Skip to main content
NCBI home page
Dementia and Geriatric Cognitive Disorders EXTRA logoLink to Dementia and Geriatric Cognitive Disorders EXTRA
. 2022 Nov 14;12(3):131–149. doi: 10.1159/000526226

Comparing the Working Memory Capacity with Cognitive Flexibility, Cognitive Emotion Regulation, and Learning Styles of University Students: A Domain General View

Kahraman Guler a,*, Aylin Aydin b
PMCID: PMC9710454  PMID: 36465839

Abstract

Introduction

The goal of this study was to investigate the relationship of working memory capacity with the use of cognitive emotion regulation strategies, cognitive flexibility level, and learning styles of university students.

Methods

In the present study (N = 39), the participants completed the Emotion Regulation Questionnaire, Cognitive Flexibility Scale, Kolb Learning Styles Inventory, and Vermunt Learning Styles Inventory and three WM capacity (WMC) tasks that are Rotation Span Task, Operation Span Task, and Symmetry Span Task. Their WMCs were assessed, and the relationship of it was compared with cognitive emotion regulation, cognitive flexibility, and learning styles.

Results

The results indicated that there is a significant difference and negative correlation (r= −0.341) between Operation Span Task and refocus on planning. Findings of the research indicated correlations between emotion regulation strategies and between cognitive flexibility and two emotion regulation strategies that are refocus on planning (r = 0.528) and positive reappraisal (r = 0.574). Only one learning style that is Processing Strategies in Vermunt Learning Style Inventory was found significantly different in terms of the cognitive flexibility level of the participants. The results also indicated a positive correlation between verbal and spatial WM tasks which support the domain general view for WMC.

Conclusions

Further studies are advised to be conducted between cognitive emotion regulation strategies and working memory capacity as these findings may have significant implications for understanding the correlation between memory and emotion.

Keywords: Working memory capacity, Cognitive emotion regulation, Cognitive flexibility, Learning styles

Introduction

The tendency of developing strategies for managing less familiar situations is a human-specific feature that can be capsuled within executive function set in prefrontal cortex. The learning environment of individuals is one of those places in which unfamiliar or less familiar information is most likely to encounter, and both executive functions and strategies are necessitated. Strategies for managing cognitive tasks can be needed in memory for recalling the necessary information and using it even when it is manipulated and also in cognitive emotion regulation for regulating negative emotions as in reappraisal. The necessity for using strategies is also of great importance during the encoding of information, internalization, and learning of it. The common grounds of all these different components necessary for managing cognitive tasks are that they are related to executive functions. WM capacity (WMC), for instance, refers to the manipulation of information during cognitive activity [1], and it, as an intellectual functioning, refers to a limited capacity in which necessary information are temporarily stored and manipulated at the same time [2]. A large body of research has focused on the WMC of individuals, and recent research in this vein suggests that individuals with high WMC are more accurate in cognitive tasks [3], and it is related with fluid intelligence [4], information compression [5], attention [6, 7], cognitive flexibility [8, 9] all of which are executive functions of human cognition.

The WMC and Cognitive Emotion Regulation

According to McRae and Gross [10], emotion regulation is required when people feel the need for using strategies against discrepancies between their emotional state and realities. Managed unconsciously, cognitive emotion regulation is an ability to balance emotions of the limbic system at the cognitive level which is enacted by the prefrontal cortex. Studies indicate that people who are high in WMC tend to use more emotion regulation strategies [11], and WM training improves emotion regulation ability [12, 13, 14]. The relationship between these two components must be a subject to more studies as these studies indicated.

The WMC and Cognitive Flexibility

Cognitive flexibility, the ability to engage in two or more cognitive processes simultaneously, is an important executive function of human cognition [15]. This ability can be associated with complex cognitive functions which are possible to decrease with aging and is highly associated with the use of strategies from the cognitive perspective [16]. WMC and cognitive flexibility were found to predict creative thinking processes [17], and studies comparing these two components of executive functions can be conducted more in order to understand the relation between them in the cognitive level. Considering that someone who is assumed to have a high level of cognitive flexibility uses the transition between information or the cognitive control of information effectively, it can be concluded that these individuals may have the capacity to use the information in their WMC effectively.

Current Aims

The current study aimed to find out the relationship among various executive functions necessary for cognitive tasks that are WMC, cognitive emotion regulation, cognitive flexibility, and the relation of those components with learning styles of individuals which are another basic strategy that individuals develop for the learning environment. To that end, the participants were determined as individuals with high or low WMC after taking three complex span tasks in order to assess their WM abilities.

Materials and Methods

Participants

The participants were 39 young undergraduates from Istanbul Gelisim University. All participants received a detailed information about the process and gave a written consent before participation. Students completed one questionnaire, one scale, two inventories, and three memory tasks details of which are shared in the relevant subtitle. All measurements were provided in their native language, Turkish.

Materials

Emotion Regulation Questionnaire

Developed by Garnefski, Kraaij ve Spinhoven [18], the Emotion Regulation Questionnaire is a 5-point Likert scale consisting of 36 questions. The Cronbach alpha coefficients range between 0.67 and 0.81. The subdimensions of scales are self-blame, acceptance, rumination, positive refocusing, refocus on planning, positive reappraisal, putting into perspective, catastrophizing, and other-blame.

Cognitive Flexibility Scale

The scale was developed by Martin and Rubin [19], and it is a one-factor scale consisting of 12 questions, internal consistency of which is 0.74, and its Cronbach alpha coefficient is 0.77.

Kolb Learning Styles Inventory and Vermunt Learning Styles Inventory

Kolb Learning Style Inventory: This short inventory was developed by Kolb [20] in which 4 learning styles are designated. The learners are defined as Diverger, Converger, Assimilator, and Accommodator which are determined by 12 questions.

Vermunt Learning Styles Inventory: Developed by Vermunt [21], the Vermunt Learning Styles Inventory consists of 120 questions with 20 subscales and four factors that are Processing Strategies, Regulation Strategies (metacognition), Mental Models of Learning, and Learning Orientations.

WM Complex Span Tasks

WM capacities of participants were measured with complex span tasks short version of which was reorganized by Foster and his colleagues [22]. These tasks are Rotation Span Task, Operation Span Task, and Symmetry Span Task, and all three tasks take almost 1 h to complete depending on the speed of students.

Operation Span Task: In Operation Span Task, participants are given some letters to remember, and mathematical operations are used for distraction. Participants answer the maths questions, click on the button on the screen, and decide if the answer on the screen is true or false for the previous question. Following this, a letter appears on the screen, and it is expected from the participants to keep it in their minds. Participants experience 5 mathematical operations and letters in each block with an 0.5 s interval, and depending on the performance of the student, the number of mathematical operations and letters change, in the range of 3–7. After this process is completed, participants are asked to pick the letter on the letter table set in the correct sequence.

Rotation Span Task: In Rotation Span Task, participants are expected to remember 16 arrays given in 2 sizes with 8 directions. The participants are expected to remember the direction of the arrays and their sizes. As a distractor of memory, they are given a letter in various dimensions and expected to decide if the letter is rotated correctly which necessitates a visualization of it in mind. The process starts with arrays that are expected to be remembered and is followed with letters which are to be rotated in mind. Participants need to click on the screen and tick off the correct arrays out of the array set. In each block of the task, there are 4 sets, and the array sets differ between 2 and 5.

Symmetry Span Task: Symmetry Span Task expects participants to keep red squares in their mind as a part of the memory test. Following this stage, they are also expected to decide if the line appearing on their screen is symmetrical which is a distractor for the squares in memory. The process includes squares to memorize and lines to decide as symmetrical. At the end of each block before reaching the whole process, participants are given a set of squares to choose the red squares given before. The whole process includes 3 blocks and 12 sets, and the squares to remember differs between 2 and 5.

Procedure

This study includes both face-to-face interaction and a distance participation process. The participants first attended 3 WM tasks, completing time of which was almost 1 h. During these complex span tasks, the participants were left alone in the class after the process was detailed on a white board. The absolute scores of the complex span tasks were used in the analysis. Following this face-to-face process, the learning styles inventories were shared with them online via Google Forms. As these inventories included more than a hundred questions, other tests were not assigned to them on the same day. Within the same week, the data for Cognitive Emotion Regulation Scale and Cognitive Flexibility were obtained via a Google Forms link. The survey was terminated after completing the online processes.

Results

The normal distribution was determined with the Skewness-Kurtosis coefficient values, and all values of all variations were found between −2 and +2. This result indicated that the distribution was normal. The confidence interval used for all analyses was determined as 95%. In the Operation Span Task, 51.3% of the participants scored low in their WMC, and 48.7% of them scored high in their WMC. In the Rotation Span Task, 51.3% of the participants scored low in their WMC, and 48.7% of them scored high in their WMC. In the Symmetry Span Task, 53.8% of the participants scored low in their WMC, and 46.2% of them scored high in their WMC. The Kolb Learning Styles of the participants indicated that 12.8% of the participants were Accommodator, 74.4% of them were Diverger, and 12.8% of them were Assimilator. The Vermunt Learning Styles of the participants indicated that 30.8% of the participants had Processing Strategies, 15.4% of them had Learning Orientations, while 53.8% of them had Mental Models of Learning (Tables 1, 2, 3, 4, 5, 6, 7).

Table 1.

One-way ANOVA results for the comparisons of Kolb Learning Style results with the results of Cognitive Flexibility Scale and Cognitive Emotion Regulation

N X Ss. Sum of square SD Mean of square F p value
Cognitive Flexibility Scale
 Accommodator 5 59.00 8.00 17.20 2 8.60 0.15 0.857
 Diverger 29 58.41 7.59 2,000.23 36 55.56
 Assimilator 5 56.60 5.68 2,017.44 38
 Total 39 58.26 7.29
Acceptance
 Accommodator 5 13.20 5.40 7.69 2 3.85 0.37 0.693
 Diverger 29 14.31 2.98 374.21 36 10.39
 Assimilator 5 13.40 1.52 381.90 38
 Total 39 14.05 3.17
Positive refocusing
 Accommodator 5 10.80 3.96 27.77 2 13.88 0.88 0.425
 Diverger 29 12.28 3.65 570.59 36 15.85
 Assimilator 5 10.00 5.79 598.36 38
 Total 39 11.79 3.97
Refocus on planning
 Accommodator 5 14.60 3.97 0.77 2 0.38 0.04 0.963
 Diverger 29 15.00 3.09 370.00 36 10.28
 Assimilator 5 14.80 3.11 370.77 38
 Total 39 14.92 3.12
Positive reappraisal
 Accommodator 5 14.00 3.16 41.53 2 20.76 2.72 0.079
 Diverger 29 16.55 2.80 274.37 36 7.62
 Assimilator 5 14.40 1.95 315.90 38
 Total 39 15.95 2.88
Putting into perspective
 Accommodator 5 12.40 2.30 30.45 2 15.22 1.36 0.269
 Diverger 29 15.07 3.54 401.86 36 11.16
 Assimilator 5 14.80 2.68 432.31 38
 Total 39 14.69 3.37
Rumination
 Accommodator 5 14.40 5.13 7.08 2 3.54 0.36 0.700
 Diverger 29 14.90 2.77 353.89 36 9.83
 Assimilator 5 16.00 2.92 360.97 38
 Total 39 14.97 3.08
Self-blame
 Accommodator 5 13.40 4.98 17.21 2 8.60 0.84 0.440
 Diverger 29 11.90 2.79 368.69 36 10.24
 Assimilator 5 10.80 3.56 385.90 38
 Total 39 11.95 3.19
Catastrophizing
 Accommodator 5 10.00 4.90 41.60 2 20.80 1.67 0.203
 Diverger 29 8.21 3.44 448.76 36 12.47
 Assimilator 5 11.00 2.35 490.36 38
 Total 39 8.79 3.59
Other-blame
 Accommodator 5 10.20 6.06 16.84 2 8.42 0.39 0.680
 Diverger 29 10.83 4.35 778.14 36 21.61
 Assimilator 5 12.60 5.03 794.97 38
 Total 39 10.97 4.57
Open in a new tab

Results did not indicate any significant difference between the subscales of Cognitive Flexibility or Cognitive Emotion Regulation when they were compared with the Kolb Learning Styles of the participants (p > 0.05). * p < 0.05: one-way ANOVA.

Table 2.

One-way ANOVA results for the comparisons of Vermunt Learning Style results with the results of Cognitive Flexibility Scale and cognitive emotion regulation

N X Ss. Sum of squares SD Mean of squares F p value
Cognitive Flexibility Scale
 Processing Strategies 12 62.75 6.03 501.11 2 250.56 5.95 0.006*
 Learning Orientations 6 51.83 6.88 1,516.32 36 42.12
 Mental Models of Learning 21 57.52 6.63 2,017.44 38
 Total 39 58.26 7.29
Acceptance
 Processing Strategies 12 13.92 2.84 4.34 2 2.17 0.21 0.814
 Learning Orientations 6 14.83 3.06 377.56 36 10.49
 Mental Models of Learning 21 13.90 3.48 381.90 38
 Total 39 14.05 3.17
Positive refocusing
 Processing Strategies 12 12.75 4.16 25.66 2 12.83 0.81 0.454
 Learning Orientations 6 12.50 3.73 572.70 36 15.91
 Mental Models of Learning 21 11.05 3.96 598.36 38
 Total 39 11.79 3.97
Refocus on planning
 Processing Strategies 12 16.58 2.15 49.57 2 24.78 2.78 0.076
 Learning Orientations 6 14.67 3.08 321.20 36 8.92
 Mental Models of Learning 21 14.05 3.34 370.77 38
 Total 39 14.92 3.12
Positive reappraisal
 Processing Strategies 12 16.75 1.96 11.65 2 5.82 0.69 0.509
 Learning Orientations 6 15.33 3.39 304.25 36 8.45
 Mental Models of Learning 21 15.67 3.20 315.90 38
 Total 39 15.95 2.88
Putting into perspective
 Processing Strategies 12 13.25 2.67 46.56 2 23.28 2.17 0.129
 Learning Orientations 6 14.17 3.37 385.75 36 10.72
 Mental Models of Learning 21 15.67 3.54 432.31 38
 Total 39 14.69 3.37
Rumination
 Processing Strategies 12 15.33 2.90 15.57 2 7.78 0.81 0.452
 Learning Orientations 6 13.50 4.09 345.40 36 9.59
 Mental Models of Learning 21 15.19 2.91 360.97 38
 Total 39 14.97 3.08
Self-blame
 Processing Strategies 12 12.17 3.04 27.28 2 13.64 1.37 0.267
 Learning Orientations 6 10.00 2.53 358.62 36 9.96
 Mental Models of Learning 21 12.38 3.35 385.90 38
 Total 39 11.95 3.19
Catastrophizing
 Processing Strategies 12 8.42 3.65 2.61 2 1.30 0.10 0.908
 Learning Orientations 6 8.83 3.82 487.75 36 13.55
 Mental Models of Learning 21 9.00 3.66 490.36 38
 Total 39 8.79 3.59
Other-blame
 Processing Strategies 12 11.00 4.99 56.40 2 28.20 1.37 0.266
 Learning Orientations 6 13.67 5.43 738.57 36 20.52
 Mental Models of Learning 21 10.19 3.98 794.97 38
 Total 39 10.97 4.57
Open in a new tab

Results did not indicate any significant difference between the subscales of cognitive emotion regulation when they were compared with the Vermunt Learning Styles of the participants (p > 0.05). However, the means obtained from the Cognitive Flexibility Scale (F (2, 36) = 5.95, p < 0.05) were significant in terms of the educational status of the participants, and a comparative analysis was conducted. Levene's test was conducted afterward which indicated that the variations were homogenous (p > 0.05). The data obtained from the Scheffe test indicated that the participants having Processing Strategies as a learning style scored higher in cognitive flexibility when it is compared with the ones having learning orientations as a learning style.

*

p < 0.05: one-way ANOVA.

Table 3.

One-way ANOVA results for the comparisons of Kolb Learning Style results with the Operation Span, Rotation Span and Symmetry Span Task scores

Kolb learning style N X Ss. Sum of square SD Mean of squares F p value
Operation Span Task partial score
 Accommodator 5 28.60 8.56 574.16 2 287.08 1.09 0.348
 Diverger 29 36.31 17.14 9,518.61 36 264.41
 Assimilator 5 26.60 15.77 10,092.77 38
 Total 39 34.08 16.30
Rotation Span Task partial score
 Accommodator 5 12.00 3.94 56.27 2 28.13 0.79 0.463
 Diverger 29 15.38 6.29 1,285.63 36 35.71
 Assimilator 5 16.20 5.40 1,341.90 38
 Total 39 15.05 5.94
Symmetry Span Task partial score
 Accommodator 5 13.80 6.61 88.87 2 44.44 0.58 0.567
 Diverger 29 17.97 9.10 2,772.57 36 77.02
 Assimilator 5 19.20 8.38 2,861.44 38
 Total 39 17.59 8.68
Open in a new tab

The scores obtained from the Operation Span, Rotation Span, and Symmetry Span Tasks indicate no significance result according to the Kolb Learning Styles of the participants (p >0.05). * p < 0.05: one-way ANOVA.

Table 4.

One-way ANOVA results for the comparisons of Vermunt Learning Style results with the Operation Span - Rotation Span - Symmetry Span Task scores

Vermunt learning style N X Ss. Sum of square SD Mean of squares F p value
Operation Span Task partial score
 Processing Strategies 12 29.25 15.37 641.95 2 320.97 1.22 0.306
 Learning Orientations 6 30.67 22.54 9,450.82 36 262.52
 Mental Models of Learning 21 37.81 14.68 10,092.77 38
 Total 39 34.08 16.30
Rotation Span Task partial score
 Processing Strategies 12 16.17 6.53 22.73 2 11.37 0.31 0.735
 Learning Orientations 6 14.17 5.38 1,319.17 36 36.64
 Mental Models of Learning 21 14.67 5.94 1,341.90 38
 Total 39 15.05 5.94
Symmetry Span Task partial score
 Processing Strategies 12 16.58 9.08 19.78 2 9.89 0.13 0.883
 Learning Orientations 6 17.50 11.40 2,841.65 36 78.93
 Mental Models of learning 21 18.19 8.02 2,861.44 38
 Total 39 17.59 8.68
Open in a new tab

The scores obtained from the Operation Span, Rotation Span, and Symmetry Span Tasks indicate no significance result according to the Vermunt Learning Styles of the participants (p > 0.05). * p < 0.05: one-way ANOVA.

Table 5.

Correlations of WM complex span tasks

Operation Span Rotation Span Symmetry Span
Operation Span
 Pearson correlation 1 0.175 0.090
 Sig. (2-tailed) 0.287 0.584
 N 39 39 39
Rotation Span
 Pearson correlation 0.175 1 0.521**
 Sig. (2-tailed) 0.287 0.001
 N 39 39 39
Symmetry Span
 Pearson correlation 0.090 0.521** 1
 Sig. (2-tailed) 0.584 0.001
 N 39 39 39
Open in a new tab

When the scores obtained from the Operation Span, Rotation Span, and Symmetry Span Tasks were compared with each other, the result indicated that there is a significant correlation between Rotation Span and Symmetry Span Tasks. Results did not indicate any significant correlation between other complex span tasks.

**

Correlation is significant at the 0.01 level (2-tailed).

Table 6.

Correlations of WM complex span tasks and cognitive emotion regulation strategies

WM − Operation Span Task − partial score WM − Symmetry Span Task − partial score WM − Rotation Span Task − partial score Acceptance Positive refocusing Refocus on planning Positive reappraisal Putting into perspective Rumination Self blame Catastrophizing Other-blame
WM − Operation Span Task − partial score
 Pearson correlation 1 0.090 0.175 0.159 –0.073 –0.341* 0.028 0.032 0.072 0.186 0.045 –0.213
 Sig. (2-tailed) 0.584 0.287 0.333 0.661 0.034 0.865 0.849 0.662 0.257 0.785 0.193
 Ν 39 39 39 39 39 39 39 39 39 39 39 39
WM − Symmetry Span Task − partial score
 Pearson correlation 0.090 1 0.521** 0.223 0.124 0.036 0.184 0.301 0.056 –0.085 –0.069 –0.122
 Sig. (2-tailed) 0.584 0.001 0.173 0.451 0.829 0.261 0.062 0.736 0.605 0.674 0.458
 Ν 39 39 39 39 39 39 39 39 39 39 39 39
WM − Rotation Span Task − partial score
 Pearson correlation 0.175 0.521** 1 0.137 0.061 0.081 0.109 0.120 0.290 0.093 0.066 –0.112
 Sig. (2-tailed) 0.287 0.001 0.406 0.713 0.624 0.508 0.466 0.073 0.572 0.690 0.496
 Ν 39 39 39 39 39 39 39 39 39 39 39 39
Accepteance
 Pearson correlation 0.159 0.223 0.137 1 –0.020 –0.039 –0.049 0.208 0.558** 0.414** 0.331* 0.227
 Sig. (2-tailed) 0.333 0.173 0.406 0.904 0.812 0.769 0.203 0.000 0.009 0.039 0.165
 Ν 39 39 39 39 39 39 39 39 39 39 39 39
Positive refocusing
 Pearson correlation –0.073 0.124 0.061 –0.020 1 0.674** 0.408** 0.196 –0.093 –0.294 –0.273 –0.122
 Sig. (2-tailed) 0.661 0.451 0.713 0.904 0.000 0.010 0.232 0.574 0.069 0.093 0.459
 Ν 39 39 39 39 39 39 39 39 39 39 39 39
Refocus on planning
 Pearson correlation –0.341* 0.036 0.081 –0.039 0.674** 1 0.613** 0.320* –0.019 –0.162 –0.337* –0.031
 Sig. (2-tailed) 0.034 0.829 0.624 0.812 0.000 0.000 0.047 0.907 0.325 0.036 0.849
 Ν 39 39 39 39 39 39 39 39 39 39 39 39
Positive reappraisal
 Pearson correlation 0.028 0.184 0.109 –0.049 0.408** 0.613** 1 0.396* –0.169 –0.201 –0.474** –0.258
 Sig. (2-tailed) 0.865 0.261 0.508 0.769 0.010 0.000 0.013 0.304 0.220 0.002 0.113
 Ν 39 39 39 39 39 39 39 39 39 39 39 39
Putting into perspective
 Pearson correlation 0.032 0.120 0.208 0.196 0.320* 0.396* 1 0.184 0.023 0.019 –0.002
 Sig. (2-tailed) 0.849 0.062 0.466 0.203 0.232 0.047 0.013 0.262 0.890 0.911 0.989
 Ν 39 39 39 39 39 39 39 39 39 39 39 39
Rumination
 Pearson correlation 0.072 0.056 0.290 0.558” –0.093 –0.019 –0.169 0.184 1 0.536** 0.506** 0.106
 Sig. (2-tailed) 0.662 0.736 0.073 0.000 0.574 0.907 0.304 0.262 0.000 0.001 0.519
 Ν 39 39 39 39 39 39 39 39 39 39 39 39
Self-blame
 Pearson correlation 0.186 –0.085 0.093 0.414” –0.294 –0.162 –0.201 0.023 0.536** 1 0.440** 0.157
 Sig. (2-tailed) 0.257 0.605 0.572 0.009 0.069 0.325 0.220 0.890 0.000 0.005 0.340
 Ν 39 39 39 39 39 39 39 39 39 39 39 39
Catastrophizing
 Pearson correlation 0.045 –0.069 0.066 0.331* –0.273 –0.337* –0.474** 0.019 0.506** 0.440† 1 0.506**
 Sig. (2-tailed) 0.785 0.674 0.690 0.039 0.093 0.036 0.002 0.911 0.001 0.005 0.001
 Ν 39 39 39 39 39 39 39 39 39 39 39 39
Other-blame
 Pearson correlation –0.213 –0.122 –0.112 0.227 –0.122 –0.031 –0.258 –0.002 0.106 0.157 0.506** 1
 Sig. (2-tailed) 0.193 0.458 0.496 0.165 0.459 0.849 0.113 0.989 0.519 0.340 0.001
 Ν 39 39 39 39 39 39 39 39 39 39 39 39
Open in a new tab

As indicated in the table, the results of the study pointed out that there is a positive strong correlation between Rotation Span Task and Symmetry Span Task (r = 0.521); a negative correlation between Operation Span Task and refocus on planning (r = −0.341); a positive correlation between acceptance and rumination (r = 0.558), self–blame (r = 0.414), catastrophizing (r = 0.331), positive refocusing, and refocus on planning (r = 0.674); a positive correlation between positive reappraisal and refocus on planning (r = 0.408); a positive correlation between putting into perspective and refocus on planning (r = 0.320), positive reappraisal (r = 0.396); a negative correlation between catastrophizing and positive reappraisal (r = −474); a positive correlation between self–blame and rumination (r = 0.536); a positive correlation between catastrophizing and self-blame (r = 0.440), other-blame (r = 506). Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

Table 7.

Correlations of Cognitive Flexibility Scale with cognitive emotion regulation strategies

Cognitive Flexibility Scale Acceptance Positive refocusing Refocus on planning Positive reappraisal Putting into perspective Rumination Self-blame Catastrophizing Other-blame
Cognitive Flexibility Scale
 Pearson correlation 1 –0.055 0.122 0.528** 0.574** 0.308 0.055 0.054 –0.258 –0.134
 Sig. (2-tailed) 0.738 0.459 0.001 0.000 0.056 0.738 0.745 0.112 0.416
 Ν 39 39 39 39 39 39 39 39 39 39
Acceptance
 Pearson correlation –0.055 1 –0.020 –0.039 –0.049 0.208 0.558** 0.414** 0.331* 0.227
 Sig. (2-tailed) 0.738 0.904 0.812 0.769 0.203 0.000 0.009 0.039 0.165
 Ν 39 39 39 39 39 39 39 39 39 39
Positive refocusing
 Pearson correlation 0.122 –0.020 1 0.674** 0.408** 0.196 –0.093 –0.294 –0.273 –0.122
 Sig. (2-tailed) 0.459 0.904 0.000 0.010 0.232 0.574 0.069 0.093 0.459
 Ν 39 39 39 39 39 39 39 39 39 39
Refocus on planning
 Pearson correlation 0.528** –0.039 0.674** 1 0.613** 0.320* –0.019 –0.162 –0.337* –0.031
 Sig. (2-tailed) 0.001 0.812 0.000 0.000 0.047 0.907 0.325 0.036 0.849
 Ν 39 39 39 39 39 39 39 39 39 39
Positive reappraisal
 Pearson correlation 0.574** –0.049 0.408** 0.613** 1 0.396* –0.169 –0.201 –0.474** –0.258
 Sig. (2-tailed) 0.000 0.769 0.010 0.000 0.013 0.304 0.220 0.002 0.113
 Ν 39 39 39 39 39 39 39 39 39 39
Putting into perspective
 Pearson correlation 0.308 0.208 0.196 0.320* 0.396* 1 0.184 0.023 0.019 –0.002
 Sig. (2-tailed) 0.056 0.203 0.232 0.047 0.013 0.262 0.890 0.911 0.989
 Ν 39 39 39 39 39 39 39 39 39 39
Rumination
 Pearson correlation 0.055 0.558** –0.093 –0.019 –0.169 0.184 1 0.536** 0.506** 0.106
 Sig. (2-tailed) 0.738 0.000 0.574 0.907 0.304 0.262 0.000 0.001 0.519
 Ν 39 39 39 39 39 39 39 39 39 39
Self-blame
 Pearson correlation 0.054 0.414** –0.294 –0.162 –0.201 0.023 0.536** 1 0.440** 0.157
 Sig. (2-tailed) 0.745 0.009 0.069 0.325 0.220 0.890 0.000 0.005 0.340
 Ν 39 39 39 39 39 39 39 39 39 39
Catastrophizing
 Pearson correlation –0.258 0.331* –0.273 –0.337* –0.474** 0.019 0.506** 0.440** 1 0.506**
 Sig. (2-tailed) 0.112 0.039 0.093 0.036 0.002 0.911 0.001 0.005 0.001
 Ν 39 39 39 39 39 39 39 39 39 39
Other-blame
 Pearson correlation –0.134 0.227 –0.122 –0.031 –0.258 –0.002 0.106 0.157 0.506** 1
 Sig. (2-tailed) 0.416 0.165 0.459 0.849 0.113 0.989 0.519 0.340 0.001
 Ν 39 39 39 39 39 39 39 39 39 39
Open in a new tab

The results of the study indicated that there is a positive strong correlation between cognitive flexibility and refocus on planning (r = 0.528) and positive reappraisal (r = 574). ** Correlation is significant at the 0.01 level (2-tailed).

Discussion and Conclusion

The results of the present study indicated that scores obtained from the Symmetry Span Task which is a spatial WM task are correlated with the scores of Rotation Span Task which is a verbal and nonspatial task (shown in Fig. 1). The correlation between a spatial and verbal task indicates that individuals who get a high WM score in Symmetry Span Task also scores high in the Rotation Span Task. Studies conducted with individuals with psychological or neurological disorders mostly highlight the difference between spatial and verbal WMC. Taking an earlier study conducted with patients with Parkinson disease having mild clinical symptoms as an example, it indicated that their spatial WMC was weaker when compared to verbal and visual WMC [23]. Also, a very recent study investigated the difference between verbal and visual WMC with depressive individuals and found that only spatial WMC of individuals with unipolar depression and bipolar II depression was impaired [24]. There are some other researches indicating the difference between verbal and spatial WMC in healthy children. A study conducted with children indicated that verbal and spatial WMC have different neural bases in children [25]. Additionally, verbal WMC was found in a study as a good predictor for reading comprehension in children rather than the spatial WM [26]. There are also other studies indicating the relationship between reading skill and verbal WMC [27, 28]. Another study questioning the role of age for WMC found that while verbal WMC is protected across years, the capacity for visual-spatial WM declines in time [29]. Results of the abovementioned studies support the domain specific view in the field of working memory. On top of all these studies pointing the difference between verbal and spatial WM and supporting the domain specific view, the present study conducted with healthy university students indicate a positive correlation between the visual and verbal WMC of individuals. Our study is in contrast to other research and supports the domain general view. There are also similar research suggesting a common storage for verbal, spatial, or visuospatial WM as indicated in the present study [30, 31, 32]. When evaluated in terms of multitasking, the result of a very recent study researching the verbal and visuospatial WMC within a multitasking concept in which a Bayesian state-trace analysis was used indicated that while dealing with more than one task at the same time a common pool for verbal and visuospatial data are used which supports the domain general view [33]. Considering all of these studies, it can be speculated that domain general and domain specific tendencies may differ with regard to the scenario, cognitive load, age, psychological state, and other relevant indicators. The present study supports the domain general view with its analysis made between verbal and spatial WMC with a continuous correlation, and it is suggested to conduct the complex span tasks with larger groups.

Fig. 1.

Fig. 1

Open in a new tab

Rotation span versus symmetry span (r = 0.521).

Apart from the domain general view regarding the complex working memory span tasks, the present study also aimed to make a comparison between working memory capacity and use of cognitive emotion regulation strategies. Operation Span Task is another nonspatial and verbal task measuring the WMC of an individual. The result indicates that the WMC of individuals measured with a verbal task that is the Operation Span Task is significantly different from one of the adaptive cognitive emotion regulation strategies of individuals that is the refocus on planning strategy (t(37) = 2.46, p < 0.05) (shown in Fig. 2) and is strongly correlated with it (r = −0.341). This strategy refers to focusing on possible steps to take to manage emotions [34]. There is a negative correlation between WMC and an emotion regulation strategy which indicates that as the WMC of individuals decreases the possibility of individuals use refocus on planning increases. Being strongly related to fluid intelligence [35, 36, 37, 38], executive functions [39, 40, 41], and executive attention [42], WMC has been an important concern of cognitive studies in recent years. This result contributes to the field in terms of comparing the working memory capacity with a cognitive strategy developed against emotions. Refocus on planning is an adaptive strategy, and an earlier study showed that refocus on planning contributes to resilience in patients with depression and anxiety disorders [43]. A study conducted more recently also indicated that higher use of refocus on planning strategy is associated with more positive emotions and anxiety symptoms are lower only when more planning is adopted [44]. The finding of this study demonstrates that as the WMC gets lower, a refocus on planning strategy is used more against emotions which might be interpreted as being more in need of using a strategy when being low in terms of memory capacity. As WM is strongly and positively correlated with the fluid intelligence and executive functions, individuals with low WMC who have problems with executive functions might need more strategies for managing their emotions and need to refocus on planning and vice versa. This study highlights the need to find the relationship between cognitive strategies against emotions and WMC. On top of that, as far as we know, no studies had been performed before about the comparison of WMC to cognitive emotion regulation strategies. The present study also aimed to fill this gap and highlights the relation between WM and cognitive emotion regulation.

Fig. 2.

Fig. 2

Open in a new tab

Operation span versus refocus on planning (r = −0.341).

The study also revealed strong correlations among cognitive emotion regulation strategies. For instance, acceptance is the allowance of emotional experience and feelings as they are [45], and it is associated with positive outcomes [46]. It is a helping strategy for managing emotions when compared to nonacceptance which ends up with psychopathology [47, 48] and anxiety disorders [48, 49]. As a cognitive emotion regulation strategy, acceptance contributes to reducing anxiety or depression [50]. Another study indicated that when patients with functional dyspepsia are compared to healthy people, the healthy ones tend to use more acceptance which is an adaptive cognitive emotion regulation strategy [51]. However, in an earlier study, acceptance was found positively correlated with depressive symptoms in major depressive disorder outpatients [52]. Another study with high school students having nonsuicidal self-injury reported higher levels of acceptance [53]. There are various studies on acceptance as a cognitive emotion regulation strategy most of which point both to positive and negative aspects of it. In this study, results obtained from the scores of college students showed that as the use of acceptance decreases, the use of rumination also decreases and vice versa (shown in Fig. 3). Rumination is a maladaptive cognitive emotion regulation strategy which is about thinking continually on feelings resulted from negative situations. For instance, euthymic bipolar disorder and major depressive disorder individuals reported increased use of rumination [54]. Besides this, a study conducted with adolescents showed that individuals with internalizing problems scored significantly higher on the cognitive emotion regulation strategies of rumination [55]. Another study with children showed that rumination mediated the relationship between anxiety problems and their consequent interference [56]. Being a quite negative strategy, rumination is mostly related with negative results of emotion. The result of this study shows that acceptance and rumination are positively correlated which supports the studies proving the relation of acceptance with depression and anxiety despite being an adaptive one. In an earlier study conducted with college students [57], other-blame, rumination, acceptance, and reduced positive reappraisal were found related to maladaptive anger suppression, and the authors also advised to reevaluate the place of acceptance as an adaptive strategy. Acceptance was also found positively correlated in the present study with self-blame which is a maladaptive strategy (shown in Fig. 4). Self-blame is also a maladaptive cognitive emotion regulation strategy. An earlier study also indicated that self-blame, acceptance, and catastrophizing are positively correlated with depressive symptoms in major depressive disorder outpatients [52] which is consistent with the results of this study pointing to the negative aspects of acceptance. The result of the study shows that as the use of acceptance as a strategy increases the use of catastrophizing also increases (shown in Fig. 5). In a study conducted with adolescents, catastrophizing was found strongly related to all forms of violent behavior [58]. In another study, somatic complaints were significantly related to a more frequent use of maladaptive cognitive coping strategies, such as catastrophizing about negative life events [59]. Also, individuals with euthymic bipolar disorder and major depressive disorder reported increased use catastrophizing [54]. Another study with children showed that catastrophizing mediated the relationship between anxiety problems and their consequent interference [56]. Thus, the result of the study is consistent with the results of the whole study which might necessitate putting the strategy of acceptance into a negative place

Fig. 3.

Fig. 3

Open in a new tab

Acceptance versus rumination (r = 0.558).

Fig. 4.

Fig. 4

Open in a new tab

Acceptance versus self-blame (r = 0.414).

Fig. 5.

Fig. 5

Open in a new tab

Acceptance versus catastrophizing (r= 0.331).

On the other hand, the present study indicated positive correlations among adaptive cognitive emotion regulation strategies. Positive refocusing is about focusing on positive experiences and is an adaptive cognitive emotion regulation strategy. An earlier study found specific relationships between externalizing problems and positive refocusing in adolescents [55]. A recent study also found that positive refocusing contribute to reducing anxiety or depression [50]. Results of this study complies with the results of previous studies and indicates that as positive refocusing increases, the strategy of refocus on planning also increases (shown in Fig. 6). Refocus on planning is an unconscious strategy developed to manage a negative situation at the cognitive level [60]. It is another adaptive cognitive emotion regulation strategy and is mostly related to resilience. Refocus on planning, positive reappraisal, and less rumination were found in a study to contribute to resilience in patients with depression and anxiety disorders [43]. A recent study indicated that high school students with nonsuicidal self-injury reported lower levels of refocus on planning [53]. Another recent research conducted with healthcare professionals indicated that positive refocusing can protect them from the destructive effects of moral distress which is directly and positively associated with burnout and protect their well-being [61]. The result of present study indicates a positive correlation between two adaptive cognitive emotion regulation strategies that are positive refocusing and refocus on planning which complies with previous studies. On the other hand, positive reappraisal, another adaptive coping style, was found inversely related to depression in a study [57] and was found to reduce the effect of bullying on anxiety [62]. The result of present study complies well with previous research that positive reappraisal and refocus on planning are also positively correlated adaptive cognitive emotion regulation strategies (shown in Fig. 7). The present study again found that refocus on planning is positively correlated with putting into perspective which is another adaptive cognitive emotion regulation strategy (shown in Fig. 8). Putting into perspective is an adaptive coping style and is beneficial in the absence of treatment [63]. Thus, an expected finding of the present study indicated that there is a positive correlation between those adaptive cognitive emotion regulation strategies.

Fig. 6.

Fig. 6

Open in a new tab

Positive refocusing versus refocus on planning (r = 0.674).

Fig. 7.

Fig. 7

Open in a new tab

Positive reappraisal versus refocus on planning (r = 0.408).

Fig. 8.

Fig. 8

Open in a new tab

Putting into perspective versus refocus on planning (r = 0.320).

Positive reappraisal is an adaptive coping style, mostly related to well-being and beneficial for patients suffering from PTSD [63]. The present findings indicated that there is a positive correlation between putting into perspective and positive reappraisal (shown in Fig. 9). Positive reappraisal is defined as reinterpreting the current negative situation in order to see the positive sides of it, and a recent study indicated that high-trait-anxious women use the positive reappraisal strategy inefficiently [64]. Positive reappraisal was also negatively correlated with depressive symptoms in major depressive disorder outpatients in an earlier study [52] (Lei et al., 2014). The same study also indicated that catastrophizing is positively correlated with depressive symptoms in major depressive disorder outpatient. Being a maladaptive strategy, catastrophizing is mostly found relevant with negative moods. In a study researching the violent behavior, catastrophizing was found strongly related to all forms of violent behavior [58]. In another study, somatic complaints were significantly related to a more frequent use of maladaptive cognitive coping strategies, such as blaming oneself, ruminating, and catastrophizing about negative life events [59]. Also, euthymic bipolar disorder and major depressive disorder individuals reported increased use of rumination, catastrophizing, and self-blame as stated above [54]. Apart from the studies performed by adults, a study with children also showed that catastrophizing, rumination, and other-blame mediated the relationship between anxiety problems and their consequent interference [56]. The present study supports the literature and shows a negative correlation between adaptive and maladaptive cognitive emotion regulation strategies that are catastrophizing and positive reappraisal (shown in Fig. 10).

Fig. 9.

Fig. 9

Open in a new tab

Putting into perspective versus positive reappraisal (r = 0.396).

Fig. 10.

Fig. 10

Open in a new tab

Catastrophizing versus positive reappraisal (r = −0.474).

The results of the present study indicated positive correlations among maladaptive cognitive emotion regulation strategies as well. Self-blame is a maladaptive coping strategy. It is positively correlated with depressive symptoms in major depressive disorder outpatients and somatic complaints [52, 59]. Rumination is also another maladaptive coping strategy that is related to anxiety problems [56]. The present study found that there is a positive correlation between self-blame and rumination which are two maladaptive strategies (shown in Fig. 11). The findings demonstrate a consistency with prior findings in the literature. Self-blame, catastrophizing, and other-blame are also maladaptive coping strategies, and in terms of cognitive emotion regulation, they are not beneficial as shown by most of the studies conducted in this field. Self-blame and catastrophizing are positively correlated with depressive symptoms, and euthymic bipolar disorder and major depressive disorder individuals use catastrophizing and self-blame as a coping strategy [52, 54]. As stated before, self-blame and catastrophizing are two maladaptive cognitive emotion regulation strategies, and catastrophizing is strongly related to violent behavior and somatic complaints [58, 59]. A study conducted with children also showed that catastrophizing, rumination, and other-blame mediated the relationship between anxiety problems and their consequent interference [56]. Another study with a large sample of unemployed people indicated that they go to maladaptive strategies that are self-blame, other-blame, and catastrophizing mostly. In a recent study, it was found that self-blame, other-blame, and catastrophizing are positively related to problematic online gaming [65]. The present study contributes to the existing information about maladaptive strategies and shows that catastrophizing is positively correlated with self-blame as well as other-blame (shown in Fig. 12, 13).

Fig. 11.

Fig. 11

Open in a new tab

Self-blame versus rumination (r = 0.536).

Fig. 12.

Fig. 12

Open in a new tab

Catastrophizing versus self-blame (r = 0.440).

Fig. 13.

Fig. 13

Open in a new tab

Catastrophizing versus other-blame (r = 0.506).

The analysis of cognitive flexibility and cognitive emotion regulation scales also revealed positive correlations in the present study. Refocus on planning, an adaptive strategy, and a coping style which were found in previous research as being highly related to resilience, well-being, and higher use of refocus on planning as a cognitive emotion regulation strategy are associated with more positive emotions while also anxiety symptoms are lower only when more planning is adopted [44, 53]. On the other hand, cognitive flexibility is defined as being aware of alternatives for a situation and also being willing to adapt to this situation as well as having self-efficacy against this adaptation [19]. In this study, a strong correlation was found for cognitive flexibility and refocus on planning (shown in Fig. 14). A possible interpretation of this result could be that individuals with high cognitive flexibility also use adaptive cognitive emotion regulation strategies. This result is in agreement with another result of this study. Another strong correlation was found for cognitive flexibility and positive reappraisal which is an adaptive cognitive emotion regulation strategy (shown in Fig. 15). It is about seeing the positive sides of negative situations and was found in previous research as correlated with resilience while being beneficial for patients suffering from depression, anxiety, and PTSD [43, 63]. Since refocus on planning and positive reappraisal are adaptive cognitive emotion regulation strategies, they are being correlated with cognitive flexibility levels of individuals. These results indicate that as the cognitive flexibility level of individuals increases, their chance of using adaptive cognitive emotion regulation strategies also increases.

Fig. 14.

Fig. 14

Open in a new tab

Cognitive flexibility versus refocus on planning (r = 0.528).

Fig. 15.

Fig. 15

Open in a new tab

Cognitive flexibility versus positive reappraisal (r = 0.574).

In the present study, one another main concern was to investigate the relations of learning styles with cognitive components regarding cognitive emotion regulation and cognitive flexibility. However, results did not indicate any significant difference or correlation for learning styles measured with Vermunt and Kolb Learning Styles Inventories. In addition to that, only the means obtained from the Cognitive Flexibility Scale (F(2, 36) = 5.95, p < 0.05) were significant in terms of the educational status of the participants, and a comparative analysis was conducted. The Levene's test was conducted afterward which indicated that the variations were homogenous (p > 0.05). The data obtained from the Scheffe test indicated that the participants having Processing Strategies as a learning style scored higher in cognitive flexibility when it is compared with the ones having Learning Orientations as a learning style. Processing Strategies are mainly related to cognitive functions that are knowledge, understanding, and skill [66]. The relationship between cognitive flexibility and a cognitive-based learning style that is processing Strategies is consistent with their main definitions. However, there is no study with regard to the relationship between cognitive flexibility and learning styles. Detailed research is hereby advised for handling a possible relation between cognitive features and learning style dominance.

Limitations

In this study, it was aimed to investigate possible correlations and differences between cognitive emotion regulation strategies, WM tasks, cognitive flexibility, and learning styles. There are important results collected in the present study, and a future study with a larger group would make great contribution to the field of cognitive and educational psychology.

Statement of Ethics

The study has been approved by the Ethics Committee of Rectorate in Istanbul Gelisim University in Turkey with 2021/21 meeting number. The participants filled out a consent form before attending the sessions of the study.

Conflict of Interest Statement

The authors declare no conflict of interest.

Funding Sources

The authors did not receive support from any organization for the submitted work.

Author Contributions

Kahraman Guler and Aylin Aydin collaboratively formulated the research question, conceptualized the study, analyzed the data, and wrote the manuscript.

Data Availability Statement

All data, analysis code, and research materials are available at (https://osf.io/hf5tc/?view_only=03dde01b7e1149a0ab7b0c019a9b4267).

Acknowledgments

We especially thank all our students who volunteered their time to our study.

Funding Statement

The authors did not receive support from any organization for the submitted work.

References

  • 1.Baddeley A, Hitch G. Working memory. Psychol Learn Motiv. 1974;8:47–89. [Google Scholar]
  • 2.Baddeley A. The episodic buffer, a new component of WM? Trends Cogn Sci. 2000;4((11)):417–423. doi: 10.1016/s1364-6613(00)01538-2. [DOI] [PubMed] [Google Scholar]
  • 3.Woehrle JL, Magliano JP. Time flies faster if a person has a high working-memory capacity. Acta Psychol. 2012;139((2)):314–319. doi: 10.1016/j.actpsy.2011.12.006. [DOI] [PubMed] [Google Scholar]
  • 4.Kane MJ, Hambrick DZ, Conway ARA. Working memory capacity and fluid intelligence are strongly related constructs: comment on Ackerman, Beier, and Boyle (2005) Psychol Bull. 2005;131((1)):66–71. doi: 10.1037/0033-2909.131.1.66. [DOI] [PubMed] [Google Scholar]
  • 5.Chekaf M, Gauvrit N, Guida A, Mathy F. Compression in working memory and its relationship with fluid intelligence. Cogn Sci. 2018;42((Suppl 3)):904–922. doi: 10.1111/cogs.12601. [DOI] [PubMed] [Google Scholar]
  • 6.Rhodes S, Cowan N. Attention in working memory: attention is needed but it yearns to be free. Ann N Y Acad Sci. 2018;1424((1)):52–63. doi: 10.1111/nyas.13652. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Ricker TJ, Nieuwenstein MR, Bayliss DM, Barrouillet P. Working memory consolidation: insights from studies on attention and working memory. Ann N Y Acad Sci. 2018;1424((1)):8–18. doi: 10.1111/nyas.13633. [DOI] [PubMed] [Google Scholar]
  • 8.Blackwell KA, Cepeda NJ, Munakata Y. When simple things are meaningful, WM strength predicts children's cognitive flexibility. J Exp Child Psychol. 2009;103((2)):241–249. doi: 10.1016/j.jecp.2009.01.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Stavroulaki V, Kazantzaki E, Bitsios P, Sidiropoulou K, Giakoumaki SG. The Effects of WM Training on Cognitive Flexibility in Man. Brain Function Assessment in Learning. BFAL. In: Frasson C, Kostopoulos G, editors. Lecture notes in computer science. Vol. 10512. Cham: Springer; 2017. [Google Scholar]
  • 10.McRae K, Gross JJ. Emotion regulation. Emotion. 2020;20((1)):1–9. doi: 10.1037/emo0000703. [DOI] [PubMed] [Google Scholar]
  • 11.Jasielska A, Kaczmarek L, Brońska A, Dominiak M, Niemier K, Patalas D, et al. The relationship between working memory and emotion regulation strategies. Roczniki Psychologiczne. 2015;18((4)):567–578. [Google Scholar]
  • 12.Xiu L, Zhou R, Jiang Y. Working memory training improves emotion regulation ability: evidence from HRV. Physiol Behav. 2016 Mar 1;155:25–29. doi: 10.1016/j.physbeh.2015.12.004. [DOI] [PubMed] [Google Scholar]
  • 13.Martin RC, Bridgett DJ, Mayes LC, Rutherford HJ. Maternal working memory, emotion regulation, and responsivity to infant distress. J Appl Develop Psychol. 2020;71:101202. [Google Scholar]
  • 14.Deng Y, Hou L, Chen X, Zhou R. Working memory training improves emotion regulation in drug abstainers: evidence from frontal alpha asymmetry. Neurosci Lett. 2021;742:135513. doi: 10.1016/j.neulet.2020.135513. [DOI] [PubMed] [Google Scholar]
  • 15.Magnusson KR, Brim BL. The aging brain. In: Brim BL, editor. Reference module in biomedical sciences. Burlington, MA: Elsevier; 2014. [Google Scholar]
  • 16.Miyake A, Friedman NP, Emerson MJ, Witzki AH, Howerter A, Wager TD. The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks, A latent variable analysis. Cogn Psychol. 2000;41((1)):49–100. doi: 10.1006/cogp.1999.0734. [DOI] [PubMed] [Google Scholar]
  • 17.Figueroa I. [Doctoral dissertation] George Mason University; 2017. Do WM capacity and cognitive flexibility predict creative thinking and production? [Google Scholar]
  • 18.Garnefski N, Kraaij V, Spinhoven P. Negative life events, cognitive emotion regulation and emotional problems. Per Indiv Diff. 2001;30((8)):1311–1327. [Google Scholar]
  • 19.Martin MM, Rubin RB. A new measure of cognitive flexibility. Psychol Rep. 1995;76((2)):623–626. [Google Scholar]
  • 20.Kolb DA. Experience as the source of learning and development. Upper Sadle River: Prentice Hall; 1984. [Google Scholar]
  • 21.Vermunt JD. Inventory of learning styles (ILS) Tilburg, The Netherlands: Tilburg University, Department of Educational Psychology; 1994. [Google Scholar]
  • 22.Foster JL, Shipstead Z, Harrison TL, Hicks KL, Redick TS, Engle RW. Shortened complex span tasks can reliably measure working memory capacity. Mem Cognit. 2015;43((2)):226–236. doi: 10.3758/s13421-014-0461-7. [DOI] [PubMed] [Google Scholar]
  • 23.Owen AM, Iddon JL, Hodges JR, Summers BA, Robbins TW. Spatial and non-spatial working memory at different stages of Parkinson's disease. Neuropsychologia. 1997;35((4)):519–532. doi: 10.1016/s0028-3932(96)00101-7. [DOI] [PubMed] [Google Scholar]
  • 24.Li Z, Chen J, Feng Y, Zhong S, Tian S, Dai Z, et al. Differences in verbal and spatial WM in patients with bipolar II and unipolar depression: an MSI study. BMC Psychiatry. 2021;21((1)):1–11. doi: 10.1186/s12888-021-03595-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Demir ÖE, Prado J, Booth JR. The differential role of verbal and spatial working memory in the neural basis of arithmetic. Dev Neuropsychol. 2014;39((6)):440–458. doi: 10.1080/87565641.2014.939182. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Oakhill J, Yuill N, Garnham A. The differential relations between verbal, numerical and spatial WM abilities and children's reading comprehension. Int Electron J Elem Education. 2011;4((1)):83–106. [Google Scholar]
  • 27.De Jong PF. In: WM and education. Academic Press; 2006. Understanding normal and impaired reading development: a WM perspective; pp. p. 33–60. [Google Scholar]
  • 28.Carretti B, Borella E, Cornoldi C, De Beni R. Role of working memory in explaining the performance of individuals with specific reading comprehension difficulties: a meta-analysis. Learn Individ Differ. 2009;19((2)):246–251. [Google Scholar]
  • 29.Swanson HL. Verbal and visual-spatial WM: what develops over a life span? Dev Psychol. 2017;53((5)):971–995. doi: 10.1037/dev0000291. [DOI] [PubMed] [Google Scholar]
  • 30.Jarrold C, Tam H, Baddeley AD, Harvey CE. How does processing affect storage in working memory tasks? Evidence for both domain-general and domain-specific effects. J Exp Psychol Learn Mem Cogn. 2011;37((3)):688–705. doi: 10.1037/a0022527. [DOI] [PubMed] [Google Scholar]
  • 31.Li D, Christ SE, Cowan N. Domain-general and domain-specific functional networks in working memory. Neuroimage. 2014;102((Pt 2)):646–656. doi: 10.1016/j.neuroimage.2014.08.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Ashkenazi S, Rosenberg-Lee M, Metcalfe AWS, Swigart AG, Menon V. Visuo–spatial working memory is an important source of domain-general vulnerability in the development of arithmetic cognition. Neuropsychologia. 2013;51((11)):2305–2317. doi: 10.1016/j.neuropsychologia.2013.06.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Vergauwe E, von Bastian CC, Kostova R, Morey CC. Storage and processing in working memory: a single, domain-general resource explains multitasking. J Exp Psychol General. 2022;151((2)):285–301. doi: 10.1037/xge0000895. [DOI] [PubMed] [Google Scholar]
  • 34.Garnefski N, Van Den Kommer T, Kraaij V, Teerds J, Legerstee J, Onstein E. The relationship between cognitive emotion regulation strategies and emotional problems, comparison between a clinical and a non-clinical sample. Eur J Personal. 2002;16((5)):403–420. [Google Scholar]
  • 35.Cochrane A, Green CS. Trajectories of performance change indicate multiple dissociable links between working memory and fluid intelligence. NPJ Sci Learn. 2021;6((1)):33. doi: 10.1038/s41539-021-00111-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Kanerva K, Kalakoski V. The predictive utility of a working memory span task depends on processing demand and the cognitive task. Appl Cogn Psychol. 2016;30((5)):681–690. [Google Scholar]
  • 37.Lucidi A, Loaiza V, Camos V, Barrouillet P. Assessing working memory capacity through time-constrained elementary activities. J Gen Psychol. 2014;141((2)):98–112. doi: 10.1080/00221309.2013.870121. [DOI] [PubMed] [Google Scholar]
  • 38.Unsworth N, Redick TS, Heitz RP, Broadway JM, Engle RW. Complex working memory span tasks and higher-order cognition: a latent-variable analysis of the relationship between processing and storage. Memory. 2009;17((6)):635–654. doi: 10.1080/09658210902998047. [DOI] [PubMed] [Google Scholar]
  • 39.Cartwright KB. Insights from cognitive neuroscience, the importance of executive function for early reading development and education. Early Educ Dev. 2012;23((1)):24–36. [Google Scholar]
  • 40.Miyake A, Friedman NP. The nature and organization of individual differences in executive functions, Four general conclusions. Curr Dir Psychol Sci. 2012;21((1)):8–14. doi: 10.1177/0963721411429458. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Willoughby MT, Kupersmidt JB, Voegler-Lee ME. Is preschool executive function causally related to academic achievement? Child Neuropsychol. 2012;18((1)):79–91. doi: 10.1080/09297049.2011.578572. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Unsworth N, Engle RW. The nature of individual differences in working memory capacity: active maintenance in primary memory and controlled search from secondary memory. Psychol Rev. 2007;114((1)):104–132. doi: 10.1037/0033-295X.114.1.104. [DOI] [PubMed] [Google Scholar]
  • 43.Min JA, Yu JJ, Lee CU, Chae JH. Cognitive emotion regulation strategies contributing to resilience in patients with depression and/or anxiety disorders. Compr Psychiatry. 2013;54((8)):1190–1197. doi: 10.1016/j.comppsych.2013.05.008. [DOI] [PubMed] [Google Scholar]
  • 44.Sacchi L, Dan-Glauser E. Never too late to plan, “Refocus on planning” as an effective way to lower symptoms and difficulties in emotion regulation during the COVID-19 first lockdown. Emotion. 2021;21((7)):1483–1498. doi: 10.1037/emo0001039. [DOI] [PubMed] [Google Scholar]
  • 45.Levitt JT, Brown TA, Orsillo SM, Barlow DH. The effects of acceptance versus suppression of emotion on subjective and psychophysiological response to carbon dioxide challenge in patients with panic disorder. Behav Ther. 2004;35((4)):747–766. [Google Scholar]
  • 46.Hayes SC, Strosahl KD, Wilson KG. Acceptance and commitment therapy: an experiential approach to behavior change. New York, NY: Guilford Press; 1999. [Google Scholar]
  • 47.Ehring T, Quack D. Emotion regulation difficulties in trauma survivors, The role of trauma type and PTSD symptom severity. Behav Ther. 2010;41((4)):587–598. doi: 10.1016/j.beth.2010.04.004. [DOI] [PubMed] [Google Scholar]
  • 48.Tull MT, Barrett HM, McMillan ES, Roemer L. A preliminary investigation of the relationship between emotion regulation difficulties and posttraumatic stress symptoms. Behav Ther. 2007;38((3)):303–313. doi: 10.1016/j.beth.2006.10.001. [DOI] [PubMed] [Google Scholar]
  • 49.McLaughlin KA, Mennin DS, Farach FJ. The contributory role of worry in emotion generation and dysregulation in generalized anxiety disorder. Behav Res Ther. 2007;45((8)):1735–1752. doi: 10.1016/j.brat.2006.12.004. [DOI] [PubMed] [Google Scholar]
  • 50.Wang QQ, Fang YY, Huang HL, Lv WJ, Wang XX, Yang TT, et al. Anxiety, depression, and cognitive emotion regulation strategies in Chinese nurses during the COVID19 outbreak. J Nurs Manag. 2021;29((5)):1263–1274. doi: 10.1111/jonm.13265. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Mazaheri M, Afshar H, Nikneshan S, Adibi P. Cognitive emotion regulation strategies in patients with functional dyspepsia and healthy controls: a comparative study. Adv Biomed Res. 2016;5:196. doi: 10.4103/2277-9175.190937. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Lei H, Zhang X, Cai L, Wang Y, Bai M, Zhu X. Cognitive emotion regulation strategies in outpatients with major depressive disorder. Psychiatry Res. 2014;218((1–2)):87–92. doi: 10.1016/j.psychres.2014.04.025. [DOI] [PubMed] [Google Scholar]
  • 53.Madjar N, Segal N, Eger G, Shoval G. Exploring particular facets of cognitive emotion regulation and their relationships with nonsuicidal self-injury among adolescents. Crisis; 2019. [DOI] [PubMed] [Google Scholar]
  • 54.Wolkenstein L, Zwick JC, Hautzinger M, Joormann J. Cognitive emotion regulation in euthymic bipolar disorder. J Affect Disord. 2014;160:92–97. doi: 10.1016/j.jad.2013.12.022. [DOI] [PubMed] [Google Scholar]
  • 55.Garnefski N, Kraaij V, van Etten M. Specificity of relations between adolescents' cognitive emotion regulation strategies and internalizing and externalizing psychopathology. J Adolesc. 2005;28((5)):619–631. doi: 10.1016/j.adolescence.2004.12.009. [DOI] [PubMed] [Google Scholar]
  • 56.Rodríguez-Menchón M, Orgilés M, Fernández-Martínez I, Espada JP, Morales A. Rumination, catastrophizing, and other-blame, the cognitive-emotional regulation strategies involved in anxiety-related life interference in anxious children. Child Psychiatry Hum Dev. 2021;52((1)):63–76. doi: 10.1007/s10578-020-00988-5. [DOI] [PubMed] [Google Scholar]
  • 57.Martin RC, Dahlen ER. Cognitive emotion regulation in the prediction of depression, anxiety, stress, and anger. Pers Individ Differ. 2005;39((7)):1249–1260. [Google Scholar]
  • 58.Bao P, Jing J, Yang WH, Li XH, Cai YS. Violence-related behaviors among adolescents and its association with cognitive emotion regulation strategies. World J Pediatr. 2016;12((1)):82–87. doi: 10.1007/s12519-015-0014-6. [DOI] [PubMed] [Google Scholar]
  • 59.Garnefski N, van Rood Y, De Roos C, Kraaij V. Relationships between traumatic life events, cognitive emotion regulation strategies, and somatic complaints. J Clin Psychol Med Sci. 2017;24((2)):144–151. doi: 10.1007/s10880-017-9494-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Li L, Zhu X, Yang Y, He J, Yi J, Wang Y, et al. Cognitive emotion regulation, characteristics and effect on quality of life in women with breast cancer. Health Qual Life Outcomes. 2015;13((1)):51–10. doi: 10.1186/s12955-015-0242-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Bernuzzi C, Setti I, Maffoni M, Sommovigo V. From moral distress to burnout through work-family conflict, the protective role of resilience and positive refocusing. Ethics Behav. 2021:1–23. [Google Scholar]
  • 62.Garnefski N, Kraaij V. Bully victimization and emotional problems in adolescents, Moderation by specific cognitive coping strategies? J Adolesc. 2014;37((7)):1153–1160. doi: 10.1016/j.adolescence.2014.07.005. [DOI] [PubMed] [Google Scholar]
  • 63.Wisco BE, Sloan DM, Marx BP. Cognitive emotion regulation and written exposure therapy for posttraumatic stress disorder. Clin Psychol Sci. 2013;1((4)):435–442. doi: 10.1177/2167702613486630. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Qi S, Basanovic J, Wang L, Xiang S, Hu W, Yi X. Regulation of negative emotions through positive reappraisal and distancing in high-trait-anxious women. J Affect Disord. 2020;267:191–202. doi: 10.1016/j.jad.2020.02.027. [DOI] [PubMed] [Google Scholar]
  • 65.Kökönyei G, Kocsel N, Király O, Griffiths MD, Galambos A, Magi A, et al. The role of cognitive emotion regulation strategies in problem gaming among adolescents, a nationally representative survey study. Front Psychiatry. 2019;10:273. doi: 10.3389/fpsyt.2019.00273. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Vermunt JD, Donche VA. A learning patterns perspective on student learning in higher education: state of the art and moving forward. Educ Psychol Rev. 2017;29((2)):269–299. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All data, analysis code, and research materials are available at (https://osf.io/hf5tc/?view_only=03dde01b7e1149a0ab7b0c019a9b4267).

Articles from Dementia and Geriatric Cognitive Disorders EXTRA are provided here courtesy of Karger Publishers

RESOURCES