Analogical Mapping Research Articles

Backside laser testing of ICs for SET sensitivity evaluation

A new experimental approach combining backside laser testing and analog mapping is presented. A new technique for integrated circuits (ICs) backside preparation by laser ablation is evaluated. The methodology is applied to the study of single-event transient (SET) sensitivity on a linear IC.

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.

Analogical priming via semantic relations.

Research on semantic memory has often tacitly treated semantic relations as simple conduits for spreading activation between associated object concepts, rather than as integral components of semantic organization. Yet conceptual relations, and the role bindings they impose on the objects they relate, are central to such cognitive tasks as discourse comprehension, inference, problem solving, and analogical reasoning. The present study addresses the question of whether semantic relations and their bindings can influence access to semantic memory. The experiments investigated whether, and under what conditions, presenting a prime pair of words linked by 1 of 10 common semantic relations would facilitate processing of a target pair of words linked by the same relation. No effect was observed when participants merely read the prime; however, relational priming was observed under instructions to note and use the semantic relations. Participants were faster at making a lexical decision or naming a word on a related pair of target words when that pair was primed with an analogously related pair of words than when the prime pair consisted of either two unrelated words or two words linked by some other relation. This evidence of analogical priming suggests that under an appropriate strategic set, lexical decisions and naming latencies can be influenced by a process akin to analogical mapping.

Integrating structure and meaning: a distributed model of analogical mapping

AbstractIn 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.

Integrating analogical mapping and general problem solving: the path-mapping theory

This article describes the path-mapping theory of how humans integrate analogical mapping and general problem solving. The theory posits that humans represent analogs with declarative roles, map analogs by lower-level retrieval of analogous role paths, and coordinate mappings with higher-level organizational knowledge. Implemented in the ACT-R cognitive architecture, the path-mapping theory enables models of analogical mapping behavior to incorporate and interface with other problem-solving knowledge. Path-mapping models thus can include task-specific skills such as encoding analogs or generating responses, and can make behavioral predictions at the level of real-world metrics such as latency or correctness. We show that the path-mapping theory can successfully account for the major phenomena addressed by previous theories of analogy. We also describe a path-mapping model that can account for subjects’ incremental eye-movement and typing behavior in a story-mapping task. We discuss extensions and implications of this work to other areas of analogy and problem-solving research.

Disorder and two-particle interaction in low-dimensional quantum systems

We review some of the recent results on two-interacting particles (TIP) in low-dimensional disordered quantum models. Special attention is given to the mapping of the problem onto random band matrices. In particular, we construct two simple, seemingly closely related examples for which an analogous mapping leads to incorrect results. We briefly discuss possible reasons for this discrepancy based on the physical differences between the TIP problem and our examples.

Integrating analogical mapping and general problem solving: the path‐mapping theory

AbstractThis article describes the path‐mapping theory of how humans integrate analogical mapping and general problem solving. The theory posits that humans represent analogs with declarative roles, map analogs by lower‐level retrieval of analogous role paths, and coordinate mappings with higher‐level organizational knowledge. Implemented in the ACT‐R cognitive architecture, the path‐mapping theory enables models of analogical mapping behavior to incorporate and interface with other problem‐solving knowledge. Path‐mapping models thus can include task‐specific skills such as encoding analogs or generating responses, and can make behavioral predictions at the level of real‐world metrics such as latency or correctness. We show that the path‐mapping theory can successfully account for the major phenomena addressed by previous theories of analogy. We also describe a path‐mapping model that can account for subjects' incremental eye‐movement and typing behavior in a story‐mapping task. We discuss extensions and implications of this work to other areas of analogy and problem‐solving research.

Representation and processing of structures with binary sparse distributed codes

The schemes for compositional distributed representations include those allowing on-the-fly construction of fixed dimensionality codevectors to encode structures of various complexity. Similarity of such codevectors takes into account both structural and semantic similarity of represented structures. We provide a comparative description of sparse binary distributed representation developed in the framework of the associative-projective neural network architecture and the more well known holographic reduced representations of T.A. Plate (1995) and binary spatter codes of P. Kanerva (1996). The key procedure in associative-projective neural networks is context-dependent thinning which binds codevectors and maintains their sparseness. The codevectors are stored in structured memory array which can be realized as distributed auto-associative memory. Examples of distributed representation of structured data are given. Fast estimation of the similarity of analogical episodes by the overlap of their codevectors is used in the modeling of analogical reasoning both for retrieval of analogs from memory and for analogical mapping.

Trace and antitrace maps for aperiodic sequences: Extensions and applications

We study aperiodic systems based on substitution rules by means of a transfer-matrix approach. In addition to the well-known trace map, we investigate the so-called ``antitrace'' map, which is the corresponding map for the difference of the off-diagonal elements of the $2\ifmmode\times\else\texttimes\fi{}2$ transfer matrix. The antitrace maps are obtained for various binary, ternary, and quaternary aperiodic sequences, such as the Fibonacci, Thue-Morse, period-doubling, Rudin-Shapiro sequences, and certain generalizations. For arbitrary substitution rules, we show that not only trace maps, but also antitrace maps exist. The dimension of our antitrace map is $r(r+1)/2,$ where r denotes the number of basic letters in the aperiodic sequence. Analogous maps for specific matrix elements of the transfer matrix can also be constructed, but the maps for the off-diagonal elements and for the difference of the diagonal elements coincide with the antitrace map. Thus, from the trace and antitrace map, we can determine any physical quantity related to the global transfer matrix of the system. As examples, we employ these dynamical maps to compute the transmission coefficients for optical multilayers, harmonic chains, and electronic systems.

Geographic Information Systems (GIS) for the Determination of Inundation Maps of Lake Mogan, Turkey

The use of geographic information systems (GIS) allows for a very efficient analysis of spatial hydrologic data for water resources projects especially for water control studies. In this respect, flood hydrographs for Mogan basin, Turkey for different durations and return periods are determined by using Soil Conservation Service (SCS) method. The basic parameter of the SCS method, curve number (CN), is evaluated by using GIS employing digital land use and hydrologic soil group data derived from analog maps. A Digital Elevation Model (DEM) of Mogan basin is constructed by making use of topographic maps with a scale of 1:25,000. Flood hydrographs for these three productive subbasins of Lake Mogan are attached and superimposed to obtain the total flood hydrographs. Total flood hydrographs entering into Lake Mogan are routed by Puls Method to determine the outflow hydrographs from the lake; then, associated lake levels are computed. Furthermore, inundation maps of Lake Mogan are obtained; these results are presented by GIS techniques.

Toward Neuroanatomical Models of Analogy: A Positron Emission Tomography Study of Analogical Mapping

Several brain regions associated with analogical mapping were identified using 15O-positron emission tomography with 12 normal, high intelligence adults. Each trial presented during scanning consisted of a source picture of colored geometric shapes, a brief delay, and a target picture of colored geometric shapes. Analogous pictures did not share similar geometric shapes but did share the same system of abstract visuospatial relations. Participants judged whether each source–target pairing was analogous (analogy condition) or identical (literal condition). The results of the analogy-literal comparison showed activation in the dorsomedial frontal cortex and in the left hemisphere; the inferior, middle, and medial frontal cortices; the parietal cortex; and the superior occipital cortex. Based on these results as well as evidence from relevant cognitive neuroscience studies of reasoning and of executive working memory, we hypothesize that analogical mapping is mediated by the left prefrontal and inferior parietal cortices.

The impact of anxiety on analogical reasoning

The effect of state anxiety on analogical reasoning was investigated by examining qualitative differences in mapping performance between anxious and non-anxious individuals reasoning about pictorial analogies. The working-memory restriction theory of anxiety, coupled with theories of analogy that link complexity of mapping with working-memory capacity, predicts that high anxiety will impair the ability to find correspondences based on relations between multiple objects relative to correspondences based on overlap of attributes between individual objects. Anxiety was induced in one condition by a stressful speeded subtraction task administered prior to the analogy task. Anxious participants produced fewer relational responses and more attribute responses than did non-anxious participants, both in the absence of explicit instructions to find relational mappings (Experiment 1) and after receiving such instructions (Experiment 2). The findings support the postulated links among anxiety, working memory, and the ability to perform complex analogical mapping.

Digital Cadastral Maps in Land Information Systems

This paper presents the problem of cadastral maps. The hitherto existing cadastre, consisting of paper maps and land registers, is now becoming insufficient. Its shortcomings force developments leading to its improvement. One of the ways is the creation of a Land Information System. A digital cadastral map is the main component of this system. The structure and information content of the map is presented, its differences from analogue maps are shown, and the process of map creation is described. A digital cadastral map can be the basis for additional thematic layers, successively converting it into a complex system for management of administrative units. Some examples of such layers and their usage are presented here.

Analogical Reasoning and Modeling in the Sciences

This paper aims at integrating the work onanalogical reasoning in Cognitive Science into thelong trend of philosophical interest, in this century,in analogical reasoning as a basis for scientificmodeling. In the first part of the paper, threesimulations of analogical reasoning, proposed incognitive science, are presented: Gentner's StructureMatching Engine, Mitchel's and Hofstadter's COPYCATand the Analogical Constraint Mapping Engine, proposedby Holyoak and Thagard. The differences andcontroversial points in these simulations arehighlighted in order to make explicit theirpresuppositions concerning the nature of analogicalreasoning. In the last part, this debate in cognitivescience is applied to some traditional philosophicalaccounts of formal and material analogies as a basisfor scientific modeling, like Mary Hesse`s, and tomore recent ones, that already draw from the work inArtificial Intelligence, like that proposed byAronson, Harre and Way.

The isolation and mapping of disease resistance gene analogs in maize.

Many of the plant disease resistance genes that have been isolated encode proteins with a putative nucleotide binding site and leucine-rich repeats (NBS-LRR resistance genes). Oligonucleotide primers based on conserved motifs in and around the NBS of known NBS-LRR resistance proteins were used to amplify sequences from maize genomic DNA by polymerase chain reaction (PCR). Eleven classes of non-cross-hybridizing sequences were obtained that had predicted products with high levels of amino acid identity to NBS-LRR resistance proteins. These maize resistance gene analogs (RGAs) and one RGA clone obtained previously from wheat were used as probes to map 20 restriction fragment length polymorphism (RFLP) loci in maize. Some RFLPs were shown to map to genomic regions containing virus and fungus resistance genes. Perfect cosegregation was observed between RGA loci and the rust resistance loci rp1 and rp3. The RGA probe associated with rp1 also detected deletion events in several rp1 mutants. These data strongly suggest that some of the RGA clones may hybridize to resistance genes.

Analogy just looks like high level perception: why a domain-general approach to analogical mapping is right

Hofstadter and his colleagues have criticized current accounts of analogy, claiming that such accounts do not accurately capture interactions between processes of representation construction and processes of mapping. They suggest instead that analogy should be viewed as a form of high level perception that encompasses both representation building and mapping as indivisible operations within a single model. They argue specifically against SME, our model of analogical matching, on the grounds that it is modular, and offer instead programs such as Mitchell and Hofstadter's Copycat as examples of the high level perception approach. In this paper we argue against this positionon two grounds. First, we demonstrate that most of their specific arguments involving SME and Copycat are incorrect. Second, we argue that the claim that analogy is high-level perception, while in some ways an attractive metaphor, is too vague to be useful as a technical proposal. We focus on five issues: (1) how perceptionrelates to analogy,(2) how flexibilityarises in analogicalprocessing, (3) whether analogy is a domain-general process, (4) how micro-worlds should be used in the study of analogy, and (5) how best to assess the psychological plausibility of a model of analogy. We illustrate our discussion with examples taken from computer models embodying both views.

Constraints on analogical inference

The ability to reason by analogy is particularly important because it permits the extension of knowledge of a target domain by virtue of its similarity to a base domain via a process of analogical inference. The general procedure for analogical inference involves copying structure from the base to the target in which missing information is generated, and substitutions are made for items for which analogical correspondences have already been found. A pure copying with substitution and generation process is too profligate to be useful, and so constraints must be placed on what information is to be carried over. In this paper, the importance of systematicity as a constraint on inference is explored in four studies in which subjects find correspondences between domains and also make inferences. This work suggests that people prefer to make inferences of information connected to systematic correspondences between domains. A second important theme of this paper is that violations of one-to-one mapping can lead to inconsistent object substitutions in inference. The data reveal no such inconsistent substitutions in people's inferences, suggesting that they do respect one-to-one mapping in analogical inference. These findings are discussed relative to four prominent computational models of analogical mapping and inference.

Constraints on Analogical Inference

The ability to reason by analogy is particularly important because it permits the extension of knowledge of a target domain by virtue of its similarity to a base domain via a process of analogical inference. The general procedure for analogical inference involves copying structure from the base to the target in which missing information is generated, and substitutions are made for items for which analogical correspondences have already been found. A pure copying with substitution and generation process is too profligate to be useful, and so constraints must be placed on what information is to be carried over. In this paper, the importance of systematicity as a constraint on inference is explored in four studies in which subjects find correspondences between domains and also make inferences. This work suggests that people prefer to make inferences of information connected to systematic correspondences between domains. A second important theme of this paper is that violations of one‐to‐one mapping can lead to inconsistent object substitutions in inference. The data reveal no such inconsistent substitutions in people's inferences, suggesting that they do respect one‐to‐one mapping in analogical inference. These findings are discussed relative to four prominent computational models of analogical mapping and inference.

An approach to quantitative compositional profiling at near-atomic resolution using high-angle annular dark field imaging

The compositional profile of a GaAs Al 0.6Ga 0.4As interface is investigated through analysis of a high resolution high-angle annular dark field image. Image calculations are carried out using a multislice code that incorporates thermal diffuse scattering, and an algorithm is developed for quantitative matching between these images and the experimental image. The resulting compositional profile (with monolayer spatial resolution) is compared with an analogous quantitative chemical mapping experiment. The extension of this new technique to high resolution compositional mapping (in two dimensions) is briefly explored.

The general unified theory of intelligence: its central conceptions and specific application to domains of cognitive science

Preface General Unified Theory of Intelligence The Generality of Abstract Rules in Intelligent Reasoning The Generality of Creative Mechanisms in Intelligent Reasoning The Generality of Analogical Mapping in Intelligent Reasoning The Generality of Explanatory Coherence in Intelligent Reasoning The Generality of Symbolic Connectionist Models in Intelligent Reasoning The Generality of the SOAR System in Intelligent Reasoning Summary and Conclusion Appendix: Expert Systems and a General Unified Theory of Intelligence Bibliography