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McGraw-Hill Companies. Pardo, J. The anterior cingulate cortex mediates processing selection in the Stroop attentional conflict paradigm. Proceedings of the National Academy of Sciences, 87 1 , Qiu, F. Study on improving fluid intelligence through cognitive training system based on Gabor stimulus. Raven, J. Manual for Raven's progressive matrices and vocabulary scales. Search MIT. Search websites, locations, and people. Enter keywords to search for news articles: Submit. Browse By. The rise and fall of cognitive skills.
Neuroscientists find that different parts of the brain work best at different ages. Publication Date :. Press Inquiries. Press Contact : Sarah McDonnell. Phone: Fax: Caption : Researchers have been running large-scale experiments on the Internet, where people of any age can become research subjects.
Their websites feature cognitive tests designed to be completed in just a few minutes. Shown here is a "pattern completion test" inspired by their website, testmybrain. Caption :. Credits :. Older pilots took longer to learn to use the simulators, but performed better than younger pilots at avoiding collisions.
Flow is the mental state of being completely present and fully absorbed in a task Csikszentmihalyi, When in a state of flow, the individual is able to block outside distractions and the mind is fully open to producing.
Additionally, the person is achieving great joy or intellectual satisfaction from the activity and accomplishing a goal. Further, when in a state of flow, the individual is not concerned with extrinsic rewards. Other characteristics of creative people identified by Csikszentmihalyi include curiosity and drive, a value for intellectual endeavors, and an ability to lose our sense of self and feel a part of something greater.
In addition, he believed that the tortured creative person was a myth and that creative people were very happy with their lives. According to Nakamura and Csikszentmihalyi people describe flow as the height of enjoyment.
Accordingly, a series of studies from Braver and colleagues showed a selective age-related impairment in proactive control, whereas the use of a reactive control strategy seems to remain intact [ 14 , 38 , 39 ]. Changes in cognitive control strategy in healthy older adults were notably investigated with the AX-CPT task [ 38 ]; see also [ 14 , 39 ]. The classical AX-CPT paradigm requires participants to respond as quickly and accurately as possible to a specific target occurring after a specific cue.
With this task, Braver et al. Moreover, older adults were less disturbed by the A probe in non-target trials smaller difference in reaction times [RTs] between AY and BY trials than in the young group. These results suggest that older adults showed impairments affecting context representations and updating inasmuch as they had poorer BX performance slower RTs but intact accuracy and better AY performance greater accuracy than younger adults.
Given that reactive control is defined as a transient reactivation of context representations following the occurrence of the probe, it may be assumed that older adults tended to rely on this kind of cognitive control because of their high accuracy on BX trials, which suggests that their access to context representations is spared, and their slower RTs on BX trials, suggesting that these context representations were reactivated when the probe occurred.
Therefore, Braver et al. This assumption seems to be supported by the enhanced age-related performance on AY trials, which could be interpreted as an age-related decline in the use of context representations to anticipate the probe proactive control. Thus, empirical evidence reveals an age-related decline in proactive control abilities. However, to our knowledge, no study has directly tested, in the context of the DMC framework, the hypothesis that the age-related decrease in proactive control abilities could in fact be influenced by the existence of less efficient general cognitive processes such as processing speed and fluid intelligence.
Answering this question should improve our understanding of cognitive control and the variations occurring on these mechanisms with age.
Given the dynamic relationship between cognitive control, fluid intelligence and processing speed, as well as the age-related changes in these cognitive domains, the objective of this study was to explore the possible impact of fluid intelligence level and processing speed on the decline of proactive control abilities in healthy aging.
Using a modified Sternberg paradigm [ 23 ], proactive and reactive control abilities were compared in young and older participants using three approaches: 1 an initial large sample of participants; 2 a subsample of young and older participants matched for fluid intelligence level; and 3 another subsample of young and older participants matched for processing speed.
We hypothesized that a decrease in proactive control abilities would be observed in the initial sample but that no difference in performance would emerge when the influence of any age-related decline in fluid intelligence and processing speed was controlled. All participants had normal or corrected vision and hearing, were native French speakers and none reported any medical, neurological or sensory defects, or use of medication likely to alter cognitive functioning.
The cognitive status of the older participants was checked with the Mattis Dementia Rating Scale [ 40 ] see Table 1. All older participants had a total score equal to or greater than range — , which constitutes the cut-off score to distinguish between healthy aging and dementia [ 41 ].
Participants were tested individually in a quiet, well-lit room. The two conditions of the probe recency Sternberg task were presented on a microcomputer with a inch color monitor using E-Prime software version 1. For the probe recency task, participants were seated in front of the computer screen at approximately 50 cm from the display.
The order of task administration was counterbalanced across participants such that half of the participants performed the high-interference condition of the probe recency task before the low-interference condition and the other half performed first the low-interference condition. In this item non-verbal reasoning test, each item contains a pattern with a missing piece.
The subject has to infer the rules underlying the pattern and apply these rules to discover which of the answer options provides the correct completion. Completion was self-paced. The score obtained is assumed to provide a reliable measure of reasoning and fluid intelligence level. This task requires participants to write, as quickly and accurately as possible, the corresponding numbers in front of symbols with the help of a correspondence table see Table 1.
The score obtained is thought to reveal basic processing speed abilities. Participants were presented series of trials consisting in groups of four consonants target groups. They had to maintain these items in memory for a short retention interval, after which they were given a single probe item and had to decide whether this probe matched one of the items previously presented in the target group. The time course of a trial was as follows see Figure 1 : First, a fixation cross was displayed for ms, followed by the visual presentation of the group of four consonants for ms.
A blank screen was then displayed for ms, followed by the probe letter. The probe letter remained on the screen until the response was given, with a maximum response time allowed of 15 s. Finally, a blank screen was presented again for ms before the beginning of the next trial. Participants had to indicate, as quickly and accurately as possible, by pressing one of two response keys, whether the probe letter was present or not in the four-letter target group of the current trial.
Task conditions of the Sternberg paradigm. The four task conditions determined by the nature of the probe items. Recent negative trials constituted the interfering trials , and recent positive trials the facilitating trials.
Non-recent negative and positive trials represent control trials, used to calculate interference and facilitation effects respectively see below. In the present study, only interference effects will be discussed. To manipulate the recruitment of proactive and reactive control processes, two versions of the probe recency task were created by varying the ratio of recent positive and recent negative trials in each version see Table 2 for items distribution.
Distribution of the probe items in the two parts of the probe recency task Raw number of trials. In order to meet the assumptions of homogeneity of variances and normality, statistical analyses were performed on logarithmic transformed RTs and arcsine transformed accuracy scores.
Nevertheless, for the sake of clarity, figures were created using the means of the raw values. With regard to accuracy, the reverse interference index non-recent negative — recent negative was calculated. Consequently, high scores are indicative of considerable sensitivity to interference for both RTs and accuracy.
First, in order to determine the age-related effect on proactive and reactive cognitive control processes, two repeated measures ANOVAs were performed on RTs and accuracy scores comparing the two groups of participants, with task condition high or low interference level as repeated measure factor.
Planned contrasts were used to test the effect of age in proactive and reactive control. Older adults were expected to have more difficulties to manage interference in the high-interference condition reflecting proactive control than younger adults. However, no significant difference was expected between the two groups in the condition thought to favor the use of reactive control strategy low-interference condition.
In addition, given that a significant difference in educational level was evidenced between young and older adults, the same repeated measures ANOVAs were conducted including educational level as covariate. To investigate the potential influence of age-related differences in cognitive resources on the selective age-related decline in proactive control, two statistical approaches were used.
First, repeated measures ANOVAs were conducted on subgroups within the participant sample that included young and older adults who were matched on the basis of their performance on fluid intelligence and processing speed tasks, respectively. Score ranges were defined to create the largest subgroups of young and older participants demonstrating similar performance in both fluid intelligence and processing speed to avoid significant differences between subsamples.
With regard to the effect of fluid intelligence level, 25 young adults and 25 older adults with Raven scores between 48 and 53 were included. Similarly, for processing speed, 25 young adults Code score between 61 and 89 and 29 older adults Code score between 61 and 93 were considered. The use of matched groups for fluid intelligence abilities on one hand and for processing speed on the other hand should provide a first evidence of the potential impact of cognitive resources on the postulated specific age-related decline in proactive control.
Indeed, if the tendency to use proactive control to deal with interference in high-interference conditions is only sensitive to aging, differences should persist in paired participants. However, if between-group differences disappeared, a potential impact of cognitive resources might be suspected.
Again, planned comparisons were conducted between paired subgroups in the high- and low-interference conditions to observe the potential selectivity of the results in the high-interference condition reflecting proactive control.
Eta squared is generally interpreted as the proportion of variance of the dependent variable that is related to the factor. Traditionally, values of.
Second, the privileged relationship that could exist between proactive control and cognitive resources was more directly investigated in the whole sample of participants with hierarchical linear regression analyses on the performance in the high-interference condition.
In a first step, hierarchical linear regressions were conducted to assess whether the age-related variance in interference sensitivity in the high-interference condition remain significant after partialling out the percentage of variance explained by cognitive resources. Afterwards, hierarchical regression models were constructed to measure variance of performance in the high-interference condition that might be explained by cognitive resources after controlling for age-related variance.
While only interference effects were reported in the present work, for the sake of completeness, raw performance on the whole task was reported in Table 3. A 2 group: young vs. Interference sensitivity in high proactive and low reactive interference conditions for young vs.
A Mean reaction times ms ; B Accuracy proportions. Error bars represent standard errors. Concerning accuracy Figure 2 B , the 2 group: young vs. Finally, two 2 group: young vs. In sum, the analyses of RTs revealed a selective age-related decline in proactive control high-interference condition whereas reactive control abilities seem to be preserved.
However, accuracy analyses evidenced a very small number of errors in both high- and low-interference conditions and did not show any effect of age on interference sensitivity. Therefore, to improve our understanding of cognitive control mechanisms and the effects of age on these processes, it seems relevant to explore whether fluid intelligence level and processing speed might influence this age-related decrease in continuous management of interference.
Due to the absence of an age-related decline in cognitive control in terms of accuracy, only RTs were considered in the subsequent analyses. To examine the potential influence of fluid intelligence on proactive control abilities in aging, subgroups were formed within the participant sample.
Demographic data and cognitive assessment in subgroups of participants matched for fluid intelligence and processing speed. As Table 1 shows, young adults performed significantly better than older adults on the Code task, confirming the postulated age-related decline in processing speed.
Separate ANOVAs were performed on the subgroups matched for fluid intelligence level and processing speed. Indeed, in the case of a specific effect of aging, the age-related difference in proactive control should remain significant.
A 2 paired subgroup: young vs. Mean reaction times ms ; Error bars represent standard errors. In order to more directly test the influence of cognitive resources fluid intelligence and processing speed on the observed age-related decline in proactive control, two hierarchical linear regression analyses were performed on RTs in the high-interference condition.
First, hierarchical regressions were conducted to assess whether the age-related variance in interference sensitivity in the high-interference condition remain significant after controlling for variance explained by cognitive resources Models A.
Results are summarized in Table 7 A. The model 1A provides a simple linear regression assessing the amount of variance in the high-interference condition that could be attributed to age.
In order to verify whether the contribution of age might be reduced to a non-significant account after controlling for cognitive resources-related variance in the high-interference condition, the model 2A includes fluid intelligence and processing speed performance as cognitive resources predictor.
A Results from the hierarchical linear regressions performed on the high-interference condition to investigate the age-related influence on interference sensitivity after controlling for cognitive resources. B Results from the hierarchical linear regressions performed on the high-interference condition to investigate the cognitive resources-related influence on interference sensitivity after controlling for age.
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