A taxonomy of part‐whole or meronymic relations is developed to explain the ordinary English‐speaker's use of the term “part of” and its cognates. The resulting classification yields six types of meronymic relations: 1. component‐integral object (pedal‐bike), 2. member‐collection (ship‐fleet), 3. portion‐mass (slice‐pie), 4. stuff‐object (steel‐car), 5. feature‐activity (paying‐shopping), and 6. place‐area (Everglades‐Florida). Meronymic relations ore further distinguished from other inclusion relations, such as spatial inclusion, and class inclusion, and from several other semantic relations: attribution, attachment, and ownership. This taxonomy is then used to explain cases of apparent intransitivity in merological syllogisms, and standard form syllogisms whose premises express different inclusion relations. The data suggest that intransitivities arise due to equivocations between different types of semantic relations. These results are then explained by means of the relation element theory which accounts for the character and behavior of semantic relations in terms of more primitive relational elements. The inferential phenomena observed are then explained by means of a single principle of element matching.

Evidence regarding the relationships between problem perception and expertise has customarily been obtained indirectly, through contrasting-group studies such as expert-novice comparisons. Differences in perception have been attributed to differences in expertise, although the groups compared generally differ on a number of other major attributes (e.g., aptitude). This study explored the relationship between perception and proficiency directly. Students' perceptions of the structure of mathematical problems were examined before and after a monthlong intensive course on mathematical problem solving. These perceptions were compared with experts' perceptions. Subjects sorted problems on the basis of similarity. Hierarchical clustering analysis of the sorting data indicated that novices perceive problems on the basis of "surface structure" (i.e., words or objects described in the problem statement). After the course the students perceived problem relatedness more like the experts-according to principles or methods relevant for problem solution. Thus, criteria for problem perception shift as a person's knowledge bases become more richly structuredT

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There is a rich variety of semantic relations in natural languages. Subjects' perceptions of similarities among relations were studied for a wider variety of relations than had been used in previous studies. Forty subjects sorted 31 cards bearing five example pairs of each of 31 semantic relations. Subjects were able both to distinguish the relations and to perceive their similarities. A hierarchical clustering analysis of the sorting data indicated that the subjects perceived five families of semantic relations (contrasts, class inclusion, similars, case relations, and part-wholes). The five families were distinguished in terms of three properties of semantic relations: contrasting/noncontrasting, logical/pragmatic, and inclusionlnoninclusion. Within each family, relations also were sorted in ways consistent with their defining properties. Relations were therefore viewed not as unanalyzable primitives, but in terms of the relational properties that distinguished them.Semantic relations between concepts are basic components of language and thought (Bierwisch, 1970; H. H. Clark, 1970;Evanechko & Maguire, 1972;Evens, Litowitz, Markowitz, Smith, & Werner, 1980;Katz, 1972;Leech, 1974;Miller, 1969; Miller & JohnsonLaird, 1976;Osgood & Richards, 1973;Palermo, 1978).Relying on associative and introspective data, Wundt (1893) identified and classified a wide range of relations. Wundt recognized two general classes: outer associations (part-wholes, word sequences) and inner associations (categorical relationships, similars, coordination, causal relations). At least 13 other classification systems were proposed prior to 1911 (Warren, 1921).More recently, researchers have been directly concerned with the study of semantic relations in two related areas: the development of general models of semantic memory (Anderson, 1976;Norman & Rumelhart, 1975; Schank, 1973) and the study of semantic decisions (Cohen, 1977;Danks & Glucksberg, 1980;Kintsch, 1980;Smith, 1977). In both areas, research has focused on a limited number of relations, and the similarities between relationships have not been explored. General models of semantic memory have been developed to account for the comprehension and representation of propositional knowledge (Anderson, 1976;Norman & Rumelhart, 1975; Schank, 1973). In these models, information is represented by a network oflabeled relaPreparation of this article was supported in part by a faculty research grant from Trenton State College to the first author. We thank Mary Crawford for comments on an earlier version of this paper. Requests for reprints should be sent to Roger Chaffin, Department of Psychology, Trenton State College, Trenton, NJ 08625. tions between nodes that stand for concepts. Relations serve as unanalyzed, primitive terms; consequently, their number has been restricted in the interest of economy. As a result, only a fraction of the total number of relations in the English language have been considered. The scope and power of current models would be enhanced by encompassing the wid...

Use of color for representing health data on maps raises many unanswered questions. This research addresses questions about which colors allow accurate map reading and which colors map users prefer. Through the combination of a review of previous color research and an experiment designed to test specific combinations of colors on maps, criteria were established and evaluated for selecting colors for choropleth maps of mortality data. The color-selection criteria provide pairs of hues for diverging schemes that avoid naming and colorblind confusions. We also tested sequential and spectral schemes. Our results show that color is worth the extra effort and expense it adds to map making because it permits greater accuracy in map reading. In addition, people prefer color maps over monochrome maps. Interestingly, scheme preference is affected by levels of clustering within mapped distributions. In this research, people preferred spectral and purple/green hue combinations. Contrary to our expectations, spectral schemes are effective if designed to include diverging lightness steps suited to the logical structure of mapped data. Diverging schemes produce better rate retrievals than both spectral and sequential schemes, however. In addition, diverging schemes place better emphasis on map clusters than sequential schemes. Thus map effectiveness is improved by use of diverging schemes. Our interdisciplinary research connects geographers with epidemiologists through concern about map symbolization and map reading, strengthening a significant area of collaboration. Providing guidelines that improve the design of customized color schemes will assist map makers in all disciplines in gaining insights about their data.