Can cognitive aging research benefit from PM research?

As shown, laboratory PM research and naturalistic PM research can benefit from each other. In a broader perspective, one might speculate whether cognitive aging research per se can profit from PM research. In other words, it is an important question whether the

comprehension of the mechanisms in prospective remembering might provide deeper insights into adult age differences in other cognitive domains and how PM research helps us to better understand cognitive aging.

As outlined, a common feature of many theoretical approaches in cognitive aging research such as the speed theory of cognitive aging (e.g., Salthouse, 1996) or the inhibition-deficit theory of cognitive aging (e.g., Hasher & Zacks, 1988) is that they attempt to isolate a single cognitive function which is (a) considered to be relevant for a wide range of cognitive tasks and (b) declines with age. Specifically, it is suggested that the age decline in the identified cognitive function mediates between aging per se and the decline in cognitive performance. A fundamental question in this context is whether such a single cognitive ability is sufficient to explain age-related developments in a wide range of cognitive performance. At least from the perspective of prospective remembering, this question can clearly be answered with “no”. In line with other findings in PM research, the present work suggests that reducing the multifaceted effects of aging to a group of cognitive factors (or even a single cognitive function) is too constricted to explain cognitive aging, particularly in complex cognitive behavior such as prospective remembering. Specifically, research showed that age-related differences in PM are not entirely accounted for by cognitive abilities (even if multiple cognitive measures are simultaneously considered). This suggests that it is necessary to

consider the interplay of different cognitive and non-cognitive variables. In other words, when studying complex cognitive behavior such as prospective remembering, current cognitive aging approaches are too limited to explain the multidimensional effects of aging.

Considering only a single functional decline may be sufficient to explain age deficits in a

local cognitive ability that is measured with a highly experimentally controlled testing procedure. However, this may be already too limited when a more complex task such as PM is studied (although in a controlled laboratory procedure). The reason is that complex tasks involve multiple processes that require the correct interplay of various abilities. It seems comprehensible that no single factor can fully explain the interaction of all those involved mechanisms. This limitation will become particularly evident when the complex ability is studied outside the control of a laboratory testing procedure, that is, in everyday life. Hence, naturalistic PM is an illustration for the failure of attempts to explain real-world abilities with a single mechanism. This is because in real life, PM involves many other non-cognitive factors that cannot be fully captured by a single cognitive function. Other examples of complex cognitive abilities in real world may be multitasking in aviation and space travel, planning in large-scale projects such as city planning, or problem-solving in catastrophes such as natural disasters. These examples emphasize that to explain cognitive abilities (and their age-related development) in real world, broader theoretical approaches are needed that explicitly consider the interplay of several cognitive and non-cognitive variables as well as moderating factors to fully explain and comprehend the multifaceted processes and their effects.

Moreover, for research on other complex cognitive abilities, the approach to specify the multiple processes involved in complex cognition and their demands on certain cognitive abilities (as it has been defined for PM; see Kliegel et al., 2011) could give helpful directions.

In detail, the usual behavioral procedure in cognitive assessment is that younger and older adults perform a cognitive task and after that, the output (i.e., task performance) is evaluated.

This may reveal that older adults show a lower performance than younger adults. To explain the deficit, conditions may then be varied with respect to a factor that is thought to represent an underlying ability. Older adults may then show larger deficits in one condition compared to the other one. The problem with this approach is that descriptive dimensions are mixed with

possible underlying mechanisms. This is because when comparing two different conditions in complex tasks, there will be probably a difference in many other features than only in the one ability that is thought to be studied. For example, when the research aim is to study the demand on processing resources, the task difficulty may be experimentally varied so that it contains more or less elements or sub-steps to process. In a PM task, this could concern that in one condition, participants have to realize five intentions, while there are only two in the other condition. In a problem-solving task, this could analogously concern that participants have to consider five rules (that define the specific problem) in one condition, while there are only two rules to be considered in the other condition. Those manipulations may concern the demand on processing resources, but the two conditions may also differ with respect to other potentially relevant dimensions. For example, this may concern motivational aspects (e.g., a more difficult task may be demotivating if it is too difficult) or memory load (i.e., there are five versus two elements to be kept active in memory). Hence, performance differences cannot be traced back (only) to different demands on processing resources because many other factors are simultaneously at work. Thus, conclusions concerning the underlying mechanism (e.g., processing resources, as in the example) would be invalid. To solve this problem, it is necessary to disentangle the descriptive and the explanatory aspects. For this purpose, it could be helpful to specify the multiple processes involved in the studied complex cognitive behavior and their demands on certain cognitive abilities. In detail, this proposition is based on the assumption that each complex process can be divided into smaller

sub-processes, which again consist of many much smaller sub-steps. Then, for each sub-step, it could be specified what exactly is required to successfully perform the respective sub-step.

This task analysis allows isolating the specific demands on certain cognitive abilities in specific phases within the large and complex process. In addition, other variables (such as non-cognitive aspects) and moderating factors could be integrated into the derived process model. Then, it could be evaluated which components are actually affected by the

experimental manipulation. In the aforementioned example, the manipulation of task difficulty may affect the demands on processing resources, memory load, motivational aspects, and many others. With this detailed knowledge, underlying mechanisms could be specified that are able to explain the observed behavioral results, such as why younger and older adults differ in their performance and which factors moderate those effects. In sum, the approach to specify the multiple involved processes and their demands on certain cognitive abilities could give helpful directions to understand the multifaceted effects of aging also in other domains of complex cognitive behavior.

Another way how cognitive aging research could benefit concerns that naturalistic PM research has identified aspects that seem to be helpful for older adults to compensate

cognitive deficits. It is an interesting question whether those aspects are also supportive to maintain a good performance in other cognitive domains. In other words, can PM research give indications which conditions may be beneficial for other cognitive behavior? As PM research showed that compensation has a strong impact in real life, one could expect that those effects on other domains of cognitive behavior may become particularly evident in real-world situations. For example, older adults are good partners for a conversation because they do not interrupt their opposite (or only very rarely). Hence, in those situations they are able to successfully inhibit upcoming tendencies to interrupt their opposite (e.g., a tendency to immediately tell their own thoughts while the other is still speaking). One could argue that this may be due to their high motivation to listen what the other is saying or due to personality traits such as their politeness or agreeableness. In fact, this example shows that although older adults usually have an inhibition deficit in the laboratory, they are able to compensate this deficit in certain situations in real life. It is an interesting question what is helpful in those situations to overcome the inhibition deficit. As noted, potential mechanisms may concern motivational aspects or personality dimensions such as politeness.

Another real-world example where older adults are able to compensate cognitive deficits is during shopping. They make a list of things they planned to buy. In fact, this strategy is a memory aid that reduces the amount of information that has to be kept active in memory. This helps to not forget the articles that they want to buy even when the distraction in everyday life is high. Kliegel et al. (2007) showed that older adults can benefit from their real-world experience in errand-planning tasks. This suggests that older adults may know well about their memory deficits and situations in which their memory is vulnerable to detrimental factors such as everyday stress or high distraction. This shows that although older adults usually have a memory deficit in the laboratory, they are able to overcome this deficit in certain situations in everyday life. As noted, a potential mechanism behind the compensation effect may concern the use of reminders such as a shopping list. These two real-world examples in which older adults are able to compensate cognitive deficits might stimulate future research to specify the involved mechanisms in detail. Yet, factors identified in PM research such as motivational aspects, personality traits, and reminder use may be potentially relevant also in this context.

As outlined, recent findings from laboratory PM research indicate that such aspects could be also beneficial for older adults when tested in the laboratory. Thus, these factors may also be potentially relevant for other cognitive abilities in the laboratory. For example, in a list recall test (where a list of words has to be learned and recalled), mnemonic strategies such as classifying words into a handful of categories (such as fruits, animals, or furniture) may be helpful to compensate age-related memory deficits. Similarly, for associated recall (where learned pairs of words have to be recalled), generating a “reason” why both words are associated could be helpful. An example would be to image that an elephant (first word) wears a hat (second word). Clearly, compensation in highly demanding tests such as executive functions seems more difficult. However, future research might clarify whether there are conditions which allow the use of compensatory strategies in those tests and whether this

improves the performance. In a further step, the underlying mechanisms may be specified using the task analysis approach (as outlined for PM in Section 7.2.4.1).

Taken together, cognitive aging research can benefit from PM research. Specifically, PM research teaches us that broader theoretical approaches are needed that explicitly consider the interplay of several cognitive and non-cognitive variables as well as moderating factors to fully explain and comprehend the multifaceted effects of aging. An approach that could give helpful directions in this context is to specify the multiple involved processes and their demands on certain cognitive abilities. Beyond that, factors that were identified in naturalistic PM research seem to have also potential to explain individual and age differences in other cognitive domains.

7.2.4.7 Adult age differences in PM and the broader field of cognitive aging - Conclusions