Evans’ 1968 ANALOGY system was the first computer model of analogy. This paper demonstrates that the structure mapping model of analogy, when combined with high‐level visual processing and qualitative representations, can solve the same kinds of geometric analogy problems as were solved by ANALOGY. Importantly, the bulk of the computations are not particular to the model of this task but are general purpose: We use our existing sketch understanding system, CogSketch, to compute visual structure that is used by our existing (...) analogical matcher, Structure Mapping Engine (SME). We show how SME can be used to facilitate high‐level visual matching, proposing a role for structural alignment in mental rotation. We show how second‐order analogies over differences computed via analogies between pictures provide a more elegant model of the geometric analogy task. We compare our model against human data on a set of problems, showing that the model aligns well with both the answers chosen by people and the reaction times required to choose the answers. (shrink)

In order to develop sophisticated models of the core domains of knowledge that support complex cognitive processing in infants and children, developmental psychologists have mapped out the content of these knowledge domains. This research strategy may provide a blueprint for advancing research on adult cognitive processing. I illustrate this suggestion with examples from analogical reasoning and decision making.

This paper applies recent work on scripts and stories developed as tools of evidential reasoning in artificial intelligence to model the use of argument from analogy as a rhetorical device of persuasion. The example studied is Gerry Spence’s closing argument in the case of Silkwood v. Kerr-McGee Corporation, said to be the most persuasive closing argument ever used in an American trial. It is shown using this example how argument from analogy is based on a similarity premise where similarity between (...) two cases is modeled using the device of a story scheme from the hybrid theory of legal evidential reasoning (Bex in Arguments, stories and criminal evidence: a formal hybrid theory. Springer, Dordrecht 2011). It is shown how the rhetorical strategy of Spence’s argumentation in the closing argument interweaves argument from analogy with explanation through three levels. (shrink)

Accounts of analogical inference are usually categorized into four broad groups: abductive, deductive, inductive and sui generis. The purpose of this paper is to defend a sui generis model of analogical inference. It focuses on the sui generis account, as developed by Juthe [2005, 2009, 2015, 2016] and Botting’s [2017] criticism of it. This paper uses the pragmadialectical theory of argumentation as the methodological framework for analyzing and reconstructing argumentation. The paper has two main points. First, that Juthe’s arguments against (...) a deductive interpretation of prima facie analogy argumentation remain unaffected by Botting’s criticism, which means that many of the reasons against deductive reformulation of analogy argumentation still stand. The additional argument, which Botting himself brings up, that a deductive interpretation cannot account for the cumulative effect of analogies, just provides further reason to reject deductivism. The second main point of this paper is that an inductive interpretation of analogical inference also fails. There are constitutional differences between inductive and analogical inference that cannot be bridged. The result is a firm defense of the sui generis view of analogical inference. (shrink)

Empirical studies indicate that analogy consists of two main processes: retrieval and mapping. While current theories and models of analogy have revealed much about the mainly structural constraints that govern the mapping process, the similarities that underpin the correspondences between individual representational elements and drive retrieval are understood in less detail. In existing models symbol similarities are externally defined but neither empirically grounded nor theoretically justified. This paper introduces a new model (EMMA: the environmental model of analogy) which relies on (...) co‐occurrence information provided by LSA (Latent Semantic Analysis; Landauer & Dumais, 1997) to ground the relations between the symbolic elements aligned in analogy. LSA calculates a contextual distribution for each word encountered in a corpus by counting the frequency with which it co‐occurs with other words. This information is used to define a model that locates each word encountered in a high‐dimensional space, with relations between elements in this space representing contextual similarities between words. A series of simulation experiments demonstrate that the environmental approach to semantics embodied in LSA can produce appropriate patterns of analogical retrieval, but that this semantic measure is not sufficient to model analogical mapping. The implications of these findings, both for theories of representation in analogy research and more general theories of semantics in cognition, are explored. (shrink)

We present a model of similarity‐based retrieval that attempts to capture three seemingly contradictory psychological phenomena: (a) structural commonalities are weighed more heavily than surface commonalities in similarity judgments for items in working memory; (b) in retrieval, superficial similarity is more important than structural similarity; and yet (c) purely structural (analogical) remindings e sometimes experienced. Our model, MAC/FAC, explains these phenomena in terms of a two‐stage process. The first stage uses a computationally cheap, non‐structural matcher to filter candidate long‐term memory (...) items. It uses content vectors, a redundant encoding of structured representations whose dot product estimates how well the corresponding structural representations will match. The second stage uses SME (structure‐mapping engine) to compute structural matches on the handful of items found by the first stage. We show the utility of the MAC/FAC model through a series of computational experiments: (a) We demonstrate that MAC/FAC can model patterns of access found in psychological data; (b) we argue via sensitivity analyses that these simulation results rely on the theory; and (c) we compare the performance of MAC/FAC with ARCS, an alternate model of similarity‐based retrieval, and demonstrate that MAC/FAC explains the data better than ARCS. Finally, we discuss limitations and possible extensions of the model, relationships with other recent retrieval models, and place MAC/FAC in the context of other recent work on the nature of similarity. (shrink)

Theories of analogical reasoning have viewed relational structure as the dominant determinant of analogical mapping and inference, while assigning lesser importance to similarity between individual objects. An experiment is reported in which these two sources of constraints on analogy are placed in competition under conditions of high relational complexity. Results demonstrate equal importance for relational structure and object similarity, both in analogical mapping and in inference generation. The human data were successfully simulated using a computational analogy model (LISA) that treats (...) both relational correspondences and object similarity as soft constraints that operate within a limited-capacity working memory; but not with a model (SME) that treats relational structure as pre-eminent. (shrink)

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. (shrink)

The ability to make informative comparisons is central to human cognition. Comparison involves aligning two representations and placing their elements into correspondence. Detecting correspondences is a necessary component of analogical inference, recognition, categorization, schema formation, and similarity judgment. Connectionist Analogy Builder (CAB) determines correspondences through a simple iterative computation that matches elements in one representation with elements playing compatible roles in the other representation while simultaneously enforcing structural constraints. CAB shows promise as a process model of comparison as its performance (...) can be related to human performance (e.g., solution trajectory, error patterns, time‐on‐task). Furthermore, CAB's bounded working memory allows it to account for the inherent capacity limitations of human processing. CAB's strengths are its parsimony, transparency of operations, and ability to generate performance predictions. In this paper, CAB is evaluated against benchmark phenomena from the analogy literature. (shrink)

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. (shrink)