Children's Ability to Detect Semantic Contradictions

Glass, Arnold L.; Holyoak, Keith J.; Kossan, Nancy E.

doi:10.2307/1128911

Cited by 7 publications

(5 citation statements)

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“…We can grasp, for example, that “love” and “hate” are related to one another in much the same way as “rich” and “poor,” and that “blindness” and “sight” are related in the same way as “poverty” and “money” are. It is known that the ability to reason about abstract semantic relations emerges during early childhood (2), with children being taught the concepts of antonym and synonym in elementary school (3); however, how abstract relations might be learned remains unclear.…”

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confidence: 99%

Emergence of analogy from relation learning

Holyoak

2019

Proc. Natl. Acad. Sci. U.S.A.

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By middle childhood, humans are able to learn abstract semantic relations (e.g., antonym, synonym, category membership) and use them to reason by analogy. A deep theoretical challenge is to show how such abstract relations can arise from nonrelational inputs, thereby providing key elements of a protosymbolic representation system. We have developed a computational model that exploits the potential synergy between deep learning from “big data” (to create semantic features for individual words) and supervised learning from “small data” (to create representations of semantic relations between words). Given as inputs labeled pairs of lexical representations extracted by deep learning, the model creates augmented representations by remapping features according to the rank of differences between values for the two words in each pair. These augmented representations aid in coping with the feature alignment problem (e.g., matching those features that make “love-hate” an antonym with the different features that make “rich-poor” an antonym). The model extracts weight distributions that are used to estimate the probabilities that new word pairs instantiate each relation, capturing the pattern of human typicality judgments for a broad range of abstract semantic relations. A measure of relational similarity can be derived and used to solve simple verbal analogies with human-level accuracy. Because each acquired relation has a modular representation, basic symbolic operations are enabled (notably, the converse of any learned relation can be formed without additional training). Abstract semantic relations can be induced by bootstrapping from nonrelational inputs, thereby enabling relational generalization and analogical reasoning.

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Emergence of analogy from relation learning

Holyoak

2019

Proc. Natl. Acad. Sci. U.S.A.

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“…For example, they learn that the relations larger and smaller have a special relationship to each other (a type of antonym). Analyses of corpora of child speech have identified systematic use of such gradable antonyms by children aged 2-5 years (Jones & Murphy, 2005), and experimental studies show that by at least 6 years of age children can use such concepts metaphorically (Gardner, 1974), and are aware that antonyms are contradictory (Glass, Holyoak, & Kossan, 1977). Children even-tually understand that a pair of concepts like larger-smaller is related in basically the same way as the pair faster-slower, allowing them to see that such pairs of relations form analogies.…”

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Bayesian analogy with relational transformations.

Chen

Holyoak

2012

Psychological Review

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How can humans acquire relational representations that enable analogical inference and other forms of high-level reasoning? Using comparative relations as a model domain, we explore the possibility that bottom-up learning mechanisms applied to objects coded as feature vectors can yield representations of relations sufficient to solve analogy problems. We introduce Bayesian analogy with relational transformations (BART) and apply the model to the task of learning first-order comparative relations (e.g., larger, smaller, fiercer, meeker) from a set of animal pairs. Inputs are coded by vectors of continuousvalued features, based either on human magnitude ratings, normed feature ratings (De Deyne et al., 2008), or outputs of the topics model (Griffiths, Steyvers, & Tenenbaum, 2007). Bootstrapping from empirical priors, the model is able to induce first-order relations represented as probabilistic weight distributions, even when given positive examples only. These learned representations allow classification of novel instantiations of the relations and yield a symbolic distance effect of the sort obtained with both humans and other primates. BART then transforms its learned weight distributions by importance-guided mapping, thereby placing distinct dimensions into correspondence. These transformed representations allow BART to reliably solve 4-term analogies (e.g., larger:smaller::fiercer:meeker), a type of reasoning that is arguably specific to humans. Our results provide a proof-of-concept that structured analogies can be solved with representations induced from unstructured feature vectors by mechanisms that operate in a largely bottom-up fashion. We discuss potential implications for algorithmic and neural models of relational thinking, as well as for the evolution of abstract thought. Keywords: analogy, relation learning, generalization, Bayesian modelsSupplemental materials: http://dx.doi.org/10.1037/a0028719.supp One of the hallmarks of human reasoning is the ability to form representations of relations between entities and then to reason about the higher order relations between these relations. Whereas concepts such as larger and smaller, for example, are first-order relations, potentially derivable by comparing features of individual objects, a relation such as opposite is a higher order relation between relations (Gentner, 1983). The capacity to represent and reason with higher order relations has been considered central to human analogical thinking

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“…If the properties of the two concepts are compared, numerous mismatches will quickly be found regardless of the order in which comparisons are made. This accumulated negative evidence will indicate that the two categories are mutually exclusive (Collins & Loftus, 1975;Glass, Holyoak, & Kossan, 1977;McCloskey & Glucksberg, 1979).…”

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Role of antonymy relations in semantic judgments.

Glass¹,

Holyoak²,

Kiger³

1979

Journal of Experimental Psychology: Human Learning and Memory

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Decision times for sentences or word pairs involving direct (e.g., boy-girl) or indirect (e.g., boy-sister) antonyms were measured in a sentence verification task and a lexical decision task. In Experiment 1 false sentences involving the direct antonyms were disconfirmed faster than sentences involving the indirect antonyms, even though the former were rated as more closely related in meaning. In Experiment 2 a smaller advantage for the direct antonyms was found in a lexical decision task, although "word" decisions were made more quickly for both types of antonym pairs than for pairs of unrelated words. Experiment 2 also investigated the degree of semantic facilitation obtained for category-instance word pairs. No significant latency differences were found for lexical decisions involving word pairs consisting of a category name (e.g., bird) and either a high frequency (e.g., robin) or low frequency (e.g., chicken) instance. The locus of the positive effect of semantic relatedness on falsification time for sentences involving antonyms is discussed in light of these results, as is the locus of the "typicality effect" obtained in previous semantic memory studies. An important theoretical contrast that has emerged in models of semantic memory involves the processes by which people evaluate sentences asserting semantic relations such as category membership (e.g., A rose is a flower). Feature models assume that such decisions are based on a comparison of elementary features or attributes that define the subject and predicate con

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Children's Ability to Detect Semantic Contradictions

Cited by 7 publications

References 4 publications

Emergence of analogy from relation learning

Emergence of analogy from relation learning

Bayesian analogy with relational transformations.

Role of antonymy relations in semantic judgments.

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