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    ABSTRACT: This chapter identifies two basic components of spatial intelligence, based on analyses of performance on tests of spatial ability and on complex spatial thinking tasks in domains such as mechanics, chemistry, medicine, and meteorology. The first component is flexible strategy choice between mental imagery (or mental simulation more generally) and more analytic forms of thinking. Research reviewed here suggests that mental simulation is an important strategy in spatial thinking, but that it is augmented by more analytic strategies such as task decomposition and rule-based reasoning. The second is meta-representational competence [diSessa, A. A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and Instruction, 22, 293–331], which encompasses ability to choose the optimal external representation for a task and to use novel external representations productively. Research on this aspect of spatial intelligence reveals large individual differences in ability to adaptively choose and use external visual–spatial representations for a task. This research suggests that we should not just think of interactive external visualizations as ways of augmenting spatial intelligence, but also consider the types of intelligence that are required for their use.
    Psychology of Learning and Motivation - PSYCH LEARN MOTIV-ADV RES TH. 01/2010; 52:265-297.
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    Spatial Cognition and Computation 06/2010; 10:83-93. · 1.22 Impact Factor
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    ABSTRACT: The characterization, identification, and understanding of spatial patterns are central concerns of geography. Deeply rooted in the notion that geographic location matters, one testable assumption is that near things are more related than distant things—a concept often referred to as Tobler's first law of geography. One means of quantifying this assumption is using measures of spatial autocorrelation. Several such measures have been developed to test whether a pattern is indeed clustered, or dispersed, or whether it is, from a statistical perspective, random. To shed light on how spatial patterns are understood from a cognitive perspective, this article reports results from studies of spatial pattern interpretation represented in maps. For the purpose of experimental validation, we used a two-color map. We systematically varied the ratio of the colors as well as the level of significance of clustering and dispersion; we targeted two groups: experts and nonexperts. The task for both experts and nonexperts was to sort patterns according to five specified categories of spatial autocorrelation structures. The results show clearly that patterns are understood on the basis of the dominant color, by both experts and nonexperts. A third experiment, using a free classification paradigm, confirmed the dominance of the color effect. These results are important, as they point to critical aspects of pattern perception and understanding that need to be addressed from the perspective of spatial thinking, especially how people relate concepts of randomness with spatial patterns (represented in maps). La caracterización, identificación y entendimiento de los patrones espaciales son preocupaciones centrales de la geografía. Profundamente arraigada en la noción de que la localización geográfica importa, un supuesto probable es que las cosas cercanas tienen más relación que las cosas distantes – concepto a menudo referido como la primera ley de la geografía de Tobler. Uno de los medios para cuantificar este supuesto es el uso de medidas de autocorrelación espacial. Varias de tales medidas han sido desarrolladas para probar si un patrón en verdad es aglomerado, o disperso, o si desde una perspectiva estadística es aleatorio. Para aclarar cómo son entendidos los patrones espaciales desde una perspectiva cognitiva, este artículo informa sobre estudios de interpretación de patrones espaciales representados en mapas. Nosotros utilizamos dos mapas a color para los efectos de validación experimental. Sistemáticamente variamos la razón de los colores lo mismo que el nivel de significación de la aglomeración y la dispersión; pusimos la mira en dos grupos: expertos e inexpertos. La tarea para ambos grupos fue igual, o sea clasificar los patrones de acuerdo con cinco categorías específicas de estructuras de autocorrelación espacial. Los resultados claramente muestran que los patrones son entendidos con base en el color dominante, tanto por expertos como inexpertos. Un tercer experimento, en el que se utilizó un paradigma de clasificación libre, confirmó el dominio del efecto del color. Tales resultados son importantes en la medida en que éstos apuntan a aspectos críticos del patrón de percepción y entendimiento que necesita abocarse desde la perspectiva del pensar espacial, especialmente cómo la gente relaciona conceptos de aleatoriedad con patrones espaciales (representados en mapas).
    Annals of the Association of American Geographers 09/2011; 101(5):1011-1031. · 2.17 Impact Factor

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