Model Of Analogical Reasoning Research Articles

An experimental and simulation study of the impact of emotional information on analogical reasoning

We investigated whether and how emotional information would affect analogical reasoning. We hypothesized that task-irrelevant emotional information would impair performance whereas task-relevant emotional information would enhance it. In Study 1, 233 undergraduates completed a novel version of the People Pieces Task (Emotional Faces People Task), an analogical reasoning task in which the task characters displayed emotional or neutral facial expressions (within-participants). The emotional faces were relevant or irrelevant to the task (between-participants). We simulated the behavioral results using the Learning and Inference with Schemas and Analogies (LISA) model of relational reasoning. LISA is a neurally plausible, symbolic-connectionist computational model of analogical reasoning. In comparison to neutral trials, participants were slower but more accurate on emotion-relevant trials, and were faster but less accurate on emotion-irrelevant trials. Simulations using the LISA model demonstrated that it is possible to account for the effects of emotional information on reasoning in terms of how emotional stimuli attract attention during a reasoning task. In Study 2, 255 undergraduates completed the Emotional Faces People Task at either a high- or low-working memory load. The high working memory load condition of Study 2 replicated the findings of Study 1, showing that participants were more accurate on emotion-relevant trials than on emotion-irrelevant trials; in Study 2, this increased accuracy could not be accounted for by a speed-accuracy tradeoff. The working memory manipulation influenced the manner in which the congruence (with the correct answer) of emotion-irrelevant emotion influenced performance. Simulations using the LISA model showed that manipulating the salience of emotion, the error penalty, as well as vigilance (which determines the likelihood that LISA will notice it has attended to an irrelevant relation), could reasonably reproduce the behavioral results of both low and high working memory load conditions of Study 2.

The structure of analogical reasoning in bioethics.

Casuistry, which involves analogical reasoning, is a popular methodological approach in bioethics. The method has its advantages and challenges, which are widely acknowledged. Meta-philosophical reflection on exactly how bioethical casuistry works and how the challenges can be addressed is limited. In this paper we propose a framework for structuring casuistry and analogical reasoning in bioethics. The framework is developed by incorporating theories and insights from the philosophy of science: Mary Hesse's ideas on horizontal and vertical relations in analogical reasoning in the sciences, Paul Bartha's articulation model of analogical reasoning and Daniel Steel's insights on mechanism-based extrapolation in biomedical research. Adopting our framework results in two practical benefits: it sets methodological standards for analogical reasoning and enables us to compare and evaluate diverging lines of analogical reasoning in a systematic way. Adopting the framework also has theoretical benefits: it helps to understand how analogical reasoning can have moral normativity; it pinpoints exactly where moral principles or theories enter analogical reasoning; and it helps to understand why casuistry is an attractive method in bioethics and in applied ethics more generally.

Understanding the What and When of Analogical Reasoning Across Analogy Formats: An Eye-Tracking and Machine Learning Approach.

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.

Prospects for Analogue Confirmation

AbstractIn analogical reasoning, observations about one or more source domains provide varying degrees of support for a conjecture about a target domain. Norton (2021) challenges the usefulness of formal models of analogical inference. Other philosophers (Dardashti et al. 2019) develop just such formal models in order to show how analogue experiments can confirm a hypothesis, even when the target domain is inaccessible. This paper defends the value of quasi-formal models of analogical reasoning. Such models are broadly compatible with Norton’s position, but help to clarify the structure of analogical reasoning and to identify basic requirements for a good analogical inference.

Deductive Analogy Defended

In (2015) André Juthe proposed a novel sui generis model of analogical reasoning and argued that where deductivist accounts failed, his account succeeded. In (2018) I argued that most of Juthe’s arguments against deductivism failed; identifying five criteria, I argued that Juthe’s account did no better than deductivist accounts with regard to four of them. Replying in (2019), Juthe argues that my objections miss their mark, that his own account does meet the criteria and that deductivist accounts fail to meet them, and that this does provide his own account with strong, albeit inconclusive, evidence in its favour. In this paper I will defend my case against these new arguments.

Verbal and non-verbal analogical reasoning in the emergent literacy period: an exploratory analysis of the influence of AR modality on listening comprehension, phonological awareness and lexical skills (Razonamiento analógico verbal y no verbal en el período de alfabetización emergente: un análisis exploratorio de la influencia de modalidad de AR sobre la comprensión auditiva, la conciencia fonológica y las habilidades léxicas)

ABSTRACT Despite the important role assigned to analogical reasoning (AR) in cognition and language, the relationship between this ability and language skills in the emergent literacy period has not been sufficiently studied, especially in Spanish speakers. To explore this link, we examined two modalities of AR (verbal and non-verbal) as predictors of linguistic skills in Spanish-speaking preschoolers with typical language development. A descriptive, cross-sectional study was conducted and non-probability sampling was used. Participants were assessed on listening comprehension, lexical skills, phonological awareness and verbal and non-verbal AR. The sample consisted of 48 monolingual Spanish-speaking children from 4.1 to 6.0 years old. Hierarchical regression analysis identified a general model of age and verbal AR as significant predictors of overall emergent literacy performance and also pointed out the different contributions of the verbal and non-verbal AR to models of listening comprehension, lexical skills and phonological awareness. These findings contribute to the discussion on the dynamics of this relationship in the emergent literacy period and underscore the need to analyse specific analogical modalities in models of language development.

Editorial Introduction: Exploring policy diffusion, collaborative governance, and policy conflict

Editorial Introduction: Exploring policy diffusion, collaborative governance, and policy conflict

Competition among Stimulus–Stimulus Relations in an Intelligence-Test Format

A behavior analytic model of analogical reasoning has been developed over the past two decades. Applying this model, we analyzed 12 visual analogies of the Test Of Nonverbal Intelligence (TONI-2) and predicted competition between stimulus-stimulus relations based on the number of nonarbitrary relations shared between the sample and the comparisons. Thirty-three adult participants solved the selected items while their eye movements were monitored using an eye-tracking device. For every item, a repeated measures ANOVA showed that observation duration was significantly higher in the response alternatives that shared more nonarbitrary relations with the sample (p < 0.001). The correct comparison was observed for a longer period of time, followed by the higher rank distractors, and then the rest. The effect size ranged from 0.293 to 0.764. The systematic application of the behavior analytic model of analogical reasoning allowed us to predict the behavior of participants taking an intelligence test based on visual analogies, at least to some extent. This work extends the application of the behavior analytic model of analogical reasoning to a rather complex situation (i.e., a published intelligence test).

Analogical reasoning, control and executive functions: A developmental investigation with eye-tracking

We use eye-tracking to study the development of analogical reasoning in 5-year-olds, 8-year-olds, adolescents and adults in the A:B:C:D paradigm. We observed significant differences between groups in the way they explored the space of possible answers to analogy problems. Looking times showed that adults first studied the possible relations between A and B and, thereafter, they moved to C and the solution set. Children, by contrast, tended to start with the C item and organized their search around this item. Children's and adults' saccade patterns differed at the beginning and the end of the trial. Children monitored their search less efficiently than adults (fewer saccades from the solution set to the A–B pair at the end of the trial). Looking patterns associated with errors and correct trials also differed from the start of the trial, suggesting that different search strategies lead to different outcomes. Results are contrasted with current models of analogical reasoning and are discussed in terms of the interaction between the development of executive functions and the control and integration of the information pertaining to the analogy problem.

Casuistry as common law morality.

This article elaborates on the relation between ethical casuistry and common law reasoning. Despite the frequent talk of casuistry as common law morality, remarks on this issue largely remain at the purely metaphorical level. The article outlines and scrutinizes Albert Jonsen and Stephen Toulmin's version of casuistry and its basic elements. Drawing lessons for casuistry from common law reasoning, it is argued that one generally has to be faithful to ethical paradigms. There are, however, limitations for the binding force of paradigms. The most important limitations--the possibilities of overruling and distinguishing paradigm norms--are similar in common law and in casuistry, or so it is argued. These limitations explain why casuistry is not necessarily overly conservative and conventional, which is one line of criticism to which casuists can now better respond. Another line of criticism has it that the very reasoning from case to case is extremely unclear in casuistry. I suggest a certain model of analogical reasoning to address this critique. All my suggestions to understand and to enhance casuistry make use of common law reasoning whilst remaining faithful to Jonsen and Toulmin's main ideas and commitments. Further developed along these lines, casuistry can appropriately be called "common law morality."

Large-Scale Analogical Reasoning

Cognitive simulation of analogical processing can be used to answer comparison questions such as: What are the similarities and/or differences between A and B, for concepts A and B in a knowledge base (KB). Previous attempts to use a general-purpose analogical reasoner to answer such questions revealed three major problems: (a) the system presented too much information in the answer, and the salient similarity or difference was not highlighted; (b) analogical inference found some incorrect differences; and (c) some expected similarities were not found. The cause of these problems was primarily a lack of a well-curated KB and, and secondarily, algorithmic deficiencies. In this paper, relying on a well-curated biology KB, we present a specific implementation of comparison questions inspired by a general model of analogical reasoning. We present numerous examples of answers produced by the system and empirical data on answer quality to illustrate that we have addressed many of the problems of the previous system.

Facilitation of equivalence – equivalence responding: generalization of relational responses

Equivalence-equivalence responding (Eq-Eq) has become a behaviour analytic model of analogical reasoning. In previous works it was demonstrated that the exposition to a non-arbitrary relational task (facilitation procedure) improves performance in Eq-Eq tasks. In the present work we attempted to analyze the role of task components: arbitrary or non-arbitrary relational responses, role as sample or comparisons, and relating relations. In the first experiment, we devised four facilitation procedures combining two dimensions: simple or compound sample or comparisons and arbitrary or non-arbitrary relations among compound stimuli. In the second experiment two facilitation procedures including compound stimuli were tested. In one condition arbitrary relations worked as sample, and non-arbitrary relations as comparison. In the other condition its function was reversed. All procedures were effective to improve Eq-Eq to different extents, being arbitrary relational responses the key element. These results show generalization between non-arbitrary and arbitrary responses, and add further support to Eq-Eq responding as operant behaviour.

Effect of age and type of reinforcer in the equivalence – equivalence by a partition procedure

Equivalence – Equivalence responding (Barnes et al., 1997), based on derived or non-explicitly trained relational responding, supports a behaviour-analytic model of analogical reasoning. Conditional discriminations are the most common procedure used to train its prerequisites. In this exploratory work we test Vaughan’s (1988) simple discrimination procedure instead to derive Eq-Eq responses in children. Two factors were assessed: type of reinforcer used (primary or secondary) and age of participants (9-10 or 12-13 years). The procedure successfully leaded to the derivation of equivalence – equivalence responses, and both factors influenced the results: selecting older children and applying primary reinforcement leaded to faster learning and better results in the equivalence – equivalence test. No interaction between factors was found. This training procedure can provide a new way to investigate the behavioural prerequisites of this important ability

Development of Reflection Support System by Applying a Cognitive Model of Analogical Reasoning

Creative innovation is achieved by observing the features of objects. This paper terms such a skill as ``the discovering viewpoints'' and proposes a framework for developing them. Our proposal implements the cognitive models of analogical reasoning that have been developed in the field of cognitive science. In the framework of this paper, learners are presented with an example of graphic composition, and they carry out the task by modifying the example. The cognitive models of analogical reasoning compute the similarities between the presented example and the learners' work. The computed similarities are assumed to be the estimations of the learners' viewpoints in the task. The learners receive the outputs of computations and reflect on their viewpoints. During the reflection, they look for hidden features in the graphics by manipulating three parameters of the similarity computations (the feature space, the abstractness, and the consistency). We developed a prototype system that implements the above framework, and conducted an experiment in which the developed system was used by the learners. As a result, we confirmed the workability of the system by analyzing the log files obtained in the experiment. Furthermore, we confirmed the effects of learning support on the development of discovering viewpoints by analyzing the subjective evaluations on the learners'.

A computational account of children’s analogical reasoning: balancing inhibitory control in working memory and relational representation

Theories accounting for the development of analogical reasoning tend to emphasize either the centrality of relational knowledge accretion or changes in information processing capability. Simulations in LISA (Hummel & Holyoak, 1997, 2003), a neurally inspired computer model of analogical reasoning, allow us to explore how these factors may collaboratively contribute to the development of analogy in young children. Simulations explain systematic variations in United States and Hong Kong children's performance on analogies between familiar scenes (Richland, Morrison & Holyoak, 2006; Richland, Chang, Morrison & Au, 2010). Specifically, changes in inhibition levels in the model's working-memory system explain the developmental progression in US children's ability to handle increases in relational complexity and distraction from object similarity during analogical reasoning. In contrast, changes in how relations are represented in the model best capture cross-cultural differences in performance between children of the same ages (3-4 years) in the United States and Hong Kong. We use these results and simulations to argue that the development of analogical reasoning in children may best be conceptualized as an equilibrium between knowledge accretion and the maturation of information processing capability.

Relational Reasoning in a Neurally-Plausible Cognitive Architecture: An Overview of the LISA Project

Human mental representations are both flexible and structured—properties that, together, present challenging design requirements for a model of human thinking. The Learning and Inference with Schemas and Analogies (LISA) model of analogical reasoning aims to achieve these properties within a neural network. The model represents both relations and objects as patterns of activation distributed over semantic units, integrating these representations into propositional structures using synchrony of firing. The resulting propositional structures serve as a natural basis for memory retrieval, analogical mapping, analogical inference, and schema induction. The model also provides an a priori account of the limitations of human working memory and can simulate the effects of various kinds of brain damage on thinking.

The role of perceptually represented structure in analogical problem solving

Current models of analogical reasoning assume that representations of source examples and target problems occur in an amodal format--that is, a representation whose construction and processing are independent of activity in the perceptual and motor cortices of the brain. We examined the possible use of kinesthetic information--perceptual structures associated with the sensation of space and force--in the representation of source examples and target problems. Participants who recreated a source story while acting out the key elements were more likely to access the story when later working on the target problem than were participants who only verbally recreated the story or who verbally recreated it as well as sketched it. We argue that enactment made kinesthetic and spatial features more salient in participants' source story representations and that this aided performance. These results suggest that current models of analogical reasoning might be improved by including perceptual information as part of their representational schemes.

Relating derived relations as a model of analogical reasoning: reaction times and event-related potentials.

The current study aimed to test a Relational Frame Theory (RFT) model of analogical reasoning based on the relating of derived same and derived difference relations. Experiment 1 recorded reaction time measures of similar-similar (e.g., "apple is to orange as dog is to cat") versus different-different (e.g., "he is to his brother as chalk is to cheese") derived relational responding, in both speed-contingent and speed-noncontingent conditions. Experiment 2 examined the event-related potentials (ERPs) associated with these two response patterns. Both experiments showed similar-similar responding to be significantly faster than different-different responding. Experiment 2 revealed significant differences between the waveforms of the two response patterns in the left-hemispheric prefrontal regions; different-different waveforms were significantly more negative than similar-similar waveforms. The behavioral and neurophysiological data support the RFT prediction that, all things being equal, similar-similar responding is relationally "simpler" than, and functionally distinct from, different-different analogical responding. The ERP data were fully consistent with findings in the neurocognitive literature on analogy. These findings strengthen the validity of the RFT model of analogical reasoning and supplement the behavior-analytic approach to analogy based on the relating of derived relations.

Relational Reasoning in a Neurally Plausible Cognitive Architecture

Human mental representations are both flexible and structured—properties that, together, present challenging design requirements for a model of human thinking. The Learning and Inference with Schemas and Analogies (LISA) model of analogical reasoning aims to achieve these properties within a neural network. The model represents both relations and objects as patterns of activation distributed over semantic units, integrating these representations into propositional structures using synchrony of firing. The resulting propositional structures serve as a natural basis for memory retrieval, analogical mapping, analogical inference, and schema induction. The model also provides an a priori account of the limitations of human working memory and can simulate the effects of various kinds of brain damage on thinking.

Relational Integration, Inhibition, and Analogical Reasoning in Older Adults.

The difficulty of reasoning tasks depends on their relational complexity, which increases with the number of relations that must be considered simultaneously to make an inference, and on the number of irrelevant items that must be inhibited. The authors examined the ability of younger and older adults to integrate multiple relations and inhibit irrelevant stimuli. Young adults performed well at all but the highest level of relational complexity, whereas older adults performed poorly even at a medium level of relational complexity, especially when irrelevant information was presented. Simulations based on a neurocomputational model of analogical reasoning, Learning and Inference with Schemas and Analogies (LISA), suggest that the observed decline in reasoning performance may be explained by a decline in attention and inhibitory functions in older adults.