problem solving crystallized and fluid intelligence

Fluid Intelligence vs. Crystallized Intelligence

Ayesh Perera

B.A, MTS, Harvard University

Ayesh Perera, a Harvard graduate, has worked as a researcher in psychology and neuroscience under Dr. Kevin Majeres at Harvard Medical School.

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Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Fluid intelligence refers to the ability to reason and solve novel problems, independent of any knowledge from the past. It involves the capacity to identify patterns, solve puzzles, and use abstract reasoning. On the other hand, crystallized intelligence refers to the ability to use knowledge, facts, and experience that one has accumulated over time. It includes vocabulary, general world knowledge, and the application of learned information.

Key Takeaways

Our general intelligence , which enables us to learn and recall, comprises our fluid intelligence and crystallized intelligence.
Fluid intelligence involves comprehension, reasoning, and problem-solving, while crystallized intelligence involves recalling stored knowledge and past experiences.
Fluid intelligence and crystallized intelligence rely on distinct brain systems despite their interrelationship in the performance of many tasks.
Various tools are used to measure fluid and crystallized intelligence, and new research suggests that fluid intelligence can be improved, although it was hitherto supposed to be static.

Hand put the last piece of jigsaw puzzle to complete the mission

Our capacity to learn the novel and recall the past is called general intelligence (Cattell, 1963). It is a construct of psychometric investigations of human intelligence and our cognitive abilities.

General intelligence encapsulates correlations among various cognitive tasks which can be categorized into two subdivisions (Cattell, 1971). These are fluid intelligence and crystallized intelligence.

The theory of fluid v. crystallized intelligence simultaneously challenges and extends what was once supposedly the single construct of general intelligence.

Cattell’s Theory of Intelligence

The theory of fluid v. crystallized intelligence was first postulated as a psychometrically based theory by psychologist Raymond B. Cattell in 1963.

Cattell argued that fluid intelligence and crystallized intelligence are two categories of general intelligence.

In his book Intelligence, Its Structure, Growth, and Action, Cattell identified one component of general intelligence as embodying a fluid quality and being directable to any problem (Cattell, 1987).

He proceeded to identify the other component as a part invested in the areas of crystalized skills. He pointed out that the latter involves knowledge acquisition and crystallized skills, which can be upset individually without impacting others.

The two concepts of fluid intelligence and crystallized intelligence were further developed by Cattell’s former student and cognitive psychologist John Leonard Horn (Horn & Cattell, 1967).

Fluid Intelligence

Fluid intelligence is the capacity to think speedily and reason flexibly to solve new problems without relying on past experience and accumulated knowledge.

Fluid intelligence allows us to perceive and draw inferences about relationships among variables and to conceptualize abstract information, which aids problem-solving. It is correlated with essential skills such as comprehension and learning.

As Raymond Cattell (1967) pointed out, it is a capacity to “perceive relationships independent of previous specific practice or instruction related to those relationships”.

Examples of the use of fluid intelligence include solving puzzles, constructing strategies to deal with new problems, seeing patterns in statistical data, and engaging in speculative philosophical reasoning (Unsworth, Fukuda, Awh & Vogel, 2014).

Horn (1969) pointed out that fluid intelligence is formless and relies only minimally upon acculturation and prior learning, which includes both formal and informal education.

He further contended that fluid intelligence is capable of flowing into a myriad of diverse cognitive activities. Consequently, the ability to solve abstract problems and engage in figural analyses and classifications, Horn argued, is dependent upon one’s level of fluid intelligence (Horn, 1968).

Fluid intelligence has long been thought to peak during the late 20s before beginning to decline (Cacioppo & Freberg 2012) gradually.

The decline of fluid intelligence is likely to be related to the deterioration of neurological functioning but may also decline as it is used less frequently during older age.

graph showing fluid and Crystallized Intelligence across the lifespan

This decline of fluid intelligence has been attributed to the brain’s local atrophy in the right cerebellum , age-related changes in the brain, and a want of training (Cavanaugh & Blanchard-Fields, 2006).

Recent research, however, challenges previous assumptions and suggests that certain parts of fluid intelligence may not peak until even age 40.

Measurements of Fluid Intelligence

Woodcock-johnson tests of cognitive abilities.

The Third Edition of Woodcock-Johnson Tests of Cognitive Abilities comprises concept formation, which involves categorical thinking, and analysis synthesis, which involves sequential reasoning (Woodcock, McGrew & Mather, 2001).

Concept formation herein requires the inference of underlying rules to solve puzzles presented in ascending order of difficulty (Schrank & Flanagan 2003).

Analysis synthesis, on the other hand, requires the learning and the oral presentation of solutions to logic puzzles that emulate a mathematics system. The association of procedural learning with muscle memory can make certain actions second nature (Bullemer, Nissen, & Willingham, 1989).

Raven’s Progressive Matrices

Raven’s Progressive Matrices evaluate the capacity to discern relationships among various mental representations (Raven, Raven & Court 2003).

It is a non-verbal multiple-choice test that requires the completion of several drawings based on the test takers’ ability to notice pertinent features based on the spatial positioning of several objects (Ferrer, O”Hare & Bunge 2009).

Wechsler Intelligence Scales for Children

The Wechsler Intelligence Scales for Children, Fourth Edition, relies exclusively on visual stimuli and is a non-verbal test that consists of a matrix reasoning test and a picture concept assessment (Wechsler, 2003).

The picture concept task evaluates a child’s capacity to discern the underlying traits governing a set of materials while the matrix reasoning test assesses the child’s ability to begin with stated governing traits/rules to identify the solution to a novel problem (Flanagan & Kaufman, 2004).

The solution herein is the picture for a puzzle that fits the stated rule.

What Is Crystallized Intelligence?

Crystallized Intelligence refers to the ability to utilize skills and knowledge acquired via prior learning (Horn, 1969). The use of crystallized intelligence involves the recalling of pre-existing information as well as skills.

Examples of the use of Crystallized Intelligence, on the other hand, include recalling historical events and dates, remembering geographical locations, building one’s vocabulary, and reciting poetic texts (Horn, 1968).

Crystallized Intelligence results from accumulated knowledge, including knowledge of how to reason, language skills and an understanding of technology. This type of intelligence is linked to eduction, experience and cultural background and is measured by tests of general information.

The use of crystallized intelligence involves the recalling of pre-existing information as well as skills. For example, knowing how to ride a bike or read a book.

Horn (1969) explained that Crystallized Intelligence is a “precipitate out of experience” which stems from a prior application of fluid intelligence.

Effectively completing tasks involving language mechanics (such as vocabulary building) and general information relies on one’s Crystallized Intelligence.

Crystallized Intelligence rises gradually and remains stable throughout adulthood until it begins to decline after age 60 (Cavanaugh & Blanchard-Fields, 2006).

Despite the observance of this general trend, the age at which Crystallized Intelligence reaches its peak is yet to be ascertained (Desjardins, Warnke & Jonas, 2012).

Measurements of Crystallized Intelligence

The c-test .

The C-Test is a text completion test initially proposed as a foreign language proficiency test that provides an integrative measure of crystallized intelligence (Baghaei & Tabatabaee-Yazdi, 2015).

The underlying construct of the C-Test corresponds to the abilities undergirding the language component of crystallized intelligence.

However, research implies that the careful selection of texts from relevant domains of knowledge can enable the C-Test to measure the factual knowledge component of crystallized intelligence as well.

The Wechsler Adult Intelligence Scale (WAIS)

The revised form of the Wechsler Adult Intelligence Scale, which has been used since 1981, comprises five performance and six verbal subtests (Kaufman & Lichtenberger 2006).

These verbal tests include comprehension, information, digit span, vocabulary, similarities, and arithmetic (Wechsler Adult Intelligence Scale-Revised). Most of these verbal tests are widely construed as capable of measuring crystallized intelligence.

How the Intelligence Types Work Together

While fluid intelligence and Crystallized Intelligence are distinct, it is important to note the multiplicity of the tasks that involve both these components. For instance, in taking a math exam, one may rely on one’s fluid intelligence to construct a strategy to respond to the given questions within the allocated time.

However, at the same time, one might have to utilize one’s Crystallized Intelligence to recall various mathematical concepts and theories in providing the correct answers.

Likewise, an entrepreneur might have to use her fluid intelligence to identify a new opportunity in the market. However, creating a product to meet consumer demand might require past knowledge and, therefore, the use of her Crystallized Intelligence.

Despite this manifest interrelationship, Crystallized Intelligence is not a type of fluid intelligence that has crystalized over time (Cherry, 2018). However, the investment of fluid intelligence via the learning of new information produces Crystallized Intelligence.

In other words, the critical analyses of problems via fluid intelligence creates and transfers information to long-term memory which constitutes a part of crystallized cntelligence.

Can Fluid Intelligence Be Improved?

Because crystallized intelligence is known to improve over time and remain stable with age, it is generally acknowledged that education and experience increase crystallized intelligence (Cavanaugh & Blanchard-Fields, 2006). However, the approach to fluid intelligence has been characterized by complexity.

Until recently, it was widely held that fluid intelligence is static, largely determined by genetic factors, and therefore, could not be altered. However, some research has suggested that fluid intelligence can be improved.

During some experiments conducted in 2008 by psychologist Susanne M. Jaeggi, 70 participants were subjected to daily tasks and regular training to improve their fluid intelligence (Jaeggi, Buschkuehl, Jonides & Perrig, 2008).

At the end of the period, a notable rise in the participants’ fluid intelligence was observed. A similarly done study by Qiu, Wei, Zhao, and Lin also supported Jaeggi’s conclusions (Qiu, Wei, Zhao, & Lin, 2009).

The key themes are challenging oneself by learning new skills, problem-solving, working memory training, and exposure to intense cognitive tasks systematically and with increasing difficulty. This seems to drive neural changes that facilitate enhanced fluid reasoning abilities.

Physical exercise – Aerobic exercise like running or swimming can help boost fluid intelligence by promoting brain plasticity and growth.
Learning new skills – Actively challenging oneself by learning new complex skills and solving mentally demanding problems can improve fluid intelligence over time. The activities should be progressively more difficult.
Working memory training – Targeted training on working memory tasks, like memory games or mental math sequences, can help improve problem-solving capacities related to fluid intelligence.
Action video game training – Training with fast-paced action video games that require quick reactions and on-the-fly decision-making provided some cognitive stimulation that improved fluid intelligence-related performance.
Brain stimulation – Non-invasive brain stimulation techniques like tDCS and TMS showed some preliminary evidence for temporarily and modestly improving reasoning skills tied to fluid intelligence, but more research is still needed.
Getting good sleep – Getting enough good quality sleep supports cognitive function and consolidation of memories important for neural changes underlying learning, which relates to fluid intelligence capacity.

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Fluid vs. Crystallized Intelligence

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

problem solving crystallized and fluid intelligence

Aaron Johnson is a fact checker and expert on qualitative research design and methodology.

Fluid Intelligence

Crystallized intelligence.

Differences
Intelligence Tests
Improving Intelligence
How They Work Together

Fluid vs. crystallized intelligence is one of many theories of intelligence in psychology. Fluid intelligence involves the ability to reason and think flexibly, whereas crystallized intelligence refers to the accumulation of knowledge, facts, and skills that are acquired throughout life.

The theory of fluid vs. crystallized intelligence was first proposed by psychologist Raymond Cattell ; he further developed it along with his student John Horn. The theory suggests that intelligence is composed of different abilities that interact and work together to produce overall individual intelligence.

People often claim that their intelligence seems to decline as they age. However, research suggests that while fluid intelligence begins to decrease after adolescence, crystallized intelligence continues to increase throughout adulthood.

Cattell defined fluid intelligence as "the ability to perceive relationships independent of previous specific practice or instruction concerning those relationships." Fluid intelligence involves being able to think and reason abstractly and solve problems. This ability is considered independent of learning, experience, and education.

When you encounter an entirely new problem that cannot be solved with your existing knowledge, you must rely on fluid intelligence to solve it.

Fluid intelligence examples include:

Coming up with problem-solving strategies
Interpreting statistics
Philosophical reasoning
Solving puzzles or abstract problems

Fluid intelligence tends to decline during late adulthood. Certain cognitive skills associated with fluid intelligence also tend to decline as people reach later adulthood.

Crystallized intelligence involves knowledge that comes from prior learning and past experiences.

What Is Crystallized Intelligence?

Crystallized intelligence is based upon facts and rooted in experiences. As we age and accumulate new knowledge and understanding, crystallized intelligence becomes stronger.

Crystallized intelligence examples include:

Memorizing text
Memorizing vocabulary
Recalling how to do something
Remembering dates and locations

As you might expect, this type of intelligence tends to increase with age. The more learning and experience you have, the more you build up your crystallized intelligence.

Differences Between Fluid and Crystallized Intelligence

There are several ways in which each intelligence type is distinct.

Refers to current ability

Involves openness to learning new things

Decreases with age

Refers to prior learning

Involves recalling specific facts

Increases with age

Fluid intelligence along with its counterpart, crystallized intelligence, are both factors of what Cattell referred to as general intelligence .

While fluid intelligence involves our current ability to reason and deal with complex information around us, crystallized intelligence involves learning, knowledge, and skills that are acquired over a lifetime.

Despite its name, crystallized intelligence is not a form of fluid intelligence that has become "crystallized." Instead, the two facets of general intelligence are considered separate and distinct.

Changes in Fluid vs. Crystallized Intelligence

Fluid and crystallized intelligence tend to change throughout life, with certain mental abilities peaking at different points .

Fluid intelligence has long been believed to peak quite early in life, but research published in 2015 suggests that some aspects of fluid intelligence may peak as late as age 40. Crystallized intelligence does tend to peak later in life, hitting its apex around age 60 or 70.

Some things to remember about fluid and crystallized intelligence:

Both types of intelligence increase throughout childhood and adolescence.
Crystallized intelligence continues to grow throughout adulthood.
Many aspects of fluid intelligence peak in adolescence and begin to decline progressively beginning around age 30 or 40.

Fluid and Crystallized Intelligence Tests

It's thought that standard IQ tests don't entirely capture a person's fluid and crystallized intelligence levels. So, what tests can measure these intelligence types?

Tests that measure fluid intelligence:

Raven's Progressive Matrices Test (RPM) is a non-verbal assessment that asks a person to examine various shapes and pick from a choice of shapes to complete a pattern.
Woodcock-Johnson Test of Cognitive Abilities measures cognitive skill and achievement; it's often given to children to assess them for advanced academic courses.
Wechsler Intelligence Scale for Children measures verbal, reasoning, and memory skills. It is primarily administered to children between the age of six and 16.

Tests that measure crystallized intelligence:

Vocabulary and general knowledge tests
Wechsler Adult Intelligence Scale (WAIS) is a measure of cognitive abilities developed for adults. It provides separate scores for different areas as opposed to an overall intelligence score.

Improving Fluid and Crystallized Intelligence

Past research on intelligence suggested that people really didn't have much control over their intelligence at all. Instead, it was believed that our IQ was largely determined by genetics and that training programs aimed at increasing IQ tended to have limited effectiveness.

By contrast, an analysis of previous studies published in 2014 found that it is possible to improve fluid intelligence with brain training.

What the researchers discovered, however, was that the training also increased unrelated cognitive skills, including the ability to reason and solve new problems totally independent of previously acquired knowledge.

In essence, with training, a person may be able to engage the abstraction of thoughts and ideas as readily as applying knowledge-based reasoning.

How to Improve Fluid Intelligence

Challenge yourself
Mix up your routine
Think creatively
Socialize on a regular basis

Crystallized intelligence, on the other hand, is something that can be improved through learning. The more accumulated knowledge you have, the more crystallized intelligence you will possess.

How to Improve Crystallized Intelligence

Learn a new language
Learn a new skill
Take a class

Seeking new knowledge helps build your crystallized intelligence over time, but challenging yourself with new experiences can improve your fluid intelligence as well.

How Fluid and Crystallized Intelligence Work Together

Does one of these intelligence types tend to be more important?

Both types of intelligence are equally important in everyday life. For example, when taking a psychology exam , you might need to rely on fluid intelligence to come up with a strategy to solve a statistics problem, while you must also employ crystallized intelligence to recall the exact formulas you need to use.

Though each is a distinct type of intelligence, fluid and crystallized intelligence are intertwined. Crystallized intelligence is formed through the investment of fluid intelligence when information is learned.

By using fluid intelligence to reason and think about problems , the information can then be transferred to long-term memory so that it can become part of crystallized intelligence.

Keep in Mind

Study participants usually engage in intensive and difficult brain training tasks over relatively short periods of time. This doesn't mean the same techniques can't be applied to your own life. The principles are the same.

Seek out new challenges. Gains in intelligence don't come from sticking to the same old routines. Keep exploring new things in life and keep learning new things. Tackle learning a new language. Take piano lessons. Visit a new country and learn about the people and culture.

All of these types of activities keep your brain engaged, challenged, and focused on learning new things in new ways.

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Psychologily

Unlocking the Power of Fluid Intelligence: How to Boost Your Brain’s Problem-Solving Abilities

Have you ever heard of fluid intelligence? It’s a fascinating concept refers to our ability to think and reason abstractly, solve problems, and adapt to new situations. Unlike crystallized intelligence, which is based on knowledge and experience, fluid intelligence is considered to be independent of learning and education.

Research has shown that fluid intelligence declines as we age while crystallized intelligence remains relatively stable. However, there are ways to improve our fluid intelligence, such as engaging in challenging mental activities like puzzles, learning a new language, or playing a musical instrument.

Understanding the differences between fluid and crystallized intelligence can help us better understand our cognitive abilities and how to improve them. In this article, we’ll explore the concept of fluid intelligence in more detail, including its definition, how it differs from crystallized intelligence and ways to improve it. So, let’s dive in and learn more about this fascinating topic!

Understanding Fluid Intelligence

As we go through life, we face various problems that require us to think and reason abstractly. This is where fluid intelligence comes into play. Fluid intelligence is thinking logically and solving problems in new and unfamiliar situations. It is considered to be independent of education, experience, and learning.

When we encounter a problem that cannot be solved with our existing knowledge, we must rely on fluid intelligence to devise a solution. For example, if we face a complex math problem we have never seen before, we must use our fluid intelligence to reason and solve the problem.

One of the critical features of fluid intelligence is its adaptability. It allows us to adapt to new situations and environments quickly. This is because fluid intelligence is not tied to specific knowledge or skills. Instead, it is a general cognitive ability that can be applied to various tasks.

It is important to note that fluid intelligence is not the only type of intelligence. Another type of intelligence is crystallized intelligence, based on our knowledge and experience. While both types of intelligence are essential, fluid intelligence is instrumental in situations where we need to think on our feet and develop creative solutions.

Understanding fluid intelligence can help us better appreciate the importance of reasoning and problem-solving in our daily lives. By developing our fluid intelligence, we can become more adaptable and better equipped to handle new and unfamiliar situations.

Critical Characteristics of Fluid Intelligence

Fluid intelligence is the ability to reason, think abstractly, and solve problems independently of prior knowledge or experience. It is considered one of the primary components of intelligence and is essential for adapting to new situations and learning new things.

Several critical characteristics of fluid intelligence distinguish it from other types of intelligence:

Flexibility : Fluid intelligence involves thinking flexibly and adapting to new situations. This means coming up with new solutions to problems and adjusting one’s thinking as needed.
Complexity : Fluid intelligence is often associated with the ability to deal with complex problems. This involves identifying patterns, relationships, and connections between different pieces of information.
Speed : Fluid intelligence is characterized by quick thinking and rapidly processing information. This allows individuals to identify and solve problems quickly.
Working Memory : Fluid intelligence is closely linked to working memory, the ability to hold and manipulate information in one’s mind. This allows individuals to keep multiple pieces of information in mind and use them to solve problems.
Novelty : Fluid intelligence is particularly important when dealing with new or unfamiliar situations. This involves being able to identify and apply new concepts and ideas.

Fluid intelligence makes individuals think flexibly, adapt to new situations, and solve complex problems. By understanding the critical characteristics of fluid intelligence, we can better appreciate the importance of this type of intelligence in our daily lives.

Fluid Intelligence vs Crystallized Intelligence

When it comes to intelligence, there are different types that we can measure. One of the most popular theories of intelligence is the distinction between fluid and crystallized intelligence. In this section, we will discuss the distinctive features of fluid and crystallized intelligence and their interrelation and interdependence.

Distinctive Features

Fluid intelligence is our ability to reason, solve problems, and think abstractly. It is the ability to adapt to new situations and find solutions to problems we have never encountered. For example, when faced with a complex mathematical problem or a logical puzzle, we use our fluid intelligence to find a solution.

On the other hand, crystallized intelligence is the accumulation of knowledge, facts, and skills we have acquired throughout our lives. It is the product of our experiences, education, and cultural background. For example, when we read a book or watch a documentary, we use our crystallized intelligence to understand and remember the information.

Interrelation and Interdependence

While fluid and crystallized intelligence are distinct, they are interrelated and interdependent. In many situations, we need both types of intelligence to perform well. For example, when we are learning a new language, we need to use our fluid intelligence to understand the grammar and syntax of the language. However, we also need our crystallized intelligence to remember the vocabulary and rules of the language.

Research has shown that fluid intelligence tends to decline with age, while crystallized intelligence tends to increase. As we age, we may become less able to adapt to new situations and solve complex problems. Still, we may become better at using our accumulated knowledge and experience to understand and remember information.

Fluid and crystallized intelligence are two distinct but interrelated types of intelligence. While fluid intelligence allows us to adapt to new situations and solve complex problems, crystallized intelligence is the accumulation of knowledge and skills we have acquired throughout our lives. Both types of intelligence are essential for our success in life, and we need to use them to perform well in many situations.

Measurement of Fluid Intelligence

When it comes to measuring intelligence, many different tests can be used. One of the most essential types of intelligence is fluid intelligence, which refers to the ability to reason, problem-solve, and think abstractly. In this section, we will discuss some of the common tests used to measure fluid intelligence and how these tests are scored and interpreted.

Common Tests

Several tests are commonly used to measure fluid intelligence. One such test is the Raven’s Progressive Matrices (RPM), which involves completing a series of visual puzzles that require abstract reasoning. Another test is the Cattell Culture Fair Intelligence Test, designed to minimize cultural bias and measure abstract reasoning ability.

Other tests commonly used to measure fluid intelligence include the Wechsler Adult Intelligence Scale (WAIS), which includes subtests that measure fluid intelligence, such as matrix reasoning and picture completion. The Kaufman Assessment Battery for Children (KABC) is another test that measures fluid intelligence and other aspects of cognitive functioning.

Scoring and Interpretation

When it comes to scoring and interpreting tests of fluid intelligence, there are several things to keep in mind. First, it is essential to understand that these tests are not measures of overall intelligence but specific aspects of intelligence, such as abstract reasoning.

In terms of scoring, most tests of fluid intelligence are standardized, which means that scores are compared to those of a representative sample of the population. This allows for a more accurate interpretation of individual scores.

Interpretation of scores on tests of fluid intelligence can be complex, but generally speaking, higher scores are associated with better problem-solving ability, adaptability, and the ability to learn quickly. However, it is essential to remember that these tests are just one way of measuring intelligence and that many factors contribute to overall cognitive functioning.

Tests of fluid intelligence are essential for measuring specific aspects of cognitive functioning. By understanding the common tests used to measure fluid intelligence and how these tests are scored and interpreted, we can better understand our cognitive strengths and weaknesses, as well as those of others.

Factors Affecting Fluid Intelligence

As we age, it is natural for our cognitive abilities to decline. However, several factors can affect our fluid intelligence: the ability to reason and think flexibly. This section will explore some factors that can affect fluid intelligence.

Age is one of the most significant factors affecting fluid intelligence. As we age, our ability to reason and think flexibly tends to decline. This decline typically starts in early adulthood and continues throughout our lives. However, it is essential to note that only some experience the same degree of decline. Some people may experience a more significant decline in their fluid intelligence than others.

Our physical and mental health can also affect our fluid intelligence. For example, certain medical conditions, such as Alzheimer’s disease, can significantly impact our cognitive abilities. Factors such as poor nutrition, lack of exercise, and chronic stress can also contribute to a decline in our fluid intelligence.

Environment

Our environment can also play a role in our fluid intelligence. For example, growing up in a stimulating environment with access to educational resources can help to improve our cognitive abilities. On the other hand, growing up in a deprived environment with limited access to educational resources can have the opposite effect. Exposure to toxins and pollutants in our environment can also harm our cognitive abilities.

Several factors can affect our fluid intelligence, including age, health, and environment. While some of these factors are beyond our control, there are steps we can take to help maintain our cognitive abilities as we age. These steps include staying physically active, eating a healthy diet, engaging in mentally stimulating activities, and reducing exposure to toxins and pollutants.

Improving Fluid Intelligence

Our cognitive abilities naturally decline as we age, including our fluid intelligence. However, there are steps we can take to improve and maintain our fluid intelligence. This section will discuss two ways to improve fluid intelligence: cognitive training and healthy lifestyle choices.

Cognitive Training

Cognitive training involves exercises and activities that challenge our brains and improve our cognitive abilities. Research has shown that cognitive training can improve fluid intelligence, among other cognitive abilities.

One type of cognitive training is working memory training. Working memory is the ability to hold and manipulate information in our minds for a short period. By practicing working memory tasks, such as remembering a list of numbers or letters, we can improve our working memory and, in turn, our fluid intelligence.

Another type of cognitive training is reasoning training. Reasoning involves the ability to think logically and solve problems. By practicing reasoning tasks, such as puzzles or brainteasers, we can improve our reasoning skills and, again, our fluid intelligence.

Healthy Lifestyle Choices

In addition to cognitive training, healthy lifestyle choices can improve our fluid intelligence. Here are some examples:

Getting enough sleep: Sleep is essential for cognitive function, including fluid intelligence. Aim for 7-9 hours of sleep per night.
Exercise: Exercise has been shown to improve cognitive function, including fluid intelligence. Aim for at least 30 minutes of moderate exercise per day.
Healthy diet: A healthy diet, rich in fruits, vegetables, whole grains, and lean protein, can also improve cognitive function.
Stress management: Chronic stress can adversely affect cognitive function, including fluid intelligence. To reduce stress, practice stress management techniques, such as meditation or deep breathing.

Incorporating cognitive training and healthy lifestyle choices into our daily routines allows us to improve and maintain our fluid intelligence, even as we age.

Frequently Asked Questions

Does fluid intelligence decrease as we age.

Yes, fluid intelligence tends to decline as we age. This decline can be caused by a variety of factors such as chronic stress, depression, anxiety, brain injury or disease, and drug and alcohol abuse. However, it’s important to note that not everyone experiences the same degree of decline and there are ways to improve and maintain fluid intelligence.

What’s the difference between fluid and crystallized intelligence?

Fluid intelligence involves the ability to reason and think flexibly, whereas crystallized intelligence refers to the accumulation of knowledge, facts, and skills that are acquired throughout life. While fluid intelligence is more closely related to problem-solving and adapting to new situations, crystallized intelligence is more related to general knowledge and expertise in certain areas.

How can we measure fluid intelligence?

There are various tests that can be used to measure fluid intelligence, such as Raven’s Progressive Matrices, the Cattell Culture Fair Intelligence Test, and the Kaufman Assessment Battery for Children. These tests typically involve tasks that require reasoning, problem-solving, and pattern recognition.

Can fluid intelligence be improved?

Yes, fluid intelligence can be improved through various means such as engaging in intellectually stimulating activities, practicing mindfulness and meditation, and getting regular physical exercise. Additionally, certain cognitive training programs have been shown to improve fluid intelligence.

Is there a correlation between fluid intelligence and IQ?

Yes, there is a correlation between fluid intelligence and IQ, but they are not the same thing. IQ tests typically measure both fluid and crystallized intelligence, as well as other factors such as processing speed and working memory.

What are the practical applications of fluid intelligence?

Fluid intelligence has practical applications in many areas, such as problem-solving, decision-making, and innovation. It is particularly important in fields such as science, engineering, and technology, where the ability to think creatively and adapt to new situations is crucial. Additionally, fluid intelligence has been linked to academic and occupational success.

Fluid Versus Crystallized Intelligence: What’s the Difference?

Archaeology

Ph.D., Psychology, Fielding Graduate University
M.A., Psychology, Fielding Graduate University
B.A., Film Studies, Cornell University

The theory of fluid and crystallized intelligence proposes that there are two distinct kinds of intelligence. Fluid intelligence refers to the ability to reason and solve problems in unique and novel situations, while crystallized intelligence refers to the ability to use knowledge acquired through past learning or experience.

The theory was first proposed by psychologist Raymond B. Cattell and developed further with John Horn.

Fluid vs. Crystallized Intelligence

The theory contends that there are two distinct types of intelligence. It challenges, and extends, the concept of g, or generalized intelligence factor.
Fluid intelligence is the ability to use logic and solve problems in new or novel situations without reference to pre-existing knowledge.
Crystallized intelligence is the ability to use knowledge that was previously acquired through education and experience.
Fluid intelligence declines with age, while crystallized intelligence is maintained or improved.

Origin of the Theory

The theory of fluid intelligence challenges the idea of generalized intelligence factor (known as g ), which contends that intelligence is a single construct. Instead, Cattell contended that there are two independent intelligence factors: “fluid” or g f intelligence, and "crystallized” or g c intelligence.

As he explained in his 1987 book Intelligence: Its Structure, Growth, and Action , Cattell referred to the ability to reason as fluid intelligence because it “has the ‘fluid' quality of being directable to almost any problem.” He referred to knowledge acquisition as crystalized intelligence because it “is invested in the particular areas of crystallized skills which can be upset individually without affecting the others.”

Fluid Intelligence

Fluid intelligence refers to the ability to reason, analyze, and solve problems. When we use fluid intelligence, we aren’t relying on any pre-existing knowledge. Instead, we are using logic, pattern recognition, and abstract thinking to solve new problems.

We use fluid intelligence when we encounter novel, often nonverbal tasks, such as math problems and puzzles. Fluid intelligence also plays a role in the creative process, as when someone picks up a paintbrush or starts plucking on a piano with no prior training.

Fluid intelligence is rooted in physiological functioning . As a result, these abilities start to decline as people age, sometimes starting as early as their 20s.

Crystallized Intelligence

Crystallized intelligence refers to the knowledge you acquire through experience and education. When you use crystallized intelligence, you reference your pre-existing knowledge: facts, skills, and information you learned in school or from past experience.

You utilize crystallized intelligence when you encounter tasks that require the use of previously acquired knowledge, including verbal tests in subjects like reading comprehension or grammar. Given its reliance on the accumulation of knowledge, crystallized intelligence is typically maintained or even increased throughout one's lifetime.

How the Intelligence Types Work Together

Although fluid and crystallized intelligence represent two distinct sets of abilities, they can and often do work together. For example, when cooking a meal, you use crystallized intelligence to understand and follow the instructions in a recipe, and use fluid intelligence when modifying spices and other ingredients to suit your tastes or dietary requirements. Similarly, when taking a math test, the formulas and math knowledge (like the meaning of a plus sign) comes from crystallized intelligence. The ability to develop a strategy to complete a complicated problem, on the other hand, is the product of fluid intelligence.

Fluid intelligence is often used when learning new things. When you encounter a new subject, you use your fluid intelligence to understand the material through logical and analysis. Once you understand the material, the information will be incorporated into your long-term memory, where it can develop into crystallized knowledge.

Can Fluid Intelligence Be Improved?

While crystalized intelligence improves or remains stable with age, fluid intelligence is known to decline fairly rapidly after adolescence. Several studies have investigated whether it is possible to improve fluid intelligence.

In 2008, psychologist Susanne M. Jaeggi and her colleagues conducted experiments in which four groups of young, healthy participants performed a highly demanding working memory (short-term memory) task every day. The groups performed the task for 8, 12, 17, or 19 days respectively. The researchers found that participants’ fluid intelligence improved following the training, and that the more training participants underwent, the more their fluid intelligence improved. Their study concluded that fluid intelligence can, in fact, improve through training.

Another study using a similar protocol backed up Jaeggi’s results, but subsequent studies have not replicated the findings, so the results of Jaeggi’s study are still considered controversial.

Cattell, Raymond B. Intelligence: Its Structure, Growth, and Action . Elsevier Science Publishers, 1987.
Cherry, Kendra. “Fluid Intelligence vs. Crystallized Intelligence” Verywell Mind , 2018. https://www.verywellmind.com/fluid-intelligence-vs-crystallized-intelligence-2795004
Chooi, Weng-Tink, and Lee A. Thompson. “Working Memory Training Does Not Improve Intelligence in Healthy Young Adults.” Intelligence , vol. 40, no. 6, 2012, pp. 531-542.
Dixon, Roger A., et al. “Cognitive Development in Adulthood and Aging.” Handbook of Psychology, vol. 6: Developmental Psychology, edited by Richard M. Lerner, et al., John Wiley & Sons, Inc., 2013.
Jaeggi, Susanne M., et al. “Improving Fluid Intelligence with Training on Working Memory.” Proceedings of the National Academy of Sciences of the United States of America , vol. 105, no. 19, 2008, pp.6829-6833,
Qiu, Feiyue, et al. “Study on Improving Fluid Intelligence Through Cognitive Training System Based on Gabor Stimulus.” Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering , IEEE Computer Society, Washington, DC, 2009. https://ieeexplore.ieee.org/document/5454984/
Redick, Thomas S., et al. “No Evidence of Intelligence Improvement After Working Memory Training: A Randomized, Placebo-Controlled Study.” Journal of Experimental Psychology: General , vol. 142, no. 2, 2013, pp. 359-379, http://psycnet.apa.org/doiLanding?doi=10.1037%2Fa0029082
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In This Article Expand or collapse the "in this article" section Crystallized and Fluid Intelligence

Introduction, historical context.

To g or Not to g ?
Extended Gf-Gc Theory of Intelligence
Other Theoretical Accounts of Human Cognitive Abilities
Lifespan Cognitive Development
The Investment Hypothesis
Neuroscientific Evidence for Gf-Gc
Genetic and Epigenetic Evidence for Gf-Gc
Environmental Influences on Intelligence
Gf-Gc and School/Education Achievement
Cohort Effects in Intelligence
Gf-Gc and Working Memory
Gf-Gc and Executive Functions
Gf-Gc and Personality, Emotional Intelligence, and Social Intelligence
Gf-Gc and Cognitive Training

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Crystallized and Fluid Intelligence by Paolo Ghisletta , Thierry Lecerf LAST MODIFIED: 23 June 2023 DOI: 10.1093/obo/9780199828340-0207

The Gf-Gc theory of cognitive abilities is a psychometric theory of intelligence based on intelligence and ability tests. Evidence in favor of the theory has largely been gathered via factor analytic and developmental studies of intelligence, but also with respect to school/education achievement and biological/genetic evidence. The theory was first formulated in terms of fluid and crystallized cognitive abilities by Raymond Cattell in 1943. The theory posits its fundamental assumption, that intelligence (or rather, human cognitive abilities) is not a unitary construct but entails the lifelong coordination of at least two classes of abilities: fluid (Gf), which refers to the ability of understanding relationships among the components of an abstract problem and using such relationships to solve the problem, and crystallized (Gc), which refers to the knowledge accumulated through experiences. Fluid abilities are general in nature, in that they can be applied to any novel abstract situation that requires solving a novel problem, while crystallized abilities are specific, in that they require specific knowledge (learned from one’s cultural milieu) to solve familiar problems (this distinction is similar to, and partially built upon, what Donald Hebb proposed in 1942 in terms of Intelligence A and Intelligence B). Two particular aspects of this hypothesis, which set it apart from previous ones, are its structural and its kinematic predictions. Structural evidence in favor of the Gf-Gc hypothesis comes from many studies showing that by relying on factor-analytic methods it is possible to demonstrate that a single factor of intelligence, called g , does not describe the relations among broad cognitive abilities, representing primary-level abilities, as well as models with multiple factors. Kinematic predictions come from studies that analyze age gradients or, more directly, age changes in cognitive performance. These studies conclude that while during childhood both classes of abilities increase, starting in young adulthood fluid abilities decrease, while crystallized abilities remain constant (or may even increase). A third set of predictions is dynamic in nature, and it is captured especially by the investment theory, which postulates that during childhood fluid abilities are necessary to accumulate crystallized ones. In practice, it is certainly not simple to measure the two broadest abilities in adults; with respect to Gc abilities, because of the influence of experience, interest, motivation, professional skills, and years since schooling (on which most general Gc tests are based, like vocabulary); and as for Gf abilities, especially in older adults, processing speed generally decreases during adulthood, and most Gf tasks are timed, which leads to confounds between the two classes of cognitive tasks. Opponents of the theory have pointed out that, in practice, Gf is highly collinear with a general factor of intelligence ( g ), while Gc abilities further contribute little in terms of individual differences in cognitive performance.

In 1943, the psychologist Raymond B. Cattell (b. 1905–d. 1998) published a highly influential paper in Psychological Bulletin entitled “The Measurement of Adult Intelligence” (he also presented portions of the paper at the forty-ninth annual meeting of the American Psychological Association in 1941 in the form of Cattell 1941 ). Therein, Cattell noted the increase of adult testing during World War I, first by the US government to recruit and attribute specific positions within the army’s hierarchy, then more generally to assess intellectual abilities in children and adults. Cattell 1943 lists forty-four existing intelligence tests, which cover various domains, such as written and oral verbal, nonverbal, and perceptual abilities. However, Cattell also points out that such tests are neither standardized nor published, while the majority were conceived for college students (hence are probably not generalizable to a wider population). In the end, he notes a dearth of intelligence tests for adults of a general population. Cattell also observes that the heterogeneity of the tests stems from different theoretical perspectives on intelligence, but also from a variety of methodologies employed to validate such tests. Within this historical context, Cattell outlines the foundations of the hypothesis of fluid and crystallized ability (Gf-Gc). While this hypothesis somewhat resembles a number of other then-current propositions about intellectual human abilities, such as Intelligence A and Intelligence B ( Hebb 1942 ), power intelligence and speed intelligence ( Lorge 1936 ), and the distinction between energy and engines of intellect ( Spearman 1927 ), Cattell points out the salient differences that warrant the originality of his Gf-Gc hypothesis. Nevertheless, as affirmed in Cattell 1943 (p. 179) and as pointed out in the recent review Brown 2016 , Cattell’s hypothesis relies heavily on the proposition of Intelligence A and Intelligence B ( Hebb 1941 , Hebb 1942 ), to the point that, according to Brown 2016 , “Cattell’s Gf-Gc theory of intelligence” should be named “the Hebb-Cattell theory.” Kent 2017 and Kaufman, et al. 2020 provide a brief overview of the origin of the theory of fluid and crystallized intelligence. Schneider and McGrew 2018 provides the last revision of the CHC taxonomy, which integrates the Cattell-Horn model and Carroll’s theory.

Brown, Richard E. 2016. Hebb and Cattell: The genesis of the theory of fluid and crystallized intelligence. Frontiers in Human Neuroscience 10 (December): 606.

DOI: 10.3389/fnhum.2016.00606

Brown reviews the currently existing evidence about the origins of the Gf-Gc hypothesis of intelligence, and he concludes that “Cattell’s theory was Hebb’s idea” (p. 9) and that “The theory of fluid and crystallized intelligence therefore, should be called the Hebb-Cattell theory” (p. 11).

Cattell, Raymond B. 1941. Some theoretical issues in adult intelligence testing. Psychological Bulletin 38:592.

This abstract corresponds to Cattell’s oral contribution to the forty-ninth annual meeting of the American Psychological Association (1941, Northwestern University, Evanston, IL, USA) and is cited as the first evidence of Cattell naming his theory of fluid and crystallized intelligence. However, the abstract does not contain that nomenclature. This presentation was later elaborated upon to become the influential paper Cattell 1943 .

Cattell, Raymond B. 1943. The measurement of adult intelligence. Psychological Bulletin 40.3: 153–193.

DOI: 10.1037/h0059973

This is the seminal paper most often cited as the first written account of the Gf-Gc theory of intelligence by Cattell. This paper was in progress in 1941, and portions of it were presented in Cattell 1941 .

Hebb, Donald O. 1941. Clinical evidence concerning the nature of normal adult test performance. Psychological Bulletin 38:593.

This is the abstract of Hebb’s oral contribution to the same APA conference attended by Cattell that year. According to evidence reported in Brown 2016 , during this conference Hebb first presents his hypothesis of Intelligence A and B, which is later elaborated upon and renamed by Cattell as Gf and Gc.

Hebb, Donald O. 1942. The effect of early and late brain injury upon test scores, and the nature of normal adult intelligence. Proceedings of the American Philosophical Society 85.3: 275–292.

The author proposes a hypothesis about intelligence in normal (i.e., non-injured) adults. The hypothesis posits that in any performance of intellectual ability, two factors are involved: the power of reasoning (Intelligence A) and skill (Intelligence B), which is largely due to experience. These obviously resemble Cattell’s Gf and Gc, respectively. Cattell was strongly inspired by this hypothesis.

Kaufman, Alan S., W. Joel Schneider, and James C. Kaufman. 2020. Psychometric approaches to intelligence. In Human intelligence: An introduction . Edited by Robert J. Sternberg, 67–103. Cambridge, UK: Cambridge Univ. Press.

In this chapter, the authors provide an overview of the psychometric approaches to intelligence. Cattell’s theory of Gf and Gc, and the “expanded Gf – Gc theory” are described. Other psychometric models are presented like Spearman’s, Thurstone’s, Carroll’s, and CHC models.

Kent, Phillip. 2017. Fluid intelligence: A brief history. Applied Neuropsychology: Child 6.3: 193–203.

DOI: 10.1080/21622965.2017.1317480

Provides a historical overview of the origins of the Gf-Gc theory of cognitive abilities, with several citations by Cattell and Horn to exemplify their perspective. Furthermore, it examines the neuropsychological and neurological components of fluid intelligence, which, according to Kent, remain unclear.

Lorge, I. 1936. The influence of the test upon the nature of mental decline as a function of age. Journal of Educational Psychology 27.2: 100–110.

DOI: 10.1037/h0059078

The author administered eleven rather different intelligence tests to 142 adults aged twenty to over seventy years. Some tests (called “power”) had no time limits for administration, while others (called “speed”) imposed time constraints. The author observes that the “speed” tests led to stronger negative age gradients than the timed “power” tests. He argues that “speed” tests contaminate power with speed measurements.

Schneider, W. Joel, and Kevin S. McGrew. 2018. The Cattell-Horn-Carroll theory of cognitive abilities. In Contemporary intellectual assessment: Theories, tests, and issues . 4th ed. Edited by Dawn P. Flanagan and Erin M. McDonough, 73–163. New York: Guilford.

A very detailed description of the origin and advances of the Gf-Gc theory of Cattell and Horn integrated within Carroll’s analysis, resulting in the Cattell-Horn-Carroll theory (the most comprehensive and empirically supported psychometric theory of the structure of intelligence). New broad abilities were introduced, such as emotional intelligence (Gei).

Spearman, Charles. 1927. The abilities of man: Their nature and measurement . London: Macmillan.

In this seminal book, Spearman outlines the work he and his students carried out for twenty-three years on the nature of intelligence. He describes his theory of intelligence, where each intellectual assessment is due to two factors, referred to as “energy” of intellect (called g , the general factor of intelligence) and the “engine” of intellect (called s , the specific or special factor). Many scholars only remember the former g component.

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Decoding Cognitive Science: Fluid vs Crystallized Intelligence (Cognitive Abilities)

by Team Experts
July 2, 2023 July 3, 2023

Discover the Surprising Differences Between Fluid and Crystallized Intelligence – Unlock Your Cognitive Abilities Today!

In summary, cognitive abilities such as fluid and crystallized intelligence, problem-solving skills, memory capacity, learning potential, information processing , reasoning ability, and mental flexibility are crucial for personal and professional success . However, various risk factors such as lack of education, poor environmental conditions, and limited access to resources can hinder cognitive development and limit one’s ability to acquire and utilize these skills. It is important to recognize the importance of cognitive abilities and work towards improving them to achieve personal and professional goals .

What is the Difference Between Fluid and Crystallized Intelligence in Cognitive Development?

Can memory capacity affect fluid or crystallized intelligence, how does information processing differ between individuals with high levels of fluid vs crystallized intelligence, the importance of mental flexibility in understanding the relationship between fluid and crystallized intelligence, how do fluid intelligence, crystallized intelligence, problem-solving skills, memory capacity, learning potential, information processing, reasoning ability & mental flexibility relate to each other, common mistakes and misconceptions, related resources.

Perceptions of the malleability of fluid and crystallized intelligence.
Link between fluid/crystallized intelligence and global/local visual abilities across adulthood.
Neurocognitive reorganization between crystallized intelligence, fluid intelligence and white matter microstructure in two age-heterogeneous developmental cohorts.
The influence of fluid and crystallized intelligence on the development of knowledge and skills.
Erratum to “Neurocognitive reorganization between crystallized intelligence, fluid intelligence and white matter microstructure in two age-heterogeneous developmental cohorts” [Dev. Cogn. Neurosci. 41 (2020) 100743].

Intelligence

Too much crystallized thinking lowers fluid intelligence, how can you improve fluid intelligence in an era of crystallized intelligence.

Posted December 26, 2013 | Reviewed by Ekua Hagan

In a digital age—that puts a premium on facts, figures, and data—crystallized intelligence has become disproportionately valued over fluid intelligence. A wide range of new studies is finding that motor skills, hand-eye coordination, aerobic conditioning, and daily physicality are important for maintaining working memory and fluid intelligence.

Fluid intelligence is the capacity to think logically and solve problems in novel situations, independent of acquired knowledge. Fluid intelligence involves the ability to identify patterns and relationships that underpin novel problems and to extrapolate these findings using logic.

On the other hand, crystallized intelligence is the ability to utilize information, skills, knowledge, and experience in a way that could be measured on a standardized test. Crystallized intelligence represents your lifetime of cerebral knowledge, as reflected through your vocabulary, general explicit knowledge, and Trivial Pursuit- types of declarative memory of people, places, and things.

Although there is some controversy and debate on the best ways to improve fluid intelligence, studies are showing a strong link between non-academic pursuits and improved fluid intelligence. I have written a wide range of Psychology Today blog posts about improving cognitive function through physical activity, playing a musical instrument, making art, improving motor skills, meditation , daydreaming, and getting a good night's sleep. The ultimate goal of The Athlete’s Way is to identify daily habits that optimize the function of the brain, body, and mind throughout a person’s lifespan.

Many experts believe that one of the backlashes of overemphasizing standardized testing as part of "no child left behind" is that young Americans are gaining crystallized intelligence at the expense of their fluid intelligence. As the father of a 6-year-old, I am determined to encourage my daughter to flex both her crystallized intelligence and fluid intelligence every day and would encourage other parents to do the same.

I hated school when I was growing up and did terribly on standardized tests. My SAT scores were barely above average. My older sister, on the other hand, literally got double 800s on her SATs and was a national merit scholar. Throughout my childhood , the unspoken family framework was that my older sister had the "book" smarts, and I had the "athletic" smarts. I never had a chip on my shoulder because I didn’t like reading books or being in school. I wanted to be outside playing, listening to music, or just hanging out with friends. How was your "intelligence" categorized by your parents and teachers when you were growing up?

My father was a neuroscientist and a neurosurgeon and often got frustrated with me for not flexing my "cerebral" muscle. Once I got really into sports and decided to become a professional athlete, he would regularly say things to me like “Chris, there’s a big part of your brain that you’re forgetting to flex and it’s going to shrink.”

In my dad’s eyes, the cerebrum was the seat of cerebral, or intellectual thinking and the cerebellum was the seat of "cerebellar" implicit knowledge and muscle memory. If I didn’t flex my prefrontal cortex and gain new explicit knowledge, he believed that my cerebrum would lose volume and connectivity. To a degree, he was probably right. I realize now the ideal is to maintain a healthy balance of all four brain hemispheres by creating daily habits that engage both crystallized and fluid intelligence throughout your lifespan.

Hampshire College: Non Satis Scire

The main reason I went to Hampshire College is that they don’t have tests or grades. The second reason I went to Hampshire College was because with my SAT scores, I didn’t get in anywhere else. I applied to Hampshire because I didn’t think it would require much cerebral muscle.

What I realize now because of all the research I do on neuroscience and peak performance is that at Hampshire, the neural volume and connectivity of my cerebellum was benefitting from all the running, biking, swimming, meditation, yoga, and art-making I was doing regularly. The fact that I never had to cram my head full of crystallized facts actually fortified my fluid intelligence. Yes, because I never had to take a test or memorize anything, my crystallized intelligence is far below average but my fluid intelligence is probably above average.

The motto of Hampshire College is "Non Satis Scire," which means “to know is not enough." The philosophy is that crystallized intelligence doesn’t really get you that far in the real world—especially in the age of Google. Hampshire wanted to teach us fluid intelligence and emphasized the importance of every individual filtering crystallized information through his or her very unique lens and connecting the dots in new and original ways.

Fluid intelligence is directly linked to creativity and innovation. The book smarts of crystallized intelligence can only take a person so far in the real world. Depriving children of recess and forcing them to sit still in a chair cramming for a standardized test literally causes their cerebellum to shrink and lowers fluid intelligence.

The “Super 8” Fluid Intelligence Loop Connects All Four Brain Hemispheres

Like every son, I craved my father’s approval and wanted him to be proud of me. Even after I broke a Guinness World Record, I felt that in my father’s eyes, I still wasn’t enough. I grew up thinking that my dad sort of considered me a "dumb jock" or "hippie." So, I decided that I was going to get a book deal and publish a book about sports and neuroscience.

My dad published a book called Fabric of Mind in the 1980s. I knew that of all his accomplishments, publishing a book with Viking was the one he boasted about the most. I knew that the key to getting a book deal was to get a good agent, so I set out to find an agent. Jonathan Cane, who got me started as a competitive athlete back in the '80s—and is my founding co-partner at City Coach—was working on a book with an agent named Giles Anderson and connected me with the Anderson Literary Agency. Giles is an amazing agent and got me a book deal with St. Martin’s Press to write The Athlete’s Way: Sweat and the Biology of Bliss .

My father was so impressed that I had gotten a book deal with a major publisher and it really changed our relationship. Finally, for the first time in my life, I had earned his approval. There’s something really sad about that. How much did it take to make me worthy of love and belonging in his eyes? Ack. But anyway...

Over the next two years, my father and I spoke almost every day and I picked his brain for everything that he knew about neuroscience. It was a perfect father-son partnership because my athletic perspective on everything actually informed his thinking and we came up with the idea of shifting the focus of left brain- right brain to a new model of up brain-down brain between the cerebrum and cerebellum. The cerebrum being the "conscious" book brain, and the cerebellum being the " subconscious " muscle memory brain.

At the time, I was trying to say that "left brain-right brain" was wrong and that the salient divide in the cranial globe was not east-west, but north-south between the "up brain" (cerebrum) and "down brain" (cerebellum). I realize now I may have been half right. My hypothesis now is that all four hemispheres need to work together to optimize brain connectivity. Again, this seems so obvious. I don't know why it took me so long to connect the dots.

The most recent neuroscientific research has confirmed that there really is a difference between the left and right hemispheres. But I believe the goal for optimal brain connectivity isn’t just across the corpus callosum of the cerebral hemispheres. Optimal brain function needs to include connectivity of the cerebellar hemispheres via the vermis (which divides the cerebellum) and the midbrain which connects the "big brain" (cerebrum) with the "little brain" (cerebellum).

Beyond that, I have a hunch that when the two hemispheres of the cerebrum and the two hemispheres of the cerebellum become a "superfluid" entity with zero friction and zero viscosity, your mind breaks free to another dimension of consciousness. When every cell of your brain, body, and mind are acting in perfect unison, you are in a state of what I call superfluidity .

That split-brain model became the foundation of The Athlete’s Way . A few years later, when I was working on a proposal for a book called Origins of Imagination , I started to notice that creative greats tended to make some type of physical activity a part of their daily routine. I also noticed that the "eureka" moments often happened when the researcher, artist, or writer had stepped away from the microscope, canvas, or typewriter. The "a-ha" moments happened when a creative person was moving our doing something that used implicit, cerebellar memory.

I also knew that as a writer, I was similar to Joyce Carol Oates in that when I ran, I could visualize and rework entire paragraphs, structure subheadings, and connect new ideas in a way that I couldn’t when I was just sitting still. But what was the neuroscience of this? I was kind of stumped until one day I was walking home and bumped into my friend Maria on Commercial Street in Provincetown. Maria is a poet. I told her about all the research I was doing on the daily habits of creative people and how physical activity was key to creating "superfluidity" of thinking.

Without missing a beat, Maria looked at me and said, “I ride the elliptical trainer for at least 40 minutes every day. When I start moving my arms and legs back and forth, the poetry just starts to come out of me.” As she moved her arms and legs to emulate riding the elliptical, suddenly I realized that the bipedal motion was engaging all four hemispheres and that connectivity optimized brain function and led to fluid intelligence.

I ran home and drew this diagram of the two hemispheres of the cerebrum and the two hemispheres of the cerebellum working together in what I call a "Super 8 Fluid Intelligence Loop." When you bring the cerebellum into the creative or "intellectual" process, crystallized thinking becomes more fluid (or superfluid on a good day).

Fine-Tuned Motor Skills Linked to Fluid Intelligence

On December 23, 2013, researchers in Switzerland announced that they had discovered that humans with higher “motor excitability”—which is linked to fine-tuned motor skills—have a better working memory, which is linked to improved fluid intelligence.

Researchers from the Psychiatric University Clinics (UPK Basel) and the Faculty of Psychology in Basel have found that the excitability of the motor cortex is directly linked to improved working memory performance. "The motor cortical excitability can be easily studied with transcranial magnetic stimulation ," says Nathalie Schicktanz, doctoral student and first author of the study.

In the present study , that included 188 healthy young subjects, the scientists were able to show that subjects with high motor excitability had increased working memory performance as compared to subjects with low excitability. "By measuring the excitability of the motor cortex, conclusions can be drawn as to the excitability of other cortical areas," says Schicktanz.

Over the past few years, I have had my antennae up for any research that could prove this hunch. It’s been very exciting to wake up every morning and see cutting edge research confirming the link between physical activity, motor skills, and improved cognitive function. I am still putting the pieces of this puzzle together but this new study from Switzerland is one more piece towards solving this riddle.

The new study titled “Motor Threshold Predicts Working Memory Performance in Healthy Humans” was published in December 2013 in Annals of Clinical and Translational Neurology. The research was conducted by scientists from the Transfacultary Research Platform at the University of Basel. By measuring motor excitability, were able to measure general cortical excitability and related working memory and cognitive performance.

Conclusion: The Importance of Maintaining Working Memory Throughout Your Life

My first book was published a few months before my father passed away in 2007. He died of a heart attack reading The New York Times in a reclining chair. When my sister and I went to Florida to empty out his house, we found stacks and stacks of my hardcover book in his study, and copies of the book were scattered throughout the house.

I felt a sense of peace knowing that my father died knowing that I had published a book. I believe that nobody should ever feel a "need for achievement" or drive for perfection in order to feel worthy of love and belonging. This is one reason I object to crystallized intelligence standardized test scores dictating education . It's also why I make sure my daughter understands that making an effort and pouring your heart into something that you love is all that really matters, regardless of whether you get a gold medal, an A+, or no recognition at all.

Interestingly, since my dad's death, I feel as if he "passed the torch" to me. I have such a joyful passion for carrying on his legacy as a neuroscientist. I wake up every morning eager to see what researchers around the world are discovering how the brain works and sharing that with the general reader. As a neuroscientist, my father grew frustrated with the limitations of brain imaging technology. Although there is still a long way to go, he would be thrilled to see the advances made by things like the connectome project.

People of all ages need to keep their working memory strong in order to maintain fluid intelligence. In a sedentary digital age full of standardized testing, crystallized intelligence is monopolizing our brains and causing some regions to shrink and become disconnected.

It causes me great concern for myself and my daughter's generation that people—especially children—are totally out of balance between crystallized and fluid intelligence. The book proposal I’m working on now is called “ SUPERFLUIDITY: Daily Habits That Optimize Brain Connectivity for a Lifespan of Health, Happiness , and Personal Bests ” and is geared towards upping the fluid intelligence quotient for people from all walks of life and generations.

If you’d like to read more on this topic, check out my Psychology Today blog posts:

" The Neuroscience of Imagination "
“ Hand-Eye Coordination Improves Cognitive and Social Skills ”
" Can Physical Activities Improve Fluid Intelligence? "

Christopher Bergland is a retired ultra-endurance athlete turned science writer, public health advocate, and promoter of cerebellum ("little brain") optimization.

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Fluid Intelligence

December 8, 2023

Explore the distinctions between fluid intelligence's adaptability and crystallized intelligence's knowledge-based application.

Main, P. (2023, December 8). Fluid Intelligence. Retrieved from www.structural-learning.com/post/fluid-intelligence

What is Fluid Intelligence?

Fluid intelligence is the mental capacity to deal with new challenges and solve problems without prior knowledge. It's a facet of intellectual abilities central to reasoning, pattern recognition, and abstract thinking. This type of intelligence is independent of learning and experience, distinguishing itself from crystallized intelligence, which is built through learning and cultural influences.

Raymond B. Cattell, a prominent psychologist, introduced the distinction between fluid and crystallized intelligence. He proposed that fluid intelligence peaks in early adulthood and diminishes with age, making it a vital area of study within developmental psychology. Recognizing the mutable nature of fluid intelligence is crucial for educators, as it affects how students process new information and adapt to unfamiliar tasks.

Cattell’s contributions to our understanding of fluid intelligence have profound implications. His work paved the way for more nuanced intelligence testing, moving beyond rote knowledge and focusing on an individual's adaptability and problem-solving skills . Today, his influence is evident in the tools we use to measure cognitive agility and in the strategies developed to enhance this critical component of intellect.

The Neurobiological Science of Fluid Intelligence

Fluid intelligence is a critical component of cognitive processes and is considered one of the primary types of intelligence.

The neurological foundations of fluid intelligence are rooted in the brain's ability to form and manipulate mental representations through abstract reasoning. This cognitive domain is distinct from learned skills and is more about the mind’s agility and adaptability.

Neurologically, fluid intelligence involves several brain regions, including the prefrontal cortex, which is responsible for complex behaviour s such as planning, decision-making, and moderating social behaviour . It is also associated with the dorsolateral prefrontal cortex, which governs executive functions such as working memory and cognitive flexibility.

These areas work in tandem during fluid intelligence tasks, enabling the brain to process and analyze new information without relying on past experiences.

Moreover, neural pathways and networks play a significant role in fluid intelligence. White matter tracts in the brain, which facilitate communication between different regions, are integral for the swift transmission of neural signals necessary for the mental activities linked with fluid intelligence. The efficiency and health of these tracts can affect cognitive processing speed and accuracy, influencing how well one can think abstractly and solve novel problems.

Age-related changes also impact the neurobiological basis of fluid intelligence. Studies show that as we age, there can be a decline in the volume and functioning of the brain areas associated with fluid cognition. Despite this, engaging in mentally stimulating activities can help maintain and even enhance these cognitive functions.

Understanding the neuroscientific aspects of fluid intelligence provides valuable insights into how educators can support and develop these cognitive abilities in students . By designing fluid intelligence tasks that challenge and stimulate the brain's problem-solving and reasoning capabilities, teachers can help learners maintain and improve this vital aspect of their intellectual development.

Cognitive Abilities

Cognitive abilities refer to the mental skills and processes that enable us to understand, learn, and problem-solve. These abilities are crucial for everyday functioning, as they encompass a wide range of processes such as memory, attention, language, reasoning, and perception.

Understanding cognitive abilities is essential for educators, psychologists, and healthcare professionals, as it can help in diagnosing and supporting individuals with cognitive impairments or developmental delays . In this article, we will explore the different types of cognitive abilities and their impact on daily life, as well as discuss strategies for enhancing and improving these skills. W

e will also delve into the importance of cognitive abilities in various aspects of life, including education, career success, and overall well-being. Finally, we will examine the role of cognitive abilities in the aging process and ways to maintain and preserve these skills as we grow older.

Short-Term Memory

Short-term memory and working memory are closely related but distinct cognitive processes. Short-term memory refers to the temporary storage of information, while working memory involves the manipulation and processing of that information. Improvements in working memory can impact short-term memory by enhancing the ability to store and retrieve information more efficiently.

Short-term memory plays a crucial role in processing speed, as it allows individuals to quickly access and utilize information. This, in turn, influences fluid intelligence, as processing speed is a key component of cognitive flexibility and problem-solving abilities.

Experimental studies have shown that training programs aimed at improving working memory can lead to significant enhancements in short-term memory and fluid intelligence. These programs often involve tasks designed to challenge and strengthen working memory capacity , such as remember and manipulate sequences of numbers or letters. The findings from these studies suggest that targeted training can have a positive impact on cognitive abilities.

In conclusion, improvements in working memory can directly impact short-term memory, and both play a critical role in processing speed and fluid intelligence. Training programs focused on enhancing working memory have shown promise in improving short-term memory and fluid intelligence, highlighting the potential for cognitive enhancement through targeted interventions .

Long-Term Memory

Long-term memory and fluid intelligence are closely related, as improvements in processing speed and working memory can have a significant impact on long-term memory. Processing speed and working memory are crucial for encoding and retrieving information, which are essential processes for long-term memory formation .

Additionally, fluid intelligence, which involves the ability to solve new problems and adapt to new situations, relies heavily on working memory and processing speed.

Research studies confirming the impact of working memory gains on IQ have potential implications for education and cognitive development . By improving working memory and related skills, individuals can potentially see increases in their fluid intelligence and long-term memory abilities, leading to improved academic performance and problem-solving skills.

The relationship between long-term memory and fluid intelligence is complex, with improvements in processing speed and working memory playing a crucial role. Various programs use of brain-based software and the potential implications of working memory gains on IQ highlight the interconnectedness of cognitive abilities and the potential for targeted interventions to enhance cognitive functioning .

Attention Control

Attention control refers to the ability to focus and sustain attention on a particular task while ignoring distractions. It is a crucial component of cognitive function, as it allows individuals to effectively process information, make decisions, and perform tasks. Attention control is measured through various tasks that assess different aspects of attentional abilities.

The visual enumeration task measures individuals' ability to quickly and accurately identify a specific number of items within a visual array, providing insight into their visual attention and counting abilities. Multiple object tracking assesses the capacity to simultaneously monitor and track multiple moving objects, reflecting the ability to divide attention and track multiple stimuli.

The Attentional Network Task evaluates three different attentional networks - alerting, orienting, and executive control - and their contributions to overall attentional abilities. The Useful Field of View visual search task measures individuals' ability to process and respond to visual information within a specific field of view, reflecting their visual attention span and processing speed.

These tasks provide valuable information about specific aspects of attention control, contributing to a better understanding of individuals' attentional abilities and cognitive function. By assessing attention control, researchers and practitioners can gain insights into individuals' cognitive abilities and develop targeted interventions to support and enhance attentional skills.

Crystallised and Fluid Intelligence Summary

Executive Functions

Executive functions refer to a set of cognitive skills that are crucial for managing and organizing information, making decisions, solving problems, and controlling impulses. These skills play a vital role in our daily lives, including academic and work performance, social interaction , and emotional regulation. For example, the ability to focus on tasks, set goals, and follow through with plans are all part of executive functioning.

The prefrontal cortex, the part of the brain responsible for higher-level cognitive functions, is the control center for executive functions. It coordinates and regulates these skills, allowing individuals to make sound decisions and maintain self-control .

However, executive functions can be impacted by developmental or acquired conditions such as ADHD and traumatic brain injury. Individuals with ADHD often struggle with impulse control and decision-making, while those who have experienced traumatic brain injury may have difficulty with problem-solving and planning.

Understanding and supporting executive function skills is essential in managing these conditions and fostering overall cognitive function and well-being. Therefore, the assessment and support of executive functions are critical in promoting success in various aspects of life.

Abstract Thinking

Working memory plays a crucial role in abstract thinking by allowing individuals to temporarily hold and manipulate information in their mind. The components of working memory, including the central executive, phonological loop, and visuospatial sketchpad , help individuals process and manipulate complex abstract concepts.

Empirical evidence has shown that working memory capacity is strongly linked to fluid intelligence, which involves reasoning and problem-solving abilities in novel situations.

Individual differences in fluid intelligence have been associated with the capacity limit of working memory, with higher working memory capacity being linked to higher fluid intelligence. Theoretical models and empirical studies have supported this association, suggesting that working memory capacity may serve as a cognitive bottleneck that limits an individual's ability to process and manipulate abstract information .

These findings have significant implications for understanding the cognitive mechanisms underlying abstract thinking and problem-solving abilities. They suggest that working memory plays a key role in the ability to think abstractly and solve complex problems, and that individual differences in working memory capacity may contribute to differences in fluid intelligence.

Understanding the relationship between working memory and abstract thinking can provide insights into how to improve problem-solving abilities and foster creative thinking skills .

11 Examples of Fluid and Crystallized Intelligence

As we have seen, Fluid intelligence is the ability to think abstractly, reason, identify patterns, solve problems, and discern relationships without relying on pre-existing knowledge. Crystallized intelligence, on the other hand, involves using learned knowledge and experience.

The following examples showcase the dynamic nature of fluid intelligence, which is more adaptable and improvisational, and crystallized intelligence, which relies on the accumulation and application of knowledge .

Understanding the interplay between these two types of intelligence is crucial in educational settings , as it can guide how teaching and learning are approached for different cognitive tasks. Fluid intelligence is often at play when students encounter new information, whereas crystallized intelligence is used when they draw upon what they have already mastered.

Here's how these two forms of intelligence can manifest:

Fluid Intelligence Examples:

Solving Puzzles : Tackling new brain teasers without prior exposure.
Learning a New Language : Deciphering syntax and grammar rules from scratch.
Canonical Attention Tasks : Focusing on a novel task despite distractions.
Articulatory Suppression Task : Recalling a new list of words while speaking another word repeatedly.
Dual Tasks : Simultaneously performing a new memory task and a secondary task without previous practice.
Grammatical Reasoning Task : Applying rules of a newly learned grammar to form correct sentences .
Complex Span Tasks : Remembering and processing new information while being engaged in a complex, unrelated activity.

Crystallized Intelligence Examples:

Literature Analysis : Using knowledge from previous readings to interpret themes in a new novel.
Historical Contextualization : Applying historical dates and events to understand current geopolitical scenarios.
Mathematical Problems : Employing long-learned formulas to calculate your monthly budget.
Scientific Reasoning : Using established scientific principles to hypothesize the outcome of an experiment.
Language Proficiency : Utilizing vocabulary and linguistic rules acquired over years to write an essay.

Increasing Fluid Intelligence with Training

Numerous studies have shown that specific training programs can significantly increase fluid intelligence. These programs often involve working memory exercises, pattern recognition tasks, and problem-solving activities. The key to training fluid intelligence lies in challenging the brain to think in new and unfamiliar ways, forcing it to adapt and become more flexible in its thinking processes.

Additionally, physical exercise has also been linked to improved fluid intelligence, as it has been shown to promote the growth of new neurons in the brain. The potential for increasing fluid intelligence through training offers promising implications for individuals looking to enhance their cognitive abilities and for educators seeking to design effective interventions for their students.

Continued training in this area offers an exciting opportunity for personal and educational growth.

Jaeggi et al. Study on Fluid Intelligence with Training

Jaeggi et al. conducted a study to investigate the impact of training on fluid intelligence, which refers to the ability to think and reason systematically and solve problems independently of acquired knowledge. The study involved participants undergoing a series of cognitive training tasks aimed at improving working memory, attention, and problem-solving skills. The researchers used a pretest-posttest design to measure the participants' fluid intelligence before and after the training.

The results obtained from the study showed a significant improvement in the participants' fluid intelligence after undergoing the training. This suggests that cognitive training can have a positive impact on an individual's ability to think and reason effectively.

Fluid intelligence is crucial in learning and problem-solving as it enables individuals to adapt to new situations, analyze information, and think critically. The findings of the study contribute to our understanding of cognitive development by highlighting the potential for training to enhance fluid intelligence, emphasizing the malleability of cognitive abilities, and providing insight into effective strategies for improving cognitive skills.

The study by Jaeggi et al. demonstrates the importance of fluid intelligence in cognitive functioning and presents promising implications for the development of cognitive training interventions.

John Horn's Study on Fluid Ability and Mental Ability

John Horn's study on fluid ability and mental ability in relation to intelligence testing focuses on his conceptualization of fluid intelligence as an innate, biologically based capacity for flexible thinking, learning, reasoning, and perceiving complex relationships.

Horn proposes that fluid ability is a key component of intelligence and plays a crucial role in problem-solving and adapting to new situations. He argues that fluid ability is distinct from crystallized intelligence, which is based on learned knowledge and experiences.

Horn's contribution to the refinement of models of fluid intelligence and its relationship to other mental faculties has led to a better understanding of the complexities of intelligence. He has also played a significant role in the development and use of the Woodcock–Johnson Tests of Cognitive Abilities, Third Edition to assess gf, or "general fluid reasoning ability." This assessment tool helps to measure an individual's fluid intelligence and provides valuable insights into their cognitive capabilities.

Horn's research and contributions have advanced our understanding of fluid ability and its role in intelligence testing, leading to the development of more accurate and comprehensive models of cognitive abilities.

Previous Studies on Neurological Conditions and Reasoning Processes

Previous studies have examined the role of working memory training in improving cognitive performance in individuals with neurological conditions. These studies have focused on conditions such as traumatic brain injury, stroke, and neurodegenerative diseases.

Experimental designs in these studies have often involved pre-and post-training assessments of cognitive function, with some using control groups to compare the effects of working memory training. Training methods typically involve engaging individuals in tasks designed to challenge working memory capacity and cognitive control, such as dual n-back tasks or visuospatial working memory exercises.

The outcomes of these studies have shown mixed results, with some indicating modest improvements in working memory and reasoning processes following training, while others have found limited transfer effects to broader cognitive functions. Additionally, the effectiveness of working memory training appears to vary depending on the specific neurological condition being studied.

The evidence suggests that working memory training may have potential benefits for cognitive performance in individuals with neurological conditions , but further investigation is needed to better understand the optimal training methods and the generalizability of the effects across different populations.

Matrix Reasoning Tasks for Assessing Fluid Intelligence

Matrix reasoning tasks are a popular method for assessing an individual's fluid intelligence, or the ability to solve abstract problems and think critically. These tasks require the test-taker to identify patterns and relationships within a series of shapes and symbols, and then apply the identified rules to solve new problems.

By measuring a person's ability to discern complex patterns and make logical connections, matrix reasoning tasks provide valuable insight into their cognitive abilities. This form of assessment has proven to be a reliable and valid measure of general intelligence and is commonly used in educational and clinical settings to evaluate reasoning and problem-solving skills.

Understanding the importance and application of matrix reasoning tasks is essential for educators, psychologists, and researchers seeking to gain a deeper understanding of an individual's cognitive functioning.

Overview of Matrix Reasoning Task Performance

The Matrix Reasoning task is a non-verbal test that assesses an individual's reasoning ability using visual stimuli. Test-takers are presented with a series or sequence of visual patterns and are asked to choose the correct picture that fits the pattern from an array of options. This task requires the ability to solve novel problems and make logical connections between different elements in the visual stimuli.

Performance on the Matrix Reasoning task is linked to working memory, as individuals need to hold and manipulate visual information in their mind to identify the patterns and make appropriate choices. Working memory allows individuals to temporarily store and manipulate information, which is crucial for reasoning and problem-solving tasks like Matrix Reasoning.

The use of visual stimuli and the non-verbal nature of the test ensure that individuals from diverse linguistic and cultural backgrounds can participate and showcase their reasoning abilities without being hindered by language barriers. Overall, the Matrix Reasoning task provides a valuable assessment of an individual's ability to reason and solve problems using visual patterns and is an important tool in cognitive assessment.

5 Additional Ways of Measuring Fluid Intelligence

Continuing from the previous discussion on fluid intelligence, there are several other methods to measure this type of cognitive capacity:

Composite Measure : A composite measure involves combining the results of various cognitive tasks to provide a more holistic assessment of fluid intelligence. This can include a mix of verbal and non-verbal challenges that require abstract thinking and problem-solving without leaning on pre-existing knowledge.
Evaluation of Age Differences : Certain tests are designed to evaluate age-related differences in fluid intelligence. These tasks may adjust in complexity to suit different age groups, allowing for a fair assessment of fluid intelligence across the lifespan.
Memory Components Analysis : Some assessments focus on the memory components of fluid intelligence. These tasks test an individual’s ability to encode , store, and retrieve information, particularly when dealing with new and complex data.
Advanced Progressive Matrices : Building upon the standard Progressive Matrices, advanced versions provide more challenging sequences that require deeper abstract reasoning and stronger manipulation of mental representations.
Journal of Intelligence Methods : Articles and studies from the Journal of Intelligence often describe innovative cognitive tasks developed for research purposes. These tasks are at the forefront of measuring fluid intelligence, designed to push the boundaries of cognitive assessment.

These five methods complement previously mentioned strategies, offering a multifaceted approach to evaluating an individual’s fluid intelligence. They are crucial for researchers and educators who aim to understand and enhance this vital aspect of human cognition.

9 Ways to Promote Neuroplasticity and Mental Adaptability

Neuroplasticity is the brain's remarkable ability to reorganize itself by forming new neural connections throughout life. This adaptability allows the brain to compensate for injury, disease, and adjusts to new situations or changes in the environment. For teachers and educators, promoting neuroplasticity is about fostering an environment that encourages continuous learning and cognitive flexibility.

Here are nine ways to promote neuroplasticity and mental adaptability:

Engage in Psychological Tasks : Encourage students to participate in activities that challenge their problem-solving skills and cognitive flexibility.
Implement Computation Span Tasks : Use activities that require students to hold and manipulate information in their working memory, enhancing executive function.
Introduce Alphabet Tasks : Alphabet-based exercises can help improve attention to detail and expand working memory.
Assign Secondary Tasks : During learning activities, include a secondary task to build the ability to manage multiple streams of information.
Utilize Cognitive Measures : Incorporate tests and activities that serve as cognitive measures to stimulate the brain's processing speed and adaptability.
Measure Intelligence : Regularly engage students in exercises that are traditionally used as a measure of intelligence to challenge their reasoning and logic skills.
Promote Executive Condition : Encourage tasks that require planning, decision-making, and sequencing, which are essential aspects of executive functions.
Establish Causal Relationships : Teach students to identify causal relationships in complex scenarios, which enhances critical thinking and cognitive complexity.
Encourage Novel Experiences : Introduce new and varied experiences that require students to adjust and learn, promoting mental adaptability and growth.

By integrating these practices into teaching strategies, educators can enhance their students' cognitive functions and contribute to their mental resilience. Neuroplasticity is not only about recovering from deficits but also about maximizing cognitive potential, making these strategies integral to a well-rounded educational approach .

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Cattell’s Theory of Intelligence

Fluid and crystallized intelligence

In psychology, fluid and crystallized intelligence (abbreviated Gf and Gc, respectively) are factors of general intelligence originally identified by Raymond Cattell.

Fluid intelligence or fluid reasoning is the capacity to think logically and solve problems in novel situations, independent of acquired knowledge. It is the ability to analyze novel problems, identify patterns and relationships that underpin these problems and the extrapolation of these using logic. It is necessary for all logical problem solving, especially scientific, mathematical and technical problem solving. Fluid reasoning includes inductive reasoning and deductive reasoning.

Crystallized intelligence is the ability to use skills, knowledge, and experience. It should not be equated with memory or knowledge, but it does rely on accessing information from long-term memory.

The terms are somewhat misleading because one is not a “crystallized” form of the other. Rather, they are believed to be separate neural and mental systems. Crystallized intelligence is indicated by a person’s depth and breadth of general knowledge, vocabulary, and the ability to reason using words and numbers. It is the product of educational and cultural experience in interaction with fluid intelligence.

Fluid and crystallized intelligence are thus correlated with each other, and most IQ tests attempt to measure both varieties. For example, the Wechsler Adult Intelligence Scale (WAIS) measures fluid intelligence on the performance scale and crystallized intelligence on the verbal scale. The overall IQ score is based on a combination of these two scales.

Theoretical development

Fluid and crystallized intelligence are discrete factors of general intelligence, or g. Although formally recognized by Cattell, the distinction was foreshadowed by Charles Spearman who originally developed the theory of g and made a similar observation regarding the difference between eductive and reproductive mental ability.

According to Cattell, “…it is apparent that one of these powers… has the ‘fluid’ quality of being directable to almost any problem. By contrast, the other is invested in particular areas of crystallized skills which can be upset individually without affecting the others.” Thus, his claim was that each type, or factor, was independent of the other, though many authors have noted an apparent interdependence of the two.

Fluid versus crystallized

Fluid intelligence includes such abilities as problem-solving, learning, and pattern recognition. Evidence is consistent with the view that Gf is more affected by brain injury. Fluid intelligence is predominant in individuals with Autism spectrum disorders, including Asperger syndrome.

Crystallized intelligence is possibly more amenable to change as it relies on specific, acquired knowledge. For example, a child who has just learned to add numbers now owns a new piece of crystallized intelligence; but his or her general ability to learn and understand, Gf, has not been altered. An example of the flexibility of, or ability to revise, crystallized intelligence can be seen in beliefs about Santa Claus. A five year-old child may believe that Santa Claus lives at the North Pole. Later, when the child is eight years old, he learns there is no Santa Claus. His belief that Santa lives at the North Pole was then invalidated and new knowledge is gained: there is no Santa Claus. The prior knowledge was revised in order to accommodate the new learning. Vocabulary tests and the verbal subscale of the Wechsler Adult Intelligence Scale are considered good measures of Gc.

Not surprisingly, people with a high capacity of Gf tend to acquire more Gc knowledge and at faster rates. This is sometimes called investment. Researchers have found that criminals have disproportionately low levels of crystallized intelligence. This may be a result of these people investing their ability into skills that are not measured on IQ tests.

Some researchers have linked the theory of fluid and crystallized intelligence to Piaget’s conception of operative intelligence and learning. Fluid ability and Piaget’s operative intelligence both concern logical thinking and the education of relations. Crystallized ability and Piaget’s treatment of everyday learning reflect the impress of experience. Like fluid ability’s relation to crystallized intelligence, Piaget’s operativity is considered to be prior to, and ultimately provides the foundation for, everyday learning.

Factor structure

Fluid intelligence generally correlates with measures of abstract reasoning and puzzle solving. Crystallized intelligence correlates with abilities that depend on knowledge and experience, such as vocabulary, general information, and analogies. Paul Kline identified a number of factors that shared a correlation of at least r=.60 with Gf and Gc. Factors with median loadings of greater than 0.6 on Gf included induction, visualization, quantitative reasoning, and ideational fluency. Factors with median loadings of greater than 0.6 on Gc included verbal ability, language development, reading comprehension, sequential reasoning, and general information. It may be suggested that tests of intelligence may not be able to truly reflect levels of fluid intelligence. Some authors have suggested that unless an individual was truly interested in the problem presented, the cognitive work required may not be performed because of a lack of interest. These authors contend that a low score on tests which are intended to measure fluid intelligence may reflect more a lack of interest in the tasks rather than inability to complete the tasks successfully.

Measurement of fluid intelligence

There are various measures that assess fluid intelligence. The Cattell Culture Fair IQ test, the Raven Progressive Matrices (RPM), and the performance subscale of the WAIS are measures of Gf. The RPM is one of the most commonly used measures of fluid abilities. It is a non-verbal multiple choice test. Participants have to complete a series of drawings by identifying relevant features based on the spatial organization of an array of objects, and choosing one object that matches one or more of the identified features. This task assesses the ability to consider one or more relationships between mental representations or relational reasoning. Propositional analogies and semantic decision tasks are also used to assess relational reasoning.

Standardized IQ tests such as those used in psychoeducational assessment also include tests of fluid intelligence. In the Woodcock-Johnson Tests of Cognitive Abilities Gf is assessed by two tests: Concept Formation (Test 5) in the Standard Battery and Analysis Synthesis (Test 15) in the Extended Battery. On Concept Formation tasks,the individual has to apply concepts by inferring the underlying “rules” for solving visual puzzles that are presented in increasing levels of difficulty. Individuals at the preschool level have to point to a shape that is different from others in a set. As the level of difficulty increases, individuals increasingly demonstrate an understanding of what constitutes a key difference (or the “rule”) for solving puzzles involving one to one comparisons, and on later items identifying common differences among a set of items. For more difficult items, individuals need to understand the concept of “and” (e.g. solution must have some of this and some of that) and the concept of “or” (e.g. to be inside a box, the item must be either this or that). The most difficult items require fluid transformations and cognitive shifting between the various types of concept puzzles that the examinee has worked with previously.

Concept Formation tasks assess inductive reasoning ability. In the Analysis-Synthesis test, the individual has to learn and orally state the solutions to incomplete logic puzzles that mimic a miniature mathematics system. The test also contains some of the features involved in using symbolic formulations in other fields such as chemistry and logic. The individual is presented with a set of logic rules, a “key” that is used to solve the puzzles. The individual has to determine the missing colors within each of the puzzles using the key. Complex items present puzzles that require two or more sequential mental manipulations of the key to derive a final solution. Increasingly difficult items involve a mix of puzzles that require fluid shifts in deduction, logic, and inference. Analysis Synthesis tasks assess general sequential reasoning.

In the Wechsler Intelligence Scale for Children-IV (WISC IV) the Perceptual Reasoning Index contains two subtests that assess Gf: Matrix Reasoning, which involves induction and deduction, and Picture Concepts, which involves induction. In the Picture Concepts task, children are presented a series of pictures on two or three rows and asked which pictures (one from each row) belong together based on some common characteristic. This task assesses the child’s ability to discover the underlying characteristic (e.g. rule, concept, trend, class membership) that governs a set of materials. Matrix Reasoning also tests this ability as well as the ability to start with stated rules, premises, or conditions and to engage in one or more steps to reach a solution to a novel problem (deduction). In the Matrix Reasoning test, children are presented a series or sequence of pictures with one picture missing. Their task is to choose the picture that fits the series or sequence from an array of five options. Since Matrix Reasoning and Picture Concepts involve the use of visual stimuli and do not require expressive language they are considered to be non-verbal tests of Gf.

Development and physiology

Fluid intelligence, like reaction time, peaks in young adulthood and then steadily declines. This decline may be related to local atrophy of the brain in the right cerebellum. Other researchers have suggested that a lack of practice, along with age-related changes in the brain may contribute to the decline. Crystallized intelligence increases gradually, stays relatively stable across most of adulthood, and then begins to decline after age 65.

Working memory capacity is closely related to fluid intelligence, and has been proposed to account for individual differences in Gf. Furthermore, recent research suggests that cognitive exercise can increase working memory and also improve Gf.

Improving fluid intelligence with training on working memory

According to David Geary, Gf and Gc can be traced to two separate brain systems. Fluid intelligence involves the dorsolateral prefrontal cortex, the anterior cingulate cortex, and other systems related to attention and short-term memory. Crystallized intelligence appears to be a function of brain regions that involve the storage and usage of long-term memories, such as the hippocampus.

Susanne M. Jaeggi, from the University of Michigan, found that healthy young adults, who practiced a demanding working memory task (dual n-back) approximately 25 minutes per day for between 8 and 19 days, had statistically significant increases in their scores on a matrix test of fluid intelligence taken before and after the training than a control group who did not do any training at all. [Jaeggi, Susanne M.; Buschkuehl, Martin; Jonides, John; and Perrig, Walter J. (2008). “Improving fluid intelligence with training on working memory.” PNAS- Proceedings of the National Academy of Sciences]

More recently, a study conducted in Hangzhou, China, at the University of Technology supports Jaeggi’s results, independently. After student subjects were given a 10 day training regime, based on the dual-n back working memory theory, their scores on the Raven’s Standard Progressive Matrices, were found to have increased significantly. [Qiu Feiyue; Wei Qinqin (2010). “Study on Improving Fluid Intelligence through Cognitive Training System Based on Gabor Stimulus” Information Science and Engineering]

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Research Article

Fluid Intelligence and Psychosocial Outcome: From Logical Problem Solving to Social Adaptation

* E-mail: [email protected]

Affiliation Laboratory of Cognitive and Social Neuroscience, Universidad Diego Portales, Santiago, Chile

Affiliation Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Cognitive Neurology (INECO) and Institute of Neuroscience, Favaloro University, Buenos Aires, Argentina

Affiliations Cognitive Development Center, Universidad Diego Portales, Santiago, Chile, Faculty of Education, Universidad Diego Portales, Santiago, Chile

Affiliation Doctoral Program in Education, Pontificia Universidad Católica de Chile, Santiago, Chile

Affiliation Cognitive Development Center, Universidad Diego Portales, Santiago, Chile

Affiliations Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Cognitive Neurology (INECO) and Institute of Neuroscience, Favaloro University, Buenos Aires, Argentina, Laboratory of Cognitive and Social Neuroscience, Universidad Diego Portales, Santiago, Chile, National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina

David Huepe,
María Roca,
Natalia Salas,
Andrés Canales-Johnson,
Álvaro A. Rivera-Rei,
Leandro Zamorano,
Aimée Concepción,
Facundo Manes,
Agustín Ibañez

Published: September 21, 2011
https://doi.org/10.1371/journal.pone.0024858
Reader Comments

While fluid intelligence has proved to be central to executive functioning, logical reasoning and other frontal functions, the role of this ability in psychosocial adaptation has not been well characterized.

Methodology/Principal Findings

A random-probabilistic sample of 2370 secondary school students completed measures of fluid intelligence (Raven's Progressive Matrices, RPM) and several measures of psychological adaptation: bullying (Delaware Bullying Questionnaire), domestic abuse of adolescents (Conflict Tactic Scale), drug intake (ONUDD), self-esteem (Rosenberg's Self Esteem Scale) and the Perceived Mental Health Scale (Spanish adaptation).

Lower fluid intelligence scores were associated with physical violence, both in the role of victim and victimizer. Drug intake, especially cannabis, cocaine and inhalants and lower self-esteem were also associated with lower fluid intelligence. Finally, scores on the perceived mental health assessment were better when fluid intelligence scores were higher.

Conclusions/Significance

Our results show evidence of a strong association between psychosocial adaptation and fluid intelligence, suggesting that the latter is not only central to executive functioning but also forms part of a more general capacity for adaptation to social contexts.

Citation: Huepe D, Roca M, Salas N, Canales-Johnson A, Rivera-Rei ÁA, Zamorano L, et al. (2011) Fluid Intelligence and Psychosocial Outcome: From Logical Problem Solving to Social Adaptation. PLoS ONE 6(9): e24858. https://doi.org/10.1371/journal.pone.0024858

Editor: Antonio Verdejo García, University of Granada, Spain

Received: March 30, 2011; Accepted: August 22, 2011; Published: September 21, 2011

Copyright: © 2011 Huepe et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This research was supported by Asistencia Técnica Educativa (ATE) Proyecto Da-Vinci, The National Scientific and Technical Research Council (CONICET) and by the Foundation for Research in Cognitive Neurosciences (FINECO). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Fluid intelligence has been defined as the ability to think logically and solve problems in novel situations, independent of acquired knowledge [1] . Fluid intelligence reflects an individual's capacity for abstract thought and reasoning and it contrasts with so-called “crystallized intelligence” [2] , which depends on previous knowledge and educational achievement. Undoubtedly, fluid intelligence is relevant to the process of analyzing novel problems, identifying patterns and relationships that underpin these problems and using logical extrapolation. Several tests have been proposed to measure this important function; the Raven Progressive Matrices (RPM) is the most widely-used task [3] . RPM is a psychometric non-verbal multiple choice test that evaluates the global index of intelligence. This index is traditionally inferred from a general factor of the underlying intelligence quotient (IQ) known as the g factor [3] . Although the RPM presents some caveats related to the fact that it is an extended and general measure, i.e., different processes seem to influence the RPM score, specifically, perceptual processing and analytic or analogical reasoning [4] ,those criticisms are expected for any general static cognitive measure. In fact, RPM is the most widely-used measure of g [5] . The g factor is thought to directly reflect a broad factor underlying several cognitive functions, such as observation and reasoning and, in this sense, evaluates a general intellectual capacity [6] . Usually, this measure is employed to evaluate perceptive skills and is recommended as a standard assessment in school populations [6] . During the application of the RPM, participants identify relevant information based on their perception of the spatial organization of an array of objects and their task consists of filling in the missing piece of a set of patterns. (See the Measures section for more details.)

Neuroanatomically, fluid intelligence has been related to frontal functioning [7] . Frontal lobe lesions have been found to affect performance on tests of fluid intelligence [7] – [9] and functional imaging studies that measure g have shown increased extensive activity in the frontal area of the brain [10] – [13] .

Alongside the view that the frontal lobe constitutes the neural basis of fluid intelligence, frontal lobe functioning has also been linked to executive functioning and complex social behavior. Support for this comes from several lesion studies, which show that frontal lobe damage can alter behavior and social adaptation [14] – [22] .

The relationship between fluid intelligence, novel problem solving and executive dysfunction has been extensively studied [7] – [11] , [23] – [28] and the relationship between fluid intelligence and abstract reasoning [29] – [30] has already been established. However, research into the relationship between fluid intelligence and the behavioral domain is very scarce, especially as it pertains to psychosocial domains.

The term “psychosocial adaptation” refers to the quality of life in terms of social activities and relationships, sense of control and self-image. It includes multiple dimensions, such as social behavior, emotional regulation and the development of habits [31] . Given these characteristics, any approach that would be appropriate for assessing psychosocial adaptation would need to be both socially focused and broad enough to be able to capture an individual's subjective experience in several social domains, such as bullying, self- esteem, mental health and drug intake, among others.

Five social domains are particularly important for psychosocial adaptation: abuse of children and adolescents [32] – [36] , bullying [37] – [41] , drug use [32] , [42] – [49] , self-esteem [50] – [52] and mental health problems [50] – [58] . Moreover, the experience of abuse and bullying are positively associated with substance use and depression in children and adolescents [40] , [59] – [63] as well as with negative effects on self-esteem [64] – [67] . To our knowledge, the relationship between said domains and fluid intelligence has not yet been studied.

Psychosocial adaptation is diagnostically and prognostically connected to neurological diseases [68] , brain injury [69] , aging, dementia and old age psychiatry [70] – [72] , mental illness [73] – [76] , ADHD and child/adolescent psychiatry [77] – [81] , bipolar disorder and depression [82] , schizophrenia [83] – [86] and epilepsy [87] – [88] , to cite some examples. Neuropsychological and neurological assessments dominate the current neuropsychological and neuropsychiatric literature, but psychosocial considerations, in both the normal and the psychiatric/neurological population, are important. Psychosocial functioning represents an ecological evaluation of everyday adaptation and cognition, interlinked with cognition and emotion [89] .

To our knowledge, no study with a large sample size, i.e., a random-probabilistic sample, has previously assessed the relationship between fluid intelligence and multiple measures of psychosocial adaptation. In the present study, we aimed to analyze this relationship by recruiting 2370 participants (controlled for educational level) who were tested on fluid intelligence using Raven's Progressive Matrices (RPM) and psychosocial outcomes, including measures of child abuse antecedents, bullying, self-esteem, mental health and drug use. In brief, we found that the lower the RPM scores were, the lower the level of psychosocial adaptation. Our results provide evidence of a strong association between fluid intelligence and psychosocial adaptation, suggesting that fluid intelligence is not only related to executive functions but is also a central component of the ability to adapt to social contexts.

Materials and Methods

Participants.

This study was part of a Chilean regional county survey conducted during 2010 and designed to evaluate the psychosocial factors associated with academic achievement in scholars from socially vulnerable contexts. Only students belonging to public schools of Santiago de Chile were evaluated and all of the county's 21 public schools were included. Participants were recruited from primary schools, in the UK system, preparatory school covering the ages of 11–14 years old. From a total of 21 schools, 2370 students (age = 11.9 years, S.D. = 1.33; Sex = 46.8% female) were recruited using a random-probabilistic sample(maximum variance of 95% confidence with ±5% sample error). The students' ages ranged from 10–14 years, with the majority in the range of 11–13 years ( Table 1 ). The participants came from socially vulnerable contexts. As expected for this population, the parents of the participants presented lower educational levels (75% completed only primary or secondary studies, without technical or university instruction). All schools that participated in this study approved the research. All participants and their parents or legal guardians gave signed, voluntary consent following the Declaration of Helsinki. This study was approved by the ethics committee of the Universidad Diego Portales – Santiago de Chile.

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https://doi.org/10.1371/journal.pone.0024858.t001

Fluid intelligence.

The Standard Progressive Matrices version of the RPM was used as a measure of general intelligence, or g factor [3] . We used a standardized version for the sample under assessment [6] . The RPM included 60 spatial tasks divided into five blocks of 12 trials, from easiest to hardest. In each trial, participants were asked to complete a series of drawings by identifying the relevant features based on the spatial organization of an array of objects and choosing one object that matched one or more of the identified features.

Psychosocial adaptation.

To assess bullying behavior, the Delaware Bullying Questionnaire [90] was used, which included two sub-scales of violent behavior. One sub-scale assessed the respondent as victimizer ( α = .75); it contained questions such as “Within this year, how often have you done these kind of things at school:” “been part of a group that mocked a classmate who was alone”; “been part of a group that started a fight with another group”; “been part of a group that attacked members of another group”; “been part of a group that physically attacked a classmate who is alone”, among others. Another sub-scale assessed the respondent as victim ( α = .72). It contained questions such as “have you been molested, while alone, by a group from your school”; “have you been physically attacked, while alone, by a group from your school”; “have you been in a group that has been attacked by another group”, among others. Answer options included “never”, “once”, “twice”, “three or four times” and “five times or more”. Both scales measured the event using the following options: it did not happen (1); it happened once during the year (2); it happened more than once during the year (3).

Domestic abuse of adolescents was assessed using the Conflict Tactic Scale (CTS) [91] . The subscales of psychological violence ( α = .80) contained statements such as “has stopped talking to you for several days”; “has told you he/she didn't love you”; “has mocked you in front of other people”, among others. The questions assessing moderate physical violence ( α = .84) included, among others, “has thrown things at you”; “has pulled your hair or ears”; “has pushed or shaken you”. Intense physical violence (α = .89) was assessed using statements such as “has given you a beating”; “has kicked, bitten or has given you a punch”; “has burned you with something (cigar, iron or hot water)”. The time scale included the following response options: “every day or almost every day”; “more than twice a week”; “more than twice a month”; “less than twice a month”; “has not happened in the last year, but it happened as a child” and “never”. Then, monthly and annual prevalence were calculated.

To assess drug intake, we used an international standardized scale [46] that evaluated monthly and annual prevalence of drug intake, specifically cannabis, cocaine, inhalants and non-prescribed stimulants (methylphenidate and methamphetamines).

Self-esteem was assessed using the Rosenberg's Self-Esteem Scale ( α = .86) [92] . This scale includes statements such as “In general, I am happy with myself”; “Sometimes I feel like I am good at nothing”; “I feel like I have some good qualities”. Each item was measured on a scale ranging from “totally agree”, “agree”, “disagree”, to “totally disagree”. Rosenberg's Self-Esteem Scale ranks respondents on a scale between 0 (low self-esteem) and 20 (high self-esteem).

Mental health problems were assessed using a Spanish adaptation of the Perceived Mental Health Scale [93] . The Perceived Mental Health Scale includes questions such as “In the last four weeks, have you felt sad?”; “In the last four weeks, have you had attitude problems at school?”; “In the last four weeks, have you felt tired all the time?” The Mental Health Scale ranks respondents on a scale between 0 (no mental health problems) and 20 (maximum mental health problems).

The assessments were performed by a team of trained social psychologists ( n = 4) in each educational institution. In order to avoid cheating, the students were divided into two to four classrooms and the overall process of RPM and psychosocial test application was carefully supervised by the team of psychologists. The average duration of the assessment was 29 minutes ( S.D. = 9.0) for the RPM and around 20 minutes for the psychosocial measures. There was a deadline of 45 minutes for the RPM and 30 for the psychosocial scale assessment. Only participants who finished all assessments within the time interval provided were included in the current study. Assessments were carried out at twenty-one institutions over a period of one month. The children's parents were notified about the procedure by the authorities of the educational institutions.

Statistical Analysis.

The data were analyzed using SPSS software (Statistical Package for the Social Sciences, version 17.0). To assess the association between RPM scores and each measure of violence, correspondence analysis (CA) was used [94] . CA is a descriptive measure to represent contingency tables, i.e., tables in which the frequency of two or more qualitative variables are collected from a group of elements. CA allows the representation of the interdependence among variables measured using a nominal scale. This technique transforms non-metric data (ordinal and categorical variables) into metric data, allowing one-dimensional reduction (as a factorial analysis) and perceptual mapping (as a multidimensional analysis). In addition, ANOVA and χ 2 were used as tests of independence. For the χ 2 correlations, Cramer's V was computed. Cramer's V ranges between 0 and 1 to indicate the strength of association between two variables. For pairwise comparisons, Tukey's HSD post hoc tests were performed. To determine the relevance of the relationships, measures of the effect size w (for proportions) and d (for mean differences) were calculated [95] . The calculation of effect sizes allows the assessment of the magnitude of relationships beyond the mere reporting of p -values, which only specify the existence of statistically significant relationships. The calculation of effect sizes should temper the concerns about finding significant results solely on the basis of a large sample size and help avoid treating every significant result equally. To control for confounding variables, logistic regressions were run between the RPM and the binary variables, including parental educational levels, as predictors. ANCOVA was used to achieve the same control in evaluating the relationship between RPM and our measures of mental health and self-esteem.

Five levels of scoring for the RPM were constructed in order to relate fluid intelligence to psychosocial adaptation. The total RPM index for each of the percentiles 5, 10, 25, 50, 75, 90 and 95 for each age group were obtained. Based on those indexes five scores were obtained ( Table 2 ).

https://doi.org/10.1371/journal.pone.0024858.t002

RPM and Bullying

The Delaware Bullying Questionnaire showed that 1 out of 3 (30.3%) students reported having exhibited violent behavior in the last year; 18.2% of the participants reported more than two episodes of violent behavior against other students and 51.5% reported that they had never performed a physical assault on another student. When the relationship between these results and the RPM scores was analyzed, the CA revealed a significant effect (χ 2 (8, N = 3692) = 109.62, p <.001). To facilitate the interpretation of these data and following technical suggestions [92] , table 3 shows the χ 2 distances between the categories of each variable.

https://doi.org/10.1371/journal.pone.0024858.t003

Reduced or absent bullying behavior was associated with higher RPM scores. On the contrary, repetitive bullying behavior was related to lower RPM scores. The bi-space diagram shows the association between RPM scores and bullying behavior ( Figure 1 ).

Circles display groups of categories close together.

https://doi.org/10.1371/journal.pone.0024858.g001

Regarding victimization, similarly to the victimizer scales, 1 out of 3 (30.1%) students reported having been exposed to violent behavior in the last year; 16% of the participants were exposed to more than two episodes of violent behavior against them; 52% reported to have never been exposed to physical assault from other students. When the relationship between victimization and RPM scores was analyzed, the CA revealed a significant effect (χ 2 (8, N = 3704) = 67.03, p <.001). As before, Table 4 shows the χ 2 distances between the categories of each variable and Figure 1 the bi-spatial diagram.

https://doi.org/10.1371/journal.pone.0024858.t004

Similar to the results of the victimizer scale, a high correspondence was observed between reports of being a victim of violence more than once a year and lower RPM scores. At the same time, higher RPM scores seemed to be a protective factor against other's aggression.

RPM and drug intake

Cannabis consumption showed an annual prevalence of 3%, followed by coca paste (2.2%) and cocaine (2%). The annual prevalence of use of all cocaine-related drugs, i.e., cocaine, coca paste and crack, was 3.3%; followed by inhalants (2.7%) and non-prescribed stimulants (2.2%). The composite score of drug intake presented an annual prevalence of 5.5%. This composite measure of drug consumption showed a significant association with RPM scores (χ 2 (4, N = 3734) = 36.48, p <.001, V = .10) and a medium effect size (Cohen's w = .42). Lower scores of RPM were associated with higher percentages of drug use. Figure 2a shows the percentages for each RPM score.

(a) RPM scores and annual prevalence of drug intake (%). (b) RPM scores and annual prevalence's percentages of moderate (MPV) and intense physical violence (IPV). (C) RPM scores and self-esteem (Means and S.D .). RPM score and perceived mental health (Means and S.D. ). In each subfigure (a, b, c, d), RPM measures of fluid intelligence are presented from left (Higher levels of intelligence) to right (Lower levels of intelligence).

https://doi.org/10.1371/journal.pone.0024858.g002

RPM and abuse of adolescents

The adolescent abuse scale showed a high annual prevalence of within-family psychological violence: 52.5%, 1 out of 2 participants had been a victim of family violence. The annual prevalence of moderate physical violence was 40.2% and the prevalence of intense physical violence was 21.4%. RPM scores were highly associated with the annual prevalence of moderate physical violence (χ 2 (4, N = 3735) = 12.80, p = .012, V = .06, Cohen's w = .07 small effect size) and intense physical violence (χ 2 (4, N = 3735) = 25.50, p <.001, V = .08; Cohen's w = .16, small effect size; see Figure 2b ). Nevertheless, no association between psychological violence and RPM scores (4, N = 3735) = 4.48, p = 0.35, ns ) was found.

RPM and self-esteem

The self-esteem scale yielded an average score of 21.25 ( S.D. = 5.74; range between 0-lower and 30-higher). A one-way ANOVA with the 5 RPM scores as a within-subjects factor revealed a strong effect ( F (4, 3711) = 30.75, p <.001). Post hoc comparisons (Tukey's HSD test) show that in participants with lower RPM scores, lower reports of self-esteem were observed ( Figure 2c ). All post hoc comparisons were statistically significant at p <.001, except score 1 vs. score 2 and score 4 vs. score 5, which were not significant. The effect sizes of significant comparisons ranged from small to large (Cohen's d range = .20 to .77).

RPM and perceived mental health

The average total score for mental health-related problems reported was 6.88 ( S.D. = 4.76; range between 0-lower and 20-higher mental health-related problems). A one-way ANOVA with the 5 RPM scores as a within-subjects factor yielded a significant effect ( F (4, 3699) = 8.67, p <.001). Similar to the results for the self-esteem measures, an inverse linear-like relationship was observed: the lower the RPM score, the higher the number of health problems reported ( Figure 2d ). Except for the pairwise comparison between scores 1–2, score 3–4 and score 4–5, which were not significant, all other comparisons yielded significant effects (Tukey's HSD Test, p <.001). The effect sizes of the significant comparisons were small (Cohen's d range = .14 to .36).

RPM effects controlling for parental education

As noted above, we controlled for two possible effects that could weaken or even cancel out our results: socioeconomic status and parental educational level. The first was fixed within the design because all of the population came from the same socioeconomic group and had a socioeconomic status of middle-low and lower. Despite this, the intelligence scores varied and showed a normal distribution, independent of socioeconomic status, which in this study was a constant. Therefore, the effects can be seen as being independent of this condition. Parental educational level was measured in terms of years of study (0 to 8 points) and was covaried with each of the relationships that were tested above. For the association between the RPM score and the measurement of bullying as the perpetrator, “Bully”, a logistic regression was used, with the following predictors: RPM score, father's educational level and mother's educational level. ‘Bully’ was coded as a dependent variable as 0 = Never, with a prevalence of 48.5% and 1 = Once a year or twice or more during the year, with a prevalence of 51.5%).RPM had an important effect in the expected direction (low RPM score –lower IQ– greater chance to trigger bullying, Table 5 ). We also found a similar outcome with ‘Bullied’ as the dependent variable. (This was coded 0 = Never, with a prevalence of 52% and 1 = Once a year or twice or more during the year, with a prevalence of 48%.) In the case of “Bullied” only RPM was statistically significant in the predicted direction ( Table 5 ).A similar finding was observed for the variable ‘use of illicit drugs’, annual prevalence (0 = Never, 1 = Yes, Table 5 ); and ‘intense physical violence’ ( Table 5 ). For “moderate physical violence”, all effects were significant ( Table 5 ). However, the most relevant outcome is that the RPM effects described remain significant over and above the influence of the parental educational levels. Finally, for the associations between RPM with self-esteem and perceived mental health, we used ANCOVAs. In both cases, the effects found for RPM were retained. For self-esteem F (4, 3531) = 28.59, p <.001. The effects of the parental educational levels were not significant. For mental health, F (4, 3520) = 8.91, p <.001. Again, the effects of the parental educational levels were not significant.

https://doi.org/10.1371/journal.pone.0024858.t005

This is the first large sample study to assess the relationship between fluid intelligence and psychosocial adaptation. The overall results of our study suggest that fluid intelligence has a strong association with psychosocial measures. We found a linear relationship between RPM and measures of physical violence as the victimizer: the lower the RPM, the higher the violence score. We found a similar relationship between RPM and measures of violence pertaining to the victim role. No relation between RPM and psychological violence was found. Drug intake (especially for cannabis, cocaine and inhalants) was higher when RPM was lower. Self-esteem reports were modulated by 5 levels of RPM (from lower to higher) in a simple way: the lower the RPM, the lower the self-esteem. Finally, similar results were found for the mental health measurement: higher rates of health were observed when RPM was higher. All effects remained even when covaried with parental educational level.

Deficits in fluid intelligence, executive functioning and social adaptation have been described after frontal lobe lesions [14] – [18] , [20] – [22] . Because all of these deficits have been linked to a common brain area, it is important to investigate the relationship between these deficits. While the relationship between executive functioning and fluid intelligence has already been established, investigations assessing the relationship between the latter and social adaptation are limited. Our data indicate that fluid intelligence is associated not only with executive function but is also a relevant component of psychosocial adaptation.

Complex modern societies demand a strong capacity for social adaptation. Bullying and violence, addictive behavior, perceived mental health and self-esteem are strongly linked with quality of life [96] – [102] . Our data evidence a straightforward association between levels of fluid intelligence and the degree of social adaptation. This is a novel result and opens up a new branch of research relating “cold” measures of intelligence to “hot” measures of socially-dependent behavior. Nevertheless, literature from other domains has produced some evidence of this relationship. For instance, Perry et al. [102] reported predictors of outcome in 332 children, aged 2–7 years, enrolled in the community-based Intensive Behavioral Intervention (IBI) program in Ontario, Canada and found that in the subset of children who had an IQ score available at intake (n = 151), there were significant and strong correlations between initial IQ and all outcome variables, mainly scales of adaptive behavior. Psychological adaptation problems, e.g., attention deficits, violence, patterns of antisocial, impulsive, norm-violating, sensation seeking and externalizing tendencies and substance use, have been linked to behavioral disinhibition [103] . In turn, behavioral disinhibition has been associated with reduced working memory and short-term memory capacity, as well as with lower IQ [104] . Various studies have shown an association between IQ in childhood or early adulthood and mortality in later life [105] – [109] . Consistent with our results, cognitive research has shown that sensitive parenting is linked with higher child IQ [110] . On the other hand, children who witness domestic violence tend to have significantly lower IQs [111] than their non-exposed peers [112] – [113] . There is also consistent evidence that relates low intelligence and delinquency [114] – [117] . As an example, Koolhof et al. [114] found that delinquents with low IQ were more behaviorally and cognitively impulsive than higher IQ delinquents. Additionally, low IQ offenders exhibited greater deficiency in empathy and guilt feelings that those with high IQ. Impulsivity, therefore, appears to be a key characteristic of low IQ.

Even though the aforementioned studies suggest a link between IQ and behavioral outcomes, no previous study has directly assessed the association between fluid intelligence and psychosocial adaptation. Ours is the first study to look for this association using a larger cohort and several measures of psychological adaptation. In addition, our sample is random and probabilistic, which is an uncommon design in studies of its type, they are usually carried out using convenience sample or intentional samples and provides greater generalizability of results and greater statistical power.

Our results need to be extended and replicated along several dimensions. First, our sample represents a socially vulnerable population and further studies should asses the relationship between fluid intelligence and psychosocial adaptation in other socioeconomic groups. In addition, further studies should include not only measures of fluid intelligence but also measures of crystallized intelligence to compare the effects of educational and cultural experience in interaction with fluid intelligence. Also, the inclusion of more objective and quantitative measures of psychosocial adaptation, such as experimental designs or brain studies, would provide an interesting, if challenging, approach to study the relationship between social adaptation and fluid intelligence.

Does low fluid intelligence itself make a person more vulnerable to social adaptation problems? Or is it that fluid intelligence is correlated with the situation in which a person lives? For example, certainly lower intelligence is correlated with lower family income, so are we seeing the effects of poverty? Unfortunately, this is impossible to answer these questions with our data and further research is needed to address this topic. Nevertheless, at least some data suggest that the range of fluid intelligence is to some extent independent of socioeconomic and educational levels. Because we found that the effects of fluid intelligence on all measures of psychosocial adaptation remain once both socioeconomic and educational levels are covaried, we speculate that fluid intelligence is not completely dependent on socio-education. The association between fluid intelligence and psychosocial adaptation is very consistent in our data, the higher the first, the higher the second, independent of socioeconomic status and socio-educational level, which was tested with statistical techniques for covariance.

A second issue concerning our results is the possible relationship between high fluid intelligence and social desirability. Specifically, could it be that fluid intelligence affects the tendency to answer questions in a socially desirable way? Unfortunately, we don't have any direct way to address this point and the possibility that social desirability could act as a moderator effect in children with high RPM scores cannot be discarded. However, the relationship between a high RPM score and better psychosocial adaptation could be interpreted, from our point of view, as suggesting that children with higher fluid intelligence exhibit more adaptive behaviors than those who show low scores. Fundamentally, this can be sustained in students who report being less frequent victims of bullying at school, which in turn also correlates with a lower prevalence of domestic violence. Precisely, greater fluid intelligence implies that subjects will use the most effective strategies to deal with becoming a victim of aggression, both in school and at home. Therefore, the fact that children show higher fluid intelligence shouldn't necessarily mean they avoid giving a sincere response about their situation. Future studies assessing implicit, not only explicit, measures of psychosocial adaptation will help to clarify the possible role of the moderator effect.

Finally, the relationship between executive function (EF), psychosocial adaptation and fluid intelligence calls for research. It is known that EFs are comprised of self-monitoring abilities. These functions are essential to goal-directed behavior, allowing us to maintain, update and integrate information to adapt and move within our environment [118] .

This is the first report suggesting a clear relationship between fluid intelligence and psychosocial adaptation evaluated in several domains. These results call for a new branch of research that combines a neurocognitive approach to fluid intelligence with study of psychosocial adaptation.

Acknowledgments

We wish to thank two anonymous reviewers for their helpful criticism in earlier versions of the manuscript.

Author Contributions

Conceived and designed the experiments: DH MR LZ AI. Performed the experiments: DH LZ AC. Analyzed the data: DH AAR-R. Contributed reagents/materials/analysis tools: DH LZ AI. Wrote the paper: DH MR NS AC-J FM AI.

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Encyclopedia of Autism Spectrum Disorders pp 1310–1311 Cite as

Fluid Intelligence

Francesca Happé 2
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Fluid reasoning

Fluid intelligence (abbreviated Gf) is the ability to reason quickly, think abstractly, and problem-solve, independent of acquired knowledge. Cattell ( 1971 ) proposed two factors underlying general intelligence: fluid and crystallized intelligence . Tests of fluid intelligence tap current reasoning ability and are considered to be more “culture-fair,” being less affected by differences in learning experience or test familiarity. Raven’s Progressive Matrices, and other tasks with novel stimuli, are thought to tap fluid intelligence.

People with ASD often do well on certain tests thought to measure fluid intelligence, such as Raven’s Matrices (Hayashi et al., 2008 ) and Block Design (Shah & Frith, 1993 ). Interpretation of these peaks in an uneven intelligence test profile has varied: many fluid intelligence tests tap visuospatial problem solving, which may be superior to verbal reasoning in many people with autism. Alternatively, Dawson et al. ( 2007 )...

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References and Readings

Cattell, R. B. (1971). Abilities: Their structure, growth, and action . New York: Houghton Mifflin.

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Dawson, M., Soulières, I., Gernsbacher, M. A., & Mottron, L. (2007). The level and nature of autistic intelligence. Psychological Science, 18 , 657–662.

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Hayashi, M., Kato, M., Igarashi, K., & Kashima, H. (2008). Superior fluid intelligence in children with Asperger’s disorder. Brain and Cognition, 66 , 306–310.

Scheuffgen, K., Happé, F., Anderson, M., & Frith, U. (2000). High “Intelligence”, low “IQ”? Speed of processing and measured IQ in children with autism. Development and Psychopathology, 12 , 83–90.

Shah, A., & Frith, U. (1993). Why do autistic individuals show superior performance on the block design task? Journal of Child Psychology and Psychiatry, 34 , 1351–1364.

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Happé, F. (2013). Fluid Intelligence. In: Volkmar, F.R. (eds) Encyclopedia of Autism Spectrum Disorders. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1698-3_1731

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Crystallized and fluid intelligence of university students with intellectual disability who are fully integrated versus those who studied in adapted enrichment courses

Hefziba lifshitz.

1 ID Majoring Program, Department of Special Education, School of Education, Bar-Ilan University, Ramat-Gan, Israel

Jay Verkuilen

2 Educational Psychology and Public Health, The Graduate Center, The City University of New York, New York, New York, United States of America

Shlomit Shnitzer-Meirovich

3 Levinsky College, Tel-Aviv, Israel

Carmit Altman

4 School of Education, Bar-Ilan University, Ramat-Gan, Israel

Associated Data

All relevant data are within the paper and its Supporting Information files.

Inclusion of people with intellectual disability (ID) in higher postsecondary academic education is on the rise. However, there are no scientific criteria for determining the eligibility for full inclusion of students with ID in university courses. This study focuses on two models of academic inclusion for students with ID: (a) separate adapted enrichment model: students with ID study in separate enrichment courses adapted to their level; (b) full inclusion model: students with ID are included in undergraduate courses, receive academic credits and are expected to accumulate the amount of credits for a B.A.

(a) To examine whether crystallized and fluid intelligence and cognitive tests can serve as screening tests for determining the appropriate placement of students with ID for the adapted enrichment model versus the full inclusion model. (b) To examine the attitudes towards the program of students with ID in the inclusion model.

Method/Procedure

The sample included 31 adults with ID: students with ID who were fully included ( N = 10) and students with ID who participated in the adapted enrichment model ( N = 21). Crystallized and fluid intelligence were examined (WAIS-III, Wechsler, 1997) and Hebrew abstract verbal tests (Glanz, 1989). Semi-structured interviews were conducted in order to examine the attitudes of students in the inclusion model towards the program.

Outcomes and results

The ANOVAs indicate that the most prominent difference between the groups was in vocabulary, knowledge and working memory. ROC analysis, a fundamental tool for diagnostic test evaluation, was used to determine the students’ eligibility for appropriate placement in the two models. Seven tests distinguished between the groups in terms of sensitivity and specificity. The interviews were analyzed according to three phases.

Conclusions/Implications

The results indicate that students with ID are able to participate in undergraduate courses and achieve academic goals. The general IQ and idioms test seem to be best determiners for appropriate placement of students with ID to one of the two models. The included students with ID are motivated and self-determined in continuing in the program.

Introduction

Despite the increasing trends of postsecondary education (PSE) for adults with intellectual disability (ID) in colleges and universities, studies to date have focused mainly on the social aspects of PSE [ 1 ], i.e. the attitudes of the traditional students and lecturers towards students with ID, the academic accommodations [ 2 ] and the impact of PSE on their employment [ 3 ], without relating to the cognitive aspect of inclusion. In all academic PSE programs, the students’ eligibility is based on educational and employment experiences, interests and motivation to undertake the course, and the support structures they have around them [ 2 ]. As Plotner and Marshall point out: "there are few if any research guidelines to help program developer, prepare and plan PSE" ([ 4 ],pp.59). There is no procedure of scientific criteria for determining the eligibility for full inclusion of students with ID in academic university courses [ 4 ]. Our study attempts to shed light on this issue.

Intellectual disability

Traditionally, classification systems of the mental retardation (MR) revolved primarily around the range of IQ scores achieved by people who met the criteria of an IQ score two or more standard deviations (IQ 70–75) below the mean [ 5 ]. The most common classification scheme involved grouping people based on IQ into one of four subgroups: mild (IQ from 55–75), moderate (IQ from 40 to 55), severe (IQ from 25 to 40) and profound (IQ below 25). In 2010, according to the Rosa’s Law and the US federal law (Rosa’s Law) [ 6 ], the term "Intellectual disability" was confirmed as an alternative to "mental retardation". The traditional definition of mental retardation of the American Association of Mental Retardation [ 5 ] has been replaced by a new one defined by the American Association of Intellectual and Developmental Disability [ 7 , 8 ] and by the Individuals with Disabilities Education Act [ 9 , 10 ]. Intellectual disability is characterized by significant limitations in both intellectual functioning and in adaptive behavior including conceptual, social, and practical adaptive skills. This disability arises before age 18. The 2002 and 2010 American Association of Intellectual and Developmental Disability (AAIDD) classification manuals [ 7 – 8 ] suggested four levels of support (intermittent, limited, extensive, pervasive) as an alternative to the four IQ levels; however, these levels were not intended to form a classification system in the same way as the four levels of mental retardation (mild, moderate, severe, profound) and therefore the traditional classification system is still in use [ 8 ]. The definition of ID in the DSM-5 [ 11 ] is similar to that of the AAIDD from 2002. According to the DSM-5, the intellectual deficit of individuals with ID is largely manifested in a lack of understanding, reasoning, problem-solving, planning, abstract thinking, learning from experience and academic learning ([ 11 ], pp.33). Nevertheless, the classification system was revised again to include the four levels of ID (mild, moderate, sever and profound) which are based not on IQ, but on adaptive behavior skills.

Postsecondary academic education for adults with ID

Despite the above-mentioned cognitive difficulties of individuals with ID, inclusion in postsecondary academic education (PSE) for adults with ID, with and without Down syndrome, is on the rise [ 4 ]. The universities of Alberta, Canada [ 1 ], were the first to open their doors to students with ID, followed by Flinders and Deakin universities in Australia [ 3 ]. In the US, there are 200 programs across 37 states for students with ID [ 4 ]. Many of these programs focus on social, vocational and life skills and students with ID study in separate courses tailored for their level [ 4 ]. Other programs have an academic orientation in which students with ID attend and audit selected undergraduate classes and receive a support system [ 1 – 2 ]. In all of these programs, students with ID receive a certification for auditing the courses.

As far as we know, there are three adults with Down syndrome (DS) in the world who succeeded in completing a bachelor’s degree: Aya Iwamo finished her BA in literature (Iwamo, Japan) [ 12 ], Pablo Pineda [ 13 ] finished his BA in education and works as an actor (Spain) and another student with Down syndrome completed a BA (USA) and works as an assistant to an English teacher in Germany (details are hard to track). The Empowerment Project at the School of Education at Bar-Ian University is a program that seeks to replicate these results and provide PSE for individuals with ID.

Empowerment Project: Two models of inclusion of adults with ID at the Bar-Ilan University

UN Convention on the Rights of Persons with Disabilities states: "Parties shall ensure an inclusive education system at all levels and lifelong learning directed to: The full development of human potential, talent and creativity… sense of dignity and self-worth” [ 14 ]. In line with the UN agenda, The Machado Chair for Research and Human Development, School of Education at Bar-Ilan University launched the Empowerment Project, which is based on two models of university inclusion for adults with ID.

Model 1: Separate model—Adapted enrichment courses [ 4 ]

Students with mild ID attended the School of Education at Bar-Ilan University once a week for four academic courses adapted to their level, including: Developmental Psychology, Sociology, Geography, Self-advocacy, etc. The lecturers were MA students in the ID Program of the School of Education. The purpose of this stage is the acquisition of academic knowledge in various domains that may be relevant to their lives.

In the second year, the same students were included in a typical undergraduate research seminar on "Lifelong Learning of Individuals with Disability". As part of the seminar, students with typical development (TD) and students with ID study subjects related to self-advocacy. There was peer-learning between the two groups. The students with ID were exposed to the concept of research via the use of self-concept, hope and optimism questionnaires. Their task was to interview two friends with ID using the above questionnaires. They were taught to score questionnaires and even to insert the data into the Excel software, while the traditional students performed the statistical analysis of the data that the students with ID collected. Both groups analyzed the results, drew conclusions and presented their research, including theoretical background, method, results and discussion via a PowerPoint presentation.

The educational objectives were: to acquire knowledge on academic subjects that may be relevant to this population, to develop strategies for learning, to access the university’s libraries, to conduct small research projects and to use the computer lab. The social objectives were: to expose students with ID to the traditional students (with typical development) in class and during breaks, to expand the friendship circle of students with ID, to empower and strengthen their self-image, confidence, and quality of life, and to construct positive attitudes towards individuals with disability among the traditional students. The students received a certificate of participation upon completion of this project.

Model 2: Full inclusion with support [ 4 ]

After two years of running the adapted enriched model, we decided to administer a series of crystallized and fluid tests using the WAIS-III [ 15 ] and Hebrew abstract verbal tests [ 16 ]. As stated, there were no scientific criteria for determining the appropriate placement of students with ID for full inclusion in regular university courses, therefore, it was decided that students with ID who exhibited a general IQ of 60 and above would be assigned to the full inclusion model, whereas those who exhibited a general IQ under 60 would remain in the adapted enrichment model. Currently, 10 students with ID are included in undergraduate courses (four with Down syndrome, one with Williams syndrome, one with Kabuki syndrome and four with no specific etiology). To date, these students have completed the following undergraduate courses: "Introduction to Special Education", "Intellectual Disability", "Informal Education”, "Computers for Children with Special Needs", "Informal Education", "Theories of Special Education", "Strategies of Cognitive Modifiability" and "Judaism". These courses were chosen according to their relevance to the life of individuals with ID. The students were registered through the university as auditors (for these specific courses), which is a position that allows students to receive academic credits if they fulfill the requirements of the course. With the assistance of a special education teacher who accompanies the students during the courses (see Method section), the students participating in this project have so far received 11 academic credits (out of 64 credits needed for the BA degree) for completing the requirements for the courses. The ultimate goal of this project is to enable students with ID to complete the remaining 53 credits required for a B.A. Only students with ID who participated in Model 1 (enrichment courses), could move to the Model 2 (full inclusion); however, not all the students with ID who participated in Model 1 could move to Model 2 (see Method section).

The theoretical basis for the Empowerment Project

The Empowerment Project was anchored in several theories: The Compensation Age Theory, the Structural Cognitive Modifiability (SCM) and the Cognitive Reserve theories.

The Compensation Age Theory (CAT) [ 17 ] postulated that chronological age (CA) plays an important role in determining the cognitive ability of individuals with ID, beyond their mental age (MA). The CAT claims that in later years there is compensation for the developmental delay experienced by individuals with ID in their early years. More specifically, their intelligence and cognitive performance may continue to increase until their 50s, thus modifying their ID at an advanced age. Furthermore, not only endogenous factors (age, etiology, IQ level), but also exogenous factors (lifestyle) influence cognitive functioning. Our vision for the Empowerment Project is to design a PSE experience that will enhance the cognitive functioning of adults with ID as postulated by the CAT and as was found by our previous research [ 18 ].

The Structural Cognitive Modifiability (SCM) Theory was at the base of the CAT. The SCM [ 19 , 20 ] postulates that the human organism is a system open to its environment and accessible to change as a result of environmental intervention, even in the presence of three formidable obstacles usually believed to prevent change: age, etiology, and severity of limitation.

The Cognitive Reserve Theory (CRT) [ 21 ] states that living into old age in terms of cognitive functioning depends on the degree or quality of ‘reserve’ (remaining resources) in the brain. The CRT posits that higher cognitive reserve in the form of brain networks that are more efficient or have greater capacity in face of increased demands is what enables people to perform at higher levels of task difficulty. One might argue that individuals with ID have a lower cognitive reserve due to their lower level of intelligence, fewer opportunities for cognitive education and cognitive leisure activities. Our argument [ 17 ] was that the cognitive reserve of individuals with ID should be examined within the population with ID, and not compared to the general population.

Lifshitz and colleagues found that adults with ID can benefit from focused cognitive interventions aimed at strengthening specific cognitive skills that are prone to age deterioration, such as abstract verbal skills, orientation in time and space, memory [ 22 ], analogical reasoning [ 23 ] and metaphoric language [ 24 ]. In these studies, adults with ID gained more from mediation than adolescents with ID with the same cognitive level.

Crystallized and fluid intelligence in adults with ID

McGrew [ 25 ] re-defined the Horn-Cattell model [ 26 ] of crystallized and fluid intelligence. Crystallized intelligence is defined as “a person’s acquired knowledge of the language, information and concepts of a specific culture” (25p.5). Crystallized intelligence is considered a “maintained” ability that increases into a person’s 60s and then declines. Fluid intelligence is defined as “the use of deliberate and controlled mental operations to solve novel problems that cannot be performed automatically” (25p.5). It is associated with frontal executive function [ 27 ], working memory, analogies and understanding of metaphors. Fluid intelligence is a “vulnerable” ability, peaking into one’s early 20s and then declining [ 25 , 27 ]. The crystallized and fluid tests used in this study (see Method section) can be regarded as markers for these constructs.

Research on crystallized and fluid intelligence among adults with non-specific ID (NSID) and with Down syndrome is scant. In Devenny et al. [ 28 ], memory (which is considered a fluid test) of adults with NSID and Down syndrome improved until their 40s and was maintained until their 50s. Contrary to the crystallized and fluid evolution in the general population, Kittler, Krinsky-McHale, and Devenny [ 29 ] found deterioration in the verbal subtests of the WISC-R over a 7-year period among adults with ID. However, Facon [ 30 ] found a similar evolution of the WAIS-R [ 31 ] verbal and performance scales among adults with NSID and with Down syndrome and that of adults with typical development. In the present study, crystallized and fluid intelligence were examined by the WAIS-III [ 15 ] and by a series of Hebrew verbal-crystallized and fluid tests.

Goals of the study

This study examined crystallized and fluid intelligence and cognitive tests of the fully included students with ID ( N = 10) compared to students with ID in the adapted enrichment courses ( N = 21). The study’s goals were: (1) To delineate scientific criteria of eligibility for full inclusion of students with ID who participate in adapted enrichment courses, i.e. to examine whether a general IQ of 60 can be a good predictor of success of students with ID in undergraduate courses; (2) to identify the cutoff points in each of the intelligence and cognitive tests that is, to develop screening tests in order to better classify students who are most appropriate for the full inclusion model, and whether certain students should not move beyond the adapted model; (3) to examine the attitudes towards the program of students with ID who are fully included in undergraduate courses.

Materials & methods

Participants.

The sample included 31 adults with ID who participated in the postsecondary Empowerment Project, which is a two-model academic inclusion project for adults with ID offered by the School of Education, Bar-Ilan Ufniversity (BIU). The students were recruited to the program by the Israel Association of Persons with Down Syndrome and by the Division of ID in the Israeli Ministry of Social Affairs and Social Services. Due to a lack of scientific criteria for eligibility of adults with ID to participate in PSE [ 2 ], the decision on their adaptation in PSE was based on their level of ID according to the DSM-5 [ 11 ], their reading ability and their interest in participating in PSE. Participants met the following criteria: Adults with mild ID [ 11 ], who knew to read and write, were independent in Activities of Daily Living (including arriving at the university campus by public transportation independently), without maladaptive behavior. We did not administer an intelligence test at the beginning of the program in order to preclude and biases. All of the participants worked in vocational centers or in the open market in the morning (with no significant differences between the fully included and those who study in adapted enrichment courses) three times a week and participated in leisure activities in the afternoons. As previously stated, all of the participants could read, but their level of reading or comprehension was not assessed. The current sample was divided into two groups according to the above-mentioned models:

Etiology: Of the ten students in the full inclusion model, four were persons with Down syndrome, one women has Williams syndrome, one woman has Kabuki syndrome and the other four are persons with ID with a non-specific etiology (NSID). Of the students studying in the adapted enrichment model, eight have Down syndrome and 13 have NSID.
Separate adapted enrichment model: 21 (68%) Students with ID participated in adapted enrichment courses (henceforth: the adapted enrichment group, Chronological age (CA) = 26–40; Mean CA = 35.79, SD = 6.86). They attend the university once a week for four academic hours. The courses were adapted to the level of the participants by the traditional MA students of the School of Education at the Bar Ilan University. In addition to the academic knowledge imparted in the courses, we worked on cognitive skills, such as working and long-term memory, executive function and meta-cognitive skills, self-regulation skills such as learning for an examination, preparing homework and self-management skills, i.e. searching in the web and using technological devices such as laptops, etc. In the separate adapted enrichment model, students with ID received a certificate of participation in this project. All participants in this group reside in residential facilities of adults with ID under the supervision of the Division of Intellectual Disability of the Israeli Ministry of Social Affairs and Social Services. The students in this group studied in special education schools.
Full inclusion model: 10 students with ID (32%) were fully included in undergraduate courses (CA = 26–51; MCA = 31.14, SD = 5.84, with no significant difference in CA between those in the adapted model versus those in the inclusion model, t (28) = 1.64, p > .05). These participants lived at home with their parents. Three students studied in special education schools and four in regular schools.

The process of identifying of the students with ID who were assigned to the full inclusion model: After two years of running the adapted enrichment program, we identified 13 relatively highly capable students. Their higher level was expressed mainly in their advanced understanding, verbal expression and memory in comparison to the other 21 that participated in the adapted enriched program. At this point, we decided to administer the intelligence and cognitive battery. The purposes were (a) to establish criteria that will enable us to determine the eligibility for participation of students with ID in the full inclusion versus the adapted enrichment models and (b) to identify future candidates who may be suitable for either program. One might argue that since we administered the tests two years after starting the adapted courses program, their previous education may have impacted the results; however, our claim is that the students with ID in the adapted enrichment model also learned academic material, which could have similarly influenced their IQ level as well as IQ of the students in the inclusion model.

The findings indicated that the 13 highly capable students exhibited general IQ of 60 and above, while the remaining 21 participants in the adapted enriched program exhibited an IQ of 50–60. It was decided that students with ID who exhibited a general IQ of 60 and above would be assigned to the full inclusion model, whereas those who exhibited a general IQ under 60 would remained in the adapted enrichment model. According to this criteria, 13 students with ID were assigned to the full inclusion model; however, three of them were excluded after the preparatory program due to behavioral-emotional problems and inability to complete course requirements. At this point, we found out that intelligence is not the only criteria of eligibility for the participation of persons with ID in full inclusion, but emotional maturity over time should also be taken into consideration. We excluded the three students with ID from the study.

The criteria for participation in the full inclusion model: Participation for two years in the adapted enrichment model, an IQ of 60 and above, and emotional maturity with no mal adaptive behavior.

Preparatory program towards full inclusion and learning strategies: Prior to the academic year, the students with ID who were assigned to the full inclusion model participated in a two month preparatory program which aimed to prepare them for their inclusion in undergraduate courses. To achieve this goal we used the Universal Design for Learning strategies (UDL, CAST) [ 32 ] with adaptations to students with ID. The UDL has defined and adopted three main principles for learning: (a) Provide multiple means of representation: The aim is promote resourceful, knowledgeable students by providing options for comprehension, language and perception. We combine this principal with Bloom’s taxonomy for population with ID [ 33 ] for population with ID [ 33 ] (b) Providing multiple means of actions and expression: The aim is to promote strategic, goal directed students by providing options to experience executive function, options for expression and communication and providing options for experience self regulations. Easy to read—Making written information easier to understand for people with learning disabilities [ 34 ], (c) Providing multiple means of engagement: The aim is to promote purposeful, motivated students by providing options for Self regulation, sustaining efforts and persistence and for recruiting interests. The academic skills, strategies and cognitive process are presented in Appendix 1.

Support system during the courses: The 10 participants in the included group were divided into two groups of five students. Each group studied in different courses and had a special education teacher accompanying them. We trained two special teachers to work with the students with ID according to Universal design learning [ 32 ]. For each academic hour in the university course they received an additional academic hour of preparation from the special education teacher. Prior to the beginning of the course, the special education teacher got the curriculum of the courses and the content of every lesson from the lecturers. A reader was composed for each course, which included relevant reading materials for the course. In that way the teachers could prepare the students with ID towards the next lesson in every course. During these preparatory courses, the teachers worked with the students with ID according to the didactic strategies and cognitive process mentioned above. They also prepared the students with ID towards exams and helped them in performing the course work.

The criteria for choosing the undergraduate courses: The pedagogical committee of the project chose courses from the social sciences which are based on verbal knowledge and that has content that is relevant to the world of adults with ID, such as "Introduction to Special Education" (2 credits), "Intellectual Disability" (1 credit), "Computer Programming for Special Needs" (1 credit), "Informal Education" (2 credits), "Theories of Special Education" (2 credits), "Strategies of Cognitive Modifiability" (1 credits) and "Judaism" (2 credits). All courses were applicable and relevant for this population by dealing with special education and informal education, the rationale and values of leisure activities and the residence framework for individuals with disabilities, Jewish humanistic values and their implication to everyday life, and constructing educational programs for special populations. This fact served as a scaffolding for their self-efficacy to cope with these courses. They participated in the course actively and shared their life experience as persons with disability. The lecturers at this courses received training in using the ULD and the other strategies mentioned above.

Criteria of success of the students with ID in the regular undergraduate courses: In some courses, the final score is determined by exams and in some, the final score is determined by course task. The criteria required of the students with ID in the traditional undergraduate courses were the same criteria that were required from the traditional students. In Israel, the scale scores in exams range between 0–100 with a passing score of 60.

In some courses the exam is a multiple choice questionnaire which was difficult for students with ID. We developed a method of teaching them to answer multiple choice questions. Their results which followed our teaching improved as captured by the students’ scores which ranged between 60–70. In courses in which there were no exams, the final score is comprised of course assignments: group work during class, reading articles and presenting them, course work and the combination of these tasks. Course works’ score is comprised of strict criteria of expressive written ability, using scientific terms, and reading academic articles. The same criteria are used for examining the students with ID. to these criteria the scores of the students with ID range between 65–80. Up to date, they earned 11 academic credits.

Assessment materials

Crystallized and fluid intelligence were examined by the Wechsler Intelligence Test for Adults, WAIS-III HEB [ 35 ] that represents crystallized and fluid intelligence skills in a typical population. In addition to the IQ scores and the verbal/perceptual scales, four other indexes were derived from the verbal and performance sub-scales: Comprehension index composed of vocabulary, similarity and knowledge, Perceptual index composed of picture completion, block design and matrices, Memory index composed of arithmetic, digit forward and backward, series letters and numbers and Speed processing index composed of coding and signs.

The crystallized and fluid tests battery were examined using the MANN—Abstract Verbal Thinking Test [ 16 ] which examines verbal abstract thinking and verbal intelligence abilities. Four tests were found to be appropriate for the population with ID [ 36 , 37 ]: synonyms, classification, contrast and verbal analogies. Semantic and phonemic fluency tests [ 38 ] were also administered, as well as Homophone Meaning Generation Test –HMGT [ 39 ].

The results of a confirmatory factor analysis with Varimax rotation confirmed two different clusters (the minimum loading set point was .35). The first type contained the following tests: semantic fluency, synonyms, contrast, and classification which accounted for 41.39% of the variance. The second type contained the following tests: phonemic fluency, verbal analogy, Homophone Meaning Generation Test—HMGT and idioms tests which accounted for 37.59% of the variance. Three academic scholars (one is an expert in the Hebrew language, and two are experts in psychology) confirmed the division into two clusters with an inter-rater reliability of 90%. The tests in the first cluster are of the crystallized type, and their solution involved cultural and environmental experience and knowledge based on what can be acquired through life experience. The second cluster was of the fluid type. Although these tests were also verbal, they demanded the use of deliberate and controlled mental abstract operations which could not be performed automatically and could not be acquired through the environment [ 25 ]. Table 1 presents the rotated component matrix of the different tests.

The crystallized battery: The Synonyms test (12 items) [ 16 ] examines verbal abstract thinking or verbal intelligence abilities, and is highly correlated with the Verbal Wechsler intelligence tests. The participants were presented with a key word and were asked to find a similar word from a list of five other words (e.g., wall: gate, path, way, balcony, side). Correct answers were given 1 point (range 0–12; test-retest reliability = .90; α = .91). Classification (12 items) [ 16 ] is aimed at assessing comparative behavior. The participants are presented with a list of four words and have to find a concept or a single trait that best characterizes the list (e.g., love, hate, worry, joy: trips, voices, liquid, feelings). A correct answer was given one point ( α = .72). Contrasts (12 items) [ 16 ]: The participants have to find the opposite word out of five alternatives (e.g., floor: ceiling, bag, cabinet, wall, balcony). A correct answer (range 0–12) was given one point (test-retest reliability = .82). Semantic fluency [ 38 ]: The number of words generated in one minute for each of the following three semantic categories: animals, fruits and vegetables, was obtained. The score is the sum of the words generated for all three categories (test-retest reliability = .86).

The fluid battery: Phonemic fluency test [ 38 ]: This test requires the ability to deal with novelty and is a culturally unbiased nonverbal test [ 27 ]. The number of words generated in one minute for the letters bet (/b/), gimel (/g/), and shin (/š /) was obtained. Instructions were as follows: “I want you to say as many Hebrew words as possible that begin with a certain letter”. The score is the sum of the words generated for all three letters (test-retest reliability = .86; α = .91). Verbal analogies (12 items) [ 16 ] examines the understanding of verbal analogies. The participants are presented with a pair of words (key pair—A:B) and a list of five words that can relate to a C term in a second pair in the same way that the words relate in the key pair (e.g., hat is to head as shoe is to: arm, eyes, ears, foot, and forehead). A correct answer was given one point (rang 0–12) (test-retest reliability = .80). Homophone Meaning Generation Test—HMGT [ 39 ] examines the ability to shift between the different meanings of a homophone (10 items in Hebrew). The participants were instructed to say all meanings of the homophone, e.g. "He wrote a letter". A correct answer was awarded one point (range 1–10) (test-retest reliability = .83). Idiom Comprehension (12 items) [ 39 ] examines understanding of figurative meanings of idioms (20 items, e.g., "he got cold feet"). Four interpretations were offered: a correct idiomatic interpretation; a literal interpretation; a literal distracter; an unrelated interpretation. Correct answers were awarded one point (range 0–20) (test-retest reliability = .71).

Qualitative analysis—Semi-structured interviews

A qualitative method of semi-structured interviews [ 40 , 41 ] was carried out among students with ID in the full inclusion model (three years after participating in the full inclusion model). The interviews enabled in-depth understanding of the meanings, perceptions and feelings of the students with ID towards studying in the university in regards to coping with successes and failures. They were asked the following four questions; “Is studying in undergraduate courses at the university important to you? Why?", "How do you cope with difficult tasks?", "What is the contribution of the full inclusion program to your life?", "Would you like to continue in the program and why"?

Content analysis [ 42 , 43 ] was performed according to three stages of qualitative research according to Shkedi [ 43 ] as follows: (a) Deriving simple categories as well as sub-categories according to participants’ answers, (b) Mapping the categories (understanding the relation and the association between categories and deriving new categories), and (c) Generating the core category or constructing theoretical themes. Three judges analyzed the answers: one academic scholar in the field of ID, one academic scholar in the field of qualitative research and the head of the ATID—Israeli Association for Individuals with Down Syndrome. A 90% inter-rater reliability was found.

Authorizations were obtained from the Ethics Committee School of Education Bar Ilan University and the Division of Individuals with ID in the Ministry of Social Affairs and Social Services who approved the participants’ consent. In addition, written consent for participation was obtained from the participants’ parents/guardians. All participants in the adapted model and inclusions groups signed an adapted informed written consent form for participation in program (the adapted or full inclusion models. We clarified that they can quit the program whenever they want. The study’s aim and procedure were explained to all participants, who signed an adapted informed written consent form for participation in scientific research. We took the original consent forms in studies where typically developing students took part in and adapted it with the rules of Easy to Read [ 34 ] for participants with ID. Participants read and signed the consent form. We also orally clarified that there is no obligation to participate in the study. According to the Normalization principal [ 44 ] for students who participate in scientific studies, the participants in this study chose payment or a gift for investing their time and effort.

The WAIS-III was administered in a quiet room by a psychologist who is an expert in the field of ID at the School of Education, individually for each participant for about 90 minutes. The aforementioned Hebrew MANN verbal intelligence tests [ 16 ], as well as the homophones, semantic and phonemic fluency were administered individually to each participant by MA students in ID in a small room at the School of Education. This stage was conducted in two sessions which lasted about 45 minutes, with a 15-minute break between sessions. After three years of participating in the full inclusion model, we conducted the semi-structured interview with each participant which lasted one hour. The interviews were held by MA students in ID who has expertise in conducting interviews.

Statistical analysis

Crystallized and fluid data were analyzed using parametric statistical procedures, e.g. ANOVAs, and non-parametric procedures such as the ROC analysis. A one-way MANOVA’s with the two groups (integration/enrichment) as the independent variables and the different subscales as dependent variables was carried out.

The ROC (Receiver Operating Characteristic) curve was used in order to determine the diagnostic metrics of crystallized and fluid variables evaluation [ 45 ]. In our case, for instance, the IQ intelligence test was evaluated in an attempt to decide the adaptation of a student with ID to the full inclusion versus the adapted enrichment model of inclusion in the academic world (see Zweig and Campbell [ 45 ] for technical results relevant to the ROC analysis).

In a ROC curve, the true positive rate (sensitivity) is plotted as a function of the false positive rate (1-specificity) for different cutoff points of a parameter. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold. The area under the ROC curve (AUC) is a measure of how well a parameter can distinguish between two diagnostic groups (i.e., students with ID participating in the full inclusion versus those who study in the adapted enrichment program). AUC has a value between 0 and 1, with higher values indicating greater distinction between groups. An AUC = 0.5 indicates chance performance, essentially no better than randomly allocating participants to groups while an AUC = 1 would indicate perfect distinction. In this study, we consider measures with AUC greater than 0.8. We also examined the bootstrap confidence intervals for it, which are the most accurate for small samples, ensuring that the measure at least does not drop below 0.5.

For a particular cutoff, sensitivity measures the probability of the test, indicating that a student is suitable for the full inclusion program (given that they fulfilled all course requirements—tasks and final examination), while those in the adapted enrichment program are suitable for their program according to the tasks that are given in this course. Specificity measures the probability of the test indicating that a student would be unsuitable for the full inclusion program given that they did not fulfill their course requirements (e.g., failed the course examination and/or course tasks), while those in the adapted enrichment program are not suitable for the program because they exhibit higher scores in the screening tests and could be moved to the full inclusion group. A good test is both sensitive and specific. We also consider the percent of correct classification, which is an overall measure of performance for a given cutoff. A good cutoff generates a high percent of correct classification.

For every possible cutoff point or criterion value selected for discriminating between two populations, there are some cases with which the cutoff point of each test for the inclusion model will be correctly classified as true positive (.i.e., the student is suitable for the program), or classified as false negative (i.e., the student is not suitable for the program). On the other hand, some students who are in the adapted enrichment model will be correctly classified as true negative (i.e., the student is not suitable for the program and can move to the full inclusion program), but some cases in this group will be classified as false positive (i.e., the student is suitable for the adapted enrichment program).

Due to the small sample size, Shapiro-Wilk analyses were performed for each of the dependent variables. The findings indicated that all dependent variables were normally distributed in both groups (all p s > .05). Parametric analyses were therefore performed for each group separately.

Differences in WAIS-III intelligence test and the cognitive battery among students with ID in the full inclusion and the adapted enrichment groups

We were eager to elucidate the differences between the students in the full inclusion model and those in the adapted enrichment program, beyond general IQ scores. In order to determine the differences between the full inclusion versus the adapted enrichment groups in the WAIS-III intelligence tests, a one-way MANOVA was performed with the two groups (inclusion/enrichment) as the independent variable and performances in the five verbal and five perceptual subscales as dependent variables. Significant differences in the WAIS subscales were found, F (10,19) = 5.76, p < .001, η p 2 = .75, indicating higher performances in the full inclusion group compared to the adapted enrichment group. Means, SDs and F values of the full scale of the WAIS five verbal and performance IQ subscales are presented in Table 2 and Fig 1 . A one-way MANOVA was also performed, with the two groups (inclusion/enrichment) as the independent variable and performances in the four IQ indexes as dependent variables. Significant differences in the IQ indexes were found, F (4,24) = 11.88, p < .001, η p 2 = .66, indicating higher performances in the full inclusion group compared to the adapted enrichment group.

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* p < .05,

** p < .01,

*** p < .001

The findings indicated that students with ID in the full inclusion group exhibited significantly higher scores in general IQ (IQ = 68.70; SD = 4.24;IQ = 56.59; SD = 3.94) for the full inclusion and adapted enrichment groups, respectively), in the verbal IQ (IQ = 72.90; SD = 6.59; IQ = 59.68; SD = 4.69 for the full inclusion and adapted enrichment groups, respectively) and in the performance IQ (M = 68.00, SD = 5.46;M = 60.64, SD = 5.47 for the full inclusion and adapted enrichment groups, respectively).

The Verbal IQ subscales: A one-way ANOVA was performed for each verbal IQ subscales. The most prominent difference (according to the effect size and SD) between the groups lies in vocabulary, followed by knowledge, digit span backward and forward and similarities. Thus, vocabulary, knowledge and working memory are the most prominent subscales that differentiate between the groups.
The performance IQ subscales: A one-way ANOVA was performed for each verbal IQ subscales. The most prominent difference between the groups according to the effect size and (SD) lies in Signs, Matrix and Block design. Thus, the most prominent differences between the full inclusion and the adapted enrichment groups lie in the verbal ability, followed by working memory. In addition, significant differences were found in Signs—a measure of speed as well as in Matrix and Block design.
Differences in the indexes: A one-way ANOVA was performed for each index, indicating that the most prominent differences between the students in the full inclusion group lies in the comprehension index ( M = 82.33, SD = 10.69; M = 62.55, SD = 10.31 for the full inclusion and adapted enrichment groups, respectively), memory index ( M = 69.72 SD = 12.26; M = 53.60, SD = 4.22 for the full inclusion and adapted enrichment groups, respectively) and the speed index. However, no significant differences were found between the two groups in the perceptual index.
The crystallized and fluid tests: A one-way MANOVA was performed with the two groups (inclusion/enrichment) as the independent variable and the four crystallized tests as dependent variables. Significant differences in the performances on the crystallized tests were found, F (4,24) = 5.96, p < .01, η p 2 = .51, indicating better performance in the full inclusion group compared to the adapted enrichment group.

A one-way MANOVA was performed with the two groups (inclusion/enrichment) as the independent variable and performances in the four fluid tests as dependent variables. Significant differences in the performances on the fluid tests were found, F (4,24) = 3.97, p < .05, η p 2 = .52, indicating better performance in the full inclusion group compared to the adapted enrichment group. Means, SDs and F values of the crystallized and fluid tests are presented in Table 3 .

Separate analyses for each of the crystallized and fluid subscales indicated significant differences in all four crystallized tests: semantic fluency, synonym, contrast and classification ( Table 3 ). Significant differences were also found in three of the fluid tests: phonemic fluency, analogies and idioms.

The cutoff points of intelligence and cognitive tests of the full inclusion and the adapted enrichment groups

ROC analysis was performed in order to consider the cutoff point in each of the intelligence tests and the Hebrew crystallized and fluid tests. We considered three measures: the sensitivity (probability that a student with ID in the full inclusion group would be correctly identified), specificity (probability that a student with ID in the adapted enrichment group would be correctly screened), and the Area Under the Curve (AUC) as an overall measure.

Sensitivity and specificity are conditional probabilities that show the value of a measure for identifying positive and negative cases, respectively. We consider 0.80 as a good value for AUC, sensitivity and/or specificity. Of the 24 tests, seven exhibit highest sensitivity and specificity (sensitivity and/or specificity above .8), as seen in Table 4 .

AUC = Area under curve

The seven variables that had AUC, sensitivity and specificity that distinguished between the two distributions overall were: IQG, IQV, comprehension index, vocabulary, knowledge, synonyms and idioms ( Table 4 ). Table 5 presents the number of persons in the full inclusion model and in the adapted enrichment model who fell within the cutoff points as indicated by the ROC curve analysis in these tests.

Nine of the ten students with ID in the full inclusion group matched the profile suggested by the ROC curve analysis ( Table 5 ). The tenth participant in the full inclusion group, an individual with Down syndrome, did not seem to fit well from the beginning, and had difficulties throughout the course. In synonyms, two participants (both with Down syndrome) scored below the cutoff point.

Greater variation was found in the adapted enrichment group. None of the participants in the adapted enrichment group scored above the cutoff point in general IQ, making them distinctly different from the full inclusion group. Idioms produced only one participant above the cutoff point. As for verbal IQ, Comprehension Index, Knowledge, three participants scored below the cutoff point; however, these were not the same participants in the various tests. This variation in the number of the participants who scored above the cutoff in the various tests may be due to individual differences between participants with ID and shall be explored further in the discussion. The vocabulary index produced four participants above the cutoff point. It should be noted that in all cases, there was close to 90% precision in the full inclusion group and above 80% precision among the adapted enrichment group. Thus, the most reliable variables are the general IQ and idioms.

In conclusion, the findings revealed two ways of differentiating between students in the full inclusion group and those in the adapted enrichment group. The MANOVAs revealed significant differences between the groups in almost all subscales of the intelligence tests and the crystallized and fluid tests. It also ranked the differences between the groups. ROC analysis determined the appropriate placement of the students in the full inclusion group and the adapted enrichment group as well as those who score below the cutoff point in the full inclusion group and those who scored above the cutoff in the adapted enrichment group. IQG, IQV, comprehension index, vocabulary, knowledge, synonyms and idioms had high specificity and sensitivity which may further our understanding of the cognitive and language resources that enable full inclusion of students with ID.

Analysis of the qualitative interview

Semi-structured interviews yielded several themes- their thoughts about the program, feelings of failure and success, and their way of coping with obstacles- related to the attitudes of participants to the program. The mapping stage according to Shkedi [ 43 ] revealed that the perception of undergraduate courses that our participants took can be explained by the three components of attitudes [ 46 , 47 ]: cognitive, emotional and motivational (a combination of cognition and behavior, see Discussion section) components. Table 6 presents samples of the answers.

Three issues are at the core of the discussion: (a) Attribution of the different patterns in intelligence and crystallized and fluid tests between the groups (according to the MANOVA’s), (b) The criteria for determining the eligibility of the students with ID for the two models of inclusion in the academic world: the adapted enrichment model and the full inclusion model (according to the ROC analysis), (c) The cognitive, emotional and motivational attitudes of the included students with ID to their program.

The difference in intelligence and cognitive battery between the adapted enrichment and the full inclusion groups

The findings indicate that the most prominent difference between the students who are fully included and those who study in the adapted enrichment model is the general IQ, which exceeds the 68 mark in the included group, compared to 56 mark in the adapted enrichment group. Significant differences were found in the verbal IQ of the full inclusion group which crossed the 72 mark (the range of three participants was 77–81), while the verbal IQ of the adapted enrichment group was approximately 59. The perceptual IQ of the full inclusion group was 68, compared to 60 in the adapted enrichment group.

In our opinion, the differences in verbal and perceptual subscales between the groups represent the developmental approach as well as the statistical approach and beyond the goals of this study, are important in understanding the nature of ID. The developmental approach is ecological, dynamically oriented and represents the progress of individuals with ID primarily in domains that that can be developed by their engagement with environmental challenges. Crystallized intelligence (verbal IQ) is considered to be culturally-dependent [ 25 , 27 , 48 ]. Vocabulary and knowledge are acquired through educational and leisure opportunities as well as via life experience. The statistical approach relates to the deviation from the statistical mean of the population. It reflects the fluid intelligence, which is more associated with general intelligence g [ 49 ] and expressed by the ability to solve novel problems and situations. Thus, the differences between the groups in both types of intelligence represent a developmental as well as a statistical gap.

Our participants were defined as individuals with mild ID according to new AAIDD ID definitions [ 7 , 8 ] and the DSM -5 [ 11 ]. All students in the full inclusion group exhibited higher verbal IQ, and in the Hebrew crystallized tests [ 16 ], i.e. synonyms, semantic fluency, contrast and classification (One student with William syndrome (characterized by higher verbal ability) [ 50 ] and a student with NSID received 81 in the verbal IQ, while another student with Kabuki syndrome received 77). The difference in the crystallized tests between the groups could be attributed to the type of residence and learning environment.

All students in the full inclusion model lived at home with their parents while most of the students with ID in the adapted enrichment model lived in community residences. Home culture is more promising and provides more opportunities for nurturing the cognitive ability than community residence. Furthermore, three students in the full inclusion model studied in special education schools and seven in regular schools, while all students with ID in the adapted enrichment model studied in special education schools. Thus, the environment culture (type of residence and school) could be the causal factor in the difference in verbal abilities between the included students with ID versus those who study in the adapted enrichment model.

Scientists asked whether academic knowledge is domain specific, i.e. correlated with crystallized and/or fluid intelligence tests. Ackerman [ 51 ] examined the contributions of fluid and crystallized intelligence in predicting individual differences in academic knowledge between middle-age adults (age 21–62) who earned a B.A in exact sciences versus social sciences. It was found that crystallized intelligence had a considerable explanatory power in predicting knowledge in the social sciences, whereas fluid intelligence predicted knowledge in exact sciences. The gap in verbal scores found in our study among students with ID who are fully included, versus their peers who study in the adapted enrichment courses, supports the same effect that was found in populations with typical development [ 51 ]. The students with ID in the current study are fully included in education courses at the School of Education (Bar-Ilan University) which falls under the umbrella of the Faculty of Social Sciences and Jewish courses, under the broader category of humanism. Thus, their higher crystallized intelligence ability represent a normal phenomenon for typical students who study in social science.

However, the significant differences between the full inclusion and the adapted enrichment groups was also grounded in the perceptual IQ which is considered of fluid type, mainly, in Block design and Matrix. These two tests are used as measures of IQ in the abbreviated version of the WAIS ™ [ 52 ] in population with typical development [ 53 ] as well as in a population with ID [ 54 ]. The students in the full inclusion model exhibited higher scores in the Hebrew fluid tests [ 16 ], including idioms, analogies and phonemic fluency which require a higher level of abstract thinking beyond the verbal ability acquired through the environment and are considered as fluid type.

The gap in fluid tests (perceptual IQ and the fluid Hebrew tests), indicate that the variance between the students with ID in the included and the adapted models was more than culture-dependent (residence and learning environment), rather it reflects the basic dilemma of intelligence theory [ 25 , 26 ] and the nature of ID. Although crystallized intelligence is acquired and impacted by the environment, scientist claim that fluid intelligence influence the crystallized intelligence as well [ 49 , 55 ]. That is, the degree of environment influence on intelligence depends on fluid intelligence. Thus, the difference between the students with ID in full inclusion and adapted enrichment models are more "structural" than developmental, and stem from individual differences in the g– the basic general intelligence [ 49 ] between the two groups [ 48 , 56 ]. However, the higher fluid intelligence, mainly general intelligence, of students with ID in the full inclusion model, served as their additional cognitive resource which enables them to be fully included in undergraduate courses, as compared to the students with ID in the adapted group.

Screening tool for determining eligibility for full inclusion of students with ID

The differences in the various cognitive tests between the students with ID in the full inclusion versus the adapted enrichment groups raised a clinical question: Which of the intelligence tests and the crystallized and/or fluid battery could serve as a screening tool for determining the appropriate placement of students with ID into the full inclusion group as opposed to the adapted enrichment models?

For this purpose, we used the ROC analysis, which differentiates between the groups in term of AUC sensitivity and specificity [ 45 ]. Of 25 intelligence measures (including the indexes and cognitive battery), seven could serve as screening tools for determining the appropriate placement of students with ID to the full inclusion group and the adapted enrichment models in terms of their AUC, sensitivity and specificity, which could not be captured by the MANOVA’s. These variables were: General IQ, verbal IQ, vocabulary, knowledge, and the comprehension index as well as synonyms (crystallized test) and idioms (fluid test).

Table 4 indicates that of the 10 students with ID in the full inclusion model, nine scored on or above the ROC cutoff point in all tests. One student—a woman with Down syndrome, scored below the cutoff points (IQG = 59, IQV = 61, vocabulary = 2, comprehension index = 67, knowledge = 5, synonyms = 4, idioms = 13). Although she did not fail in any of the courses, she revealed a lower level of understanding in the undergraduate courses and needed more mediation in terms of time and learning strategies. Furthermore, her scores in two courses (Introduction to Special Education and Intellectual Disability) were low compared to the other students with ID. Another student, also a woman with Down syndrome, received one point under the cutoff in synonyms, but in all the other tests her scores were at or above the cutoff point (IQG = 68, IQV = 66, vocabulary = 5, comprehension index = 67, knowledge = 7, synonyms = 4, idioms = 9). She also followed the courses’ requirements, did not fail any course and exhibited creativity in different domains.

It is noteworthy that the lower scores of two students with Down syndrome on some of the verbal tests is correlated with the cognitive profile of this etiology. Wang and Bellugi [ 50 ] found that individuals with Williams syndrome exhibit strength in verbal tasks and deficit in visual-spatial tasks. Contrastingly, individuals with Down syndrome exhibited deficit in verbal tasks, but preserved visual-spatial tasks [ 57 ]. However, the level of understanding of individuals with Down syndrome is higher than their production ability [ 58 ]. This feature of participants with Down syndrome might serve as an explanation for the fact that despite their lower verbal scores in accordance with the cutoff score, these two students did not fail any of the course requirements. This may be due to additional mediation that was provided to them during the year by the special education teacher as well as their higher motivation to succeed. We currently face the ethical dilemma of whether to keep the woman who scored below the cutoff point in all seven tests in the full inclusion program, which is time and money consuming. The other option is to allow her to study in undergraduate courses without the requirements. From an educational point of view, the results of the ROC analysis can serve as an operative tool for determining the amount and content of mediation that should be provided to the students with ID in the full inclusion model.

The general IQ and idioms scores seem to be the best indicators for determining whether a student was suitable for the adapted enrichment or the full inclusion program. Based on their cutoff scores, either none (for general IQ) or one (for idioms) student can move to the full inclusion model. On the other tests, the scores of 3–4 students were above the cutoff point, but those were not the same individuals, and each exhibited strengths and weaknesses in different tests.

In conclusion, this study was a first attempt to predict the appropriate placement of individuals with ID for undergraduate courses. This issue should be researched cautiously, thoroughly and longitudinally. The ROC analysis assists in the evaluation of the eligibility of students with ID for adapted enrichment versus full inclusion. One important conclusion that emerged from this study is associated with the current DSM-5 [ 11 ] definition of ID. As stated, IDD (Intellectual Developmental Disorder) is a disorder with onset during the developmental period that includes both intellectual and adaptive functioning deficits in conceptual, social, and practical domains. According to DSM-5, the deficits in intellectual functions of persons with ID lie in reasoning, problem-solving, planning, abstract thinking, judgment, academic learning and learning from experience. In light of the Empowerment Project and the results of the current study, it can be said that under certain mediation and support circumstances, students with ID are able to actively participate in undergraduate academic learning and achieve academic goals. However, amendment of this DSM-5 claim can be possible only after our students complete their courses for the BA degree.

This study raises several educational dilemmas. The first is related to the type of undergraduate courses in which adults with ID could be included. Currently, these individuals have succeeded in the first two years of social science courses in an undergraduate education program, which are based on crystallized abilities. Will they be able to cope with third year courses? This question remains unanswered at present.

Attitudes towards the program of participants in the included model

The semi-structured interviews revealed several themes that express the attitudes of the students with ID in the inclusion model towards the program. We did not use any formal measure of quality of life but the interviews allowed us a glimpse at the students with ID’s thoughts and feelings towards their academic life. We analyzed the results according to Shkedi’s [ 43 ] three phases of qualitative research. In the "mapping stage" (see Results section) we categorize the answers of the participants into cognitive, emotional and motivational components of attitudes [ 46 , 47 ]. The themes that emerged from the theoretical stage grounded the answers of the participants in theories of motivation [ 59 , 60 , 61 ] and self-determination [ 62 ].

Motivation is defined as a process that initiates, guides and maintains goal oriented behaviors or activates, directs, and sustains, even though the task involves barriers and obstacles [ 62 ]. Motivation emphasizes the universal will towards growth and development [ 62 ] and is associated with energy, direction, persistence, intention and activation and involves biological, social, and cognitive forces of the behavior. Maeher and Midgley [ 61 ] related to three components of motivation in learning: Activation which involves the decision to initiate (or to maintain) a behavior and continued effort towards a goal, despite the potential existence of obstacles. Persistence is the amount of time, energy and resources that are invested (behavior). Intensity (also known as quality) relates to the thought (cognition) and emotion put towards the goal (there are other interpretations to this concept).

Self-determination theory (SDT) [ 62 ] is a macro theory of human motivation and personality that deals with an individual’s inherent growth tendencies and innate psychological needs. It is concerned with the motivation behind choices made without external influence and interference.

It seems that the students with ID included in undergraduate courses were characterized by higher motivation and self-determination. They were self-determined to continue in the program despite the difficulties and to pay the ensuing "cost". They invested time and energy in the program, not only at the university, but at home, because they were aware of the cognitive and emotional contribution of participating in the program ( Table 6 ).

The need for appreciation and respect in Maslow [ 59 , 60 ] is based on the hierarchal motivational theory. When the needs at the bottom three levels have been satisfied (physical needs, safety, esteem and belonging) are fulfilled, it becomes increasingly important to gain the respect and appreciation of others. The answers of the students with ID ( Table 6 ) indicate that learning in undergraduate courses and achieving academic accomplishments, played a role in fulfilling their esteem needs, the confidence in their abilities, and empowerment of their self-image.

The last need in the hierarchy is self-actualization , or the desire to fulfill one’s individual potential. "What a man can be, he must be," Maslow explained ([ 60 ], pp.203), referring to the need of people to achieve their full potential as human beings. Our study indicated that despite the limitations imposed by their disability, the concept "self-actualization" can be expanded to include people with ID. Learning at the university in undergraduate courses brought the students to a level of functioning previously absent from their behavioral repertoire, from both a cognitive and emotional point of view and helped them to facilitate their human potential and fulfill their self-actualization.

Limitations and future research

Generalization from this research should be regarded with caution, due to the small sample size and the specific undergraduate courses that were studied in our program. However, the Empowerment Program is the first of its kind (students with ID are expected to fulfill the requirements of undergraduate courses and receive academic credits) in Israel and the world over. This was, therefore, the largest sample that could be found. Further research using other screening tests, such as working memory and long-term memory, are needed in order to examine the criteria for participation of students with ID in the full inclusion model. In this study, we use a semi-structured interview in order to learn about the attitudes of students with ID towards the inclusion program. It is recommended to use other formal measures to examine other personal characteristics such as: motivation, curiosity, creativity and emotional intelligence as well as quality of life. The appropriateness of the criteria should be tested in longitudinal studies, where participants will be followed along their academic studies to discover the number of courses they can take simultaneously, the type of courses and the support system and mediation necessary to help them succeed. Our study focuses on social sciences. It is recommended to examine whether the tests that were used in the current study are suitable for other academic programs for adults with ID: such as technical college or liberal arts college.

Educational implications

All the students with ID in the full inclusion program live at home. The division of ID in the Welfare Ministry should take this fact into account and consider the provision of a more home-oriented environment to people with ID living in community residences, in order to facilitate participation in postsecondary education in academic settings.

Three students in the full inclusion model studied in special education schools and the other four in mainstream schools. All the students in the adapted enrichment group studied in special education schools. The Israeli Special Education Act 5748–1988 [ 63 ]; 5755–2005 [ 64 ], following the United States “Education for All Handicapped Children Act” [ 65 ], recommends inclusion of students with special needs in traditional classrooms, in a less restrictive environment. ID is the second most common disability after learning disability (19,559 students with learning disability and 12,382 students with ID) [ 66 ] but only 5% of students with mild ID are included in regular schools [ 66 ]. The educational curriculum and transition curricula are directed towards preparation of adolescents (16–21) with ID for life as adults focusing on adaptive behavioral, instrumental and vocational skills. PSE all over the world and in Israel indicate that adults with mild ID can benefit from academic learning and a portion can even be fully included into undergraduate courses. The education system should therefore construct an appropriate syllabus for this population, both in mainstream schools and within special education classes, which will be more cognitively-oriented in order to allow more students to study further in a college or university for at least one year (stages 1–2) and to fulfill their potential by being included in undergraduate courses.

Supporting information

S1 appendix, funding statement.

The authors received no specific funding for this work.

Data Availability

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Fluid vs Crystallized Intelligence In Psychology
Fluid intelligence refers to the ability to reason and solve novel problems, independent of any knowledge from the past. It involves the capacity to identify patterns, solve puzzles, and use abstract reasoning. On the other hand, crystallized intelligence refers to the ability to use knowledge, facts, and experience that one has accumulated over time. It includes vocabulary, general world ...
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Fluid intelligence is your ability to learn, assess, and navigate new situations. Crystallized intelligence is accumulated knowledge you can recall as needed. Problem-solving uses both ...
Fluid Intelligence vs. Crystallized Intelligence
Fluid vs. crystallized intelligence is one of many theories of intelligence in psychology. Fluid intelligence involves the ability to reason and think flexibly, whereas crystallized intelligence refers to the accumulation of knowledge, facts, and skills that are acquired throughout life. ... Solving puzzles or abstract problems; Fluid ...
Fluid Intelligence: Definition, Examples, & Psychology
Problem-solving: Fluid intelligence helps in quickly identifying patterns and relationships within information, which is crucial for solving complex problems. ... Fluid intelligence and crystallized intelligence are both important aspects of cognitive functioning. The difference in these two types of intelligence was initially proposed by ...
PDF 23 Problem-Solving and Intelligence
In this chapter, we discuss the link between intelligence and problem-solving in terms of contemporary ideas concerning both. To preview, we argue that the ability to solve problems is not just an aspect or feature of intelligence. - it is the essence of intelligence. The chapter is organized into ve major sections.
Fluid and crystallized intelligence
Fluid intelligence is the ability to solve novel reasoning problems and is correlated with a number of important skills such as comprehension, problem-solving, and learning. Crystallized intelligence, on the other hand, involves the ability to deduce secondary relational abstractions by applying previously learned primary relational abstractions.
Unlocking the Power of Fluid Intelligence: How to Boost Your Brain's
Fluid intelligence is thinking logically and solving problems in new and unfamiliar situations. It is considered to be independent of education, experience, and learning. ... While fluid and crystallized intelligence are distinct, they are interrelated and interdependent. In many situations, we need both types of intelligence to perform well ...
Fluid vs. Crystallized Intelligence: What's the Difference?
Fluid intelligence is the ability to use logic and solve problems in new or novel situations without reference to pre-existing knowledge. Crystallized intelligence is the ability to use knowledge that was previously acquired through education and experience. Fluid intelligence declines with age, while crystallized intelligence is maintained or ...
Crystallized and Fluid Intelligence
Fluid abilities are general in nature, in that they can be applied to any novel abstract situation that requires solving a novel problem, while crystallized abilities are specific, in that they require specific knowledge (learned from one's cultural milieu) to solve familiar problems (this distinction is similar to, and partially built upon ...
Intelligence (Crystallized/Fluid)
Crystallized intelligence reflects the results of prior learning but does not necessarily require new problem-solving. Examples include measures of vocabulary and fund of knowledge. Fluid intelligence, in contrast, is the ability to think abstractly and solve novel problems. An example would be pattern completion tests, which require analysis ...
Fluid Intelligence Emerges from Representing Relations
Cattell Raymond B. Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology. 1963; 54:1-22. doi: 10. ... Insight problem solving is strongly related to working memory capacity and reasoning ability. Journal of Experimental Psychology: General. 2018; 147:257-81. doi: 10.1037/xge0000378. ...
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Tell us about core facets of problem-solving, crystallized and fluid intelligence. Crystallized intelligence is the capacity to solve problems that depend on our prior knowledge and experience. We ...
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Introduction. Fluid intelligence has been defined as the ability to think logically and solve problems in novel situations, independent of acquired knowledge .Fluid intelligence reflects an individual's capacity for abstract thought and reasoning and it contrasts with so-called "crystallized intelligence" , which depends on previous knowledge and educational achievement.
Decoding Cognitive Science: Fluid vs Crystallized Intelligence
In summary, cognitive abilities such as fluid and crystallized intelligence, problem-solving skills, memory capacity, learning potential, information processing, reasoning ability, and mental flexibility are crucial for personal and professional success.However, various risk factors such as lack of education, poor environmental conditions, and limited access to resources can hinder cognitive ...
Too Much Crystallized Thinking Lowers Fluid Intelligence
Fluid intelligence involves the ability to identify patterns and relationships that underpin novel problems and to extrapolate these findings using logic. On the other hand, crystallized ...
Fluid and Crystallized Intelligence
Fluid and Crystallized Intelligence. ... Study after study has shown that specialized problem-solving depends upon the acquisition of schematic forms of reasoning appropriate to the domain at hand. Furthermore, these schema are very largely acquired by extensive practice. This has been shown in domains as far apart as chess, physics, and economics.
Fluid Intelligence
Fluid intelligence is crucial in learning and problem-solving as it enables individuals to adapt to new situations, analyze information, and think critically. ... 11 Examples of Fluid and Crystallized Intelligence. As we have seen, Fluid intelligence is the ability to think abstractly, reason, identify patterns, solve problems, and discern ...
Discrepancy in Fluid and Crystallized Intelligence: An Early Cognitive
In conclusion, crystallized-fluid intelligence discrepancy analysis has a strong potential in predicting the onset of cognitive decline. It provides a roadmap to predict the latent cognitive decline in older populations ahead of time, who may not be clinically manifesting symptoms of cognitive decline at the start of follow-up, since such ...
Cattell's Theory of Intelligence
Fluid and crystallized intelligence In psychology, fluid and crystallized intelligence (abbreviated Gf and Gc, respectively) are factors of general intelligence originally identified by Raymond Cattell. Fluid intelligence or fluid reasoning is the capacity to think logically and solve problems in novel situations, independent of acquired knowledge. It is the ability to analyze novel problems ...
Fluid Intelligence and Psychosocial Outcome: From Logical Problem
Introduction. Fluid intelligence has been defined as the ability to think logically and solve problems in novel situations, independent of acquired knowledge .Fluid intelligence reflects an individual's capacity for abstract thought and reasoning and it contrasts with so-called "crystallized intelligence" , which depends on previous knowledge and educational achievement.
Fluid Intelligence
Fluid intelligence (abbreviated Gf) is the ability to reason quickly, think abstractly, and problem-solve, independent of acquired knowledge. Cattell proposed two factors underlying general intelligence: fluid and crystallized intelligence.Tests of fluid intelligence tap current reasoning ability and are considered to be more "culture-fair," being less affected by differences in learning ...
Testing Relations of Crystallized and Fluid Intelligence and the
Intelligence models (Horn and Cattell, 1966) suggest that g can be divided into two separate general factors, namely fluid g (reasoning and problem solving, independent of acquired knowledge) and crystallized g (accumulated information and verbal skills).
Crystallized and fluid intelligence of university students with
Fluid intelligence is a "vulnerable" ability, peaking into one's early 20s and then declining [25, 27]. The crystallized and fluid tests used in this study (see Method section) can be regarded as markers for these constructs. Research on crystallized and fluid intelligence among adults with non-specific ID (NSID) and with Down syndrome is ...

Fluid Intelligence vs. Crystallized Intelligence

Key Takeaways

Cattell’s Theory of Intelligence

Fluid Intelligence

Measurements of Fluid Intelligence

Raven’s Progressive Matrices

Wechsler Intelligence Scales for Children

What Is Crystallized Intelligence?

Measurements of Crystallized Intelligence

The Wechsler Adult Intelligence Scale (WAIS)

How the Intelligence Types Work Together

Can Fluid Intelligence Be Improved?

Further Reading

Fluid vs. Crystallized Intelligence

Fluid Intelligence

What Is Crystallized Intelligence?

Differences Between Fluid and Crystallized Intelligence

Changes in Fluid vs. Crystallized Intelligence

Fluid and Crystallized Intelligence Tests

Improving Fluid and Crystallized Intelligence

How to Improve Fluid Intelligence

How to Improve Crystallized Intelligence

How Fluid and Crystallized Intelligence Work Together

Keep in Mind

Unlocking the Power of Fluid Intelligence: How to Boost Your Brain’s Problem-Solving Abilities

Understanding Fluid Intelligence

Critical Characteristics of Fluid Intelligence

Fluid Intelligence vs Crystallized Intelligence

Distinctive Features

Interrelation and Interdependence

Measurement of Fluid Intelligence

Common Tests

Scoring and Interpretation

Factors Affecting Fluid Intelligence

Environment

Improving Fluid Intelligence

Cognitive Training

Healthy Lifestyle Choices

Frequently Asked Questions

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Fluid Versus Crystallized Intelligence: What’s the Difference?

Fluid vs. Crystallized Intelligence

Origin of the Theory

Fluid Intelligence

Crystallized Intelligence

How the Intelligence Types Work Together

Can Fluid Intelligence Be Improved?

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