A Clinician's Guide to Normal Cognitive Development in Childhood

Elisabeth Hollister Sandberg, Becky L. Spritz, Elisabeth Hollister Sandberg, Becky L. Spritz, Elizabeth Hollister Sandberg, Becky Spritz

• Publisher: Routledge
• Year: 2011
• Print ISBN: 9780415991834
• eBook ISBN: 9781136914799
• Topic: Psychologie
• Subtopic: Kognitive Psychologie & Kognition

Clinicians and practitioners-in-training can often lose sight of the normal developmental landscape that underlies behavior, especially in the field of cognitive development. It exists in an insular bubble within the broader field of psychology, and within each sub-domain there is a wide continuum between the anchors of atypical and optimal development. Clinicians need to learn, and to be reminded of, the unique peculiarities of developing cognitive skills in order to appreciate normal developmental phenomena.

In A Clinician's Guide to Normal Cognitive Development in Childhood, every chapter provides students and established professionals with an accessible set of descriptions of normal childhood cognition, accompanied by suggestions for how to think about normal development in a clinical context. Each sub-topic within cognitive development is explicated through a succinct presentation of empirical data in that area, followed by a discussion of the ethical implications. With an extensive review of data and clinical practice techniques, professionals and students alike will benefit enormously from this resource.

IV
Developing Reason and Executive Control

Take what you know about the world and use it to build more knowledge. Reasoning is the ultimate form of cognitive bootstrapping. Generalizing, hypothesizing, deducing, and evaluating—the hallmarks of sophisticated thought—are skills that develop very gradually, and in very knowledge-baseddriven ways, over childhood. Because of this, “reasoning” with children sometimes feels as futile as bashing one’s head into a brick wall (over and over and over). As Sandberg and McCullough describe in Chapter 10, small logical victories (it is snowing, so I must wear boots) at early ages give the illusion of more abstract competence and don’t foretell that regression in reasoning comes with the burden of worldly knowledge (and that precedes moving to the higher plane of abstract thought).

Moving beyond the brass tacks of inductive, deductive, and systematic thinking, reasoning can be examined with respect to the content-specific domains of any field. White-Ajmani and O’Connell describe for us the development of children’s moral reasoning in Chapter 11, an area of particular relevance for clinicians and educators. They provide useful guidelines for interpreting children’s decisions about good/bad, right/wrong, and do/don’t.

Finally, Holler and Greene (Chapter 12) sketch the picture of the child as gatekeeper and tender of his cognitive kingdom. Effective rulers possess the capacity for organization, planning, control, and flexibility over cognitive processes such as attention and memory. By grounding this discussion in developmental theory, emphasis is placed on the process of learning how to become an effective ruler, a process that begins in childhood and continues well into adulthood.

10
The Development of Reasoning Skills

ELISABETH HOLLISTER SANDBERG and MARY BETH McCULLOUGH

Reasoning, the subject of this chapter, is perhaps one of the most obviously complex and sophisticated cognitive activities in which people engage. Even simple reasoning tasks require the active manipulation of stored information, and many reasoning tasks take us well into the domain of metacognition (thinking about thinking). Although developmental trajectories can be articulated for content-specific types of reasoning (e.g., quantitative reasoning, reasoning about object constancy, moral reasoning), we concern ourselves in this chapter with the description and analysis of three broad classes of domain-general forms of reasoning: inductive reasoning, deductive reasoning, and scientific reasoning.

Briefly defined, inductive reasoning is the process of moving from the specific to the general—taking data from individual observations and experiences and using those data to form more global, generalized rules about the world. Conversely, deduction is the process of moving from the general to the specific—taking established general premises and applying rules of logic to draw valid conclusions that would apply to new specific instances. Scientific reasoning is arguably a step up from induction and deduction in that it is the most complex and explicit of the forms of reasoning. Scientific reasoning should not be confused with knowledge about science; it is a method, not a content area. One could think of it as systematic reasoning. It involves formulating and testing hypotheses, gathering and evaluating evidence, and drawing conclusions that represent links between hypotheses and evidence. Inductive and deductive reasoning are both essential components of scientific reasoning.

The cognitive skills required for all three of these reasoning processes show considerable developmental variability across childhood and into adolescence. For reasons not entirely clear (faulty reasoning perhaps?), we adults make a lot of erroneous assumptions about the sophistication of children’s reasoning skills. As scholars of human behavior, our academic expectations of reasoning from children diverge from the intuitive expectations we evidence in typical day-to-day interaction with children.

In clinical and educational contexts, we routinely expect children to generalize information and skills from one situation to another, to use general rules to guide behavior, and to make connections between causally–related events. We expect these skills from even very young children, ages 3 and 4; yet the skills required to draw inferences develop gradually over childhood. The most sophisticated form of reasoning—in which you develop an idea that is testable—does not develop until adolescence. Yet who among us (yes, even the authors) hasn’t been guilty of asking a five-year-old something along the lines of “What do you think would happen if you were allowed to eat candy at every meal?” followed by looking pointedly at the child, confident that our implicit message carried its intended weight?

Inductive, deductive, and scientific reasoning are stacked in ascending order based on the cognitive resources required to execute them. Successful application of all forms of informal and formal reasoning requires manipulating knowledge or information to gain new insights into or to draw new conclusions about new data. An important theme that arises from consideration of the cognitive skills of informal and formal reasoning is that of accumulated knowledge and experience. Reasoning by induction, or analogy, is greatly facilitated by knowledge and familiarity. The more information that a child possesses and can access, the easier it is to draw analogies. Deep understanding of the elements to be relationally mapped, or the domains from which and to which generalizations are made, facilitates extracting the necessary similarities and relationships required for transfer of information. On the other hand, reasoning by deduction, the drawing of logical inferences, is clouded by associated knowledge and experience. Although young children cannot solve truly abstract logical problems (if P, then Q; P, so Q), with concrete referents early logical proficiencies can be observed. Being able to generate evidence of possibility—utterly irrelevant to questions of argument validity—is easier with an extensive base of knowledge and experience. Similarly, scientific reasoning is heavily influenced by prior knowledge and beliefs. Past experience serves as the foundation for theory building, and theories drive hypotheses. Closely held beliefs about the how’s and why’s of life may not be evidence based, but play an enormous role in the scientific approach to questions. In sum, what you know makes it easier to be inductive, but what you know gets in the way of logic and systematic inquiry.

The Development of Inductive Reasoning

In a sense, inductive reasoning is the most pragmatic form of reasoning—the one that drives much of our day-to-day learning. We observe the consequences of the behaviors of ourselves and others, and we subsequently apply our knowledge of those behavior-consequence pairs to new situations. We find regularity in, and make generalizations from, our daily experiences and encounters through inductive/analogical reasoning. Common manifestations of inductive reasoning include understanding intentionality, reasoning about causality, construction of categories, and relational mapping skills. Under carefully controlled experimental conditions, with task demands stripped down to the bare minimum, developmental scientists have found the rudiments of inductive reasoning at surprisingly early ages (Chen, Sanchez, & Campbell, 1997; Goswami & Reese, 1996).

Categorical Reasoning

Most categorical reasoning is inductive in nature (Coley et al., 2005; Gelman, 2003). Children search for similarities between sets of objects, and use the relational similarities to generalize information from member to member or set to set. Once one has developed a category—dogs, candy, doctors—new members to the category are presumed to share certain characteristics with the existing set. Similarities between existing, specific members of the category are generalized to new members of that category. In most day-to-day learning circumstances, making generalizations of this sort is an extremely efficient and useful way to go about approaching new objects and events in the world. Even though the child does not possess an exact, explicit, formal definition of what makes a creature a dog (as opposed to a cat, small bear, or very large rodent), the child does have a practical definition of dog built up from functionally relevant features such as: furry body, four-legged, tail wagging, domesticity, potential danger, and so on. When introduced to a new animal, “this is my dog, Sparky,” categorical features of dog-ness are extended to Sparky. The more dog features that are stored for the category of dog, the more that can be extended. If, for example, the child’s concept of dog includes: likes to play fetch, licks your face, chews bones, and obeys commands, the child will assume that Sparky will do these things. If the child’s concept of dog includes only: jumps on you and knocks you down, then that is all that can be extended to Sparky. The response to meeting Sparky is predictable when you know the categorical assumptions, but we rarely do.

Practical Implications of Inductive Categories. Generalizations of categorical information creep into children’s behaviors in clinical settings. Because the specific features that define categories for individual children are collected ad hoc from accumulated memories of instances and events, the nature of a category definition is unknowable to an outside party (see Sparky example, above). When knowledge structures are not collectively shared, it can be very difficult as a clinician to anticipate the types of generalizations or working assumptions that a child will make to new category instances. What is the child’s working representation of dogs? Or babysitters? Or vegetables? Or games? Imagine that you ask a small child, “Do you want to play a game with me?” How are you to know that the child’s shocked resistance is based on a uniquely defined concept of game that involves getting smacked on the head by his older brother? Because this insight is impossible, we do not predicate our interactions with children on the ambiguity of idiosyncratic generalizations (questioning all of which...

Table of contents

• Contents
• Contributors
• Introduction
• I Communicating With Children
• II Understanding Others’ Perspectives
• III Children’s Memory
• IV Developing Reason and Executive Control
• Conclusion
• Index