Correlated properties in natural categories

Color–function categories that prime infants to use color information in an object individuation task

“…Infants also possess more meaningful or conceptual categories (Mandler, 1992), such as the distinction between kinds of objects (animate vs. inanimate or natural kinds vs. artifacts) or kinds of mechanical interactions (containment vs. support or inert vs. self-propelled), that allow them to interpret and make predictions about the outcome of physical and social events (e.g., Baillargeon, 1998Baillargeon, , 2004Leslie, 1994;Meltzoff & Moore, 1995;Premack, 1990;Spelke & Woodward, 1995). In adults, these are often referred to as natural categories (Ross & Murphy, 1999) and a great deal of effort has been placed on understanding how adults and children use these categories to draw inferences about the properties an object will possess or the purpose/function of an object (Gelman & Coley, 1990;Gelman & Koenig, 2003;Gelman & Markman, 1986;Kalish & Gelman, 1992;Lopez, Atran, Coley, Medin, & Smith, 1997;Malt, Ross, & Murphy, 1995;Malt & Smith, 1984;Markman, 1989;Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976;Ross & Murphy, 1999). These kinds of categories are evident very early in development, are relatively stable, and are gradually shaped over time by infants' and children's everyday experiences in the physical and social world.…”

Feature-feature causal relations and statistical co-occurrences in object concepts

“…First, the vast majority of previous research on feature correlations in natural concepts has focused on the basic level, and feature cooccurrence at this level provides insights into conceptual representations and processing (Ahn et al, 2002;Malt & Smith, 1984). More pragmatically, we possess feature production norms for 541 living and nonliving things, which allows the computation of statistical co-occurrences for a large number of feature pairs across a sizeable sample of concepts.…”

“…For example, Malt and Smith (1984) examined how typicality judgments are affected by broken correlations, which occur when only one member of a correlated feature pair appears in a concept (such as having wings, but not being able to fly). Not all correlated feature pairs had the same effect on typicality ratings, leading Malt and Smith to speculate that knowledge of statistical co-occurrences may be limited to only certain salient relations.…”

“…Not all correlated feature pairs had the same effect on typicality ratings, leading Malt and Smith to speculate that knowledge of statistical co-occurrences may be limited to only certain salient relations. Ahn et al (2002) hypothesized that the feature pairs in Malt and Smith (1984) that influenced typicality ratings were those for which participants possess a theory-based relation. Participants rated the correlational magnitude of Malt and Smith's feature pairs, and these ratings were used to distinguish between explicit (high magnitude) and implicit (low magnitude) pairs.…”

“…Moreover, for some correlated feature pairs, people possess a theory for why they are correlated, such as the fact that has wings is causally related to flies. It has been shown multiple times that people are aware (and can learn) that certain features co-occur within concepts (ChinParker & Ross, 2002;Malt & Smith, 1984). Furthermore, knowledge of feature co-occurrences and causal relations has been shown to influence performance in a range of tasks, from offline feature inferences (Gelman, 2003) to online feature verification (McRae, Cree, Westmacott, & de Sa, 1999).…”