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We see the external world as consisting not only of objects and their parts, but also of relations that hold between them. Visual analogy, which depends on similarities between relations, provides a clear example of how perception supports reasoning. Here we report an experiment in which we quantitatively measured the human ability to find analogical mappings between parts of different objects, where the objects to be compared were drawn either from the same category (e.g., images of two mammals, such as a dog and a horse), or from two dissimilar categories (e.g., a chair image mapped to a cat image).more »Free, publicly-accessible full text available January 1, 2023
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Human reasoning goes beyond knowledge about individual entities, extending to inferences based on relations between entities. Here we focus on the use of relations in verbal analogical mapping, sketching a general approach based on assessing similarity between patterns of semantic relations between words. This approach combines research in artificial intelligence with work in psychology and cognitive science, with the aim of minimizing hand coding of text inputs for reasoning tasks. The computational framework takes as inputs vector representations of individual word meanings, coupled with semantic representations of the relations between words, and uses these inputs to form semantic-relation networks formore »Free, publicly-accessible full text available January 1, 2023
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Analogy problems involving multiple ordered relations of the same type create mapping ambiguity, requiring some mechanism for relational integration to achieve mapping accuracy. We address the question of whether the integration of ordered relations depends on their logical form alone, or on semantic representations that differ across relation types. We developed a triplet mapping task that provides a basic paradigm to investigate analogical reasoning with simple relational structures. Experimental results showed that mapping performance differed across orderings based on category, linear order, and causal relations, providing evidence that each transitive relation has its own semantic representation. Hence, human analogical mappingmore »Free, publicly-accessible full text available January 1, 2023
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Computational models of verbal analogy and relational similarity judgments can employ different types of vector representations of word meanings (embeddings) generated by machine-learning algorithms. An important question is whether human-like relational processing depends on explicit representations of relations (i.e., representations separable from those of the concepts being related), or whether implicit relation representations suffice. Earlier machine-learning models produced static embeddings for individual words, identical across all contexts. However, more recent Large Language Models (LLMs), which use transformer architectures applied to much larger training corpora, are able to produce contextualized embeddings that have the potential to capture implicit knowledge of semanticmore »Free, publicly-accessible full text available January 1, 2023
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A key property of human cognition is its ability to generate novel predictions about unfamiliar situations by completing a partially-specified relation or an analogy. Here, we present a computational model capable of producing generative inferences from relations and analogs. This model, BART-Gen, operates on explicit representations of relations learned by BART (Bayesian Analogy with Relational Transformations), to achieve two related forms of generative inference: reasoning from a single relation, and reasoning from an analog. In the first form, a reasoner completes a partially-specified instance of a stated relation (e.g., robin is a type of ____). In the second, a reasonermore »Free, publicly-accessible full text available January 1, 2023
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Understanding abstract relations, and reasoning about various instantiations of the same relation, is an important marker in human cognition. Here we focus on development of understanding for the concept of antonymy. We examined whether four- and five-year-olds (N= 67) are able to complete an analogy task involving antonyms, whether language cues facilitate children’s ability to reason about the antonym relation, and how their performance compares with that of two vector-based computational models. We found that explicit relation labels in the form of a relation phrase (“opposites”) improved performance on the task for five-year-olds but not four-year-olds. Five-year-old (but not four-year-old)more »Free, publicly-accessible full text available January 1, 2023
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Many computational models of reasoning rely on explicit relation representations to account for human cognitive capacities such as analogical reasoning. Relational luring, a phenomenon observed in recognition memory, has been interpreted as evidence that explicit relation representations also impact episodic memory; however, this assumption has not been rigorously assessed by computational modeling. We implemented an established model of recognition memory, the Generalized Context Model (GCM), as a framework for simulating human performance on an old/new recognition task that elicits relational luring. Within this basic theoretical framework, we compared representations based on explicit relations, lexical semantics (i.e., individual word meanings), andmore »Free, publicly-accessible full text available January 1, 2023
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Fitch, T. ; Lamm, C. ; Leder, H. ; Teßmar-Raible, K. (Ed.)Is analogical reasoning a task that must be learned to solve from scratch by applying deep learning models to massive numbers of reasoning problems? Or are analogies solved by computing similarities between structured representations of analogs? We address this question by comparing human performance on visual analogies created using images of familiar three-dimensional objects (cars and their subregions) with the performance of alternative computational models. Human reasoners achieved above-chance accuracy for all problem types, but made more errors in several conditions (e.g., when relevant subregions were occluded). We compared human performance to that of two recent deep learning models (Siamesemore »
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We review recent theoretical and empirical work on the emergence of relational reasoning, drawing connections among the fields of comparative psychology, developmental psychology, cognitive neuroscience, cognitive science, and machine learning. Relational learning appears to involve multiple systems: a suite of Early Systems that are available to human infants and are shared to some extent with nonhuman animals; and a Late System that emerges in humans only, at approximately age three years. The Late System supports reasoning with explicit role-governed relations, and is closely tied to the functions of a frontoparietal network in the human brain. Recent work in cognitive sciencemore »