Introduction
The increase of information we manage every day is changing the way our cognitive system interacts with the environment, requiring us increasing multitasking abilities, and impacting on our psychological well-being. The continuous spreading of our attention to multiple types of stimuli might clash with the efficiency with which our cognitive system processes the information needed to accomplish everyday requests. So, how can we control the environment, rather than being controlled by it?
Adaptive cognitive control (ACC) is the set of mental tools allowing to adapt behavior to our goals, and it is predictive of well-being, quality of life, academic and work success, couple harmony [1]. It includes core cognitive components such as selective attention , interference control , task switching , response inhibition , self-control [2,3], and it builds on the ability to implicitly generate, and flexibly update internal predictive models, based on bottom-up contextual changes.
Although there are available tools for ACC assessment [4–6], to date there is no comprehensive protocol assessing it as a context-dependent associative learning process. Despite its crucial role, it is not clear yet: i) how does ACC change across the adult life-span (between 20 and 86 years of age), and ii) which are the constructs/factors able to account for individual differences in the efficiency of ACC?
To answer these questions, we developed a novel assessment battery, by combining extant ACC tasks (e.g., [4–6]) with an induced context-dependent associative learning, by means of a covert, block-wise probabilistic manipulation of stimulus-stimulus or stimulus-response contingencies. This innovative logic induces participants to extract implicit changes in stimuli regularities, and to adjust their behavior consequently. Through the induction of context-dependent associative learning, it becomes possible to assess behavior modulation as a function of global and local dynamic changes in ACC demands. The individual responses to global and local manipulation will thus allow us to dynamically study ACC as a function of contextual demands, to track ACC changes throughout the life-span, and to investigate, for the first time, ACC relationship with personality factors (e.g., introversion, intolerance of uncertainty, impulsivity) and core aspects of cognitive functioning (e.g., working memory –WM–, cognitive reserve –CR).
Methodology
The protocol includes three ACC tasks, two tasks assessing core cognitive abilities, and five questionnaires. All tasks include visual stimuli and are anticipated by a block of practice.
The ACC tasks assess the following components:
- Global/local perceptual processing . This task, derived from Navon’s paradigm [6], involves the presentation of compound stimuli: a large letter (global level) composed of smaller letters (local level), which could be the same (congruent) or different (incongruent) as the large ones.
- Conflict control . This task, derived from the Attentional Network Test [4], requires participants to judge the direction of a central arrow flanked by arrows pointing in the same (congruent) or different (incongruent) direction.
- Self-control . In this adaptation of the Balloon Analogue Risk Task [5], participants have to press a button to progressively inflate a balloon, obtaining a certain amount of money each time. After each push they can stop (and cash the money), or go on (to get more), risking to lose all the money if the balloon explodes. Each balloon color is covertly associated to a short- or wide-range of allowable pushes before it explodes, so that one is advantageous and the other is risky.
As a main novelty element, in all the ACC tasks the trial proportion is list-wise [7], and varied between a ‘predictable’ (70/30% incongruent/congruent ratio) and ‘unpredictable’ (30/70% ratio) condition. This manipulation allows a direct comparison of responses across tasks. Furthermore, all tasks will include a final transfer block, with a re-set 50% proportion congruency, in order to test whether ACC translates to unbiased contexts. Behavioral performance is measured in terms of response times (RT) and accuracy, and it is possible to derive both global and local measures of ACC components. The estimated duration of ACC tasks is about 10 minutes each.
The cognitive tasks assess:
- WM , as measured by the N-back task [7], in which participants are presented with a stream of stimuli, and for each stimulus they have to decide whether it matches the one presented N items before.
- CR , as assessed by means of proxy measures such as education, and occupational attainment [8].
The estimated length of cognitive tasks is about 5 minutes each.
Furthermore, we measure the following personality traits and mood states:
- Big Five personality dimensions , as assessed through the 10-item Big Five Inventory [9].
- Intolerance of Uncertainty , measured through the Intolerance of Uncertainty Scale [10].
- Impulsiveness , as measured through the Barratt Impulsiveness Scale [11].
- Mood states , reflected by the presence of depressive/anxious symptoms, as measured with Beck Depression [12] and Anxiety [13] Inventories in adults participants, or with Geriatric Depression Scale [14] in elderly participants.
Finally, participants fill a brief debriefing questionnaire assessing motivation, overall interest towards the tasks, and explicit awareness of the main experimental manipulation (i.e., congruent/incongruent trial proportion).
The estimated completion time for questionnaires is about 5 minutes each.
Ethical approval
The protocol will be submitted to the Ethics Committee evaluation of the University of Padua.
Sample size
According to a priori power analysis (α = .05, power = 80%, effect size ( f ) = .1) the target sample size is 787. Anticipating 25% exclusions based on a priori-defined criteria, we plan to recruit a larger sample of 1000 participants, aged between 20 and 86.
Study cost
With 1000 subjects, £6.66/hour payment, and 90-min completion length the estimated study cost is £10,000.
Pre-registration
The experimental design, exclusion criteria, exploratory hypotheses, and analysis plan will be pre-registered on the Open Science Framework before data collection. We plan to manage potential confounding variables, as well as individual variability, by considering them as random factors in mixed-effects models.
Open Science Commitment
The protocol, the full dataset, and the analysis syntax will be made available upon publication on Open Science Framework repository.
References
|1. |Moffitt TE, Arseneault L, Belsky D, Dickson N, Hancox RJ, Harrington H, et al. A gradient of childhood self-control predicts health, wealth, and public safety. Proc Natl Acad Sci. 15 febbraio 2011;108(7):2693–8. |
|2. |Diamond A. Executive Functions. Annu Rev Psychol. 2013;64(1):135–68. |
|3. |Braver TS. The variable nature of cognitive control: a dual mechanisms framework. Trends Cogn Sci. 1 febbraio 2012;16(2):106–13. |
|4. |Fan J, McCandliss BD, Sommer T, Raz A, Posner MI. Testing the Efficiency and Independence of Attentional Networks. J Cogn Neurosci. 1 aprile 2002;14(3):340–7. |
|5. |Lejuez CW, Read JP, Kahler CW, Richards JB, Ramsey SE, Stuart GL, et al. Evaluation of a behavioral measure of risk taking: The Balloon Analogue Risk Task (BART). J Exp Psychol Appl. 2002;8(2):75–84. |
|6. |Navon D. Forest before trees: The precedence of global features in visual perception. Cognit Psychol. 1 luglio 1977;9(3):353–83. |
|7. |Jaeggi SM, Buschkuehl M, Perrig WJ, Meier B. The concurrent validity of the N-back task as a working memory measure. Memory. 1 maggio 2010;18(4):394–412. |
|8. |Opdebeeck C, Quinn C, Nelis SM, Clare L. Does cognitive reserve moderate the association between mood and cognition? A systematic review. Rev Clin Gerontol. agosto 2015;25(3):181–93. |
|9. |Rammstedt B, John OP. Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. J Res Personal. 1 febbraio 2007;41(1):203–12. |
|10. |Carleton RN, Norton MAPJ, Asmundson GJG. Fearing the unknown: A short version of the Intolerance of Uncertainty Scale. J Anxiety Disord. 2007;21(1):105–17. |
|11. |Patton JH, Stanford MS, Barratt ES. Factor structure of the barratt impulsiveness scale. J Clin Psychol. 1995;51(6):768–74. |
|12. |Beck AT, Steer RA, Brown G. Beck Depression Inventory, 2nd ed. San Antonio, TX, USA: The Psychological Corporation; 1996. |
|13. |Beck AT, Steer RA. Beck Anxiety Inventory Manual. San Antonio, TX, USA: The Psychological Corporation Harcourt Brace & Company; 1993. |
|14. |Yesavage JA, Brink TL, Rose TL, Lum O, Huang V, Adey M, et al. Development and validation of a geriatric depression screening scale: A preliminary report. J Psychiatr Res. 1 gennaio 1982;17(1):37–49.|