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Chapter 1
Introduction
Cognition is one of the most important and basic functions of the human brain, which
is most important for survival. The mental processes involved in cognition are attention,
memory, thought, problem-solving, language, and learning. All the cognitive processes are
related and work with each other. For example, our memory of previous events plays an
important role in solving the problems. Cognitive flexibility also affects our judgment and
memory (Cherry, 2020).
The ability to process multiple dissimilar concepts simultaneously or separately can
be a highly regarded skill that is overlooked. However, increasing the intensity and workload
on the number of processes one can think of can lead to memory losses and deficits, which
can cause difficulties with problem-solving in certain situations (Young, Zavelina, & Hooper,
2008). The present study examines the relationships between three main factors; cognitive
flexibility, memory deficits, and problem-solving.
Having good executive functioning skills is particularly important. Therefore, success,
especially in young adults, has higher executive functioning after most tasks. Executive
functioning includes cognitive flexibility, problem-solving skills, decision-making ability,
reasoning (Whitney, 2017). In addition, having higher cognitive flexibility increases the
chances of success in life as a person with higher cognitive flexibility can adapt according to
the situation (Cherry, 2020). Cognitive flexibility is important on both micro and macro
levels as, without it, humans may not function sufficiently, and it helps us adapt to new
situations. Cognitive flexibility helps us come up with new ways of solving problems (Miller,
2021).
In day-to-day problem-solving tasks, one must have a balanced adaptive behavior to
enable these tasks' success while also preventing failure to recall the tasks. The decision-
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making control comes from the cognitive control response of stability and flexibility, and in
effect, the balance of these determines the control output (Serrien & O'Regan, 2019). It is
clear that the cognitive response declines with age and results in difficulty adapting to new
situations and environments (Magnusson & Brim, 2014). However, other factors include
certain mental and physical disorders, quality of life and upbringing, and other significant
factors. In turn, the decline of these cognitive control measures leads to problems with
memory and the ability to recall tasks and information either while undertaking the tasks or
with time. The challenges associated with problem solving and memory arise from these
cognitive measures (Ophir, Nass, Wagner, 2009).
Cognitive flexibility is very important to us daily, and practicing cognitive flexibility
skills or multitasking causes physiological changes to our brain. It creates new neural
pathways in our brain, which enhances our cognitive flexibility skills and thinking, which in
turn helps us to come up with more creative solutions to the problems. In addition to that,
cognitive flexibility also helps us with divergent thinking (Miller, 2021). If people are not
cognitively flexible, they cannot develop different strategies to solve problems effectively.
Neuroimaging has shown that cognitive flexibility depends upon frontal and striatal brain
regions. The frontal region of the brain is also linked with other high functions such as
decision making and problem-solving (Sahakian, Langley & Leong, 2021)
Problem-solving is one of the most important cognitive processes. Because of
problem-solving skills and cognitive flexibility, we know there is not a single and same way
to solve all the problems we face. So, we develop different, creative, and new ideas to solve
the problems. All of our cognitive processes work with each other to carry out the function
properly, and that is why we need to use strategies to improve our cognitive skills and
abilities (Goldman, 2020). According to MasterClass (2020), cognition plays a crucial role in
effective problem-solving.
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Cognitive Flexibility and Problem Solving
Cognitive flexibility is the person's ability to think about two or more different and
diverse concepts simultaneously. It is also defined as the capability to change one's mind or
alter one's thinking process when faced with a demanding situation with seemingly no other
solutions. The fMRIs of the human brain display that when a person is engaged in task
switching, his brain regions that contain the prefrontal lobe, posterior parietal cortices, basal
ganglia, and anterior cingulate are stimulated (Braem & Egner, 2018). Problem-solving is the
daily process of identifying, analyzing, and resolving problems or issues (Cherry, 2020).
Problem is described as something that causes trouble but may not instantaneously know
what to do. Recently, Amico (2021) found out that the best problem solvers had two traits:
they were cognitively diverse, which means they were curious, experimental, intriguing, and
nurturing in nature. They were also psychologically safe, which refers to being appreciative,
controlling, competitive, hierarchical, and flexible.
Much research has been conducted on cognitive flexibility and problem-solving.
Kalis, Fuesting, and Cody (2019) assessed the role of grit and cognitive flexibility in
problem-solving. Results revealed that grit perseverance predicted performance on hard vs.
easy Sudoku puzzles, and it was also associated with effort on hard and extremely hard
puzzles. Other than that, mediation analysis revealed that grit perseverance indicated
amplified efforts through decreased cognitive flexibility.
Another research (Adler, & Benbunan-Fich, 2015) suggests that multitasking has a
negative impact on problem-solving, especially when the performer is forced to switch tasks
and the problem to be solved is considered difficult. However, if subjects find the problem to
be solved easily, their performance is good even with multitasking. The association between
cognitive flexibility and problem solving was also examined in Stevens's (2019) research.
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According to the study, children at risk are more likely to provide less problem-solving
strategies if cognitively more flexible.
Lee et al. (2021) conducted a study that examined the effect of age on cognitive
flexibility in older adults. They studied the brains of healthy older adults using functional
MRI. They concluded that age does not directly affect cognitive flexibility, but as a person
aged, brain inflexibility increased, and it mediated the reduced cognitive flexibility in older
people.
Cañas, Quesada, and Antolí (2003) explored cognitive flexibility in complex problem
solving and how a person adapts to the situation. The results revealed that when an
environment changes, the participants' performance gets negatively affected. However, this
negative impact is dependent on the problem-solving strategy the person chooses to solve the
problem in that complex situation. Earlier, Dennis (2017) also found that if a deficit in
cognitive flexibility, the person may show more rigidity in behaviors. It can be assumed that
rigidity in behavior is observed; there may be a negative problem-solving orientation as not
looking for a problem from different perspectives is not execrated. Consequently, a person
will just get fixated on one solution even if it is not suitable.
Hafine (2018) examined pre-service teachers to assess their cognitive flexibility and
interpersonal problem-solving skills. Based on the analyzed data, it was concluded that preservice teachers who showed high cognitive flexibility also solved problems with persistence
and constructively. They had good problem-solving skills than the less cognitively flexible
teachers. It has been brought to attention by Gabrys et al. (2018) that cognitive flexibility
depends on attentional processes. That is why more control and attention are required when
assessing a new situation and planning action. Important tasks need to be assessed beforehand
when facing a new situation to not interfere with the task. Based on the study (Gabrys et al.,
2018), it can be inferred that how problems are solved in new and novel situations is affected
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by our cognitive flexibility. If someone is more flexible, there will be a higher chance that
s/he will assess the new situations more efficiently and will produce solutions
Theoretical Framework
Many theories also shed light on the relationship between cognitive flexibility and
problem-solving. According to the theory “functional fixedness” by Dunker (1945), human
thinking processes are inflexible or rigid by nature. They find it rather hard to find new and
novel ways to use objects to solve different problems. Contrary to that, Gestalt's (1945)
problem-solving theory also explains the relationship between cognitive flexibility and
problem-solving. According to this theory, problem-solving occurs with sudden insight.
When the person restructures the problem in his mind and looks at it from a different
approach or perspective, this only happens if the person is cognitively flexible enough to find
new and novel ways to solve a problem.
Other than that, the four-category model by Anderson and Reidy (2012) of executive
functioning also emphasizes the relationship of cognitive flexibility with problem-solving. It
suggests that executive functions are divided into four domains: attentional control, goal
setting, information processing, and cognitive flexibility. These domains overlap and work
with each other to perform executive functions properly. When a person is cognitively
flexible, he can direct his attentional control to the stimulus presented to him and process the
information to solve different problems. This theory can also infer that if a person is less
flexible, he is less likely to allocate his attentional resources to different problems, thus
failing to solve them.
Furthermore, Cognitive Flexibility Theory (Spiro & Jehng, 1990) also proposed that
individuals who can perform a single task in multiple ways are also more adaptive. They can
understand conditional environmental fluctuations and change how they behave and respond
to a particular stimulus. Thus, more flexible individuals can solve problems more efficiently
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and productively. To summarize this, based on the research as mentioned above (Adler, &
Benbunan-Fich, 2015; Cañas, Quesada, Antolí, & Fajardo, 2003; Kalis, Fuesting, & Cody,
2019; Magnusson, & Brim, 2014; Stevens, 2019), it can be said that cognitive flexibility is
related to problem-solving and directly affects how individuals solve their problems and how
many attentional resources they allocate in given situations. Along with problem-solving
skills, cognitive flexibility is also associated with memory deficits, signifying how daily
memory problems are directly and indirectly linked with cognitive flexibility.
Cognitive Flexibility and Memory Deficits
Memory deficits can be described as the failure to retrieve the information already
stored or track the information attentionally or attentively. It is also known as
absentmindedness, described as the failure to retrieve or encode memories due to lack of
attention (Carriere, Cheyne, & Smilek, 2008; Schacter, 1999).
Several researchers explored the relationship between cognitive flexibility and
Memory Deficits. A few of these research are mentioned in this study. One study (Nweze &
Nwani, 2020) explored the role of inhibition and working memory in two cognitive flexibility
skills: switch cost and mixing cost. The results revealed that cognitive flexibility skills were
significantly correlated with inhibitions and working memory. Inhibition was positively
correlated with switch cost. On the other hand, working memory was negatively correlated
with mixing cost.
Gabrys et al. (2018) found that cognitive flexibility relied on attentional resources.
More focus is required to do things in novel and inexperienced situations. Based on the
research results, it could be assumed that cognitive flexibility has a relationship with attention
and memory as things are encoded and stored and then retrieved from our memory only when
we pay attention. Earlier, Epsy and Bull (2005) also aimed to find the relation of cognitive
flexibility with short-term memory in children who were in preschool. The study results
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indicated that the children who had better short-term memory span could easily shift their
focus from one thing to another. They thus were more cognitively flexible than those who
had poor memory.
Another research conducted by Farrant et al., (2017) inquired into the relationship
between attention and cognitive flexibility. The research findings indicated that cognitive
flexibility and attention are negatively correlated. Individuals who were more cognitively
flexible showed lower levels of inattention which led to lesser deficits in attentional tracking.
Individuals with higher cognitive flexibility could devote more attention to their work
without getting distracted.
Some researchers like Koslov, Mukerji, Hedgpeg and Lewis-Peacock (2019) explored
the relationship between cognitive flexibility and prospective memory. Prospective memory
is the ability to recall things in the future or hold up the execution of a certain goal until the
appropriate time (Dismukes, 2012). The study concluded that individuals who have
demonstrated more cognitive flexibility also had good prospective memory.
Other than that, Maramis et al. (2021) conducted a study on rats to study depression.
They concluded that working memory and cognitive flexibility go hand in hand. Impairment
in cognition preceded depression, and since cognitive flexibility and working memory were
related, impairment in both functions occurred simultaneously. Furthermore, Ashraf,
Shameem, and Najam (2021) found out that deficits in executive functions also affected
social adjustment and reading deficits. Initiation and working memory deficits were
significant mediators between reading deficits and social adjustment.
Metacognition was also a significant mediator of the relationship between reading
deficits and internalization/ externalization of behaviors (Ashraf, Najam, Jibeen, 2020)
Moreover, higher cognitive flexibility is associated with better working memory, and
lower cognitive flexibility is associated with poor memory. It was concluded in Blackwell,
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Cepeda, and Munakata's (2009) study. They also figured that those children who persevered
were slower in responding to different questions and children who were more cognitively
flexible and were able to switch their attention easily were sharp and more responsive.
Though many researchers suggest that cognitive flexibility is associated with good memory,
another research by Tello-Ramos et al. (2019) concluded that cognitive flexibility and
memory were a trade-off in animals. When animals were more cognitively flexible, they
showed poorer memory retention, and when they were less flexible, they were good at
retaining memory.
Arshad et al. (2020) also reexamined Pakistani physical therapy students to see how
good their memory and cognitive flexibility were. The study results showed that female
students showed more cognitive flexibility while male students had better memory retention.
Another research by Dev, Javed, and Bai (2021) revealed that memory and cognitive
impairment go side-by-side in Alzheimer’s patients. As memory gets worse, cognition,
including language, problem-solving, mental flexibility, and judgment, also worsens. Other
than that, memory deficits were also significantly correlated with getting injured more in
Alzheimer’s patients.
Theoretical Framework
The relation between cognitive flexibility and memory deficits can be explained by an
"attentional inertia" (Kirkham, Cruess, & Diamond, 2003). This theory states that
perseveration happens when someone focuses on one task and it becomes hard to shift focus.
It results in attentional deficits and ultimately memory deficits when a person shifts tasks too
quickly. Another theory that also sheds light on the relationship between the two study
variables is the interference theory proposed by Allport and Wylie (1999, 2000). According
to this theory of attention, when a person swiftly shifts his attention from one task to another,
the previous actions affect the new actions because of the sudden shift in attention. There are
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memory and attentional deficits if the time between stopping the presentation of the old
stimulus and presenting the new stimulus increases.
To conclude this, the relationship between cognitive flexibility and memory deficits
has been explored by several studies (For example; Blackwell, Cepeda, & Munakata, 2009;
Carriere, Cheyne, Smilek, 2008; Epsy & Bull. 2005; Farrant et al., 2017; Koslov, Mukerji,
Hedgpeg, & Lewis-Peacock, 2019; Nweze, & Nwani, 2020; Schacter, 1999). These studies
suggest a relationship between cognitive flexibility and several types of memories and
memory deficits.
Moderating Role of Memory Deficits
Though several studies above examined the direct associations of cognitive flexibility
with problem-solving skills and memory deficits, moderated links are yet to be explored.
Passolunghi and Pazzaglia (2004) investigated the role of memory updating in cognitive
processes and arithmetic word problem-solving in a sample of children. The result of the
study suggested that children with a higher ability of memory updating had better cognitive
functioning. They performed better on problem-solving, computational tests, and recall
problems. The children with poor memory updating ability showed poorer cognitive process
and arithmetic word problem-solving skills. Any such findings for the samples of young are
not available.
Ihle, Gouveia, Gouveia, and Kliegel (2020) investigated the role of self-report
memory problems in decline in executive functioning. The study results indicated that adults
who had less engagement in leisure time activities showed more decline in executive
functioning over 6 years. Another study carried out by Peng, Namkung, Barnes, and Sun
(2016) explored the moderating effect of memory. In the meta-analysis, the association
between working memory and mathematics and the possible moderators that include different
domains of working memory were explored. The moderation analysis revealed that domains
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of working memory are a significant moderator, and mathematics were comparably
associated with verbal, numerical, and visuospatial working memory. Due to cognitive
deficits, the association between mathematics and working memory was stronger in
individuals with mathematical difficulties.
Demographic Correlates
In any psychological phenomenon, the role of certain personal, economic, social, and
cultural characteristics cannot be ignored. As every psychological behavior is exercised,
executed, and evaluated in a given context, it is very important to understand such factors'
roles. Piccolo et al. (2016) conducted a study on Brazilian children to explore the relationship
of socioeconomic status with intelligence quotient, memory, executive functions, and
language. The results demonstrated that the socioeconomic status of the Brazilian children
impacted their cognitive performance, and it affected their language, memory, intelligence,
and executive functions. Besides that, gender plays a crucial role in cognitive flexibility and
memory deficits. Arshad and colleagues (2020) discovered that men had better memory while
women had good cognitive flexibility.
Gender also plays a role in problem-solving skills, as it was concluded in a study by
Gallagher and colleagues (2000) that men had better problem-solving skills than women.
D'Zurilla et al. (1998) also discovered gender differences in the positive and negative
problem orientation. They also concluded that some gender differences in problem-solving
were associated with age, such as there were also gender differences observed in impulsive
problem-solving styles in young adults. Khasawneh did a recent study (2021) to find students'
cognitive flexibility with learning disabilities. The research results suggested that the male
students who had learning disabilities were more cognitively flexible than the female
students.
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Cognitive flexibility is also associated with age. Children and older adults showed
patterns of brain dynamics associated with poor cognitive flexibility compared to young
adults (Kupis et al., 2021). Lee et al. (2021) also confirmed that cognitive flexibility gets
reduced in older adults and that brain inflexibility mediates the relationship between age and
reduced cognitive flexibility.
In conclusion, the association between cognitive flexibility and cognitive performance
is noted, including memory and problem-solving. Furthermore, the role of demographic
characteristics such as age, gender, and socioeconomic status should also be considered as
they influence cognitive flexibility, memory deficits, and problem-solving.
To summarize this, the relationship between cognitive flexibility, memory deficits,
and problem-solving has been explored by many researchers in past studies (Adler, &
Benbunan-Fich, 2015; Blackwell, Cepeda, & Munakata, 2009; Carriere, Cheyne, Smilek,
2008; Cañas; Epsy & Bull. 2005; Farrant et al., 2017; Kalis, Fuesting, and Cody, 2019;
Koslov, Mukerji, Hedgpeg, & Lewis-Peacock, 2019; Nweze, & Nwani, 2020; Schacter, 1999
Quesada, Antolí, & Fajardo, 2003; Magnusson, & Brim, 2014; Passolunghi, & Pazzaglia,
2004; Peng, Namkung, Barnes, & Sun, 2016; Stevens, 2019) and most of the studies
indicated that these variables are related to each other. The present study is an effort to
explore the relationship of these variables simultaneously.
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Chapter 2
Literature Review
In this section of the study, studies explaining the significant associations between
cognitive flexibility, problem-solving skills, and memory deficits are documented.
A study by Kalis, Fuesting, and Cody (2019) aimed to find the role of perseverance in
solving sudoku. The study aimed to find the purpose of grit and cognitive flexibility in
problem-solving. In the experimental study, 117 female participants were taken. Two studies
were conducted in this research. The result of study one revealed that on hard vs. easy
puzzles, grit-perseverance predicted the performance. Study 2 also revealed that grit
perseverance was correlated with the effort on hard and extremely hard puzzles. Other than
that, mediation analysis revealed that grit perseverance indicated amplified efforts through
decreased cognitive flexibility.
Khasawneh (2021) conducted research to find out the cognitive flexibility of the
students who had a learning disability in English and found its relationship with other
variables such as gender and class. For the study, 380 students were taken. A scale to assess
cognitive flexibility was designed for them. The study results concluded that the gender
differences in cognitive flexibility of students with learning disabilities were significant.
Males were more flexible than females, and students from the second grade showed more
flexibility than students from the first grade.
Adler and Benbunan-Fich (2015) conducted an experimental study to find the effects
of multitasking on problem-solving and performance. A total of 636 individuals took part in
the study, where they were divided into three groups. One group could switch tasks at its
discretion, the other was forced to switch tasks, and the third group had to solve the problems
in a sequential order without switching tasks. The results of the experiment revealed that
when the problem was difficult, the group which could switch tasks at its discretion
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performed better than the other two groups, and when the problem was easy, the group which
was forced to multitask performed better than the other two groups.
A correlational study by Stevens (2019) aimed to find the relation between cognitive
flexibility abilities, social problem solving, teachers' view of children's social skills, and
problematic behavior. For this study, 86 children between the ages of 4.5 to 6 years old,
particularly from lower socioeconomic status. The study results indicated a significant link
between social problem solving, cognitive flexibility, and social outcomes. The students who
showed better cognitive flexibility provided fewer problem-solving strategies and showed
better social skills.
Other than that, Nweze and Nwani (2020) explored the relationship between cognitive
flexibility, working memory, and inhibition. In the study, 110 adolescents from Nigerian
schools were taken, 58 were males, and 45 were females. The gender of 7 students was
unidentified hence not reported. The study performed four computerized tasks to assess their
cognitive flexibility, including switching cost and mixing cost, working memory, and
inhibition. The study results revealed that the inhibition was positively and significantly
correlated with switching cost, and working memory was negatively linked with mixing cost.
So, the hypothesis that cognitive flexibility is associated with inhibition and working memory
was proven right in this research.
An experimental study was conducted by Epsy and Bull (2005). This study inquired
into inhibitory processes and individual differences in the short-term memory in young
children from preschool. The total number of participants in the study were 183 between the
age of 3 years to 6 years (M =4.84 years, SD =.5 years). The young children were divided
into three different groups based on their ability to recall the string of digits. The study results
showed that children who had good memory or recalled more digits could easily shift their
focus and disengage from shifting trials than those with poor short-term memory.
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Farrant and colleagues (2017) assessed the link between attention and cognitive
flexibility. A sample of 70 students was taken, and they were given inattention and difficult
questionnaires to fill. The study hypothesized that cognitive flexibility was associated with
attention and focus. The study revealed that individuals with more advanced and developed
cognitive flexibility showed lesser inattention. Such individuals were able to give more
attention to their tasks and ultimately had lesser deficits in attentional tracking.
An experimental study was carried out by Koslov, Mukerji, Hedgpeth, and LewisPeacock (2019) to find the relation of cognitive flexibility with prospective memory. Fifty
females were included in experiment 1, and 28 females were included in experiment 2. The
experiment's findings established that cognitive flexibility was very adaptive and associated
with increased performance on the prospective memory tasks in the study participants.
A cross-sectional study was conducted by Arshad et al. (2020) to evaluate cognitive
flexibility and memory in physical therapy students. A total of 227 students from DPT were
taken between 19 to 25. Five tests were used to assess cognitive flexibility and memory:
Brown-Peterson and delayed recall, free recall, month backward test, Stroop test, and
cognitive flexibility. In the study, 172 participants were female, while 55 were males. The
study results revealed that female students showed better cognitive flexibility than male
students, and male students showed better memory than female students.
Another indigenous study by Tariq and Adil (2020) explored the role of cognitive
rigidity as a mediator between the avoidant temperament and social adjustments in the
teachers. A total of 300 participants, out of which 150 were school teachers and 150 were
teachers at madaris in Lahore and Sargodha, took part in the study. The study's findings
concluded a negative relationship between the avoidant approach and cognitive rigidity in the
teachers who worked in schools, and this relationship was positive in madaris teachers.
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Channa and colleagues (2017) also aimed to investigate the role of metacognitive
strategies on reading skills. This study was carried out on first-year students of Quaid-eAzam University of Engineering Science and Technology. The qualitative instruments used
in the study involved semi-structured interviews with the teachers and observations in the
classroom. The findings revealed that using more metacognitive strategies help with reading
skills, comprehension, and attention of the first-year students
Another study to explore the relationship between cognitive flexibility and working
memory in children was conducted by Blackwell, Cepeda, & Munakata (2009). It was an
experimental study in which 42 children aged 5 and 6 years were the study sample. Out of
these 42 children, 30 persevered, and 12 switched tasks. The children's tasks were box
completion, then offset reaction time, after this 3D and 1D card sort, and finally probabilistic
selection. The experiment concluded that the participants with higher cognitive flexibility or
switched tasks had good working memory, and those who perseverated had bad.
To find the relation between cognitive flexibility with interpersonal problem-solving
skills in pre-service teachers, Hanife (2018) conducted research. The study design was a
descriptive correlational study. Cognitive Flexibility Inventory (CFI) and Interpersonal
Problem-Solving Inventory (IPSI) were used to collect the data of 531 pre-service teachers
who had studied in the Teacher Training Department in Fall semester, 2017-18. This study
concluded that the pre-service teachers who were more flexible were also more likely to solve
the problems persistently and constructively. Other than that, there were also differences
according to gender and maternal education status.
Researchers Passolunghi and Pazzaglia (2004) explored the link between memory and
problem-solving was also explored by researchers Passolunghi & Pazzaglia (2004) in an
experimental study. In the study, two groups of 35 children in 4th grade were taken out of the
total sample of 89. They were divided into groups based on their good or poor memory
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updating ability, assessed using an updating task. Both groups in the study were required to
solve arithmetic problems and recall relevant information from another problem.
Furthermore, the spans, PMA verbal, and computational tests were also administered. The
research results revealed that children who had better memory updating skills also had good
cognitive processing abilities. However, both groups performed the same on PMA verbal
subtests.
Ihle, Gouveia, Gouveia, and Kliegel (2020) conducted a longitudinal study to explore
the association between self-report memory problems followed by the decline in executive
functioning 6 years after and the role of engagement in leisure activities. Eight hundred
ninety-seven adults (M= 74.33) were assessed on the Traill Making Test two times by the
time difference of 6 years. The study results concluded that the role of memory problems
could be a significant predictor of the decline of executive functions. Moreover, individuals
who had less engagement in leisure activities showed a decline in executive functions over 6
years.
A meta-analysis of 110 studies was done to investigate the relationship between
mathematics and working memory and identify the potential moderators of this relationship,
which included different domains of working memory and different types of mathematical
skills. A significant association was found between mathematics and working memory. The
moderation analysis revealed that the verbal, numeric, and visuospatial working memory
domain significantly moderates the relationship between mathematics and working memory.
The link between mathematics and working memory was also stronger in the individuals who
had mathematical difficulties due to cognitive deficits (Peng, Namkung, Barnes, & Sun,
2016).
Piccolo et al. (2016) investigated 419 Brazilian children between the ages of 6-12
from private and public schools in Porto Alegre to investigate the role of socioeconomic
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status on cognitive performance, intelligence, language, executive functions, and memory.
The results revealed that the participants' socioeconomic status impacted the children's
cognitive performance, memory, language, and executive functions. Especially the
socioeconomic status had a stronger impact on the younger children, I.e., up to the age of 9
years. After that age, many other factors such as home environment, schooling, and social
environment could mitigate the socioeconomic impact.
Age is also associated with cognitive flexibility. Kupis et al. (2021) conducted a study
to examine the brain dynamics which underly cognitive flexibility during lifespan. For the
study, 601 individuals from 6 to 85 were taken. Age was examined the interaction of brain
dynamics in the three neurocognitive networks, medial frontoparietal, midcingulate-insular,
and lateral frontoparietal networks. The study's findings exhibited that children and older
adults displayed brain dynamics associated with poor cognitive flexibility than young adults.
To conclude the studies mentioned above, it can be inferred that there is a significant
relationship between cognitive flexibility, memory deficits, and problem-solving. People with
higher cognitive flexibility sometimes show more memory deficits and sometimes lesser
ones, but the non-directional relationship is obvious. Cognitive flexibility has also shown a
relationship with problem-solving in previous researches.
Rationale of the Study
Many researches (Koslov, Mukerji, Hedgpeth, & Lewis-Peacock, 2019; Gómez-Ariza
et al., 2016; Stevens, 2009; Hanife, 2018) have explored the areas of cognition, memory, and
problem-solving, but in the local context, the literature insufficiently seems to explore these
dimensions. In Pakistan, the field of cognitive psychology is relatively undiscovered. So,
exploring it further in Pakistan is necessary. Research by Arshad and colleagues (2020) has
been conducted on memory and cognitive flexibility in Pakistan, but other possible related
aspects, such as problem-solving, need to be addressed.
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Other than that, this research is a unique addition to the previous literature as it
explores cognitive flexibility, memory deficits, and problem-solving in the young Pakistani
population. No research has been conducted to explore the relationship between these three
variables simultaneously before. Furthermore, the relationship between cognitive flexibility
and memory deficits is disputed as some of the research suggests that there is a negative
correlation (Farrant et al. 2017; Koslov, Mukerji, Hedgpeg, Lewis-Peacock, 2019; Gabrys et
al., 2018), and some suggest there is a positive correlation (Epsy & Bull, 2005; Tello-Ramos,
Branch, Kozlovsky, Pitera, Pravosudov, 2019). This study will explore how this relationship
turns out in the Pakistani context.
In addition, memory problems are often observed in older adults in most empirical
studies. This study will explore memory problems about cognitive flexibility and problemsolving in younger adults. Also, this study is quantitative. Many studies (Ashraf, 2020; Lee et
al., 2021) on these variables are experimental, but the quantitative study will also help us
study how people perceive their cognitive flexibility, memory deficits, and problem-solving.
Objective of the Study
The objective of the current study is to observe the relation of cognitive flexibility with
problem-solving in context with moderating role of memory deficits in young adults.
1. The objective of this study is to find an association between cognitive flexibility,
memory deficits and problem-solving.
2. To explore the role of personal characteristics in the link between cognitive
flexibility,
memory deficits and problem-solving.
Hypotheses of the Study
Based on the previous researches following hypotheses have been formulated:
1. Cognitive flexibility is associated with memory deficits and problem-solving.
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2. Cognitive flexibility predicts problem-solving in adults.
3. Memory failure moderates the relationship between cognitive flexibility and problemsolving.
4. Personal characteristics of participants likely to play a role in suggested moderated
link
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Chapter 3
Methodology
Research Design
The correlational research design was used in the current study as the objectives of the
present research is to investigate the relationship of cognitive flexibility and memory deficits
with problem-solving in the Pakistani young adults
Participants and Sampling Strategy
The participants included in this study were young adults from Pakistan. The total
number of participants in the current research was 298. Male to women participants were
proportionate (47:53) between 18-28 (M: 22.89, SD: 1.99). The data was collected by
convenience sampling strategy. 90% of the data was gathered through online surveys, and the
rest was collected by approaching the participants directly. The response rate of the
participants was approximately 60% because the people were less likely to fill the
questionnaires online. The medium through which the participants were approached for data
collection were Facebook, Instagram, WhatsApp, LinkedIn, email, and face-to-face. The data
was collected during the third wave of Covid-19 from July 2021 to August 2021
Inclusion/Exclusion Criteria
To filter out the participants, a screening questionnaire based on the
inclusion/exclusion criteria of the study was added in the survey form. Only the educated
participants who comprehended English well were contacted because the survey was English.
Other than that, only the participants in Pakistan were approached and included in the study.
Furthermore, participants who sought any psychological or psychiatric assistance and who
had any mental or physical disability were excluded from the sample.
Operational Definition of Study Variables
The operational definitions of the constructs used in the current study are mentioned
below.
21
Cognitive Flexibility
Cognitive flexibility is defined as the person’s willingness to change according to the
situations and be aware of other alternatives (Martin & Rubin, 1995).
Memory Deficits
Memory deficits are characterized by retrieval failures, failure in attentional tracking,
and unidentified memory deficits (Royle & Lincoln, 2009).
Problem Solving
Problem-solving is characterized by the cognitive-behavioral process through which
people try to identify ways to find solutions to problems they go through in daily life
(D'zurilla, Nezu, & Maydeu-Olivares, 2004).
Tools of Assessment
Demographic Questionnaire
A demographic questionnaire was added, which included questions about the age,
gender (male/ female), education (post-graduate, graduate, undergraduate and intermediate),
perceived socioeconomic status (lower class, lower-middle-class, middle class, upper-middleclass, and upper-class), and family system (joint family/ nuclear family).
Cognitive Flexibility Scale (CFS: Martin & Rubin, 1995)
CFS consists of 12 items which are marked on a 6-point Likert scale. The response
options on CFS range from 1=strongly disagree to 6=strongly agree. Four of the 12 items are
reversed scored. Sample items included: “I can communicate an idea in many different
ways.” (item no.3). The score on this scale ranges from 12-72, and high cognitive flexibility
scores indicate more cognitive flexibility. The validity studies by the original authors and
other successive authors show that this scale shows good concurrent, construct, and criterion
validity. The reliability of CFS is 0.77. In this research, the alpha coefficient of the scale is
0.73.
22
Everyday Memory Questionnaire-Revised (EMQ-R: Royle & Lincoln, 2009)
Everyday memory questionnaire is a self-report measure of failures in memory or
perception about one’s memory. This scale consists of 13 items marked on a 5-point Likert
scale. The response options on this questionnaire are 4= once or more in a day, 3= more than
once a week but less than once a day, 2= more than once a month but less than once a week,
1= about once in a month, and 0=once or less in a month. The lowest score is 0, and the
highest is 52 on EMQ-R. It has three subscales that are retrieval failures (n=7). Sample item
included: “Completely forgetting to do things you said you would do, and things you planned
to do” failure in attentional tracking (n=4) (sample item: Getting the details of what someone
told you mixed up and confused) and unidentified memory deficits (n=2) (Example:
Forgetting where things are normally kept, or looking for them in the wrong place). High
scores indicate more memory deficits in daily routine. The reliability tests conducted by the
original authors show good internal reliability of 0.89 in healthy participants. In the current
study, the scale showed overall good alpha reliability of 0.83.
Social Problem-Solving Inventory- Revised (SPSI-R: D'Zurilla, Nezu, & MaydeuOlivares, 1996)
SPSI-R consists of 25 items which comprise five subscales which are negative
problem orientation (NPO, n=5), positive problem orientation (PPO, n=5), avoidant problemsolving style (APSS, n=5), impulsive style problem-solving (ISPS, n=5), and rational
problem solving (RPS, n=5). It is a 5-point Likert scale. The responses on this scale are rated
on a 5-point Likert-type scale which ranges from 0=not at all true of me to 4=extremely true
of me. The scores range from 0 to 20 on each subscale of problem-solving. A higher score on
any subscale indicates the presence of the individual problem-solving style. In the past study,
the internal consistency ranged from 0.73 to 0.86 (Hawkins, Sofronoff, & Sheffield, 2009).
23
The alpha coefficient of the SPSI-R in the current study ranged from 0.75 to 0.83 (see table
1).
Ethical Considerations
Thesis Committee approved the present study of the Department of Humanities,
COMSATS University Islamabad, Lahore campus. The study participants were briefed about
the objective of the current study, and online informed consent was also taken from the
participants. They were also informed that their participation in the study was completely
voluntary. If they wanted to withdraw from the study at any point, they were free to do so
without any penalty. They were told that the personal information they shared would be kept
confidential and would only be used for research purposes. Moreover, the safety and respect
of the participants were also considered. No participants’ safety or dignity was compromised
in the research. In addition, the confidentiality and privacy of the participants were also
assured.
Statistical Analysis
In the current study, the data was processed and analyzed using descriptive and
inferential statistics. Descriptive statistics were analyzed by calculating the mean (M),
standard deviation (SD), frequencies (f), percentage (%), graphs, Skewness, Kurtosis, and
alpha coefficients. On the other hand, inferential statistics were also estimated by running the
Pearson Product Moment Correlational Analysis and Regression Analysis using Process
Macross (Andrew and Hayes, 2006) on SPSS version 26.
24
Chapter 4
Results
Analysis Plan
This study section addresses preliminary analysis, descriptive analysis, and
inferential analysis. The missing values, outliers, and random responses were identified and
cleaned out in a preliminary analysis. Then, descriptive analysis was run to calculate the
frequencies, percentages, mean, standard deviation, skewness, kurtosis, and alpha
coefficients. Then, inferential analysis was performed, including Pearson Product Moment
Correlation Analysis and Regression Analysis.
Preliminary Analysis
The data was cleaned in the study's first phase, and the response rate was checked.
The response rate of the participants was 100% because a google form was generated to
collect the participants' responses, and it was made sure that the participants responded to
each question on the questionnaire. The responses were directly downloaded from the
google form in an excel sheet, and then they were exported to SPSS for further analysis.
Descriptive Analysis
Table 1 shows the descriptive characteristics of the sample. In the current study, the
participants were young adults between 18 to 28 years; the mean age (n=298) was 22.89
years, and the standard deviation was 1.99. The study's total sample comprised 298 young
adults (men= 142, women=156). The reported education level of young adults was divided
into four categories. People who were in intermediate comprised 31.7% (n=71) of the sample,
58.4% (n=129) of the individuals were graduates, and post-graduates constituted 10% (n=22)
of the sample. The socioeconomic status of the participants is divided into five categories. In
this study, 1% (n=4) of the participants belonged to lower socioeconomic class, 3% (n=9) of
the participants were from a lower middle class, 50% (n=149) of the sample comprised of
25
middle-class individuals, 41% (n=122) participants were from an upper middle class, and 5%
(n=14) participants belonged to the upper class. The family system was divided into two
categories: joint family (n=204, 69%) and nuclear family (n=94, 31%).
Table 1
Descriptive Characteristics of Study Participants
Variables
M
SD
Min-Max
Age (years)
22.89
1.99
18-28
f
%
100%
Gender
Female
156
53%
Male
142
47%
Intermediate
70
31.7%
Graduate
129
58.4%
Post-graduate
22
10%
Lower class
4
1%
Lower middle class
9
3%
Middle class
149
50%
Upper-middle-class
122
41%
Upper class
14
5%
Joint family
204
69%
Nuclear family
94
31%
Education
Socioeconomic status
Family System
26
Table 2
Cronbach’s Alpha Reliability of Study Variables
n
M
SD
α
Min-Max
S
K
Variables
Actual
Observed
Cognitive Flexibility
12
48.6
7.91
.71
12-72
27-65
-.34
-.33
Memory Deficits
13
18.39
9.39
.83
0-52
0-42
.34
-.60
i. Retrieval
7
10.92
5.9
.76
0-28
0-26
.34
-.67
ii. Attentional tracking
4
5.32
3.6
.66
0-16
0-16
.45
-.42
iii. Unidentified
2
2.14
1.99
.57
0-8
0-8
.87
.12
Problem Solving
25
i. Negative Problem Orientation
5
8.46
4.75
.82
0-20
0-20
.25
-.69
ii. Positive Problem Orientation
5
11.1
4.46
.80
0-20
0-20
-.09
-.59
iii. Impulsivity/carelessness style
5
7.87
4.27
.75
0-20
0-20
.32
-.28
iv. Avoidance Style Scale
5
8.21
4.58
.80
0-20
0-20
.25
-.56
v. Rational Problem Solving
5
10.97
4.32
.83
0-20
0-20
-.03
-.63
Table 2 shows Cronbach’s alpha reliability, the number of items, mean, standard
deviation, internal consistency, actual and observed maximum and minimum scores of the
variables, skewness, and kurtosis. Alpha coefficients for all the scales and subscales were
assessed to measure internal consistency. The alpha coefficient for the study variables ranged
from 0.57 to 0.83. The alpha reliability value above 0.9 is considered excellent, the
value between 0.75 to 0.9 is considered good, and the alpha value 0.5 to 0.75 is considered
moderate. The alpha value below 0.5 shows poor reliability (Koo & Li, 2016). The
Cronbach’s alpha reliability value for cognitive flexibility was .71, moderate. For memory, it
was found .83. Though failure in attentional tracking and unidentified memory failure
27
showed comparatively low but acceptable alpha reliability, which is .66 and .57 respectively,
collectively, it was sufficient (0.83). The reliability of retrieval failure is 0.76. Cronbach’s
alpha reliability of all the problem-solving domains ranged from .75 to .83, which is
satisfactory (See table 2).
Table 3 showed a correlation between the demographics and study variables. It
demonstrated that there was a significant relationship between gender and positive problem
orientation (r= .23***), impulsive problem solving (r= .11*) and rational problem solving (r=
.17**). Education has a significant association with retrieval failures (r= -.15*) and avoidant
style scale (r= .12*). Furthermore, there was a significant link between socioeconomic status
and unidentified memory deficits (r= .15*).
Cognitive flexibility had significant negative correlation with overall everyday
memory deficits (r= -.14*) and two of its subscales; unidentified memory deficits (r= -.21***)
and failure in attentional tracking (r= -.16**). Cognitive flexibility also had a significant
negative relationship with negative problem orientation (r= -.15*) and avoidant problem
solving (r= -.15*) and a strong, significant and positive association with positive problems
orientation (r= .52***) and rational problem solving (r= .52***).
Everyday memory deficits also had a significant positive correlation with its negative
problem orientation (r= .33***), impulsive problem solving (r=.23***), and avoidant problem
solving (r=.29***) (See table 3).
28
Table 3
Relationship between Demographic Characteristics, Cognitive Flexibility, Memory Deficits, and Problem Solving. (N=298)
1. Age
2. Gender
3. Education
4. Socioeconomic status
1
-
2
3
4
5
***
.02 .26
-.01 .05
6
.01
7
-.02
8
.03
9
-.01
10
-.04
11
-.03
12
.08
13
-.04
14
-.005
15
.07
.02
-.14* .09
.10
-.06
-.07
-.06
-.03
.04
.23***
.11*
.08
.17**
-
.15** -.03 -.02 -.09
.02
-.03
-.15*
.03
-.01
.10
.12*
.01
-
-
5. Family Structure
6. Cognitive Flexibility
7. Everyday Memory Deficits
8. Unidentified Memory Deficits
9. Failures in Attentional Tracking
10. Retrieval Failures
11. Negative Problem Orientation
12. Positive Problem Orientation
13. Impulsive/ Carelessness Style
14. Avoidance Style Scale
15. Rational Problem Solving
.08 -.05
.05
.15*
-.01
.03
-.02
-.08
.05
-.03
-.01
-
.02
.04
.06
.05
.02
-.08
.02
.02
-.07
.01
-
-.14* -.21*** -.16**
-.06
-.15* .52***
-.11
-.15* .52***
-
.66*** .87*** .93*** .33*** -.03
-
.56*** .46*** .20*** -.10
-
.29*** .29***
.02
.21*** .18*** -.04
.66*** .33*** -.05
.29*** .28***
.01
.28***
.01
.24*** .26***
.04
-
.11
.62*** .68*** .20***
-
.26*** .16** .77***
-
-
.65*** .25***
-
.22***
-
Note: *=p< .05, **=p < .01, ***=p < .001; Gender: female=1, male=2; Education: intermediate=1, undergraduate=2, graduate=3, post-graduate=4; Socioeconomic status; lower class=0, lower-middle class=1,
middle class=2, upper middle class=3, upper class=4; Family system: nuclear family=0, joint family=1
29
Regression Analysis
A series of moderation analyses were executed by running regression analysis through
Model 1 Process V4.0 by Hayes (2016) process on SPSS version 26. The moderating role of
unidentified memory deficits associated with cognitive flexibility and negative problem
orientation was estimated in the first moderation analysis. The results revealed that cognitive
flexibility was a significant predictor (β= -.22***) of negative problem orientation, and
unidentified memory deficits were significant moderators (β= -2.89**). The interaction term
between cognitive flexibility and unidentified memory deficits indicated that the moderator
was a significant predictor (β= .07***). At low levels of unidentified memory deficits, the
relation between cognitive flexibility and negative problem orientation was negative. The
correlation remained negative but weaker at moderate levels of unidentified memory deficits.
At the high levels of unidentified memory deficits, the association between cognitive
flexibility and negative problem orientation became positive (See figure 1).
In the second moderation analysis, the moderating role of unidentified memory
deficits was estimated to link cognitive flexibility and positive problem orientation. The
moderation analysis did not reveal any significant moderator effects.
The third moderation analysis revealed that cognitive flexibility was a significant
predictor (β= -.19***) and unidentified memory deficits are a significant moderator (β: 2.45**) of avoidance style problem-solving. The interaction term between cognitive flexibility
and unidentified memory deficits showed that moderator was a significant predictor (β=
.06***). At the low level of unidentified memory deficits, the relationship between cognitive
flexibility and avoidant style problem solving was negative. At a moderate level of
unidentified memory deficits, the association between cognitive flexibility and avoidant style
problem solving was still negative but was weaker. At a high level of unidentified memory
30
deficits, the link between cognitive flexibility and avoidant style problem solving became
positive (See figure 2).
In the fourth analysis, the moderation did not reveal any significant moderator effects
between cognitive flexibility and rational problem-solving (see table 5).
Figure 1
Moderating Role of Unidentified Memory Deficits Between Cognitive Flexibility and
Negative Problem Orientation
Table 6 showed the moderation analysis did not reveal any significant moderator
effects of retrieval failures in the link between cognitive flexibility and negative problem
orientation, positive problem orientation, avoidant problem-solving style, and rational
problem-solving style.
31
Table 4
Standard Regression Weights of Moderation Analysis with Unidentified Memory Deficits
Negative Problem
Positive Problem
Avoidance Style
Rational Problem
Orientation
Orientation
Scale
Solving
Measures
β
SE
β
SE
β
SE
β
SE
Constant
18.25***
2.54
-1.18
2.15
16.99***
2.47
-2.17
2.07
Cognitive Flexibility (CA)
-.22***
.05
.25***
.04
-.19***
.05
.26*
.04
Unidentified Memory Deficits (UMD)
-2.89**
.82
-.91
.69
-2.45**
.80
-.51
0.67
UMD x Problem Solving
.07***
.02
.02
.01
.06***
.02
.01
.01
R2
.10
.27
.08
.28
ΔR2
.05
.01
.04
.01
Note: *=p< .05, **=p < .01, ***=p < .001
32
Figure 2
Moderating Role of Unidentified Memory Deficits Between Cognitive Flexibility and
Avoidance Style
Table 7 indicates that failure in attentional tracking was also not a significant
moderator in the relationship between cognitive flexibility and negative problem orientation,
positive problem orientation, avoidant problem-solving style, and rational problem-solving
style. Table 8 shows that the moderating role of total memory deficits in the relationship
between cognitive flexibility and negative problem orientation, positive problem orientation,
impulsive/careless problem-solving style, and rational problem orientation is insignificant.
33
Table 5
Standard Regression Weights of Moderation Analysis with Retrieval Failures
Negative Problem
Positive Problem
Avoidance Style
Rational Problem
Orientation
Orientation
Scale
Solving
Measures
β
SE
β
SE
β
SE
β
SE
Constant
9.29**
3.57
-5.37
3
12.27***
3.56
-2.91
2.9
Cognitive Flexibility
-.06
.07
.32***
.05
-.12
.06
.27***
.05
Retrieval Failures
.26
.29
.19
.24
-.04
.28
-.02
.24
Retrieval Failures x Problem Solving
-.01
.01
-.01
.01
.01
.01
.01
.01
R2
.09
.27
.08
.28
∆R2
.00
.00
.00
.00
Note: *=p< .05, **=p < .01, ***=p < .001
34
Table 6
Standard Regression Weights of Moderation Analysis with Failures in Attentional Tracking
Negative Problem
Positive Problem
Avoidance Style
Rational Problem
Orientation
Orientation
Scale
Solving
Measures
β
SE
β
SE
β
SE
β
SE
Constant
12.13***
3.01
-5.08*
2.56
13.27***
2.92
-5.49*
2.46
Cognitive Flexibility
-.12
.05
.32
.05
-.14
.05
.32
.04
Failure in Attentional Tracking (FAT) -.18
.47
.33
.42
-.49
.45
.40
.38
FAT x Problem Solving
.01
.01
-.01
.01
.01
.01
-.01
.01
R2
.12
.27
.10
.28
∆R2
.00
.01
.01
.00
Note: *=p< .05, **=p < .01, ***=p < .001
35
Table 8
Standard Regression Weights of Moderation Analysis with Total Memory Deficits Between Cognitive Flexibility and Negative problem
Orientation, Positive Problem Orientation, Avoidant Style and Rational Problem Solving
Negative Problem
Positive Problem
Avoidance Style
Rational Problem
Orientation
Orientation
Scale
Solving
Measures
β
SE
β
SE
β
SE
β
SE
Constant
10.94*
3.78
-4.93
3.22
13.72***
3.67
-4.35
3.10
Cognitive Flexibility
-.10
.07
.32***
.06
-.15*
.07
.29***
.06
Memory Deficits
.02
.18
.08
.15
-.13
.17
.05
.15
Memory Deficits x Problem Solving
.01
.01
-.01
.01
.01
.01
-.01
.01
R2
.11
.27
.10
.28
∆R2
.01
.01
.01
.00
Note: *=p< .05, **=p < .01, ***=p < .0
36
Summary
The results indicate a significant negative association between cognitive flexibility
and memory deficits. Furthermore, cognitive flexibility has a significant and positive
relationship with positive problem orientation and rational problem-solving. It shows a
significant and negative association with negative problem orientation and avoidant problemsolving style. Moreover, unidentified are a significant moderator in the relationship between
cognitive flexibility and negative problem orientation and avoidant problem-solving style.
37
Figure 3
Emerged Moderation Model
38
Chapter 5
Discussion
The objective of the current study was to investigate the association between
cognitive flexibility and problem-solving in young adults. Other than that, the research also
aimed to explore the moderating role of memory deficits in the relationship between
cognitive flexibility and problem-solving. The current research findings demonstrated the
link between cognitive flexibility and problem solving and the moderating role of memory
deficits in the relationship between cognitive flexibility and problem-solving. This part of the
current research aims to discuss the current results in light of the previous studies, theories,
and local context.
One of the current study hypotheses explored the association between cognitive
flexibility, memory deficits, and problem-solving in young adults. The results proved an
association between cognitive flexibility, memory deficits, and problem-solving. Previous
research was also supported current study findings as Adler and Benbunan-Fich (2015)
explored the relationship between multitasking and problem-solving. The research concluded
that multitasking had, in fact, a negative effect on problem-solving. Especially when the
performer had to multitask without his will, the task he had to perform was considered
difficult. However, if the task is considered easy, the performer can easily switch tasks
without having a negative impact on his performance.
Results of the current study were also consistent with the study by Hanife (2018). He
investigated cognitive flexibility and its relationship with interpersonal problem-solving skills
in pre-service teachers. He concluded that pre-service teachers who showed more cognitive
flexibility also showed good problem-solving skills. They solved the problems with more
consistency. Furthermore, they were more persistent than the less flexible teachers. Stevens
(2019) also investigated to explore the link between cognitive flexibility and problem-solving
39
strategies provided by the children who were at risk. He found a negative association between
cognitive flexibility and the strategies to solve problems provided by at-risk children. Thus,
he concluded that at-risk children who were more flexible provided less problem-solving
strategies than those who were more flexible.
Another study by Epsy and Bull (2005) also supported the hypothesis and findings of
our study that cognitive flexibility is associated with memory deficits. They explored the
relationship between short-term memory and cognitive flexibility in preschool children. The
study results concluded that children who had good short-term memory were also cognitively
more flexible, and children who had poor short-term memory showed less cognitive
flexibility.
Farrant et al. (2017) investigated the relationship of attention with cognitive
flexibility. They found that attention is negatively correlated with cognitive flexibility. More
flexible individuals showed lower levels of inattention. Thus, individuals with high cognitive
flexibility experienced fewer deficits in attentional tracking. Besides that, Koslov, Mukerji,
Hedgpeg, & Lewis-Peacock (2019) investigated the relationship between cognitive flexibility
and prospective memory. The finding of this study too was consistent with the present study.
They found an association between cognitive flexibility and prospective memory. They
deduced that individuals with higher cognitive flexibility also had good prospective memory.
The current study results are also supported by research carried out by Blackwell,
Cepeda, and Munakata (2009). They conducted a study on children to explore the
relationship between cognitive flexibility and working memory. The study's findings
concluded that higher cognitive flexibility is associated with good working memory
performance, and lower cognitive flexibility is associated with more deficits in the working
memory. Furthermore, they also found that children who persevered were slower in
40
responding to questions. In contrast, children who could switch attention quickly whenever
they needed to were sharp and more responsive.
The current study's findings were also justified in the light of different theoretical
frameworks. Gestalt theory of Problem Solving (1952) suggested that problem-solving
happens when a person gets sudden insight. It is only possible if a person is cognitively
flexible enough to have a sudden burst of insight. If a person is flexible, he will know he has
more options to solve a problem and find novel ways to solve the problem.
The theory of "attentional inertia" is another theoretical framework supporting our
study's hypothesis that cognitive flexibility and memory deficits are correlated with each
other. This theory states that when a person is focusing on one thing, preservation happens,
and all of his attention is focused on that object only at that time. If he tries to shift his focus
from one thing to another all of a sudden, he may lose focus, and failure in attention happens,
which leads to failure in memory because he was unable to track information and could not
encode it at that time.
The local context of Pakistan also tried to explain cognitive flexibility and its
relationship with memory in physical therapy students. Arshad and colleagues (2020)
conducted a cross-sectional study and concluded that cognitive flexibility and memory were
related. Furthermore, male students had better memory retention, and female students showed
more cognitive flexibility.
Another indigenous study conducted by Tariq and Adil (2020) supports the current
study's findings. Their study aimed to find the mediating role of cognitive rigidity in the
association between avoidant approach and social adjustment in the teachers of madaris and
schools. The results concluded a relationship between cognitive rigidity and avoidant
approach or temperament. However, this relationship was negative in school teachers and
positive in madaris’ teachers.
41
Research carried out by Channa et al. (2017) affirms the result of the current study.
They conducted a study to observe the effect of metacognitive strategies on reading and
comprehension abilities in the Pakistani context. They concluded that higher metacognition
has a positive effect on comprehension. Using more metacognitive strategies was associated
with good learning ability, comprehension, and attention.
The moderating role of memory deficits is also supported by research conducted by
Peng, Namkung, Barnes, & Sun (2016). In this meta-analysis association between working
memory and mathematics was explored with all the possible moderators, including the
domains of working memory and mathematics skills. The results revealed that verbal,
numerical, and visuospatial working memory are significant moderators of the relationship
between mathematics and working memory.
The third hypothesis of the current study was that demographic characteristics of the
sample were correlated with the study variables. The results of the current study are also
supported by past research. A study conducted by Piccolo and his colleagues (2016) aimed to
explore the impact of the socioeconomic status of Brazilian children on their IQ, cognition,
language, memory, and executive functioning. The results were consistent with the current
study that socioeconomic status impacted memory and executive functions.
The “Response Style theory” by Nolen-Hoeksema (1987) also supported the current
research. According to the response style theory, gender played an important role in
responding to the problems and dealing with them. The current study also revealed that
gender was significantly correlated with positive problem orientation, impulsive or careless
problem-solving style, and rational problem-solving style.
Limitations And Suggestions
Like all other studies, the present study also has some limitations and drawbacks that
can be rectified in future research. The study sample only included young adults, excluding
42
children, teenagers, middle-aged adults, and old adults. According to the statistics (Statistia,
2020), 34.82% of the population in Pakistan is between the ages of 0-14, 60.83% are between
the ages of 15-64, and 4.35% are over the age of 65. So, if this sample was also included, we
could generalize the results to a broader category of individuals living in Pakistan. On that
account, it is recommended and suggested to explore cognitive flexibility, memory deficits,
and problem-solving in different age groups simultaneously in the future.
Other than that, the data was collected from the urban areas in the current study. Thus,
the results cannot be generalized to the population of rural areas as well. According to
Trading Economics (2021), 62.84% of the population in Pakistan is from rural areas. That
being so, individuals from rural areas should at least comprise 50% of the study population.
Furthermore, a cross-cultural study could also be carried out by collecting data from other
cultures and comparing it with Eastern culture. The findings would be more diverse in that
case and could be generalized globally.
Another limitation of the study was using self-report measures to assess cognitive
flexibility, memory deficits, and problem-solving. Though the measures used in the study
were reliable, they only assessed the participants' perception of their cognitive flexibility,
memory deficits, and problem-solving. Experimental studies can be conducted on this topic
to explore it further.
Before exploring this topic, another aspect that should be considered is that the data
was collected through an online survey in the present study. Online surveys could
compromise and minimize the authenticity of the data. Hence, it would be better to collect the
data in face-to-face meetings.
Implications and Benefits
The current research has many implications and potential benefits for the researchers,
practitioners, and psychotherapists in conducting research and assessments. The current
43
research findings are beneficial and of interest for theoretical and applied psychologists and
other relevant fields. The current study concluded that cognitive flexibility is negatively
associated with memory deficits, negative problem orientation and avoidant problem-solving
style, and positively with positive problem orientation and rational problem-solving style.
The current study's findings demonstrated that cognitive flexibility leads to lesser
deficits in memory and attention. Hence, the practitioners can explore ways to improve
cognitive flexibility to deal with memory deficits in daily life. Practitioners can also devise
ways to improve cognitive flexibility to improve effective problem-solving in young adults.
Results could also help initiate educational workshops and programs to educate
individuals about cognitive flexibility, its impact on their memory and problem solving, and
how they can use it to improve their daily lives as memory and problem-solving skills are
among the most vital components of our cognition. If individuals learn to improve their
cognitive flexibility, they can ultimately improve their lives for the better.
Conclusion
The overall findings of the current study concluded that cognitive flexibility is
negatively associated with memory deficits in young adults. Cognitive flexibility is also
negatively linked with negative problem orientation and avoidant problem-solving style and
positively correlated with positive problem orientation and rational problem-solving style.
Education was also negatively correlated with memory deficits. The moderation analysis
revealed that unidentified memory deficits were a significant moderator in the relationship
between cognitive flexibility and negative problem orientation and avoidant problem-solving
style.
The present study is a novel, and unique contribution to society and academia as
cognitive flexibility, memory deficits, and problem-solving and their relationship have not
been explored together before. This study will aid individuals in understanding the role of
44
cognitive flexibility and how it affects their daily lives by affecting their memory and
problem-solving styles. Programs can improve cognitive flexibility, which affects memory
and problem-solving.
45
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