Structure, Cognition, and Activity--论文代写范文精选

从事一个推理任务学习既简单又复杂，寻常的例子是值得注意的。成功的学习需要一个复杂的类别结构,一个一维的规则。尽管参与者可能依赖于一种或两种规则,注意力策略并无影响。下面的essay代写范文进行详述。

Abstract
The increasing effort required for learning increasingly complex structures may stem, in part, from the particular activity—classification—that dominates laboratory studies on categorization (for review, see Markman & Ross, 2003). The interplay between structural, cognitive, and activity-related constraints becomes obvious when one contrasts alternative uses of categories (ibid.)—e.g., classifying an object versus inferring its uncertain features. Whereas classification focuses attention on between-category featural information, those engaged in feature inference appear to allocate attention to within-category featural information, especially to specific feature values and the withincategory correlations among these values (Yamauchi & Markman, 1998, 2000a, 2000b). 

For example, if one needed to classify a particular brazier as either a tagine or a dutch oven, one would attend to the category differentiating features—ceramic vs. cast iron, conical lid vs. flat lid. Alternatively, if one needed to infer whether a particular pot was appropriate for braising a mutton shank, one would attend to the width, depth, and thickness of the pot as well as how tightly the lid fits. Those engaged in a classification task tend to learn simpler, more compressible, category structures more easily than 14 complex category structures (ibid.). Those engaged in an inference task learn both simple and complex category structures with comparable effort (ibid.).

Activities that involve the indirect learning of categories—e.g, looking for patterns among stimuli or rating their pleasantness—appear similar to inference tasks in attentional strategy and the effort expended in learning simple versus complex category structures (Love, 2002, 2003). Minda & Ross (2004) serves as a noteworthy example of indirect category learning. Participants predicted the food allotment for a sequence of imaginary animals. Successful predictions required the learning of a complex category structure, where both a unidimensional rule and family resemblance (multidimensional rule) predicted the food allotment. While participants could rely on either or both rules, attentional strategies varied with whether or not a classification task (direct category learning) preceded the prediction task (indirect category learning). Those who classified animals before predicting food allotments relied on the simple rule; those who only predicted food allotment distributed their attention across multiple dimensions (see also Ross, 1997, 1999).

Person-Related Constraints 
Other constraints derive from the person (Murphy & Medin, 1985), including his or her prior knowledge (e.g, Chi, Feltovich, & Glaser, 1981; Gauthier, Williams, Tarr, & Tanaka, 1998; Tanaka & Taylor, 1991; Murphy & Wright, 1984; Schvaneveldt et al., 1985), current goals (Barsalou, 1983; Ratneshwar, Barsalou, Pechmann, & Moore, 2001), and the situational heuristics (e.g, Gluck, Shohamy, & Myers, 2002) that connect prior knowledge and current goals. These individual differences may elicit varying prior 15 expectations of how the objects, actions, and events of a new, rare, or unfamiliar activity relate to one another (c.f., Murphy & Medin, 1985). Moreover in trying to induce the actual relationships among the objects, actions, and events, differing individuals might use different heuristics (Lin & Murphy, 1997). In this way, person-related constraints can enhance or impede the learning of novel categories. 

For example, when Bonita and Belle build their computational simulation of Vanishing Bee Syndrome, they are likely to model complex interactions among variables like microwaves and genetically modified crops based on their prior expectations of a syndrome. Likewise, they are likely to exclude simple factors like mite infestation, which might account for many symptoms of the syndrome. Usually, when faced with contrary evidence, the category learner abandons misapplied prior hypotheses (Livingston & Andrews, 1995). The absence of glass cartridge fuses in the lighting aisle of the hardware store will prod Alpha and Beta towards the circuitry aisle. When prior hypotheses bear some resemblance to the observed evidence, though, mistaken hypotheses can persist and impede learning (ibid.). A simulation that includes microwaves and genetically modified crops but excludes mite infestation will not help beekeepers.

What a category is called—the category label—and whether or not it is called anything at all can affect the learning and use of a category. Such effects fall short of the notion that language is thought (Davidson, 1975) or that language determines thought (Whorf, 1956), but goes far beyond the notion that category labels serve little purpose beyond that of another diagnostic feature among other diagnostic features (Anderson, 16 1991). Category labels appear to serve three interrelated functions: (1) as conceptual cues, (2) as conceptual manipulatives, (3) and as conceptual manipulators.

By the principle of contrast (E.V. Clark, 1987), different category labels signal different concepts. For example, labeling objects by different names can help children individuate those objects (Xu, 2002) and often leads them to look for differences among differently-labeled objects (Katz, 1963; Landau & Shipley, 2001) and for similarities among objects with the same name (Loewenstein & Gentner, 2005; Smith, Jones, & Landau, 1996; Waxman & Markow, 1995). Similarly, adults learn event categories better when verbs and/or syntax covaries with the events (Cabrera & Billman, 1996). （essay代写)

Also, children often treat unknown labels as category labels for unknown objects and as feature labels for known objects (Markman & Wachtel, 1988). Finally, the mere presence of category labels can cue the category learner to look for meaningful patterns in what he or she perceives. Children tend to pay more attention to labeled categories than to unlabeled categories (Balaban & Waxman, 1997; Waxman & Booth, 2001) and adults learn labeled categories more quickly than unlabeled categories (Lupyan, Rakison, & McClelland, 2007). In all, category labels appear to make abstractions concrete and implicit judgements explicit (A. Clark, 2006, Vygotsky, 1986 [1962]).（essay代写)

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