When Do Words Promote Analogical Transfer?

Son, Ji Young; Doumas, Leonidas A. A.; Goldstone, Robert L.

doi:10.7771/1932-6246.1079

Cited by 27 publications

(25 citation statements)

References 70 publications

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“…However abstracting relational similarity requires going beyond merely matching up parts and studies suggest that accurate mapping does not always coincide with a useful understanding of relational structure (e.g., Novick & Holyoak, 1991; Son, Doumas, & Goldstone, in press). …”

Section: Discussionmentioning

confidence: 99%

Connecting instances to promote children’s relational reasoning

Son

Smith

Goldstone

2011

Journal of Experimental Child Psychology

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The practice of learning from multiple instances seems to allow children to learn about relational structure. The experiments reported here have focused on two issues regarding relational learning from multiple instances: (1) what kind of perceptual situations foster such learning and (2) how particular object properties, such as complexity or similarity, interact with relational learning. Two kinds of perceptual situations were of interest here: simultaneous view, where instances are viewed at once, and sequential view, instances are viewed one at a time, one right after the other. We examine the influence of particular perceptual situations and object properties using two tests of relational reasoning: a common match-to-sample task (where new instances are compared to a common sample) and a variable match-to-sample task (where new instances are compared to a sample that varies on each trial). Experiments 1 and 2 indicate that simultaneous presentation of even highly dissimilar instances, one simple and one complex, effectively connects them together and improves relational generalization in both match-to-sample tasks. Experiment 3 showed simple samples are more effective than complex ones in the common match-to-sample task. However, when one instance is not used a common sample and various pairs of instances are simply compared (Experiment 4), simple and rich instances are equally effective at promoting relational learning. These results bear on our understanding of how children connect instances and how those initial connections affect learning and generalization.

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Section: Discussionmentioning

confidence: 99%

Connecting instances to promote children’s relational reasoning

Son

Smith

Goldstone

2011

Journal of Experimental Child Psychology

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“…For instance, preschool-aged children were better able to recognize and take advantage of structural commonalities between two physical models when the spatial locations of one of the models were meaningfully labeled (e.g., in, on, under;Loewenstein & Gentner, 2005). For adults as well, labels that emphasize structural relations can promote transfer to new situations that are structurally similar (Son, Doumas, & Goldstone, 2010).…”

Section: Discerning Deep Structurementioning

confidence: 99%

The Import of Knowledge Export: Connecting Findings and Theories of Transfer of Learning

Day

Goldstone

2012

Educational Psychologist

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“…For example, given a number of examples of instances where one object is larger than another object, DORA can extract and learn a structured representation of the invariant properties of all those larger things (e.g., it learns a predicate for larger from the properties that are invariant across instances of one larger and one smaller object). The resulting representations support successful reasoning in a wide range of relational tasks (see, e.g., Doumas & Hummel, 2010;Doumas et al, 2008;Hamer & Doumas, 2013;Martin & Doumas, 2017; Morrison et al, 2012;Son et al, 2010; a more detailed description of the model is also given below). The copyright holder for this preprint (which was .…”

Section: Learning Structured Representations From Experiencementioning

confidence: 88%

How we learn things we don’t know already: A theory of learning structured representations from experience

Doumas

Puebla

Martin

2017

Preprint

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How a system represents information tightly constrains the kinds of problems it can solve.Humans routinely solve problems that appear to require structured representations of stimulus properties and relations. Answering the question of how we acquire these representations has central importance in an account of human cognition. We propose a theory of how a system can learn invariant responses to instances of similarity and relative magnitude, and how structured relational representations can be learned from initially unstructured inputs. We instantiate that theory in the DORA (Discovery of Relations by Analogy) computational framework. The result is a system that learns structured representations of relations from unstructured flat feature vector representations of objects with absolute properties. The resulting representations meet the requirements of human structured relational representations, and the model captures several specific phenomena from the literature on cognitive development. In doing so, we address a major limitation of current accounts of cognition, and provide an existence proof for how structured representations might be learned from experience. KEYWORDS: relation learning, predicate learning, neural networks, similarity, relative magnitude, invariance, learning structured representations . CC-BY-NC-ND 4.0 International license not peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was . http://dx.doi.org/10.1101/198804 doi: bioRxiv preprint first posted online Oct. 18, 2017; Learning structured representations 3 To reason relationally is to reason about objects based on the relations that those objects play, rather than based on the literal features of those objects (see, e.g., Holyoak, 2012;Holyoak & Thagard, 1995). For example, when we make an analogy between the nucleus of an atom and the sun, we do so based on a common relation-e.g., that both nuclei and suns are larger than their orbiting bodies (planets and electrons respectively)-despite the fact that nuclei and suns are otherwise not particularly similar. Humans routinely draw inferences based on relations, from the mundane ("my kid won't eat a portion that big"), to the sublime ("the cardinal number of the reals between 0-1 is larger than the cardinal number of the positive integers"), and relational reasoning has been shown to importantly contribute to abilities such as analogy (e.g., Holyoak & Thagard, 1995), categorisation (e.g., Medin, Goldstone, & Gentner, 1993), concept learning (e.g., Doumas & Hummel, 2004, 2013, and visual cognition (e.g., Biederman, 1987, Hummel, 2013. In fact, the capacity to represent and reason about relations has been posited as the key difference in human and non-human animal cognition (Penn, Holyoak, & Povinelli, 2008).Perhaps the most plausible explanation of how humans are able to reason relationally is that we can represent relations as abstract structures that take arguments-i.e., as predicates (see, e.g., Holyoak, 2012;Holyoak ...

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When Do Words Promote Analogical Transfer?

Cited by 27 publications

References 70 publications

Connecting instances to promote children’s relational reasoning

Connecting instances to promote children’s relational reasoning

The Import of Knowledge Export: Connecting Findings and Theories of Transfer of Learning

How we learn things we don’t know already: A theory of learning structured representations from experience

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