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Problem-Solving Strategies and Obstacles

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From deciding what to eat for dinner to considering whether it's the right time to buy a house, problem-solving is a large part of our daily lives. Learn some of the problem-solving strategies that exist and how to use them in real life, along with ways to overcome obstacles that are making it harder to resolve the issues you face.

What Is Problem-Solving?

In cognitive psychology , the term 'problem-solving' refers to the mental process that people go through to discover, analyze, and solve problems.

A problem exists when there is a goal that we want to achieve but the process by which we will achieve it is not obvious to us. Put another way, there is something that we want to occur in our life, yet we are not immediately certain how to make it happen.

Maybe you want a better relationship with your spouse or another family member but you're not sure how to improve it. Or you want to start a business but are unsure what steps to take. Problem-solving helps you figure out how to achieve these desires.

The problem-solving process involves:

• Discovery of the problem
• Deciding to tackle the issue
• Seeking to understand the problem more fully
• Researching available options or solutions
• Taking action to resolve the issue

Before problem-solving can occur, it is important to first understand the exact nature of the problem itself. If your understanding of the issue is faulty, your attempts to resolve it will also be incorrect or flawed.

Problem-Solving Mental Processes

Several mental processes are at work during problem-solving. Among them are:

• Perceptually recognizing the problem
• Representing the problem in memory
• Considering relevant information that applies to the problem
• Identifying different aspects of the problem
• Labeling and describing the problem

Problem-Solving Strategies

There are many ways to go about solving a problem. Some of these strategies might be used on their own, or you may decide to employ multiple approaches when working to figure out and fix a problem.

An algorithm is a step-by-step procedure that, by following certain "rules" produces a solution. Algorithms are commonly used in mathematics to solve division or multiplication problems. But they can be used in other fields as well.

In psychology, algorithms can be used to help identify individuals with a greater risk of mental health issues. For instance, research suggests that certain algorithms might help us recognize children with an elevated risk of suicide or self-harm.

One benefit of algorithms is that they guarantee an accurate answer. However, they aren't always the best approach to problem-solving, in part because detecting patterns can be incredibly time-consuming.

There are also concerns when machine learning is involved—also known as artificial intelligence (AI)—such as whether they can accurately predict human behaviors.

Heuristics are shortcut strategies that people can use to solve a problem at hand. These "rule of thumb" approaches allow you to simplify complex problems, reducing the total number of possible solutions to a more manageable set.

If you find yourself sitting in a traffic jam, for example, you may quickly consider other routes, taking one to get moving once again. When shopping for a new car, you might think back to a prior experience when negotiating got you a lower price, then employ the same tactics.

While heuristics may be helpful when facing smaller issues, major decisions shouldn't necessarily be made using a shortcut approach. Heuristics also don't guarantee an effective solution, such as when trying to drive around a traffic jam only to find yourself on an equally crowded route.

Trial and Error

A trial-and-error approach to problem-solving involves trying a number of potential solutions to a particular issue, then ruling out those that do not work. If you're not sure whether to buy a shirt in blue or green, for instance, you may try on each before deciding which one to purchase.

This can be a good strategy to use if you have a limited number of solutions available. But if there are many different choices available, narrowing down the possible options using another problem-solving technique can be helpful before attempting trial and error.

In some cases, the solution to a problem can appear as a sudden insight. You are facing an issue in a relationship or your career when, out of nowhere, the solution appears in your mind and you know exactly what to do.

Insight can occur when the problem in front of you is similar to an issue that you've dealt with in the past. Although, you may not recognize what is occurring since the underlying mental processes that lead to insight often happen outside of conscious awareness .

Research indicates that insight is most likely to occur during times when you are alone—such as when going on a walk by yourself, when you're in the shower, or when lying in bed after waking up.

How to Apply Problem-Solving Strategies in Real Life

If you're facing a problem, you can implement one or more of these strategies to find a potential solution. Here's how to use them in real life:

• Create a flow chart . If you have time, you can take advantage of the algorithm approach to problem-solving by sitting down and making a flow chart of each potential solution, its consequences, and what happens next.
• Recall your past experiences . When a problem needs to be solved fairly quickly, heuristics may be a better approach. Think back to when you faced a similar issue, then use your knowledge and experience to choose the best option possible.
• Start trying potential solutions . If your options are limited, start trying them one by one to see which solution is best for achieving your desired goal. If a particular solution doesn't work, move on to the next.
• Take some time alone . Since insight is often achieved when you're alone, carve out time to be by yourself for a while. The answer to your problem may come to you, seemingly out of the blue, if you spend some time away from others.

Obstacles to Problem-Solving

Problem-solving is not a flawless process as there are a number of obstacles that can interfere with our ability to solve a problem quickly and efficiently. These obstacles include:

• Assumptions: When dealing with a problem, people can make assumptions about the constraints and obstacles that prevent certain solutions. Thus, they may not even try some potential options.
• Functional fixedness : This term refers to the tendency to view problems only in their customary manner. Functional fixedness prevents people from fully seeing all of the different options that might be available to find a solution.
• Irrelevant or misleading information: When trying to solve a problem, it's important to distinguish between information that is relevant to the issue and irrelevant data that can lead to faulty solutions. The more complex the problem, the easier it is to focus on misleading or irrelevant information.
• Mental set: A mental set is a tendency to only use solutions that have worked in the past rather than looking for alternative ideas. A mental set can work as a heuristic, making it a useful problem-solving tool. However, mental sets can also lead to inflexibility, making it more difficult to find effective solutions.

How to Improve Your Problem-Solving Skills

In the end, if your goal is to become a better problem-solver, it's helpful to remember that this is a process. Thus, if you want to improve your problem-solving skills, following these steps can help lead you to your solution:

• Recognize that a problem exists . If you are facing a problem, there are generally signs. For instance, if you have a mental illness , you may experience excessive fear or sadness, mood changes, and changes in sleeping or eating habits. Recognizing these signs can help you realize that an issue exists.
• Decide to solve the problem . Make a conscious decision to solve the issue at hand. Commit to yourself that you will go through the steps necessary to find a solution.
• Seek to fully understand the issue . Analyze the problem you face, looking at it from all sides. If your problem is relationship-related, for instance, ask yourself how the other person may be interpreting the issue. You might also consider how your actions might be contributing to the situation.
• Research potential options . Using the problem-solving strategies mentioned, research potential solutions. Make a list of options, then consider each one individually. What are some pros and cons of taking the available routes? What would you need to do to make them happen?
• Take action . Select the best solution possible and take action. Action is one of the steps required for change . So, go through the motions needed to resolve the issue.
• Try another option, if needed . If the solution you chose didn't work, don't give up. Either go through the problem-solving process again or simply try another option.

You can find a way to solve your problems as long as you keep working toward this goal—even if the best solution is simply to let go because no other good solution exists.

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. doi:10.3389/fnhum.2018.00261

Dunbar K. Problem solving . A Companion to Cognitive Science . 2017. doi:10.1002/9781405164535.ch20

Stewart SL, Celebre A, Hirdes JP, Poss JW. Risk of suicide and self-harm in kids: The development of an algorithm to identify high-risk individuals within the children's mental health system . Child Psychiat Human Develop . 2020;51:913-924. doi:10.1007/s10578-020-00968-9

Rosenbusch H, Soldner F, Evans AM, Zeelenberg M. Supervised machine learning methods in psychology: A practical introduction with annotated R code . Soc Personal Psychol Compass . 2021;15(2):e12579. doi:10.1111/spc3.12579

Mishra S. Decision-making under risk: Integrating perspectives from biology, economics, and psychology . Personal Soc Psychol Rev . 2014;18(3):280-307. doi:10.1177/1088868314530517

Csikszentmihalyi M, Sawyer K. Creative insight: The social dimension of a solitary moment . In: The Systems Model of Creativity . 2015:73-98. doi:10.1007/978-94-017-9085-7_7

Chrysikou EG, Motyka K, Nigro C, Yang SI, Thompson-Schill SL. Functional fixedness in creative thinking tasks depends on stimulus modality .  Psychol Aesthet Creat Arts . 2016;10(4):425‐435. doi:10.1037/aca0000050

Huang F, Tang S, Hu Z. Unconditional perseveration of the short-term mental set in chunk decomposition .  Front Psychol . 2018;9:2568. doi:10.3389/fpsyg.2018.02568

National Alliance on Mental Illness. Warning signs and symptoms .

Mayer RE. Thinking, problem solving, cognition, 2nd ed .

Schooler JW, Ohlsson S, Brooks K. Thoughts beyond words: When language overshadows insight. J Experiment Psychol: General . 1993;122:166-183. doi:10.1037/0096-3445.2.166

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Cognitive Predictors of Everyday Problem Solving across the Lifespan

Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas

Christopher Hertzog

School of Psychology, Georgia Institute of Technology

Denise C. Park

An important aspect of successful aging is maintaining the ability to solve everyday problems encountered in daily life. The limited evidence today suggests that everyday problem solving ability increases from young adulthood to middle age, but decreases in older age.

The present study examined age differences in the relative contributions of fluid and crystallized abilities to solving problems on the Everyday Problems Test (EPT; [ 1 ]). We hypothesized that due to diminishing fluid resources available with advanced age, crystallized knowledge would become increasingly important in predicting everyday problem solving with greater age.

Two hundred and twenty-one healthy adults from the Dallas Lifespan Brain Study, aged 24–93 years, completed a cognitive battery that included measures of fluid ability (i.e., processing speed, working memory, inductive reasoning) and crystallized ability (i.e., multiple measures of vocabulary). These measures were used to predict performance on the Everyday Problems Test.

Everyday problem solving showed an increase in performance from young to early middle age, with performance beginning to decrease at about age of fifty. As hypothesized, fluid ability was the primary predictor of performance on everyday problem solving for young adults, but with increasing age, crystallized ability became the dominant predictor.

This study provides evidence that everyday problem solving ability differs with age, and, more importantly, that the processes underlying it differ with age as well. The findings indicate that older adults increasingly rely on knowledge to support everyday problem solving, whereas young adults rely almost exclusively on fluid intelligence.

An important aspect of successful aging is maintaining the ability to solve everyday problems encountered in daily life. Instrumental Activities of Daily Living (IADLs) represent one important domain of these problems. IADLs are complex behaviors required for independent management of one’s life, including adherence to complex medical regimens, ability to use increasingly complex communication devices, and management of financial resources [ 2 ]. Other everyday problems involve situations where a conflict is present or a goal cannot be reached without some inferential reasoning [ 3 ]. Cross-sectional data show that the practical ability to solve everyday problems increases from young adulthood until middle age [ 4 – 6 ], but that older age is characterized by diminishing performance[ 5 – 8 ].

One reason for peak performance during middle adulthood in everyday problem solving may be that middle-aged adults have the ideal balance of fluid and crystallized resources needed for everyday problem solving. Crystallized ability represents accumulated experience and knowledge of the world, and is typically measured by vocabulary and general knowledge. It does not decline, and may even grow, well into late adulthood [ 9 , 10 ]. In contrast, fluid ability – the ability to abstract and perform efficient mental operations – shows consistent age-related decline beginning in the 20s [ 10 ], but nevertheless, performance is still relatively high in middle-aged adults [ 11 ]. Fluid ability is best measured by different types of inductive and deductive reasoning tasks, and is closely related to the construct of processing resources [ 12 ] as operationalized by working memory [ 13 ].

Previous studies have found fluid ability to be an important predictor of everyday problem solving in healthy older adults [ 7 , 8 , 14 – 18 ]. Gross et al. [ 16 ] found that although memory, reasoning and processing speed were all significant predictors for everyday functioning and everyday problem solving, inductive reasoning (measured by Letter Series, Word Series, and Letter Sets tasks) independently accounted for the most variance in everyday functioning (measured by Everyday Problems Test, the Observed Tasks of Daily Living, and the Timed Instrumental Activities of Daily Living test). Willis et al. [ 19 ] also showed that older adults who underwent reasoning training showed less functional decline in IADLs than an untrained control group, indicating the importance of reasoning for everyday problem solving.

Everyday problem solving is also related to other aspects of fluid ability that decline with age, especially working memory and processing speed. Importantly, age-related decreases in working memory, using traditional measures that include Reading Span, Computation Span, and Operation Span tasks, have been strongly associated with lower performance on everyday problem solving tasks [ 14 , 20 ]. Age-related slowing in processing speed has also been associated with decreased everyday problem solving [ 8 , 21 ]. Rebok and colleagues [ 22 ] reported evidence that older adults who had extensive training on processing speed in the ACTIVE trial reported less difficulty in performing IADL’s ten years after training, suggesting that such an intervention confers protection in later life. In sum, there is little doubt that fluid ability plays an important role in everyday problem solving.

What is less certain is the role that crystallized ability and knowledge play in everyday problem solving. There have been a few studies that examined the joint contributions of both fluid ability and crystallized ability to everyday problem solving and all suggest an important role of fluid ability [ 7 , 14 , 15 , 17 , 21 , 23 , 24 ]. However, the importance of crystallized ability in everyday problem solving seems to be different depending on the age range of the sample included in the study. Three studies in older adults all found that both fluid and crystallized ability played very important roles in everyday problem solving. Diehl and colleagues [ 15 ] used structural equation modeling and found that both fluid and crystallized abilities had significant paths to everyday problem solving, measured by Observed Tasks of Daily Living (OTDL). In addition, the effects of memory and speed on OTDL were mediated by crystallized ability, indexed by vocabulary. Burton [ 21 ] used hierarchical regression and found that verbal ability, measured by verbal fluency and vocabulary tasks, predicted performance in Everyday Problems Test beyond the effect of fluid ability and demographic variables (e.g., age, education). Allaire and Marsiske [ 14 ] also found relationship between vocabulary and some domains of everyday problem solving, measured by Everyday Cognition Battery. However, studies including middle-aged adults yielded somewhat different conclusions on the role of crystallized ability in the relation to everyday problem solving. Kimbler [ 7 ] studied healthy middle-aged and older adults (age 50 to 92) and found no relationship between performance in vocabulary and Everyday Problems Test. Thornton and colleagues [ 24 ] reported that, although in a sample of healthy adults and chronic disease patients, crystallized ability (measured by ETS vocabulary) mediated age effects on performance in Everyday Problems Test, the relationship was not significant when the analysis was limited only to healthy adults.

These findings suggest that there is a discrepancy in the role of crystallized ability in predicting everyday problem solving across the adulthood lifespan. A potential explaination is that there may be an age-related shift in the contribution of fluid versus crystallized abilities in solving everyday problems. This shift can only be detected by using a lifespan sample with broader age range. We are aware of only two adult lifespan studies on the cognitive predictors of performance in everyday problem solving [ 6 , 23 ]. In both studies, the correlation of fluid and crystallized cognitive predictors to everyday problem solving (practical problem solving in [ 6 ]) was significant. However, when the effects of age and education on everyday problem solving were controlled, neither predictor was significant [ 6 ], limiting the understanding of their respective contribution. Moreover, we were unable to find any studies that include young, middle-age and older adults that examined how age affects the contribution of cognitive predictors to everyday problem solving. Therefore, the present study focuses on two important and unresolved issues. First, what are the strength of the contributions of fluid and crystallized abilities to everyday problem solving? And second, do these contributions shift in importance as a function of age?

Park has argued that older adults maintain performance on many cognitive tasks by increasingly relying on knowledge and experience to compensate for declines in fluid abilities [ 25 ]. Congruent with this perspective, Baltes also suggested that crystallized ability can compensate, to some extent, for age-related declines in processing efficiency with advanced age [ 26 ]. In support of this theorizing, Hedden et al. reported that performance on a verbal memory task was mediated by fluid abilities in young and middle-aged adults, but that older adults relied more on vocabulary (an index of crystallized ability) for optimal performance [ 27 ]. In the present study, we determine whether such an age-related shift occurs for everyday problem solving in an adult lifespan sample. We predicted that young adults who are rich in cognitive resources such as speed, working memory and reasoning would rely on fluid processing for success; however, as age increased, crystallized ability would play an increasingly important role in everyday problem solving.

Participants

A total of 221 healthy adults from the Dallas Lifespan Brain Study (DLBS; 148 women, 73 men, age range: 24–93 years, Mini-Mental Status Examination scores ≥ 26, mean = 28.37) were recruited locally from the community. All participants were right-handed with normal or corrected to normal vision. Participants with any of following conditions were excluded: history of major psychiatric or neurological disorder, history of prescription drug abuse/illegal drug use, and/or any head trauma. Participants were compensated fifteen dollars per hour for their participation. They completed two 2.5-hour sessions that are described below.

Each participant completed a battery of cognitive tests as well as the Everyday Problems Test (EPT) [ 1 ]. This comprehensive battery included both paper-and-pencil and computerized tasks. The cognitive constructs assessed and the tasks associated with each construct included the following:

Processing speed was measured by Digit Comparison [ 28 ], WAIS-III Digit Symbol [ 29 ] and Pattern Comparison task taken from NIH Toolbox Cognition Battery (NIHTB-CB) [ 30 ].

Working memory was measured by the Spatial Working Memory (SWM) task of Cambridge Neuropsychological Test Automated Battery (CANTAB) [ 31 ], WAIS-III Letter-Number Sequencing [ 29 ] and NIHTB-CB List Sorting [ 30 ].

Inductive reasoning was measured by Educational Testing Service (ETS) Letter Sets [ 32 ], Raven’s Progressive Matrices [ 33 ], and Stockings of Cambridge (SOC) of CANTAB [ 31 ].

Crystallized ability was measured by NIHTB-CB Picture Vocabulary [ 30 ], NIHTB-CB Oral Reading Recognition [ 30 ] and the ETS Advanced Vocabulary Scale [ 32 ]. Although the ETS Vocabulary task was timed, we made sure that no participants failed to complete the task because of the time limit so the performance on this task was not affected by their speed.

Everyday problem solving ability was measured by the Everyday Problems Test (EPT) [ 1 ]. It is a paper-pencil task that has 42 questions, which assesses the ability to solve tasks that are important to live independently in our society. The EPT is comprised of seven scales that include problems from domains of Health/Medications, Meal Preparation/Nutrition, Phone Usage, Consumer/Shopping, Financial Management, Household Management, and Transportation. For each of these seven scales, participants are presented with three sample stimuli (e.g., prescription drug label, bus schedule, catalog order form) and two questions about each stimulus. Figure 1 is an example of one EPT stimulus with two questions based on the stimulus. The performance on this task is measured as the total number of correct answers to the 42 questions. Compared to other neuropsychological tasks that assessed traditional problem solving ability, everyday problems test (EPT) was designed to be a better indicator of problem solving performance in real-life. Schmitter-Edgecombe and colleagues [ 34 ] found that EPT was strongly associated with directly observed everyday functioning performance in real world, and therefore considered to be a valid and useful measure for assessing everyday functioning in cognitively healthy older population.

a . Age-related differences in fluid ability. Fluid ability is comprised of the measures on processing speed, working memory and inductive reasoning. b . Age-related differences in crystallized ability. Crystallized ability is comprised of ETS Advanced Vocabulary Scale, NIHTB-CB Picture Vocabulary and NIHTB-CB Oral Reading Recognition. Figure 3c . Age-related differences in everyday problem solving. Everyday problem solving is measured by number of correct answers on the Everyday Problems Test (EPT).

Data Analyses

Altogether, there were 13 tasks subjected to analyses: three measures each for processing speed, working memory, inductive reasoning, crystallized ability, and a single measure of everyday problem solving. We created standard scores for the 12 cognitive measures that were used for further analyses. A confirmatory factor analyses (CFA) validated the expected factor structure of cognitive measures, Χ 2 (60) = 147.941, p < .001, CFI = .953, RMSEA = .081, SRMR = .076 (see Figure 2 ). The standardized scores for each crystallized and fluid test were averaged to produce composite crystallized and fluid scores in a standard-score (z-score) metric in the aggregate cross-sectional sample.

Confirmatory Factor Analysis of cognitive tasks, after controlling for age. Χ 2 (60)= 147.941, p < .001, CFI = .953, RMSEA = .081, SRMR = .076.

To test the hypothesis that crystallized intelligence would be a more potent predictor of everyday problem solving for older adults, relative to earlier ages, we conducted a hierarchical moderated regression analysis with age, fluid ability, and crystallized ability as predictors, using product variables to capture interactions. Prior to evaluating the interaction effects, we introduced quadratic age effects to test for possible curvilinearity in the relation of age and the variables to the EPT score. This approach was taken because curvilinear age relations were expected in abilities [ 35 ] and everyday problem solving performance, and because methodological studies have shown that failing to account for curvilinear relations of predictors to dependent variables in the context of moderated regression can create spurious product variable effects that are an artifact of curvilinear relations of both predictors to the dependent variable [ 36 ]. To foreshadow our results, we did detect curvilinear relations of age and abilities to EPS tests, requiring that moderated regression tests for age × ability interaction effects include quadratic terms for each predictor variable.

Linear predictor terms were first centered at the sample mean, and then squared predictors were computed to reduce collinearity issues in the multiple regression. Significant moderated regression effects were decomposed by computing simple slopes at the mean and at ± 1 SD of the predictor variables.

To further understand age differences in the predictive utility of fluid and crystallized abilities for everyday problem solving, we used bootstrapping to examine the regression coefficients for each of the three age groups (young, middle-aged, older). Finally, to assess the stability of the observed effects across individual problem solving domains, we examined the contributions of fluid and crystallized abilitites for each of the seven domains in everyday problem solving for young, middle-aged, and older adults.

Demographic Data and Age-related Differences

Demographic data are presented in Table 1 , broken down by 3 age groups (Young: 24–49 years old; Middle: 50–69 years old; Old: 70–93 years old). The three age groups differed on years of formal education, F (2, 218) = 6.16, p =.002, with young adults having somewhat higher levels than the other two age groups. Means and standard deviations of cognitive measures and EPT scores are also presented in Table 1 . For descriptive purposes, we presented age effects associated with fluid ability, crystallized ability and everyday problem solving score in scatter plots ( Figure 3 ). Figure 3a portrays a significant linear age-related decrease in fluid ability, R 2 = .626, R 2 adjusted = 625, F(1, 212) = 355.312, p < .001, and the quadratic relationship was also statistically significant, R 2 = .64, R 2 adjusted = .637, F(2, 211) = 187.72, p < .001. In contrast, crystallized ability ( Figure 3b ) did not have a significant linear relationship with age, p = .628. However, there was a significant quadratic relationship between crystallized ability and age, R 2 = .038, R 2 adjusted = .029, F(2, 217) = 4.258, p = .015, with increasing performance until about age 59. We also examined both linear and quadratic relationships between everyday problem solving ability and age. While the simple linear relationship showed significance, R 2 = .237, R 2 adjusted = .234, F(1, 219) = 68.091, p < .001, adding age 2 significantly improved the model, ΔR 2 = .105, ΔF = 34.810, p <.001, suggesting a quadratic relation with age was a better fit for everyday problem solving ability ( Figure 3c ), R 2 = .342, R 2 adjusted = .336, F (2, 218) = 56.707, p < .001, with the peak performance at 47.2 years of age.

Demographic and descriptive data.

Cognitive Predictors Across the Lifespan

We used hierarchical multiple regression to examine the role that fluid and crystallized abilities play in solving everyday problems. In the first model, we included years of education and linear and quadratic components for age. Then in the second model, we added fluid ability and crystallized ability as cognitive predictors. In the third model, we included quadratic components (crystallized 2 and fluid 2 ) to examine if there was a curvilinear relationship between cognitive predictors and everyday problem solving. In the fourth model, we added interactions among fluid ability, crystallized ability and age. Each of aforementioned steps improved the fit of the overall model significantly ( Table 2 ). We also examined a further model that included interactions between cognitive ability and age 2 , and found that it did not improve the model significantly. Therefore, the fourth model was chosen as the final model depicting the relationship between cognitive predictors and everyday problem solving across the lifespan. As shown in Table 2 , Model 4 explained a substantial amount of variance in everyday problem solving, R 2 = .683, R 2 Adjusted = 666. There was a main effect of age, age 2 , fluid ability, and crystallized ability on everyday problem solving. Although the quadratic terms of fluid ability and crystallized ability were not each statistically significant in the final model, adding quadratic terms of these predictors significantly improved the fit of the model. The partial residual plots of crystallized ability ( Figure 4a ) and fluid ability ( Figure 4b ) showed that these two predictors both evidenced a similar curvilinear pattern to everyday problem solving. Curvilinearity occurred because for lower ability participants compared to those of higher ability, cognitive ability had a stronger relationship to everyday problem solving.

a . Partial residual plot of crystallized ability. b . Partial residual plot of fluid ability. For both cognitive predictors, the effect of crystallized and fluid ability follows a similar curvilinear pattern regardless of age and the other cognitive level: for people who have lower cognitive ability, the level of cognitive ability has a strong effect on everyday problem solving, while for people who have high cognitive ability, higher cognitive ability does not affect everyday problem solving as much.

Hierarchical Multiple Regression.

Critically, we also found a significant Age × Crystallized ability interaction, b = 0.046, SEb = 0.016, t (201) = 2.943, β = .152, p = .004, 95% CI = [0.015, 0.076], indicating the relationship between crystallized ability and everyday problem solving differed across the lifespan. In order to better interpret the significant interaction, simple slopes (displayed in Figure 5 ) for the relationship between crystallized ability and everyday problem solving were tested for younger age (−1 SD below the mean), middle age (mean), and older age (+1 SD above the mean). Simple slope tests showed that the relationship of crystallized ability to everyday problem solving at a younger age was not significant, b = 0.708, SEb = 0.433, t (201) = 1.636, β = .125, p = .103, 95% CI = [−0.146, 1.562]. However, both the middle age model, b = 1.576, SEb = 0.292, t (201) = 5.391, β = .279, p < .001, 95% CI = [0.999, 2.152], and the older age model, b = 2.44, SEb = 0.397, t (201) = 6.141, β = .432, p < .001, 95% CI = [1.656, 3.223], revealed a significant positive association between crystallized ability and everyday problem solving. We then tested the difference between regression coefficients across models, and found that the effect of crystallized ability was stronger for both old ( z = −3.027, p = .001) and middle age ( z = −1.719, p = .043) compared to young, and that the effect was even stronger for the old age compared to middle, ( z = −1.753, p = .04), suggesting that crystallized ability played a continuously increasingly important role in solving everyday problems as age increased. Note that the interaction between fluid and crystallized ability was not significant ( p = .351), suggesting that the contribution of crystallized ability did not change across people with different fluid ability, after age-related effects taken into account.

Simple slopes of Age × Crystallized ability. Simple slope was not significantly different from 0 at Age = 40 (1SD below mean), but was significant at Age = 59 (mean age) and Age = 78 (1SD above mean). Based on comparison using z-tests, the effect of crystallized ability was stronger at older age ( z = −3.027, p = .001) and middle age ( z = −1.719, p = .043), than at a younger age, and the effect was even stronger at a older age than middle, ( z = −1.753, p = .04).

Comparing Cognitive Predictors in Three Age-groups

To further examine which cognitive predictor – fluid or crystallized ability – was more important for everyday problem solving at different stages of the lifespan, we generated bootstrapped standard errors for regression coefficients in three age subgroups: younger adults (24–49 years old), middle-aged adults (50–69 years old), and older adults (70–93 years old). In each multiple regression, the predictor variables were age, fluid ability, crystallized ability, fluid 2 , crystallized 2 and the fluid × crystallized interaction. This model was derived from Model 4 used for the whole sample with first order age-related effects removed since this analysis was on each age group. We generated 95% confidence intervals (CI) using bias-corrected and accelerated (BCa) bootstrap (with 1000 iterations in each group) as presented in Table 3 . We then compared the BCa CI using a conservative rule by examining the overlap of confidence intervals [ 37 ]. Put simply, the rule assesses whether the 95% confidence intervals have less than 50% proportion overlap, expressed as a proportion of average margin of error. If the result is affirmative, the two estimates are significantly different ( p < .05). As shown in Figure 6 , for the young group, the lower end of 95% CI of the crystallized ability parameter was below zero, confirming its non-significance and that only the fluid ability value was predictive, as we found in simple slope analysis. For the middle age, the 95% CIs of fluid and crystallized abilities overlapped more than 50%, suggesting that both were predictive but not significantly different in middle-aged adults. Finally, for the older group, the predictive utility of crystallized ability was significantly larger than fluid ability, with the proportion overlap = 42.8%, p < .05. Hence, in middle-aged and older adults, everyday problem solving was associated with both fluid and crystallized abilities. Importantly for older adults, crystallized ability was a significantly stronger predictor compared to fluid ability (see Figure 6 ).

95% BCa CI for fluid and crystallized regression coefficients. In older adults, everyday problem solving was predicted more by crystallized ability than fluid ability, proportion overlap = 42.8%, p <.05.

Regression coefficient estimates and 95% BCa CI in three age groups.

We also note that we found no evidence for a Fluid × Crystallized interaction within any age group. The absence of the interaction suggests that fluid and crystallized ability made independent contributions to everyday problem solving, regardless of level of performance on either ability.

In a final analysis, we assessed the stability of the effects of fluid and crystallized ability for each of the seven problem-solving domains, within each age group, using the same bootstrapping approach. The main finding was that for older adults, crystallized ability played an important role for all EPT domains except meal preparation , which was marginally significant. In addition, fluid ability was significant for shopping, finance and meal preparation in older adults (see Table 4 ). Table 4 also shows that for young adults, fluid ability was significant for finance, household and transportation , and for finance, medication and transportation in middle-aged adults. Crystallized ability played no significant role for young adults, and significantly predicted only shopping in middle age.

Regression coefficient estimates and 95% BCa CI for seven EPT domains.

The main goal of this study was to understand how fluid and crystallized ability differ across the lifespan in predicting everyday problem solving. We hypothesized that due to diminished fluid resources with age, crystallized knowledge would become increasingly important in predicting everyday problem solving as a function of age. Congruent with this hypothesis, crystallized ability (measured by verbal knowledge in this study) played a more important role in predicting everyday problem solving as age increased. In contrast, fluid ability (measured by speed, working memory, and inductive reasoning) consistently explained variance for all age groups. This pattern of findings suggests that older adults are relying more on crystallized knowledge to solve everyday problems, whereas young adults rely more heavily on the efficiency of basic cognitive-mechanisms (e.g., processing speed, working memory, inductive reasoning) that comprise fluid ability.

Past studies have been inconclusive about the relative roles of crystallized versus fluid abilities in everyday problem solving at different ages, because none that have examined this issue have included a lifespan sample. The inclusion of the entire adult lifespan was an important feature of the present study, as it allowed us to begin to clarify when in the lifespan crystallized knowledge assumes importance in everyday problem solving. We began to observe a small contribution of crystallized ability to everyday problem solving in middle age, with a large contribution at older ages. The present findings provide clear evidence for the importance of including middle-aged samples in studies.

We also note that the present findings replicate a pattern reported by Hedden et al. [ 27 ] for a very different task—a verbal cued recall task that required participants to memorize associations between paired cues and target words. Hedden et al [ 27 ] used crystallized and fluid ability to predict performance on the verbal recall task. Just as reported in the present study, they found that crystallized ability (vocabulary scores) explained more variance for older compared to middle-aged and young adults. The similarity of the findings for these two very different tasks suggests that increasing reliance on crystallized ability may be a general characteristic of aging. Buttressing this conclusion, was the finding that crystallized ability accounted for significant variance in older adults in six of the seven EPT domains, suggesting that the breadth of the effect was reliable across domains. Moreover, the crystallized ability effect was nearly absent in the young and middle-aged adults, with only one significant effect for shopping in the middle-aged.

The notion that age differentially affects the type of cognitive ability drawn upon to perform everyday cognitive tasks has not received much attention in the literature. The present findings suggest that crystallized knowledge may help older adults maintain cognitive function in the face of declining fluid ability. Other studies of problem-solving support this interpretation. For example, older adults actually showed better problem-solving abilities than young and middle-aged adults when they were presented with problems associated with social conflict and interpersonal conflict. The solution to these types of problems rely more on wisdom and a broad range of social experiences rather than fluid ability [ 38 ]. Similarly, there is evidence that older adults develop adaptive, experience-based heuristics for solving everyday problems and make decisions that minimize the need to rely on fluid reasoning [ 39 ]. Conversely, there are also domains where crystallized ability makes a scant contribution, even for older adults. We suggest that these would be domains that require extensive on-line processing, such as constantly switching and updating information of different ingredients and procedures when cooking.

It is also important to recognize that everyday problem solving ability is a crucial skill that greatly affects older adults’ life quality, but few studies have examined the predictive utility of respondent-based, laboratory problem solivng tasks (such as the EPT) in the real world. In support of the use of such laboratory measures, there is a small body of evidence suggesting that the EPT explains substantial variance in every day functioning [ 17 , 34 , 40 ]; but much more research is needed. Moreover, the EPT consists of sets of questions that address well defined, but relatively narrow everyday problems. Real world problems are typically more complex, are more open-ended (ill-defined), and are comprised of many smaller interrelated problems that require different aspects of knowledge, skills and abilities. Thus, the EPT may not adequately mirror the complexity of real world problems. Additional investigation of ability predictors of everyday problem solving tasks would help to address this concern.

A limitation of this study is that crystallized ability was measured by vocabulary tasks, which have been traditionally considered as a proxy of knowledge and experience in cognitive psychology studies and everyday problem solving research. However, we acknowledge that a broader assessment of crystallized ability would incorporate experience and other types of world knowledge. Future research with more comprehensive assessment of knowledge and experience beyond measures of vocabulary may help to understand the individual differences in people’s utilization of cognition in solving everyday problems. One option might be to assess expertise and familiarity participants have in each problem solving domain in an effort to understand how life experiences asset problem solving. Similar strategies could be adapted to different problem solving paradigms.

We also recognize that it would be ideal to have longitudinal data on both cognitive and everyday problem solving so that the changing relationship between cognitive measures and everyday performance could be assessed as people grow and age. Cross-sectional designs are vulnerable to cohort differences and age × selection confounds. Finally, the compensatory role of crystallized ability may be maximized in high-functioning samples of older adults. Participants in this study were well-educated (mean years of education = 16.6); individuals with lower levels of educational attainment may not show the same degree of compensatory benefit (although we found no evidence of fluid × crystallized interactions in predicting EPS performance). It would therefore be useful to evaluate these relationships in a more representative sample of the population that included low-education individuals.

In conclusion, the present study suggests that young adults may solve everyday problems based on cognitive resources and mechanisms that are traditionally associated with effective problem solving. However, crystallized knowledge becomes a more predominant influence on everyday problem solving in older adults.

Example questions of the Everyday Problems Test.

Acknowledgments

This work was supported by National Institute on Aging at the National Institutes of Health (grant number 5R37AG006265-29 to D. C. P.).

Contributor Information

Xi Chen, Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas.

Christopher Hertzog, School of Psychology, Georgia Institute of Technology.

Denise C. Park, Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas.

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In This Article Expand or collapse the "in this article" section Problem Solving and Decision Making

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Problem Solving and Decision Making by Emily G. Nielsen , John Paul Minda LAST REVIEWED: 26 June 2019 LAST MODIFIED: 26 June 2019 DOI: 10.1093/obo/9780199828340-0246

Problem solving and decision making are both examples of complex, higher-order thinking. Both involve the assessment of the environment, the involvement of working memory or short-term memory, reliance on long term memory, effects of knowledge, and the application of heuristics to complete a behavior. A problem can be defined as an impasse or gap between a current state and a desired goal state. Problem solving is the set of cognitive operations that a person engages in to change the current state, to go beyond the impasse, and achieve a desired outcome. Problem solving involves the mental representation of the problem state and the manipulation of this representation in order to move closer to the goal. Problems can vary in complexity, abstraction, and how well defined (or not) the initial state and the goal state are. Research has generally approached problem solving by examining the behaviors and cognitive processes involved, and some work has examined problem solving using computational processes as well. Decision making is the process of selecting and choosing one action or behavior out of several alternatives. Like problem solving, decision making involves the coordination of memories and executive resources. Research on decision making has paid particular attention to the cognitive biases that account for suboptimal decisions and decisions that deviate from rationality. The current bibliography first outlines some general resources on the psychology of problem solving and decision making before examining each of these topics in detail. Specifically, this review covers cognitive, neuroscientific, and computational approaches to problem solving, as well as decision making models and cognitive heuristics and biases.

General Overviews

Current research in the area of problem solving and decision making is published in both general and specialized scientific journals. Theoretical and scholarly work is often summarized and developed in full-length books and chapter. These may focus on the subfields of problem solving and decision making or the larger field of thinking and higher-order cognition.

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Age Differences in Everyday Problem-Solving Effectiveness: Older Adults Select More Effective Strategies for Interpersonal Problems

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Fredda Blanchard-Fields, Andrew Mienaltowski, Renee Baldi Seay, Age Differences in Everyday Problem-Solving Effectiveness: Older Adults Select More Effective Strategies for Interpersonal Problems, The Journals of Gerontology: Series B , Volume 62, Issue 1, January 2007, Pages P61–P64, https://doi.org/10.1093/geronb/62.1.P61

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Using the Everyday Problem Solving Inventory of Cornelius and Caspi, we examined differences in problem-solving strategy endorsement and effectiveness in two domains of everyday functioning (instrumental or interpersonal, and a mixture of the two domains) and for four strategies (avoidance–denial, passive dependence, planful problem solving, and cognitive analysis). Consistent with past research, our research showed that older adults were more problem focused than young adults in their approach to solving instrumental problems, whereas older adults selected more avoidant–denial strategies than young adults when solving interpersonal problems. Overall, older adults were also more effective than young adults when solving everyday problems, in particular for interpersonal problems.

DESPITE cognitive declines associated with advancing age ( Zacks, Hasher, & Li 2000 ), older adults function independently. Furthermore, evidence is equivocal as to the impact that cognitive decline has on older adults' abilities to navigate complicated social situations (see, e.g., Cornelius & Caspi, 1987 ; Marsiske & Willis, 1995 ). Some research suggests that older adults are more effective than young adults when solving everyday problems (Cornelius & Caspi; Blanchard-Fields, Chen, & Norris, 1997 ; Blanchard-Fields, Jahnke, & Camp, 1995 ; Blanchard-Fields, Stein, & Watson, 2004 ). Our goal in the current study was to examine age differences in (a) the strategies selected to solve everyday problems from different problem domains and (b) how effective these strategy choices are relative to ideal everyday problem solutions.

Blanchard-Fields and colleagues (1995 , 1997 , 2004 ) demonstrated that older adults are equally likely, if not more likely, than young adults to choose proactive strategies to directly confront instrumental problems. However, when they are facing interpersonal problems, older adults are more likely than young adults to choose passive emotion regulation strategies. Differential strategy preferences may reflect a maturing of the strategy repertoire of older adults. As people age, experience may hone strategy preferences on the basis of successes and failures, making it easier for older adults to invest energy into strategies that have been effectively used when dealing with problems in the past.

The important issue is what constitutes effective strategy use. Past research defines it as one's sensitivity to the context that is underlying problems when one is selecting strategies ( Blanchard-Fields et al., 1995 ), the number of strategies and one's satisfaction with problem solution ( Thornton & Dumke, 2005 ), or the evaluation of strategy choices on an everyday problem-solving inventory against a panel of external judges ( Cornelius & Caspi, 1987 ). In the current study we examined the latter approach to problem-solving effectiveness from the level of domain-specific strategy use in order to simultaneously investigate age differences in effective problem solving and age differences in strategy selection (i.e., differential strategy preference related to context). We sought to replicate past research examining interpersonal and instrumental problem-solving contexts, while also determining whether age differences in strategy preferences actually lead to more effective problem solving in the two domains. Because domain effects are sensitive to the amount of overlap that is allowed between problem definitions when problems are classified (e.g., Artistico, Cervone, & Pezzuti, 2003 ), we expanded the typical instrumental–interpersonal dichotomy by adding a mixed-problem domain to describe problems that are not unambiguously instrumental or interpersonal.

We expected older adults to show a greater preference than young adults for emotion-focused strategies when they were solving interpersonal problems. For instrumental problems, we expected older adults to prefer more problem-focused strategies than did young adults. We also expected older adults to have higher effectiveness scores than young adults ( Cornelius & Caspi, 1987 ). Finally, we expected older adults to be more effective than young adults in their application of emotion-focused strategies.

Participants

We recruited young adults ( n = 53, with 36 women and 17 men; age = 18–27 years, M = 20.6, SD = 1.6) and older adults ( n = 53, with 33 men and 20 women; age = 60–80 years, M = 68.9, SD = 4.9) from a southeastern metropolitan area. Participants were primarily Caucasian (∼77%) and reported similar levels of education (i.e., some college). On average, both groups indicated good health [young adults, M = 3.49, SE = 0.08; older adults, M = 3.15, SE = 0.09; t (1, 102) = 2.89, p <.01].

Everyday problem-solving task

We selected 24 of 48 hypothetical problems from the Everyday Problem Solving Inventory (EPSI; Cornelius & Caspi, 1987 ). We randomly selected 4 problems from each of the six original problem domains (i.e., home management, information use, consumer issues, conflicts with friends, work-related issues, and family conflicts). We presented participants with a single manifestation of each strategy type tailored to each problem (without strategy labels) and asked them to indicate how likely they were to use each of four strategies to solve each problem: avoidance–denial, passive dependence, planful problem solving, and cognitive analysis (see Table 1 for strategy definitions).

Dependent variables

Strategy endorsement ratings indicated participants' preferred methods for solving hypothetical everyday problems. Higher scores represented greater endorsement of a particular strategy. We calculated effectiveness scores for each domain and strategy by correlating participant strategy endorsement ratings with those of a panel of external judges ( Cornelius & Caspi, 1987 ). 1 Correlations (range: r = −1.0 to r = 1.0) represented the degree of similarity between a participant's responses and the ideal solutions nominated by judges. Large positive correlations indicated effective problem solving.

Classification of problem type

For each problem indicate whether it is an (A) instrumental problem, or (B) interpersonal problem. Instrumental problems involve competence concerns and stem from complications that arise when one is trying to accomplish, achieve, or get better at something. Instrumental problems are situations in which one is having difficulty achieving something that is personally relevant. Interpersonal problems involve social/interpersonal concerns and stem from complications that arise when one is trying to reach an outcome that involves other people. Interpersonal problems are situations in which one is dealing with a social conflict or obstacle in a relationship. Please provide only one classification per problem.

We conducted 2 (age: young, old) × 3 (domain: instrumental, mixed, interpersonal) × 4 (strategy: avoidance–denial, passive dependence, planful problem solving, cognitive analysis) mixed-model analyses of variance on the strategy endorsement and effectiveness scores. Age was the between-subjects factor. We followed each analysis of variance by contrasts to examine age differences for each strategy by domain.

Strategy endorsement ratings

For each domain (interpersonal, instrumental, or mixed), we calculated average endorsement ratings for each strategy type (e.g., avoidance–denial). Analyses indicated that main effects of domain, F (2, 312) = 34.57 (η p 2 =.25, p <.001), and strategy, F (2, 312) = 265.54 (η p 2 =.72, p <.001), were qualified by Strategy × Age, F (3, 312) = 5.46 (η p 2 =.05, p =.001), Domain × Strategy, F (6, 624) = 46.59 (η p 2 =.31, p <.001), and Domain × Strategy × Age, F (6, 624) = 5.30 (η p 2 =.05, p <.001), interactions. The patterns of age differences in strategy endorsement varied by domain (see Table 2 for mean strategy endorsement ratings). For instrumental problems, young adults preferred avoidance–denial more than old adults did, t (104) = 2.26 ( p <.05), whereas old adults preferred passive dependence, t (104) = 2.28 ( p <.05), planful problem solving, t (104) = 3.74 ( p <.001), and cognitive analysis, t (104) = 3.30 ( p <.01), more than young adults did. For mixed problems, young adults preferred avoidance–denial, t (104) = 4.36 ( p <.001), and passive dependence, t (104) = 3.87 ( p <.001), more than old adults did. The opposite pattern held for interpersonal problems. Old adults preferred avoidance–denial, t (104) = 2.15 ( p <.05), and cognitive analysis, t (104) = 2.39 ( p <.05), more than young adults did. Old adults also marginally preferred passive dependence more than young did, t (104) = 1.42 ( p =.08, one-tail).

Effectiveness scores

For each domain and each strategy, we calculated an overall effectiveness score across problems by correlating each participant's strategy endorsement ratings with the effectiveness ratings of the judges (e.g., avoidance–denial strategies for each interpersonal problem and judges' average rating for avoidance–denial for the same problems). Analyses indicated main effects of age, F (1, 92) = 7.15 (η p 2 =.07, p <.01), and domain, F (2, 184) = 18.66 (η p 2 =.17, p <.001). Older adults ( M = 0.46, SE = 0.02) were more effective than young adults ( M = 0.39, SE = 0.02) in their overall choice of strategies ( Cornelius & Caspi, 1987 ). These main effects were qualified by Domain × Age, F (2, 184) = 3.04 (η p 2 =.03, p =.05), and Domain × Strategy, F (6, 552) = 44.19 (η p 2 =.32, p <.001), interactions (see Table 2 for mean strategy effectiveness scores). Although both age groups were more effective at solving instrumental problems (young adults, M = 0.40, SE = 0.02; old adults, M = 0.48, SE = 0.02) and mixed problems (young adults, M = 0.50, SE = 0.03; old adults, M = 0.50, SE = 0.03) than interpersonal problems, young adults were especially less effective than old adults at solving interpersonal problems (young adults, M = 0.27, SE = 0.03; old adults, M = 0.41, SE = 0.03).

Although the Domain × Strategy × Age interaction failed to reach significance, F (6, 552) = 1.67 (η p 2 =.02, p =.13), we conducted planned contrasts to investigate age differences in problem-solving effectiveness for each strategy by domain. For interpersonal problems, old adults were more consistent than young adults in endorsing avoidance–denial, t (103) = 1.90 ( p <.05, one-tail), passive dependence, t (104) = 1.30 (only marginal at p =.10, one-tail), planful problem solving, t (105) = 1.65 ( p <.05), and cognitive analysis, t (96) = 1.72 ( p <.05), at levels that were deemed to be effective by the judges. For instrumental problems, old adults were more consistent than young adults in endorsing avoidance–denial, t (104) = 4.21 ( p <.001), at the level deemed to be effective by the judges. No age differences emerged for mixed problems. 2

Consistent with past research, in our research the older adults preferred more passive emotion-focused strategies (e.g., avoidance or passive dependence) than the young adults did when facing interpersonal problems, and they preferred more proactive strategies such as planful problem solving (in combination with emotion regulation strategies) for instrumental problems ( Blanchard-Fields et al., 1995 , 1997 ; Watson & Blanchard-Fields, 1998 ). In contrast, young adults used similar amounts of planful problem solving, irrespective of the type of problem. It is interesting to note that young adults preferred (a) more passive emotion-focused strategies in mixed problems and (b) more avoidance emotion-focused strategies in instrumental problems than older adults. Perhaps young adults are motivated to behave more passively when managing personally relevant achievement-oriented problems, especially those involving potentially awkward social interactions. This deserves further research.

Second, we moved beyond previous indices of effectiveness by basing problem-solving efficacy on the degree of similarity in strategy endorsement between participants and a panel of judges to control for individual differences in strategy accessibility. Older adults were more effective at solving problems than young adults were (which is similar to the findings of Cornelius & Caspi, 1987 ). More importantly, we found that older adults' greater effectiveness was driven by strategy selection within interpersonal problems. Extending past research, we assessed effectiveness at the level of the problem domain and at the level of specific strategies. Thus, it is not simply that older people use more or less of a strategy in various domains; they use these strategies appropriately (as determined by panel effectiveness scores) to match the context of the problem. This adaptivity may be crucial to interpersonal problems. Although proactive strategies are typically key to resolving causes of problems (e.g., Thornton & Dumke, 2005 ), older adults' use of passive (emotion regulation) strategies may buffer them from intense emotional reactions in order to maintain tolerable levels of arousal given increased vulnerability and reduced energy reserves (Consedine, Magai, & Bonanno, 2003).

One limitation of the EPSI is that effective solutions tend to be biased toward instrumental strategies. Nevertheless, we still find older adults to be more effective in their application of emotion-focused strategies in the interpersonal domain. Future research must include a greater balance in situations in which both problem-focused and emotion-focused strategies are judged effective. Another limitation is that the EPSI problem contexts are sparse. Thus, problem appraisal could possibly play a role in producing age differences in strategy preference. Past research demonstrates age differences in problem definitions ( Berg et al., 1998 ) and goals evoked when approaching problems ( Strough, Berg, & Sansone, 1996 ). A third limitation of the current study is that we did not control for age relevance of each problem. Future research should address how age relevance influences problem-solving effectiveness, especially as it pertains to emotion regulation in interpersonal problems and to whether age differences in effectiveness are maintained for the oldest-old individuals.

Given recent interest in the role of emotion in older adulthood, these findings are significant because they provide further evidence for the capacity of older adults to draw on accumulated experience in socioemotional realms to solve problems successfully. Older adults' strategy use suggests that they are capable of complex and flexible problem solving. Furthermore, whereas advancing age is associated with cognitive decline, such declines do not readily translate into impaired everyday problem-solving effectiveness. Instead, both types of developmental trajectories exist in tandem and may even complement one another.

Cornelius and Caspi (1987) recruited 23 judges to determine which of four strategies could be used to effectively solve a series of everyday problems. Of these 23 judges, 18 were “laypersons without formal training in psychology” and 5 were “graduate students majoring in developmental psychology” (p. 146). Overall, the panel consisted of young adults ( n = 9, ages 24–40, M = 28.4), middle-aged adults ( n = 8, ages 44–54, M = 50.3), and older adults ( n = 6, ages 62–72, M = 67.3). Ten members of the panel were men and 13 were women. Given that the panel (a) consisted of such small samples from each of the three age groups, (b) was probably sampled from a single geographic region, and (c) was sampled about 20 years ago, it is possible that the effective solutions endorsed by this particular panel are not entirely representative of those effective solutions that might be offered by individuals sampled today and who are living in different regions of the country. Future research should examine the metric properties of the EPSI to see if the effective solutions reported by the earlier panel (Cornelius & Caspi) are consistent with those endorsed by a more current sample of everyday problem solvers.

If we examine the effectiveness scores by using the six original EPSI domains, the results replicate those of Cornelius and Caspi (1987) . Older adults were more effective than younger adults in the consumer (young adults, M = 0.20, SE = 0.04; old adults, M = 0.36, SE = 0.04), t (104) = 2.80 ( p <.01), home (young adults, M = 0.37, SE = 0.04; old adults, M = 0.45, SE = 0.03), t (104) = 1.75 ( p <.05, one-tail), information (young adults, M = 0.61, SE = 0.03; old adults, M = 0.66, SE = 0.03), t (104) = 1.32 ( p <.10, one-tail), and work (young adults, M = 0.53, SE = 0.04; old adults, M = 0.61, SE = 0.03), t (104) = 1.69 ( p <.05, one-tail), domains.

Decision Editor: Thomas M. Hess, PhD

Problem Solving Strategies Included in the Everyday Problem Solving Inventory.

Mean Strategy Endorsement and Problem-Solving Effectiveness Ratings by Age and Domain.

Notes : Strategy endorsement ratings ranged from 1 (definitely would not do) to 5 (definitely would do). Problem-solving effectiveness scores ranged from r = −1.0 to r = 1.0. Parenthetical material represents the extreme ends of the strategy endorsement ratings. ADE = Avoidance–denial, PD = passive dependence, PPS = planful problem solving, and CA = cognitive analysis.

EPSI Problems Used in the Current Study.

This research was supported by the National Institute on Aging under Research Grant AG-11715, awarded to Fredda Blanchard-Fields.

Artistico, D., Cervone, D., Pezzuti, L. ( 2003 ). Perceived self-efficacy and everyday problem-solving among young and older adults. Psychology and Aging , 18 , 68 -79.

Berg, C. A., Strough, J., Calderone, K. S., Sansone, C., Weir, C. ( 1998 ). The role of problem definitions in understanding age and context effects on strategies for solving everyday problems. Psychology and Aging , 13 , 29 -44.

Blanchard-Fields, F., Chen, Y., Norris, L. ( 1997 ). Everyday problem solving across the adult life span: Influence of domain specificity and cognitive appraisal. Psychology and Aging , 12 , 684 -693.

Blanchard-Fields, F., Jahnke, H., Camp, C. ( 1995 ). Age differences in problem-solving style: The role of emotional salience. Psychology and Aging , 10 , 173 -180.

Blanchard-Fields, F., Stein, R., Watson, T. L. ( 2004 ). Age differences in emotion-regulation strategies in handling everyday problems. Journals of Gerontology: Psychological and Social Sciences , 59B , P261 -P269.

Consedine, N., Magai, C., Bonanno, G. ( 2002 ). Moderators of the emotion inhibition-health relationship: A review and research agenda. Review of General Psychology , 6 , 204 -228.

Cornelius, S. W., Caspi, A. ( 1987 ). Everyday problem solving in adulthood and old age. Psychology and Aging , 2 , 144 -153.

Marsiske, M., Willis, S. L. ( 1995 ). Dimensionality of everyday problem solving in older adults. Psychology and Aging , 10 , 269 -283.

Strough, J., Berg, C. A., Sansone, C. ( 1996 ). Goals for solving everyday problems across the life span: Age and gender differences in the salience of interpersonal concerns. Developmental Psychology , 32 , 1106 -1115.

Thornton, W. J. L., Dumke, H. A. ( 2005 ). Age differences in everyday problem-solving and decision-making effectiveness: A meta-analytic review. Psychology and Aging , 20 , 85 -99.

Watson, T. L., Blanchard-Fields, F. ( 1998 ). Thinking with your head and your heart: Age differences in everyday problem-solving strategy preferences. Aging, Neuropsychology, and Cognition , 5 , 225 -240.

Zacks, R. T., Hasher, L., Li, K. Z. H. ( 2000 ). Human memory. In T. A. Salthouse & F. I. M. Craik (Eds.), Handbook of aging and cognition (2nd ed., pp. 293–357). Mahwah, NJ: Erlbaum.

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7.3 Problem-Solving

Learning objectives.

By the end of this section, you will be able to:

• Describe problem solving strategies
• Define algorithm and heuristic
• Explain some common roadblocks to effective problem solving

   People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

The study of human and animal problem solving processes has provided much insight toward the understanding of our conscious experience and led to advancements in computer science and artificial intelligence. Essentially much of cognitive science today represents studies of how we consciously and unconsciously make decisions and solve problems. For instance, when encountered with a large amount of information, how do we go about making decisions about the most efficient way of sorting and analyzing all the information in order to find what you are looking for as in visual search paradigms in cognitive psychology. Or in a situation where a piece of machinery is not working properly, how do we go about organizing how to address the issue and understand what the cause of the problem might be. How do we sort the procedures that will be needed and focus attention on what is important in order to solve problems efficiently. Within this section we will discuss some of these issues and examine processes related to human, animal and computer problem solving.

PROBLEM-SOLVING STRATEGIES

   When people are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

Problems themselves can be classified into two different categories known as ill-defined and well-defined problems (Schacter, 2009). Ill-defined problems represent issues that do not have clear goals, solution paths, or expected solutions whereas well-defined problems have specific goals, clearly defined solutions, and clear expected solutions. Problem solving often incorporates pragmatics (logical reasoning) and semantics (interpretation of meanings behind the problem), and also in many cases require abstract thinking and creativity in order to find novel solutions. Within psychology, problem solving refers to a motivational drive for reading a definite “goal” from a present situation or condition that is either not moving toward that goal, is distant from it, or requires more complex logical analysis for finding a missing description of conditions or steps toward that goal. Processes relating to problem solving include problem finding also known as problem analysis, problem shaping where the organization of the problem occurs, generating alternative strategies, implementation of attempted solutions, and verification of the selected solution. Various methods of studying problem solving exist within the field of psychology including introspection, behavior analysis and behaviorism, simulation, computer modeling, and experimentation.

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them (table below). For example, a well-known strategy is trial and error. The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

   Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Further problem solving strategies have been identified (listed below) that incorporate flexible and creative thinking in order to reach solutions efficiently.

Additional Problem Solving Strategies :

• Abstraction – refers to solving the problem within a model of the situation before applying it to reality.
• Analogy – is using a solution that solves a similar problem.
• Brainstorming – refers to collecting an analyzing a large amount of solutions, especially within a group of people, to combine the solutions and developing them until an optimal solution is reached.
• Divide and conquer – breaking down large complex problems into smaller more manageable problems.
• Hypothesis testing – method used in experimentation where an assumption about what would happen in response to manipulating an independent variable is made, and analysis of the affects of the manipulation are made and compared to the original hypothesis.
• Lateral thinking – approaching problems indirectly and creatively by viewing the problem in a new and unusual light.
• Means-ends analysis – choosing and analyzing an action at a series of smaller steps to move closer to the goal.
• Method of focal objects – putting seemingly non-matching characteristics of different procedures together to make something new that will get you closer to the goal.
• Morphological analysis – analyzing the outputs of and interactions of many pieces that together make up a whole system.
• Proof – trying to prove that a problem cannot be solved. Where the proof fails becomes the starting point or solving the problem.
• Reduction – adapting the problem to be as similar problems where a solution exists.
• Research – using existing knowledge or solutions to similar problems to solve the problem.
• Root cause analysis – trying to identify the cause of the problem.

The strategies listed above outline a short summary of methods we use in working toward solutions and also demonstrate how the mind works when being faced with barriers preventing goals to be reached.

One example of means-end analysis can be found by using the Tower of Hanoi paradigm . This paradigm can be modeled as a word problems as demonstrated by the Missionary-Cannibal Problem :

Missionary-Cannibal Problem

Three missionaries and three cannibals are on one side of a river and need to cross to the other side. The only means of crossing is a boat, and the boat can only hold two people at a time. Your goal is to devise a set of moves that will transport all six of the people across the river, being in mind the following constraint: The number of cannibals can never exceed the number of missionaries in any location. Remember that someone will have to also row that boat back across each time.

Hint : At one point in your solution, you will have to send more people back to the original side than you just sent to the destination.

The actual Tower of Hanoi problem consists of three rods sitting vertically on a base with a number of disks of different sizes that can slide onto any rod. The puzzle starts with the disks in a neat stack in ascending order of size on one rod, the smallest at the top making a conical shape. The objective of the puzzle is to move the entire stack to another rod obeying the following rules:

• 1. Only one disk can be moved at a time.
• 2. Each move consists of taking the upper disk from one of the stacks and placing it on top of another stack or on an empty rod.
• 3. No disc may be placed on top of a smaller disk.

  Figure 7.02. Steps for solving the Tower of Hanoi in the minimum number of moves when there are 3 disks.

Figure 7.03. Graphical representation of nodes (circles) and moves (lines) of Tower of Hanoi.

The Tower of Hanoi is a frequently used psychological technique to study problem solving and procedure analysis. A variation of the Tower of Hanoi known as the Tower of London has been developed which has been an important tool in the neuropsychological diagnosis of executive function disorders and their treatment.

GESTALT PSYCHOLOGY AND PROBLEM SOLVING

As you may recall from the sensation and perception chapter, Gestalt psychology describes whole patterns, forms and configurations of perception and cognition such as closure, good continuation, and figure-ground. In addition to patterns of perception, Wolfgang Kohler, a German Gestalt psychologist traveled to the Spanish island of Tenerife in order to study animals behavior and problem solving in the anthropoid ape.

As an interesting side note to Kohler’s studies of chimp problem solving, Dr. Ronald Ley, professor of psychology at State University of New York provides evidence in his book A Whisper of Espionage  (1990) suggesting that while collecting data for what would later be his book  The Mentality of Apes (1925) on Tenerife in the Canary Islands between 1914 and 1920, Kohler was additionally an active spy for the German government alerting Germany to ships that were sailing around the Canary Islands. Ley suggests his investigations in England, Germany and elsewhere in Europe confirm that Kohler had served in the German military by building, maintaining and operating a concealed radio that contributed to Germany’s war effort acting as a strategic outpost in the Canary Islands that could monitor naval military activity approaching the north African coast.

While trapped on the island over the course of World War 1, Kohler applied Gestalt principles to animal perception in order to understand how they solve problems. He recognized that the apes on the islands also perceive relations between stimuli and the environment in Gestalt patterns and understand these patterns as wholes as opposed to pieces that make up a whole. Kohler based his theories of animal intelligence on the ability to understand relations between stimuli, and spent much of his time while trapped on the island investigation what he described as  insight , the sudden perception of useful or proper relations. In order to study insight in animals, Kohler would present problems to chimpanzee’s by hanging some banana’s or some kind of food so it was suspended higher than the apes could reach. Within the room, Kohler would arrange a variety of boxes, sticks or other tools the chimpanzees could use by combining in patterns or organizing in a way that would allow them to obtain the food (Kohler & Winter, 1925).

While viewing the chimpanzee’s, Kohler noticed one chimp that was more efficient at solving problems than some of the others. The chimp, named Sultan, was able to use long poles to reach through bars and organize objects in specific patterns to obtain food or other desirables that were originally out of reach. In order to study insight within these chimps, Kohler would remove objects from the room to systematically make the food more difficult to obtain. As the story goes, after removing many of the objects Sultan was used to using to obtain the food, he sat down ad sulked for a while, and then suddenly got up going over to two poles lying on the ground. Without hesitation Sultan put one pole inside the end of the other creating a longer pole that he could use to obtain the food demonstrating an ideal example of what Kohler described as insight. In another situation, Sultan discovered how to stand on a box to reach a banana that was suspended from the rafters illustrating Sultan’s perception of relations and the importance of insight in problem solving.

Grande (another chimp in the group studied by Kohler) builds a three-box structure to reach the bananas, while Sultan watches from the ground.  Insight , sometimes referred to as an “Ah-ha” experience, was the term Kohler used for the sudden perception of useful relations among objects during problem solving (Kohler, 1927; Radvansky & Ashcraft, 2013).

Solving puzzles.

   Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below (see figure) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

How long did it take you to solve this sudoku puzzle? (You can see the answer at the end of this section.)

   Here is another popular type of puzzle (figure below) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Did you figure it out? (The answer is at the end of this section.) Once you understand how to crack this puzzle, you won’t forget.

   Take a look at the “Puzzling Scales” logic puzzle below (figure below). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

What steps did you take to solve this puzzle? You can read the solution at the end of this section.

Pitfalls to problem solving.

   Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

   Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in the table below.

Were you able to determine how many marbles are needed to balance the scales in the figure below? You need nine. Were you able to solve the problems in the figures above? Here are the answers.

   Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.

References:

Openstax Psychology text by Kathryn Dumper, William Jenkins, Arlene Lacombe, Marilyn Lovett and Marion Perlmutter licensed under CC BY v4.0. https://openstax.org/details/books/psychology

Review Questions:

1. A specific formula for solving a problem is called ________.

a. an algorithm

b. a heuristic

c. a mental set

d. trial and error

2. Solving the Tower of Hanoi problem tends to utilize a  ________ strategy of problem solving.

a. divide and conquer

b. means-end analysis

d. experiment

3. A mental shortcut in the form of a general problem-solving framework is called ________.

4. Which type of bias involves becoming fixated on a single trait of a problem?

a. anchoring bias

b. confirmation bias

c. representative bias

d. availability bias

5. Which type of bias involves relying on a false stereotype to make a decision?

6. Wolfgang Kohler analyzed behavior of chimpanzees by applying Gestalt principles to describe ________.

a. social adjustment

b. student load payment options

c. emotional learning

d. insight learning

7. ________ is a type of mental set where you cannot perceive an object being used for something other than what it was designed for.

a. functional fixedness

c. working memory

Critical Thinking Questions:

1. What is functional fixedness and how can overcoming it help you solve problems?

2. How does an algorithm save you time and energy when solving a problem?

Personal Application Question:

1. Which type of bias do you recognize in your own decision making processes? How has this bias affected how you’ve made decisions in the past and how can you use your awareness of it to improve your decisions making skills in the future?

anchoring bias

availability heuristic

confirmation bias

functional fixedness

hindsight bias

problem-solving strategy

representative bias

trial and error

working backwards

Answers to Exercises

algorithm:  problem-solving strategy characterized by a specific set of instructions

anchoring bias:  faulty heuristic in which you fixate on a single aspect of a problem to find a solution

availability heuristic:  faulty heuristic in which you make a decision based on information readily available to you

confirmation bias:  faulty heuristic in which you focus on information that confirms your beliefs

functional fixedness:  inability to see an object as useful for any other use other than the one for which it was intended

heuristic:  mental shortcut that saves time when solving a problem

hindsight bias:  belief that the event just experienced was predictable, even though it really wasn’t

mental set:  continually using an old solution to a problem without results

problem-solving strategy:  method for solving problems

representative bias:  faulty heuristic in which you stereotype someone or something without a valid basis for your judgment

trial and error:  problem-solving strategy in which multiple solutions are attempted until the correct one is found

working backwards:  heuristic in which you begin to solve a problem by focusing on the end result

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Everyday Problem Solving: Experience, Strategies, and Behavioral Intentions

• Published: January 2004
• Volume 11 , pages 9–18, ( 2004 )

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• Julie Hicks Patrick 1 &
• JoNell Strough 1  

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Experience and strategic flexibility predict performance on solving hypothetical everyday problems, although their relation to real-world behavior is largely unknown (e.g., M. Diehl, S. L. Willis, & K. W. Schaie, 1995). Moreover, few studies have examined highly salient, rare-event problems such as relocation decisions. This study tested whether experience and strategic flexibility, in addition to demographic variables, were related to intentions to relocate. Ninety-five adults ( M age = 72.1 years) completed problem-solving vignettes, provided information regarding previous experience with problems related to living arrangements, and stated personal relocation intentions. Logistic regression analyses showed that both experience (Odds Ratio ( OR ) = 40.6) and the number of strategies generated ( OR = 3.0) were significantly associated with an increased likelihood of considering a late-life relocation. The benefits of linking lab-based assessments of everyday problem solving to real-world behavior are discussed.

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Variability and harshness shape flexible strategy-use in support of the constrained flexibility framework

The brief case for everyday problems: a proposal of two brief alternate forms of the Everyday Problems Test

Multitasking as a choice: a perspective.

Baltes, P. B., & Staudinger, U. M. (2000). Wisdom: A metaheuristic (pragmatic) to orchestrate mind and virtue toward excellence. American Psychologist, 55, 122-126.

Google Scholar  

Berg, C. A., & Klaczynski, P. A. (1996). Practical intelligence and problem-solving: Searching for perspectives. In F. Blanchard-Fields & T. M. Hess (Eds.), Perspectives on cognitive change in adulthood and aging (pp. 323-357). New York: McGraw-Hill.

Berg, C. A., Meegan, S. P., & Klaczynski, P. (1999). Age and experiential differences in strategy generation and information requests for solving everyday problems. International Journal of Behavioral Development, 23, 615-639.

Berg, C. A., & Sternberg, R. J. (1985). A triarchic theory of intellectual development during adulthood. Developmental Review, 5, 334-370.

Berg, C. A., Strough, J., Calderone, K. S., Sansone, S., & Weir, C. (1998). The role of problem definitions in understanding age and context effects on strategies for solving everyday problems. Psychology and Aging, 13, 29-44.

Blanchard-Fields, F. & Chen, Y. (1996). Adaptive cognition and aging. American Behavioral Scientist, 39, 231-248.

Blanchard-Fields, F., Jahnke, H. C., & Camp, C. J. (1995). Age differences in problem-solving style: The role of emotional salience. Psychology and Aging, 10, 173-180.

Bland, J. M., & Altman, D. G. (2000). The odds ratio. BMJ, 320 1468.

Bosman, E. A., & Charness, N. (1996). Age-related differences in skilled performance and skill acquisition. In F. Blanchard-Fields & T. M. Hess (Eds.), Perspectives on cognitive change in adulthood and aging (pp. 428-453). New York: McGraw-Hill.

Choi, N. G. (1996). Older persons who move: Reasons and health consequences. Journal of Applied Gerontology, 15, 325-345.

Cornelius, S., & Caspi, A. (1987). Everyday problem-solving in adulthood and old age. Psychology and Aging, 2, 144-153.

Denney, N. W., & Pearce, K. A. (1989). A developmental study of practical problem solving in adults. Psychology and Aging, 4, 438-442.

Diehl, M., Willis, S. L., & Schaie, K. W. (1995). Everyday problem solving in older adults: Observational assessment and cognitive correlates. Psychology and Aging, 10, 478-491.

Faul, F., & Erdfelder, E. (1992). GPOWER: A priori, post hoc, and compromise power analysis for MS-DOS (computer program). Bonn, FRG: Bonn University, Department of Psychology.

Hoyer, W. J., & Rybash, J. M. (1994). Characterizing adult cognitive development. Journal of Adult Development, 1, 7-12.

Juster, F. T. (1997). On the measurement of expectations, uncertainty, and preferences. Journal of Gerontology: Social Sciences, 52B, S237-S239.

Landauer, T. K. (1989). Some bad and some good reasons for studying memory and cognition in the wild. In L.W. Poon, D. C. Rubin, & B. A. Wilson (Eds.), Everyday cognition in adulthood and late life (pp. 116-125). Cambridge, UK: Cambridge University Press.

Lawton, M. P. (1999). Environmental design features and the well-being of older persons. In Michael Duffy et al. (Eds.),Handbook of counseling and psychotherapy with older adults.(pp. 350-363). New York: Wiley.

Marsiske, M., & Willis, S. L. (1995). Dimensionality of everyday problem solving in older adults. Psychology and Aging, 10, 269-283.

Meacham, J. A., & Emont, N. C. (1989). The interpersonal basis of everyday problem solving. In J. D. Sinnott (Ed.), Everyday problem solving (pp. 7-23). New York: Praeger.

Morrow, D., Leirer, V., Altieri, P., & Fitzsimmons, C. (1994). When expertise reduces age differences in performance. Psychology and Aging, 9, 134-148.

Robison, J. T., & Moen, P. (2000). A life-course perspective on housing expectations and shifts in late midlife. Research on Aging, 22,–533.

Silverstein, M., & Angelelli, J. J. (1998). Older parents' expectations of moving closer to their children. Journal of Gerontology: Social Sciences, 53B, S153-S163.

Sinnott, J. D. (1998). The development of logic in adulthood: Postformal thought and its applications. New York: Plenum.

Spivack, G., & Shure, M. B. (1982). The cognition of social adjustment. In B. B. Lahey, & A. E. Kazdin (Eds.), Advances in child psychology (pp. 323-372). New York: Plenum.

Strough, J., Berg, C. A., & Sansone, C. (1996). Goals for solving everyday problems across the life span: Age and gender differences in the salience of interpersonal concerns. Developmental Psychology, 32, 1106-1115.

Strough, J., Cheng, S., & Swenson, L. M. (2002). Preferences for collaborative and individual everyday problem solving in later adulthood. International Journal of Behavioral Development, 26, 26-35.

Strough, J., Patrick, J. H., & Swenson, L. M. (2002). Strategies for solving everyday problems faced by grandparents: The role of experience. In B. Hayslip, & J. H. Patrick (Eds.), Working with custodial grandparents (pp. 257-275). New York: Springer.

Strough, J., Patrick, J. H., Swenson, L. M., Cheng, S., & Barnes, K. A. (2003). Collaborative everyday problem solving: Interpersonal relationships and problem dimensions. International Journal of Aging and Human Development, 56, 43-66.

Willis, S. L., Dolan, M. M., & Bertrand, R. M. (1999). Problem solving on health-related tasks of daily living. In D. C. Park, R. W. Morrell, & K. Shifren (Eds.), Processing of medical information in aging patients: Cognitive and human factors perspectives (pp. 199-219). Hillsdale, NJ: Erlbaum.

Willis, S. L., & Schaie, K. W. (1993). Everyday cognition: Taxonomic and methodological considerations. In J. M. Puckett and H. W. Reese (Eds.), Mechanisms of everyday cognition (pp. 33-53). Hillsdale, NJ: Erlbaum.

Yates, J. F., & Patalano, A. L. (1999). Decision making and aging. In D. C. Park, R. W. Morrell, & K. Shifren (Eds.), Processing of medical information in aging patients: Cognitive and human factors perspectives (pp. 31-54). Hillsdale, NJ: Erlbaum

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Patrick, J.H., Strough, J. Everyday Problem Solving: Experience, Strategies, and Behavioral Intentions. Journal of Adult Development 11 , 9–18 (2004). https://doi.org/10.1023/B:JADE.0000012523.31728.f7

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7.3 Problem Solving

Learning objectives.

• Describe problem solving strategies
• Define algorithm and heuristic
• Explain some common roadblocks to effective problem solving

People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

Problem-Solving Strategies

When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them ( Table 7.2 ). For example, a well-known strategy is trial and error . The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Everyday Connection

Solving puzzles.

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below ( Figure 7.8 ) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

Here is another popular type of puzzle ( Figure 7.9 ) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Take a look at the “Puzzling Scales” logic puzzle below ( Figure 7.10 ). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

Pitfalls to Problem Solving

Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

Link to Learning

Check out this Apollo 13 scene where the group of NASA engineers are given the task of overcoming functional fixedness.

Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in Table 7.3 .

Please visit this site to see a clever music video that a high school teacher made to explain these and other cognitive biases to his AP psychology students.

Were you able to determine how many marbles are needed to balance the scales in Figure 7.10 ? You need nine. Were you able to solve the problems in Figure 7.8 and Figure 7.9 ? Here are the answers ( Figure 7.11 ).

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ORIGINAL RESEARCH article

The value of removing daily obstacles via everyday problem-solving theory: developing an applied novel procedure to increase self-efficacy for exercise.

• 1 Department of Psychology, Baruch College, City University of New York, New York, NY, USA
• 2 University of Rome “La Sapienza,”, Rome, Italy

The objective of the study was to develop a novel procedure to increase self-efficacy for exercise. Gains in one’s ability to resolve day-to-day obstacles for entering an exercise routine were expected to cause an increase in self-efficacy for exercise. Fifty-five sedentary participants (did not exercise regularly for at least 4 months prior to the study) who expressed an intention to exercise in the near future were selected for the study. Participants were randomly assigned to one of three conditions: (1) an Experimental Group in which they received a problem-solving training session to learn new strategies for solving day-to-day obstacles that interfere with exercise, (2) a Control Group with Problem-Solving Training which received a problem-solving training session focused on a typical day-to-day problem unrelated to exercise, or (3) a Control Group which did not receive any problem-solving training. Assessment of obstacles to exercise and perceived self-efficacy for exercise were conducted at baseline; perceived self-efficacy for exercise was reassessed post-intervention (1 week later). No differences in perceived challenges posed by obstacles to exercise or self-efficacy for exercise were observed across groups at baseline. The Experimental Group reported greater improvement in self-efficacy for exercise compared to the Control Group with Training and the Control Group. Results of this study suggest that a novel procedure that focuses on removing obstacles to intended planned fitness activities is effective in increasing self-efficacy to engage in exercise among sedentary adults. Implications of these findings for use in applied settings and treatment studies are discussed.

Introduction

Regular physical activity is associated with numerous positive health outcomes such as reduced risk of cardiovascular disease, diabetes, and increased quality of life ( Colcombe et al., 2004 ; Hamman et al., 2006 ; Warburton et al., 2006 ; Flöel et al., 2009 ; Reddigan et al., 2011 ). Despite the known benefits of exercise, many people struggle to engage in and maintain a regular exercise routine. Less than 25% of Americans aged 15 and older exercise regularly ( Bureau of Labor Statistics, 2009 ), while many other individuals perceive physical activity to be a psychological challenge ( Walcott-McQuigg and Prohaska, 2001 ). When younger adults are interviewed about the obstacles that stop them from exercising, some refer to physical barriers such as lack of transportation to the gym, while the large majority report psychological barriers, such as lack of motivation ( Dishman et al., 2009 ).

Research has shown that success with initiating and maintaining a physical activity program is related to one’s self-efficacy perceptions, or one’s perceived ability to overcome self-reported obstacles to exercise ( Bandura, 1997 ; Dishman et al., 2009 ; Doerksen et al., 2009 ; Prohaska et al., 2000 ). It would seem then, that increasing self-efficacy for exercise may be an effective vehicle for facilitating initiation and maintenance of physical activity.

This paper aims to demonstrate that the psychological underpinnings of the perceived self-efficacy for physical activity are related to people’s ability to overcome their self-reported obstacles to exercise. In so doing, we capitalized on recent problem-solving theoretical advances and applications ( Allaire and Marsiske, 2002 ; Artistico et al., 2003 , 2010 , 2011 ; Pezzuti et al., 2009 ) and developed a novel experimental procedure to use in the context of physical activity. Specifically we addressed the challenges faced by individuals entering into an exercise routine by providing a structured problem-solving analysis for each obstacle that could interfere with exercise adherence.

Increasing Self-Efficacy for Exercise: An Everyday Problem-Solving Novel Procedure

Self-efficacy is a cognitive mediator in the individual’s ability to overcome setbacks, challenges, and obstacles ( Bandura, 1986 ; Caprara and Cervone, 2000 ). Perceptions of self-efficacy contribute to human adjustment and achievement in several cognitive and emotional domains ( Bandura and Cervone, 1983 ; Bandura, 1986 ). To foster self-efficacy, Bandura (1997) recommended that investigators carefully design research that examines the determinants of the task that needs to be accomplished (in our case to remove obstacles that interfere with adherence to physical activities). Indeed, it has been convincingly shown that the most powerful source of performance is mastery of experiences ( Bandura, 1977 , 1997 ). In the domain of exercise, this could mean that individuals should first be able to “master” what stops them from exercising. The uniqueness of perceived obstacles is also relevant because one’s self-efficacy can be explained by idiosyncratic patterns of activation between self-knowledge and appraisals of the obstacles to be solved ( Cervone, 2004 ).

Specifically, one needs to analyze the factors that undermine self-efficacy for exercise from the individual perspective. Research has demonstrated that a perceived lack of time, energy, sense of inability to exercise, and lack of social support from family or friends may differentially impact individuals ( Dishman et al., 2004 , 2009 ). A lack of problem-solving skills will lead to self-doubt and impede the formation of a robust sense of self-efficacy.

When people are committed to partaking in health activities, they may fail to act on their intentions because of situational factors. Among those situational factors are social, interpersonal, and intra-personal everyday problems. By training individuals to generate strategies for solving such everyday problems, the intention is to facilitate their daily adherence to exercise and reduce attrition from exercise programs. This is consistent with systematic research that demonstrates the way in which people who wish to exercise commonly confront everyday problems that impede their intended pursuits ( Lee et al., 2008 ; Roessler and Ibsen, 2009 ).

Our goal is to show that young adults’ participation in exercise activity may hinge on their ability to solve everyday problems. Here we are mainly concerned with issues of perceived self-efficacy for exercise. We studied the interplay between everyday problem-solving and self-efficacy in the context of physical exercise. This is novel in the literature as no prior research has established a causal relationship between self-efficacy, exercise, and everyday problem-solving. We sought to improve everyday problem-solving skills first by having participants identify obstacles and solutions to their individual exercise-related problems, and second by encouraging them to consider additional solutions via idiographic experimental procedures. Gains in people’s ability to solve everyday problems are expected to increase self-efficacy for exercise. This notion was driven by previous research on everyday problem-solving which is extensively reviewed below (e.g., Reitman, 1964 ; Simon, 1973 ; Allaire and Marsiske, 2002 ; Artistico et al., 2003 , 2010 ; Blanchard-Fields, 2007 ; Pezzuti et al., 2009 ).

Everyday Problem-Solving Theory

Everyday problem-solving ability is one’s capacity to overcome day-to-day obstacles. Researchers have focused on three intertwined elements in framing the study of everyday problem-solving: solution generation, problem-solving space, and the problem’s root cause. Solution generation refers to the cognitive process of conceptualizing and choosing obstacle-relevant strategies. The problem-solving space ( Reitman, 1964 ; Simon, 1973 ) contains an everyday problem’s fundamental elements, the comprehension of which is required for effective solution generation. The root cause or causes of an everyday problem refer to the events or situations that acted as primary determinants of the problem-space. Below we briefly discuss these three elements in greater detail.

Solution generation

The ability to aptly identify an increasing number of strategies or solutions to a specific problem is central to everyday problem-solving ( Allaire and Marsiske, 2002 ; Artistico et al., 2003 , 2010 ). In complex and dynamic environments the exact level of success that will be achieved by a given strategy cannot be known. Inherent uncertainties in the environment in which a problem might take place (e.g., work, home, recreational settings), make it impossible to know which strategy will prove to be the most effective for a given decision maker. To the extent that one is able to generate multiple solutions to everyday problems, there will be viable alternatives available in case a strategy fails. One’s ability to solve everyday problems is a rather malleable cognitive ability. Indeed, researchers have previously demonstrated that it is possible to increase individuals’ solution generation ability ( Pezzuti et al., 2009 ) by helping the problem-solver to address gaps in the problem-solving space.

The problem-solving space

The “problem-solving space” is a concept that was originally introduced by Reitman (1964) and Simon (1973) in order to guide our understanding of the underlying elements of an everyday problem. The problem-solving space is defined by three elements: the initial state, the means, and the final state. Everyday problems are considered ill-defined when one of the three elements is missing or not stipulated in the formulation of the problem ( Allaire and Marsiske, 2002 ). Problem-space theory ( Simon, 1973 ) proposes that in order to solve ill-defined problems, people must first fill in the gaps in their mental representation of the problem-space. In many cases the initial state is ambiguous and the means by which the final state is reached can be multiple. For example, if someone wishes to overcome feelings of loneliness, social contact may be increased in a variety of ways. However, if the lonely individual were to reflect on and identify the antecedents of those feelings of loneliness (e.g., he just moved to a different city), then he or she would be able to more aptly direct solution generation (e.g., increasing time spent in public or private events in the new city).

The root cause of the problem

The example above demonstrates the importance of root cause identification in the problem-solving process. The problem-solver can better define the problem-space when he or she understands the problem’s root causes. Such comprehension allows descriptions of the problem’s nature beyond those available from a consideration of its surface-level features. Those root-level descriptions fill gaps in mental representations of the initial and final states of a problem. Solutions (i.e., the means ) flourish at each deeper level of definition. The optimal problem-solver will be able to identify the sub-goals or elements for everyday problems, allowing him or her to generate and propose alternative types of solutions to the problem according to its underlying elements ( Allaire and Marsiske, 2002 ).

When using everyday problem-solving theory, investigators should provide individuals with a large number of problem-solving strategies alongside plausible root causes of the problem, thereby fostering connections between root causes and solutions. Further, because the intention to exercise is often correlated with perception of social support, and inversely related to the amount of perceived stress ( Doerksen et al., 2009 ), a careful study design should include individual difference measures of both perceived stress and social support ( Prohaska et al., 2000 ). Aspects of perceived self-knowledge not directly relevant to the challenges at hand can impact everyday problem-solving ability ( Cervone, 2004 ).

Generating dynamic solutions to everyday problems and identifying root causes of problems should help participants reduce the impact of obstacles that interfere with regular exercise activities by increasing their sense of self-efficacy. We expect that newly acquired problem-solving strategies attained via our brief experimental procedure will significantly enhance self-efficacy perceptions. Specifically, we hypothesized an interaction between the type of condition (experimental) and the time of the assessment (after the treatment) on self-efficacy for exercise. We tested this main hypothesis experimentally, after controlling for the effects of perceived stress and social support.

Materials and Methods

Participants.

Participants were undergraduate students from a large university in the Mid-Atlantic U.S. who volunteered for the study in exchange for partial research credit toward their course requirements. The study was advertised online through a college subject pool website and specifically targeted sedentary college students. Inclusion criteria for the study were: not currently engaged in a regular exercise program, tried but failed to do so in the past 4 months, and intention to begin an exercise program in the near future.

Sixty-five subjects provided informed consent to participate in the study, but only 55 completed the study procedures (one participant misunderstood the selection criteria, whereas the other nine participants did not return for the second session of the study). The sample size was compared with the power analysis on research linking everyday problem-solving and self-efficacy ( Artistico et al., 2003 ). The power analysis (0.80) suggested by Keppel (1991) indicated that a sample of 50 subjects would produce a statistically significant effect size.

The sample was ethnically (30.9% Hispanic or Latino) and racially (33.3% Asian, 31.5% Caucasian, 9.3% Black or African American, 25.9% other or did not report) diverse. The sample was composed of male (40%) and female (60%) younger adults ( M = 21.38, SD = 4.48). The study groups (see Procedures immediately below) did not differ significantly by sex (χ 2 = 2.71, p = 0.26), ethnicity (χ 2 = 0.98, p = 0.61), or race (χ 2 = 6.17, p = 0.41).

Participants who met study inclusion criteria attended the first laboratory session lasting approximately 30 min in which they signed informed consent documents, underwent a screening procedure to confirm that (1) they were not engaged in an exercise program and (2) had intention to exercise, and completed baseline questionnaires. Participants returned to complete a second laboratory session 1 week later. During the second laboratory session, participants were randomly assigned to one of three conditions: Experimental Group ( n = 18), Control Group with Problem-solving Training ( n = 22), or Control Group (without problem-solving training; n = 15). The training procedure for the Experimental Group and Control Group with Problem-solving Training lasted approximately 45 min (see Intervention Conditions section); following the training procedure participants completed a second set of questionnaires, which took approximately 15 min. Participants in the Control Group returned to the laboratory for Session 2 only to complete study questionnaires.

To ensure privacy and to create a tranquil environment for the study, participants worked individually in a lab facility. The experimenter was in the background to prompt participants about the next task. Although the study instructions stressed to the participants to take a break if needed, the experimenter did not notice that any breaks were taken. Assessments were presented via computer (measures) or on paper (the training sessions). Once participants completed the second session, they were debriefed about the purpose of the study. The Institutional Review Board of the City University of New York approved the study procedures.

Intervention conditions

Experimental group. Participants in this group received tailored materials which were designed to specifically address challenges with exercise (Part A). All participants were asked to list their three primary reasons for not exercising. Participants then engaged in the following assignments: (a) generate alternative solutions to ideographically identified obstacles, (b) identify potential root causes of the obstacles, (c) connect solutions with the root causes of the obstacles, and (d) recall the solutions generated. These are discussed in more detail below.

The goal of the solution generation phase (Part A) was to compose a tailored list of potentially viable solutions for each participant, as has been done in prior work on everyday problem-solving (cf. Pezzuti et al., 2009 ; Artistico et al., 2010 ). The first strategy was to look at failed prior attempts to solve the problem, as reported by the participant. This was important because we did not want to activate heuristics that would prime failure. The second strategy was to use a “thinking-out-loud” procedure in which we asked a small group of subject matter experts to generate as many solutions as possible. The third strategy was to integrate information obtained from specialized literature. Specifically, we consulted Medline and PsycInfo databases along with some self-help websites in order to offer participants as many of the most viable solutions as possible.

Type of solution validation testing . In the current study, we offered solutions to participants that represented a mix between interpersonal or instrumental ways to solve everyday problems. Because in the everyday problem-solving literature ( Blanchard-Fields, 2007 ), the way one approaches a problem or problem-solving style (interpersonal or instrumental) has an impact on solution generations, we tested such impact by using one independent sample of 93 undergraduate students coming from a similar population. The specific goal was to see if participants preferred interpersonal or instrumental solutions (independent variable) to problems related to physical exercise. Each participant rated 12 solutions. Neither the type of solution (interpersonal or instrumental) nor the participants’ preference between an interpersonal versus an instrumental way to solve problems produced any significant effects ( F 5;86 = 0.34) on perceived self-efficacy, as assessed by a standard scale on self-efficacy and exercise (cf. Plotnikoff and Higginbotham, 2002 ).

The final product was a bank of solutions, which was mapped ideographically to each participant’s problem according to two principles that we specifically developed for this procedure. The first principle is the exclusion of failed strategies (this could vary between individuals), and the second is independence of strategies (this could vary within individuals). According to the first principle, we would exclude strategies that participants have already tried in the past. Hence, two participants with the same problem would not necessarily be provided the same solutions. However, for experimental consistency, each participant received the same number of total solutions (33 solutions for each of the three major problems) yielding a total of 99 solutions. We also allowed participants to generate solutions via a write-in response in addition to the ones we provided. No participant used the write-in space to offer additional solutions. To apply the second principle (independence of strategies), we made sure that no two sets of solutions were identical.

The goal of the second phase (Part B) was to identify the potential root causes of the three obstacles to exercise. We employed the same three strategies and two guiding principles discussed above to identify four major root causes for each problem, yielding a total of 12 root causes.

The goal of the third phase (Part C) was for participants to complete a written exercise designed to integrate Parts A and B. We asked our participants to assign one or more solutions from Part A to each of the root causes identified in Part B, which were the causes that were relevant to them.

The goal of the fourth phase (Part D), was for participants to “think back” to the problem they had previously worked on, and to remember as many solutions as possible for that specific problem, without looking back at their previous pages. The task was to say out loud as many solutions as possible that were recalled (or to write them down on a blank paper).

In doing so, the novel experimental procedure process included two components that have proven important in cognitive-behavioral problem-solving interventions: (1) The process of solution generation, that is, the ability to find many effective ways to solve the same problem and (2) reasoning about everyday problem definitions. Specifically, in this case we focused on the participant’s ability to consider contingencies or conditions such as hidden or underlying aspects.

Control group with problem-solving training

Participants in this group received standard materials derived from Artistico et al. (2010) . The experimental procedure for this group was essentially identical to the procedure described above, but instead of focusing on exercise-related obstacles and solutions, we asked participants to work on a typical day-to-day problem (e.g., how to increase social contact with others, or how to cope with feelings of separation from a partner). Specifically, in Part A participants were presented with a list of solutions related to solving interpersonal problems of a day-to-day nature. There were 33 solutions for each of the problems presented (three problems total), and they were normatively the same for each participant. In Part B participants were presented with plausible root causes of the problems from Part A. In Part C the goal was to link solutions to root causes. Finally, in Part D the participants recounted out loud (or in writing) as many solutions as possible for the same problem.

Control group (without problem-solving training)

Participants in the control group did not receive any information on how to overcome their obstacles to exercise. Participants in this group were asked to simply complete measures related to self-efficacy for engaging in physical activity.

All the measures were presented at baseline and only the Self-Efficacy to Exercise (SEE) scales were re-presented during the post treatment procedure. The demographic questions were presented at the end of the study.

Obstacles to exercise

The Obstacles to Exercise Survey (OES) was developed to identify participants’ primary reasons for not exercising. The OES is a self-administered questionnaire that consists of a series of questions aimed at identifying the participant’s three primary reasons for not exercising (e.g., lack of motivation, low energy). Specifically the OES is comprised of 18 questions that help the participant identify and explore challenges related to exercise engagement (e.g., have you attempted in the past to overcome the reason or problem that makes it difficult for you to exercise?). The perceived difficulty of the problem is also assessed using a 1–10 scale. The internal consistency of the challenge posed by the three problems was calculated (α = 0.86).

Self-efficacy to exercise scales

We also developed the SEE Scales. The SEE is a six-scale survey that measures an individual’s expectations of his or her self-efficacy when faced with barriers to exercise. For five of the scales (brisk walking, running, cycling, swimming, lifting weights), the participant responds on a scale from 1 (“You cannot accomplish the specific behaviors described”) to 10 (“You can certainly accomplish the specific behaviors described”) in order to describe their current level of confidence that they could exercise for various lengths of time or intensity (I can run 1–3 miles a week or I can run 1–3 miles everyday). The sixth scale on the survey asks participants to write in one activity that is their preferred way to exercise and then to rate that item on the same scale used for the previous five scales. To measure changes in self-efficacy, this measure was employed in both laboratory sessions. SEE scales were developed by closely following Bandura’s (1997) guiding principles. The coefficient of reliability α of the SEE ranged from 0.89 to 0.95 (session 1) and α = 0.90 to α = 0.95 (session 2).

Perceived social support

Perceived social support was assessed using the Social Provisions Inventory (SPI – Cutrona and Russell, 1987 ), which consists of 24 questions that assess an individual’s relationship with other people based on a four-point scale (Strongly disagree, Disagree, Agree, and Strongly agree). High numbers on this scale indicate satisfactory levels of perceived social support. The sub-dimensions of the SPI were computed to assess the SPI internal consistency (α = 0.86).

Perceived stress

Perceived stress was assessed with the Perceived Stress Scale ( Cohen and Williamson, 1988 ), which consists of four questions that rate an individual’s feelings and thoughts during the past month based on a five-point scale (Never, Almost never, Sometimes, Fairly often, and Very often). High numbers on this scale indicate the presence of stress. We assessed the internal consistency (α = 0.84) of the scale in our sample.

Other measures

The experimenter completed the screening by interviewing the participants about the time elapsed since they exercised regularly (at least 4 months ago), and about the intention to exercise in the near future (yes/no). A standard questionnaire was administered to all participants to assess demographic characteristics such as gender, age, and race.

Statistical Analyses

A mixed between-within factorial design was implemented to test our main hypothesis. The three experimental groups comprised the between factor, with time (baseline and post-intervention) as the within factor. The primary dependent variables were levels of perceived self-efficacy for exercise at both time points. To test our main hypothesis, we conducted a repeated measures MANOVA where the within factor was represented by the assessment pre- and post-training, and the between factor was represented by the three different study groups.

Baseline Results and Preliminary Analysis

No significant differences (computed with a MANOVA) were observed among study groups on obstacles to exercise, self-efficacy, perceived social support, perceived stress (Table 1 ). At the outset of the analysis we correlated all the specific self-efficacy scales (running, brisk walking, swimming, lifting weights, and cycling). The “other activities” self-efficacy scale was not analyzed because the participants’ responses varied greatly from person to person (e.g., yoga, rock-climbing, dancing, etc.). The results indicated that all the scales were inter-correlated. The minimum correlation was r = 0.35, p < 0.05; and the maximum correlation was r = 0.78; p < 0.001.

Table 1 . Assessment of the three groups regarding selection criteria, background characteristics, and self-efficacy for exercise before training .

As part of the preliminary analysis, we looked at the obstacles to exercise reported by participants across study conditions. We computed frequencies by combining nuances of the same problems. For example time pressure when deciding to exercise (too much work, or not enough hours in a day, or too much homework) was reported by 50.3% of participants (each participant reported three obstacles) in the total obstacle count, followed by lack of energy (14.5%), feelings of shyness (I feel shy at the gym) or inadequacy (not too well coordinated) combined (12.7%), lack of intrinsic (i.e., exercising is boring) or extrinsic motivation (i.e., do not see the benefits of exercising) combined (9%) or social support (6%) from family and friends (i.e., I do not like to exercise alone), and inability to find a suitable space to work out (about 3%) plus other reasons (i.e., never played sports in my life). The correlation between the perceived challenge posed by the obstacles to exercise (aggregate score of the three obstacles reported) and the perceived self-efficacy for exercise was significant ( r = −0.39; p < 0.01) with a negative valance – the more the perception of the challenge, the less the sense of self-efficacy for exercise.

Main Results

The multivariate tests confirmed the hypothesized interaction. The interaction was between the condition (experimental, control with training, control without training) and the time of assessment of the SEE scales (before and after the training). The significant effect of the interaction was driven by an increase in perceived self-efficacy in the experimental group with F 2,52 = 4.98 ( p < 0.02, η 2 = 0.16).

This increase in self-efficacy by the experimental group at post-intervention was significantly greater than the increase in self-efficacy in the other two groups. Specifically, the mean level of perceived self-efficacy was higher in the experimental group than in the control group with training: t 38 = 2.95; p < 0.01. Also the self-efficacy mean level of the participants in the experimental group was greater than the one of the control group: t 31 = 2.49, p < 0.02. Within group analysis showed that participants in the experimental group reported almost a standard deviation increase with respect to baseline ( t 17 = −3.35, p < 0.003): their post-training self-efficacy was M = 6.40 (SD = 1.04). Mean levels of self-efficacy of participants in the other two conditions (control with training, t 21 = 1.25, p = 0.23, or control t 14 = −0.73, p = 0.48) did not change significantly after the intervention. In Figure 1 , we depicted the average perceived self-efficacy levels for the three groups as a function of treatment (pre- or post-intervention).

Figure 1. It depicts average levels of self-efficacy across the three groups before and after the intervention .

Covariates and Other Analysis

We correlated self-efficacy scores before and after the procedure with perceived stress ( r = −0.22) and perceived social support ( r = 0.16): these correlations were not significant. Additionally, we did not find any significant co-variation in perceived stress ( F 1,50 = 0.43, ns) or social support ( F 1,50 = 1.88, ns), with self-efficacy measured before or after the intervention. Table 2 shows solutions that were chosen most frequently by the participants in the experimental group.

Table 2 . Most frequently chosen solutions for every day obstacles that hinder exercise .

The results clearly indicated that it is possible to increase self-efficacy for exercise by improving everyday problem-solving ability. This was the goal of the study. Specifically, we achieved our goal by enhancing participants’ understanding of the obstacles or barriers that had been interfering with their intended exercise pursuits. The experimental procedure was tailored in order to capture within-person variability in participants’ perceived SEE. Each person may have reported differently similar problems or different problems altogether. Our work was designed to capitalize on these individual differences. The data showed that challenges varied from person to person in subtle ways. For example, a person might have reported time as being her biggest challenge because of family obligations whereas another person was unable to set time aside from school or friends.

The importance of analyzing several strategies to exercise obstacles was an asset of the new experimental procedure. Participants were guided to think divergently about their problems by exploring alternative solutions. Solution generation was proposed as a way to increase problem-solving, but the implementation of the solution was carefully linked to possible root causes of the problem. In everyday problem-solving theory, the link between solution generation and identification of root causes of the problem is considered one of the best ways to approach ill-defined problems.

Other investigators have also studied well-defined problems. The intent of well-defined everyday problem-solving research is to study the logical generation of solutions that can be considered optimal because all the nuances of the problems could be eliminated or controlled ( Diehl et al., 2005 ; Willis et al., 2006 ). Here we were concerned mainly with such nuances. The point of the study was to show that even if problems are nominally the same (time pressure), the nuances of the problem can be addressed at the individual level rather than at the group level. We, instead of treating the problem “time” as the same for everybody, offered a tailored problem-solving strategy to each participant.

The results also indicated that the experimental manipulation was strong in comparison to other types of everyday problem-solving. For instance, work by Artistico et al. (2010) , found a large number of solutions for problems that were relevant to younger adults (e.g., break-up with a boyfriend). When we applied the same solutions to ensure internal validity to the study (recall that the participants in the control group were exposed to these problem-solving strategies), participants in the control group did not show a significant increase in their self-efficacy for exercise. This null result documents that there was no task demand in our experiment; information learned by our participants in the control conditions did not translate into an increased self-efficacy in the exercise domain.

The magnitude of the change in self-efficacy reported by the experimental group was notable. Although measurement of behavior change (i.e., engagement in physical activity) was beyond the scope of this study, these results, in combination with the documented relationship between self-efficacy and behavior change, provide preliminary evidence for examining the utility of a problem-solving focused training in the context of a physical activity intervention. Changes in self-efficacy offer insights into the next step, that is, to follow through a designed action ( Bandura, 1997 ). Our newly developed SEE scales “speak about” real exercise activities such as brisk walking. For example, brisk walking could be taught in a physical activity program.

Our study naturally contains limitations, including sample size, scope, and age of the participants. The sample comes from a similar educational background, where the external resources are the same (i.e., free gym on campus), thus the challenge of exercising could pose slightly different motivational demands. The age of the participants, scope of the study, and sample size could be addressed within a research plan that targets a more heterogeneous class of individuals. This research is in fact a germinal step toward the development of a large-scale analysis of one’s ability to overcome barriers and psychological blocks when entering an exercise routine. It will be useful to replicate these findings with an older population coming from a more experientially diverse background. As stated in the specialized literature on everyday problem-solving (see Blanchard-Fields, 2007 for a quick overview), age differences are noteworthy in everyday problem-solving ability. Also, because of the limited scope of the study (individual change in perceived self-efficacy), we did not assess the health status of the participants. This limitation can be overcome in a behavioral modification study where health indicators such as a body mass index are typically measured.

Despite the limitations, the study possesses strengths. For one, we experimentally increased perception of self-efficacy via everyday problem-solving ability in the domain of exercise. We as well other researchers obtained similar correlational findings in the past ( Artistico et al., 2003 ; Pezzuti et al., 2009 ), but never studied the individual’s perception of self-efficacy for exercise as a “dependent variable.” Thus, the findings are novel. Second, we intended to measure shifts in people’s confidence in their ability to exercise through a systematic approach to problem-solving (the systematic approach is the cognition of mental efforts to a practical sense of being able to exercise). Although not measured directly in our study, research on health intervention has documented that self-efficacy perceptions predict behavioral modification in a variety of clinical settings.

Applied research could benefit from the development of these findings. For instance, given the known beneficial effects of physical exercise, it would be useful to apply panel study methods to foster knowledge learned during the lab procedures in real life scenarios. The vision for the future is a longitudinal study where the objective is to capitalize on individual differences (at the individual level) and not just group differences regarding self-efficacy, everyday problem-solving, and exercise. Generating solutions and reasoning about everyday problem definitions can be used as the foci of intervention procedures where the goal is to provide people with alternative ways of achieving health promotion via self-efficacy for exercise.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The study was supported by the Research Foundation of the City University of New York (RF # 64688-0042). We are very grateful to the Research Foundation personnel for their input about the logistical aspects of the study. We are also very thankful to the psychology department of the Baruch College of the City University of New York for their help with online recruitment procedures. Last but not least we would like to thank the Research Experience for Undergraduate program at Baruch for the help with selection of the participants and the execution of the study. We are especially grateful to Stephanie Scutari who helped with the validation testing of the solutions.

Allaire, J. C., and Marsiske, M. (2002). Well- and ill-defined measures of everyday cognition: relationship to elders’ intellectual ability and functional status. Psychol. Aging 17, 101–115.

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

Artistico, D., Berry, J., Black, J., Cervone, D., Lee, L., and Orom, H. (2011). “Psychological functioning in older age: a self-efficacy analysis,” in The Oxford Handbook of Reciprocal Adult Development and Learning , ed. C. H. Hoare (New York: Oxford University Press), 215–247.

Artistico, D., Cervone, D., and Pezzuti, L. (2003). Perceived self-efficacy and everyday problem solving among young and older adults. Psychol. Aging 18, 68–79.

Artistico, D., Orom, H., Cervone, D., Krausse, S., and Houston, E. (2010). Everyday challenges: the influence of contextual factors on everyday problem solving among young, middle-aged, and older adults. Exp. Aging Res. 36, 230–247.

Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84, 191–215.

Bandura, A. (1986). Social Foundation of Thoughts and Action: A Social Cognitive Theory . Englewood Cliffs, NJ: Prentice-Hall.

Bandura, A. (1997). Self-Efficacy: The Exercise of Control . New York: Freeman and Company.

Bandura, A., and Cervone, D. (1983). Self-evaluative and self-efficacy mechanisms governing the motivational effects of goal systems. J. Pers. Soc. Psychol. 45, 1017–1028.

CrossRef Full Text

Blanchard-Fields, F. (2007). Everyday problem solving and emotion: an adult developmental perspective. Curr. Dir. Psychol. Sci. 16, 26–31.

Bureau of Labor Statistics. (2009). Sports and Exercise . Available at: http://www.bls.gov/spotlight/2008/sports/

Caprara, G. V., and Cervone, D. (2000). Personality: Determinants, Dynamics, and Potentials . Cambridge: Cambridge University Press.

Cervone, D. (2004). The architecture of personality. Psychol. Rev. 111, 184–203.

Cohen, S., and Williamson, G. (1988). “Perceived stress in a probability sample of the U.S.,” in The Social Psychology of Health: Claremont Symposium on Applied Social Psychology , eds S. Spacapam and S. Oskamp (Newbury Park, CA: Sage), 31–67.

Colcombe, S. J., Kramer, A. F., Erickson, K. I., Scalf, P., McAuley, E., Cohen, N. J., et al. (2004). Cardiovascular fitness, cortical plasticity, and aging. Proc. Natl. Acad. Sci. U.S.A. 101, 3316–3321.

Cutrona, C. E., and Russell, D. (1987). “The provisions of social relationships and adaptation to stress,” in Advances in Personal Relationships , Vol. 1, eds W. H. Jones and D. Perlman (Greenwich, CT: JAI Press), 37–67.

Diehl, M., Marsiske, M., Horgas, A. L., Rosenberg, A., Saczynski, J. S., and Willis, S. L. (2005). Revised observed tasks of daily living: a performance-based assessment of everyday problem-solving in older adults. J. Appl. Gerontol. 24, 211–230.

Dishman, R. K., Motl, R. W., Saunders, R. P., Felton, G., Ward, D. S., Dowda, M., et al. (2004). Self-efficacy partially mediates the effect of a school-based physical-activity intervention among adolescent girls. Prev. Med. 38, 100–108.

Dishman, R. K., Saunders, R. P., Motl, R. W., Dowda, M., and Pate, R. R. (2009). Self-efficacy moderates the relation between declines in physical activity and perceived social support in high school girls. J. Pediatr. Psychol. 34, 441–451.

Doerksen, S. E., Umstattd, M. R., and McAuley, E. (2009). Social cognitive determinants of moderate and vigorous physical activity in college freshmen. J. Appl. Soc. Psychol. 39, 1201–1213.

Flöel, A., Ruscheweyh, R., Krüger, K., Willemer, C., Winter, B., Völker, K., et al. (2009). Physical activity and memory functions: are neurotrophins and cerebral gray matter volume the missing link? Neuroimage 49, 2756–2763.

Pubmed Abstract | Pubmed Full Text

Hamman, R. F., Wing, R. R., Edelstein, S. L., Lachin, J. M., Bray, G. A., Delahanty, L., et al. (2006). Effect of weight loss with lifestyle intervention on risk of diabetes. Diabetes Care 29, 2102–2107.

Keppel, G. (1991). Design and Analysis: A Researcher’s Handbook . Upper Saddle River, NJ: Prentice-Hall, Inc.

Lee, L., Arthur, A., and Avis, M. (2008). Using self- efficacy theory to develop interventions that help older people overcome psychological barriers to physical activity: a discussion paper. Int. J. Nurs. Stud. 45, 1690–1699.

Pezzuti, L., Artistico, D., Cervone, D., Tramitolo, C., and Black, J. (2009). Assessing the efficacy of a brief everyday problem solving training program for older adults. TPM 16, 111–127.

Plotnikoff, R. C., and Higginbotham, N. (2002). Protection motivation theory and exercise behavior change for the prevention of coronary heart disease in a high-risk, Australian representative community sample of adults. Psychol. Health Med. 7, 87–98.

Prohaska, T. R., Peters, K., and Warren, J. S. (2000). Sources of attrition in a church-based exercise program for older African-Americans. Am. J. Health Promot. 14, 380–385.

Reddigan, J. I., Ardern, C. I., Riddell, M. C., and Kuk, J. L. (2011). Relation of physical activity to cardiovascular disease mortality and the influence of cardiometabolic risk factors. Am. J. Cardiol. 108, 1426–1431.

Reitman, W. R. (1964). Heuristic Decision Procedures Open Constraints and the Structure of Ill-Defined Problems , Chap. 15. New York: John Wiley & Sons Inc., 282–315.

Roessler, K. K., and Ibsen, B. (2009). Promoting exercise on prescription: recruitment, motivation, barriers and adherence in a Danish community intervention study to reduce type 2 diabetes, dyslipidemia and hypertension. J. Public Health (Bangkok) 17, 187–193.

Simon, H. A. (1973). The structure of ill-structured problems. Artif. Intell. 4, 181–201.

Walcott-McQuigg, J. A., and Prohaska, T. R. (2001). Factors influencing participation of African American elders in exercise behavior. Public Health Nurs. 18, 194–203.

Warburton, D. E., Nicol, C. W., and Bredin, S. S. (2006). Health benefits of physical activity: the evidence. Can. Med. Assoc. J. 174, 801–809.

Willis, S. L., Tennstedt, S. L., Marsiske, M., Ball, K., Elias, J., Koepke, K. M., et al. (2006). Long-term effects of cognitive training on everyday functional outcomes in older adults. JAMA 296, 2805–2814.

Keywords: self-efficacy, physical activity, everyday problem-solving theory, everyday problem-solving training, idiosyncratic methods

Citation: Artistico D, Pinto AM, Douek J, Black J and Pezzuti L (2013) The value of removing daily obstacles via everyday problem-solving theory: developing an applied novel procedure to increase self-efficacy for exercise. Front. Psychology 4 :20. doi: 10.3389/fpsyg.2013.00020

Received: 01 November 2012; Paper pending published: 06 December 2012; Accepted: 09 January 2013; Published online: 29 January 2013.

Reviewed by:

Copyright: © 2013 Artistico, Pinto, Douek, Black and Pezzuti. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

*Correspondence: Daniele Artistico, Department of Psychology, Baruch College, The City University of New York, One Bernard Baruch Way, 55 Lexington Avenue, New York, NY 10010, USA. e-mail: daniele.artistico@baruch.cuny.edu

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• DOI: 10.5860/choice.27-1214
• Corpus ID: 141912311

Everyday Problem Solving: Theory and Applications

• Published 18 January 1989
• Psychology, Education

137 Citations

The relevance of logical thinking and cognitive style to everyday problem solving among older adults, evaluating a measure of everyday problem solving for use in african americans., frontal lobe dysfunction and everyday problem-solving: social and non-social contributions., emotion and everyday problem solving in adult development, everyday problem solving across the adult life span: influence of domain specificity and cognitive appraisal., age and experiential differences in strategy generation and information requests for solving everyday problems, everyday problem solving in older adults: observational assessment and cognitive correlates., perceived self-efficacy and everyday problem solving among young and older adults., social problem solving as a mediator of stress-related depression and anxiety in middle-aged and elderly community residents, contexts, functions, forms, and processes of collaborative everyday problem solving in older adulthood.

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Everyday problem solving across the adult life span: influence of domain specificity and cognitive appraisal

Affiliation.

• 1 School of Psychology, Georgia Institute of Technology, Atlanta 30332-0170, USA. [email protected]
• PMID: 9416636

Differences in problem-solving strategies for situations varying in three domains, consumer, home management, and conflict with friends, were examined among younger, middle-aged, and older adults. In addition, this study examined the influence of perceived ability to resolve the problem, controllability, and causal attributions on strategy selection. In the 2 instrumental domains, older adults were more problem focused in their approach than adolescents and younger adults, whereas adolescents and younger adults selected more passive-dependent strategies. In the more interpersonal domain, conflict with friends, older adults tended to select avoidant-denial strategies more so than younger adults. Finally, across domains, the greater the perceived ability to resolve a problem the less the avoidant-denial strategy was selected. The importance of distinguishing between social and instrumental problem solving and of examining the cognitive appraisal of a problem situation are discussed.

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• Focus on Opportunities or Limitations? Their Effects on Older Workers' Conflict Management. Yeung DY, Ho AK. Yeung DY, et al. Front Psychol. 2020 Nov 5;11:571874. doi: 10.3389/fpsyg.2020.571874. eCollection 2020. Front Psychol. 2020. PMID: 33224065 Free PMC article.
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12 Ways to Cope With Frustration: How Effective Are Yours?

Goal-oriented stressful obstacles are inevitable. are you ready to tackle them.

Updated August 7, 2024 | Reviewed by Davia Sills

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• Even when someone thinks they understand an issue well enough to resolve it, it often can defy solutions.
• Many solutions for frustrations have downsides not immediately apparent that are later recognized as harmful.
• Ignoring distress or relying on procrastination to indefinitely evade it won’t make the issue go away.
• When one has a growth mindset, problem-solving is the single most important step in resolving frustrations.

Frustration and Its Many Discontents

How many times have you, anxious to remedy a perplexing problem, encountered so many obstacles that you threw up your hands in defeat? And when you readied yourself for another chance to resolve it, how did it go?

Sad to say, like everyone else, you will confront situations that bedevil you because they turn out to be more complicated and difficult than you could have imagined.

Even when you believe you understand the matter in question well enough to deal with it successfully, all too often, it will begin to aggravate you.

It hardly matters whether the particular subject is political, cultural, religious, literary, or anything else: like your computer seemingly laughing at you for your futile efforts to implement something that worked just fine when you did it in the past.

Following are 12 methods that people frequently use to cope with adverse circumstances, which, given their assumptions and expectations, cause them considerable frustration.

I’ll start with the least helpful approaches so you can estimate how often you may unwittingly have defaulted to them.

Then, I’ll enumerate more positive ways of dealing with bothersome problems. That is, ways that likely would work better for you, especially because they avoid the various downsides of what in the moment might seem to work but later turn out to hurt you (and your relationships).

Least Effective Ways of Coping With Frustrations

1. Anger and Aggression : I list this maladaptive track first because when people are frustrated, they feel out of control. Here, anger is an immediate, ready-made (pseudo-)solution for restoring control. For you’re blaming another person for your burdensome feelings.

In so doing, you’re proclaiming that your frustration doesn’t have anything to do with your inadequacy or incompetence, but theirs.

Plus, however short-lived, vengefully acting out against others provides you with a handy, self-confirming sense of interpersonal power. But—as in revenge begets revenge—such retaliation endangers relationships. So this is hardly the way to go.

2. Avoidance: Perhaps the single most common escape route from the vexing feeling of not knowing how to handle something effectively is to avoid it entirely—to put it out of your mind; to “exile” it.

In fact, virtually all the adverse ways of dealing with frustration can be viewed as avoidance-motivated.

Yet ignoring your distress or relying on procrastination to evade it indefinitely will not make the issue go away. Over time, such avoidance will probably make things worse, exacerbating the frustration you experienced originally.

3. Abusing Substances or Activities: Whether it’s seeking to revivify your comfort level through drinking, drugs, emotional (i.e., not hunger-related) eating, misusing any other potentially detrimental substance, or engaging in physically distracting activities—from sports to (excessive) shopping to gambling—these all-too-convenient “frustration detours” will eventually prove toxic to you.

For one thing, you can become addicted to anything you use repeatedly to evade coping directly with what’s frustrating you. And for another, as you increasingly depend on these escapist ways of liberating yourself from unpleasant emotions, your health—mental as well as physical—will suffer.

Your life will become unbalanced and overloaded with unresolved annoyances, worries, and hang-ups.

4. Withdrawal From Others: If your frustrations concern relationships, it’s natural to be tempted to retreat from those who have caused them.

But while it may offer some relief from interpersonal frustration, such isolation (whether from friends or family) is also likely to augment whatever anger, depression , or anxiety you experienced with them earlier.

Moreover, the loneliness that will likely ensue from this social flight will probably feel more painful than whatever precipitated your disengagement earlier.

More Effective Ways of Coping With Frustrations.

1. Mindfulness : Practicing mindfulness is about focusing on the present, moment to moment, nonjudgmentally observing your feelings without falling victim to them. Being mindful endows you with more clarity about present-day circumstances and will accurately align your behaviors with your sense of purpose.

2. Various Self-Calming, Emotional Detachment Techniques: Meditation , deep breathing, visualization , guided imagery, and other beneficial relaxation methods can reduce the heightened stress levels linked to personal frustrations.

You’ll cope with your indecisiveness and inner travails much better when you can reduce a level of emotional arousal that may have left you feeling muddled and disorganized.

3. Exercise and Physical Activity Generally: Before exploring the deeper roots of your frustrations and reasoning your way through their thickets, you need to get yourself into the right frame of mind.

You’ll manage your frustrations more effectively when you can lift your spirits through movement. And it may not matter whether that movement comes from yoga, bicycling, running, dancing, or even gardening.

It’s often noted that getting physical releases feel-good chemicals. And biochemically, these endorphins can prompt you to take resolute action to settle what’s been so unsettling.

4. Activating Your Support Network: When you feel unsteady or discombobulated, you may be overreacting to a minor situation, exaggerating its relevance or importance to your welfare.

Sharing your concerns with a friend who knows you well, or with a therapist can offer you a revised and far less negative perspective on what’s causing you frustration, so you’re better prepared to handle it.

Hence, don’t hesitate to avail yourself of the more measured insights that another, less emotionally involved individual might provide you.

5. Journaling: Writing down worrisome thoughts and feelings is another way to clarify whether your viewpoint may be incongruous with the actual facts of the situation.

Similar to external support, it can also be a valuable tension reliever.

6. Cultivating an Attitude of Gratitude : Overly focusing on unresolved frustrations makes them harder to address logically.

On the contrary, temporarily redirecting your attention to more favorable aspects of your life can foster a more lucid mindset, which then can facilitate developing a more objective appreciation of what’s troubling you. That way, you’re empowered to return to your dilemma with more understanding and openness .

7. Reexamining Your Goals : It may be that your feelings of overwhelm are mostly related to the goals you’ve set yourself. These goals may be too ambitious, too daunting, and unrealizable. Therefore, consider revising these goals to make them more modest and, thus, more achievable.

8. Problem-Solving: Obviously, this is the single, most important step in resolving your frustrations. Everything I’ve discussed so far is to assist you in tackling this final step with maximum confidence and minimal self-doubt.

You may need to (a) “chunk” large tasks into smaller ones so they feel more manageable, (b) brainstorm, (c) systematically evaluate potential solutions, and (d) revise parts of your plan as you recognize their deficits.

Cultivating the healthiest possible attitude toward what’s been keeping you stuck will enable you to finally move beyond the impasse that’s so agitated you.

Approaching your frustrations proactively and with (unsentimental) optimism almost guarantees that you’ll discover the most tenable solution for them.

And remember, not all problems are resolvable—so, ironically, your ultimate solution could be accepting what, practically, is unchangeable.

© 2024 Leon F. Seltzer, Ph.D. All Rights Reserved.

Leon F. Seltzer, Ph.D. , is the author of Paradoxical Strategies in Psychotherapy and The Vision of Melville and Conrad . He holds doctorates in English and Psychology. As of mid-July 2024, Dr. Seltzer has published some 590 posts, which have received over 54 million views.

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Sticking up for yourself is no easy task. But there are concrete skills you can use to hone your assertiveness and advocate for yourself.

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