آزمایش محدودیت های انعطاف پذیری شناختی در افراد مسن: برنامه ای برای کنترل توجه

Abstract Laboratory based training studies suggest that older adults can benefit from training in tasks that tap control aspects of attention. This was further explored in the present study in which older and younger adults completed an adaptive and individualized dual-task training program. The testing-the-limits approach was used [Lindenberger, U., & Baltes, P. B. (1995). Testing-the-limits and experimental simulation: Two methods to explicate the role of learning in development. Human Development, 38, 349–360.] in order to gain insight into how attentional control can be improved in older adults. Results indicated substantial improvement in overlapping task performance in both younger and older participants suggesting the availability of cognitive plasticity in both age groups. Improvement was equivalent among age groups in response speed and performance variability but larger in response accuracy for older adults. The results suggest that time-sharing skills can be substantially improved in older adults.

Introduction In the past few years a number of laboratory-based cognitive interventions have been designed in an attempt to improve specific aspects of cognitive functioning in seniors (see Kramer & Willis, 2003 for a recent review of this literature). In most cases, older and younger adults participated in extensive practice with laboratory-based paradigms that have been used to identify age-related deficits in memory, attention, problem solving, etc. A variety of results have been observed in these training studies. For example, in some studies, older and younger adults showed similar patterns of training benefits. This has been shown for instance in visual-search tasks in which participants must find a target among visual distractors. Both older and younger adults learned to perform visual search tasks at the same rate and both age groups achieved automatized search with extensive practice (Ho and Scialfa, 2002 and Scialfa et al., 2000). However, other studies have shown that younger adults, but not older adults, achieve automatized search in tasks that combine visual and memory search (Rogers, 1992 and Rogers et al., 1994). In the memory domain, training programs using different mnemonic techniques have shown positive results in older adults, which suggests that they can benefit from memory training. However, the improvements are typically larger in younger adults (see Verhaeghen, Marcoen, & Goossens, 1992). Interestingly, some studies have also reported larger training benefits for older than for younger adults. For example, a larger improvement in the performance of older compared to younger adults has been reported in a study involving extensive practice in multiple memory-search tasks (Baron & Mattila, 1989) and in dual-task performance (Kramer, Larish, Weber, & Bardell, 1999). A larger benefit of training in older than younger adults has also been observed in a task that requires preparing for a motor response (Bherer & Belleville, 2004). An interesting feature of these studies was the use of feedback and/or instruction conditions intended to assist the participants in developing effective strategies to better perform and coordinate the tasks. Providing participants with active feedback to encourage the development of effective strategies might be important for older adults to develop greater cognitive skills over the course of training. Indeed, this would appear to be particularly important given previous demonstrations of age-related deficits in metacognitive skills such as self-monitoring and information management (Dunlosky et al., 2003 and Murphy et al., 1987). Although this hypothesis is appealing, further studies are needed to disentangle the effect of the training protocol compared to mere practice on cognitive functioning in older adults. Together the studies reviewed above clearly indicate that older adults can learn new skills. Thus, latent cognitive potential (i.e., cognitive reserve) exists even in old age and laboratory-based cognitive training may be an effective approach to develop this potential. However, given the small number of behavioral intervention and cognitive training studies that have been reported, the differences between the methodologies employed, and the fact that not all studies have produced positive results, conclusions remain speculative as to how cognitive vitality can be improved and maintained in old age. Many open questions remain with regard to the potential benefit of cognitive and behavioral interventions: (a) What are the determinant factors of an efficient cognitive stimulation program? (b) What are the limits of cognitive reserve, or what is the range of cognitive plasticity and how and when is it reduced during aging? (c) Does the range of cognitive reserve vary among cognitive processes or domains (see Baltes & Kliegl, 1992)? Of course, further empirical studies would help to provide answers to these questions. Moreover, the use of a theoretical framework would also be of great value to categorize the existing findings and, perhaps more importantly, to predict the direction of cognitive change with regards to the type of intervention provided and the cognitive functions targeted. One insightful way to examine an individual’s latent potential or range of cognitive reserve is the testing-the-limits approach (Kliegl, Smith, & Baltes, 1989). The rationale of this approach is that detailed analyses of time compressed stimulating experiences will provide valuable information on the developmental mechanisms and range of medium and long-term developmental changes (Lindenberger & Baltes, 1995). The testing-the-limits approach aims to establish the boundaries of potential development or range of cognitive plasticity. To do so, cognitive performance is assessed under three conditions. First, baseline level of cognitive performance is assessed under standardized conditions. Then, performance is assessed in optimized conditions, designed to maximize motivation and performance, in order to measure baseline reserve, which refers to the current maximum potential of cognitive performance that can be achieved under idealized conditions ( Kliegl et al., 1989). Finally, performance is assessed following cognitive training under optimized conditions as used to measure baseline reserve, in order to measure the maximum cognitive plasticity, or maximum latent potential of an individual. This is referred to as the developmental reserve. The testing-the-limits approach has been proposed to approximate the limits of developmental capacity and as such, as an efficient way to obtain a detailed picture of an individual’s potential under “idealized” experiential conditions ( Lindenberger & Baltes, 1995). Baltes and colleagues have argued that this approach can lead to identification of genuine age-related cognitive decline, rather than overestimate age-related differences due to unpracticed or non-optimized conditions of testing, assuming that age-related differences in reserve capacity are more accurately assessed near the limits of performance. Application of the testing-the-limits approach to the memory domain (Baltes and Kliegl, 1992 and Kliegl et al., 1989), using an intervention program with the Method of Loci to improve memory performance (this mnemonic strategy relies on the association of the to-be-remembered words to different well-known locations), indicated that both older and younger adults show cognitive reserve. However, the improvement was smaller in seniors than it was in young participants, suggesting reduced cognitive plasticity in older adults. The robustness of this finding led the authors to conclude that it expressed a fundamental neurobiological limit due to the aging process. Baltes and Kliegl (1992) also discussed their results in terms of cognitive domains, arguing that the reduced cognitive reserve in older adults may involve fluid intelligence, or mechanical aspects of cognition, sometimes referred to as process-based or control functions, and that this finding may not generalize to other cognitive domains. Although it is true that the memory processes assessed by the authors can be considered as mechanical aspects of cognitive functioning, the limited use of the testing-the-limits approach in the memory domain reduces the potential generalizability of this finding, even within the broad domain of fluid intelligence. The goal of the present study is to assess potential cognitive plasticity in controlled attentional processes through the testing-the-limits approach. It has been frequently suggested that attentional control processes are particularly sensitive to age and that this may be related (McDowd & Shaw, 2000) to the substantial modifications observed in the frontal and prefrontal areas of the cerebral cortex during aging (Raz, 2000). Older adults’ difficulty in performing concurrent tasks is one of the most well documented executive control deficits in the cognitive aging literature (Hartley, 1992, Kramer and Larish, 1996 and McDowd and Shaw, 2000). In the past few years, an increasing number of studies have used the Psychological Refractory Period (PRP) paradigm to investigate age-related deficits in overlapping task performance (Allen et al., 2002, Glass et al., 2000, Hartley, 2001 and Hartley and Little, 1999). Typically this paradigm involves the performance of simple tasks with different stimulus onset asynchronies (SOA) (e.g., identifying a letter presented on a computer screen and discriminating between a high or low tone). The increased reaction time in the second task with decreasing SOA between the two tasks is used as a measure of dual-task costs. This measure along with the systematic manipulation of different task parameters has been employed to identify the cognitive processes that serve as the source of processing bottlenecks in dual-task performance (Pashler & Johnston, 1998). Studies with older adults performing PRP tasks have led to diverging results with respect to the nature and source of age-related differences in dual-task costs. For instance, Hartley and Little (1999) reported larger dual-task costs in older adults compared to younger adults only when the two tasks required manual responses (see also Hartley, 2001). As a result of these findings, Hartley concluded that the age-related deficit observed in dual-tasks is localized to response generation processes. Glass et al. (2000) also reported larger dual-task costs (greater PRP effects) in older adults but concluded that the observed age-related performance deficit has three sources: general slowing, process-specific slowing and the use of a more cautious task-coordination strategy. However, Allen et al. (2002) observed equivalent magnitude PRP effects for younger and older adults even when the two tasks required manual responses. They concluded that parallel processing that enables efficient dual-task performance is relatively age-invariant, at least in some conditions. It thus seems that the source of age-related difference in dual-task performance could be linked to both, task-coordination strategies (Glass et al., 2000) and parallel processing (Allen et al., 2002). Moreover, both appear to develop as a result of training. Kramer et al. (1999) showed improved task-coordination strategies in dual tasks, and Allen et al. (2002) reported evidence of parallel processing with practice. However, in a recent study, Maquestiaux, Hartley, and Bertsch (2004) observed that extensive practice did not allow parallel execution of two concurrent tasks in a PRP paradigm. It is thus possible that practice alone does not favor the development of efficient dual-task performance strategies. Indeed, such strategies may only develop when subjects are explicitly trained, through individualized adaptive feedback and task prioritization instructions, to concurrently perform multiple tasks (Kramer et al., 1995 and Kramer et al., 1999). Thus the source of age-related differences in dual task performance remains unclear. Although the extensive research of Hartley, 2001 and Hartley and Little, 1999 suggests that older adults often show larger dual-task deficits when both tasks require manual responses, exceptions have been noted (Allen et al., 2002), which suggests that older adults’ dual-task deficits in some conditions could be partly explained by age-related differences in task coordination strategies (Glass et al., 2000). Moreover, Glass et al.’s (2000) proposal suggests that inducing efficient task-coordination strategies combined with practice may reduce age-related deficits in dual-task performance. In other words, using an efficient task-coordination strategy along with sufficient practice should help older adults to perform concurrent tasks. Rephrased in the testing-the-limits terminology described previously, age-related difficulty in performing concurrent tasks may be reduced near the limits of optimal performance (see also Kramer et al., 1999). The present study investigates dual-task performance skills in older and younger adult participants in an experimental protocol that enables the assessment of the three levels of cognitive performance identified in the testing-the limits approach. Dual-task performance was assessed at the baseline level of performance, the baseline reserve (or the current level of latent potential) and the developmental reserve (or the maximum level of cognitive plasticity). This approach has the potential to elucidate the source(s) of age-related differences in the ability to coordinate the performance of multiple tasks and also to extend the application of the testing-the-limits methodology to other cognitive processes and abilities. Recent studies suggest that age-related difference in executive control also lead to increased performance variability in older adults. In a recent report, West, Murphy, Armilio, Craik, and Stuss (2002) looked at different measures of performance variability in older and younger adults and observed that both between-person variability (diversity) and within-individual variability (dispersion) are greater in older individuals in tasks that put heavy demand on executive control. Moreover, while diversity was larger in older adults at initial testing only (at the 1st of 4 sessions) in the executive condition, age-related differences in within-person variability persisted despite four days of testing. In the context of cognitive training for attentional control, it is of interest to assess whether training in the testing-the-limits conditions will lead to reduced within person variability. In the present study, we explored age-related differences in between-person variability and within-person variability in the context of dual-task training. To our knowledge, the impact of training on response variability in older compared to younger adults has never been assessed within the context of the testing-the-limits approach.

. Results The dependent variables of interest were mean response time (RT) and accuracy and measures of response time variability. Incorrect responses were not included in the analyses and trials were also rejected if RT was longer than 3000 ms or shorter than 100 ms. Statistical analyses of the data were performed with SPSS (SPSS Inc.), which provides adjusted alpha levels (Greenhouse-Geisser) for within-subject factors. An effect is reported significant here according to the adjusted alpha level when required, that is when Mauchly’s test of sphericity was significant (SPSS, 1997). Effect sizes (η2) are also reported. 3.1. Mean reaction time Fig. 2 shows the mean RT of older and younger adults in both the auditory and the visual discrimination tasks during experimental blocks. Mean RTs are shown for the single pure trials, the single-mixed trials performed within the mixed block and the dual-mixed trials. Clearly, in both age groups RT became shorter from the baseline level of performance (i.e., the pre-training block) to the first session of training (baseline reserve), and improved again over the course of training (developmental reserve). The improvement in response speed appeared equivalent in both age groups, in both tasks and in single- and dual-task trials. Mean reaction time (ms) for older and younger adults as a function of conditions ... Fig. 2. Mean reaction time (ms) for older and younger adults as a function of conditions of testing (i.e., baseline level, baseline reserve and developmental reserve). Performance is shown for the three trial types; single-pure, single-mixed and dual-mixed trials. Figure options A mixed-design ANOVA was performed with Age group as the between subject factor and Task (auditory and visual), Session (baseline performance, baseline reserve, developmental reserve) and Trial type (single-pure, single-mixed and dual-mixed) as within subjects factors. The analyses indicated that older adults produced slower responses overall (934 ms) compared to younger adults (634 ms) as indicated by a main group difference, F(1, 22) = 22.19, p < .001, η2 = .50. Moreover, a significant main effect of Trial type was observed, F(2, 44) = 143.55, p < .001, η2 = .87. In fact, repeated-contrasts, which provide a comparison of RT difference between two consecutive levels of a repeated measure ( SPSS, 1997) indicated that all participants responded faster in the single-pure trials (522 ms) compared to the single-mixed trials (811 ms), F(1, 22) = 103.76, p < .001, η2 = .83, and faster in the single-mixed compared to the dual-mixed trials (1019 ms), F(1, 22) = 148.95, p < .001, η2 = .87. The main effect of training session was also significant, F(2, 44) = 63.21, p < .001, η2 = .74. Overall performance improved significantly from the baseline assessment (909 ms) to baseline reserve (794 ms), F(1, 22) = 27.89, p < .001, η2 = .56, and even more so from the baseline reserve to developmental reserve (649 ms), F(1, 22) = 53.98, p < .001, η2 = .71. Two interactions were significant. The Age × Trial type interaction, F(2, 44) = 7.72, p < .001, η2 = .26, was significant which suggests that the RT difference across trial types varied between age groups. The difference between groups across trial types can be localized by computing task-set cost and dual-task cost shown in Fig. 3. Mean task-set cost and dual-task cost in older and younger adults as a function ... Fig. 3. Mean task-set cost and dual-task cost in older and younger adults as a function of conditions of testing (i.e., baseline level, baseline reserve and developmental reserve). Figure options ANOVAs performed on these scores with Group as between subject factor and Task and Session as within subject factors, indicated that both task-set cost, F(1, 22) = 7.50, p < .01, η2 = .25, and dual-task cost, F(1, 22) = 4.33, p < .05, η2 = .17, were larger in older compared to younger participants. However, further analyses indicated that the Age × Trial type interaction was no longer significant once baseline speed of responses was controlled for, which suggests that age-related general slowing largely accounts for the group difference in task-set and dual-task costs in the present study. 1 The Session × Trial type interaction was also significant, F(4, 88) = 27.63, p < .001, η2 = .56, indicating that improvement across sessions differed among task-set and dual-task costs (see Fig. 3). In fact, results from the ANOVA performed on task-set cost indicated that although it tended to improve from baseline to baseline reserve, F(1, 22) = 3.71, p = .067, η2 = .14, significant improvement in task-set cost was significant only from baseline reserve to developmental reserve, F(1, 22) = 20.30, p < .001, η2 = .48. However, dual-task cost significantly improved from baseline to baseline reserve, F(1, 22) = 10.47, p < .01, η2 = .32, and again from baseline reserve to developmental reserve, F(1, 22) = 7.02, p < .02, η2 = .240. Finally, it is important to emphasize that no interaction involving Age group was observed, which suggests that older adults and younger adults showed the same pattern and the same magnitude of improvement over the course of training and that the differential improvement between task-set and dual-task costs was equivalent in both age groups. 3.2. Accuracy Percentages of correct answers are shown in Fig. 4. Overall, older adults (86%) produced fewer accurate responses than young adults (95%), as indicated by a group main effect, F(1, 22) = 13.54, p < .001, η2 = .38. A main effect of session indicated that in both groups accuracy improved through training, F(2, 44) = 3.18, p < .05, η2 = .13. Moreover, the effect of trial type reached significance, F(2, 44) = 7.57, p < .001, η2 = .26. This was due to a significant decrease in accuracy from single-pure to single-mixed trials (significant task-set cost), F(1, 22) = 12.16, p < .01, η2 = .36. Although, this effect seems larger in older adults, the group difference in task-set cost was not significant, F(1, 22) = 3.47, p = .076, η2 = .14. Two interactions were significant. The Group × Session interaction was significant, F(2, 44) = 3.57, p < .05, η2 = .13. Repeated-contrasts indicated no age group difference in improvement between baseline and baseline reserve, F(1, 22) < 1. However, older adults benefited from training to a greater extent than younger adults, from baseline reserve to developmental reserve, F(1, 22) = 4.57, p < .05, η2 = .17. Moreover, and as observed with RT data, the Session × Trial type interaction was significant, F(4, 88) = 3.16, p < .05, η2 = .13. This was due to a larger improvement in accuracy from the baseline reserve to developmental reserve in single-mixed compared to single-pure trial (improvement in task-set cost), F(1, 22) = 4.50, p < .05, η2 = .17, which was not observed when single-mixed trials are compared to dual-mixed trials (no change in dual-task cost in accuracy). Percentages of correct responses for older and younger adults as a function of ... Fig. 4. Percentages of correct responses for older and younger adults as a function of conditions of testing (i.e., baseline level, baseline reserve and developmental reserve). Performance is shown for the three trial types; single-pure, single-mixed and dual-mixed trials. Figure options 3.3. Between and within persons variability To assess whether between-person variability was larger in older than younger adults and if it changed with training, we computed a Coefficient of Variability (see Table 2). This measure was preferred over standard deviation (SD) since it offers the advantage of taking into account the absolute mean (see Morse, 1993). This is important in the context of cognitive aging since in RT tasks slower responders often produce larger SDs. Thus, a larger SD could be the product of general slowing. Indeed, West et al. (2002) reported that in some studies an age-related difference in variability was explained by general slowing. In the present study, the group CV was computed using group SD deviation and group mean (CV = SD/M ∗ 100) in the eighteen experimental conditions (2 Task × 3 Trial types × 3 Sessions) for both older and younger adults. As can be seen in Table 2, the results indicated that between-person variability was larger overall in older (24) than younger (19) adults. An ANOVA comparing CV of older and younger adults in the 18 experimental conditions showed that the group difference was significant, F(1, 34) = 4.25, p < .05, η2 = .22. We then assessed change in between-person variability as a function of trial types and session, separately for both older and younger adults. In both groups, variability depended of trial types, F(2, 15) = 5.65, p < .01, η2 = .43, in older adults and, F(2, 15) = 13.3, p < .001, η2 = .64, in younger adults. Respectively for single-pure, single-mixed and dual-mixed trials, mean CV were 16, 29, 27 in older adults and 14, 20, 24 in younger adults. Repeated contrasts indicated that between-subjects variability was smaller in single-pure trials compared to single-mixed trials (p < .05, for both groups), whereas CV did not differ between single- and dual-task trials within the mixed block. Moreover, CV did not change as a function of training in both older and younger adults since there was no significant difference as a function of session. Table 2. Group Coefficient of Variability (CV = SD/mean ∗ 100) and Individual Coefficient of Variability (ICV = ISD/mean ∗ 100) as a function of trial types and sessions for both older and younger adults Group Trial types Baseline level Baseline reserve Developmental reserve Group Coefficient of Variability (CV) Older Single-pure 16.31 16.23 16.32 Single-mixed 26.20 30.74 28.98 Dual-mixed 23.87 27.40 30.36 Younger Single-pure 16.86 13.04 11.27 Single-mixed 20.18 22.22 17.93 Dual-mixed 22.44 26.87 21.31 Individual Coefficient of Variability (ICV) Older Single-pure 38.17 40.87 30.83 Single-mixed 36.68 32.22 28.54 Dual-mixed 33.72 34.40 35.48 Younger Single-pure 29.81 29.27 25.06 Single-mixed 30.42 26.85 23.37 Dual-mixed 35.36 35.91 31.32 In the absence of performance variability between task, data were pooled for the auditory and the visual tasks. Table options Within-participants variability was measured by computing individual coefficients of variability (ICV = ISD/individual mean ∗ 100), using individual standard deviation (ISD) computed for each participant in each experimental condition of interest. Table 2 shows the ICVs in each training session (Baseline performance, Baseline reserve, Developmental reserve) and for each trial types (single pure and mixed trials, and dual-mixed trials). An ANOVA performed on these data indicated that within-participant variability is larger in older adults (35) compared to younger adults (30), F(1, 22) = 6.54, p < .02, η2 = .23. Moreover, within-subject variability decreased with training as indicated by a main effect of session, F(2, 44) = 11.10, p < .001, η2 = .34. The improvement from Baseline performance (34) to Baseline reserve (33) was not significant, F(1, 22) < 1, whereas improvement from Baseline reserve to Developmental reserve was significant, F(1, 22) = 15.96, p < .001, η2 = .42, reaching 29 in the last training session. A main effect of trial type, F(2, 44) = 7.53, p < .01, η2 = .26, showed that ICV differed according to trial types. In fact ICV was larger in dual-mixed trials (34) compared to single-mixed trials (30). The Group × Trial type interaction, F(2, 44) = 5.94, p < .01, η2 = .21, was significant. This was due to a larger increase in ICV between dual-mixed and single-mixed trials in younger compared to older adults, F(1, 22) = 6.17, p < .02, η2 = .22.