Problem Solving

One of the most important ways in which people use their intellectual abilities is to solve problems. Think for a minute about everyday life and the number of problem-solving situations one encoun­ters in school, on the job, in relationships, driving a car, and so forth. Each of these settings requires one to analyze complex situations quickly, to apply knowledge, and to create solutions, sometimes in a matter of seconds.

Some people tend to be better at dealing with certain problems than with others. Why is that? One possible explanation has to do with the kinds of abilities we use regularly versus the abilities we use only occasionally. Nancy Denney proposed a more formal version of this explanation, which we will consider next.

Denney’s Model of Unexercised and Optimally Exercised Abilities. Denney (1984) postulates that intellectual abilities relating to problem solving follow two types of developmental functions. One of these functions represents unexercised, or unpracticed, ability, and the other represents optimally trained, or optimally exercised, ability. Unexercised ability is the ability a normal, healthy adult would exhibit without practice or training. Fluid intelligence is thought to be an example of untrained ability, because, by definition, it does not depend on experience and is unlikely to be formally trained (Horn & Hofer, 1992). Optimally exercised ability is the ability a normal, healthy adult

would demonstrate under the best conditions of train­ing or practice. Crystallized intelligence is an example of optimally exercised ability, because the component skills (such as vocabulary ability) are used daily.

Denney argues that the overall developmental course of both abilities is the same: They tend to increase until late adolescence or early adulthood and slowly decline thereafter. At all age levels there is a difference in favor of optimally exercised ability, although this difference is less in early childhood and old age. As the developmental trends move away from the hypothetical ideal, Denney argues, the gains seen in training programs will increase. As we noted earlier in our discussion of attempts to train fluid intelligence, it appears that this increase occurs.

Practical Problem Solving. Denney’s model spurred considerable interest in how people solve practical problems. Based on the model, adults should per­form better on practical problems than on abstract ones like those typically used on standardized intel­ligence tests. Tests of practical problem solving would use situations such as the following (Denney et al., 1982): “Let’s say that a middle-aged woman is frying chicken in her home when, all of a sudden, a grease fire breaks out on top of the stove. Flames begin to shoot up. What should she do?” (p. 116).

Findings from studies examining how well adults solve problems like this are mixed (Cornelius, 1990; Denney, 1990). Although most researchers find better performance on practical problems, how this performance differs with age is unclear. Some inves­tigators (e. g., Denney & Pearce, 1989) find that performance peaks during midlife and decreases after that, as predicted by Denney’s model. Other researchers (e. g., Cornelius & Caspi, 1987) find con­tinued improvement at least until around age 70.

Performance on practical problems clearly in­creases from early adulthood to middle age. Dif­ferences among studies occur in the age at which maximal performance is attained and in the direction and degree of change beyond midlife. However, some important differences among these studies could explain the differences in findings. One key difference is that several different measures are used in assessing practical problem-solving ability.

Marsiske and Willis (1995) addressed this issue by using three separate measures of practical problem solving and seeing if they converged on a single set of abilities. Their results were enlightening. Each measure proved reliable and apparently valid, but they were not strongly interrelated and their rela­tion to age also varied. Thus competence in solving practical problems is a multidimensional construct, much like intelligence itself, calling into question whether a global construct of practical problem solving exists at all.

One way to assess practical problem solving in more focused terms is to create measures with clearly identifiable dimensions that relate to specific types of problems. This is what Diehl, Willis, and Schaie (1995) did by creating the Observed Tasks of Daily Living (OTDL) measure. The OTDL consists of three dimen­sions, which reflect three specific problems in every­day life: food preparation, medication intake, and telephone use. Each of these dimensions also reflects important aspects of assessing whether people can live independently, a topic we explored in Chapter 5. Diehl et al. showed that performance on the OTDL is directly influenced by age, fluid intelligence, and crystallized intelligence and indirectly by perceptual speed, memory, and several aspects of health. These results provide important links between practical problem solving and basic elements of psychometric intelligence and information processing. However, more recent study findings indicate that basic mea­sures of inductive reasoning, domain-specific knowl­edge, memory, and working memory were related to everyday assessments of each of these abilities (Allaire & Marsiske, 1999, 2002; Thornton & Dumke, 2005). Allaire and Marsiske (1999, 2002) conclude that everyday problems reflecting well-structured challenges from activities of daily living show a strong relationship to traditional psychometric abilities.

The search for relations between psychometric intelligence and practical problem-solving abilities is only one way to examine the broader linkages with intellectual functioning. It focuses on the degree to which everyday problem solving is a manifesta­tion of underlying intellectual abilities (Berg, 2008). However, recall that post-formal thinking is grounded in the ways in which people conceptualize situations.

Indeed, much of the research that led to the discovery of post-formal thought involved presenting adults with lifelike problems; Blanchard-Fields’s (1986) study that we considered earlier is an excellent example. This approach enlarges the scope of what we consider everyday problem solving to include not just cognitive abilities, but also social, motivational, and cultural fac­tors influencing how we solve problems (Berg, 2008).

For example, solving practical problems also involves the role of emotionality. Remember that one of the key aspects of post-formal thinking is the integration of emotion and logic. Blanchard-Fields, Jahnke, and Camp (1995) took this as a starting point and carefully manipulated the emotionality of problems. As described in more detail in the How Do We Know? feature, they found important age differences in problem-solving styles that were highly dependent on whether the problem situation was emotionally salient.

Another important factor that influences the way we solve everyday problems is the context in which the problem occurs. Do we use the same strategies when solving a family conflict between two siblings as we do when solving a conflict over the leading role in a project at work? The answer is no. Interestingly, however, age differences reveal that younger adults are more likely to use a simi­lar strategy across problem-solving contexts: self­action in order to fix the problem. Older adults, on the other hand, are more likely to vary their strategy given the problem-solving context. For example, in interpersonal conflict problems (e. g., family con­flict) they use more emotion-regulating strategies (i. e., managing their emotions) whereas in more instrumental situations (e. g., dealing with defec­tive merchandise) they use self-action strategies (return the product) (Blanchard-Fields et al., 1997). Blanchard-Fields et al. (1997) argue that as we grow older and accumulate more everyday experience, we become more sensitive to the problem context and use strategies accordingly.

There are also individual differences in the way the same problem situation is interpreted. In other words, how individuals represent problems differs and could vary across the life span as developmen­tal life goals change (Berg et al., 1998). Berg and colleagues (Berg et al., 1998; Strough et al., 1996) find that there are age differences in how individu­als define their own everyday problems. Overall, middle-aged older adults defined problems more in terms of interpersonal goals (e. g., getting along with a person or spending more time with an individual), whereas adolescents and young adults focused more on competence goals (e. g., losing weight or studying for an exam). Furthermore, problem-solving strate­gies fit the problem definitions. For example, older adults defined problems more in terms of interper­sonal concerns and subsequently reported strategies such as regulating others or including others, whereas competence goals resulted in strategies that involved more self-action. Along these lines Artistico, Cervone, and Pezzuti (2003) found that older adults were more confident and generated more effective solutions to problems that were typical of the life stage of older adults. Finally, Blanchard-Fields, Mienaltowski, and Seay (2007) found that older adults were rated as more effective in their everyday problem-solving strategy use than younger adults across all types of problem situations.

What can we conclude from the research on practical problem solving? First, practical problem­solving abilities are multidimensional and may not even interrelate strongly with each other. Second, the developmental functions of these abilities are com­plex and may differ somewhat across abilities. Third, the relations between practical problem-solving abilities and psychometric intelligence are equally complex. Finally, the close connection between solv­ing practical problems and emotion and motivation may prove fruitful in furthering our understanding of individual differences in abilities. In short, solving practical problems offers an excellent way to discover how all the topics we have considered in this chapter come together to produce behavior in everyday life.

Expertise

On many basic information-processing tasks, younger adults clearly outperform older adults. Yet many people in their 60s and some in their 70s hold jobs that demand complex decision making, abstract reasoning, and memory for a lot of infor­mation. How do they do it?

268 CHAPTER 7

HOW DO
WE KNOW?