The Impact of Fluid Intelligence on Academic Performance through Working Memory
There are two types of intelligence: fluid and crystallised. While both serve an important role in cognitive function and understanding, this essay will primarily focus on fluid intelligence. Fluid intelligence can be defined by one’s ability to think in abstract terms and interpret new concepts regardless of past knowledge. This can be thought of as adapting to new situations—especially situations that are unexpected or unfamiliar. Unfamiliar situations test people’s intelligence due to their spontaneous nature, which often relates to real-life contexts. While school and overall academics is based more on applying crystallised knowledge (ingrained knowledge), the natural flow of life is more unstructured and complex, thus necessitating the need to use fluid intelligence to adapt to situations of any type. Humans have evolved to a point where some level of fluid intelligence is expected in order to maintain intellectual dominance as a species. This influences academic ability because it allows humans to process and manipulate information quicker than other species, advancing the development of humanity collectively. In this essay paper, the topics that will be discussed include the correlation between working memory and fluid intelligence, factors that affect the training of working memory, learning and knowledge in visual working memory, fluid intelligence and working memory effect on academic performance, and the real life implications to working memory and fluid intelligence.
In this research paper, the link between fluid intelligence and working memory, and their effect on academic performance, will be discussed. Fluid intelligence is one’s ability to extract patterns and solve problems in new situations without previous knowledge. It involves an individual’s ability to reason and deduce solutions from unfamiliar circumstances. Another important feature in the essay is working memory, which is one’s ability to retain and manipulate small amounts of information temporarily. The central executive system is largely responsible for controlling working memory, and it works on directing attention (to the subject at hand), maintaining goals, memory retrieval, and decision-making (McCabe et al., 2011). In the same scope as working memory is short-term memory, which is a person’s ability to store information in their mind for a short period of time (LibreTexts Social Sciences). However, it is not the same as working memory because working memory includes both the storage and manipulation of information, whereas short term memory is the cognitive capacity to retain a small quantity of information within the mind and have it easily accessible for a brief duration or short period of time (Kendra Cherry).
Correlation Between Working Memory and Fluid Intelligence
Working memory refers to the cognitive system responsible for temporarily storing and manipulating information during complex cognitive tasks. It involves the active maintenance of information and the ability to update and manipulate it in real time. Working memory capacity varies among individuals and is limited in terms of the amount of information it can hold simultaneously. Fluid intelligence is a component of general intelligence that relates to the ability to reason, solve novel problems, and think abstractly. It involves the ability to identify patterns, make connections between different concepts, and adapt to new situations. Fluid intelligence is believed to be less dependent on prior knowledge and is considered to reflect innate cognitive abilities. The connection between working memory and fluid intelligence lies in the fact that working memory plays a crucial role in supporting fluid intelligence.
Working memory acts as a temporary workspace where incoming information is held, processed, and manipulated. It allows individuals to mentally juggle and integrate different pieces of information relevant to a specific task. This processing ability is critical for tasks that require fluid intelligence, such as problem-solving and reasoning. Working memory enables individuals to hold and manipulate multiple pieces of information simultaneously, facilitating cognitive flexibility. Cognitive flexibility involves the ability to switch between different mental representations, consider various perspectives, and generate new solutions. This flexibility is an essential aspect of fluid intelligence.
Tasks that require fluid intelligence often involve mentally demanding processes, such as maintaining relevant information, inhibiting irrelevant information, and updating mental representations. Working memory capacity influences an individual's ability to handle these complex tasks effectively. A larger working memory capacity allows for better storage and manipulation of information, potentially leading to enhanced performance in tasks requiring fluid intelligence. Research has shown that individuals with higher working memory capacity tend to perform better on tasks that require fluid intelligence. However, it is important to note that working memory is just one component contributing to fluid intelligence, and there are other factors involved as well, such as processing speed, attention control, and long-term memory. Nonetheless, working memory capacity is considered a significant cognitive factor underlying fluid intelligence.
A study performed by Timothy Salthouse and Jeffrey Pink (2008) found that there is a positive correlation between working memory and fluid intelligence. They found that the correlation between working memory and fluid intelligence was .74, and .66. In their experiment they had recruited participants from flyers, ⅔ of them were women and had completed at least three years of college on average. Participants met for three sessions over a two-week period where they performed cognitive ability tests and working memory tests. These tests included Raven’s Progressive Matrices, Letter Sets, Shipley Abstraction, Spatial Relations, Paper Folding, and Form Boards for Gf; Logical Memory, Multiple Trial Word Recall, and Paired Associates for episodic memory; Digit Symbol Substitution, Letter Comparison, and Pattern Comparison for perceptual speed; and WAIS Vocabulary, Picture Vocabulary, Synonym Vocabulary, and Antonym Vocabulary for vocabulary.
The study utilized three storage-plus-processing tasks: Operation Span (OSpan), Symmetry Span (SSpan), and Reading Span (RSpan). Participants were required to perform a processing component while simultaneously remembering a series of items. The tasks involved different combinations of storage and processing components, such as verifying arithmetic operations or making judgments about symmetry or sentence meaningfulness. The number of items to be remembered varied across trials, and the primary measure of performance was the accurate recall of the items in the correct sequence. The tasks were designed to familiarize participants with both storage and processing aspects and ensure no correlation between set size and trial number.
In Study 1, the OSpan and SSpan tasks showed a correlation of .52, while in Study 2, the correlations were .61 (OSpan and SSpan), .71 (OSpan and RSpan), and .56 (SSpan and RSpan). The correlations between WM tasks and Raven's Progressive Matrices were .49 (OSpan) and .60 (SSpan) in Study 1, and .52 (OSpan), .57 (SSpan), and .49 (RSpan) in Study 2. Additionally, the correlation between the latent constructs of WM and Gf was .86 (Study 1) and .74 (Study 2), indicating a strong relationship. The correlations remained significant even after accounting for age effects. These findings confirm the strong correlation between WM and Gf, suggesting a connection between the two constructs.
Knowledge and Vision: Learning and knowledge in visual working memory
The subject of working memory training has gained significance, specifically in relation to the potential for training on working memory tasks to not only apply to various stimuli but also enhance other skills such as fluid intelligence. The impact of working memory training on other abilities is a subject of debate, as indicated by Chooi and Thompson (2012), Shipstead, Redick, and Engle (2012), and Thompson et al. (2013). However, the majority of studies suggest that consistent training on a specific working memory task enhances performance on that task (Thompson et al., 2013).
Regarding visual working memory, research has focused on the effects of repeated exposure to the same memory displays on visual working memory capacity, exploring the influence of stimuli (Olson & Jiang, 2004; Olson, Jiang, & Moore, 2005). Nevertheless, these studies have not provided conclusive evidence that familiarity with the stimuli alone can increase capacity. For instance, one study revealed that learning did not amplify the amount of information retained, but it did enhance memory performance by redirecting attention to subsequently tested items (Olson et al., 2005).
Nevertheless, the research from the book “Psychology of Learning and Motivation” by Timothy F. Brady and Hayden M. Schill demonstrates that visual working memory performance consistently improves when participants are taught novel associations between unrelated elements, such as colored circles (Brady, Konkle, & Alvarez, 2009). The repeated exposure to stimulus pairs, such as red next to yellow, led participants to remember more colors as they acquired this association. This increase in capacity persisted even after considering the possibility of participants using perceptual "guessing" strategies. Brady, Konkle, and Alvarez (2009) illustrated that participants did not falsely guess "yellow" when an item happened to be paired with a different color (e.g., red next to blue instead of yellow). These capacity improvements occurred despite the absence of broader conceptual meanings attached to the elements, thus suggesting that exposure or familiarity alone can enhance working memory capacity, specifically in the context of paired stimuli. Therefore, when dealing with simple stimuli like letters and colors, although exposure to independent stimuli alone does not enhance working memory performance (Chen, Yee Eng, & Jiang, 2006; Eng, Chen, & Jiang, 2005; Olson & Jiang, 2004), constructing items into larger associative units can lead to improvements in working memory (Vogt). Ultimately, while the impact of working memory training on various abilities is still debated, research suggests that consistent training on specific tasks can enhance performance. Furthermore, studies on visual working memory indicate that exposure to paired stimuli, even without broader conceptual meanings, can lead to improvements in working memory capacity. Moreover, research on visual working memory suggests that being exposed to paired stimuli, even when they lack broader conceptual meanings, can have a positive impact on working memory capacity as it can influence academic achievement by enhancing working memory capacity, improve attention and focus, increase information retention, ability to transfer cognitive skills, and the readiness to learn. This implies that the ability to retain and manipulate visual information could be enhanced through such exposure, which in turn could potentially influence academic achievement.
Factors that affect the training of working memory
Mental health conditions such as anxiety and depression can affect working memory, as well as physical factors like intelligence, age, personality, and gender.
Firstly, anxiety can affect working memory since people with anxiety are more prone to get distracted by the stimuli causing their anxiety and diverge their attention from the task at hand. This negatively impacts their working memory since they occupy some of their attention on subjects unrelated to the task at hand rather than placing complete focus on the current task. According to Elliman, Green, Rogers, and Finch (1997), anxiety causes people to have pre-occupying thoughts and subvocalization which uses up the necessary cognitive resources for optimal working memory function (Blasiman and Was, 2018). In a study by Sorg and Whitney in 1992, participants were separated into groups of high or low anxiety participants, and they were exposed to both stressful and non-stressful situations. The high anxiety group had decreased verbal working memory in response to stressful situations, but not for non-stressful situations.
Like anxiety, depression also has an adverse effect on working memory by causing pre-occupying thoughts in an individual. A study of 395 mothers and fathers of newborns found that depression affected 16.2% of mothers and 5.2% of fathers. Compared to the non-depressed individuals, the depressed ones scored worse on the memory test (word span test). Not only was the working memory of the depressed group impaired, their short-term memory was also worse (Almeida et al., 2011).
Additionally, another study consisting of 20 participants with major depressive disorder, as well as a control group, were tested with the n-back task to determine if there was a relation between working memory and depression. The study found that the depressed participants had slower reaction times and reduced performance accuracy when compared to the control group (Rose & Ebmeier, 2005).
Intelligence is related with working memory capability, with fluid intelligence and working memory being highly correlated with each other. However, crystallized intelligence is not correlated with working memory. Correlations between fluid intelligence and working memory differ depending on the study used, with correlation coefficients averaging at .48, according to meta-analyses conducted by Ackerman and colleagues in 2005.
Another key factor that affects working memory is age, which is part of the reason for why education is focused on teaching children and building their knowledge as early as possible. Many studies have found that working memory declines as age increases (Babcock & Salthouse, 1990; Borella, Caretti, & De Beni, 2008; Craik & Bialystok, 2006). Just like the human body, human cognition grows from frail (infant) to strong (youth/middle age) then back to frail (old) again. This process of building up and wearing down is shown by the consolidation of mental networks in infancy, then the deterioration of these networks in older age. Salthouse states that processing speed increases from infancy to young adulthood, then declines starting from the twenties (Craik & Bialystok, 2006). Further, in their analysis of age and working memory, Bopp and Verhaeghen (2007) found moderate to large negative correlations between age and working memory. Their study included 26 younger adults and 22 older adults, with average ages and standard deviations of 19.10 years, 0.98 S.D., and 70.72, 3.54 S.D., respectively. Though both the younger and older adults performed similarly on the digit span tests, younger adults did significantly better on the Digit Symbol Modalities Test. This is shown by the mean score of 61.79 for younger adults and 44.68 for older adults with a p-value of <0.001, defining the extent to which the younger adults performed better (Bopp & Verhaeghen, 2007).
Personality can also have an effect on working memory. Among the personality tests accepted by psychologists is the Big 5, which consists of the following personality traits: Conscientiousness, Agreeableness, Neuroticism, Openness to Experience, and Introversion/Extroversion. Among these traits, psychologists most often compare working memory with introversion and extroversion. Some researchers state that extroverts have better working memory than introverts, while other researchers find no difference in working memory between the two groups. One researcher, Lieberman, conducted a study in 1995 that tested the reaction times of seven introverts and seven extroverts, who were categorized as such using the Eysenck Personality Inventory scale. However, data was lost for one individual, resulting in 13 total participants. The study unveiled faster reaction times for the extroverted group, with extroverts averaging 341.19 milliseconds, while the introverts averaged 397.28 milliseconds (Lieberman, 1999). However, because of the small sample size of the study (n<30), the data may not be significant enough to draw conclusions about the relationship between introversion/extroversion and working memory. So, the relationship between introversion/extroversion and working memory remains undetermined.
The last factor, one which may or may not affect working memory, is gender, though several researchers have reported that males perform better than women on spatial working memory tasks, while women generally perform better than men on verbal working memory tasks. Still, there is not a clear conclusion as some studies report no difference between genders in verbal tasks, with males still performing better in spacial tasks, while other studies report no differences in working memory between the two genders. Because of the inconsistency of the results in studies concerning gender and working memory, it is difficult to come to a conclusion about the effect of gender on working memory (Blasiman & Was, 2018).
Fluid Intelligence and Working Memory Effect on Academic Performance
The longitudinal connections between academic achievement and intelligence over time
Intelligence can be defined as the ability to comprehend complex concepts, adapt effectively to the environment, learn from past experiences, engage in various forms of reasoning, and overcome obstacles through thoughtful consideration (Neisser et al., 1996, p. 77). It serves as a broad mental capacity that underlies the positive relationship between test scores on different cognitive tasks, regardless of their specific nature (van der Maas et al., 2006).
Fluid intelligence, in particular, refers to an individual's capacity to solve intricate problems using intentional mental operations, such as inductive and deductive reasoning (Cattell, 1971). Situations that demand deliberate mental processes also rely on executive functions like working memory (WM), so fluid intelligence is closely associated with WM (Engle, Tuholski, Laughlin, & Conway, 1999; Kane, Hambrick, & Conway, 2005). However, it should be noted that fluid intelligence and WM are not identical (Oberauer, Schulze, Wilhelm, & Süß, 2005). For example, when solving a mathematics test question, students need to construct a mental representation based on the information provided in the question (Schnotz & Bannert, 2003). The construction of a mental model is constrained by the capacity of their working memory. WM plays a crucial role in maintaining and mentally manipulating relevant information, facilitating reasoning processes. It allows for the coordination of information and retrieval from long-term memory while engaging in tasks, thereby enabling flexible construction of connections relevant to the task at hand (Oberauer, Weidenfeld, & Hörnig, 2006).
The relationship between intelligence and academic achievement, particularly their joint development over time, remains a topic of debate despite their unquestionable association. According to Cattell's investment theory (Cattell, 1971, Cattell, 1987), fluid intelligence plays a crucial role in the acquisition of new abilities and knowledge. Fluid intelligence refers to an individual's capacity to solve complex problems through deliberate mental operations like inductive and deductive reasoning. Therefore, higher levels of fluid intelligence are associated with greater learning ability and, consequently, greater potential for academic achievement. Recent studies have demonstrated how fluid intelligence predicts growth in academic achievement, particularly in the domains of mathematics and reading. These domains are not only fundamental subjects in school but also represent opposite ends of the continuum that represents the perceived relatedness of academic domains (Helm, Mueller-Kalthoff, Nagy, & Möller, 2016). By investigating mathematics and reading, researchers aim to cover a wide range of academic domains.
Some studies have found that students with higher initial levels of fluid intelligence experience faster growth in mathematics achievement (Geary, 2011; Paetsch, Radmann, Felbrich, Lehmann, & Stanat, 2016; Primi et al., 2010). In contrast, for reading achievement, a compensatory effect has been observed: students with lower fluid intelligence show a steeper rise in their reading achievement growth rates compared to those with higher fluid intelligence (Baumert, Nagy, & Lehmann, 2012; Geary, 2011). Other studies, both in mathematics and reading, have shown that initial intelligence is related to initial achievement but not to achievement growth (Murayama, Pekrun, Lichtenfeld, & vom Hofe, R., 2013; Rescorla & Rosenthal, 2004). Another study by Lechner, Miyamoto, and Knopf (2019) found that fluid intelligence positively predicted both the initial level and the growth of mathematics and reading achievement, with larger effects observed in mathematics. These studies have a common characteristic in their research design: they model achievement growth as a function of initial intelligence but do not consider the reverse relationship, thereby overlooking changes in the reciprocal interrelations over time. Some studies that measured both fluid intelligence and achievement across multiple measurement occasions have supported the predictive effect of intelligence on achievement but not the reverse effect of achievement on intelligence (Ferrer & McArdle, 2004; Watkins, Lei, & Canivez, 2007; Watkins & Styck, 2017).
Simultaneously, it is possible that school learning directly influences the development of intelligence (Ceci, 1991). Previous studies have discovered a correlation between years of schooling and intelligence, supporting the idea that students with lower fluid intelligence tend to leave school earlier than their peers (Ceci, 1991; Strenze, 2007; Deary & Johnson, 2010). Each year of education has a positive impact on cognitive abilities (Ritchie & Tucker-Drob, 2018), with children who initially have lower intelligence scores experiencing the greatest growth through academic achievement (Hegelund, Flensborg-Madsen, Dammeyer, Mortensen, & Mortensen, 2019). Some researchers argue that different school environments and teaching methods vary in their effectiveness for students' cognitive development (Clouston et al., 2012). For example, school effectiveness research has demonstrated differences in instructional quality, with students in academic-track schools benefiting from higher-quality instruction and more cognitively engaging lessons (Reynolds et al., 2014). Considering the evidence that targeted training can lead to increased fluid intelligence (Klauer & Phye, 2008), one can speculate that a cognitively stimulating environment fosters fluid intelligence. Empirical support for these causal relationships comes from research showing that academic school tracks significantly support students' cognitive development, with greater intelligence gains observed in students on the academic track compared to those on a non-academic track (Becker, Lüdtke, Trautwein, Köller, & Baumert, 2012; Guill et al., 2017). This effect may be primarily attributed to higher instructional quality and more cognitively stimulating learning environments in academic tracks compared to other tracks. Another effect of schooling is that engagement with the learning material itself may foster the development of not only domain-specific abilities but also general cognitive abilities (i.e., psychometric intelligence). For instance, research based on twin studies has suggested a positive impact of reading ability on measures of both general intelligence and nonverbal intelligence (Harlaar, Hayiou-Thomas, & Plomin, 2005; Ritchie, Bates, & Plomin, 2015).
Lastly, research has demonstrated a reciprocal relationship between fluid intelligence and achievement, indicating that they are mutually interconnected over time. Several studies utilizing various analytical approaches, such as change score models or cross-lagged panel models, have observed an interaction between different cognitive abilities, including intelligence, reading achievement, and vocabulary, throughout their developmental trajectories. These studies indicate that initial intelligence influences changes in achievement, and vice versa (Ditton & Krüsken, 2009; Ferrer, Shaywitz, Holahan, Marchione, & Shaywitz, 2010; Kievit et al., 2017; Kievit, Hofman, & Nation, 2019; Rindermann, Flores-Mendoza, & Mansur-Alves, 2010; van der Maas et al., 2006). In some studies, the effect of achievement on later intelligence was found to be stronger than the effect of intelligence on later achievement (Ditton & Krüsken, 2009). However, other studies, including a recent meta-analysis (Peng, Wang, Wang, & Lin, 2019), supported the existence of a reciprocal effect with both effects being of similar magnitude. According to this line of research, fluid intelligence is not only a predictor of learning but also an outcome of learning. These findings support the notion that utilizing fluid intelligence during the learning process leads to the acquisition of enhanced learning skills, which, in turn, fosters fluid intelligence (Kievit et al., 2017; van der Maas et al., 2006).
In summary, while there is substantial evidence supporting a relationship between fluid intelligence and achievement, the exact cause-and-effect nature of this relationship remains somewhat unclear. Previous longitudinal research has taken three approaches to investigate the connections between fluid intelligence and achievement, and each approach has provided evidence supporting its respective predictive pattern. These approaches examine whether (1) academic achievement is predicted by fluid intelligence, (2) fluid intelligence is predicted by academic achievement, and (3) there are reciprocal relationships between fluid intelligence and academic achievement.
The associations/relationship between fluid intelligence and academic achievement vary across different academic domains
While it is generally believed that there are connections between the development of intelligence and domain-specific academic achievement across various academic domains, the strength of these connections may vary between domains (Sternberg, Kaufman, & Grigorenko, 2008). Previous research has supported this notion. Academic domains differ in terms of their cognitive demands, content, and utilization of learning strategies (Lehto, 1995; Riding, Grimley, Dahraei, & Banner, 2003). For example, learning mathematics requires the use of mental operations such as making inferences, forming concepts, classifying, identifying relationships, and solving problems (Rohde & Thompson, 2007). Therefore, fluid intelligence plays a crucial role in acquiring fundamental mathematical skills like counting, as well as more complex skills such as solving word problems or engaging in algebra (Fuchs, Fuchs, Compton, Hamlett, & Wang, 2015). On the other hand, reading comprehension focuses more on understanding complex texts, drawing inferences, and utilizing cognitive abilities such as reasoning, discerning relationships, cognitive flexibility, and updating skills, which are highly correlated with fluid intelligence (Friedman et al., 2006). However, empirical evidence regarding domain differences has been inconsistent. Some studies have shown that the predictive power of fluid intelligence for academic achievement did not significantly differ between mathematics and reading, or that it was relatively similar (Lauermann, Meißner, & Steinmayr, 2020; Peng et al., 2019; Roth et al., 2015). In contrast, other studies have indicated a stronger influence of fluid intelligence on mathematics achievement compared to reading (Lechner et al., 2019; Watkins & Styck, 2017).
Standardised Test Scores vs. Grades Achievements
Depending on the specific aims of the study, domain-specific academic achievements are typically assessed using either standardised achievement test scores or grades. Standardised test scores provide a more objective measure of academic achievement, while grades are subjective evaluations given by teachers and are susceptible to bias. Factors such as student characteristics (e.g., sex, motivation) and teacher characteristics (e.g., cognitive abilities) can influence the grades assigned to students. As a result, previous research has shown that intelligence explains less variance in grades compared to achievement test scores (Brandt, Lechner, Tetzner, & Rammstedt, 2019; Jansen, Lüdtke, & Schroeders, 2016; Lauermann et al., 2020; Steinmayr & Meißner, 2013).
There is substantial evidence of a significant association between intelligence and standardised test scores, as well as between intelligence and grades. In large-scale assessments in Germany, correlations between intelligence and standardised mathematics test scores ranged from r = .38 to r = 0.72 (Saß, Kampa, & Köller, 2017). A recent meta-analysis reported a population correlation of ρ = 0.54 between intelligence and various grades (Roth et al., 2015). However, studies investigating the effects of intelligence on later grades have produced conflicting results. While Deary, Strand, Smith, and Fernandes (2007) found strong support for the impact of intelligence on later grades, other studies did not find substantial evidence when controlling for prior grades across subjects (Soares, Lemos, Primi, & Almeida, 2015; Thorsen, Gustafsson, & Cliffordson, 2014). Another study showed mixed results depending on the academic domain of reading and mathematics (Steinmayr & Spinath, 2009). The cross-sectional studies by Brandt et al. (2019) and Lauermann et al. (2020) also demonstrated differential effects of intelligence and achievement test scores compared to grades, with a stronger association between intelligence and test scores. To the best of our knowledge, our study is the first to investigate the longitudinal relationship between fluid intelligence and both standardized test scores and grades. Both standardized achievement tests and fluid intelligence tests require domain-independent reasoning processes. Additionally, grades have been found to be influenced by various student and teacher characteristics, such as persistence and study habits (Neisser et al., 1996). Therefore, we hypothesised that the relationship between fluid intelligence and achievement test scores would be stronger than that between fluid intelligence and grades (Steffani Saß et al.).
Real Life Implications to Working Memory and Fluid Intelligence
A study (Hagmann-von Arx, Gygi, Weidmann, & Grob) examined the relationship between fluid and crystallised intelligence and different aspects of career success, both extrinsic (occupational skill level, income) and intrinsic (job satisfaction). The researchers also investigated the incremental predictive validity of conscientiousness and its facets in relation to career success. The study involved 121 participants who completed assessments of their intellectual abilities using the Reynolds Intellectual Assessment Scales (RIAS) and reported their occupational skill level, income, and job satisfaction. The Revised NEO Personality Inventory (NEO-PI-R) was used to measure conscientiousness and its facets. The findings indicated that crystallised intelligence was positively associated with occupational skill level but not with income. Furthermore, the association between crystallised intelligence and job satisfaction was negative, particularly for individuals with lower occupational skill levels, whereas it was not significant for those with higher skill levels. On the other hand, fluid intelligence did not show a significant association with career success. In addition to intelligence, conscientiousness and its facets were found to be related to different aspects of career success. Conscientiousness, as well as its facets of self-discipline, competence, and achievement striving, were associated with income. Conscientiousness and its facets of competence and achievement striving were related to job satisfaction.
Although there is a lack of evidence to suggest that fluid intelligence plays an impactful role in career success, working memory is crucial in juvenile academic development which can help with problem solving and academic achievement.
A study aimed to examine the long-term impact of working memory and IQ on learning in typically developing children over a six-year period. The research involved assessing the IQ and working memory of students at the ages of 5 and 11. Academic achievements in reading, spelling, and math were also evaluated. The findings demonstrated that working memory plays a vital role in all aspects of learning, regardless of the individual's IQ score. Importantly, working memory at the beginning of formal education was a stronger predictor of subsequent academic success than IQ during the early years. This finding challenges the conventional view that general intelligence is the primary determinant of academic success, as individuals with average IQ scores can still struggle in learning. Working memory was found to be more influential, especially when learning new information, suggesting that certain cognitive skills contribute to learning beyond mere practice effects. Another noteworthy discovery was that unlike IQ, working memory was not associated with parents' educational levels or socio-economic background. This implies that all children, regardless of their circumstances, have equal opportunities to fulfill their potential if their working memory is assessed and any problems are addressed. Working memory is considered a relatively stable trait with significant implications for academic achievement. Although it naturally improves with age, its relative capacity remains constant. Therefore, a child with low working memory compared to peers is likely to face ongoing academic challenges throughout their educational journey.
In conclusion, working memory plays a crucial role in tasks that require fluid intelligence, such as problem-solving and reasoning. It acts as a temporary workspace for processing and manipulating incoming information, allowing individuals to hold and integrate multiple pieces of relevant information simultaneously. Working memory capacity influences an individual's ability to handle complex tasks effectively, and research has shown a positive correlation between working memory and fluid intelligence. While working memory is just one component contributing to fluid intelligence, it is considered a significant cognitive factor underlying it. Factors such as processing speed, attention control, and long-term memory also play a role in fluid intelligence. Additionally, factors like anxiety, depression, neurodevelopmental disorders, intelligence, and age can affect working memory performance. The impact of working memory training on other abilities is still debated, but research suggests that consistent training on specific tasks can enhance performance. Studies on visual working memory indicate that exposure to paired stimuli, even without broader conceptual meanings, can lead to improvements in working memory capacity. The relationship between fluid intelligence and academic achievement is complex and multifaceted. Fluid intelligence predicts growth in academic achievement, particularly in mathematics and reading. Students with higher initial levels of fluid intelligence tend to experience faster growth in mathematics, while a compensatory effect has been observed for reading achievement. The reciprocal relationship between fluid intelligence and academic achievement suggests that they influence each other over time. Furthermore, schooling and the learning environment can impact the development of fluid intelligence and academic achievement. Cognitively stimulating environments, high-quality instruction, and engagement with learning materials can foster the development of fluid intelligence. The reciprocal relationship between fluid intelligence and academic achievement suggests that targeted training and a stimulating learning environment can contribute to cognitive development.Overall, understanding the relationship between working memory, fluid intelligence, and academic achievement provides insights into cognitive processes and the potential for enhancing cognitive abilities through targeted interventions and educational practices.
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