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.

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)

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)

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.

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)

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)

ABSTRACT We consider several ways in which a good understanding of modern techniques and principles in physics can elucidate ecology, and we focus on analogical reasoning between these two branches of science. Analogical reasoning requires an understanding of both sciences and an appreciation of the similarities and points of contact between the two. In the current ecological literature on the relationship between ecology and physics, there has been some misunderstanding about the nature of modern physics and its methods. (...) Physics is seen as being much cleaner and tidier than ecology. When compared to this idealized, fictional version of physics, ecology looks very different, and the prospect of ecology and physics learning from one another is questionable. We argue that physics, once properly understood, is more like ecology than ecologists have thus far appreciated. Physicists and ecologists can and do learn from each other, and, in this paper, we outline how analogical reasoning can facilitate such exchanges. (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)

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)

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)

Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...) solution for the so-called NP-complete problems will also be a solution for any other such problems. Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist. In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AI-complete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deep-learning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI. (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.

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)

In some areas of cognitive science we are confronted with ultrafast cognition, exquisite context sensitivity, and scale-free variation in measured cognitive activities. To move forward, we suggest a need to embrace this complexity, equipping cognitive science with tools and concepts used in the study of complex dynamical systems. The science of movement coordination has benefited already from this change, successfully circumventing analogous paradoxes by treating human activities as phenomena of self-organization. Therein, action and cognition are seen to be emergent in (...) ultrafast symmetry breaking across the brain and body; exquisitely constituted of the otherwise trivial details of history, context, and environment; and exhibiting the characteristic scale-free signature of self-organization. (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)

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)

Chapter LEARNING BY UNDERSTANDING ANALOGIES Russell Greiner1 ABSTRACT2 This article describes a method for learning by analogy — ie, for proposing new ...

This qualitative study aimed at exploring whether students’ successful use of analogy in learning curriculum complex science concepts was related: to the level of their understanding of a specific analogy and to their metacognitive awareness of how the analogy was to be used and of the changes produced in their own conceptual structures. In implementing a biological curriculum unit, students’ prior knowledge has been taken into account in order to examine its conceptual growth and change via a not completely (...) introduced analogy to 15 fifth graders as they were engaged in understanding the ways in which the new concepts were similar to a familiar source . Qualitative data present the children's mapping processes in elaborating the analogy and their metacognitive awareness of the meaning and purpose of the analogy itself, and their personal use of the analogy in changing initial conceptions. As hypothesised, the results showed a high positive correlation among the level of conceptual understanding of the new science topic, the level of understanding of the analogy, and the level of effective use of the analogy in integrating the new information into the pre‐existing conceptual structures. Key implications on the use of analogy for conceptual change in the classroom are outlined. (shrink)

Cognitive Science, Volume 46, Issue 4, April 2022.

In this article, after some thoughts on medieval logic and teaching, we present Thomas Murner’s text, Logica memorativa, showing some of his mnemonic strategies for the student to learn logic quickly. Murner offers a type of “flash cards” that illustrate much of the teaching of logic at the beginning of the sixteenth century. The first impression is visual, because the cards do not contain words that illustrate their content. Murner’s exposition rests on analogies between logic themes that are explained and (...) the visual images presented. (shrink)

This paper defines the form of prior knowledge that is required for sound inferences by analogy and single-instance generalizations, in both logical and probabilistic reasoning. In the logical case, the first order determination rule defined in Davies (1985) is shown to solve both the justification and non-redundancy problems for analogical inference. The statistical analogue of determination that is put forward is termed 'uniformity'. Based on the semantics of determination and uniformity, a third notion of "relevance" is defined, both logically (...) and probabilistically. The statistical relevance of one function in determining another is put forward as a way of defining the value of information: The statistical relevance of a function F to a function G is the absolute value of the change in one's information about the value of G afforded by specifying the value of F. This theory provides normative justifications for conclusions projected by analogy from one case to another, and for generalization from an instance to a rule. The soundness of such conclusions, in either the logical or the probabilistic case, can be identified with the extent to which the corresponding criteria (determination and uniformity) actually hold for the features being related. (shrink)

The problem of mortality treats death as posing a paradox for the narrative view of the self. This view, on some interpretations, needs death in order to complete a life in a manner analogous to the ending of a story. But death is inaccessible to the subject herself, and so the analogy fails. Our inability to grasp the event of our own death is thought to undermine the possibility of achieving a meaningful, coherent, or complete life on narrativist terms. Narrativist (...) attempts to solve the problem of mortality often involve exercises in "learning to be dead" in an effort to demonstrate that one's death is not entirely outside one's grasp. But there are different versions of the problem of mortality, which invite disparate solutions. I argue that most versions do not in fact pose a significant conundrum for the narrative view of the self. In order to strike at the narrative view in particular, in a fashion that is significant and a form that is paradoxical, a quite specific type of death must be at issue--one that is sudden and unanticipated. But a suggested solution to this version of the problem of mortality implies that the problem can be dissolved by disavowing certain alleged narrative presuppositions that fuel it. Those suppositions may, in turn, be essential to sustaining a uniquely narrative outlook on life and death. (shrink)

The continuing and expanding successes of behavior therapy in the treatment of psychological problems raise important questions about their scientific and philosophical bases. In this paper I examine the claims of Edward Erwin that behaviorism cannot provide an adequate philosophical basis for behavior therapy, contemporary learning theories which exclude cognitive factors as causes of behavior cannot provide an adequate empirical basis for behavior therapy; and learning theories have played only a heuristic role in the development of behavior therapy. (...) And I argue that Erwin's claims rest on two doubtful logical empiricist assumptions about scientific theory and explanation. Using different assumptions, I argue that some of the regularities involving cognitions which seem to govern behavior therapy techniques are analogical extensions of operant learning principles. On this basis I suggest some modifications of Erwin's claim about the philosophical foundations of behavior therapy. (shrink)