Cognitive Development In Childhood And Adolescence Research Paper

1. Causal Reasoning

Whereas no one theory unites the numerous disparate domains of cognitive development, some cognitive operations are common across them. Causal reasoning provides an illustrative example of how several areas of cognition develop in concert to support a higher-level skill. Reasoning can be defined as goal-directed activity that often relies on the use of inferences to reach a conclusion (DeLoache et al. 1998). Causal relations are ‘the cement of the universe’ (Mackie 1980) in that they provide systematic links between precursors and consequences. Causal reasoning thus allows for identification of the cause–effect relations observed in the world. Inhelder and Piaget (1958, 1964), founders of scholarship in cognitive development, began the tradition of research on causal reasoning in the developing child and set the stage for much of the research done to date. Motivated by the desire to understand how children and adolescents think and reason about events in the world, researchers have examined infants’ perceptions of causality, young children’s ability to perform causally linked actions in sequential order so as to achieve a goal, and older children’s abilities to infer causal mechanisms and to test for cause–effect relations (i.e., to reason scientifically).

2. Early Developments In Causal Reasoning

2.1 Perception Of Causality In Infancy

Within a few months of birth, infants demonstrate sensitivity to causes and their consequents. Oakes and Cohen (1990) showed 7-and 10-month-old infants different events in which one toy traveled across a screen and either (a) made contact with another toy, which immediately moved; (b) made contact with another toy, which moved only after a brief delay; or (c) stopped before contacting the other toy, which, after a brief delay, moved. Only the first event contained the causal elements of spatial and temporal contiguity. The researchers found that the 10-month-olds were sensitive to the causal structure of the events as demonstrated by longer looking to the events that violated causal principles. Leslie (1984) found that infants as young as six and a half months are sensitive to causal structure when simpler stimuli (e.g., colored blocks) are used.

2.2 Enacting Causal Sequences To Achieve A Goal

By the second half of the first year of life infants not only appear to recognize basic conditions of causality, they also use them to guide their own actions to reach desired goals. In Willatts (1984), nine-month-old infants were presented with a cloth within their reach on which rested a desired toy just outside their reach. The infants needed to remove a barrier in order to pull the cloth and thereby obtain the toy. Compared to infants in a control condition in which the toy rested just off the cloth, infants in the ‘causal’ condition more often removed the barrier and pulled the cloth. By 12 months of age infants are able to solve a more complex version of this means–ends task: they successfully navigate multiple steps including removing a barrier to reach a cloth, pulling the cloth to reach a string, and reeling in a toy attached to the string. By 18 months, in age-appropriate versions of means–ends tasks, children use a variety of strategies and monitor the effectiveness of their strategies for reaching their goals (see Willatts 1990 for a review).

2.3 Planning A Path From Cause To Effect

Shortly after children show appreciation of existing causal connections, they begin to create their own paths from causes to effects. In Bauer et al. (1999), 21-and 27-month-old children were required to plan a course of action to achieve an effect. The children were shown either the initial state or the goal state of a problem that required a three-step solution. Each of the steps was necessary, but not sufficient, to achieve the goal. For example, for the problem of ‘making a rattle’ (Step 1: put a block in a cup; Step 2: cover the cup and block with a second cup; Step 3: shake the cups to make a rattling sound), an experimenter modeled either the initial step of putting the block in the cup or the goal step of shaking the rattle. In both cases, the experimenter verbally provided the children with the goal of the activity (i.e., ‘make a rattle’). Note that the children were given the same amount of information (one step of the three-step solution) in each of the two conditions. When the children were shown the initial step of the causal sequence, neither the 21-nor the 27-month-olds solved the problem on more than 8 percent of the trials. In contrast, when provided with the goal step of the solution, 23 percent of the 21-month-olds and 50 percent of the 27-month-olds were able to plan a path to the goal. This study not only demonstrates the primacy of the goal state in aiding children’s production of cause–effect sequences, but also shows rapid developments in planning abilities.

2.4 Identifying Causal Relations In The Preschool Years

Whereas in the toddler years, children show facility with planning a course of action to achieve a specific effect, in the preschool years, they reason as effectively about effects, causes, and the steps that unite them. Gelman et al. (1980) trained three-and four-year-old children to read three-picture ‘stories’ that depicted the initial state of an object, a causal agent, and the object in a transformed state. For example, children were shown an intact coffee cup (initial state), a hammer (causal agent), and a broken coffee cup (transformed state). The children then were presented with a series of stories in which one of the three pictures was missing. They were asked to complete the story by selecting one of three choice cards. The researchers found that both three-and four-year-olds were able to infer the causal agent as well as the initial and transformed states. In related research it has been shown that children understand a number and variety of causal relations, including melting, cutting, and burning (Goswami and Brown 1989).

Just as important as the ability to infer causes, consequences, and causal agents is the ability to recognize that not all cause–effect relations are deterministic. In the real world, causality tends to be probabilistic. For example, if an object is tremendously heavy, pulling the cloth on which it rests might not be sufficient to retrieve it. Kalish (1998) tested three-and five-year-olds and adults’ understanding of probabilistic causal relations in the domain of illness. Whereas adults recognize that causes of illness are probabilistic (e.g., coming into contact with a sick person does not inevitably result in getting sick), children treat them as deterministic. This research suggests that the development of sensitivity to probabilistic causality is more protracted, relative to understanding of causal events with definite outcomes. Thus, lack of sensitivity to probabilistic outcomes represents a limitation of the causal understanding of preschool-age children.

3. Later Developments In Causal Reasoning

In addition to demonstrated competencies in reasoning about cause–effect relations, preschool-age children also show early manifestations of scientific reasoning. The goal of scientific reasoning is to test hypotheses in order to identify cause–effect relations. Children as young as four and five years of age will search for the causal mechanism that resulted in an effect even when they do not see it (Bullock 1984). Therefore, even very young children are sensitive to the necessity of a cause. Nevertheless, just as they initially overgeneralize and treat all cause–effect relations as deterministic (i.e., failing to recognize the probabilistic nature of some causal relations), as shown below, in the preschool and early school years, children also overgeneralize one of the strongest cues to causality, namely, contiguity.

Schlottman (1999) presented five-, seven-, and nine-year-old children and adults with a mystery box that could contain one of two mechanisms. Both mechanisms caused a bell to ring when a ball was dropped in one of two holes in the box. One causal mechanism was ‘slow’ in ringing the bell because the ball had to travel down a runway in order to ring the bell at the opposite end of the box. The other causal mechanism was ‘fast’ because the ball dropped onto a lever that acted like a seesaw and immediately rang the bell. In the task, a ball was dropped into one of two holes in the box. Seconds later, the second ball was dropped and the bell immediately rang. Participants were asked to identify which of the two balls had caused the bell to ring. When they did not have knowledge of which of the two mechanisms was in the box, participants of all ages attributed causality to the contiguous event. That is, they selected the ball that was dropped immediately before the bell rang. Even after they were informed of which mechanism was in the box, five-and seven-year-olds continued to select the contiguous event, regardless of mechanism. In contrast, when appropriate, the nine-year-olds and adults ignored contiguity and made decisions based on the properties of the mechanism. This research makes clear both the profound influence that prior knowledge and experience have on causal and scientific reasoning and the great strides in treatment of data made by children in the early elementary school years.

Even beyond the early elementary school years, individuals’ beliefs influence the ways in which they design experiments and the hypotheses that they test. Schauble (1996) asked fifth and sixth graders and noncollege adults to design experiments to identify the variables that affect the speed of a boat down a canal and the extension of a spring into water when a weight is attached. Both adults and children entered the experiment with beliefs about the objects and the relations about which they were to reason. In general, adults’ beliefs were more appropriate than children’s beliefs. For example, only 30 percent of the adults compared with 80 percent of the children expressed the belief that ‘big things weigh more than small things.’ A priori beliefs about causal variables influenced the experiments that both the children and the adults designed. Specifically, adults used their experimentation trials to understand the variables for which they did not hold prior beliefs. In contrast, children used their experiments to confirm the beliefs they held prior to investigation. This difference was partially responsible for the overall higher performance of adults in identifying the causal variables.

Schauble’s (1996) research also makes clear that children and adults differ in the systematicity with which they approach the experimental space. First, although the children and the adults conducted the same total number of experiments, the children often inadvertently duplicated their experiments (even though they were provided with data cards to record their experimental manipulations). Second, within an experiment, the children were less systematic in the conduct of trials. They were less likely to control variables across two trials and often changed two variables at once in their attempts to test causal hypotheses. Because they based their causal inferences on confounded tests, children showed lower levels of performance relative to adults. These patterns indicate that in addition to domain-specific knowledge (as assessed by prior beliefs), domain-general experimentation strategies influence children’s abilities to design experiments and to determine causal mechanisms.

Scientists are required not only to design experiments to test hypotheses, but are also required to draw conclusions on the basis of data obtained by others. In drawing conclusions, scientists must attend to a number of features, including the presence or absence of co-variation, the availability of a plausible causal mechanism, the size of the sample, and the sampling method used. Koslowski et al. (1989) presented sixth and ninth graders and college students with a series of story problems, each of which described the way evidence was gathered (direct intervention or correlation), the sample size (large or small), the causal mechanism (present or absent), and the results (co-variation or no co-variation). Participants were then asked to judge the extent to which they believed the proposed cause was responsible for an observed effect. Whereas all participants were sensitive to the co-variation of cause and effect, developmental changes were apparent in sensitivity to each of the other types of information. Sixth graders continued to give high ratings for the proposed cause when co-variation was present even when there was no causal mechanism provided and when a small sample size was used. Ninth graders provided high ratings for their confidence in the proposed cause with either a small or a large sample size, but only when a causal mechanism was present. College students showed even more refined scientific reasoning. They were less confident in the proposed cause when a causal mechanism was absent (even when co-variation was present) and when a small sample size was used. These findings suggest a developmental progression in scientific reasoning in which co-variation is primary, followed by a sensitivity to the presence of causal mechanism, with a later developing sensitivity to sample size. Notice that absent from the list of features to which college students were sensitive is the nature of the evidence: Koslowski et al.’s participants did not discriminate direct intervention studies from correlational approaches. Thus although, with development, participants evidenced greater awareness of the types of information that scientists use in their evaluation of a potential cause and effect relation, the additional distinction of sampling method is needed to truly ‘think like a scientist.’

4. Mechanisms Of Cognitive Change

From infancy through adolescence, children’s under-standing of causality undergoes tremendous refinements. Developments in several cognitive domains support these advances in causal reasoning. The abilities include analogical reasoning, attention, working memory, selection and use of strategies, and domain-specific knowledge. Developments in each domain are described in turn.

Analogical reasoning refers to the process by which knowledge is transferred from one domain to another. It is appropriate when the domains share fundamental or ‘deep structure’ similarities. Analogical reasoning plays an important role in causal and scientific thinking because it allows for extension of knowledge from a well-known or better known situation to another less well-known domain. Children as young as 16 to 20 months of age show evidence of generalization of knowledge from one domain to another (Bauer and Dow 1994). Goswami and Brown (1989) demonstrated that preschoolers can use their understanding of causal relations to complete analogies wherein the higher order relation between the elements is the causal mechanism (e.g., chocolate is to melted chocolate as a snowman is to a melted snowman). Analogical reasoning also permits transfer of strategies from one task to another. For example, Chen and Klahr (1999) found that fourth graders transferred the strategy of control of variables even after a seven-month period. With development there are changes in the efficiency and proficiency with which analogical transfer is employed (see DeLoache et al. 1998 for a review).

Attention also shows tremendous developments from infancy to adolescence. There are documented developmental differences in the ability to sustain attention on a task, to switch attentional focus between tasks, and within a task, to focus on relevant versus irrelevant features (e.g., Ruff and Lawson 1990). In research on causal reasoning, the tasks presented to young children typically require focus on a limited number of features. The absence of irrelevant features makes it easier for young children to focus on those that are related to the causal structure. As tasks and problems become more complicated and more variables and features are involved, it is necessary to differentiate what is relevant from what is irrelevant, and to ignore ‘distractions.’ A comparison of Willatts’ (1984) research on means–ends problem solving by nine-month-olds with Schlottman’s (1999) research on the use of contiguity to identify causal mechanism by five to nine-year-olds makes clear that the older children are required to attend to many more features to produce or identify the cause–effect relation. In addition, with development, people become better able to attend selectively to the variables at hand. Selective attention may promote the control of variables when designing experiments and assist children in ignoring deceptive surface-feature similarities (Chen and Siegler 2000).

Age-related changes in working memory also play a large part in the development of causal reasoning abilities. Working memory refers to the ability to hold information in mind and simultaneously process it. A major model of working memory proposes that it is comprised of short-term information stores or ‘buffers,’ access to and use of which is controlled by a central executive function that also retrieves information from long-term memory and controls action planning and goal-directed behaviors (Baddeley 1986). Over the course of development, there are age-related changes in each of these functions. Whether developmental changes are the product of increases in available memory capacity (Halford et al. 1994) or of increasingly efficient use of available capacity (Case 1992) is under debate. Nevertheless, what is clear is that with development, there are increases in the length of time over which information can be held or maintained and the facility and speed with which it can be processed (see Gathercole 1998 for a review). For example, absent sustained, focused attention or rehearsal, auditory information remains persistent in short-term memory for 12 seconds in adults, 10 seconds in 10–12 year olds, and for only eight seconds in six–seven-year-olds (Keller and Cowan 1994). As reviewed in Swanson (1996), there also are age-related increases in working memory span, or the number of items that can be recalled in the context of a concurrent processing task. As demonstrated in Schauble’s (1996) research, each of these elements plays a supportive role in causal reasoning. That is, compared with adults, children were more likely to lose sight of their plans for experimentation and were less systematic in their execution of plans. In each case, the presumed source of lower performance was difficulty re-membering what already had been tested and what had been found.

Also implicated in developments in causal reasoning are changes in the selection and use of problem-solving strategies. For example, as demonstrated in Schauble (1996), relative to children, adults are more likely to use a control of variables strategy when testing hypotheses. Developmental changes in the selection and use of more and less sophisticated and effective strategies can be characterized as a series of overlapping ‘waves’ of approaches or solutions to problems (Siegler 1996). Multiple strategies exist in the repertoire simultaneously. They compete with one another both over time and within a given problem resulting in waves of use as some strategies gradually decline and the frequency of others gradually in-creases. The implication is that strategies such as control of variables likely exist in the repertoires of both children and adults, alongside less advanced strategies of experimentation. Consistent with this suggestion, it has been shown that second graders can be trained to use a control of variables strategy (Chen and Klahr 1999). Nevertheless, the presence of the strategy in the child’s repertoire does not guarantee that it will be used when appropriate or that it will be executed successfully when it is deployed.

Finally, analogical reasoning, attention, working memory, and selection and use of strategies all are influenced by the individual’s familiarity with the domain. For example, Chi (1977) documented higher levels of reasoning by children in domains in which they had expertise (e.g., chess) than in domains in which they were relative novices. Goswami and Brown (1989) found that analogical reasoning was facilitated by knowledge of the causal mechanisms involved in the transformations that children were asked to judge. Similarly, Schauble (1996) found that both children’s and adults’ beliefs about causality influenced the hypotheses that they tested. Thus, the influence of domain knowledge is consistent throughout the life span. The facilitative effects of knowledge also extend to the component processes that support problem solving. Specifically, working memory span is greater for familiar words than for nonwords (Hulme et al. 1991). In addition, greater experience within a domain allows participants to guide their attention to the relevant attributes of the problem to be solved, and to disregard those features that are irrelevant to the solution (Goswami and Brown 1989).

5. Neurological Changes Related To Cognitive Development

Although the precise linkages are not clear, it is widely assumed that cognitive developmental changes in processes such as attention and working memory are related to developments in the neural substrates that subserve them. The prefrontal cortex supports planning and working memory and undergoes a protracted course of development (see Nelson et al. 2000 for review). For example, although adult levels of metabolic activity are approximated in the first year of life, development continues into adolescence (Chugani 1994), and frontal pruning of synapses (Huttenlocher 1994) and myelination (Jernigan et al. 1991) continue into adolescence. Thatcher (1992) demonstrated that the coherence of EEG patterns between frontal and posterior lobes increases throughout middle childhood. Case (1992) has shown that the patterns of coherence in EEG mirror the patterns in the development of attention over the same time period (see Case 1998, for a review).

Other neurological developments include brain wide changes such as myelination that results in faster transmission of impulses between neurons. There also are age-related increases in white matter density that are consistent with greater myelination of fiber tracts throughout adolescence (Paus et al. 1999). Increased speed of processing benefits working memory and attention.

6. Summary

The refinements that occur through brain development produce changes in the cognitive processes that support causal understanding. With development, children accrue a greater knowledge base, process in-formation more rapidly, attain greater attentional and strategic resources, and improve in the efficiency with which they utilize resources. These component processes support a range of behaviors that are united by the appreciation of causal mechanisms, from behaviors as basic as infants’ perception of causality to adolescents’ abilities to design and evaluate experiments to test causal relations. Although we observe qualitative differences in these behaviors through the course of development, what underlies these advances are continuous changes in the brain and the resulting cognitive component processes that develop to sup-port them.

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