Understanding analogical reasoning : viewpoints from psychology and related disciplines

Analogy and metaphor have a long history of study in linguistics, education, philosophy and psychology. Consensus over what analogy is or how analogy functions in language and thought, however, has been elusive. This paper, the first in a two part series, examines these various research traditions, attempting to bring out major lines of agreement over the role of analogy in individual human experience. As well as being a general literature review which may be helpful for newcomers to the study of analogy, this paper attempts to extract from these literatures existing theories, models and concepts which may be interesting or useful for computational studies of analogical reasoning

Learning Analogies and Semantic Relations

We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the Scholastic Aptitude Test (SAT). A verbal analogy has the form A:B::C:D, meaning "A is to B as C is to D"; for example, mason:stone::carpenter:wood. SAT analogy questions provide a word pair, A:B, and the problem is to select the most analogous word pair, C:D, from a set of five choices. The VSM algorithm correctly answers 47% of a collection of 374 college-level analogy questions (random guessing would yield 20% correct). We motivate this research by relating it to work in cognitive science and linguistics, and by applying it to a difficult problem in natural language processing, determining semantic relations in noun-modifier pairs. The problem is to classify a noun-modifier pair, such as "laser printer", according to the semantic relation between the noun (printer) and the modifier (laser). We use a supervised nearest-neighbour algorithm that assigns a class to a given noun-modifier pair by finding the most analogous noun-modifier pair in the training data. With 30 classes of semantic relations, on a collection of 600 labeled noun-modifier pairs, the learning algorithm attains an F value of 26.5% (random guessing: 3.3%). With 5 classes of semantic relations, the F value is 43.2% (random: 20%). The performance is state-of-the-art for these challenging problems

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

The Latent Relation Mapping Engine: Algorithm and Experiments

Many AI researchers and cognitive scientists have argued that analogy is the core of cognition. The most influential work on computational modeling of analogy-making is Structure Mapping Theory (SMT) and its implementation in the Structure Mapping Engine (SME). A limitation of SME is the requirement for complex hand-coded representations. We introduce the Latent Relation Mapping Engine (LRME), which combines ideas from SME and Latent Relational Analysis (LRA) in order to remove the requirement for hand-coded representations. LRME builds analogical mappings between lists of words, using a large corpus of raw text to automatically discover the semantic relations among the words. We evaluate LRME on a set of twenty analogical mapping problems, ten based on scientific analogies and ten based on common metaphors. LRME achieves human-level performance on the twenty problems. We compare LRME with a variety of alternative approaches and find that they are not able to reach the same level of performance.Comment: related work available at http://purl.org/peter.turney

Metaphorically speaking: the role of cognitive abilities in the production of figurative language

Figurative language is one of the most common expressions of creative behavior in everyday life. However, the cognitive mechanisms behind figures of speech such as metaphor remain largely unexplained. Recent evidence suggests fluid and executive abilities are important to the generation of conventional and creative metaphors. The present study investigated whether several factors of the Cattell-Horn-Carroll (CHC) model of intelligence contribute to generating these different types of metaphors. Specifically, the roles of fluid intelligence (Gf), crystallized knowledge (Gc), and general retrieval ability (Gr) were explored. Participants completed a series of intelligence tests and were asked to produce conventional and creative metaphors. Structural equation modeling was used to assess the contribution of the different factors of intelligence to metaphor production. Model results for creative metaphor showed large effects of Gf (β = .45) and Gr (β = .52), whereas Gc had a moderate effect on conventional metaphor production (β = .30). The present research extends the traditional study of divergent thinking to an area important to everyday communication, and advances a testable framework of creative cognition based on the CHC model of intelligence

Conceptual Metaphors: a review with implications for human understandings and systems practice

We provide an overview of metaphor theory and explore implications for systems practice by building on claims that metaphors are central to our ways of understanding. As stakeholders will have different understandings, each metaphor will reveal and conceal different aspects of their understandings. These differences need to be accommodated within systems practice. Our contribution in this paper is to show how metaphors can explain, appreciate and create different understandings. Further, new understandings can emerge from considering different metaphors

The Case for Dynamic Models of Learners' Ontologies in Physics

In a series of well-known papers, Chi and Slotta (Chi, 1992; Chi & Slotta, 1993; Chi, Slotta & de Leeuw, 1994; Slotta, Chi & Joram, 1995; Chi, 2005; Slotta & Chi, 2006) have contended that a reason for students' difficulties in learning physics is that they think about concepts as things rather than as processes, and that there is a significant barrier between these two ontological categories. We contest this view, arguing that expert and novice reasoning often and productively traverses ontological categories. We cite examples from everyday, classroom, and professional contexts to illustrate this. We agree with Chi and Slotta that instruction should attend to learners' ontologies; but we find these ontologies are better understood as dynamic and context-dependent, rather than as static constraints. To promote one ontological description in physics instruction, as suggested by Slotta and Chi, could undermine novices' access to productive cognitive resources they bring to their studies and inhibit their transition to the dynamic ontological flexibility required of experts.Comment: The Journal of the Learning Sciences (In Press

The Role of the Arts in Professional Education: Surveying the Field

Many educators of professionals use arts-based approaches, but often explore this within the confines of their own professional disciplines. This paper consists of a thematic review of the literature on arts and professional education, which cuts across professional disciplines in an attempt to identify the specific contribution the arts can make to professional education. The review identified five broad approaches to the use of the arts in professional education: exploring their role in professional practice, illustrating professional issues and dilemmas, developing empathy and insight, exploring professional identities and developing self-awareness and interpersonal expression. Woven through these approaches we found that the development of a more sophisticated epistemology and a critical social perspective were common outcomes of art-based work in professional education. Arts-based approaches may help learners to make a critical assessment of their own roles and identities within professions, and to consider the impact of professions in shaping the broader society

Elementary Forms of the Metaphorical Life : Tropes at Work in Durkheim’s Theory of the Religious

Peer reviewedPostprin

Making sense of theory construction: Metaphor and disciplined imagination

This article draws upon Karl Weick’s insights into the nature of theorizing, and extends and refines his conception of theory construction as ‘disciplined imagination’. An essential ingredient in Weick’s ‘disciplined imagination’ involves his assertion that thought trials and theoretical representations typically involve a transfer from one epistemic sphere to another through the creative use of metaphor. The article follows up on this point and draws out how metaphor works, how processes of metaphorical imagination partake in theory construction, and how insightful metaphors and the theoretical representations that result from them can be selected. The paper also includes a discussion of metaphors-in-use (organizational improvisation as jazz and organizational behavior as collective mind) which Weick proposed in his own writings. The whole purpose of this exercise is to theoretically augment and ground the concept of ‘disciplined imagination’, and in particular to refine the nature of thought trials and selection within it. In doing so, we also aim to provide pointers for the use of metaphorical imagination in the process of theory construction