Informational & Biological Constraints--论文代写范文精选

定量不同的概念系统的耦合与定性定量不同的机会学习能力可以产生不同的结果。有人可能争辩说,比较常见的环境概念，例如,人体产生的特定配置在一个特定的物理空间,反过来,影响抽象的概念。下面的essay代写范文进行探讨。

Abstract
Phenomena like basic-level primacy appears so pervasive that one might speculate whether biological evolution has hard-wired certain conceptual structures (e.g, Atran, 2005; Medin & Atran, 2004; Shepard, 1992; Tooby & Cosmides, 1989; Rosch, 1975). By this account, the tendencies to name and identify both natural and artificial kinds has evolved as an adaptive response to fairly persistent informational structures in the environment (Atran, 2005), and any observed deviations from such “universal” tendencies represents a devolution of the conceptual system (Atran, Medin, & Ross, 2004). To a great extent, the adaptionist account relies on discontinuities between humanbeings and other primates (Hauser, 2005). Nevertheless, many primate species exhibit homologous understandings of various concepts, such as small quantities (Dehaene, 2001; Hauser, 2000; Hauser et al., 2000, 1996), gravity (Hood, Hauser, Anderson, & Santos, 1999), distinctions between living and artifactual kinds (Hauser, 1997), and functional distinctions among artifactual kinds (ibid.). Moreover, non-human primates trained to use arbitrary symbols can make abstract judgements that are seldom recognized in the wild (Thompson, Oden, & Boysen, 1997).

For the most part, the differences between the human and non-human conceptual systems appear quantitative rather than qualitative (Deacon, 2000). A coupling of a quantitatively different conceptual system with quantitatively different capacities for opportunistic learning suffices to produce qualitatively different results (Wagner & Wagner, 2003). Add to that, a quantitatively different capacity for sharing mental states (e.g., Meltzoff & Andrew, 2007; Saxe, 2006; Tomasello, Carpenter, Call, Behne, & Moll, 2005; Bloom, 2002) via heritable media (e.g., language and artifacts; cf.; Hutchins & Hazelhurst, 1992; Cavalli-Sforza & Feldman, 1983; Vygotsky, 1978), and strongly adaptionist accounts of the human conceptual system appear, at best, unnecessary (Gould & Lewontin, 1979).

At most, one might argue that comparable sensori-motor capacities among human beings may yield comparable experiences and, thus, comparable conceptions of a common environment (Gallese & Lakoff, 2005; Pfeifer & Bongard, 2006). For instance, the particular configuration of the human body yields a particular experience of physical space, which may, in turn, yield a spatially-analogous structure to both concrete and abstract concepts (Moyer, 1973; Paivio, 1978; Tversky, 2005).

Cognitive constraints, such as limits on working memory and selective attention, represent a less controversial expression of biology. Structural and cognitive constraints appear to intermingle in the oft-cited study by Shepard, Hovland, and Jenkins (1961), where varying the number and combination of diagnostic dimensions of artificial category structures varied the time and effort required to learn those structures (see also Nosofsky, Gluck, Palmieri, Mckinley, & Glauthier, 1994; Love, 2002). Assuming that category learners attempt to optimize selective attention across diagnostic dimensions, one could attribute the increasing cognitive effort directly to increasing demands on selective attention and working memory (e.g, Nosofsky et al., 1994). More often, though, category learners rely on satisficing solutions (Simon, 1955), inferring categories from the simplest description derivable from known exemplars (Feldman, 2003b; Chater & Vitányi, 2003). For example, in the Introduction, Beta relies on the diagnostic features of the familiar category light bulb—glass object containing a filament—to identify the unfamiliar fuse as a “mini bulb.” This sort of “fast and frugal” reasoning conserves cognitive resources (e.g, Todd & Gigerenzer, 2001; Gigerenzer & Goldstein, 1996) and has been observed in categorical decision-making (Matsuka & Corter, In Press; Medin et al., 1987).

That said, the extent of one’s “frugality” depends on the compressibility of the category structure (Feldman, 2000, 2003a, 2006). For example, Shepard et al. (1961) designed each of their six category structures using three binary dimensions, or twelve bits of information. Expressed as a Boolean equation, the category structure with one diagnostic dimension and two non-diagnostic dimensions compresses to one bit, while 13 the category structure with three diagnostic and nonlinearly separable dimensions compresses no smaller than ten bits of information. In other words, the simplest heuristic for learning the latter category structure would require nothing short of memorizing each category exemplar (e.g, Allen & Brooks, 1991). In the Introduction, Bonita’s difficulty in diagnosing Vanishing Bee Syndrome attests to the excessive cognitive effort required in learning and using an incompressible category structure.（essay代写)

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