This study was designed to investigate, in the context of the DMC model , the age-related decline in cognitive control and particularly the decreased tendency to use proactive control strategies in high-interference conditions with aging. More specifically, we were interested in determining whether performance on general cognitive abilities such as fluid intelligence and processing speed might influence the selective age-related decline in proactive control.
The present study replicates previous findings [14, 38, 39] indicative of a selective age-related decline in proactive control, in association with preserved reactive control abilities. Indeed, greater sensitivity to interference was observed for older than younger adults in the high-interference condition of the Sternberg task, which is thought to favor proactive control, but not in the low-interference condition, which is postulated to involve reactive control processes. Therefore, the presence of greater sensitivity to interference (in comparison to young subjects) in a context involving a large number of interfering trials seems to indicate that older adults did not try to anticipate the occurrence of interfering items but rather tended to react following the presentation of these items [[14, 38, 39], for similar results and interpretation].
Conflict monitoring  and executive attention  theories stress the importance of active maintenance of goal-relevant information to achieve a task, particularly during high-interference situations. In line with these theories, the observed selective impairment of proactive control processes might be interpreted as a failure to maintain task-goal representations in a high-interference context see also  or, more generally, as a deficit affecting general context representations, including task-relevant information that could influence cognitive processes involved in the task . In addition, similarly to the study conducted by Bélanger, Belleville and Gauthier , our results revealed an increase in age-related interference sensitivity in high-interference situations but particularly for RTs and not for accuracy of responses. Therefore, age seems to partially affect the ability to maintain task-goal information, since older adults were slowed down by the goal-maintenance requirement but they were still able to succeed on the trials.
In addition, one important goal of this study was to determine whether the decrease in proactive control abilities in healthy aging is related to fluid intelligence level and processing speed, two general cognitive processes known to be affected by age e.g., [49–54]. For instance, Li et al.  observed an age-related decline in fluid intelligence (composite score derived from adapted psychometric tasks used in the Berlin Aging Study [50, 55]) from 40 years old. Decreased ability to efficiently manage conflict costs was also observed from the age of 50 using a flanker task. Interestingly, these authors also revealed a significant relationship between fluid intelligence and conflict costs for their older adults group while no significant influence appeared for younger adults, suggesting that the decline in cognitive resources abilities could impede the tendency to implement the most efficient strategy to perform a task.
Moreover, within the DMC background, the tendency to use proactive control was previously related to the ease or efficacy of active goal maintenance in working memory [13, 21, 56]. Thus, due to the interrelations between working memory and fluid intelligence/processing speed [27, 29, 49, 57], it seemed relevant to predict an influence of these cognitive resources on proactive control abilities. With regard to fluid intelligence, Burgess and Braver  observed that participants with high fluid intelligence were less sensitive to interference than participants with low fluid intelligence in the high-interference condition, in which proactive control is assumed to be involved. With the same kind of task, results obtained in the present study suggest that, when older and younger adults are equated for their level of fluid intelligence, the age effect on the use of proactive control in high-interference condition disappeared. Therefore, the current results are consistent with Burgess and Braver’s  earlier findings and extend the evidence to healthy aging, suggesting that fluid intelligence could influence how proactive and reactive cognitive control processes are implemented according to task requirements in both young and older adults.
Given the age-related decline in processing speed  and its influence on working memory abilities [53, 59], this study also investigated the potential influence of this factor on the age-related decline in proactive control. Similarly to fluid intelligence, processing speed seems to be involved in the tendency to anticipate in a sustained manner the interfering items in a high-interference context. Indeed, when younger and older participants were selected according to their performance on a basic processing speed task, the previously observed selective age-related difference in proactive control disappeared.
In addition, hierarchical linear regression analyses were conducted on RTs to more directly assess the influence of cognitive resources on proactive control abilities. These analyses supported the assumption of an influence of age-related decline in cognitive resources (assessed here simultaneously by fluid intelligence and processing speed) on the tendency to use proactive control strategies in a high-interference condition. Indeed, the results of a first analysis suggest that age explains a significant amount of interference sensitivity variance in the high-interference condition, assumed to favor proactive control. Moreover, this analysis also indicates that cognitive resources account for a large portion of that age-related variance in proactive control. Finally, models including age groups in a first step, followed by cognitive resources, suggested that cognitive resources add explained variance to interference sensitivity in the high-interference condition, which is not captured by age-related variance.
It should be noted that the present study provided an unexpected effect of condition. Indeed, contrary to the prediction of Braver, Gray and Burgess , the high-interference condition seems more difficult for young and older participants. Even if the reason is not clear, since few studies have used the Sternberg paradigm to explore proactive and reactive control processes, this unexpected pattern of results should not impede the exploration of age-related changes in cognitive control because we systematically compared the performance of young and older participants in each condition separately. It seems interesting to note that this pattern of response was not influenced by a more liberal response bias in the high- than in the low- interference condition. Indeed, C indices were calculated according to the signal detection theory  and revealed a significant tendency for “yes” responses that did not differ across age-groups or across control conditions. Moreover, participants that did not show the expected effect in the high-interference condition did not differ on demographic (years of education) or on neuropsychological variables (fluid intelligence, crystallized intelligence, or processing speed). Therefore, cognitive resources factors that may influence cognitive control need further investigation if we want to better understand the variability observed in healthy populations.
Brain imaging data could be very valuable in investigating the implementation of proactive and reactive control strategies in healthy aging according to the availability of cognitive resources. For instance, the DMC model  distinguished two specific patterns of cerebral activity depending on the use of proactive or reactive control strategy. Proactive control was associated with phasic activity in the lateral prefrontal cortex, whereas reactive control involved transient activation, particularly in the anterior cingulate cortex, lateral prefrontal cortex and medial temporal lobe. In accordance with these predictions, Burgess and Braver  observed in young subjects transient increased activity in the lateral prefrontal cortex during the probe period when interference expectancy was low; but sustained bilateral prefrontal cortex activity during the delay period in situations of high expectation of interference. Moreover, the high fluid intelligence group demonstrated increased activation of right lateral prefrontal regions prior to the presentation of the probe (suggesting a proactive control strategy) compared to the low fluid intelligence group, which seemed to preferentially implement reactive control strategies (i.e., probe-triggered activation on interference trials). In addition, Braver et al.  proposed that the observed shift from proactive control to reactive control in aging was also supported by changes in brain activation and neurotransmission patterns, particularly in the prefrontal cortex and in the dopamine system, respectively. Therefore, it seems relevant to hypothesize that age-related prefrontal atrophy and/or intra-cerebral dopamine levels decrease might influence the efficiency in implementing proactive control processes leading to a pattern of brain activity reflecting the involvement of reactive control [see  for such a pattern of results using the AX-CPT task].
At this time, few studies explored the neural substrates of cognitive control in young subjects [7, 61–64] and these studies used various protocols (e.g., Sternberg, Stroop and N-back tasks). Some discrepancies in the neural substrates associated to proactive and reactive control were observed according to the exact characteristics of the tasks (e.g. more or less initial requirement of reactive vs. proactive control) and explored populations (e.g., varying by the level of dopamine availability). However, the adaptation of these paradigms to aging populations should add a substantial value to the comprehension of the implementation of proactive and reactive control processes, as well as to the influence of available cognitive resources. In particular, such studies would improve the understanding of the cognitive factors that could exert an influence on the selective age-related decline in the ability to use proactive control in a high demanding environment.