Can laboratory PM research and naturalistic PM research benefit

To better understand adult age differences in prospective remembering, several propositions regarding the underlying mechanisms of adult age differences in both settings were made. Yet, it would be of further relevance to know whether understanding the mechanisms in one setting could give us a better comprehension of the effects in the other one. This means whether research on naturalistic PM can benefit from laboratory findings and whether factors that are identified in the field can provide further important indications for laboratory PM research.

Specifically, findings from Study 1 (see Chapter 4) clearly underline the potential of other factors beyond traditionally examined cognitive variables to explain individual and age differences in prospective remembering. Some of those factors investigated in Study 1 were motivational aspects and rescheduling of intentions. As outlined, a variety of non-cognitive variables may additionally account for individual and age variance in PM. Interestingly, there are already some trends in laboratory PM research to also consider some of those additional factors that have been explored in naturalistic PM research. As these trends could give fruitful directions for future laboratory PM research, some of them are exemplarily discussed in the following.

For example, Altgassen et al. (2010a) manipulated the social importance of a laboratory PM task. 40 younger adults (mean age = 24.73 years) and 40 older adults (mean age = 68.70 years) performed a PM task that had two conditions: Half of the participants received a standard instruction (i.e., they were asked to press a designated key after every two minutes. The other half received a high social importance instruction (i.e., they were asked to do the experimenter a favor by pressing a key after every two minutes). Hence, the PM task was identical in both conditions but with different emphasis on the social importance of this task. Results showed reliable PM age deficits in all conditions. However, in contrast to

younger adults, older adults' PM performance was significantly better in the social importance

condition than the standard instruction condition. Thereby, the PM age deficit was reduced in the social importance condition in comparison to the standard instruction condition (i.e., respective effect sizes -.22 versus -.65), demonstrating the potential of motivational aspects also for laboratory PM.

Moreover, Cuttler and Graf (2007; see Section 7.2.4.3, for a more detailed description of the study) also investigated the relations of personality variables with laboratory PM performance. For this purpose, a PM tasks was implemented in the laboratory testing session.

In addition, cognitive tests (including measures of retrospective memory and executive functions) were assessed. For the PM task, participants were informed that the phone in the laboratory would be unplugged to prevent any disruptions during the testing session.

Participants were asked to remember to remind the experimenter to plug it back in. They were asked to give this reminder immediately after being informed that the testing was completed.

Results showed reliable age deficits in PM performance as well as in the cognitive measures.

Higher values in the cognitive measures were significantly associated with better PM. From the personality variables, higher neuroticism was significantly associated with better PM performance. Neuroticism significantly decreased with age. In addition, a set of hierarchical regression analyses was conducted. These analyses revealed that age was no longer a significant predictor of PM performance when it was entered either after the personality variables or when it was entered after the cognitive abilities. This suggests that PM age differences were attributable to differences in personality variables as well as to differences in cognitive abilities. Moreover, the contribution of the cognitive abilities was no longer

significant when they were entered after the personality variables. On the other hand, the personality variables still significantly predicted PM when they were entered after cognitive abilities. This underlines the potential of personality traits (beyond cognitive resources) for laboratory PM.

In sum, laboratory PM research can benefit from approaches in naturalistic PM research. In other words, factors that were identified in the field seem to have potential

beyond traditionally examined cognitive variables to explain individual and age differences in laboratory PM. An interesting question is why those non-cognitive factors should be at work also in the laboratory when it is suggested that they have their major impact in the field. As noted, certain task features determine the individual impact of the factors in their interplay during the process of prospective remembering. As outlined, in a typical highly standardized laboratory design, the impact of personality traits may be rather limited. In contrast, when the PM task is modified this may increase the impact of those factors. For example, when a PM task is used that involves a highly social component such as reminding the experimenter to plug the phone back in (see Cuttler & Graf, 2007) or to do the experimenter a favor (see Altgassen et al., 2010a), the influence of personality traits and motivational variables on individual and age-related PM is immense.

The finding that in certain situations non-cognitive factors can be further important predictors of laboratory PM reflects the fact that individual and age-related differences in PM are not entirely explained when only cognitive abilities are taken into account. Hence, the question whether a single cognitive ability is sufficient to explain age-related developments in a wide range of cognitive performance can clearly be answered with “no”. At least for PM, reducing the multifaceted effects of aging to a group of cognitive factors is too constricted.

Specifically, the present work shows that although cognitive factors have a great impact on PM performance, especially in the laboratory (e.g., the higher demand on cognitive control processes in specified PM tasks; see Study 2, Chapter 5), non-cognitive factors (such as motivational aspects and the use of compensatory strategies such as reminder use and

rescheduling of intentions; see Study 1, Chapter 4) are further important predictors that should be additionally considered in laboratory PM research to better understand adult age

differences in prospective remembering.

The age benefit in naturalistic PM tasks shows that there are situations in which older adults are able to successfully compensate cognitive age deficits to maintain a good

performance. The fact that cognitive limitations can be compensated is especially relevant from an applied perspective to identify helpful strategies to enhance everyday PM

performance, particularly in older adults. Yet, an important question in this context is whether research on naturalistic PM can benefit from laboratory PM research. One approach from laboratory PM research that could give helpful directions is the attempt to specify the multiple processes involved in prospective remembering and their demands on certain cognitive

abilities. Specifically, the process model of PM (Kliegel et al., 2002, 2011) considers the four phases of intention formation, intention retention, intention initiation, and intention execution.

Each phase places specific demands on several cognitive abilities (see Section 7.2.4.1, for a detailed illustration of the different processes required in prospective remembering).

Considering this model could be helpful to specify under which circumstances compensation is supportive and how exactly those compensatory strategies support prospective

remembering. In other words, to better understand the compensation aspect in naturalistic PM, it is necessary to comprehend how those mechanisms work, unfold, and influence PM

performance in detail.

As outlined, compensation may be supportive in the intention formation phase of PM where older adults are able to maintain a good planning performance (which is an important basis for later success in intention realization. Compensation is also possible regarding intention retention and intention initiation. Such compensation strategies could be the use of reminders (see Study 1, Chapter 4). In addition, if intention execution is difficult or

impossible due to certain circumstances, compensation by rescheduling of intentions (see Study 1) may be supportive in a replanning phase (see Section 7.2.4.3, for a more detailed illustration of these mechanisms). The process approach allows to specify which cognitive deficits in detail can be compensated in which phase of PM. In addition, it would be

interesting to see whether the ability to compensate certain deficits depends on specific factors such as other cognitive abilities, motivational aspects, or external factors such as an

optimal/non-optimal time of day. Moreover, in this context, it could be specified which impact factors have that are known from laboratory PM research such as whether a PM task is time- or event-based, whether it is focal or nonfocal, or whether it contains a specified task order or not. Finally, a thorough investigation of the underlying mechanisms of certain compensation strategies could presumably be best realized with a laboratory experiment (see Section 7.3.1, for an outlook). Then, it could be evaluated whether the identified effects also hold for real life. With this detailed knowledge of the underlying mechanisms, trainings of compensation strategies can be designed that aim to enhance everyday PM performance.

Taken together, naturalistic PM research can also benefit from laboratory PM research.

Specifically, laboratory PM research offers helpful methodology such as the task analysis approach to specify the involved processes, its knowledge of potentially relevant moderating factors, or its methodological approaches to study the details of underlying mechanisms in a controlled testing procedure.

To conclude, the question whether laboratory PM research and naturalistic PM research can benefit from each other can clearly be answered with “yes”. Yet, the present work suggests that they even need to profit from each other: Both trends in PM research have identified factors that are important to understand PM age differences either in the laboratory or the field. As shown, these factors may be potentially at work in both settings. Hence, it is necessary to consider the interplay of numerous cognitive and non-cognitive variables that are known from laboratory and naturalistic PM research to fully explain and comprehend the complexity of adult age differences in prospective remembering.