23 Male
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.
Model 1 | Model 2 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Coefficient | ||||||||||
Education | 0.399 | 0.135 | 2.969 | .169 | [0.134, 0.665] | 0.018 | 0.114 | 0.162 | .008 | [−0.206, .0242] |
Age | −0.146 | 0.017 | −8.51 | −.491 | [−0.179, −0.112] | −0.152 | 0.013 | −11.436 | −.514 | [−0.179, −0.126] |
Age | −0.005 | 0.001 | −5.822 | −.324 | [−0.007, −0.003] | −0.003 | 0.001 | −4.59 | −.203 | [−0.005, −0.002] |
Fluid Ability | 1.704 | 0.278 | 6.138 | .302 | [1.157, 2.251] | |||||
Crystallized Ability | 1.859 | 0.299 | 6.219 | .329 | [1.270, 2.448] | |||||
Fluid Ability | ||||||||||
Crystallized Ability | ||||||||||
Age × Crystallized Ability | ||||||||||
Age × Fluid Ability | ||||||||||
Crystallized × Fluid | ||||||||||
Crystallized × Fluid × Age | ||||||||||
total | 40.711 | 68.777 | ||||||||
.369 | .624 | |||||||||
Adjusted | .36 | .615 | ||||||||
Δ | 70.35 | |||||||||
Δ | .255 | |||||||||
Model 3 | Model 4 (Full Model) | |||||||||
Coefficient | ||||||||||
Education | 0.044 | 0.109 | −0.407 | −.019 | [−0.17, 0.259] | 0.025 | 0.107 | 0.232 | .011 | [−0.186, 0.236] |
Age | −0.144 | 0.013 | −11.138 | −.486 | [−0.17, −0.119] | −0.143 | 0.014 | −10.02 | −.483 | [−0.171, −0.115] |
Age | −0.004 | 0.001 | −5.235 | −.223 | [−0.005, −0.002] | −0.003 | 0.001 | −5.013 | −.211 | [−0.005, −0.002] |
Fluid Ability | 1.621 | 0.267 | 6.078 | .287 | [1.095, 2.146] | 1.697 | 0.269 | 6.304 | .301 | [1.166, 2.228] |
Crystallized Ability | 1.712 | 0.293 | 5.842 | .303 | [1.135, 2.290] | 1.576 | 0.292 | 5.391 | .279 | [0.999, 2.152] |
Fluid Ability | −0.501 | 0.167 | −2.997 | −.126 | [−0.830, −0.171] | −0.342 | 0.218 | −1.569 | −.086 | [−0.773, 0.088] |
Crystallized Ability | −0.558 | 0.197 | −2.829 | −.123 | [−0.946, −0.169] | −0.334 | 0.236 | −1.414 | −.073 | [−0.799, 0.132] |
Age × Crystallized Ability | 0.046 | 0.016 | 2.943 | .152 | [0.015, 0.076] | |||||
Age × Fluid Ability | 0.008 | 0.016 | 0.503 | .024 | [−0.023, 0.039] | |||||
Crystallized × Fluid | −0.324 | 0.346 | −0.936 | −.06 | [−1.006, 0.359] | |||||
Crystallized × Fluid × Age | 0.014 | 0.014 | 1.01 | .051 | [−0.013, 0.042] | |||||
total | 56.507 | 39.367 | ||||||||
.659 | .683 | |||||||||
Adjusted | .647 | .666 | ||||||||
Δ | 10.331 | 3.858 | ||||||||
Δ | .034 | .024 |
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).
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.
Young | Middle | Older | |||||||
---|---|---|---|---|---|---|---|---|---|
Age | [−0.168, 0.169] | −0.022 | −.042 | [−0.255, −0.07] | −0.153 | −.297 | [−0.475, −0.065] | −0.302 | −.306 |
Fluid | [0.396, 2.467] | 1.703 | .395 | [0.646, 2.374] | 1.364 | .426 | [0.454, 2.661] | 1.605 | .265 |
Fluid | [−1.627, 0.688] | −0.03 | −.012 | [−1.394, 0.21] | −0.362 | −.146 | [−1.38, 0.32] | −0.675 | −.135 |
Crystallized | [−0.273, 2.745] | 0.976 | .229 | [0.18, 1.528] | 0.921 | .256 | [1.662, 4.116] | 2.753 | .502 |
Crystallized | [−1.44, 0.854] | −0.276 | −.079 | [−1.005, 1.061] | −0.237 | −.063 | [−1.471, 0.184] | −0.714 | −.173 |
Crystallized × Fluid | [−3.621, 2.059] | −0.867 | −.244 | [−1.644, 1.36] | −0.209 | −.055 | [−0.966, 1.725] | 0.511 | .084 |
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.
EPT Domain | Fluid | Crystallized | ||||
---|---|---|---|---|---|---|
Young | ||||||
Shopping | [−0.096, 0.388] | 0.143 | .178 | [0.029, 0.498] | 0.23 | .289 |
Finance | [0.084, 0.576] | 0.344 | .406 | [−0.134, 0.297] | 0.057 | .067 |
Household | [−0.015, 0.588] | 0.292 | .328 | [−0.218, 0.318] | 0.037 | .042 |
Meal | [−0.206, 0.521] | 0.263 | .249 | [−0.010, 0.562] | 0.25 | .239 |
Medication | [−0.233, 0.238] | 0.075 | .104 | [−0.047, 0.406] | 0.163 | .228 |
Phone | [−0.267, 0.390] | 0.1 | .094 | [−0.029, 0.584] | 0.238 | .227 |
Transportation | [0.018, 0.672] | 0.385 | .379 | [−0.206, 0.318] | 0.032 | .032 |
Middle-aged | ||||||
Shopping | [−0.073,0.308] | 0.075 | .087 | [0.110, 0.541] | 0.325 | .337 |
Finance | [−0.026, 0.327] | 0.174 | .248 | [−0.059, 0.366] | 0.156 | .198 |
Household | [−0.045, 0.356] | 0.142 | .186 | [−0.274, 0.180] | −0.036 | −.042 |
Meal | [0.002, 0.357] | 0.168 | .212 | [−0.028, 0.348] | 0.162 | .183 |
Medication | [0.026, 0.393] | 0.195 | .271 | [−0.092, 0.319] | 0.12 | .148 |
Phone | [0.077, 0.762] | 0.337 | .296 | [−0.094, 0.544] | 0.243 | .190 |
Transportation | [0.052, 0.519] | 0.265 | .343 | [−0.327, 0.188] | −0.046 | −.053 |
Older | ||||||
Shopping | [0.006, 0.482] | 0.253 | .236 | [0.127, 0.582] | 0.345 | .356 |
Finance | [0.071, 0.529] | 0.298 | .284 | [0.119, 0.580] | 0.345 | .363 |
Household | [−0.166, 0.449] | 0.157 | .122 | [0.042, 0.694] | 0.353 | .304 |
Meal | [0.049, 0.698] | 0.408 | .306 | [−0.001, 0.633] | 0.293 | .242 |
Medication | [−0.040, 0.428] | 0.18 | .192 | [0.098, 0.540] | 0.308 | .363 |
Phone | [−0.068, 0.574] | 0.259 | .189 | [0.100, 0.773] | 0.450 | .362 |
Transportation | [−0.028, 0.461] | 0.24 | .182 | [0.305, 0.792] | 0.528 | .443 |
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.
This work was supported by National Institute on Aging at the National Institutes of Health (grant number 5R37AG006265-29 to D. C. P.).
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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