Feb 20, 2024 · Embracing the Complexity of Problem-Solving in the Context of IQ In conclusion, the relationship between IQ and problem-solving skills is intricate, with IQ providing a crucial, though not ... ... We then discuss the predictive validity of intelligence as it relates to job performance, mortality, expertise, and academic achievement. We also discuss practical uses of intelligence tests. Finally, we consider the question of whether intelligence as problem-solving ability can be improved through training. ... Apr 8, 2018 · In practice, the way intelligence is usually tested in IQ tests, involves solving some types of problems (assuming a generous defintion of "problem"). Which essentially entails operationalizing intelligence. It's fair to say IQ tests test some specific types of problem solving. Of course the hope is that these correlate well enough with ... ... Sep 30, 2024 · The concept of Abstract Reasoning IQ: Unraveling the Cognitive Skill Behind Problem-Solving is closely related to Performance IQ. Abstract reasoning, the ability to identify patterns and relationships between concepts, is a key component of non-verbal problem-solving skills. ... 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 five major sections. In the first section, we consider the question of what a problem is and argue ... ... Crystallized intelligence represents abilities acquired through learning, practice, and exposure to education, while fluid intelligence represents a more basic capacity that is valuable to reasoning and problem solving in contexts not necessarily related to school education (Carroll, 1982). ... Feb 13, 2023 · The question of whether problem solving can increase IQ has been the subject of much research and debate. Some experts argue that engaging in problem-solving activities can improve cognitive abilities such as critical thinking, reasoning, and decision-making, which are all aspects of IQ. ... Oct 1, 1988 · Problem solving and intelligence are generally considered to be two intimately related domains (Resnick & Glaser, 1976; Sternberg, 1982). However, there is a noticeable lack of research that directly compares performance on experimental problem-solving tasks with performance on psychometric abilities instruments (Sternberg, 1982). ... Nov 11, 2023 · This groundbreaking study sought to measure problem-solving skills in various contexts, comparing the results with the IQ scores of participants. The researchers employed a range of problem-solving tasks, from logical puzzles and mathematical equations to real-world scenarios that required creative and critical thinking. The findings ... ... problem-solving ability is not just an aspect of intelligence – it is the essence of intelligence. In the next section, we briefly consider what is known about individual Figure 23.1 Example of a Raven’s-like problem (from Kunda et al., 2016). Reprinted with permission of Elsevier. Problem-Solving and Intelligence 555 ... ">
paper

is problem solving related to iq

NeuroLaunch.com

  • General Categories
  • Mental Health
  • IQ and Intelligence
  • Bipolar Disorder

Performance IQ: Unveiling the Key to Cognitive Abilities and Problem-Solving Skills

Performance IQ: Unveiling the Key to Cognitive Abilities and Problem-Solving Skills

Puzzle pieces of cognitive prowess snap into place as we delve into the realm of Performance IQ, a crucial yet often overlooked dimension of intelligence that shapes our ability to navigate complex challenges and solve problems with unparalleled finesse. This fascinating aspect of our mental capabilities has long intrigued psychologists, educators, and researchers alike, offering a window into the intricate workings of the human mind.

Picture, if you will, a world where our ability to think, reason, and problem-solve is not confined to the realm of words and language. This is the domain of Performance IQ, a concept that has been quietly revolutionizing our understanding of intelligence for decades. But what exactly is Performance IQ, and why should we care about it?

At its core, Performance IQ refers to our capacity to process and manipulate visual information, solve problems, and reason abstractly without relying on verbal skills. It’s the silent powerhouse behind our ability to navigate a bustling city, assemble flat-pack furniture, or even master the art of Tetris. While its verbal counterpart often steals the spotlight, Performance IQ plays an equally vital role in our cognitive toolkit.

The history of Performance IQ in intelligence testing is a tale of evolution and discovery. It all began in the early 20th century when psychologists realized that traditional IQ tests, heavily reliant on verbal skills, were failing to capture the full spectrum of human intelligence. Enter David Wechsler, a visionary psychologist who recognized the need for a more comprehensive approach to intelligence assessment.

Wechsler’s groundbreaking work led to the development of intelligence scales that incorporated both verbal and performance subtests. This revolutionary approach acknowledged that intelligence is multifaceted, encompassing a range of cognitive abilities beyond language skills. Suddenly, individuals who may have struggled with verbal tasks could showcase their cognitive strengths in other areas, leveling the playing field and providing a more accurate representation of their intellectual capabilities.

Unraveling the Mystery: What is Performance IQ?

To truly grasp the concept of Performance IQ, we need to dig a little deeper. Imagine your brain as a Swiss Army knife, with each tool representing a different cognitive skill. Performance IQ is like the set of tools designed for tasks that don’t require words – think of it as the pliers, scissors, and screwdriver of your mental toolkit.

But how does Performance IQ differ from its verbal sibling? While Verbal IQ focuses on language-based skills such as vocabulary, comprehension, and verbal reasoning, Performance IQ zeroes in on non-verbal problem-solving, visual-spatial abilities, and processing speed. It’s the difference between solving a crossword puzzle and completing a jigsaw – both require intelligence, but they tap into different cognitive resources.

The components of Performance IQ are like the ingredients in a complex recipe. Each element contributes to the overall flavor of our cognitive abilities. These components typically include:

1. Visual-spatial reasoning: The ability to understand and manipulate visual patterns and relationships in space. 2. Perceptual organization: The capacity to interpret and organize visual information quickly and accurately. 3. Processing speed: The efficiency with which we can perform cognitive tasks, particularly those involving visual input. 4. Non-verbal problem-solving: The skill of tackling challenges and finding solutions without relying on language.

Measuring Performance IQ is no small feat. It requires a carefully crafted set of tasks designed to assess these non-verbal cognitive abilities. Nonverbal IQ Tests: Comprehensive Guide to Measuring Intelligence Without Words offers a deep dive into the world of these assessments, shedding light on how we can quantify these elusive cognitive skills.

Peeling Back the Layers: Key Aspects of Performance IQ

Let’s zoom in on the key aspects of Performance IQ, shall we? It’s like exploring the different rooms in a fascinating cognitive mansion, each with its own unique features and functions.

First up, we have visual-spatial reasoning – the mental equivalent of being able to park a car in a tight spot or navigate through a maze. This skill allows us to manipulate mental images, understand spatial relationships, and visualize objects from different angles. It’s what helps architects design stunning buildings or allows you to tetris your luggage into the overhead compartment on a plane.

Next, we encounter perceptual organization – the brain’s filing system for visual information. This aspect of Performance IQ enables us to make sense of the visual world around us, identifying patterns, and distinguishing between relevant and irrelevant details. It’s like having a super-efficient personal assistant in your mind, sorting through the chaos of visual input and presenting you with a neatly organized summary.

Processing speed, our third key player, is all about cognitive efficiency. Think of it as the broadband connection of your brain – the faster it is, the quicker you can download and process information. This aspect of Performance IQ is crucial in today’s fast-paced world, allowing us to react swiftly to changing situations and make split-second decisions.

Last but certainly not least, we have non-verbal problem-solving skills. This is where Performance IQ really flexes its muscles, enabling us to tackle complex challenges without relying on language. It’s the mental agility that allows us to figure out how to assemble furniture without instructions or solve a Rubik’s cube.

These aspects of Performance IQ don’t exist in isolation – they work together in a beautiful cognitive symphony, each complementing and enhancing the others. The interplay between these skills is what gives Performance IQ its power and versatility.

Putting Performance IQ to the Test

Now that we’ve dissected the components of Performance IQ, you might be wondering how psychologists actually measure these elusive cognitive skills. Well, strap in, because we’re about to embark on a whirlwind tour of Performance IQ tests and assessments!

The world of Performance IQ testing is a veritable smorgasbord of cognitive challenges, each designed to probe a specific aspect of non-verbal intelligence. Common Performance IQ subtests might include tasks like:

1. Block Design: Arranging colored blocks to match a given pattern. 2. Matrix Reasoning: Identifying the missing element in a series of patterns. 3. Picture Completion: Spotting what’s missing from an incomplete image. 4. Symbol Search: Quickly scanning and matching symbols.

These subtests are often part of larger intelligence assessments, such as the Wechsler Intelligence Scales. The Wechsler scales, including versions for adults (WAIS) and children (WISC), are like the gold standard in IQ testing, offering a comprehensive evaluation of both verbal and performance abilities.

But wait, there’s more! Another heavy hitter in the Performance IQ arena is Raven’s Progressive Matrices. This test is like the ninja of non-verbal assessments – silent, efficient, and incredibly effective. It consists of a series of diagrams with a missing piece, and the test-taker must select the correct option to complete the pattern. Simple in concept, fiendishly challenging in practice!

Interpreting Performance IQ scores is where the real magic happens. It’s not just about slapping a number on someone’s cognitive abilities – it’s about understanding what that score means in the context of an individual’s overall cognitive profile. A high Performance IQ score might indicate strong visual-spatial skills and problem-solving abilities, while a lower score could suggest areas for potential improvement or the need for further assessment.

It’s crucial to remember that Performance IQ is just one piece of the cognitive puzzle. As explored in IQ Achievement Discrepancy Model: Evaluating Learning Disabilities and Cognitive Potential , there can be significant discrepancies between different aspects of cognitive functioning. This underscores the importance of a holistic approach to intelligence assessment.

The Influencers: Factors Shaping Performance IQ

Performance IQ, like any aspect of human cognition, doesn’t exist in a vacuum. It’s shaped and influenced by a myriad of factors, each leaving its unique imprint on our non-verbal cognitive abilities. Let’s pull back the curtain and examine some of these influential players.

Age and developmental stages play a significant role in Performance IQ. Our cognitive abilities don’t remain static throughout our lives – they evolve, peak, and sometimes decline as we age. For instance, processing speed tends to be at its zenith in our youth and early adulthood, while other aspects of Performance IQ, like problem-solving skills, may continue to improve well into middle age thanks to accumulated experience.

Cultural and environmental influences also leave their mark on Performance IQ. The world we grow up in shapes how we perceive and interact with visual information. For example, individuals raised in urban environments might excel at certain spatial tasks compared to those from rural areas, simply due to the different visual landscapes they’re accustomed to navigating.

Neurological factors are another crucial piece of the puzzle. The intricate wiring of our brains, influenced by both genetics and experiences, can significantly impact our Performance IQ. Conditions affecting brain structure or function can have profound effects on non-verbal cognitive abilities. This is where the fascinating field of cognitive neuroscience comes into play, unraveling the complex relationships between brain and behavior.

Learning disabilities can also have a substantial impact on Performance IQ. Conditions like visual processing disorders or spatial perception difficulties can affect performance on non-verbal tasks. However, it’s important to note that having a learning disability doesn’t necessarily equate to lower intelligence – it simply means that the brain processes information differently. This is beautifully illustrated in the exploration of Paper IQ: Unraveling the Intelligence of Paper Manufacturing , where we see how different types of intelligence can manifest in unexpected ways.

Interestingly, certain activities and experiences can potentially influence Performance IQ. For instance, the question of whether Musical Instruments and IQ: Exploring the Cognitive Benefits of Learning to Play has been a topic of much research and debate. While the jury is still out on whether playing an instrument directly increases IQ, studies have shown that it can enhance various cognitive skills, including some associated with Performance IQ.

The Power of Performance IQ: Applications and Importance

Now that we’ve dissected Performance IQ and examined its influencing factors, you might be wondering: “So what? Why does this matter in the real world?” Well, buckle up, because we’re about to explore the practical applications and importance of Performance IQ across various domains of life.

In educational settings, understanding Performance IQ can be a game-changer. It allows educators to tailor learning strategies to individual students’ strengths and weaknesses. For instance, a student with high Performance IQ but lower Verbal IQ might benefit from more visual and hands-on learning approaches. This personalized approach to education can help unlock the full potential of every student, regardless of their cognitive profile.

When it comes to career guidance and job performance, Performance IQ plays a crucial role. Certain professions, such as architecture, engineering, or graphic design, heavily rely on the skills associated with high Performance IQ. Understanding one’s cognitive strengths can guide individuals towards careers where they’re likely to excel and find satisfaction. It’s like finding the perfect puzzle piece that fits seamlessly into the jigsaw of professional life.

In the realm of cognitive rehabilitation, Performance IQ assessments can be invaluable. For individuals recovering from brain injuries or dealing with cognitive decline, understanding their non-verbal cognitive abilities can help tailor rehabilitation programs to their specific needs. It’s about working with what’s there and building upon existing strengths to improve overall cognitive function.

Research in cognitive psychology continues to uncover new insights about Performance IQ and its relationship to other aspects of cognition. For instance, studies exploring Persist IQ: Unlocking the Power of Perseverance in Intelligence have shed light on how non-cognitive factors like persistence can interact with and potentially enhance cognitive abilities, including those associated with Performance IQ.

The applications of Performance IQ extend even further into unexpected territories. Consider the fascinating world of GRE to IQ Conversion: Unraveling the Relationship Between Test Scores . While not a direct measure of Performance IQ, the GRE includes sections that tap into similar cognitive skills, illustrating how these abilities can be relevant in various assessment contexts.

Moreover, the relationship between visual perception and intelligence, as explored in Visual Perception IQ: Unraveling the Connection Between Vision and Intelligence , underscores the importance of Performance IQ in our overall cognitive functioning. Our ability to perceive and interpret visual information is intricately linked with our problem-solving skills and overall intelligence.

The concept of Abstract Reasoning IQ: Unraveling the Cognitive Skill Behind Problem-Solving is closely related to Performance IQ. Abstract reasoning, the ability to identify patterns and relationships between concepts, is a key component of non-verbal problem-solving skills. It’s like the Swiss Army knife in your cognitive toolbox, allowing you to tackle complex problems from multiple angles.

Lastly, the exploration of Visuospatial Pattern Reasoning IQ: Unveiling Its Impact on Cognitive Abilities provides deeper insights into a core aspect of Performance IQ. This skill, which involves recognizing and manipulating visual patterns, is crucial in many real-world applications, from reading maps to solving complex mathematical problems.

Wrapping It Up: The Performance IQ Puzzle

As we reach the end of our journey through the fascinating world of Performance IQ, it’s time to step back and admire the bigger picture we’ve pieced together. Like a complex jigsaw puzzle, each aspect we’ve explored contributes to a richer, more nuanced understanding of this crucial dimension of intelligence.

Performance IQ, with its focus on non-verbal cognitive abilities, offers a unique window into the human mind. It reminds us that intelligence is not a monolithic construct, but a multifaceted gem with many sparkling facets. From visual-spatial reasoning to processing speed, from perceptual organization to non-verbal problem-solving, Performance IQ encompasses a range of skills that are vital in navigating our complex world.

The future of Performance IQ research holds exciting possibilities. As our understanding of the brain and cognition continues to evolve, so too will our methods of assessing and enhancing these crucial skills. Emerging technologies, such as virtual reality and advanced brain imaging techniques, may offer new ways to measure and improve Performance IQ.

However, it’s crucial to remember that Performance IQ is just one piece of the cognitive puzzle. While it’s an important aspect of intelligence, it doesn’t tell the whole story. A truly comprehensive understanding of an individual’s cognitive abilities must consider a balance of verbal and performance skills, as well as other factors like emotional intelligence, creativity, and practical problem-solving abilities.

In conclusion, Performance IQ serves as a powerful reminder of the diversity of human cognitive abilities. It challenges us to think beyond traditional notions of intelligence and appreciate the many ways in which our minds can excel. Whether you’re an educator striving to unlock your students’ potential, a professional seeking to leverage your cognitive strengths, or simply someone fascinated by the workings of the human mind, understanding Performance IQ can open up new perspectives and possibilities.

So the next time you successfully navigate a new city without getting lost, solve a tricky puzzle, or even just appreciate a beautiful piece of art, take a moment to thank your Performance IQ. It’s been quietly working behind the scenes, helping you make sense of the visual world around you and tackle life’s non-verbal challenges with finesse.

References:

1. Wechsler, D. (2008). Wechsler Adult Intelligence Scale–Fourth Edition (WAIS–IV). San Antonio, TX: The Psychological Corporation.

2. Raven, J. (2000). The Raven’s Progressive Matrices: Change and stability over culture and time. Cognitive Psychology, 41(1), 1-48.

3. Deary, I. J., Whalley, L. J., Lemmon, H., Crawford, J. R., & Starr, J. M. (2000). The stability of individual differences in mental ability from childhood to old age: follow-up of the 1932 Scottish Mental Survey. Intelligence, 28(1), 49-55.

4. Nisbett, R. E., Aronson, J., Blair, C., Dickens, W., Flynn, J., Halpern, D. F., & Turkheimer, E. (2012). Intelligence: new findings and theoretical developments. American Psychologist, 67(2), 130-159.

5. Schellenberg, E. G. (2004). Music lessons enhance IQ. Psychological Science, 15(8), 511-514.

6. Kaufman, A. S., & Kaufman, N. L. (2004). Kaufman Brief Intelligence Test–Second Edition (KBIT-2). Circle Pines, MN: American Guidance Service.

7. Johnson, W., & Bouchard Jr, T. J. (2005). The structure of human intelligence: It is verbal, perceptual, and image rotation (VPR), not fluid and crystallized. Intelligence, 33(4), 393-416.

8. Uttal, D. H., Meadow, N. G., Tipton, E., Hand, L. L., Alden, A. R., Warren, C., & Newcombe, N. S. (2013). The malleability of spatial skills: A meta-analysis of training studies. Psychological Bulletin, 139(2), 352-402.

9. Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. Cambridge University Press.

10. Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. Basic Books.

Was this article helpful?

Would you like to add any comments (optional), leave a reply cancel reply.

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Post Comment

Related Resources

Visuospatial Pattern Reasoning IQ: Unveiling Its Impact on Cognitive Abilities

Visuospatial Pattern Reasoning IQ: Unveiling Its Impact on Cognitive Abilities

Language Learning and IQ: Exploring the Cognitive Benefits of Bilingualism

Language Learning and IQ: Exploring the Cognitive Benefits of Bilingualism

Vibe IQ: Mastering Social Intelligence in the Modern World

Vibe IQ: Mastering Social Intelligence in the Modern World

ENFP IQ: Exploring Intelligence in the Enthusiastic Idealist Personality Type

ENFP IQ: Exploring Intelligence in the Enthusiastic Idealist Personality Type

Puns and Intelligence: Exploring the Cognitive Connection

Puns and Intelligence: Exploring the Cognitive Connection

Seizures and Intelligence: Exploring the Potential Impact on Cognitive Function

Seizures and Intelligence: Exploring the Potential Impact on Cognitive Function

Music and IQ: Exploring the Cognitive Benefits of Musical Training

Music and IQ: Exploring the Cognitive Benefits of Musical Training

Cognition vs Intelligence: Key Differences and Interconnections

Cognition vs Intelligence: Key Differences and Interconnections

Aesthetic IQ: Exploring the Intersection of Beauty and Intelligence

Aesthetic IQ: Exploring the Intersection of Beauty and Intelligence

Memory and IQ: Exploring the Intricate Connection Between Cognitive Functions

Memory and IQ: Exploring the Intricate Connection Between Cognitive Functions

An official website of the United States government

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List

Intelligence and Creativity in Problem Solving: The Importance of Test Features in Cognition Research

Saskia jaarsveld, thomas lachmann.

  • Author information
  • Article notes
  • Copyright and License information

Edited by: Anatoliy V. Kharkhurin, American University of Sharjah, UAE

Reviewed by: Tanja Gabriele Baudson, Technical University of Dortmund, Germany; Massimiliano Palmiero, University of L’Aquila, Italy

*Correspondence: Saskia Jaarsveld, [email protected] Thomas Lachmann, [email protected]

This article was submitted to Cognition, a section of the journal Frontiers in Psychology

Received 2016 Jul 5; Accepted 2017 Jan 19; Collection date 2017.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

This paper discusses the importance of three features of psychometric tests for cognition research: construct definition, problem space, and knowledge domain. Definition of constructs, e.g., intelligence or creativity, forms the theoretical basis for test construction. Problem space, being well or ill-defined, is determined by the cognitive abilities considered to belong to the constructs, e.g., convergent thinking to intelligence, divergent thinking to creativity. Knowledge domain and the possibilities it offers cognition are reflected in test results. We argue that (a) comparing results of tests with different problem spaces is more informative when cognition operates in both tests on an identical knowledge domain, and (b) intertwining of abilities related to both constructs can only be expected in tests developed to instigate such a process. Test features should guarantee that abilities can contribute to self-generated and goal-directed processes bringing forth solutions that are both new and applicable. We propose and discuss a test example that was developed to address these issues.

Keywords: creative cognition, cognitive neuroscience, creative reasoning, problem space, knowledge domain

The definition of the construct a test is to measure is most important in test construction and application, because cognitive processes reflect the possibilities a task offers. For instance, a test constructed to assess intelligence will operationalize the definition of this construct, being, in short, finding the correct answer. Also, the definition of a construct becomes important when selecting tests for the confirmation of a specific hypothesis. One can only find confirmation for a hypothesis if the chosen task instigates the necessary cognitive operations. For instance, in trying to confirm the assumed intertwining of certain cognitive abilities (e.g., convergent thinking and divergent thinking), tasks should be applied that have shown to yield the necessary cognitive process.

The second test feature, problem space , determines the degrees of freedom cognition has to its disposal in solving a problem. For instance, cognition will go through a wider search path when problem constraints are less well defined and, consequently, data will differ accordingly.

The third test feature, knowledge domain , is important when comparing results from two different tests. When tests differ in problem space, it is not advisable they should differ in knowledge domain. For instance, when studying the differences in cognitive abilities between tests constructed to asses convergent thinking (mostly defined problem space) and divergent thinking (mostly ill-defined problem space), in general test practice, both tests also differ in knowledge domain. Hence, data will reflect cognition operating not only in different problem spaces, but also operating on different knowledge domains, which makes the interpretation of results ambiguous.

The proposed approach for test development and test application holds the promise of, firstly, studying cognitive abilities in different problem spaces while operating on an identical knowledge domain. Although cognitions’ operations have been studied extensively and superbly in both contexts separately, they have rarely been studied in test situations where one or the other test feature is controlled for. The proposed approach also presents a unique method for studying thinking processes in which cognitive abilities intertwine. On the basis of defined abilities, tasks can be developed that have a higher probability of yielding the hypothesized results.

The construct of intelligence is defined as the ability to produce the single best (or correct) answer to a clearly defined question, such as a proof to a theorem ( Simon, 1973 ). It may also be seen as a domain-general ability ( g -factor; Spearman, 1904 ; Cattell, 1967 ) that has much in common with meta cognitive functions, such as metacognitive knowledge, metacognitive monitoring, and metacognitive control ( Saraç et al., 2014 ).

The construct of creativity, in contrast, is defined as the ability to innovate and move beyond what is already known ( Wertheimer , 1945/1968 ; Ghiselin , 1952/1985 ; Vernon, 1970 ). In other words, it emphasizes the aspect of innovation. This involves the ability to consider things from an uncommon perspective, transcend the old order ( Ghiselin , 1952/1985 ; Chi, 1997 ; Ward, 2007 ), and explore loosely associated ideas ( Guilford, 1950 ; Mednick, 1962 ; Koestler, 1964 ; Gentner, 1983 ; Boden, 1990 ; Christensen, 2007 ). Creativity could also be defined as the ability to generate a solution to problems with ill-defined problem spaces ( Wertheimer , 1945/1968 ; Getzels and Csikszentmihalyi, 1976 ). In this sense it involves the ability to identify problematic aspects of a given situation ( Ghiselin , 1952/1985 ) and, in a wider sense, the ability to define completely new problems ( Getzels, 1975 , 1987 ).

Guilford (1956) introduced the constructs of convergent thinking and divergent thinking abilities. Both thinking abilities are important because they allow us insights in human problem solving. On the basis of their definitions convergent and divergent thinking help us to structurally study human cognitive operations in different situations and over different developmental stages. Convergent thinking is defined as the ability to apply conventional and logical search, recognition, and decision-making strategies to stored information in order to produce an already known answer ( Cropley, 2006 ). Divergent thinking, by contrast, is defined as the ability to produce new approaches and original ideas by forming unexpected combinations from available information and by applying such abilities as semantic flexibility, and fluency of association, ideation, and transformation ( Guilford, 1959 , as cited in Cropley, 2006 , p. 1). Divergent thinking brings forth answers that may never have existed before and are often novel, unusual, or surprising ( Cropley, 2006 ).

Guilford (1967) introduced convergent and divergent thinking as part of a set of five operations that apply in his Structure of Intellect model (SOI model) on six products and four kinds of content, to produce 120 different factors of cognitive abilities. With the SOI model Guilford wanted to give the construct of intelligence a comprehensive model. He wanted the model to include all aspects of intelligence, many of which had been seriously neglected in traditional intelligence testing because of a persistent adherence to the belief in Spearman’s g ( Guilford, 1967 , p. vii). Hence, Guilford envisaged cognition to embrace, among other abilities, both convergent and divergent thinking abilities. After these new constructs were introduced and defined, tests for convergent and divergent thinking emerged. Despite the fact that Guilford reported significant loadings of tests for divergent production on tests constructed to measure convergent production ( Guilford, 1967 , p. 155), over the years, both modes of thinking were considered as separate identities where convergent thinking tests associated with intelligence and divergent thinking tests with creativity ( Cropley, 2006 ; Shye and Yuhas, 2004 ). Even intelligence tests that assess aspects of intelligence that supposedly reflect creative abilities do not actually measure creativity ( Kaufman, 2015 ).

The idea that both convergent and divergent thinking are important for solving problems, and that intelligence helps in the creative process, is not really new. In literature we find models of the creative process that define certain stages to convergent and divergent thinking; the stages of purposeful preparation at the start and those of critical verification at the end of the process, respectively ( Wallas, 1926 ; Webb Young , 1939/2003 ). In this view, divergent thinking enables the generation of new ideas whereas the exploratory activities of convergent thinking enable the conversion of ideas into something new and appropriate ( Cropley and Cropley, 2008 ).

We argue that studying the abilities of divergent and convergent thinking in isolation does not suffice to give us complete insight of all possible aspects of human problem solving, its constituent abilities and the structure of its processes. Processes that in a sequence of thoughts and actions lead to novel and adaptive productions ( Lubart, 2001 ) are more demanding of cognition for understanding the situation at hand and planning a path to a possible solution, than abilities involved in less complex situations ( Jaušovec, 1999 ). Processes that yield self-generated and goal-directed thought are the most complex cognitive processes that can be studied ( Beaty et al., 2016 ). Creative cognition literature is moving toward the view that especially in those processes that yield original and appropriate solutions within a specific context, convergent and divergent abilities intertwine ( Cropley, 2006 ; Ward, 2007 ; Gabora, 2010 ).

The approach of intertwining cognitive abilities is also developed within cognitive neuroscience by focusing on the intertwining of brain networks ( Beaty et al., 2016 ). In this approach divergent thinking relates to the default brain network. This network operates in defocused or associative mode of thought yielding spontaneous and self-generated cognition ( Beaty et al., 2015 ). Convergent thinking relates to the executive control network operating in focused or analytic modes of thought, yielding updating, shifting, and inhibition ( Benedek et al., 2014 ). Defocused attention theory ( Mendelssohn, 1976 ) states that less creative individuals operate with a more focused attention than do creative individuals. This theory argues that e.g., attending to two things at the same time, might result in one analogy, while attending to four things might yield six analogies ( Martindale, 1999 ).

In the process of shifting back and forth along the spectrum between associative and analytic modes of thinking, the fruits of associative thought become ingredients for analytic thought processes, and vice versa ( Gabora, 2010 ). In this process, mental imagery is involved as one sensory aspect of the human ability to gather and process information ( Jung and Haier, 2013 ). Mental imagery is fed by scenes in the environment that provide crucial visual clues for creative problem solving and actuates the need for sketching ( Verstijnen et al., 2001 ).

Creative problem solving processes often involve an interactive relationship between imagining, sketching, and evaluating the result of the sketch ( van Leeuwen et al., 1999 ). This interactive process evolves within a type of imagery called “visual reasoning” where forms and shapes are manipulated in order to specify the configurations and properties of the design entities ( Goldschmidt, 2013 ). The originality of inventions is predicted by the application of visualization, whereas their practicality is predicted by the vividness of imagery ( Palmiero et al., 2015 ). Imaginative thought processes emerge from our conceptual knowledge of the world that is represented in our semantic memory system. In constrained divergent thinking, the neural correlates of this semantic memory system partially overlap with those of the creative cognition system ( Abraham and Bubic, 2015 ).

Studies of convergent and divergent thinking abilities have yielded innumerable valuable insights on the cognitive and neurological aspects involved, e.g., reaction times, strategies, brain areas involved, mental representations, and short and long time memory components. Studies on the relationship between both constructs suggest that it is unlikely that individuals employ similar cognitive strategies when solving more convergent than more divergent thinking tasks ( Jaušovec, 2000 ). However, to arrive at a quality formulation the creative process cannot do without the application of both, convergent and divergent thinking abilities (e.g., Kaufmann, 2003 ; Runco, 2003 ; Sternberg, 2005 ; Dietrich, 2007 ; Cropley and Cropley, 2008 ; Silvia et al., 2013 ; Jung, 2014 ).

When it is our aim to study the networks addressed by the intertwining of convergent and divergent thinking processes that are considered to operate when new, original, and yet appropriate solutions are generated, then traditional thinking tests like intelligence tests and creativity tests are not appropriate; they yield processes related to the definition of one or the other type of construct.

Creative Reasoning Task

According to the new insights gained in cognition research, we need tasks that are developed with the aim to instigate precisely the kind of thinking processes we are looking for. Tasks should also provide a method of scoring independently the contribution of convergent and divergent thinking. As one possible solution for such tasks we present the Creative Reasoning Task (CRT; Jaarsveld, 2007 ; Jaarsveld et al., 2010 , 2012 , 2013 ).

The CRT presents participants with an empty 3 × 3 matrix and asks them to fill it out, as original and complex as possible, by creating components and the relationships that connect them. The created matrix can, in principle, be solved by another person. The creation of components is entirely free, as is the generation of the relationships that connects them into a completed pattern. Created matrices are scored with two sub scores; Relations , which scores the logical complexity of a matrix and is, therefore, considered a measure for convergent thinking, and Components and Specifications , which scores the originality, fluency, and flexibility and, therefore, is considered an indication for divergent thinking (for a more detailed description of the score method, see Appendix 1 in Supplementary Material).

Psychometric studies with the CRT showed, firstly, that convergent and divergent thinking abilities apply within this task and can be assessed independently. The CRT sub score Relations correlated with the Standard Progressive Matrices test (SPM) and the CRT sub score Components and Specifications correlated with a standard creativity test (TCT–DP, Test of Creative Thinking–Drawing Production; Urban and Jellen, 1995 ; Jaarsveld et al., 2010 , 2012 , 2013 ). Studies further showed that, although a correlation was observed for the intelligence and creativity test scores, no correlation was observed between the CRT sub scores relating to intelligent and creative performances ( Jaarsveld et al., 2012 , 2013 ; for further details about the CRT’s objectivity, validity, and reliability, see Appendix 2 in Supplementary Material).

Reasoning in creative thinking can be defined as the involvement of executive/convergent abilities in the inhibition of ideas and the updating of information ( Benedek et al., 2014 ). Jung (2014) describes a dichotomy for cognitive abilities with at one end the dedicated system that relies on explicit and conscious knowledge and at the other end the improvisational system that relies more upon implicit or unconscious knowledge systems. The link between explicit and implicit systems can actually be traced back to Kris’ psychoanalytic approach to creativity dating from the 1950s. The implicit system refers to Kris’ primary process of adaptive regression, where unmodulated thoughts intrude into consciousness; the explicit system refers to the secondary process, where the reworking and transformation of primary process material takes place through reality-oriented and ego-controlled thinking ( Sternberg and Lubart, 1999 ). The interaction between explicit and implicit systems can be seen to form the basis of creative reasoning, i.e., the cognitive ability to solve problems in an effective and adaptive way. This interaction evolved as a cognitive mechanism when human survival depended on finding effective solutions to both common and novel problem situations ( Gabora and Kaufman, 2010 ). Creative reasoning solves that minority of problems that are unforeseen and yet of high adaptability ( Jung, 2014 ).

Hence, common tests are insufficient when it comes to solving problems that are unforeseen and yet of high adaptability, because they present problems that are either unforeseen and measure certain abilities contained in the construct of creativity or they address adaptability and measure certain abilities contained in the construct of intelligence. The CRT presents participants with a problem that they could not have foreseen; the form is blank and offers no stimuli. All tests, even creativity tests, present participants with some kind of stimuli. The CRT addresses adaptability; to invent from scratch a coherent structure that can be solved by another person, like creating a crossword puzzle. Problems, that are unforeseen and of high adaptability, are solved by the application of abilities from both constructs.

Neuroscience of Creative Cognition

Studies in neuroscience showed that cognition operating in ill-defined problem space not only applies divergent thinking but also benefits from additional convergent operations ( Gabora, 2010 ; Jung, 2014 ). Understanding creative cognition may be advanced when we study the flow of information among brain areas ( Jung et al., 2010 ).

In a cognitive neuroscience study with the CRT we focused on the cognitive process evolving within this task. Participants performed the CRT while EEG alpha activity was registered. EEG alpha synchronization in frontal areas is understood as an indication of top-down control ( Cooper et al., 2003 ). When observed in frontal areas, for divergent and convergent thinking tasks, it may not reflect a brain state that is specific for creative cognition but could be attributed to the high processing demands typically involved in creative thinking ( Benedek et al., 2011 ). Top-down control, relates to volitionally focusing attention to task demands ( Buschman and Miller, 2007 ). That this control plays a role in tasks with an ill-defined problem space showed when electroencephalography (EEG) alpha synchronization was stronger for individuals engaged in creative ideation tasks compared to an intelligence related tasks ( Fink et al., 2007 , 2009 ; Fink and Benedek, 2014 ). This activation was also found for the CRT; task related alpha synchronization showed that convergent thinking was integrated in the divergent thinking processes. Analyzes of the stages in the CRT process showed that this alpha synchronization was especially visible at the start of the creative process at prefrontal and frontal sites when information processing was most demanding, i.e., due to multiplicity of ideas, and it was visible at the end of the process, due to narrowing down of alternatives ( Jaarsveld et al., 2015 ).

A functional magnetic resonance imaging (fMRI) study ( Beaty et al., 2015 ) with a creativity task in which cognition had to meet specific constraints, showed the networks involved. The default mode network which drives toward abstraction and metaphorical thinking and the executive control network driving toward certainty ( Jung, 2014 ). Control involves not only maintenance of patterns of activity that represent goals and the means to achieve those ( Miller and Cohen, 2001 ), but also their voluntary suppression when no longer needed, as well as the flexible shift between different goals and mental sets ( Abraham and Windmann, 2007 ). Attention can be focused volitionally by top-down signals derived from task demands and automatically by bottom-up signals from salient stimuli ( Buschman and Miller, 2007 ). Intertwining between top-down and bottom-up attention processes in creative cognition ensures a broadening of attention in free associative thinking ( Abraham and Windmann, 2007 ).

These studies support and enhance the findings of creative cognition research in showing that the generation of original and applicable ideas involves an intertwining between different abilities, networks, and attention processes.

Problem Space

A problem space is an abstract representation, in the mind of the problem solver, of the encountered problem and of the asked for solution ( Simon and Newell, 1971 ; Simon, 1973 ; Hayes and Flowers, 1986 ; Kulkarni and Simon, 1988 ; Runco, 2007 ). The space that comes with a certain problem can, according to the constraints that are formulated for the solution, be labeled well-defined or ill-defined ( Simon and Newell, 1971 ). Consequently, the original problems are labeled closed and open problems, respectively ( Jaušovec, 2000 ).

A problem space contains all possible states that are accessible to the problem solver from the initial state , through iterative application of transformation rules , to the goal state ( Newell and Simon, 1972 ; Anderson, 1983 ). The initial state presents the problem solver with a task description that defines which requirements a solution has to answer. The goal state represents the solution. The proposed solution is a product of the application of transformation rules (algorithms and heuristics) on a series of successive intermediate solutions. The proposed solution is also a product of the iterative evaluations of preceding solutions and decisions based upon these evaluations ( Boden, 1990 ; Gabora, 2002 ; Jaarsveld and van Leeuwen, 2005 ; Goldschmidt, 2014 ). Whether all possible states need to be passed through depends on the problem space being well or ill-defined and this, in turn, depends on the character of the task descriptions.

When task descriptions clearly state which requirements a solution has to answer then the inferences made will show little idiosyncratic aspects and will adhere to the task constraints. As a result, fewer options for alternative paths are open to the problem solver and search for a solution evolves in a well-defined space. Vice versa, when task or problem descriptions are fuzzy and under specified, the problem solver’s inferences are more idiosyncratic; the resulting process will evolve within an ill-defined space and will contain more generative-evaluative cycles in which new goals are set, and the cycle is repeated ( Dennett, 1978 , as cited in Gabora, 2002 , p. 126).

Tasks that evolve in defined problem space are, e.g., traditional intelligence tests (e.g., Wechsler Adult Intelligence Scale, WAIS; and SPM, Raven , 1938/1998 ). The above tests consist of different types of questions, each testing a different component of intelligence. They are used in test practice to assess reasoning abilities in diverse domains, such as, abstract, logical, spatial, verbal, numerical, and mathematical domains. These tests have clearly stated task descriptions and each item has one and only one correct solution that has to be generated from memory or chosen from a set of alternatives, like in multiple choice formats. Tests can be constructed to assess crystallized or fluid intelligence. Crystallized intelligence represents abilities acquired through learning, practice, and exposure to education, while fluid intelligence represents a more basic capacity that is valuable to reasoning and problem solving in contexts not necessarily related to school education ( Carroll, 1982 ).

Tasks that evolve in ill-defined problem space are, e.g., standard creativity tests. These types of test ask for a multitude of ideas to be generated in association with a given item or situation (e.g., “think of as many titles for this story”). Therefore, they are also labeled as divergent thinking test. Although they assess originality, fluency, flexibility of responses, and elaboration, they are not constructed, however, to score appropriateness or applicability. Divergent thinking tests assess one limited aspect of what makes an individual creative. Creativity depends also on variables like affect and intuition; therefore, divergent thinking can only be considered an indication of an individual’s creative potential ( Runco, 2008 ). More precisely, divergent thinking explains just under half of the variance in adult creative potential, which is more than three times that of the contribution of intelligence ( Plucker, 1999 , p. 103). Creative achievement , by contrast, is commonly assessed by means of self-reports such as biographical questionnaires in which participants indicate their achievement across various domains (e.g., literature, music, or theater).

Studies with the CRT showed that problem space differently affects processing of and comprehension of relationships between components. Problem space did not affect the ability to process complex information. This ability showed equal performance in well and ill-defined problem spaces ( Jaarsveld et al., 2012 , 2013 ). However, problem space did affect the comprehension of relationships, which showed in the different frequencies of relationships solved and created ( Jaarsveld et al., 2010 , 2012 ). Problem space also affected the neurological activity as displayed when individuals solve open or closed problems ( Jaušovec, 2000 ).

Problem space further affected trends over grade levels of primary school children for relationships solved in well-defined and applied in ill-defined problem space. Only one of the 12 relationships defined in the CRT, namely Combination, showed an increase with grade for both types of problem spaces ( Jaarsveld et al., 2013 ). In the same study, cognitive development in the CRT showed in the shifts of preference for a certain relationship. These shifts seem to correspond to Piaget’s developmental stages ( Piaget et al., 1977 ; Siegler, 1998 ) which are in evidence in the CRT, but not in the SPM ( Jaarsveld et al., 2013 ).

Design Problems

A sub category of problems with an ill-defined problem space are represented by design problems. In contrast to divergent thinking tasks that ask for the generation of a multitude of ideas, in design tasks interim ideas are nurtured and incrementally developed until they are appropriate for the task. Ideas are rarely discarded and replaced with new ideas ( Goel and Pirolli, 1992 ). The CRT could be considered a design problem because it yields (a) one possible solution and (b) an iterative thinking process that involves the realization of a vague initial idea. In the CRT a created matrix, which is a closed problem, is created within an ill-defined problem space. Design problems can be found, e.g., in engineering, industrial design, advertising, software design, and architecture ( Sakar and Chakrabarti, 2013 ), however, they can also be found in the arts, e.g., poetry, sculpting, and dance geography.

These complex problems are partly determined by unalterable needs, requirements and intentions but the major part of the design problem is undetermined ( Dorst, 2004 ). This author points out that besides containing an original and a functional value, these types of problems contain an aesthetic value. He further states that the interpretation of the design problem and the creation and selection of possible suitable solutions can only be decided during the design process on the basis of proposals made by the designer.

In design problems the generation stage may be considered a divergent thinking process. However, not in the sense that it moves in multiple directions or generates multiple possibilities as in a divergent thinking tests, but in the sense that it unrolls by considering an initially vague idea from different perspectives until it comes into focus and requires further processing to become viable. These processes can be characterized by a set of invariant features ( Goel and Pirolli, 1992 ), e.g., structuring. iteration , and coherence .

Structuring of the initial situation is required in design processes before solving can commence. The problem contains little structured and clear information about its initial state and about the requirements of its solution. Therefore, design problems allow or even require re-interpretation of transformation rules; for instance, rearranging the location of furniture in a room according to a set of desirable outcomes. Here one uncovers implicit requirements that introduce a set of new transformations and/or eliminate existing ones ( Barsalou, 1992 ; Goel and Pirolli, 1992 ) or, when conflicting requirements arise, one creates alternatives and/or introduces new trade-offs between the conflicting constraints ( Yamamoto et al., 2000 ; Dorst, 2011 ).

A second aspect of design processes is their iterative character. After structuring and planning a vague idea emerges, which is the result of the merging of memory items. A vague idea is a cognitive structure that, halfway the creative process is still ill defined and, therefore, can be said to exist in a state of potentiality ( Gabora and Saab, 2011 ). Design processes unroll in an iterative way by the inspection and adjustment of the generated ideas ( Goldschmidt, 2014 ). New meanings are created and realized while the creative mind imposes its own order and meaning on the sensory data and through creative production furthers its own understanding of the world ( Arnheim , 1962/1974 , as cited in Grube and Davis, 1988 , pp. 263–264).

A third aspect of design processes is coherence. Coherence theories characterize coherence in, for instance, philosophical problems and psychological processes, in terms of maximal satisfaction of multiple constraints and compute coherence by using, a.o., connectionist algorithms ( Thagard and Verbeurgt, 1998 ). Another measure of coherence is characterized as continuity in design processes. This measure was developed for a design task ( Jaarsveld and van Leeuwen, 2005 ) and calculated by the occurrence of a given pair of objects in a sketch, expressed as a percentage of all the sketches of a series. In a series of sketches participants designed a logo for a new soft drink. Design series strong in coherence also received a high score for their final design, as assessed by professionals in various domains. Indicating that participants with a high score for the creative quality of their final sketch seemed better in assessing their design activity in relation to the continuity in the process and, thereby, seemed better in navigating the ill-defined space of a design problem ( Jaarsveld and van Leeuwen, 2005 ). In design problems the quality of cognitive production depends, in part, on the abilities to reflect on one’s own creative behavior ( Boden, 1996 ) and to monitor how far along in the process one is in solving it ( Gabora, 2002 ). Hence, design problems are especially suited to study more complex problem solving processes.

Knowledge Domain

Knowledge domain represents disciplines or fields of study organized by general principles, e.g., domains of various arts and sciences. It contains accumulated knowledge that can be divided in diverse content domains, and the relevant algorithms and heuristics. We also speak of knowledge domains when referring to, e.g., visuo-spatial and verbal domains. This latter differentiation may refer to the method by which performance in a certain knowledge domain is assessed, e.g., a visuo-spatial physics task that assesses the content domain of the workings of mass and weights of objects.

In comparing tests results, we should keep in mind that apart from reflecting cognitive processes evolving in different problem spaces, the results also arise from cognition operating on different knowledge domains. We argue that, the still contradictory and inconclusive discussion about the relationship between intelligence and creativity ( Silvia, 2008 ), should involve the issue of knowledge domain.

Intelligence tests contain items that pertain to, e.g., verbal, abstract, mechanical and spatial reasoning abilities, while their content mostly operates on knowledge domains that are related to contents contained in school curricula. Items of creativity tests, by contrast, pertain to more idiosyncratic knowledge domains, their contents relating to associations between stored personal experiences ( Karmiloff-Smith, 1992 ). The influence of knowledge domain on the relationships between different test scores was already mentioned by Guilford (1956 , p. 169). This author expected a higher correlation between scores from a typical intelligence test and a divergent thinking test than between scores from two divergent thinking tests because the former pair operated on identical information and the latter pair on different information.

Studies with the CRT showed that when knowledge domain is controlled for, the development of intelligence operating in ill-defined problem space does not compare to that of traditional intelligence but develops more similarly to the development of creativity ( Welter et al., in press ).

Relationship Intelligence and Creativity

The Threshold theory ( Guilford, 1967 ) predicts a relationship between intelligence and creativity up to approximately an intelligence quotient (IQ) level of 120 but not beyond ( Lubart, 2003 ; Runco, 2007 ). Threshold theory was corroborated when creative potential was found to be related to intelligence up to certain IQ levels; however, the theory was refuted, when focusing on achievement in creative domains; it showed that creative achievement benefited from higher intelligence even at fairly high levels of intellectual ability ( Jauk et al., 2013 ).

Distinguishing between subtypes of general intelligence known as fluent and crystallized intelligence ( Cattell, 1967 ), Sligh et al. (2005) observed an inverse threshold effect with fluid IQ: a correlation with creativity test scores in the high IQ group but not in the average IQ group. Also creative achievement showed to be affected by fluid intelligence ( Beaty et al., 2014 ). Intelligence, defined as fluid IQ, verbal fluency, and strategic abilities, showed a higher correlation with creativity scores ( Silvia, 2008 ) than when defined as crystallized intelligence. Creativity tests, which involved convergent thinking (e.g., Remote Association Test; Mednick, 1962 ) showed higher correlations with intelligence than ones that involved only divergent thinking (e.g., the Alternate Uses Test; Guilford et al., 1978 ).

That the Remote Association test also involves convergent thinking follows from the instructions; one is asked, when presented with a stimulus word (e.g., table) to produce the first word one thinks of (e.g., chair). The word pair table–chair is a common association, more remote is the pair table–plate, and quite remote is table–shark. According to Mednick’s theory (a) all cognitive work is done essentially by combining or associating ideas and (b) individuals with more commonplace associations have an advantage in well-defined problem spaces, because the class of relevant associations is already implicit in the statement of the problem ( Eysenck, 2003 ).

To circumvent the problem of tests differing in knowledge domain, one can develop out of one task a more divergent and a more convergent thinking task by asking, on the one hand, for the generation of original responses, and by asking, on the other hand, for more common responses ( Jauk et al., 2012 ). By changing the instruction of a task, from convergent to divergent, one changes the constraints the solution has to answer and, thereby, one changes for cognition its freedom of operation ( Razumnikova et al., 2009 ; Limb, 2010 ; Jauk et al., 2012 ). However, asking for more common responses is still a divergent thinking task because it instigates a generative and ideational process.

Indeed, studying the relationship between intelligence and creativity with knowledge domain controlled for yielded different results as defined in the Threshold theory. A study in which knowledge domain was controlled for showed, firstly, that intelligence is no predictor for the development of creativity ( Welter et al., 2016 ). Secondly, that the relationship between scores of intelligence and creativity tests as defined under the Threshold theory was only observed in a small subset of primary school children, namely, female children in Grade 4 ( Welter et al., 2016 ). We state that relating results of operations yielded by cognitive abilities performing in defined and in ill-defined problem spaces can only be informative when it is ensured that cognitive processes also operate on an identical knowledge domain.

Intertwining of Cognitive Abilities

Eysenck (2003) observed that there is little justification for considering the constructs of divergent and convergent thinking in categorical terms in which one construct excludes the other. In processes that yield original and appropriate solutions convergent and divergent thinking both operate on the same large knowledge base and the underlying cognitive processes are not entirely dissimilar ( Eysenck, 2003 , p. 110–111).

Divergent thinking is especially effective when it is coupled with convergent thinking ( Runco, 2003 ; Gabora and Ranjan, 2013 ). A design problem study ( Jaarsveld and van Leeuwen, 2005 ) showed that divergent production was active throughout the design, as new meanings are continuously added to the evolving structure ( Akin, 1986 ), and that convergent production was increasingly important toward the end of the process, as earlier productions are wrapped up and integrated in the final design. These findings are in line with the assumptions of Wertheimer (1945/1968) who stated that thinking within ill-defined problem space is characterized by two points of focus; one is to work on the parts, the other to make the central idea clearer.

Parallel to the discussion about the intertwining of convergent and divergent thinking abilities in processes that evolve in ill-defined problem space we find the discussion about how intelligence may facilitate creative thought. This showed when top-down cognitive control advanced divergent processing in the generation of original ideas and a certain measure of cognitive inhibition advanced the fluency of idea generation ( Nusbaum and Silvia, 2011 ). Fluid intelligence and broad retrieval considered as intelligence factors in a structural equation study contributed both to the production of creative ideas in a metaphor generation task ( Beaty and Silvia, 2013 ). The notion that creative thought involves top-down, executive processes showed in a latent variable analysis where inhibition primarily promoted the fluency of ideas, and intelligence promoted their originality ( Benedek et al., 2012 ).

Definitions of the Constructs Intelligence and Creativity

The various definitions of the constructs of intelligence and creativity show a problematic overlap. This overlap stems from the enormous endeavor to unanimously agree on valid descriptions for each construct. Spearman (1927) , after having attended many symposia that aimed at defining intelligence, stated that “in truth, ‘intelligence’ has become a mere vocal sound, a word with so many meanings that finally it has none” (p. 14).

Intelligence is expressed in terms of adaptive, goal-directed behavior; and the subset of such behavior that is labeled “intelligent” seems to be determined in large part by cultural or societal norms ( Sternberg and Salter, 1982 ). The development of the IQ measure is discussed by Carroll (1982) : “Binet (around 1905) realized that intelligent behavior or mental ability can be ranged along a scale. Not much later, Stern (around 1912) noticed that, as chronological age increased, variation in mental age changes proportionally. He developed the IQ ratio, whose standard deviation would be approximately constant over chronological age if mental age was divided by chronological age. With the development of multiple-factor-analyses (Thurstone, around 1935) it could be shown that intelligence is not a simple unitary trait because at least seven somewhat independent factors of mental ability were identified.”

Creativity is defined as a combined manifestation of novelty and usefulness ( Jung et al., 2010 ). Although it is identified with divergent thinking, and performance on divergent thinking tasks predicts, e.g., quantity of creative achievements ( Torrance, 1988 , as cited in Beaty et al., 2014 ) and quality of creative performance ( Beaty et al., 2013 ), it cannot be identified uniquely with divergent thinking.

Divergent thinking often leads to highly original ideas that are honed to appropriate ideas by evaluative processes of critical thinking, and valuative and appreciative considerations ( Runco, 2008 ). Divergent thinking tests should be more considered as estimates of creative problem solving potential rather than of actual creativity ( Runco, 1991 ). Divergent thinking is not specific enough to help us understand what, exactly, are the mental processes—or the cognitive abilities—that yield creative thoughts ( Dietrich, 2007 ).

Although current definitions of intelligence and creativity try to determine for each separate construct a unique set of cognitive abilities, analyses show that definitions vary in the degree to which each includes abilities that are generally considered to belong to the other construct ( Runco, 2003 ; Jaarsveld et al., 2012 ). Abilities considered belonging to the construct of intelligence such as hypothesis testing, inhibition of alternative responses, and creating mental images of new actions or plans are also considered to be involved in creative thinking ( Fuster, 1997 , as cited in Colom et al., 2009 , p. 215). The ability, for instance, to evaluate , which is considered to belong to the construct of intelligence and assesses the match between a proposed solution and task constraints, has long been considered to play a role in creative processes that goes beyond the mere generation of a series of ideas as in creativity tasks ( Wallas, 1926 , as cited in Gabora, 2002 , p. 1; Boden, 1990 ).

The Geneplore model ( Finke et al., 1992 ) explicitly models this idea; after stages in which objects are merely generated, follow phases in which an object’s utility is explored and estimated. The generation phase brings forth pre inventive objects, imaginary objects that are generated without any constraints in mind. In exploration, these objects are evaluated for their possible functionalities. In anticipating the functional characteristics of generated ideas, convergent thinking is needed to apprehend the situation, make evaluations ( Kozbelt, 2008 ), and consider the consequences of a chosen solution ( Goel and Pirolli, 1992 ). Convergent reasoning in creativity tasks invokes criteria of functionality and appropriateness ( Halpern, 2003 ; Kaufmann, 2003 ), goal directedness and adaptive behavior ( Sternberg, 1982 ), as well as the abilities of planning and attention. Convergent thinking stages may even require divergent thinking sub processes to identify restrictions on proposed new ideas and suggest requisite revision strategies ( Mumford et al., 2007 ). Hence, evaluation, which is considered to belong to the construct of intelligence, is also functional in creative processes.

In contrast, the ability of flexibility , which is considered to belong to the construct of creativity and denotes an openness of mind that ensures the generation of ideas from different domains, showed, as a factor component for latent divergent thinking, a relationship with intelligence ( Silvia, 2008 ). Flexibility was also found to play an important role in intelligent behavior where it enables us to do novel things smartly in new situations ( Colunga and Smith, 2008 ). These authors studied children’s generalizations of novel nouns and concluded that if we are to understand human intelligence, we must understand the processes that make inventiveness. They propose to include the construct of flexibility within that of intelligence. Therefore, definitions of the constructs we are to measure affect test construction and the resulting data. However, an overlap between definitions, as discussed, yields a test diversity that makes it impossible to interpret the different findings across studies with any confidence ( Arden et al., 2010 ). Also Kim (2005) concluded that because of differences in tests and administration methods, the observed correlation between intelligence and creativity was negligible. As the various definitions of the constructs of intelligence and creativity show problematic overlap, we propose to circumvent the discussion about which cognitive abilities are assessed by which construct, and to consider both constructs as being involved in one design process. This approach allows us to study the contribution to this process of the various defined abilities, without one construct excluding the other.

Reasoning Abilities

The CRT is a psychometrical tool constructed on the basis of an alternative construct of human cognitive functioning that considers creative reasoning as a thinking process understood as the cooperation between cognitive abilities related to intelligent and creative thinking.

In generating relationships for a matrix, reasoning and more specifically the ability of rule invention is applied. The ability of rule invention could be considered as an extension of the sequence of abilities of rule learning, rule inference, and rule application, implying that creativity is an extension of intelligence ( Shye and Goldzweig, 1999 ). According to this model, we could expect different results between a task assessing abilities of rule learning and rule inference, and a task assessing abilities of rule application. In two studies rule learning and rule inference was assessed with the RPM and rule application was assessed with the CRT. Results showed that from Grades 1 to 4, the frequencies of relationships applied did not correlate with those solved ( Jaarsveld et al., 2010 , 2012 ). Results showed that performance in the CRT allows an insight of cognitive abilities operating on relationships among components that differs from the insight based on performance within the same knowledge domain in a matrix solving task. Hence, reasoning abilities lead to different performances when applied in solving closed as to open problems.

We assume that reasoning abilities are more clearly reflected when one formulates a matrix from scratch; in the process of thinking and drawing one has, so to speak, to solve one’s own matrix. In doing so one explains to oneself the relationship(s) realized so far and what one would like to attain. Drawing is thinking aloud a problem and aids the designer’s thinking processes in providing some “talk-back” ( Cross and Clayburn Cross, 1996 ). Explanatory activity enhances learning through increased depth of processing ( Siegler, 2005 ). Analyzing explanations of examples given with physics problems showed that they clarify and specify the conditions and consequences of actions, and that they explicate tacit knowledge; thereby enhancing and completing an individual’s understanding of principles relevant to the task ( Chi and VanLehn, 1991 ). Constraint of the CRT is that the matrix, in principle, can be solved by another person. Therefore, in a kind of inner explanatory discussion, the designer makes observations of progress, and uses evaluations and decisions to answer this constraint. Because of this, open problems where certain constraints have to be met, constitute a powerful mechanism for promoting understanding and conceptual advancement ( Chi and VanLehn, 1991 ; Mestre, 2002 ; Siegler, 2005 ).

Convergent and divergent thinking processes have been studied with a variety of intelligence and creativity tests, respectively. Relationships between performances on these tests have been demonstrated and a large number of research questions have been addressed. However, the fact that intelligence and creativity tests vary in the definition of their construct, in their problem space, and in their knowledge domain, poses methodological problems regarding the validity of comparisons of test results. When we want to focus on one cognitive process, e.g., intelligent thinking, and on its different performances in well or ill-defined problem situations, we need pairs of tasks that are constructed along identical definitions of the construct to be assessed, that differ, however, in the description of their constraints but are identical regarding their knowledge domain.

One such possible pair, the Progressive Matrices Test and the CRT was suggested here. The CRT was developed on the basis of creative reasoning , a construct that assumes the intertwining of intelligent and creativity related abilities when looking for original and applicable solutions. Matched with the Matrices test, results indicated that, besides similarities, intelligent thinking also yielded considerable differences for both problem spaces. Hence, with knowledge domain controlled, and only differences in problem space remaining, comparison of data yielded new results on intelligence’s operations. Data gathered from intelligence and creativity tests, whether they are performance scores or physiological measurements on the basis of, e.g., EEG, and fMRI methods, are reflections of cognitive processes performing on a certain test that was constructed on the basis of a certain definition of the construct it was meant to measure. Data are also reflections of the processes evolving within a certain problem space and of cognitive abilities operating on a certain knowledge domain.

Data can unhide brain networks that are involved in the performance of certain tasks, e.g., traditional intelligence and creativity tests, but data will always be related to the characteristics of the task. The characteristics of the task, such as problem space and knowledge domain originated at the construction of the task, and the construction, on its turn, is affected by the definition of the construct the task is meant to measure.

Here we present the CRT as one possible solution for the described problems in cognition research. However, for research on relationships among test scores other pairs of tests are imaginable, e.g., pairs of tasks operating on the same domain where one task has a defined problem space and the other one an ill-defined space. It is conceivable that pairs of test could operate, besides on the domain of mathematics, on content of e.g., visuo-spatial, verbal, and musical domains. Pairs of test have been constructed by changing the instruction of a task; instructions instigated a more convergent or a more a divergent mode of response ( Razumnikova et al., 2009 ; Limb, 2010 ; Jauk et al., 2012 ; Beaty et al., 2013 ).

The CRT involves the creation of components and their relationships for a 3 × 3 matrix. Hence, matrices created in the CRT are original in the sense that they all bear individual markers and they are applicable in the sense, that they can, in principle, be solved by another person. We showed that the CRT instigates a real design process; creators’ cognitive abilities are wrapped up in a process that should produce a closed problem within an ill-defined problem space.

For research on the relationship among convergent and divergent thinking, we need pairs of test that differ in the problem spaces related to each test but are identical in the knowledge domain on which cognition operates. The test pair of RPM and CRT provides such a pair. For research on the intertwining of convergent and divergent thinking, we need tasks that measure more than tests assessing each construct alone. We need tasks that are developed on the definition of intertwining cognitive abilities; the CRT is one such test.

Hence, we hope to have sufficiently discussed and demonstrated the importance of the three test features, construct definition, problem space, and knowledge domain, for research questions in creative cognition research.

Author Contributions

All authors listed, have made substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpsyg.2017.00134/full#supplementary-material

  • Abraham A., Bubic A. (2015). Semantic memory as the root of imagination. Front. Psychol. 6:325 10.3389/fpsyg.2015.00325 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Abraham A., Windmann S. (2007). Creative cognition: the diverse operations and the prospect of applying a cognitive neuroscience perspective. Methods 42 38–48. 10.1016/j.ymeth.2006.12.007 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Akin O. (1986). Psychology of Architectural Design London: Pion. [ Google Scholar ]
  • Anderson J. R. (1983). The Architecture of Cognition Cambridge, MA: Harvard University Press. [ Google Scholar ]
  • Arden R., Chavez R. S., Grazioplene R., Jung R. E. (2010). Neuroimaging creativity: a psychometric view. Behav. Brain Res. 214 143–156. 10.1016/j.bbr.2010.05.015 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Arnheim R. (1962/1974). Picasso’s Guernica Berkeley: University of California Press. [ Google Scholar ]
  • Barsalou L. W. (1992). Cognitive Psychology: An Overview for Cognitive Scientists Hillsdale, NJ: LEA. [ Google Scholar ]
  • Beaty R. E., Benedek M., Silvia P. J., Schacter D. L. (2016). Creative cognition and brain network dynamics. Trends Cogn. Sci. 20 87–95. 10.1016/j.tics.2015.10.004 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Beaty R. E., Kaufman S. B., Benedek M., Jung R. E., Kenett Y. N., Jauk E., et al. (2015). Personality and complex brain networks: the role of openness to experience in default network efficiency. Hum. Brain Mapp. 37 773–777. 10.1002/hbm.23065 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Beaty R. E., Nusbaum E. C., Silvia P. J. (2014). Does insight problem solving predict real-world creativity? Psychol. Aesthet. Creat. Arts 8 287–292. 10.1037/a0035727 [ DOI ] [ Google Scholar ]
  • Beaty R. E., Silvia R. E. (2013). Metaphorically speaking: cognitive abilities and the production of figurative language. Mem. Cognit. 41 255–267. 10.3758/s13421-012-0258-5 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Beaty R. E., Smeekens B. A., Silvia P. J., Hodges D. A., Kane M. J. (2013). A first look at the role of domain-general cognitive and creative abilities in jazz improvisation. Psychomusicology 23 262–268. 10.1037/a0034968 [ DOI ] [ Google Scholar ]
  • Benedek M., Bergner S., Konen T., Fink A., Neubauer A. C. (2011). EEG alpha synchronization is related to top-down processing in convergent and divergent thinking. Neuropsychologia 49 3505–3511. 10.1016/j.neuropsychologia.2011.09.004 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Benedek M., Franz F., Heene M., Neubauer A. C. (2012). Differential effects of cognitive inhibition and intelligence on creativity. Pers. Individ. Dif. 53 480–485. 10.1016/j.paid.2012.04.014 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Benedek M., Jauk E., Sommer M., Arendasy M., Neubauer A. C. (2014). Intelligence, creativity, and cognitive control: the common and differential involvement of executive functions in intelligence and creativity. Intelligence 46 73–83. 10.1016/j.intell.2014.05.007 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Boden M. A. (1990). The Creative Mind: Myths and Mechanisms London: Abacus. [ Google Scholar ]
  • Boden M. A. (1996). Artificial Intelligence New York, NY: Academic. [ Google Scholar ]
  • Buschman T. J., Miller E. K. (2007). Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 315 1860–1862. 10.1126/science.1138071 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Carroll J. B. (1982). “The measurement of Intelligence,” in Handbook of Human Intelligence , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 29–120. [ Google Scholar ]
  • Cattell R. B. (1967). The theory of fluid and crystallized general intelligence checked at the 5-6 year-old level. Br. J. Educ. Psychol. 37 209–224. 10.1111/j.2044-8279.1967.tb01930.x [ DOI ] [ PubMed ] [ Google Scholar ]
  • Chi M. T. H. (1997). “Creativity: Shifting across ontological categories flexibly,” in Creative Thought: An Investigation of Conceptual Structures and Processes , eds Ward T., Smith S., Vaid J. (Washington, DC: American Psychological Association; ), 209–234. [ Google Scholar ]
  • Chi M. T. H., VanLehn K. A. (1991). The content of physics self-explanations. J. Learn. Sci. 1 69–105. 10.1207/s15327809jls0101_4 [ DOI ] [ Google Scholar ]
  • Christensen B. T. (2007). The relationship of analogical distance to analogical function and preinventive structure: the case of engineering design. Mem. Cogn. 35 29–38. 10.3758/BF03195939 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Colom R., Haier R. J., Head K., Álvarez-Linera J., Quiroga M. A., Shih P. C., et al. (2009). Gray matter correlates of fluid, crystallized, and spatial intelligence: testing the P-FIT model. Intelligence 37 124–135. 10.1016/j.intell.2008.07.007 [ DOI ] [ Google Scholar ]
  • Colunga E., Smith L. B. (2008). Flexibility and variability: essential to human cognition and the study of human cognition. New Ideas Psychol. 26 158–192. 10.1016/j.newideapsych.2007.07.012 [ DOI ] [ Google Scholar ]
  • Cooper N. R., Croft R. J., Dominey S. J. J., Burgess A. P., Gruzelier J. H. (2003). Paradox lost? Exploring the role of alpha oscillations during externally vs. internally directed attention and the implications for idling and inhibition hypotheses. Int. J. Psychophysiol. 47 65–74. 10.1016/S0167-8760(02)00107-1 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Cropley A. (2006). In praise of convergent thinking. Creat. Res. J. 18 391–404. 10.1207/s15326934crj1803_13 [ DOI ] [ Google Scholar ]
  • Cropley A., Cropley D. (2008). Resolving the paradoxes of creativity: an extended phase model. Camb. J. Educ. 38 355–373. 10.1080/03057640802286871 [ DOI ] [ Google Scholar ]
  • Cross N., Clayburn Cross A. (1996). Winning by design: the methods of Gordon Murray, racing car designer. Des. Stud. 17 91–107. 10.1016/0142-694X(95)00027-O [ DOI ] [ Google Scholar ]
  • Dennett D. (1978). Brainstorms: Philosophical Essays on Mind and Psychology Montgomery, VT: Bradford Books. [ Google Scholar ]
  • Dietrich A. (2007). Who’s afraid of a cognitive neuroscience of creativity? Methods 42 22–27. 10.1016/j.ymeth.2006.12.009 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Dorst K. (2004). The problem of design problems: Problem solving and design expertise. J. Design Res. 4 10.1504/JDR.2004.009841 [ DOI ] [ Google Scholar ]
  • Dorst K. (2011). The core of ‘design thinking’ and its application. Des. Stud. 32 521–532. 10.1016/j.destud.2011.07.006 [ DOI ] [ Google Scholar ]
  • Eysenck H. J. (2003). “Creativity, personality and the convergent-divergent continuum,” in Critical Creative Processes , ed. Runco M. A. (Cresskill, NJ: Hampton Press; ), 95–114. [ Google Scholar ]
  • Fink A., Benedek M. (2014). EEG alpha power and creative ideation. Neurosci. Biobehav. Rev. 44 111–123. 10.1016/j.neubiorev.2012.12.002 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Fink A., Benedek M., Grabner R. H., Staudt B., Neubauer A. C. (2007). Creativity meets neuroscience: experimental tasks for the neuroscientific study of creative thinking. Methods 42 68–76. 10.1016/j.ymeth.2006.12.001 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Fink A., Grabner R. H., Benedek M., Reishofer G., Hauswirth V., Fally M., et al. (2009). The creative brain: investigation of brain activity during creative problem solving by means of EEG and FMRI. Hum. Brain Mapp. 30 734–748. 10.1002/hbm.20538 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Finke R. A., Ward T. B., Smith S. M. (1992). Creative Cognition: Theory, Research, and Applications Cambridge, MA: MIT Press. [ Google Scholar ]
  • Fuster J. M. (1997). Network memory. Trends Neurosci. 20 451–459. 10.1016/S0166-2236(97)01128-4 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Gabora L. (2002). “Cognitive mechanisms underlying the creative process,” in Proceedings of the Fourth International Conference on Creativity and Cognition , eds Hewett T., Kavanagh T. (Loughborough: Loughborough University; ), 126–133. [ Google Scholar ]
  • Gabora L. (2010). Revenge of the ‘neurds’: Characterizing creative thought in terms of the structure and dynamics of human memory. Creat. Res. J. 22 1–13. 10.1080/10400410903579494 [ DOI ] [ Google Scholar ]
  • Gabora L., Kaufman S. B. (2010). “Evolutionary approaches to creativity,” in The Cambridge Handbook of Creativity , eds Kaufman J. S., Sternberg R. J. (Cambridge: Cambridge University Press; ), 279–300. [ Google Scholar ]
  • Gabora L., Ranjan A. (2013). “How insight emerges in a distributed, content-addressable memory,” in The Neuroscience of Creativity , eds Bristol A., Vartanian O., Kaufman J. (Cambridge: MIT Press; ), 19–43. [ Google Scholar ]
  • Gabora L., Saab A. (2011). “Creative inference and states of potentiality in analogy problem solving,” in Proceedings of the Annual Meeting of the Cognitive Science Society , Boston, MA, 3506–3511. [ Google Scholar ]
  • Gentner D. (1983). Structure mapping: a theoretical framework for analogy. Cogn. Sci. 7 155–170. 10.1207/s15516709cog0702_3 [ DOI ] [ Google Scholar ]
  • Getzels J. W. (1975). Problem finding and the inventiveness of solutions. J. Creat. Behav. 9 12–18. 10.1002/j.2162-6057.1975.tb00552.x [ DOI ] [ Google Scholar ]
  • Getzels J. W. (1987). “Creativity, intelligence, and problem finding: retrospect and prospect,” in Frontiers of Creativity Research: Beyond the Basics , ed. Isaksen S. G. (Buffalo, NY: Bearly Limited; ), 88–102. [ Google Scholar ]
  • Getzels J. W., Csikszentmihalyi M. (1976). The Creative Vision: A Longitudinal Study of Problem Finding in Art New York, NY: Wiley. [ Google Scholar ]
  • Ghiselin B. (ed.) (1952/1985). The Creative Process Los Angeles: University of California. [ Google Scholar ]
  • Goel V., Pirolli P. (1992). The structure of design problem spaces. Cogn. Sci. 16 395–429. 10.1207/s15516709cog1603_3 [ DOI ] [ Google Scholar ]
  • Goldschmidt G. (2013). “A micro view of design reasoning: two-way shifts between embodiment and rationale,” in Creativity and Rationale: Enhancing Human Experience by Design, Human-Computer Interaction Series , ed. Carroll J. M. (London: Springer Verlag; ). 10.1007/978-1-4471-2_3 [ DOI ] [ Google Scholar ]
  • Goldschmidt G. (2014). Linkography: Unfolding the Design Process Cambridge, MA: MIT Press. [ Google Scholar ]
  • Grube H. E., Davis S. N. (1988). “Inching our way up mount Olympus: The evolving-systems approach to creative thinking,” in The Nature of Creativity , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 243–270. [ Google Scholar ]
  • Guilford J. P. (1950). Creativity. Am. Psychol. 5 444–454. 10.1037/h0063487 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Guilford J. P. (1956). The structure of intellect model. Psychol. Bull. 53 267–293. 10.1037/h0040755 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Guilford J. P. (1959). “Traits of creativity,” in Creativity and its Cultivation , ed. Anderson H. H. (New York: Harper; ), 142–161. [ Google Scholar ]
  • Guilford J. P. (1967). The Nature of Human Intelligence New York, NY: McGraw-Hill, Inc. [ Google Scholar ]
  • Guilford J. P., Christensen P. R., Merrifield P. R., Wilson R. C. (1978). Alternate Uses: Manual of Instructions and Interpretation Orange, CA: Sheridan Psychological Services. [ Google Scholar ]
  • Halpern D. F. (2003). “Thinking critically about creative thinking,” in Critical Creative Processes , ed. Runco M. A. (Cresskill, NJ: Hampton Press; ), 189–208. [ Google Scholar ]
  • Hayes J. R., Flowers L. S. (1986). Writing research and the writer. Am. Psychol. 41 1106–1113. 10.1037/0003-066X.41.10.1106 [ DOI ] [ Google Scholar ]
  • Jaarsveld S. (2007). Creative Cognition: New Perspectives on Creative Thinking Kaiserslautern: University of Kaiserslautern Press. [ Google Scholar ]
  • Jaarsveld S., Fink A., Rinner M., Schwab D., Benedek M., Lachmann T. (2015). Intelligence in creative processes; an EEG study. Intelligence 49 171–178. 10.1016/j.ijpsycho.2012.02.012 [ DOI ] [ Google Scholar ]
  • Jaarsveld S., Lachmann T., Hamel R., van Leeuwen C. (2010). Solving and creating Raven Progressive Matrices: reasoning in well and ill defined problem spaces. Creat. Res. J. 22 304–319. 10.1080/10400419.2010.503541 [ DOI ] [ Google Scholar ]
  • Jaarsveld S., Lachmann T., van Leeuwen C. (2012). Creative reasoning across developmental levels: convergence and divergence in problem creation. Intelligence 40 172–188. 10.1016/j.intell.2012.01.002 [ DOI ] [ Google Scholar ]
  • Jaarsveld S., Lachmann T., van Leeuwen C. (2013). “The impact of problem space on reasoning: Solving versus creating matrices,” in Proceedings of the 35th Annual Conference of the Cognitive Science Society , eds Knauff M., Pauen M., Sebanz N., Wachsmuth I. (Austin, TX: Cognitive Science Society; ), 2632–2638. [ Google Scholar ]
  • Jaarsveld S., van Leeuwen C. (2005). Sketches from a design process: creative cognition inferred from intermediate products. Cogn. Sci. 29 79–101. 10.1207/s15516709cog2901_4 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Jauk E., Benedek M., Dunst B., Neubauer A. C. (2013). The relationship between intelligence and creativity: new support for the threshold hypothesis by means of empirical breakpoint detection. Intelligence 41 212–221. 10.1016/j.intell.2013.03.003 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Jauk E., Benedek M., Neubauer A. C. (2012). Tackling creativity at its roots: evidence for different patterns of EEG alpha activity related to convergent and divergent modes of task processing. Int. J. Psychophysiol. 84 219–225. 10.1016/j.ijpsycho.2012.02.012 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Jaušovec N. (1999). “Brain biology and brain functioning,” in Encyclopedia of Creativity , eds Runco M. A., Pritzker S. R. (San Diego, CA: Academic Press; ), 203–212. [ Google Scholar ]
  • Jaušovec N. (2000). Differences in cognitive processes between gifted, intelligent, creative, and average individuals while solving complex problems: an EEG Study. Intelligence 28 213–237. 10.1016/S0160-2896(00)00037-4 [ DOI ] [ Google Scholar ]
  • Jung R. E. (2014). Evolution, creativity, intelligence, and madness: “here be dragons”. Front. Psychol 5:784 10.3389/fpsyg.2014.00784 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Jung R. E., Haier R. J. (2013). “Creativity and intelligence,” in Neuroscience of Creativity , eds Vartanian O., Bristol A. S., Kaufman J. C. (Cambridge, MA: MIT Press; ), 233–254. [ Google Scholar ]
  • Jung R. E., Segall J. M., Bockholt H. J., Flores R. A., Smith S. M., Chavez R. S., et al. (2010). Neuroanatomy of creativity. Hum. Brain Mapp. 31 398–409. 10.1002/hbm.20874 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Karmiloff-Smith A. (1992). Beyond Modularity: A Developmental Perspective on Cognitive Science Cambridge, MA: MIT Press. [ Google Scholar ]
  • Kaufman J. C. (2015). Why creativity isn’t in IQ tests, why it matters, and why it won’t change anytime soon probably. Intelligence 3 59–72. 10.3390/jintelligence303005 [ DOI ] [ Google Scholar ]
  • Kaufmann G. (2003). What to measure? A new look at the concept of creativity. Scand. J. Educ. Res. 47 235–251. 10.1080/00313830308604 [ DOI ] [ Google Scholar ]
  • Kim K. H. (2005). Can only intelligent people be creative? J. Second. Gift. Educ. 16 57–66. [ Google Scholar ]
  • Koestler A. (1964). The Act of Creation London: Penguin. [ Google Scholar ]
  • Kozbelt A. (2008). Hierarchical linear modeling of creative artists’ problem solving behaviors. J. Creat. Behav. 42 181–200. 10.1002/j.2162-6057.2008.tb01294.x [ DOI ] [ Google Scholar ]
  • Kulkarni D., Simon H. A. (1988). The processes of scientific discovery: the strategy of experimentation. Cogn. Sci. 12 139–175. 10.1016/j.coph.2009.08.004 [ DOI ] [ Google Scholar ]
  • Limb C. J. (2010). Your Brain on Improve Available at: http://www.ted.com/talks/charles_limb_your_brain_on_improv [ Google Scholar ]
  • Lubart T. I. (2001). Models of the creative process: past, present and future. Creat. Res. J. 13 295–308. 10.1207/S15326934CRJ1334_07 [ DOI ] [ Google Scholar ]
  • Lubart T. I. (2003). Psychologie de la Créativité. Cursus. Psychologie Paris: Armand Colin. [ Google Scholar ]
  • Martindale C. (1999). “Biological basis of creativity,” in Handbook of Creativity , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 137–152. [ Google Scholar ]
  • Mednick S. A. (1962). The associative basis of the creative process. Psychol. Rev. 69 220–232. 10.1037/h0048850 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Mendelssohn G. A. (1976). Associational and attentional processes in creative performance. J. Pers. 44 341–369. 10.1111/j.1467-6494.1976.tb00127.x [ DOI ] [ Google Scholar ]
  • Mestre J. P. (2002). Probing adults’ conceptual understanding and transfer of learning via problem posing. Appl. Dev. Psychol. 23 9–50. 10.1016/S0193-3973(01)00101-0 [ DOI ] [ Google Scholar ]
  • Miller E. K., Cohen J. D. (2001). An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24 167–202. 10.1146/annurev.neuro.24.1.167 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Mumford M. D., Hunter S. T., Eubanks D. L., Bedell K. E., Murphy S. T. (2007). Developing leaders for creative efforts: a domain-based approach to leadership development. Hum. Res. Manag. Rev. 17 402–417. 10.1016/j.hrmr.2007.08.002 [ DOI ] [ Google Scholar ]
  • Newell A., Simon H. A. (1972). “The theory of human problem solving,” in Human Problem Solving , eds Newell A., Simon H. (Englewood Cliffs, NJ: Prentice Hall; ), 787–868. [ Google Scholar ]
  • Nusbaum E. C., Silvia P. J. (2011). Are intelligence and creativity really so different? Intelligence 39 36–40. 10.1016/j.intell.2010.11.002 [ DOI ] [ Google Scholar ]
  • Palmiero M., Nori R., Aloisi V., Ferrara M., Piccardi L. (2015). Domain-specificity of creativity: a study on the relationship between visual creativity and visual mental imagery. Front. Psychol. 6:1870 10.3389/fpsyg.2015.01870 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Piaget J., Montangero J., Billeter J. (1977). “La formation des correlats,” in Recherches sur L’abstraction Reflechissante I , ed. Piaget J. (Paris: Presse Universitaires de France; ), 115–129. [ Google Scholar ]
  • Plucker J. (1999). Is the proof in the pudding? Reanalyses of torrance’s (1958 to present) longitudinal study data. Creat. Res. J. 12 103–114. 10.1207/s15326934crj1202_3 [ DOI ] [ Google Scholar ]
  • Raven J. C. (1938/1998). Standard Progressive Matrices, Sets A, B, C, D & E Oxford: Oxford Psychologists Press. [ Google Scholar ]
  • Razumnikova O. M., Volf N. V., Tarasova I. V. (2009). Strategy and results: sex differences in electrographic correlates of verbal and figural creativity. Hum. Physiol. 35 285–294. 10.1134/S0362119709030049 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Runco M. A. (1991). The evaluative, valuative, and divergent thinking of children. J. Creat. Behav. 25 311–319. 10.1177/1073858414568317 [ DOI ] [ Google Scholar ]
  • Runco M. A. (2003). “Idea evaluation, divergent thinking, and creativity,” in Critical Creative Processes , ed. Runco M. A. (Cresskill, NJ: Hampton Press; ), 69–94. [ Google Scholar ]
  • Runco M. A. (2007). Creativity, Theories and Themes: Research, Development, and Practice New York, NY: Elsevier. [ Google Scholar ]
  • Runco M. A. (2008). Commentary: divergent thinking is not synonymous with creativity. Psychol. Aesthet. Creat. Arts 2 93–96. 10.1037/1931-3896.2.2.93 [ DOI ] [ Google Scholar ]
  • Sakar P., Chakrabarti A. (2013). Support for protocol analyses in design research. Des. Issues 29 70–81. 10.1162/DESI_a_00231 [ DOI ] [ Google Scholar ]
  • Saraç S., Önder A., Karakelle S. (2014). The relations among general intelligence, metacognition and text learning performance. Educ. Sci. 39 40–53. [ Google Scholar ]
  • Shye S., Goldzweig G. (1999). Creativity as an extension of intelligence: Faceted definition and structural hypotheses. Megamot 40 31–53. [ Google Scholar ]
  • Shye S., Yuhas I. (2004). Creativity in problem solving. Tech. Rep. 10.13140/2.1.1940.0643 [ DOI ] [ Google Scholar ]
  • Siegler R. S. (1998). Children’s Thinking , 3rd Edn Upper Saddle River, NJ: Prentice Hall, 28–50. [ Google Scholar ]
  • Siegler R. S. (2005). Children’s learning. Am. Psychol. 60 769–778. 10.1037/0003-066X.60.8.769 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Silvia P. J. (2008). Creativity and intelligence revisited: a reanalysis of Wallach and Kogan (1965). Creat. Res. J. 20 34–39. 10.1080/10400410701841807 [ DOI ] [ Google Scholar ]
  • Silvia P. J., Beaty R. E., Nussbaum E. C. (2013). Verbal fluency and creativity: general and specific contributions of broad retrieval ability (Gr) factors to divergent thinking. Intelligence 41 328–340. 10.1016/j.intell.2013.05.004 [ DOI ] [ Google Scholar ]
  • Simon H. A. (1973). The structure of ill structured problems. Artif. Intell. 4 1012–1021. 10.1016/0004-3702(73)90011-8 [ DOI ] [ Google Scholar ]
  • Simon H. A., Newell A. (1971). Human problem solving: state of theory in 1970. Am. Psychol. 26 145–159. 10.1037/h0030806 [ DOI ] [ Google Scholar ]
  • Sligh A. C., Conners F. A., Roskos-Ewoldsen B. (2005). Relation of creativity to fluid and crystallized intelligence. J. Creat. Behav. 39 123–136. 10.1002/j.2162-6057.2005.tb01254.x [ DOI ] [ Google Scholar ]
  • Spearman C. (1904). ‘General intelligence,’ objectively determined and measured. Am. J. Psychol. 15 201–293. 10.2307/1412107 [ DOI ] [ Google Scholar ]
  • Spearman C. (1927). The Abilities of Man London: Macmillan. [ Google Scholar ]
  • Sternberg R. J. (1982). “Conceptions of intelligence,” in Handbook of Human Intelligence , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 3–28. [ Google Scholar ]
  • Sternberg R. J. (2005). “The WICS model of giftedness,” in Conceptions of Giftedness , 2nd Edn, eds Sternberg R. J., Davidson J. E. (New York, NY: Cambridge University Press; ), 237–243. [ Google Scholar ]
  • Sternberg R. J., Lubart T. I. (1999). “The concept of creativity: Prospects and paradigms,” in Handbook of Creativity , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 3–15. [ Google Scholar ]
  • Sternberg R. J., Salter W. (1982). “The nature of intelligence and its measurements,” in Handbook of Human Intelligence , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 3–24. [ Google Scholar ]
  • Thagard P., Verbeurgt K. (1998). Coherence as constraint satisfaction. Cogn. Sci. 22 l–24. 10.1207/s15516709cog2201_1 [ DOI ] [ Google Scholar ]
  • Torrance E. P. (1988). “The nature of creativity as manifest in its testing,” in The Nature of Creativity: Contemporary Psychological Perspectives , ed. Sternberg R. J. (New York, NY: Cambridge University Press; ), 43–75. [ Google Scholar ]
  • Urban K. K., Jellen H. G. (1995). Test of Creative Thinking – Drawing Production Frankfurt: Swets Test Services. [ Google Scholar ]
  • van Leeuwen C., Verstijnen I. M., Hekkert P. (1999). “Common unconscious dynamics underlie uncommon conscious effect: a case study in the iterative nature of perception and creation,” in Modeling Consciousness Across the Disciplines , ed. Jordan J. S. (Lanham, MD: University Press of America; ), 179–218. [ Google Scholar ]
  • Vernon P. E. (ed.) (1970). Creativity London: Penguin. [ Google Scholar ]
  • Verstijnen I. M., Heylighen A., Wagemans J., Neuckermans H. (2001). “Sketching, analogies, and creativity,” in Visual and Spatial Reasoning in Design, II. Key Centre of Design Computing and Cognition , eds Gero J. S., Tversky B., Purcell T. (Sydney, NSW: University of Sydney; ). [ Google Scholar ]
  • Wallas G. (1926). The Art of Thought New York, NY: Harcourt, Brace & World. [ Google Scholar ]
  • Ward T. B. (2007). Creative cognition as a window on creativity. Methods 42 28–37. 10.1016/j.ymeth.2006.12.002 [ DOI ] [ PubMed ] [ Google Scholar ]
  • Webb Young J. (1939/2003). A Technique for Producing Ideas New York, NY: McGraw-Hill. [ Google Scholar ]
  • Welter M. M., Jaarsveld S., Lachmann T. Problem space matters: development of creativity and intelligence in primary school children. Creat. Res. J. (in press) [ Google Scholar ]
  • Welter M. M., Jaarsveld S., van Leeuwen C., Lachmann T. (2016). Intelligence and creativity; over the threshold together? Creat. Res. J. 28 212–218. 10.1080/10400419.2016.1162564 [ DOI ] [ Google Scholar ]
  • Wertheimer M. (1945/1968). Productive Thinking (Enlarged Edition) London: Tavistock. [ Google Scholar ]
  • Yamamoto Y., Nakakoji K., Takada S. (2000). Hand on representations in two dimensional spaces for early stages of design. Knowl. Based Syst. 13 357–384. 10.1016/S0950-7051(00)00078-2 [ DOI ] [ Google Scholar ]

Associated Data

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

Supplementary Materials

  • View on publisher site
  • PDF (224.4 KB)
  • Collections

Similar articles

Cited by other articles, links to ncbi databases.

  • Download .nbib .nbib
  • Format: AMA APA MLA NLM

Add to Collections

Does problem solving increase IQ?

Does problem solving increase IQ?

Problem solving is a fundamental cognitive skill that involves the ability to analyze, evaluate, and find solutions to complex problems. While it is widely acknowledged that problem solving is an important skill for success in many areas of life, including academics, employment, and personal relationships, there is debate over whether engaging in problem-solving activities can actually increase one’s intelligence quotient (IQ). In this article, we will explore this topic in depth and examine the evidence regarding whether problem solving can improve IQ.

  • Have you read these?
  • Should I get an IQ test?
  • How long is an actual IQ test?
  • Understanding IQ: Exploring the Intricacies of Intelligence Quotient

First, it’s important to define what we mean by “intelligence quotient” (IQ). IQ is a standardized measure of cognitive ability that is derived from various tests that assess different aspects of intellectual functioning, such as verbal and nonverbal reasoning, spatial awareness, and memory. IQ is often used as a predictor of academic and career success, and is commonly used to evaluate individuals in educational and clinical settings.

The question of whether problem solving can increase IQ has been the subject of much research and debate. Some experts argue that engaging in problem-solving activities can improve cognitive abilities such as critical thinking, reasoning, and decision-making, which are all aspects of IQ. According to this view, regularly engaging in challenging and complex problem-solving tasks can help to develop these skills and increase overall intellectual functioning.

One study published in the Journal of Educational Psychology found that individuals who engaged in regular problem-solving activities showed greater improvements in cognitive abilities over time than those who did not. The study, which involved nearly 500 adults, found that those who engaged in regular problem-solving activities such as puzzles, crosswords, and strategic games showed improvements in cognitive abilities such as memory, attention, and processing speed, as well as in measures of fluid intelligence, which is a key component of IQ.

Other research has also suggested that problem solving can improve cognitive abilities. One study published in the Journal of Experimental Psychology found that engaging in complex problem-solving activities can lead to improvements in working memory, which is a key aspect of cognitive ability. Another study published in the Journal of Research in Personality found that engaging in problem-solving activities can lead to improvements in reasoning abilities and overall intellectual functioning.

While these studies provide some evidence that problem solving may be associated with improvements in cognitive abilities, it is important to note that the relationship between problem solving and IQ is complex and multifaceted. For example, some research has suggested that the link between problem solving and IQ may be mediated by other factors such as motivation, self-esteem, and confidence.

In addition, it is also important to consider the role of genetics in determining IQ. While engaging in problem-solving activities may have a positive impact on cognitive abilities, there is evidence to suggest that genetic factors play a significant role in determining overall intellectual functioning. For example, a study published in the Journal of Intelligence found that genetic factors account for approximately 50% of the variance in IQ scores, indicating that individual differences in IQ are largely attributable to genetics.

Overall, while there is some evidence to suggest that engaging in problem-solving activities may improve cognitive abilities and potentially increase IQ, the relationship between these factors is complex and multifaceted. Additionally, it is important to consider the role of genetics in determining overall intellectual functioning, and to recognize that individual differences in IQ are largely determined by genetic factors.

In conclusion, while the relationship between problem solving and IQ is complex, engaging in regular problem-solving activities may have a positive impact on cognitive abilities such as critical thinking, reasoning, and decision-making. However, it is important to recognize that individual differences in IQ are largely attributable to genetics, and that engaging in problem-solving activities alone is unlikely to result in significant improvements in overall intellectual functioning.

What is the difference between IQ and Cognitive abilities?

Intelligence Quotient (IQ) and cognitive abilities are two terms that are often used interchangeably, but they are not the same thing. While IQ tests measure certain aspects of cognitive abilities, cognitive abilities refer to a wider range of mental capacities and processes. In this artic...

What is multiple intelligence theory?

The theory of multiple intelligences, first proposed by psychologist Howard Gardner in 1983, suggests that there is not just one single type of intelligence, but rather multiple forms of intelligence that can work independently or together. This theory challenges the traditional view of in...

How much does a full IQ test cost?

IQ tests, or intelligence quotient tests, are standardized assessments that aim to measure an individual’s cognitive abilities and potential. These tests are often used in educational and occupational settings to determine academic and career potential, and can be administered to ind...

What is it like to have an extremely high IQ?

IQ is a commonly used measure of cognitive abilities and potential. While there is no universally accepted definition of intelligence, IQ tests are designed to measure a range of cognitive abilities such as verbal comprehension, mathematical reasoning, and spatial visualization. A person w...

Why do different online IQ tests give me different scores?

IQ tests are designed to measure a person’s intelligence quotient, which is a numerical representation of their cognitive abilities. Traditionally, these tests have been administered in person by professionals, but with the rise of the internet, there has been an increase in online I...

Which online iq test is most accurate?

IQ, or intelligence quotient, is a measure of cognitive abilities and potential. IQ tests are standardized assessments that aim to measure an individual’s cognitive abilities and potential. These tests are often used in educational and occupational settings to determine academic and ...

Can IQ be improved?

IQ (Intelligence Quotient), is a measure of cognitive abilities and potential. IQ tests are standardized assessments that aim to measure an individual’s cognitive abilities and potential. These tests are often used in educational and occupational settings to determine academic and ca...

Why do you have to pay for an IQ test?

IQ tests, which are designed to measure cognitive abilities and potential, can come at a cost for several reasons. Firstly, professional and accurate IQ testing often requires the involvement of trained psychologists or educators. These professionals administer the test in a controlled env...

What Are the Positive & Negatives of IQ Testing?

Intelligence Quotient (IQ) testing has been a popular measure of cognitive abilities for over a century. While IQ tests provide valuable information about an individual’s cognitive abilities, strengths and weaknesses, they also have their limitations and controversies. In this articl...

University of Pennsylvania - Psychology

IQ Test

Relationship between IQ and problem-solving skills

  • November 11, 2023

is problem solving related to iq

Discovering the Connection: How IQ and Problem-Solving Abilities Interact

In the realm of psychology and cognitive studies, the intelligence quotient, commonly known as the IQ, has long been a topic of fascination and debate among scholars and the general public alike. Historically regarded as a pivotal indicator of intellectual capability, the IQ test seeks to measure a person’s cognitive abilities and potential for academic success. But the question that frequently emerges in discourse is how directly IQ relates to problem-solving skills. Recent research efforts have delved into this connection, providing intriguing insights that merit a closer look.

The study titled “Relationship between IQ and Problem-Solving Skills” brings forth compelling evidence to the ongoing conversation regarding the correlation between a person’s IQ and their proficiency in problem-solving. At its core, problem-solving represents a critical aspect of daily life and professional environments. It involves understanding complex issues, analyzing possible solutions, and implementing effective strategies to overcome challenges.

This groundbreaking study sought to measure problem-solving skills in various contexts, comparing the results with the IQ scores of participants. The researchers employed a range of problem-solving tasks, from logical puzzles and mathematical equations to real-world scenarios that required creative and critical thinking.

The findings illuminated a nuanced relationship between the two constructs. Participants who recorded higher IQ scores generally performed better on problem-solving tasks, particularly those that involved logical reasoning and pattern recognition. There was a clear trend indicating that individuals with a higher IQ found it easier to grasp the underlying structure of a problem and to apply their knowledge to formulate viable solutions.

However, the study also underscored that IQ is not the sole determinant of problem-solving success. Other factors, such as creativity, experience, domain-specific knowledge, and even emotional intelligence played significant roles. Those with a high IQ but limited experience in a certain field, for instance, could be outperformed by individuals with lower IQ scores but greater expertise in that area.

Additionally, the research highlighted that problem-solving is a multilayered skill set. It incorporates cognitive functions like working memory, information processing speed, and abstract reasoning – all of which are aspects measured by IQ tests. Nevertheless, other qualities, like persistence, flexibility in thinking, and the ability to manage frustration during challenging tasks, also contribute to effective problem-solving.

In conclusion, the study “Relationship between IQ and Problem-Solving Skills” has provided valuable insights into how these two important constructs interact. While there is a definite correlation between IQ levels and problem-solving abilities, the reality is more complex and multifaceted. Such findings have profound implications for educational practices, hiring assessments, and personal development. Individuals and institutions alike would benefit from a holistic approach that nurtures not only intellectual prowess but also a diverse set of cognitive and emotional skills to navigate the intricate problems of the modern world effectively.

Related Articles

Studies on the relationship between iq and life longevity, analysis of the effect of music training on cognitive abilities, relationship between iq and emotional regulation, studies on the impact of bilingual education on iq, analysis of the role of technology in cognitive development, relationship between iq and ethical decision-making.

  • November 2023
  • IQ Test's

IMAGES

  1. Problem solving

    is problem solving related to iq

  2. Psychology, Thinking and Intelligence, Problem Solving

    is problem solving related to iq

  3. Solving an IQ question by understanding a common technique || Make math simple

    is problem solving related to iq

  4. IQ Questions

    is problem solving related to iq

  5. The Problem with How We View IQ Tests

    is problem solving related to iq

  6. Solve This Tricky Problem And Your IQ Is 159

    is problem solving related to iq

COMMENTS

  1. The Nexus of Intellect: IQ’s Influence on Problem-Solving Skills

    Feb 20, 2024 · Embracing the Complexity of Problem-Solving in the Context of IQ In conclusion, the relationship between IQ and problem-solving skills is intricate, with IQ providing a crucial, though not ...

  2. Problem-Solving and Intelligence (Chapter 23) - The Cambridge ...

    We then discuss the predictive validity of intelligence as it relates to job performance, mortality, expertise, and academic achievement. We also discuss practical uses of intelligence tests. Finally, we consider the question of whether intelligence as problem-solving ability can be improved through training.

  3. What is the difference between problem solving and intelligence?

    Apr 8, 2018 · In practice, the way intelligence is usually tested in IQ tests, involves solving some types of problems (assuming a generous defintion of "problem"). Which essentially entails operationalizing intelligence. It's fair to say IQ tests test some specific types of problem solving. Of course the hope is that these correlate well enough with ...

  4. Performance IQ: Key to Cognitive Abilities Revealed

    Sep 30, 2024 · The concept of Abstract Reasoning IQ: Unraveling the Cognitive Skill Behind Problem-Solving is closely related to Performance IQ. Abstract reasoning, the ability to identify patterns and relationships between concepts, is a key component of non-verbal problem-solving skills.

  5. Problem-solving and intelligence. - APA PsycNet

    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 five major sections. In the first section, we consider the question of what a problem is and argue ...

  6. Intelligence and Creativity in Problem Solving: The ...

    Crystallized intelligence represents abilities acquired through learning, practice, and exposure to education, while fluid intelligence represents a more basic capacity that is valuable to reasoning and problem solving in contexts not necessarily related to school education (Carroll, 1982).

  7. Does problem solving increase IQ? - realiqtests.com

    Feb 13, 2023 · The question of whether problem solving can increase IQ has been the subject of much research and debate. Some experts argue that engaging in problem-solving activities can improve cognitive abilities such as critical thinking, reasoning, and decision-making, which are all aspects of IQ.

  8. Relationships between problem-solving and intelligence

    Oct 1, 1988 · Problem solving and intelligence are generally considered to be two intimately related domains (Resnick & Glaser, 1976; Sternberg, 1982). However, there is a noticeable lack of research that directly compares performance on experimental problem-solving tasks with performance on psychometric abilities instruments (Sternberg, 1982).

  9. Relationship between IQ and problem-solving skills

    Nov 11, 2023 · This groundbreaking study sought to measure problem-solving skills in various contexts, comparing the results with the IQ scores of participants. The researchers employed a range of problem-solving tasks, from logical puzzles and mathematical equations to real-world scenarios that required creative and critical thinking. The findings ...

  10. 23 Problem-Solving and Intelligence - gatech.edu

    problem-solving ability is not just an aspect of intelligence – it is the essence of intelligence. In the next section, we briefly consider what is known about individual Figure 23.1 Example of a Raven’s-like problem (from Kunda et al., 2016). Reprinted with permission of Elsevier. Problem-Solving and Intelligence 555