We tested whether analogical training could help children learn a key principle of elementary engineering—namely, the use of a diagonal brace to stabilize a structure. The context for this learning was a construction activity at the Chicago Children's Museum, in which children and their families build a model skyscraper together. The results indicate that even a single brief analogical comparison can confer insight. The results also reveal conditions that support analogical learning.

Children’s cognitive control and knowledge at school entry predict growth rates in analogical reasoning skill over time; however, the mechanisms by which these factors interact and impact learning are unclear. We propose that inhibitory control (IC) is critical for developing both the relational representations necessary to reason and the ability to use these representations in complex problem solving. We evaluate this hypothesis using computational simulations in a model of analogical thinking, Discovery of Relations by Analogy/Learning and (...) Inference with Schemas and Analogy (DORA/LISA; Doumas et al., 2008 ). Longitudinal data from children who solved geometric analogy problems repeatedly over 6 months show three distinct learning trajectories though all gained somewhat: analogical reasoners throughout, non-analogical reasoners throughout, and transitional – those who start non-analogical and grew to be analogical. Varying the base level of top-down lateral inhibition in DORA affected the model’s ability to learn relational representations, which, in conjunction with inhibition levels used in LISA during reasoning, simulated accuracy rates and error types seen in the three different learning trajectories. These simulations suggest that IC may not only impact reasoning ability but may also shape the ability to acquire relational knowledge given reasoning opportunities. (shrink)

In general, we know that gesture accompanying spoken instruction can help children learn. The present study was conducted to better understand how gesture can support children’s comprehension of spoken instruction and whether the benefit of teaching though speech and gesture over spoken instruction alone depends on differences in cognitive profile – prior knowledge children have that is related to a to-be-learned concept. To answer this question, we explored the impact of gesture instruction on children’s analogical reasoning ability. Children between (...) the ages of 4 and 11 years solved scene analogy problems before or after speech alone or speech and gesture instruction while their visual attention was monitored. Our behavioral results suggest a marginal benefit of gesture instruction over speech alone, but only 5-year-old children showed a distinct advantage from speech+gesture instruction when solving the post-instruction trial, suggesting that at this age, children have the cognitive profile in place to utilize the added support of gesture. Furthermore, while speech+gesture instruction facilitated effective visual attention during instruction, directing attention away from featural matches, and towards relational information, was pivotal for younger children’s success post instruction. We consider how these results contribute to the gesture-for-learning literature and consider how the nuanced impact of gesture is informative for educators teaching tasks of analogy in the classroom. (shrink)

Starting with the hypothesis that analogical reasoning consists of a search of semantic space, we used eye-tracking to study the time course of information integration in adults in various formats of analogies. The two main questions we asked were whether adults would follow the same search strategies for different types of analogical problems and levels of complexity and how they would adapt their search to the difficulty of the task. We compared these results to predictions from the literature. (...) Machine learning techniques, in particular support vector machines (SVMs), processed the data to find out which sets of transitions best predicted the output of a trial (error or correct) or the type of analogy (simple or complex). Results revealed common search patterns, but with local adaptations to the specifics of each type of problem, both in terms of looking-time durations and the number and types of saccades. In general, participants organized their search around source-domain relations that they generalized to the target domain. However, somewhat surprisingly, over the course of the entire trial, their search included, not only semantically related distractors, but also unrelated distractors, depending on the difficulty of the trial. An SVM analysis revealed which types of transitions are able to discriminate between analogy tasks. We discuss these results in light of existing models of analogical reasoning. (shrink)

While rules and exemplars are usually viewed as opposites, this paper argues that they form end points of the same distribution. By representing both rules and exemplars as (partial) trees, we can take into account the fluid middle ground between the two extremes. This insight is the starting point for a new theory of language learning that is based on the following idea: If a language learner does not know which phrase‐structure trees should be assigned to initial sentences, s/he (...) allows (implicitly) for all possible trees and lets linguistic experience decide which is the “best” tree for each sentence. The best tree is obtained by maximizing “structural analogy” between a sentence and previous sentences, which is formalized by the most probable shortest combination of subtrees from all trees of previous sentences. Corpus‐based experiments with this model on the Penn Treebank and the Childes database indicate that it can learn both exemplar‐based and rule‐based aspects of language, ranging from phrasal verbs to auxiliary fronting. By having learned the syntactic structures of sentences, we have also learned the grammar implicit in these structures, which can in turn be used to produce new sentences. We show that our model mimicks children’s language development from item‐based constructions to abstract constructions, and that the model can simulate some of the errors made by children in producing complex questions. (shrink)

The research reported in this document has been sponsored by the Air Force Office of Scientific Research, OAR, under Contract AF61 ‐640 with the European Office of Aerospace Research, United States Air Force; by the Aeronautical Systems Division of the Air Force Systems Command, United States Air Force, through the European Office of the Office of Aerospace Research, under Contract AF61‐402, and by the US Department of the Army, through its European Research Office, under Contract No. DA‐91‐591‐EUC‐3216.

Analogical cognition refers to the ability to detect, process, and learn from relational similarities. The study of analogical and similarity cognition is widely considered one of the ‘success stories’ of cognitive science, exhibiting convergence across many disciplines on foundational questions. Given the centrality of analogy to mind and knowledge, it would benefit philosophers investigating topics in epistemology and the philosophies of mind and language to become familiar with empirical models of analogical cognition. The goal of this essay (...) is to describe recent empirical work on analogical cognition as well as model applications to philosophical topics. Topics to be discussed include the epistemological distinction between implicit knowledge and explicit knowledge, the debate between empiricists and nativists, the frame problem, expertise, creativity and autism, cognitive architecture, and relational knowledge. Particular attention is given to Dedre Gentner and colleague’s structure-mapping theory – the most developed and widely accepted model of analogical cognition. (shrink)

Theory‐of‐mind (ToM) is an integral part of social cognition, but how it develops remains a critical question. There is evidence that children can gain insight into ToM through experience, including language training and explanatory interactions. But this still leaves open the question of how children gain these insights—what processes drive this learning? We propose that analogical comparison is a key mechanism in the development of ToM. In Experiment 1, children were shown true‐ and false‐belief scenarios and prompted to (...) engage in multiple comparisons (e.g., belief vs. world). In Experiments 2a, 2b, and 3, children saw a series of true‐ and false‐belief events, varying in order and in their alignability. Across these experiments, we found that providing support for comparing true‐ and false‐belief scenarios led to increased performance on false‐belief tests. These findings show that analogical comparison can support ToM learning. (shrink)

We sought to gain more insight into the effects of attention focus and time constraints on skill learning and performance in novices and experts by means of two complementary experiments using a table tennis paradigm. Experiment 1 showed that skill-focus conditions and slowed ball frequency disrupted the accuracy of experts, but dual-task conditions and speeded ball frequency did not. For novices, only speeded ball frequency disrupted accuracy. In Experiment 2, we extended these findings by instructing novices either explicitly or (...) by analogy . Explicitly instructed novices were less accurate in skill-focused and dual-task conditions than in single-task conditions. Following analogy instruction novices were less accurate in the skill-focused condition, but maintained accuracy under dual-task conditions. Participants in both conditions retained accuracy when ball frequency was slowed, but lost accuracy when ball frequency was speeded, suggesting that not attention, but motor dexterity, was inadequate under high temporal constraints. (shrink)

Analogical reasoning is a foundational tool for human learning, allowing learners to recognize relational structures in new events and domains. Here I sketch some grounds for understanding and applying analogical reasoning in social learning. The social world is fundamentally characterized by relations between people, with common relational structures—such as kinships and social hierarchies—forming social units that dictate social behaviors. Just as young learners use analogical reasoning for learning relational structures in other domains—spatial relations, verbs, (...) relational categories—analogical reasoning ought to be a useful cognitive tool for acquiring social relations and structures. (shrink)

Human cognition is striking in its brilliance and its adaptability. How do we get that way? How do we move from the nearly helpless state of infants to the cognitive proficiency that characterizes adults? In this paper I argue, first, that analogical ability is the key factor in our prodigious capacity, and, second, that possession of a symbol system is crucial to the full expression of analogical ability.

A central question in human development is how young children gain knowledge so fast. We propose that analogical generalization drives much of this early learning and allows children to generate new abstractions from experience. In this paper, we review evidence for analogical generalization in both children and adults. We discuss how analogical processes interact with the child's changing knowledge base to predict the course of learning, from conservative to domain-general understanding. This line of research leads (...) to challenges to existing assumptions about learning. It shows that it is not enough to consider the distribution of examples given to learners; one must consider the processes learners are applying; contrary to the general assumption, maximizing variability is not always the best route for maximizing generalization and transfer. (shrink)

Adult humans show exceptional relational ability relative to other species. In this research, we trace the development of this ability in young children. We used a task widely used in comparative research—the relational match-to-sample task, which requires participants to notice and match the identity relation: for example, AA should match BB instead of CD. Despite the simplicity of this relation, children under 4 years of age failed to pass this test (Experiment 1), and their performance did not improve even with (...) initial feedback (Experiment 2). In Experiments 3 and 4, we found that two kinds of symbolic-linguistic experience can facilitate relational reasoning in young children. Our findings suggest that children learn to become adept analogical thinkers, and that language fosters this learning in at least two distinct ways. (shrink)

This paper defends the thesis of learning from non-causal models: viz. that the study of some model can prompt justified changes in one’s confidence in empirical hypotheses about a real-world target in the absence of any known or predicted similarity between model and target with regards to their causal features. Recognizing that we can learn from non-causal models matters not only to our understanding of past scientific achievements, but also to contemporary debates in the philosophy of science. At one (...) end of the philosophical spectrum, my thesis undermines the views of those who, like Cartwright (Erkenntnis 70:45–58, 2009), follow Hesse (Models and Analogies in Science, Notre Dame, University of Notre Dame Press, 1963) in restricting the possibility of learning from models to only those situations where a model identifies some causal factors present in the target. At the other end of the spectrum, my thesis also helps undermine some extremely permissive positions, e.g., Grüne-Yanoff’s (Erkenntnis 70(1):81–99, 2009, Philos Sci 80(5): 850–861, 2013) claim that learning from a model is possible even in the absence of any similarity at all between model and target. The thesis that we can learn from non-causal models offers a cautious middle ground between these two extremes. (shrink)

Analogical transfer in sequence learning is presented as an example of how the type-2 problem of learning an unbounded number of isomorphic sequences is reduced to the type-1 problem of learning a small finite set of sequences. The commentary illustrates how the difficult problem of appropriate analogical filter creation and selection is addressed while avoiding the trap of strong nativism, and it provides theoretical and experimental evidence for the existence of dissociable mechanisms for type-1 (...) class='Hi'>learning and type-2 recoding. (shrink)

In attempting to fit a model of analogical problem solving to protocol data of students solving physics problems, several unexpected observations were made. Analogies between examples and exercises (a form of case‐based reasoning) consisted of two distinct types of events. During an initialization event, the solver retrieved an example, set up a mapping between it and the problem, and decided whether the example was useful. During a transfer event, the solver inferred something about the problem's solution. Many different types (...) of initialization and transfer events were observed. Poor solvers tended to follow the example verbatim, copying each solution line over to the problem. Good solvers tried to solve the problem themselves, but referred to the example when they got stuck, or wanted to check a step, or wanted to avoid a detailed calculation. Rather than learn from analogies, both Good and Poor solvers tended to repeat analogies at subsequent similar situations. A revised version of the model is proposed (but not yet implemented) that appears to be consistent with all the findings observed in this and other studies of the same subjects. (shrink)

Learning from examples is a very effective means of initial cognitive skill acquisition. There is an enormous body of research on the specifics of this learning method. This article presents an instructionally oriented theory of example-based learning that integrates theoretical assumptions and findings from three research areas: learning from worked examples, observational learning, and analogical reasoning. This theory has descriptive and prescriptive elements. The descriptive subtheory deals with (a) the relevance and effectiveness of examples, (...) (b) phases of skill acquisition, and (c) learning processes. The prescriptive subtheory proposes instructional principles that make full exploitation of the potential of example-based learning possible. (shrink)

The current paper examines whether knowledge of an ethical principle influences moral awareness and ethical decision making. Using hypothetical scenarios and a behavioral task, three experiments examine the effects of deepening people’s knowledge of ethical principles. In each study, an analogical encoding learning intervention led to greater knowledge of an ethical principle, which in turn resulted in a greater likelihood of moral awareness and making ethical decisions. These findings suggest that moral awareness is partly a matter of the (...) depth of individuals’ knowledge of ethical principles. The findings provide further reasons to link work on ethics with work on expertise and knowledge transfer as well as indicate new approaches to ethics training. (shrink)

Leech et al.'s demonstration that analogical reasoning can be an emergent property of low-level incremental learning processes is critical for analogical theory. Along with insights into neural learning based on the salience of dynamic spatio-temporal structure, and the neural priming mechanism of repetition suppression, it establishes relational primacy as a plausible theoretical description of how brains make analogies.

We present an analytical study that puts forward a hypothesis about the role of analogy in thinking. The article presents a critique of the structural mapping hypothesis in analogy as an explanation of the analogical argument and will offer an alternative explanation — the functional hypothesis of the analogical argument. It will also be shown how such functions of thinking as recognition and memory, metaphor and syllogism, generalization and ontology, deduction and induction can be realized with the help (...) of the analogy method developed by the author. The article puts forward a hypothesis that these different functions are nothing more than different manifestations of one mechanism for the dissemination of thinking patterns through analogy. In the previous article [1] we described the results of experiments on obtaining analogs of words based on the statistics of predicates extracted from texts by a simple parser. In this article, continuing the topic of analogy, we will analyze two aspects: - how similar expressions (analogous argument) are found when solving problems and learning; - what is the role of analogy in thinking, namely, what thinking processes are realized with the help of analogy. The article critically examines the most well-known approaches to explaining and attempting to implement analogy. The authors propose another hypothesis, called functional analogy, which is distinguished by the simplicity of explaining the analogical argument, greater correspondence to examples of analogy, in the authors’ opinion, and the possibility of programmatic implementation. (shrink)

Analogy making from examples is a central task in intelligent system behavior. A lot of real world problems involve analogy making and generalization. Research investigates these questions by building computer models of human thinking concepts. These concepts can be divided into high level approaches as used in cognitive science and low level models as used in neural networks. Applications range over the spectrum of recognition, categorization and analogy reasoning. A major part of legal reasoning could be formally interpreted as an (...) analogy making process. Because it is not the same as reasoning in mathematics or the physical sciences, it is necessary to use a method, which incorporates first the ability to specify likelihood and second the opportunity of including known court decisions. We use for modelling the analogy making process in legal reasoning neural networks and fuzzy systems. In the first part of the paper a neural network is described to identify precedents of immaterial damages. The second application presents a fuzzy system for determining the required waiting period after traffic accidents. Both examples demonstrate how to model reasoning in legal applications analogous to recent decisions: first, by learning a system with court decisions, and second, by analyzing, modelling and testing the decision making with a fuzzy system. (shrink)

Advances in artificial intelligence have raised a basic question about human intelligence: Is human reasoning best emulated by applying task‐specific knowledge acquired from a wealth of prior experience, or is it based on the domain‐general manipulation and comparison of mental representations? We address this question for the case of visual analogical reasoning. Using realistic images of familiar three‐dimensional objects (cars and their parts), we systematically manipulated viewpoints, part relations, and entity properties in visual analogy problems. We compared human performance (...) to that of two recent deep learning models (Siamese Network and Relation Network) that were directly trained to solve these problems and to apply their task‐specific knowledge to analogical reasoning. We also developed a new model using part‐based comparison (PCM) by applying a domain‐general mapping procedure to learned representations of cars and their component parts. Across four‐term analogies (Experiment 1) and open‐ended analogies (Experiment 2), the domain‐general PCM model, but not the task‐specific deep learning models, generated performance similar in key aspects to that of human reasoners. These findings provide evidence that human‐like analogical reasoning is unlikely to be achieved by applying deep learning with big data to a specific type of analogy problem. Rather, humans do (and machines might) achieve analogical reasoning by learning representations that encode structural information useful for multiple tasks, coupled with efficient computation of relational similarity. (shrink)

In the discipline of software development, effort estimation renders a pivotal role. For the successful development of the project, an unambiguous estimation is necessitated. But there is the inadequacy of standard methods for estimating an effort which is applicable to all projects. Hence, to procure the best way of estimating the effort becomes an indispensable need of the project manager. Mathematical models are only mediocre in performing accurate estimation. On that account, we opt for analogy-based effort estimation by means of (...) some soft computing techniques which rely on historical effort estimation data of the successfully completed projects to estimate the effort. So in a thorough study to improve the accuracy, models are generated for the clusters of the datasets with the confidence that data within the cluster have similar properties. This paper aims mainly on the analysis of some of the techniques to improve the effort prediction accuracy. Here the research starts with analyzing the correlation coefficient of the selected datasets. Then the process moves through the analysis of classification accuracy, clustering accuracy, mean magnitude of relative error and prediction accuracy based on some machine learning methods. Finally, a bio-inspired firefly algorithm with fuzzy analogy is applied on the datasets to produce good estimation accuracy. (shrink)

Despite the burgeoning research in recent years on what is called analogical reasoning and transfer, the problem of how similarity or invariant relations are fundamentally accessed is typically either unrecognized, or ignored in componential and computational analyses. The access problematic is not a new one, being outlined by the paradox found in Plato’s Meno. In order to understand the analogical-access problematic, it is suggested that the concepts of analogical relations including the lexical concept metaphor, isomorphic relation in (...) mathematics, homology in biology, stimulus generalization in psychology, transfer of learning in education, and transposition phenomena in perception, be reconceptualized as subsets of a higher-order domain as all share the problem of how invariance relations are generated and accessed. A solution is suggested based on two specific evolutionary and neurological models, coupled with findings regarding the cognitive importance of knowledge-base. The paper constitutes a reciprocal complementarity analysis of a previous paper on metaphor. A higher-order form of analogical reasoning called analogical progression is introduced. Implications for research are discussed that indicate the need for a paradigm shift. The paper concludes with a four-stage model of analogical access. (shrink)

Linguistic and non-linguistic pattern learning have been studied separately, but we argue for a comparative approach. Analogous inductive problems arise in phonological and visual pattern learning. Evidence from three experiments shows that human learners can solve them in analogous ways, and that human performance in both cases can be captured by the same models. We test GMECCS, an implementation of the Configural Cue Model in a Maximum Entropy phonotactic-learning framework with a single free parameter, against the alternative (...) hypothesis that learners seek featurally simple algebraic rules. We study the full typology of patterns introduced by Shepard, Hovland, and Jenkins (), instantiated as both phonotactic patterns and visual analogs, using unsupervised training. Unlike SHJ, Experiments 1 and 2 found that both phonotactic and visual patterns that depended on fewer features could be more difficult than those that depended on more features, as predicted by GMECCS but not by rule-seeking. GMECCS also correctly predicted performance differences between stimulus subclasses within each pattern. A third experiment tried supervised training to elicit simple rule-seeking phonotactic learning, but cue-based behavior persisted. We conclude that similar cue-based cognitive processes are available for phonological and visual concept learning, and hence that studying either kind of learning can lead to significant insights about the other. (shrink)

Are morphological patterns learned in the form of rules? Some models deny this, attributing all morphology to analogical mechanisms. The dual mechanism model (Pinker, S., & Prince, A. (1998). On language and connectionism: analysis of a parallel distributed processing model of language acquisition. Cognition, 28, 73-193) posits that speakers do internalize rules, but that these rules are few and cover only regular processes; the remaining patterns are attributed to analogy. This article advocates a third approach, which uses multiple stochastic (...) rules and no analogy. We propose a model that employs inductive learning to discover multiple rules, and assigns them confidence scores based on their performance in the lexicon. Our model is supported over the two alternatives by new "wug test" data on English past tenses, which show that participant ratings of novel pasts depend on the phonological shape of the stem, both for irregulars and, surprisingly, also for regulars. The latter observation cannot be explained under the dual mechanism approach, which derives all regulars with a single rule. To evaluate the alternative hypothesis that all morphology is analogical, we implemented a purely analogical model, which evaluates novel pasts based solely on their similarity to existing verbs. Tested against experimental data, this analogical model also failed in key respects: it could not locate patterns that require abstract structural characterizations, and it favored implausible responses based on single, highly similar exemplars. We conclude that speakers extend morphological patterns based on abstract structural properties, of a kind appropriately described with rules. (shrink)

This essay explores the benefits of utilizing non-scientific examples and analogies in teaching philosophy of science courses, or general introductory courses. These examples can help resolve two basic difficulties faced by most instructors, especially when teaching lower-level courses: first, they can prompt students to take an active interest in the class material, since the examples will involve aspects of the culture well-known to the students; second, these familiar, less-threatening examples will lessen the students' collective anxieties and open them up to (...) learning the material more easily. To demonstrate this strategy of constructing and employing non-scientific examples, a lengthy analogy between musical styles and Kuhn's theory of scientific revolutions is developed. (shrink)

In this paper I offered a friendly amendment to Paul Churchland’s a well-known criticism of Frank Jackson’s knowledge argument. According to Jackson’s argument, a hypothetical Mary, living in her darkened stimulus-impoverished environment, knows all information from physical science about the perception of color but still does not know everything, e.g., what it is like to experience the color red. Churchland offered a refutation by analogy whereby Mary is an ectoplasmologist who knows all the supposed nonphysical things that friends of the (...) hard problem of qualia believe about that phenomenon. So Mary knows everything ectoplasmic about the perception of color but still does not know everything – what it is like to experience the color red. I made a necessary adjustment whereby Mary is not just an ectoplasmologist but a dualist who knows everything physical and ectoplasmic about the perception of color. By the same reasoning, then, the Jackson-style argument would refute dualism too. I also offered a diagnosis of the problem much in the spirit of other criticisms, namely, that things like experiencing red or riding a bicycle can be fully physical even though Mary would not “know how” to experience red or ride a bicycle simply by possessing all the “know that”/propositional/book-learning about those things. (shrink)

The debate over how best to characterize inflectional morphology has been couched largely in terms of the “dual-mechanism” approach described in Pinker (Words and rules: the ingredients of language, Basic Books, 1999) versus “single-mechanism” connectionist approaches derived from Rumelhart and McClelland (On learning past tenses of English verbs, MIT, 1986). There are, however, other single-mechanism approaches. The exemplar-based or analogical models of Daelemans et al. (TimBL: Tilburg Memory-Based Learner, version 4.3 reference guide, ILK, 2002) and Skousen (Analogical (...) modeling of language, Kluwer Academic, 1989) also model inflectional usage accurately within a single-mechanism. The most striking theoretical claim peculiar to these purely analogical models is that they do not posit any resident linguistic generalizations for processing language. Instead they process new instances of usage by comparing them systematically to remembered instances of previous usage. Based on a comparison of their Minimal Generalization Model with an adaptation of Nosofsky's (Journal of Mathematical Psychology 34: 393–418, 1990) Generalized Context Model, Albright and Hayes (Cognition 90: 119–161, 2003) argue that such purely analogical models are intrinsically inadequate for modeling the English past tense. This paper shows, however, that Skousen's (Analogical modeling of language, Kluwer Academic, 1989) Analogical Model performs as well as the Minimal Generalization Model. The implications of these results for cognitive linguistics are discussed. (shrink)