Dimensions of Similarity

Attneave, Fred

doi:10.2307/1418869

Cited by 384 publications

(243 citation statements)

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“…The idea that stimuli can be represented in a multidimensional Euclidean space seems to have been first proposed by Richardson (1938). Subsequent developments are due to Torgerson (1952), Attneave (1950), Shepard (e.g., 1964Shepard (e.g., , 1980 and Lockhead (1970Lockhead ( , 1972.…”

Section: Notes To Chapter 10mentioning

confidence: 99%

Adaptive Behavior and Learning

Staddon

2016

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Every day at about 4:30, Jazz, a Hungarian Vizsla dog, leaps up on the sofa and looks out for his owner who always comes home at 5:00. He doesn't need an internal clock because he has an acute sense of smell that allows him to measure how long his master has been absent. Explaining complex behavior in simple ways, this book is a fascinating exploration of the evolution, development and processes of learning in animals. Now in its second edition, there is increased emphasis on development, evolution and dynamics; new accounts of taxic orientation, reflex induction, habituation and operant learning in organisms; more discussion of spatial learning and the processes underlying it; expanded chapters on choice and completely new chapters on molar laws, classical conditioning theories and comparative cognition. J. E. R. Staddon provides a definitive summary of contemporary theoretical understanding suitable for graduates and advanced undergraduates.

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Section: Notes To Chapter 10mentioning

confidence: 99%

Adaptive Behavior and Learning

Staddon

2016

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“…The reason why we here adopt only the former method is twofold,(1) there were few scales which represent purely one factor, and (2) amount of labor involved in their calculation. Theoretically, this method is said to follow the city block model of Attneave (1950). Even with this method, the fewer the number of scales to be considered, the more we can save the amount of calculation.…”

Section: (I) Introductionmentioning

confidence: 99%

Dimensions of Tactual Impressions (1)

Yoshida

1968

Jpn. Psychol. Res

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“…Geometric models of knowledge representation were first used in psychology to exploit the analogy to space for measuring similarity (Attneave, 1950;Torgerson, 1958Torgerson, , 1965. Concepts are modeled within a multidimensional space and their spatial distance is an indicator of semantic similarity.…”

Section: Geometric Modelsmentioning

confidence: 99%

“…block distance and r = 2 in the Euclidian distance (Suppes, Krantz, Luce, & Tversky, 1989). Similarity is then a decaying function of semantic distance d(q, c) (Attneave, 1950;Melara, Marks, & Lesko, 1992;Shepard, 1957Shepard, , 1958aShepard, , 1958b.…”

Section: Geometric Modelsmentioning

confidence: 99%

A Hybrid Semantic Similarity Measure for Spatial Information Retrieval

Schwering

Kuhn

2009

Spatial Cognition & Computation

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Semantic similarity is central to many cognitive processes and plays an important role in the way humans process and reason about information. In particular, the retrieval of knowledge from memory hinges crucially on similarity. Likewise, information retrieval systems use similarity to detect relevant information for a given query. Current information retrieval systems apply mainly syntactic techniques to determine similarity. Although such syntactic similarity measures have performed strongly with resources containing large amounts of text, they cannot appropriately cope with syntactic and semantic heterogeneity and ambiguity, if the semantics of the terms is not explicitly available. Therefore, they are rather rigid and inflexible as they cannot adapt to the user's requirements and conceptualization of the domain. Furthermore, geographic features are distinguished via their geometric and thematic data. It is often not possible to capture the complex semantics of geographic features by a single name or a textual description. Therefore spatial data is different from text documents typically found in enterprise databases or on the Web. Retrieval of spatial information requires new, intelligent retrieval mechanisms that satisfy its specific requirements. A semantics-based solution can more easily adapt to user needs and therefore increases the flexibility and usability of spatial data and retrieval methods. This paper investigates the suitability of various approaches -originally developed to explain human similarity judgement -in the context of spatial information retrieval. We propose a new, hybrid approach for semantic similarity measurement, which can represent the complex semantics of spatial data. It allows for retrieving relevant data by determining the similarity between the query and the semantic descriptions of geographic feature types within the database. The hybrid similarity measure combines the geometric structure of conceptual spaces with the relational structure of semantic nets to one, cognitively plausible knowledge representation with an inherent similarity measure.

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Dimensions of Similarity

Cited by 384 publications

References 0 publications

Adaptive Behavior and Learning

Adaptive Behavior and Learning

Dimensions of Tactual Impressions (1)

A Hybrid Semantic Similarity Measure for Spatial Information Retrieval

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