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How Do Young Children Reason about Natural Categories (Natural Kinds)?

3172 words (13 pages) Essay in Psychology

08/02/20 Psychology Reference this

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INTRODUCTION:

WHAT IS A NATURAL KIND?

It is important to know what it means to call something a natural kind.  In daily terms, a natural kind is a collection or category of things that are all the same as one another but different from some other set of things. These things may look alike on the surface or may not look alike but in some depth they are all equivalent. In the philosophical sense, a natural kind is a nonarbitrary grouping of instances that occur in the world. This categories are given by nature and not created by human minds. In a natural kind category, instances cluster together in a meaningful way because they all have something in common. A category constitutes a natural kind if every occasion of the kind looks similar and shares a collection of features that co -occur. These features can be observed and measured.  The property cluster that characterizes a natural kind is sometimes said to be homeostatic, in the sense that the properties work to maintain and reinforce one another.  The property cluster must be reliably observed for every instance of the category (Goodman, 1945).

It has been argued that concepts as mental representations of categories have no necessary or defining features, and as a consequence it is possible to define a natural kind by the similarity of instances(kripke,1972; putman,1975). In this view members of a category share a family resemblance on a characteristic set of features, and therefore concepts for categories, whether they refer to natural kinds(birds) or not (marriages), are best defined by probabilistic sets of features. However some researchers have argued that natural kinds cannot be characterized in terms of similarity, and instead propose that there is something deeper and a more basic to a natural kind some underlying casual structure or mechanism that makes a set of instances the kind that they are and not some other kind.

HOW YOUNG CHILDREN CATOGORISE NATURAL OBJECTS

Flavell & Wellman (1976, Flavell 1977) have proposed that memory phe­nomena can be divided into four types: basic processes, knowledge (semantic memory), strategies, and metamemory.

They have described two important characteristics of basic processes. First, a person is not conscious of the actual working of the process. Second, the process undergoes no significant development with age; development is complete by the end of the sensorimotor period (age 1 to 2 years). They provide three examples of basic processes:

 1. the processes by which an object is recog­nized;

 2. the processes of representation underlying recall of absent objects or events;

3. the process of cueing or associating.

They also  point out that the four types of memory phenomena are not mutually exclusive.   Without any categorization an organism could not interact profitably with the infinitely distinguishable objects and events it experiences. Therefore, even infants should be able to categorize.  Nevertheless, until recently there was little motivation to consider infant categorization abilities, since it was widely believed (Gelman 1978) that children could not categorize until they reached the stage of concrete operations (when they are 5 to 7 years old).  However, once simple categorization abilities were demonstrated in preschool children, research with infants began in earnest. Most of these studies have taken advantage of an infant’s predictable preference for novel stimuli over familiar ones. The studies use the same general procedure. First, the infant is given several familiarization trials with different category members. Then he is shown either a novel member of the same category, a novel member of a different category, or both at once. If the infant has formed a category, then he should spend significantly more time looking at a stimulus from a novel category than from the familiar one. Using this format, G. Ross ( 1 977) demonstrated that 1 2-month-old infants were able to form a variety of basic level categories. L. B. Cohen demonstrated that much younger infants (30-week-olds) were able to form the categories “female face” (L. B. Cohen & Strauss 1979) and “stuffed animal” (L. B. Cohen & Caputo 1978). Strauss (1979), using sche­matic faces, demonstrated that lO-month-olds were able to categorize an average prototype after familiarization with other category members.

Three more studies have used different techniques. Husaim & L. B. Cohen (1980), using a discrete trial discrimination learning paradigm, found that ten-month-olds could form two non criterially defined categories (of schematic animals). The infants attended to more than one attribute, and the same models that predict adult categorization behavior were able to predict infant behavior. Ricciuti (1965) examined the behavior of 12-, 18-, and 24-month-olds who were given two types of toys to play with, and found considerable evidence of categorization abilities in even the 12- month-olds. K. Nelson (1973), using 20-month-olds, has replicated this result for other basic level categories. In summary, then, there is now substantial evidence that basic level categorization should be considered a basic process.

 The second major implication of representativeness is that categories are learned more easily and more accurately if initial exposure to the category is through only representative exemplars. Two studies have shown that initial exposure to only representative exemplars is more effective than initial exposure to only non representative exemplars; the stimuli were dot patterns (Mirman 1978) and multimodal artificial stimuli with natural cate­ gory structure (Mervis & Pani 1980). Results are somewhat more equivocal when initial exposure to both representative and non representative examples is compared with initial exposure to only representative examples. Two studies (Homa & Vosburgh 1976, Goldman & Homa 1977) found that for categories with certain char­acteristics initial exposure to the full range of category membership was not worse than exposure to good examples only. However, three other studies found training on good examples superior to training on a range of examples (Mervis & Pani 1980, Hupp & Mervis 1981, Mervis & Mirman 1981).

What might make some objects more representative of their category than others? It may be useful to consider once more the previous section in which we discussed clusters of correlated attributes. These correlations are not perfect-for example, in our hypothetical set of creatures (p. 9 1), besides the two main correlated clusters (birds and mammals), there are many types of flightless birds, a few flying mammals (bats), and the duck­ billed platypus. It has been argued that the logic of attribute structures associated with gradients of representativeness within categories is parallel to the logic (described in the previous section) that predicts which level in a taxonomy will be the basic level (Rosch 1978, Mervis 1980).

Whales aren’t fish, fool’s gold isn’t gold, and glass-snakes are actually lizards. These anomalies pose a problem for theories of categorization: To any casual observer, whales look like fish, fool’s gold looks like gold, and glass-snakes seem indistinguishable from snakes. As these cases illustrate, certain categories capture more than obvious perceptual features. Fish, gold, and lizards are examples of natural kind categories, classes of objects and substances found in nature.

Mill’s observations are supported by recent work on natural kind terms (Kripke, 1971, 1972; Putnam, 1970; Schwartz, 1977, 1979). As part of more extensive analyses, Kripke and Putnam point out that natural kinds cannot be characterized by simple perceptual properties. Although features may be set forth to identify members of a natural kind category, they often do not serve as necessary and sufficient criteria. For example, whales are shaped like fish and live and swim in water as fish do, but they are not fish. Conversely, whales are mammals even though they are not furry and do not walk on land, as other mammals do. Usually, of course, perceptual features reliably identify the category of an object. Giraffes, for example, have many perceptual characteristics in common. Yet attributes common to members of a category are often impossible to know by casual inspection,

Young children, with their usual reliance on perceptual appearances and only rudimentary scientific knowledge, might not induce new information within natural kind categories. To test this possibility, category membership was pitted against perceptual similarity in an induction task. For example, children had to decide whether a shark is more likely to breathe as a tropical fish does because both are fish, or as a dolphin does because they look alike. By at least age 4, children can use categories to support inductive inferences even when category membership conflicts with appearances.

One piece of evidence that children are not solely dependent on perceptual similarity for drawing inferences comes from work by Carey (1985). She showed several groups of children between ages 4 and 10 a mechanical monkey, one that could move its arms to bang cymbals together. Children, who knew that real monkeys breathe, eat, and have baby monkeys, were asked whether the mechanical monkey could breathe, eat, and have babies too. All but one group of 4-year-old children denied that the mechanical monkey possessed these animate properties. Despite the striking perceptual similarity of these two types of objects-mechanical and real monkeys–children did not generalize facts about one to the other. These children have differentiated living things from nonliving things and therefore refused to impute properties that characterize living things to nonliving things.

Study 2

They ran a condition where category membership was conveyed by means of synonyms rather than identical labels. If children infer that objects named by synonyms (e.g., “rabbit” and “bunny”) share the same properties, then their within-category inferences cannot be based simply on identity of the labels. Unfortunately, however, there are not many synonyms

for natural kinds in the vocabulary of 4-year-olds. So to supplement the few synonyms we could find, we also included categories that were related by subset-superset relations, e.g., “rose-flower.” These pairs in addition to having different labels, would also require children to make an additional inference (e.g., that a rose is a flower) to use the common category as a basis for drawing an inductive inference. Therefore, they should underestimate the categorical inferences children would make if genuine synonyms could be found.

Method

 Subjects Subjects included 48 preschoolers, none of whom had participated in Studies 1 and la, from six nursery schools in northern California. Children were tested in three conditions: the Synonyms condition, the Arbitrary Decision condition, and the Standard condition. The age range and mean age for each condition were: Synonyms condition (4;3 to 5;8, 4;lO); Standard condition (4;0 to 5;6,4;9); Arbitrary Decision condition (4;0 to 5;6,4;9). Nineteen of the children were girls.

Procedure

The task was introduced to all children as a game, in which after every set of pictures the child would receive a sticker to place on a sheet of paper to make a picture. Before presenting the experimental items, the researcher first asked the child three easy warm-up questions (e.g., “Is this [green bow] green or is it red?“). Children in the Synonyms condition received eight problems, each consisting of a triplet of pictures constructed to be analogous to those in the earlier studies. Unlike the earlier studies, however, category membership was conveyed by means of synonyms. For example, one triplet consisted of a target rabbit, another rabbit with different appearance, and a squirrel that had long ears and looked like the target rabbit. The two rabbits in the Synonyms condition were called “rabbit” and “bunny,” respectively. Examples of items and attributes can be found in Table 4. Children in the Standard condition received the same eight sets of items as in the Synonyms condition, except that objects from the same category were given identical labels rather than synonyms. This task is a replication of Studies 1 and 2, except for the items used. So for example, on one item children saw two rabbits and a squirrel – and both rabbits were labeled “rabbit. ” Children in the Arbitrary Decision condition saw the same eight sets of items and heard the same labels for the pictures as in the Standard condition. However, the experimenter presented the child with an arbitrary, game-like task rather than with a series of inferencing problems. That is, for each problem the experimenter placed a small card with a colored dot on each of the first two pictures. For example, the experimenter placed a red dot on one rabbit and a yellow dot on the squirrel. The child then had to pick which color dot to put on the third picture (the other rabbit that looked like the squirrel): a yellow dot or a red dot.

Results and discussion

 In the Standard condition, designed to replicate Study 1, children once again drew inferences to category members above-chance (68% of the time, t (15) = 2.83, p < .OS). Moreover, as the results from the Synonyms condition indicate, children do not need to hear common labels to use common category membership to draw inferences. In the Synonyms condition, where children heard either synonyms or subclass-superclass relations to indicate common category membership, children still were basing their inferences on category membership greater than chance (63% of the time, t (15) = 3.87, p < .005). The results of this study further indicate that children have begun to differentiate between inferences where category membership is relevant from arbitrary decisions where it is not. When children were asked about arbitrary decisions, e.g., what color chip should go on a given picture, they were not significantly more likely to base that decision on the color chip they had seen placed on a common category member than a chip placed on a perceptually similar picture. Children in the Arbitrary Decision condition were performing at chance levels (57% category choices, t (15) = 1.45, p > .l). The results in the last two conditions were very close (63% versus 57%). It appears that children may have a slight bias toward basing their answers on category membership, even in the Arbitrary Decision task. Nonetheless, responses in the two conditions look clearly different when the patterns of individual children are considered: only 6% of the children in the Synonyms condition answered based on appearance more than half of the time, whereas 25% of the children in the Arbitrary Decision condition did so. In summary, children’s inferences were based on the category membership of the objects in question, and not simply on how the pictures were labeled. First, children drew inferences within each category even when category members were not given identical labels. And second, when category members have given identical labels, children relied on these category names above chance only when the task required them to draw inferences.

These results are at odds with a widely held view that children’s thinking is strongly influenced by the perceptual appearances of things. Several of our findings suggest that children are not dominated by appearances either in their conception of the structure of categories or in their use of categories to support inductions. First, most children accepted our label for the third object even though it looked more like a member of the other category. For example, one object was a squirrel with very long, rabbit-like ears. Overall, it looked more like a typical rabbit than a squirrel. Some children noted the discrepancy, some even mildly objected to the label, but for the most part, children accepted the labels for these abnormal category members. This finding is consistent with recent work by Flavell, Flavell, and Green (1983) on the development of the appearance-reality distinction. Like our subjects, preschool children in their studies accepted category labels even in the face of discrepant appearances (e.g., a sponge that looks convincingly like a rock is called a “sponge” by these children).

Finally, as Carey (1985) has argued, children must learn how natural kind categories are related to one another in a system of theory-based knowledge. Carey has found that children initially organize biological knowledge around humans as a prototype. Inferences about the biological properties of other species are based both on what children believe about people and on how similar the species is to humans. It is also possible that children are initially biased to interpret category terms this way, independent of experience. Other expectations about the structure of natural language categories appear quite early. When children as young as 18-24 months hear an object labeled with a common noun, they assume the term refers to the object as a whole rather than to one of its properties (Macnamara, 1982). By 3 or 4 years of age and possibly earlier, children expect a noun to refer to objects that are taxonomically related (e.g., a dog and a cat) even though in the absence of a label they are likely to group objects on the basis of thematic relations (e.g., a dog and a bone) (Markman & Hutchinson, 1984). The assumption that categories promote rich inductive inferences could be another early bias, one that helps children acquire category terms rapidly, organize knowledge efficiently, and induce information to novel exemplars of familiar categories. By expecting unforeseen non perceptual properties to be common to members of a kind, children could go beyond the original basis for grouping objects into a category and discover more about the category members than they knew before. Children might start out assuming that categories will have the structure of natural kinds. With development children would then refine these expectations, limiting them to properties, domains, and category types that are appropriate.

References:

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  • Flavell, J.H. (1963). The developmental psychology of Jean Piaget. Princeton, NJ: D. Van Nostrand.
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Cognitive development, Vol. 3 of P.H. Mussen (General Ed.), Handbook of child psychology.

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