Tversky 1977, which provided compelling demonstrations against all the. In this work we wanted to gain some insights into tverskys featurebased semantic similarity measure on instances in a specific ontology. In such cases, similarity will be reduced more by the distinctive features of the subject than by the distinctive features of the predicate. Tverskys featurebased similarity and beyond semantic web journal. In this work we wanted to gain some insights into tverskys and more specifically jaccards featurebased semantic similarity measure on instances in a specific ontology. He had a unique ability to master the technicalities of normative ideals and then to intuit and demonstrate experimentally their systematic violation due to the vagaries and consequences of human information processing. I have read some of his work in other collections particularly judgement under uncertainty and it.
The representativeness heuristic suggests that similarity judgments provide a basis for judgments of likelihood. Studies of similarity amos tversky itamar gati hebrew university, jerusalem any event in the history of the organism is, in a sense, unique. We develop a similarity measure, based on fuzzy logic, that exhibits several features that match experimental findings in humans. Indeed, the concept of similarity is ubiquitous in psychological theory. Tverskys contrast model 1977 systematizes this feature approach. Tversky s 1977 contrast model, unlike geometricspatial models of similarity, does not represent stimuli as points in a multidimensional space. Tversky index is a generalization of the dice similarity coe cient and the f scores. Doctor of philosophy information science, may 2005, 125 pp. In addition to being present or absent, a feature can also be missing, in the sense that the value of that feature is not known. A new model is constructed that is based on the alcove model kruschke, 1992, but it uses a feature matching similarity measure see, e. The triangle inequality differs from minimality and symmetry in that it cannot be formulated in ordinal terms.
Similarity the percepts and concepts laboratory at indiana. In a fingerprint the presence or absence of a structural fragment is represented by the presence or absence of a set bit. When we compare something we judge features, not a simple physical distance of resemblance on a mental mapchart. Hebrew university jerusalem, israel the metric and dimensional assumptions that underlie the geometric represen tation of similarity are questioned on both theoretical and empirical grounds. Similarity in categorization 381 as additive features, so there is no loss of generality if we restrict attention to the latter. Amos tversky 19371996, a towering figure in cognitive and mathematical psychology, devoted his professional life to the study of similarity, judgment, and decision making. A unique feature of the quantum similarity model is that, whereas previous models. This will in turn influence the judged similarity between objects that share these diagnostic features. An experimental note on tverskys features of similarity. The ones marked may be different from the article in the profile. Tverskys 1977 contrast model, which defines similarity as a feature contrast task and equates the degree of similarity of two stimuli to a linear combination of their common and distinctive features, explains human similarity judgments much better than the geometric models. It is possible to account for peoples intuitions or ratings of the similarities.
The tversky index, named after amos tversky, is an asymmetric similarity measure on sets that compares a variant to a prototype. Additive clustering relies on the common features measure for item similarities tversky, 1977. Semantic web 0 0 1 1 ios press tverskys featurebased similarity and beyond silvia likaveca, ilaria lombardia. The tversky index, named after amos tversky, is an asymmetric similarity measure on sets that. The contrast model can account for violations of minimality and asymmetry, as well as certain other phenomena associated with proximity data, such as systematic differences between ratings of similarity and of dissimilarity and changes in similarity with context tversky, 1977.
Lehmann graduate school of business, columbia uniciersity, new york, ny10027 usa. Common and distinctive features in stimulus similarity. Nevertheless, two general properties, irregularity and local representativeness, seem to capture the intuitive notion of randomness. This cited by count includes citations to the following articles in scholar. Features of similarity amos tversky hebrew university jerusalem, israel the metric and dimensional assumptions that underlie the geometric representation of similarity are questioned on both theoretical and empirical grounds. Ec features that determine apparent randomness differ depending on context.
As is true of the similarity of sample to population, the speci. Our method was deliberately designed to find a point of contact between the two traditions of research in heuristics the early tradition exemplified by kahneman and tverskys work, and the later tradition exemplified by the work of gigerenzer and goldstein 1996. The model is dubbed fuzzy feature contrast ffc and is an extension to a more general domain of the feature contrast model due to tversky 1977. A central assumption of the model is that the similarity of object a to object b is a function of the features common to a and b a and b, those in a but not in b symbolized ab and those in b but not in a ba.
Similarity is one of the most straightforward ways to relate two objects and guide the human perception of the world. Similarity, confusability, and the density hypothesis. A quantum geometric model of similarity psychological sciences. Computes the tversky index between two sequences the tversky index is an asymmetric similarity measure between two sets, one defined the prototype and the other the variant. The observed similarity of object a to object b, s a, b, is a function of their common features, those that are shared by both a and b, and their distinctive features, those that belong to one. Rorissa, abebe, perceived features and similarity of images. While i have an interest in behavioural economics, i have no expertise in psychology. Tversky has related degree of judged similarity between among objects to the degree of overlap of matching features. Our proposed algorithm yields the best result in terms of precisionrecall, fmeasure and dcg compared to the traditional tversky similarity measure and geometry similarity algorithm. Consequently, recognition, learning, and judgment presuppose an ability to categorize stimuli and classify situations by similarity. Using tverskys contrast model to investigate how features of. Features are usually discrete, often binary variables, but any dimension can also be represented as nested or overlapping sets of discrete features.
Semantic web 0 0 1 1 ios press tverskys featurebased. Previous studies compared the molecular similarity of marketed drugs and endogenous human metabolites endogenites, using a series of fingerprinttype encodings, variously ranked and clustered using the tanimoto jaccard similarity coefficient ts. According to tverskys diagnosticity principle, features that are used to cluster stimuli into subgroups have a higher diagnostic value and will therefore affect similarity judgments more than features that are not used to create clusters. For alpha beta 1, the index is equal to the tanimoto. Nevertheless, the works of amos tversky are immediately accessible to the outsider. Alternatively, one may estimate probability by assessing availability, or associative distance. The metric and dimensional assumptions that underlie the geometric.
A heuristic for judging frequency and probability122. Selected writings amos tversky edited by eldar shafir amos tversky 19371996, a towering figure in cognitive and mathematical psychology, devoted his profes sional life to the study of similarity, judgment, and decision making. The contrast model expresses similarity between objects as a weighted. Features of similarity and categorybased induction evan heit department of psychology university of warwick coventry cv4 7al united kingdom e. An experimental note on tversky s features of similarity. To avoid a combinatorial explosion, the valueofclairvoyance computation employs the assumption that at most one additional feature will be reported by the user. The metric and dimensional assumptions that underlie the geometric represen. As will be shown below, this approach to the judgment of likelihood leads to serious biases, because several of the factors that should be considered in assessing likelihood play no role in judgments of similarity. In other words, tverskys 1977 stated intent was that f should describe how features combine with one another, while the hyperparameters establish the balance between common features and distinctive features in assessing similarity. We show how adjusting the hyperparameters of this index allow placing emphasis on false negatives in training a network that generalizes and performs well in highly imbalanced data as it leads to high sensitivity, dice, f. Corter teachers college, columbia university amos tversky stanford university proximity data can be represented by an extended tree, which generalizes traditional trees by including marked segments that correspond to overlapping dusters. Features of similarity 329 sessment of similarity are briefly discussed in the final section.
Firstly, we propose a novel algorithm for measuring the similarity between two bim components based on their attribute information and tversky similarity. Features that are shared in the feature lists are commonalities of the pair and features that are contained in one feature set but not the other are differences of the pair. Computational models of word similarity two types of models for predicting human word similarity ratings dominate the literature. Then, the user observes one or more of these features. A new settheoretical approach to similarity is developed in which objects are. Data were collected from 150 participants who performed an image description and a similarity judgment task. Tverskys approach can also easily deal with the contextdependence of similarity judgments that goodman brought to bear against the geometrical model. It serves as an organizing principle by which individuals classify objects, form concepts, and make generalizations. The theory of similarity, known as the contrast model, was presented by tversky in features of similarity 1977 and elaborated by tversky and itamar gati in studies of similarity 1978. Structural equation modeling, correlation, and regression analyses confirmed the relationships between perceived features and similarity of objects hypothesized by tversky.
A similarity comparison involves comparing the feature lists that represent the concepts. Rather, it defines stimuli as sets of features and the similarity of any two stimuli as a linear function of a measure of their common and uniquedistinctive features. Using tverskys contrast model to investigate how features. The tversky index can be seen as a generalization of dices coefficient and tanimoto coefficient aka jaccard index. Johnson school of business admintitration, university of michigan, ann arbor, mi 48109, usa donald r. Features of similarity amos tversky similarity plays a fundamental role in theories of knowledge and behavior. It asserts that one distance must be smaller than the sum of two others, and hence it cannot be readily refuted with ordinal or even interval data. This theory provided an explanation for a number of judgmental anomalies that had been observed by other researchers. An investigation into their relationships and a test of tverskys contrast model.
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