Academic journal article Memory & Cognition

Feature-Feature Causal Relations and Statistical Co-Occurrences in Object Concepts

Academic journal article Memory & Cognition

Feature-Feature Causal Relations and Statistical Co-Occurrences in Object Concepts

Article excerpt

Influences of feature-feature statistical co-occurrences and causal relations have been found in some circumstances, but not others. We hypothesized that detecting an influence of these knowledge types hinges crucially on the congruence between the task and type of knowledge. We show that both knowledge types influence tasks that tap feature relatedness. Detailed descriptions of causal theories were collected, and co-occurrence statistics were based on feature production norms. Regression analyses tested the influences of these knowledge types in untimed relatedness ratings and speeded relatedness decisions for 65 feature pairs spanning a range of correlational strength. Both knowledge types influenced both tasks, demonstrating that causal theories and statistical co-occurrences between features influence conceptual computations.

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Lexical concepts are ubiquitous in human cognition, and language succinctly transmits a great deal of information about them via the computation of word meaning. Concepts are often described in terms of semantic features, and people readily provide features for object concepts in production tasks (McRae, Crée, Seidenberg, & McNorgan, 2005; Rosch & Mervis, 1975). For example, for the word robin, a participant might list flies, has feathers, has wings, has a beak, lays eggs, builds nests, and eats worms. Of course, people know much more about concepts than simply lists of features. In particular, they have extensive knowledge of various distributional statistics regarding environmental structure (Crée & McRae, 2003) as well as background and theory-based knowledge that influences people's use of concepts (Murphy & Medin, 1985).

An important confluence of distributional statistics and theory-based knowledge concerns the manner in which features co-occur. Features do not occur independently of one another; there is statistical structure in the patterns of feature co-occurrence across concepts. That is, there is a continuum of variation in the degree to which the presence of one feature signals the presence of another. For example, has feathers and has a beak are highly correlated because the various types of birds that have feathers are very likely to have a beak as well. In contrast, has a tail and has hooves are relatively weakly correlated because things in the world that have hooves always have a tail, but there are many types of animals that have a tail but do not have hooves. 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, Crée, Westmacott, & de Sa, 1999).

In fact, the tasks used to tap knowledge of feature co-occurrences and the manner in which people learn about concepts are both important factors in showing influences of feature co-occurrences (Chin-Parker & Ross, 2002). Although many studies have focused on categorization per se and the intentional category learning paradigm, the utility of various aspects of conceptual representation and computation is much broader than solely categorization or laboratory category learning tasks. Concepts are used for many purposes, including language comprehension and production, object recognition, conceptual combination, and making various sorts of inferences. The present research investigates the manner in which knowledge of feature-feature causal relations and statistical cooccurrences forms part of our conceptual representations and influences conceptual tasks. …

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