction. On the other hand, evidence from adults indicates that the process of solving far analogies may be a more effective scaffold for transfer of a relational strategy. We explore whether engaging with far analogies similarly induces such a strategy in preschoolers. Children were provided with the opportunity to solve either a near or far spatial analogy using a pair of puzzle boxes that varied in perceptual similarity (Experiment 1), or to participate in a control task (Experiment 2). All groups were then presented with an ambiguous spatial reasoning task featuring both object and relational matches. We were interested in the relationship between near and far conditions and two effects: (a) children's tendency to spontaneously draw an analogy when solving the initial puzzle, and (b) their tendency to privilege relational matches over object matches in a subsequent, ambiguous task. Although children were more likely to spontaneously draw an analogy in the near condition, those who attempted the far analogy were more likely to privilege a relational match on the subsequent task. We argue that the process of solving a far analogy-regardless of a learner's spontaneous success in identifying the relation-contextualizes an otherwise ambiguous learning problem, making it easier for children to access and apply relational hypotheses. (PsycINFO Database Recor
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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
Motion as manipulation: Implementation of motion and force analogies by event-file binding and action planning\ud
Tool improvisation analogies are a special case of motion and force analogies that appear to be implemented pre-conceptually, in many species, by event-file binding and action planning. A detailed reconstruction of the analogical reasoning steps involved in Rutherford's and Bohr's development of the first quantized-orbit model of atomic structure is used to show that human motion and force analogies generally can be implemented by the event-file binding and action planning mechanism. Predictions that distinguish this model from competing concept-level models of analogy are discussed, available data pertaining to them are reviewed, and further experimental tests are proposed
The Latent Relation Mapping Engine: Algorithm and Experiments
Many AI researchers and cognitive scientists have argued that analogy is the
core of cognition. The most influential work on computational modeling of
analogy-making is Structure Mapping Theory (SMT) and its implementation in the
Structure Mapping Engine (SME). A limitation of SME is the requirement for
complex hand-coded representations. We introduce the Latent Relation Mapping
Engine (LRME), which combines ideas from SME and Latent Relational Analysis
(LRA) in order to remove the requirement for hand-coded representations. LRME
builds analogical mappings between lists of words, using a large corpus of raw
text to automatically discover the semantic relations among the words. We
evaluate LRME on a set of twenty analogical mapping problems, ten based on
scientific analogies and ten based on common metaphors. LRME achieves
human-level performance on the twenty problems. We compare LRME with a variety
of alternative approaches and find that they are not able to reach the same
level of performance.Comment: related work available at http://purl.org/peter.turney
1st INCF Workshop on Sustainability of Neuroscience Databases
The goal of the workshop was to discuss issues related to the sustainability of neuroscience databases, identify problems and propose solutions, and formulate recommendations to the INCF. The report summarizes the discussions of invited participants from the neuroinformatics community as well as from other disciplines where sustainability issues have already been approached. The recommendations for the INCF involve rating, ranking, and supporting database sustainability
Multi-view constrained clustering with an incomplete mapping between views
Multi-view learning algorithms typically assume a complete bipartite mapping
between the different views in order to exchange information during the
learning process. However, many applications provide only a partial mapping
between the views, creating a challenge for current methods. To address this
problem, we propose a multi-view algorithm based on constrained clustering that
can operate with an incomplete mapping. Given a set of pairwise constraints in
each view, our approach propagates these constraints using a local similarity
measure to those instances that can be mapped to the other views, allowing the
propagated constraints to be transferred across views via the partial mapping.
It uses co-EM to iteratively estimate the propagation within each view based on
the current clustering model, transfer the constraints across views, and then
update the clustering model. By alternating the learning process between views,
this approach produces a unified clustering model that is consistent with all
views. We show that this approach significantly improves clustering performance
over several other methods for transferring constraints and allows multi-view
clustering to be reliably applied when given a limited mapping between the
views. Our evaluation reveals that the propagated constraints have high
precision with respect to the true clusters in the data, explaining their
benefit to clustering performance in both single- and multi-view learning
scenarios
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