Conceptual metaphors are not automatically accessed during idiom comprehension

Do conceptual analogies motivate idiom use and comprehension in discourse? For example, a story in which a person is described asfuming would be analogically consistent with an idiom such asblew her top, but inconsistent with an idiom such asbite his head off. Earlier work by Nayak and Gibbs(1990) had suggested that people use such analogical information during idiom comprehension. We replicated their findings inan idiom choice task, suggesting that people can indeed make use of such knowledge. However, when reading times were used to assess idiom comprehensibility, no effects of analogical consistency were found. We conclude that conceptual analogies play little, if any, role in idiom comprehension unless people have the time (and motivation) to make considered judgments.     

Building to discover: a common coding model

I present a case study of scientific discovery, where building two functional and behavioral approximations of neurons, one physical and the other computational, led to conceptual and implementation breakthroughs in a neural engineering laboratory. Such building of external systems that mimic target phenomena, and the use of these external systems to generate novel concepts and control structures, is a standard strategy in the new engineering sciences. I develop a model of the cognitive mechanism that connects such built external systems with internal models, and I examine how new discoveries, and consensus on discoveries, could arise from this external-internal coupling and the building process. The model is based on the emerging framework of common coding, which proposes a shared representation in the brain between the execution, perception, and imagination of movement.     

Interpretation of instructions: A source of individual differences in analogical reasoning

Two studies were conducted to identify individual differences in the effect of instruction on strategies used to solve figural/numerical analogies. In Study 1, students aged 9, 11, 13, and 19 years were given incomplete instructions and then were assessed for (a) consistent use of an appropriate rule for solving analogies and (b) the type and number of stimulus attributes that are incorporated in those analogical rules. Use of an analogical rule increased with age and corresponded to higher scores on a psychometric test of reasoning within each age group. In Study 2, analysis of verbal protocols was used to identify the strategies of some 9- and 11-year-olds who showed a peculiar pattern of responding in Study 1. These children attended to relevant attributes but used systematic nonanalogical rules to solve problems. When provided with more specific instructions, most of these children adopted an analogical rule but failed to incorporate relevant attributes. Results indicate that selection or construction of task-relevant solution strategies from incomplete instructions may partially account for differential performance on tests of analogical reasoning.     

The development of analogical reasoning processes.

Two experiments were conducted to test the generalizability to children of a theory of analogical reasoning processes originally proposed for adults (R. J. Sternberg, Psychological Review, 1977a, 84, 353–378; R. J. Sternberg, Intelligence, information processing, and analogical reasoning: The componential analysis of human abilities, Hillsdale, N.J.: Lawrence Erlbaum Associates, 1977b) and to examine the development of analogical reasoning processes in terms of five proposed sources of cognitive development: (a) availability of component operations; (b) strategy for combining multiple component operation; (c) strategy for combining multiple executions of the same component operation; (d) consistency in use of strategy; (e) component-operation latency and difficulty. Between 15 and 21 subjects in each of grades 2, 4, 6, and adulthood (ages 8, 10, 12, and 19 years, respectively) were tested in their ability to solve analogies of systematically varied difficulty. Performance was measured in terms of latencies for items solved correctly, latencies for all items solved, and error rates. A slightly modified version of the Stenberg, 1977(a), Stenberg, 1977(b) theory was found to be applicable to the data for each of the age levels tested. In analogies with perceptually separable attributes, change over age was found in sources (d) and (e) noted above. In analogies with perceptually integral attributes, change over age was found in sources (a), (c), (d), and (e). Developmental trends were discussed in terms of past theory and findings, and possible reasons for differences in developmental patterns between the two kinds of analogies were suggested.     

The Power of Analogies for Imagining and Governing Emerging Technologies

The emergence of new technologies regularly involves comparisons with previous innovations. For instance, analogies with asbestos and genetically modified organisms have played a crucial role in the early societal debate about nanotechnology. This article explores the power of analogies in such debates and how they could be effectively and responsibly employed for imagining and governing emerging technologies in general and nanotechnology in particular. First, the concept of analogical imagination is developed to capture the explorative and anticipatory potential of analogies. Yet analogies do not simply stimulate imagination, they also restrict it by framing emerging technologies in specific ways. Thus, second, the article argues that tracing the rhetorical and persuasive power of analogical arguments is essential for understanding how analogies are constructed to legitimise assessments, funding policies, and governance approaches. Third, the article addresses factors that account for the persuasiveness of analogies in debates about emerging technologies. The article concludes with reflections on how analogical imagination and an enhanced analogical sensibility for framing and persuasive effects can foster responsible research and innovation (RRI).     

Seek,and ye shall find: Differences between spontaneous and voluntary analogical retrieval

The present study tackles two overlooked aspects of analogical retrieval: (a) whether argumentation activities elicit a spontaneous search for analogical sources, and (b) whether strategic search can relax the superficial bias typically obtained in experimental studies of analogical retrieval. In Experiment 1, participants had to generate arguments for a target situation under three conditions: without indication to use analogies, with indication to use analogies, and with indication to search for sources within domains provided by the experimenters. Results showed that while voluntary search yields analogical retrievals reliably, the argumentation activity seldom elicits spontaneous remindings. A second set of results demonstrated that the superficial bias can be strategically relaxed, leading to a majority of distant retrievals. Experiment 2 replicated this result with the instruction to search within domains different from that of the target, and without providing a list of specific domains. The theoretical and educational implications of these findings are discussed.     

Science as Structured Imagination

This paper offers an analysis of scientific creativity based on theoretical models and experimental results of the cognitive sciences. Its core idea is that scientific creativity — like other forms of creativity — is structured and constrained by prior ontological expectations. Analogies provide scientists with a powerful epistemic tool to overcome these constraints. While current research on analogies in scientific understanding focuses on near analogies, where target and source domain are close, we argue that distant analogies — where target and source domain differ widely — are especially useful in periods of intense conceptual change. To argue this point, we discuss three case studies from the history of science: early physiologists like Harvey, early evolutionary biologists like Darwin, and recent theorists on the evolution of the human mind like Mithen.     

Finding faults: analogical comparison supports spatial concept learning in geoscience

A central issue in education is how to support the spatial thinking involved in learning science, technology, engineering, and mathematics (STEM). We investigated whether and how the cognitive process of analogical comparison supports learning of a basic spatial concept in geoscience, fault. Because of the high variability in the appearance of faults, it may be difficult for students to learn the category-relevant spatial structure. There is abundant evidence that comparing analogous examples can help students gain insight into important category-defining features (Gentner in Cogn Sci 34(5):752–775, 2010). Further, comparing high-similarity pairs can be especially effective at revealing key differences (Sagi et al. 2012). Across three experiments, we tested whether comparison of visually similar contrasting examples would help students learn the fault concept. Our main findings were that participants performed better at identifying faults when they (1) compared contrasting (fault/no fault) cases versus viewing each case separately (Experiment 1), (2) compared similar as opposed to dissimilar contrasting cases early in learning (Experiment 2), and (3) viewed a contrasting pair of schematic block diagrams as opposed to a single block diagram of a fault as part of an instructional text (Experiment 3). These results suggest that comparison of visually similar contrasting cases helped distinguish category-relevant from category-irrelevant features for participants. When such comparisons occurred early in learning, participants were more likely to form an accurate conceptual representation. Thus, analogical comparison of images may provide one powerful way to enhance spatial learning in geoscience and other STEM disciplines.     

A comparison of the classification capabilities of the 1-dimensional kohonen neural network with two pratitioning and three hierarchical cluster analysis algorithms

Neural Network models are commonly used for cluster analysis in engineering, computational neuroscience, and the biological sciences, although they are rarely used in the social sciences. In this study we compare the classification capabilities of the 1-dimensional Kohonen neural network with two partitioning (Hartigan and Späthk-means) and three hierarchical (Ward's, complete linkage, and average linkage) cluster methods in 2,580 data sets with known cluster structure. Overall, the performance of the Kohonen networks was similar to, or better than, the performance of the other methods.     

Analogical insight: toward unifying categorization and analogy

The purpose of this paper is to present two kinds of analogical representational change, both occurring early in the analogy-making process, and then, using these two kinds of change, to present a model unifying one sort of analogy-making and categorization. The proposed unification rests on three key claims: (1) a certain type of rapid representational abstraction is crucial to making the relevant analogies (this is the first kind of representational change; a computer model is presented that demonstrates this kind of abstraction), (2) rapid abstractions are induced by retrieval across large psychological distances, and (3) both categorizations and analogies supply understandings of perceptual input via construing, which is a proposed type of categorization (this is the second kind of representational change). It is construing that finalizes the unification.     

The Impact of Analogies on Creative Concept Generation: Lessons From an In Vivo Study in Engineering Design

Research on innovation often highlights analogies from sources outside the current problem domain as a major source of novel concepts; however, the mechanisms underlying this relationship are not well understood. We analyzed the temporal interplay between far analogy use and creative concept generation in a professional design team's brainstorming conversations, investigating the hypothesis that far analogies lead directly to very novel concepts via large steps in conceptual spaces (jumps). Surprisingly, we found that concepts were more similar to their preceding concepts after far analogy use compared to baseline situations (i.e., without far analogy use). Yet far analogies increased the team's concept generation rate compared to baseline conditions. Overall, these results challenge the view that far analogies primarily lead to novel concepts via jumps in conceptual spaces and suggest alternative pathways from far analogies to novel concepts (e.g., iterative, deep exploration within a functional space).     

The impact of semantic distance and induced stress on analogical reasoning: A neurocomputational account

In a study of reasoning with four-term verbal analogy problems, we explored the relationship between the effects of an acute, mild stressor and the complexity of the reasoning process. Participants judged whether analogy problems in the form A:B :: C:D were valid or invalid, on the basis of whether the relation in the A:B term matched that in the C:D term. Half of the problems contained a C:D pair semantically near the A:B pair (e.g., NOSE:SCENT :: TONGUE:TASTE), and the other half contained ones semantically far from A:B (e.g., NOSE:SCENT :: ANTENNA:SIGNAL). After an initial block without stress, participants were randomly assigned to count backward by 13?s from 1,000 while being told to go faster, or to count forward by 1?s from 0. The stress-induced participants reported a significant increase in state anxiety as compared to controls immediately after the mental arithmetic task. Stressed participants performed less accurately (as measured by d') on both near and far analogy problems, mainly due to an increase in false alarms. We were able to model the influence of semantic distance using the ??learning and inference with schemas and analogies?? (LISA) model. Our findings indicated that even mild increases in stress impair analogical reasoning. However, the decrement does not seem to directly involve the integration of relations, but rather is due to a shift in decision strategy: Under stress, people show an increased tendency to endorse analogies as valid when the terms in the individual pairs are semantically related to each other, even if the overall analogical relationship is not valid.     

Integrating structure and meaning: a distributed model of analogical mapping

In this paper we present Drama , a distributed model of analogical mapping that integrates semantic and structural constraints on constructing analogies. Specifically, Drama uses holographic reduced representations (Plate, 1994), a distributed representation scheme, to model the effects of structure and meaning on human performance of analogical mapping. Drama is compared to three symbolic models of analogy (SME, Copycat, and ACME) and one partially distributed model (LISA). We describe Drama's performance on a number of example analogies and assess the model in terms of neurological and psychological plausibility. We argue that Drama's successes are due largely to integrating structural and semantic constraints throughout the mapping process. We also claim that Drama is an existence proof of using distributed representations to model high-level cognitive phenomena.     

A uniform model of computational conceptual blending

We present a mathematical model for the cognitive operation of conceptual blending that aims at being uniform across different representation formalisms, while capturing the relevant structure of this operation. The model takes its inspiration from amalgams as applied in case-based reasoning, but lifts them into category theory so as to follow Joseph Goguen’s intuition for a mathematically precise characterisation of conceptual blending at a representation-independent level of abstraction. We prove that our amalgam-based category-theoretical model of conceptual blending is essentially equivalent to the pushout model in the ordered category of partial maps as put forward by Goguen. But unlike Goguen’s approach, our model is more suitable to capture computational realisations of conceptual blending, and we exemplify this by concretising our model to computational conceptual blends for various representation formalisms and application domains.     

Convergent and divergent thinking in verbal analogy

Individual differences in convergent and divergent thinking may uniquely explain variation in analogical reasoning ability. Across two studies we investigated the relative influences of divergent and convergent thinking as predictors of verbal analogy performance. Performance on both convergent thinking (i.e., Remote Associates Test) and divergent thinking (i.e., Alternative Uses Task) uniquely predicted performance on both analogy selection (Studies 1 and 2) and analogical generation tasks (Study 2). Moreover, convergent and divergent thinking were predictive above and beyond creative behaviours in Study 1 and a composite measure of crystallised intelligence in Study 2. Verbal analogies in Study 2 also varied in semantic distance, with results demonstrating divergent thinking as a stronger predictor of analogy generation for semantically far than for semantically near analogies. Results thus further illuminate the link between analogical reasoning and creative cognition by demonstrating convergent and divergent thinking as predictors of verbal analogy.     

A simple computational algorithm of model-based choice preference

A broadly used computational framework posits that two learning systems operate in parallel during the learning of choice preferences—namely, the model-free and model-based reinforcement-learning systems. In this study, we examined another possibility, through which model-free learning is the basic system and model-based information is its modulator. Accordingly, we proposed several modified versions of a temporal-difference learning model to explain the choice-learning process. Using the two-stage decision task developed by Daw, Gershman, Seymour, Dayan, and Dolan (2011), we compared their original computational model, which assumes a parallel learning process, and our proposed models, which assume a sequential learning process. Choice data from 23 participants showed a better fit with the proposed models. More specifically, the proposed eligibility adjustment model, which assumes that the environmental model can weight the degree of the eligibility trace, can explain choices better under both model-free and model-based controls and has a simpler computational algorithm than the original model. In addition, the forgetting learning model and its variation, which assume changes in the values of unchosen actions, substantially improved the fits to the data. Overall, we show that a hybrid computational model best fits the data. The parameters used in this model succeed in capturing individual tendencies with respect to both model use in learning and exploration behavior. This computational model provides novel insights into learning with interacting model-free and model-based components.     

The Role of Surface Similarity in Analogical Retrieval: Bridging the Gap Between the Naturalistic and the Experimental Traditions

Blanchette and Dunbar (2000) have claimed that when participants are allowed to draw on their own source analogs in the service of analogical argumentation, retrieval is less constrained by surface similarity than traditional experiments suggest. In two studies, we adapted this production paradigm to control for the potentially distorting effects of analogy fabrication and uneven availability of close and distant sources in memory. Experiment 1 assessed whether participants were reminded of central episodes from popular movies while generating analogies for superficially similar versus superficially dissimilar target situations. In Experiment 2, we modified this procedure to assess the retrieval of autobiographic memories, more familiar to participants than fictional episodes. Both studies revealed a strong effect of surface similarity on the retrieval of participants' own sources, thus suggesting that the superficial bias typically observed in experimental studies—and simulated by most computational models—does not originate in a lack of ecological validity.     

Modeling Mental Spatial Reasoning About Cardinal Directions

This article presents research into human mental spatial reasoning with orientation knowledge. In particular, we look at reasoning problems about cardinal directions that possess multiple valid solutions (i.e., are spatially underdetermined), at human preferences for some of these solutions, and at representational and procedural factors that lead to such preferences. The article presents, first, a discussion of existing, related conceptual and computational approaches; second, results of empirical research into the solution preferences that human reasoners actually have; and, third, a novel computational model that relies on a parsimonious and flexible spatio‐analogical knowledge representation structure to robustly reproduce the behavior observed with human reasoners.     

Baboons, like humans, solve analogy by categorical abstraction of relations

Reasoning by analogy is one of the most complex and highly adaptive cognitive processes in abstract thinking. For humans, analogical reasoning entails the judgment and conceptual mapping of relations-between-relations and is facilitated by language (Gentner in Cogn Sci 7:155–170, 1983; Premack in Thought without language, Oxford University Press, New York, 1986). Recent evidence, however, shows that monkeys like “language-trained” apes exhibit similar capacity to match relations-between-relations (Fagot and Thompson in Psychol Sci 22:1304–1309, 2011; Flemming et al. in J Exp Psychol: Anim Behav Process 37:353–360, 2011; Truppa et al. in Plos One 6(8):e23809, 2011). Whether this behavior is driven by the abstraction of categorical relations or alternatively by direct perception of variability (entropy) is crucial to the debate as to whether nonhuman animals are capable of analogical reasoning. In the current study, we presented baboons (Papio papio) and humans (Homo sapiens) with a computerized same/different relational-matching task that in principle could be solved by either strategy. Both baboons and humans produced markedly similar patterns of responding. Both species responded different when the perceptual variability of a stimulus array fell exactly between or even closer to that of a same display. Overall, these results demonstrate that categorical abstraction trumped perceptual properties and, like humans, Old World monkeys can solve the analogical matching task by judging the categorical abstract equivalence of same/different relations-between-relations.