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INT’L. J. AGING AND HUMAN DEVELOPMENT, Vol. 62(2) 175-184, 2006

AGE AND PLANNING TASKS: THE INFLUENCE OF ECOLOGICAL VALIDITY*

LOUISE H. PHILLIPS School of Psychology, College of Life Sciences and Medicine, University of Aberdeen MATTHIAS KLIEGEL MIKE MARTIN University of Zurich

ABSTRACT

Planning ability is important in many everyday tasks, such as cooking and shopping. Previous studies have investigated aging effects on planning, looking at either widely used laboratory-based neuropsychological tasks such as the Tower of London (TOL) or more naturalistic planning tasks, such as organizing shopping errands. In the current study, we compare the effects of normal adult aging on both the TOL and a more ecologically valid planning task, the Plan-a-Day (PAD) task. There was a reliable decline in TOL planning performance with age, but no significant correlation between age and PAD planning performance. Age-related variance was partly explained by variance in information processing speed and education. It is proposed that in more ecologically valid planning tasks, age changes in processing speed can be compensated for by task-related knowledge. Implications for everyday planning performance by older adults are considered.

*Preparation of this article was supported, in part, by a grant from Cusanuswerk, Bonn, Germany, and a grant from the German Science Foundation DFG (Ma 1895/4-1). 175 Ó 2006, Baywood Publishing Co., Inc.

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Cognitive planning is involved in a range of important life skills, such as cooking, shopping, and many occupational tasks. There is evidence that executive functions, such as planning, are better predictors of the ability to carry out daily activities in old age than more traditional cognitive measures, such as intelligence and memory (Cahn-Weiner, Malloy, Boyle, Marran, & Salloway, 2000). However, although several studies have found age effects on planning tasks (e.g., Gilhooly, Phillips, Wynn, Logie, & Della Sala, 1999; Kliegel, McDaniel, & Einstein, 2000), age does not seem to influence all aspects of planning in the same way (for a review see Phillips, MacLeod, & Kliegel, 2005). One of the most widely used neuropsychological tasks of planning is the Tower of London (TOL) task (see Shallice, 1982) in which participants are shown a start and goal set of disks placed on rods, with the disks differing in position. The task is to make a mental plan which moves the start set of disks to match the goal in the minimum moves possible, and then to physically execute that plan. Older adults make less accurate mental plans than young on the TOL (Gilhooly et al., 1999), as well as making more excess moves over the minimum necessary to solve TOL trials (Andrés & van der Linden, 2000). Lachman and Burack (1983) argue that although age differences are found in laboratory-based planning tasks, using planning materials that are more familiar might reduce or eliminate age differences. Recently, evidence has been found to support this proposal in studies indicating that there are no age differences found in shopping errand tasks where the material is more realistic (Garden, Phillips & MacPherson, 2001; Kliegel, Martin, McDaniel, & Phillips, in prep.). Also, a recent meta-analysis of prospective memory tasks, which require the planning and execution of an intention to carry out a task (Henry, MacLeod, Phillips & Crawford, 2004), indicates a substantial age-related deficit in plan execution on laboratory tasks, but an age-related benefit of the same magnitude in plan execution in naturalistic tasks. One aim of the current study is to extend these findings and investigate whether this pattern of age differences in planning on laboratory tasks along with age stability in more ecologically valid planning tasks can be found in a single sample of young and old adults. Although there is often recognition of the importance of looking at the performance of older adults in more realistic contexts, there is considerable tension between the development of methods which tap into realistic environments and experimental control and rigor (Czaja & Sharit, 2003). The task which we use to investigate more contextualised planning is called the Plan-a-Day test (PAD) (Funke & Krüger, 1993; Kohler, Poser, & Schönle, 1995) and investigates the ability to plan a work schedule. This task is based upon classical daily errands tasks (e.g., Hayes-Roth & Hayes-Roth, 1979) and requires participants to run a number of errands in a fictitious workplace setting, given specific constraints concerning errand priority and the time course of the task. This task has previously been used in neuropsychological assessment (e.g., Gouzoulis-Mayfrank, Thimm, Rezk, Hensen & Daumann, 2002), as well as

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in occupational settings (e.g., Funke & Krüger, 1993). Funke and Krüger (1995) also report data on the use of the PAD task in assessment centers and found acceptable reliability of the task. They conclude that the measure has good convergent and discriminant validity as a measure of everyday planning in workplace settings because: a) the task successfully distinguishes managers from non-managers, b) PAD performance correlates with organizational skills in the workplace, and c) PAD variance loads on a separate factor compared to other tests used in assessment centers. This task meets important criteria for ecological validity in a simulated work task identified by Czaja and Sharit (2003): sampling of tasks similar to those used in work settings and prediction of actual skills in the workplace. A further issue in relation to age differences in planning concerns the involvement of cognitive resources. There is evidence that age-related changes in many cognitive functions such as reasoning and memory correlate with changes in relatively simple measures of information processing speed (e.g., Salthouse, 1996). It is generally assumed that age-related changes in executive functions, such as planning, reflect a specific age change separate from more general changes in information processing speed. In the current article, we test whether age variation in planning tasks overlaps with variance in processing speed. Another potential explanation for age differences in planning is that older adults have poorer ability to inhibit task-irrelevant material (Hasher, Stolzfus, Zacks, & Rypma, 1991) and thus, are less able to select and focus upon the most critical information central to constructing and executing a plan. There is evidence that variance in inhibitory functioning can explain some of the age-related variance in a complex holiday planning task (Martin & Ewert, 1997). In the current study, we assess inhibition using the most common measure of this construct, the Stroop task. In sum, the first aim of the current study is to investigate the effects of adult aging on both abstract (TOL) and more contextualised (PAD) planning tasks in the same population, with the prediction that age differences should be smaller in the more ecologically-valid PAD task. A further aim is to investigate whether any age differences found in planning can be explained by variance in processing speed or inhibition. METHOD Participants Participants were 39 young (M = 24.8 years, SD = 2.0, range = 22–31; 18 male, 21 female) and 39 old (M = 69.5, SD = 5.5, range = 60–80; 10 male, 29 female) participants who reported no history of vision or hearing difficulties. The younger adults had more years of education (M = 13.2, SD = 0.9) compared to the older adults (M = 9.9, SD = 3.1), F(1, 76) = 42.8, p < .01. Thus, education will be considered in the following analyses.

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Procedure Planning Measures

The Tower of London (TOL) is a traditional laboratory planning task in which the difficulty can be varied by using different start and end states to manipulate plan length. In the current computerized version of the task, three different-colored disks were displayed on three rods that varied in size, and the participants had to move the disks using the computer keyboard. Participants were instructed to achieve the end state in the fewest possible moves possible but were not specifically told to make a full plan in advance. Various task instructions can be given in the TOL task, but there is evidence that instructions to produce a full mental plan does not reduce the number of moves needed to solve the TOL task (Phillips, Wynn, McPherson, & Gilhooly, 2001).The main dependent variable was the difference between the minimum number of moves (i.e., 50) in which the 10 given problems could be solved and the number of moves actually made by a participant. Thus, high scores on the TOL task indicate more excess moves (i.e., worse planning performance). The second, more contextualised planning measure was the Plan-a-day test (PAD). This is a computerized task in which participants have to run a number of errands in a fictitious setting given specific constraints concerning errand priority and the time course. Participants are told to imagine themselves as an employee of a company who has to carry out a number of appointments during a fictitious day. They are encouraged to carry out as many appointments as possible. The appointments all take place within the area of the company presented on the computer screen, which consists of several buildings that are scattered over a wide area. Participants are informed that each appointment can only be met at a specific time or in a specific time window. They are also prompted that the scheduling of these appointments must take into account the distances between the respective locations. Participants can always view the set of appointments they have to schedule by pressing a function key. The option to delete plan elements and modify schedules is also available. Participants are first presented with a practice trial, and only once they have completed this correctly do they continue with the “test” part of the PAD which consists of two “days” for which seven appointments have to be scheduled in 20 minutes test time each. The dependent variable is the number of errands accomplished weighted with respect to their priority. Hence, high scores on the PAD task indicate more accurate planning. Predictors

Speed of processing was assessed with the Digit-Symbol Substitution Test (DSST) from the revised Wechsler Adult Intelligence Scale (Wechsler, 1981), scored as the number of correctly translated digits within a 90s period.

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Finally, we included a color-word version of the Stroop-task in order to measure inhibition (e.g., Houx, Jolles, & Vreeling, 1993). In this task, the baseline trials consisted of four types of color bar (red, blue, green, and yellow) with instructions to name the colors as fast as possible. The interference trials consisted of the four color names printed in mismatching colors with instructions again to name the color each words is written in as fast as possible. Each Stroop condition began with practice on five items; followed by timed performance on the 20 test items (consisting of five rows of four items each). The dependent variable was the time difference between the baseline and the interference condition. Analysis Strategy

Multiple linear regression analysis was used to investigate whether age-related variance could be explained by measures of education, speed, and inhibition. A regression equation with age as the only predictor was calculated first. In order to examine if education, speed, and inhibition could explain age-related variance, a further hierarchical regression analysis was then performed. In the hierarchical regression, education was entered as the first predictor, then, DSST speed, with Stroop inhibition performance entered in a third step and age in a final step. This allows examination of correlates of the age-related variance and also addresses whether inhibitory control functions can account for variance in planning beyond slowed information processing. RESULTS Correlations between age and the planning measures are reported in Table 1. There was a highly significant relationship between age and TOL planning performance, r = 0.555, p < .001, with age explaining 31% of the TOL variance. Older adults showed much poorer planning on the TOL task (young: M = 3.5, SD = 3.4, old: M = 9.6, SD = 5.7; range of scores = 0 to 22). The correlation between age and PAD planning performance was not significant (young: M = 63.8, SD = 5.1 versus old: M = 61.3, SD = 5.7; range of scores = 45 to 68), with age explaining 4% of variance. Correlations between the planning and predictor measures are also reported in Table 1. These indicate that TOL performance correlated with education, inhibition, and speed. PAD planning performance did not relate to inhibition score but did correlate with education and speed. Next, multiple hierarchical linear regression analyses were conducted to investigate whether variance in education (Step 1), speed (Step 2), and inhibition (Step 3) mediated age effects (Step 4) in both planning tasks. Results are summarized in Table 2. For the TOL task, education and speed were both significant predictors of performance, with no remaining variance being explained by inhibition. However, even after considering education, speed, and inhibition, a small part of the age-related variance remained unexplained. Comparing the

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Table 1. Correlations between Planning Measures and Predictor Variables Age Education

Education

TOL

PAD

Stroop

–.600**

TOL

.555**

PAD

–.202

–.352** .285*

–.226

Stroop

.553**

–.412**

.309**

DSST

–.860**

.591**

–.502**

–.171 .291*

–.593**

Note: TOL = Tower of London planning task, PAD = Plan A Day task, Stroop = Difference score between Stroop inhibition and control tasks, DSST = Digit Symbol Substitution Task. *p < .05. **p < .01.

Table 2. Regression Analyses Predicting Performance on Planning Tasks Planning task TOL

PAD

DR2

p-Value

DR2

p-Value

Step 1: Education

.124

.002

.081

.013

Step 2: Speed

.133

.001

.023

.173

Step 3: Inhibition

.000

.889

.000

.917

Step 4: Age

.054

.022

.016

.263

Overall shared variance, Age and Planning

.308

.000

.041

.081

Note: TOL = Tower of London planning task, PAD = Plan A Day task, Stroop = Difference score between Stroop inhibition and control tasks, DSST = Digit Symbol Substitution Task.

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remaining age related variance in TOL planning (see Table 2, Step 4) with the initial age variance (see Table 2, final row) reveals that education and speed together explain around 83% of the age-related variance in TOL. For the PAD task, education was the only significant predictor, with speed no longer explaining variance in the hierarchical analysis. DISCUSSION The results from this study support the prediction that age differences in a laboratory-based planning task will be much greater than on a more contextualized task. Within the same group of participants there were substantial age differences in planning efficacy in the TOL task, but no significant effect of age on the PAD task, despite the fact that the PAD task was rather complex and required simultaneous consideration of multiple task elements and constraints. Both tasks showed significant correlations with a processing speed measure, and most (but not all) of the age variance in the TOL was explained by controlling for speed and education. These results suggest that both the TOL and PAD tasks are influenced by speed of processing, which decreases substantially with age. However, it seems likely that contextual background may be more important in the PAD task. It is plausible that some form of compensatory mechanism may be in operation whereby older adults are able to make use of their previous knowledge about real life scheduling to override the influence of slowed processing speed on the PAD (see e.g., Marsiske, Lang, Baltes, & Baltes, 1995). This is supported by other research (Kliegel et al., in prep) indicating that in tasks involving familiar materials older adults are good at selectively attending to task-relevant information, and this can compensate for resource changes with age. It is worth noting that even the more ecologically-valid PAD task used here demanded the use of computer skills which are likely to be underdeveloped in an older adult sample. The task therefore may still overestimate any age-related declines in planning, due to the more unfamiliar format for older adults. Note though that computerized testing is used for many studies of age effects on ecologically valid tasks; Czaja and Sharit (2003) argue that because most current workplace settings involve computer interaction, it is appropriate for simulated work sample tests to be computerized. However, it would be useful in future research into ecologically valid planning tasks in aging to: a) assess computer abilities and familiarity, b) include a non-computerized measure of planning if possible, or c) recruit young and old participants with equivalent levels of computer experience. The finding that age differences in TOL performance can be explained by considering processing speed but not inhibition scores indicates that the effects of age on this task may reflect a more global cognitive change rather than a specific executive function deficit. This fits with findings that the age changes

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in TOL planning performance are not differentially larger than changes in intelligence test performance (Crawford, Bryan, Luszcz, Obonsawin, & Stewart, 2000) and dual task results, which indicate that in younger adults the TOL specifically loads executive function while in older adults the TOL loads more general cognitive resources (Phillips, Gilhooly, Logie, Della Sala, & Wynn, 2003). In conclusion, these results have implications for the interpretation of age differences in planning tasks. It cannot be assumed that age differences on laboratory tasks, such as the TOL, imply poor ability to plan in the real world, because knowledge-based compensatory mechanisms may be in place which facilitate performance on more realistic tasks. Nor can it be assumed that age differences on the TOL reflect a specific executive deficit of cognition—instead it may be more parsimonious to interpret the majority of evidence on age effects of the TOL as indicative of a more global cognitive change with age. Finally, we propose that on planning tasks which deal with contextualised materials older adults can compensate for age-related declines in processing speed through utilization of relevant knowledge. ACKNOWLEDGMENT We would like to thank Professor Margie Lachman for her comments on the results reported here. REFERENCES Andres, A. U., & van der Linden, M. (2000). Age-related differences in supervisory attentional functions. Journal of Gerontology: Psychological Sciences, 55, 373-380. Cahn-Weiner, D.A., Malloy, P.F., Boyle, P.A., Marran, M., & Salloway, S. (2000). Prediction of functional status from neuropsychological tests in community-dwelling elderly individuals. Clinical Neuropsychologist, 14, 187-195. Crawford, J. R., Bryan, J., Luszcz, M. A., Obonsawin, M. C., & Stewart, L. (2000). The executive decline hypothesis of cognitive aging: Do executive deficits qualify as differential deficits and do they mediate age-related memory decline? Aging Neuropsychology and Cognition, 7, 9-31. Czaja, S. J., & Sharit, J. (2003). Practically relevant research: Capturing real world tasks, environments and outcomes. The Gerontologist, 43, Special Issue 1, 9-18. Funke, J., & Krüger, T. (1993). “Plan-A-Day” (PAD). Bonn: Psychologisches Institut der Universität Bonn. Funke, J., & Krüger, T. (1995). “Plan-A-Day”: Konzeption eines modifizierbaren Instruments zur Führungskräfte-Auswahl sowie erste empirische Befunde [Concept and first empirical results of a modified management assessment test]. In J. Funke & A. Fritz (Eds.), Neue Konzepte und Instrumente zur Planungsdiagnostik [New concepts and instruments for the assessment of planning abilities] (pp. 97-120). Bonn: Deutscher Psychologen Verlag.

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Garden, S., Phillips, L. H., & MacPherson, S. E. (2001). Mid-life aging, open-ended planning and laboratory measures of executive function. Neuropsychology, 15, 472-482. Gilhooly, K. J., Phillips, L. H., Wynn, V. E., Logie, R. H., & Della Sala, S. (1999). Planning processes and age in the 5 disc Tower of London task. Thinking and Reasoning, 5, 339-361. Gouzoulis-Mayfrank, E., Thimm, B., Rezk, M., Hensen, G., & Daumann, J. (2002). Memory impairment suggests hippocampal dysfunction in absintent ecstasy users. Progress in Neuropsychopharmacology and Biological Psychiatry, 27, 819-827. Hasher L., Stolzfus, E. R., Zacks, R. T., & Rypma, B. (1991). Age and inhibition. Journal of Experimental Psychology: Learning Memory and Cognition, 17, 163-169. Hayes-Roth, B., & Hayes-Roth, F. (1979). A cognitive model of planning. Cognitive Science, 3, 275-310. Henry, J. D., MacLeod, M., Phillips, L. H., & Crawford, J. R. (2004). A meta-analytic review of age effects on prospective memory. Psychology and Aging, 19, 27-39. Houx, P. J., Jolles, J., & Vreeling, F. W. (1993). Stroop interference: Aging effects assessed with the Stroop color-word test. Experimental Aging Research, 19, 209-224. Kliegel, M., Martin, M., McDaniel, M. A., & Phillips, L. H. (in prep.). Older adults’ planning: Experience can compensate for cognitive decline. Manuscript in preparation. Kliegel, M., McDaniel, M. A., & Einstein, G. O. (2000). Plan formation, retention, and execution in prospective memory: A new paradigm and age-related effects. Memory and Cognition, 28, 1041-1049. Kohler, J. A., Poser, U., & Schönle, P. W. (1995). The application of “plan-a-day” in neuropsychological assessment and therapy. In J. Funke & A. Fritz (Eds.), Neue Konzepte und Instrumente zur Planungsdiagnostik (pp. 167-181). Bonn: Deutscher Psychologen Verlag. Lachman, M. E., & Burack, O. R. (1983). Planning and control processes across the lifespan: An overview. International Journal of Behavioral Development, 16, 131-143. Marsiske, M., Lang, F. R., Baltes, P. B., & Baltes, M. M. (1995). Selective optimization with compensation: Life-span perspectives on successful human development. In R. A. Dixon & L. Bäckman (Eds.), Compensating for psychological deficits and declines: Managing losses and promoting gains (pp. 35-79). Mahwah, NJ: Erlbaum. Martin, M., & Ewert, O. (1997). Attention and planning in older adults. International Journal of Behavioral Development, 20, 577-594. Phillips, L. H., Gilhooly, K. J., Logie, R. H., Della Sala, S., & Wynn, V. (2003). Age, working memory, and the Tower of London task. European Journal of Cognitive Psychology, 15, 291-312. Phillips, L. H., MacLeod, M., & Kliegel, M. (2005). Adult aging and cognitive planning. In G. Ward & R. Morris (Eds.), The cognitive psychology of planning (pp. 111-139). Hove, UK: Psychology Press. Phillips, L. H., Wynn, V. E., McPherson, S., & Gilhooly, K. J. (2001). Mental planning and the Tower of London task. Quarterly Journal of Experimental Psychology A, 54, 579-598. Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403-428.

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Direct reprint requests to: Louise H. Phillips School of Psychology College of Life Sciences and Medicine University of Aberdeen Aberdeen AB24 2UB Scotland, UK e-mail: [email protected]