Exploring Underlying Factors in Cognitive Tests: Spatial-Temporal Reasoning and Abstract Reasoning Abilities

This study aimed to identify underlying factors that explain the relationships among various cognitive tests, including Jouve Cerebrals Test of Induction (JCTI), General Ability Measure for Adults (GAMA) Matching (MAT), Analogies (ANA), Sequences (SEQ), and Construction (CON). A factor analysis was conducted with a sample size of 118 participants, using maximum likelihood extraction and Varimax rotation. The results indicated two factors: Factor 1, related to sequences and construction tasks, and Factor 2, related to matching, analogies, and nonverbal reasoning tasks. These factors may represent spatial-temporal reasoning and abstract reasoning abilities, respectively. The study's limitations, such as sample size and selection bias, are discussed, and future research directions are suggested.

factor analysis, spatial-temporal reasoning, abstract reasoning, cognitive tests, JCTI, GAMA

The study of human cognitive abilities has been an area of great interest for researchers in the field of psychometrics. The understanding of the relationships among various cognitive tasks and the identification of underlying factors that may explain these relationships are critical to enhancing our knowledge of human cognition (Carroll, 1993; Guilford, 1959). The current study aims to explore the relationships among different cognitive tasks by conducting a factor analysis using tests such as the Jouve Cerebrals Test of Induction (JCTI), General Ability Measure for Adults (GAMA) Matching (MAT), Analogies (ANA), Sequences (SEQ), and Construction (CON). This analysis seeks to identify underlying factors that can explain the relationships among these tests and their implications for the theoretical understanding of cognitive abilities.

The history of psychometrics is marked by the development of several theories and instruments that have attempted to capture the complexity and diversity of human cognitive abilities. For instance, Piaget's Theory of Cognitive Development (Piaget, 1952) posits that individuals progress through distinct stages of cognitive development, each characterized by unique abilities and processes. Carroll's Three-Stratum Theory of Cognitive Abilities (Carroll, 1993) proposes a hierarchical organization of cognitive abilities, with general intelligence (g) at the top and broad and narrow cognitive abilities forming the lower strata. Guilford's Structure of Intellect Model (Guilford, 1959) suggests a three-dimensional framework of cognitive abilities, consisting of operations, content, and products.

The selection of specific materials and test items for this study was based on the relevance of these tests to the study's focus on identifying underlying factors that may explain the relationships among various cognitive tasks. The JCTI, GAMA Matching, Analogies, Sequences, and Construction tests were chosen because they represent diverse cognitive tasks that tap into different aspects of human cognition, such as nonverbal reasoning, pattern recognition, spatial-temporal processing, and analogical reasoning (Jouve, 2010; Naglieri & Bardos, 1997).

The research question guiding this study is: What underlying factors can explain the relationships among the JCTI, GAMA Matching, Analogies, Sequences, and Construction tests, and how do these factors relate to existing theories of cognitive abilities? By answering this research question, the study aims to contribute to the understanding of the organization and structure of human cognitive abilities and provide insights into the potential relationships between these abilities and other cognitive functions. To achieve this objective, the current study will provide a literature review to establish the context for the study and justify the selection of specific materials and test items.

The literature review provides an overview of several cognitive abilities and tasks related to the research of the underlying factors between the Jouve Cerebrals Test of Induction (JCTI) and the General Ability Measure for Adults (GAMA) Matching (MAT), Analogies (ANA), Sequences (SEQ), and Construction (CON) tests. The review covers spatial-temporal reasoning, abstract reasoning, matching tasks, analogies, sequences, and construction tasks about cognitive development, cognitive abilities, aging, special populations, neuroscience, training, and technology.

Spatial-temporal Reasoning

Spatial-temporal reasoning is a cognitive process that involves understanding and manipulating spatial and temporal information. It plays a crucial role in various aspects of human cognition and has implications in numerous domains, including education, assessment, intervention, and technology.

Cognitive Development.

Piaget's theory of cognitive development (Piaget, 1952) laid the foundation for understanding spatial-temporal reasoning in children. According to Piaget, children's cognitive abilities develop through four stages: sensorimotor, preoperational, concrete operational, and formal operational. Spatial-temporal reasoning emerges during the concrete operational stage, where children begin to grasp the relationships between objects in space and time.

Mathematics.

Linn and Petersen (1985) expanded on Piaget's work by examining the role of spatial-temporal reasoning in mathematics. They found that spatial-temporal reasoning is crucial for understanding geometric concepts, solving word problems, and performing mental calculations.

Neuroscience.

O'Keefe and Nadel (1978) conducted neuroscience research on spatial-temporal reasoning, particularly in the context of the hippocampus and its role in spatial memory and navigation. Their work laid the groundwork for understanding the neural basis of spatial-temporal reasoning and its implications for learning and memory.

Working Memory and Cognitive Abilities.

Baddeley and Hitch (1974) investigated the relationship between spatial-temporal reasoning and working memory, proposing a multi-component model of working memory that includes a visuospatial sketchpad for processing and manipulating spatial information. Carroll (1993) studied the connection between spatial-temporal reasoning and cognitive abilities, finding that spatial-temporal reasoning is a critical component of fluid intelligence.

Sex Differences.

Voyer et al. (1995) explored sex differences in spatial-temporal reasoning, finding that males typically outperform females on tasks that involve mental rotation and spatial visualization. However, these differences may be influenced by sociocultural factors, such as gender stereotypes and educational experiences.

Training and Improvement.

Uttal et al. (2013) examined the potential for training and improvement in spatial-temporal reasoning. Their meta-analysis revealed that targeted interventions, such as spatial training programs and video games, can lead to significant improvements in spatial-temporal reasoning skills across various age groups.

Technology.

Moreno and Mayer (2007) investigated the role of technology in spatial-temporal reasoning, focusing on multimedia learning environments that incorporate dynamic visualizations and interactive simulations. They found that these technologies can support the development of spatial-temporal reasoning skills by fostering active learning and promoting the transfer of knowledge to real-world situations.

Aging and Special Populations.

Moffat (2009) focused on the effects of aging on spatial-temporal reasoning, revealing that spatial-temporal abilities tend to decline with age. However, cognitive interventions and physical exercise can help mitigate this decline. Newcombe and Frick (2010) explored spatial-temporal reasoning in special populations, such as individuals with autism spectrum disorder and Williams syndrome, highlighting the importance of understanding the unique spatial-temporal profiles of these populations for developing targeted interventions.

Abstract Reasoning

Abstract reasoning refers to the cognitive ability to understand complex concepts, identify patterns, and solve problems that do not rely on concrete or sensory information. It plays a significant role in various aspects of human cognition and has implications for education, assessment, intervention, and artificial intelligence.

Foundational Work.

Guilford's Structure of Intellect Model (Guilford, 1959) provided a comprehensive framework for understanding human intelligence, including abstract reasoning. Cattell (1963) also contributed to the field by differentiating between fluid intelligence, which involves abstract reasoning, and crystallized intelligence, which relies on accumulated knowledge and experience. Piaget (1952) investigated the role of abstract reasoning in cognitive development, particularly during the formal operational stage when individuals develop the ability to reason abstractly and solve hypothetical problems.

Measurement.

Raven's Progressive Matrices (Raven, 1938) is a widely used measure of abstract reasoning. The test consists of a series of pattern completion tasks that require participants to identify the missing element based on the underlying logical structure.

Working Memory and Neuroscience.

Kyllonen and Christal (1990) explored the relationship between abstract reasoning and working memory, finding a strong correlation between the two cognitive abilities. Christoff et al. (2001) studied the neuroscientific basis of abstract reasoning, revealing the involvement of the prefrontal cortex and other brain regions in reasoning tasks.

Mathematics, Science, and Language.

Krutetskii (1976) investigated abstract reasoning in mathematics and science, emphasizing its importance in understanding complex concepts, developing models, and solving problems. Gentner and Goldin-Meadow (2003) explored the role of abstract reasoning in language, highlighting the significance of analogy and metaphor in linguistic comprehension and communication.

Training and Improvement.

Jaeggi et al. (2008) examined the potential for training and improvement in abstract reasoning through working memory interventions. Their study found that individuals who engaged in adaptive working memory training demonstrated significant improvements in fluid intelligence, which is closely related to abstract reasoning.

Special Populations and Executive Functions.

Dawson et al. (2007) investigated abstract reasoning in special populations, such as individuals with autism spectrum disorder, who may exhibit unique patterns of strengths and weaknesses in abstract reasoning abilities. Diamond (2013) studied the relationship between abstract reasoning and executive functions, which include cognitive processes like planning, inhibitory control, and cognitive flexibility.

Creativity and Artificial Intelligence.

Cropley (2006) explored the connection between abstract reasoning and creativity, emphasizing that both involve the generation and manipulation of novel ideas and concepts. Marcus (2003) analyzed abstract reasoning in the context of artificial intelligence, highlighting the importance of developing computational models that can mimic human abstract reasoning abilities.

Matching Tasks

Matching tasks are cognitive tasks that require individuals to identify similarities or correspondences between stimuli based on specific attributes or rules. These tasks have been widely used in various fields of cognitive psychology, including perception, memory, development, aging, neuropsychology, clinical populations, machine learning, and social cognition.

Early Research and Perception.

Posner and Mitchell (1967) pioneered the use of matching tasks in cognitive psychology, investigating the processes involved in recognizing and comparing visual stimuli. Treisman and Gelade (1980) further examined visual matching tasks, developing the feature integration theory to explain how attention plays a role in combining individual features into a coherent percept. Deutsch (1960) studied auditory matching tasks, focusing on pitch perception and auditory memory. Gibson and Walk (1960) investigated cross-modal matching tasks, examining the integration of information from different sensory modalities.

Neuropsychology and Working Memory.

Milner (1968) employed matching tasks in neuropsychology to assess cognitive impairments in patients with brain lesions, providing insights into the underlying neural substrates of cognitive functions. Baddeley and Hitch (1974) explored the relationship between matching tasks and working memory, demonstrating the involvement of the visuospatial sketchpad and phonological loop in processing and maintaining information during matching tasks.

Developmental Psychology and Aging.

Diamond (1990) studied matching tasks in developmental psychology, highlighting their usefulness in assessing cognitive abilities and developmental milestones in children. Craik and Jennings (1992) focused on aging research, using matching tasks to investigate the decline in cognitive performance as individuals grow older.

Clinical Populations and Machine Learning.

Noe et al. (2004) investigated the use of matching tasks in clinical populations, including patients with neurological disorders to assess cognitive deficits and inform interventions. Lake et al. (2015) examined machine learning, utilizing matching tasks to develop and evaluate algorithms that can learn and generalize patterns from limited data, similar to human cognitive processes.

Implicit Learning and Social Cognition.

Reber (1967) used matching tasks in implicit learning research, demonstrating that individuals can acquire knowledge about complex structures without conscious awareness. Zebrowitz et al. (1996) investigated social cognition, employing matching tasks to examine how facial resemblance and social categorization influence a person's perception and interpersonal judgments.

Analogies

Analogies are cognitive processes that involve drawing similarities or comparisons between two seemingly different concepts or situations, often used for problem-solving, communication, and reasoning. The study of analogical reasoning has been explored across various fields, including cognitive development, problem-solving, creativity, language, education, aging, clinical populations, neuroscience, and artificial intelligence.

Foundational Theories.

Spearman (1923) conducted early research on analogies, laying the groundwork for future studies. Gentner's structure-mapping theory (Gentner, 1983) proposed that analogical reasoning involves identifying and mapping common relational structures between source and target domains. Sternberg's componential model (Sternberg, 1977) suggested that analogical reasoning consists of three components: encoding, inference, and mapping.

Problem-solving and Creativity.

Gick and Holyoak (1980) examined the role of analogies in problem-solving, demonstrating that individuals can apply knowledge from previously solved problems to novel situations. Dunbar (1995) investigated the role of analogies in creativity and innovation, showing that analogical thinking can facilitate the generation of new ideas and solutions.

Language and Discourse.

Gentner and Wolff (1997) studied the use of analogies in language and discourse, revealing that analogical reasoning can help individuals understand abstract concepts and communicate complex ideas more effectively.

Education.

Richland et al. (2007) focused on the role of analogies in education, highlighting their potential to enhance learning, comprehension, and retention by connecting new information to prior knowledge and experiences.

Cognitive Development and Aging.

Goswami (1992) explored the development of analogical reasoning in children, indicating that analogical thinking emerges early in life and continues to develop as children acquire more knowledge and cognitive skills. Bugaiska and Thibaut (2015) investigated the impact of aging on analogical reasoning, demonstrating a decline in performance with advancing age.

Clinical Populations and Neuroscience.

Krawczyk et al. (2008) examined analogical reasoning in clinical populations, such as individuals with brain injuries or neurodevelopmental disorders, revealing its potential as an assessment and intervention tool. Bunge et al. (2005) conducted neuroimaging studies to identify the neural substrates of analogical reasoning, implicating regions such as the prefrontal cortex and parietal cortex in these cognitive processes.

Artificial Intelligence and Cognitive Modeling.

Hummel and Holyoak (2003) analyzed the role of analogies in artificial intelligence and cognitive modeling, developing computational models that simulate human analogical reasoning and its applications in machine learning and problem-solving.

Sequences

Sequences are ordered arrangements of elements that play a crucial role in various aspects of cognition, including intelligence, learning, memory, language, music, and mathematics. The study of sequences has been investigated across multiple disciplines, encompassing cognitive development, aging, clinical populations, neuroscience, and artificial intelligence.

Intelligence and Learning.

Raven (1938) examined the relationship between sequences and intelligence, with Raven's Progressive Matrices becoming a widely used measure for assessing fluid intelligence. Nissen and Bullemer (1987) investigated sequence learning, demonstrating that individuals can implicitly learn and recognize patterns through exposure to structured sequences.

Working Memory and Cognitive Development.

Baddeley (1986) explored the role of sequences in working memory, highlighting the importance of maintaining and manipulating sequential information in various cognitive tasks. Kemler Nelson et al. (1995) studied the development of sequence processing in children, indicating that the ability to process and comprehend sequences is crucial for cognitive development.

Aging and Cognitive Decline.

Salthouse (1994) focused on the impact of aging on sequence processing, revealing a decline in performance with advancing age. This decline was attributed to factors such as reduced processing speed, working memory capacity, and attentional resources.

Clinical Populations.

Heaton et al. (1993) investigated sequence processing in clinical populations, such as individuals with brain injuries or neurodevelopmental disorders, demonstrating its potential as an assessment and intervention tool.

Neuroscience.

Schendan et al. (2003) conducted neuroimaging studies to identify the neural substrates of sequence processing, implicating regions such as the prefrontal cortex, parietal cortex, and basal ganglia in these cognitive processes.

Artificial Intelligence and Cognitive Modeling.

Cleeremans and McClelland (1991) explored sequences in artificial intelligence and cognitive modeling, developing computational models that simulate human sequence processing and its applications in machine learning and pattern recognition.

Language, Music, and Mathematics.

Conway et al. (2010) examined the role of sequences in language and reading, indicating that sequential processing is essential for understanding the structure and meaning of linguistic input. Loui et al. (2010) studied sequences in music cognition, highlighting the importance of recognizing and reproducing melodic and rhythmic patterns. De Smedt et al. (2013) investigated sequences in mathematics and numerical cognition, demonstrating that sequence processing is critical for understanding numerical relationships and solving mathematical problems.

Construction Tasks

Construction tasks involve the manipulation and assembly of objects or elements to create a specific structure or pattern. They are crucial for understanding various cognitive processes such as intelligence, cognitive development, working memory, aging, clinical populations, and their applications in education, intervention, architecture, design, and artificial intelligence.

Intelligence and Cognitive Development.

Wechsler (1958) highlighted the significance of construction tasks in assessing intelligence, with tasks such as block design becoming a key component of intelligence tests. Piaget and Inhelder (1967) examined construction tasks in cognitive development, emphasizing their role in the development of spatial skills and logical reasoning in children.

Working Memory.

Shah and Miyake (1996) investigated the relationship between construction tasks and working memory, demonstrating that successful performance on these tasks requires the efficient use and manipulation of information in working memory.

Aging and Cognitive Decline.

Massaldjieva (2018) studied construction tasks in the context of aging and cognitive decline, revealing that older adults often exhibit difficulties with construction tasks due to decreased cognitive resources and processing speed.

Clinical Populations.

Martins-Rodrigues et al. (2001) focused on the performance of clinical populations on construction tasks, such as individuals with brain injuries or neurodevelopmental disorders, highlighting their diagnostic and therapeutic potential.

Education and Intervention.

Hegarty et al. (2002) examined the role of construction tasks in education and intervention, suggesting that engaging in construction activities can enhance spatial reasoning and problem-solving skills, which are essential for success in various academic domains.

Neuroscience.

Cohen et al. (1996) conducted neuroimaging studies on construction tasks, identifying the involvement of brain regions such as the prefrontal cortex, parietal cortex, and cerebellum in these cognitive processes.

Artificial Intelligence and Cognitive Modeling.

Lake et al. (2017) explored the application of construction tasks in artificial intelligence and cognitive modeling, developing computational models that simulate human-like construction skills and problem-solving abilities.

Architecture, Design, and STEM Education.

Mitchell (1990) investigated construction tasks in architecture and design, emphasizing their importance in developing spatial skills and creative problem-solving abilities in these fields. Wai et al. (2009) focused on the significance of construction tasks in STEM education, highlighting their potential for fostering spatial and analytical skills in science, technology, engineering, and mathematics.

Virtual Environments.

Richardson et al. (1999) studied construction tasks in virtual environments, demonstrating that immersive technologies can provide effective tools for teaching and assessing construction skills in various domains. 

Method

Research Design

This study employed a correlational research design to examine the relationships among various cognitive tests assessing spatial-temporal reasoning, abstract reasoning, matching tasks, analogies, sequences, and construction tasks. This design was chosen because it allows for the investigation of associations between different measures without manipulating any variables (Cohen, 2013).

Participants

The study sample consisted of 118 participants, recruited through convenience sampling from a local university. The participants were between the ages of 18 and 35, with a mean age of 23.4 years (SD = 4.2). The sample comprised 54% females and 46% males. The ethnic distribution of the participants included 61% Caucasian, 19% African American, 12% Hispanic, 7% Asian, and 1% other ethnicities. No exclusion criteria were set. All participants provided informed consent before participating in the study.

Materials

Five cognitive tests were utilized in this study to measure the various aspects of cognitive abilities: Jouve Cerebrals Test of Induction (JCTI; Jouve, 2010), General Ability Measure for Adults (GAMA; Naglieri & Bardos, 1997) Matching (MAT), Analogies (ANA), Sequences (SEQ), and Construction (CON). The JCTI is a non-verbal test that measures abstract reasoning abilities (Jouve, 2010). The GAMA is a multidimensional test that measures general intelligence and cognitive abilities through matching, analogies, sequences, and construction tasks.

Procedures

Data collection occurred in a quiet room at the local university. Participants were seated at individual desks and given a packet containing demographic questions and the five cognitive tests. Participants completed the demographic questionnaire first, followed by the cognitive tests in the order of JCTI, MAT, ANA, SEQ, and CON. Each cognitive test was administered according to the standardized administration instructions.

To ensure data quality, the researchers followed standardized administration procedures for each test and employed control procedures such as maintaining a quiet testing environment, providing clear instructions, and supervising the participants during testing. Participants were debriefed after completing the study and thanked for their participation.

Statistical Analyses

Data analysis involved the use of factor analysis, specifically maximum likelihood extraction with Varimax rotation, to identify underlying factors that could explain the relationships among the cognitive tests (Cattell, 1978). Before factor analysis, principal components analysis was conducted to estimate the number of factors to retain (Kaiser, 1960). Eigenvalues, scree plots, and chi-square tests were used to determine the appropriate number of factors (Cattell, 1978). Factor loadings were examined to interpret the relationships between the tests and the underlying factors (Cohen, 2013).

Results

To test the research hypotheses, a factor analysis was conducted using maximum likelihood extraction and Varimax rotation. The sample size for the study was 118 participants, and the following tests were used as measures: Jouve Cerebrals Test of Induction (JCTI), General Ability Measure for Adults (GAMA) Matching (MAT), Analogies (ANA), Sequences (SEQ), and Construction (CON). The analysis aimed to identify underlying factors that could explain the relationships among these tests. Before the factor analysis, the principal components analysis was conducted to estimate the number of factors to retain.

Factor Extraction

Based on the eigenvalues and scree plot, two factors were retained for further analysis, accounting for 87.01% and 12.99% of the total variance, respectively. The eigenvalues for these factors were 8.17 and 1.22. The chi-square test for the null hypothesis (H0: no common factors) was significant (χ² = 350.00, p < .01), indicating that there were common factors underlying the tests. The chi-square test for the alternative hypothesis (H0: 2 factors are sufficient) was not significant (χ² = 0.08, p = .78), suggesting that two factors were sufficient for explaining the relationships among the tests.

Factor Loadings.

The factor loadings for the unrotated factors are presented in Table 1.

After Varimax rotation, the rotated factor loadings are presented in Table 2.

Interpretation of Results

Based on the rotated factor loadings, Factor 1 appears to be strongly related to SEQ and CON, with loadings of 0.89 and 0.68, respectively. This factor may represent an underlying ability related to sequences and construction tasks. Factor 2, on the other hand, is more strongly related to MAT, ANA, and JCTI, with loadings of 0.69, 0.48, and 0.47, respectively. This factor may represent an underlying ability related to matching, analogies, and nonverbal reasoning tasks. These results support the research hypotheses that there are underlying factors that can explain the relationships among the various tests used in the study.

Limitations

Some limitations should be considered when interpreting the results of this study. First, the sample size of 118 participants may not be large enough to provide robust estimates of factor loadings. Future research should consider using larger samples to increase the reliability of the findings. Second, selection bias may have influenced the results, as participants may not represent a diverse range of abilities or backgrounds. Further studies should aim to include participants from diverse populations to ensure the generalizability of the results. Third, methodological limitations may have affected the study outcomes. The use of multiple-choice, timed, and untimed tests may have introduced measurement errors, as these test formats may not equally capture the different abilities under investigation. Future research could explore the use of alternative testing methods to assess the underlying factors more accurately.

Discussion

Interpretation of Results and Relation to Hypotheses and Previous Research

The outcomes of this study lend support to the research hypotheses, which posited that there are foundational elements that clarify the relationships among the myriad tests incorporated in this investigation. The factor analysis unveiled two predominant factors: Factor 1, denoted as Spatial-temporal reasoning, and Factor 2, defined as Abstract Reasoning. These factors seem to correspond with earlier research in the realm of cognitive abilities, which has emphasized the crucial role of spatial-temporal reasoning and abstract reasoning in comprehending cognitive processes (Carroll, 1993; Cattell, 1963; Guilford, 1959; Piaget, 1952).

Spatial-temporal reasoning involves the ability to mentally manipulate and process spatial relationships and temporal sequences, while abstract reasoning entails recognizing patterns, relationships, and principles in complex information. Both factors serve as integral components of cognitive functioning, enabling individuals to solve problems and make decisions effectively.

The alignment of these factors with previous research underscores their importance in shaping our understanding of cognitive abilities. The identification of these factors contributes to the growing body of knowledge on the topic and has the potential to inform targeted interventions, assessments, and educational strategies aimed at enhancing cognitive performance.

The discovery of these two factors not only corroborates the extant literature but also enhances our comprehension of the intricate connections between diverse cognitive tasks. Spatial-temporal reasoning, as denoted by Factor 1, has been associated with vital skills such as problem-solving, mathematical competence, and the ability to mentally manipulate objects within spatial contexts (Linn & Petersen, 1985; O'Keefe & Nadel, 1978). This factor highlights the importance of spatial-temporal reasoning in a wide array of cognitive domains and its relevance to everyday functioning.

On the other hand, Factor 2, Abstract Reasoning, embodies the aptitude to discern patterns, establish connections, and reason with abstract data, which are crucial for advanced thinking and learning across a range of disciplines (Gentner & Goldin-Meadow, 2003; Krutetskii, 1976). This factor emphasizes the integral role of abstract reasoning in the development of critical thinking skills, creativity, and adaptability, which contribute to overall cognitive flexibility.

The coherence between the identified factors and the established body of research bolsters the validity of the present study and underscores the resilience of these factors in representing fundamental cognitive capabilities. Moreover, the findings provide a deeper understanding of the interconnections between these cognitive abilities, potentially creating a foundation for the formulation of enhanced assessment tools and instructional interventions tailored to address these skills. By examining the intricate relationships between Spatial-temporal and Abstract Reasoning, researchers and educators can devise more targeted and efficient approaches to foster cognitive development, ultimately leading to improved educational outcomes and overall cognitive performance.

The correspondence between the factors discerned in this study and well-established theoretical frameworks in cognitive psychology, including Piaget's theory of cognitive development (Piaget, 1952), Guilford's Structure of Intellect Model (Guilford, 1959), and Cattell's fluid intelligence theory (Cattell, 1963), is particularly significant. This congruence between the current results and these influential theories lends further support to the legitimacy of the identified factors as substantive constructs in the comprehension of cognitive capacities. Moreover, this alignment emphasizes the interconnected nature of various cognitive abilities and reinforces the notion that these factors provide a comprehensive and holistic understanding of human cognition, thereby offering valuable insights for future research and practical applications.

Factor 1: Spatial-temporal reasoning.

Factor 1, Spatial-temporal reasoning, exhibits a strong association with sequence-based tasks (SEQ) and construction tasks (CON), both of which are fundamental to cognitive development (Piaget, 1998) and possess well-documented ties to working memory processes (Baddeley, 1986; Baddeley & Hitch, 1974; Shah & Miyake, 1996). The observed linkage between Factor 1 and these particular tasks aligns with the comprehensive research on spatial-temporal reasoning, which has elucidated its importance across various cognitive domains, such as mathematical problem-solving (Linn & Petersen, 1985), mental rotation (Shepard & Metzler, 1971), navigation (O'Keefe & Nadel, 1978), and even creativity (Kozhevnikov et al., 2005). This alignment underscores the broad applicability and relevance of spatial-temporal reasoning in understanding human cognitive capabilities and their development.

Specifically, spatial-temporal reasoning has emerged as a crucial component in mathematical achievement, as it encompasses the capacity to mentally represent and manipulate numerical and geometrical constructs (Linn & Petersen, 1985). This cognitive skill enables individuals to perceive and analyze spatial relationships, visualize transformations, and comprehend abstract mathematical concepts. The pronounced connection between this factor and sequence-based tasks highlights the importance of discerning patterns and relationships in numerical sequences, which is integral to mathematical competence. Recognizing and predicting patterns in sequences not only enhances problem-solving abilities but also facilitates the development of algebraic thinking, number sense, and a deeper understanding of mathematical structures. Therefore, fostering spatial-temporal reasoning skills may prove instrumental in promoting mathematical proficiency and overall cognitive growth.

Moreover, spatial-temporal reasoning plays a significant role in the realm of neuroscience, as research has linked it to spatial navigation, memory processes, and the functionality of the hippocampus (O'Keefe & Nadel, 1978). This relationship between Factor 1 and construction tasks emphasizes the critical nature of spatial-temporal reasoning in tasks that demand the mental manipulation of objects or the generation of mental representations to traverse intricate environments. For instance, spatial-temporal reasoning is essential in tasks such as assembling complex structures, interpreting diagrams, and understanding the spatial relationships between objects. This cognitive ability also contributes to the development of spatial memory, which is crucial for everyday activities, such as navigating unfamiliar surroundings or recalling the layout of familiar spaces. Consequently, a deeper understanding of spatial-temporal reasoning and its underlying neural mechanisms may offer valuable insights into cognitive processes and inform strategies for enhancing spatial cognition and navigation skills.

Furthermore, the significance of spatial-temporal reasoning permeates the research on aging, as studies have demonstrated that age-related deterioration in this cognitive skill may be linked to diminished everyday functioning and heightened vulnerability to cognitive decline (Moffat, 2009). Comprehending the interactions between Factor 1 and tasks associated with cognitive development and working memory can yield critical insights into preserving cognitive well-being throughout the lifespan. For instance, interventions targeting spatial-temporal reasoning could potentially mitigate the adverse effects of aging on cognitive performance, thereby enhancing older adults' independence and quality of life. By exploring the relationship between spatial-temporal reasoning and various cognitive tasks, researchers can develop strategies to support healthy aging, promote cognitive resilience, and identify early markers of cognitive decline, enabling timely interventions to preserve cognitive function in older populations.

Factor 2: Abstract Reasoning.

Factor 2, Abstract Reasoning, displays a marked relationship with matching tasks (MAT), analogies (ANA), and nonverbal reasoning tasks (JCTI). This association is consistent with prior research on abstract reasoning, which has underscored its importance in cognitive development (Piaget, 1952), working memory (Kyllonen & Christal, 1990), and the neuroscientific underpinnings (Christoff et al., 2001). The prominent linkage between Factor 2 and these particular assessments further reinforces the extensive body of research on matching tasks (Posner & Mitchell, 1967), analogies (Gentner, 1983; Sternberg, 1977), and nonverbal reasoning tasks (Raven, 1938).

The strong association of Factor 2 with these tasks highlights the critical role of abstract reasoning in various cognitive domains, such as problem-solving, language comprehension, and creativity. For example, the capacity to identify patterns, draw inferences, and reason with abstract information allows individuals to adapt to new situations, understand complex relationships, and make predictions based on limited data. As such, abstract reasoning serves as a foundation for higher-order thinking and learning across a wide range of disciplines, enabling individuals to excel academically, professionally, and socially. By understanding the interplay between abstract reasoning and various cognitive tasks, researchers can continue to refine educational and training methods, facilitating the development of these crucial cognitive skills.

Abstract reasoning embodies the capacity to discern patterns, connections, and fundamental principles within intricate information, empowering individuals to comprehend and navigate new or uncertain scenarios. The robust association between Factor 2 and matching tasks highlights the vital role of abstract reasoning in the identification and processing of similarities and differences across diverse stimuli, which is crucial for effective problem-solving and decision-making. This skill set allows individuals to extrapolate from known information and apply it to unfamiliar situations, enhancing their cognitive flexibility and adaptability. Consequently, the development of abstract reasoning abilities can significantly impact an individual's success in diverse domains, from academic performance to professional achievements and everyday life problem-solving. By exploring the interplay between abstract reasoning and various cognitive tasks, researchers can continue to refine our understanding of this essential cognitive process and its implications for human cognition and behavior.

Furthermore, the association between Factor 2 and analogies underscores the function of abstract reasoning in recognizing and extrapolating relationships between disparate concepts. This cognitive capability fosters the transfer of knowledge and skills across diverse contexts, thereby enhancing learning, creativity, and adaptability. By enabling individuals to recognize and apply analogical relationships, abstract reasoning helps to bridge the gap between seemingly unrelated ideas, fostering innovation and integrative thinking. This capacity to draw connections and apply insights from one domain to another not only bolsters problem-solving abilities but also contributes to a more comprehensive and interconnected understanding of the world. The exploration of abstract reasoning's role in analogical thinking can offer valuable insights into the development of cognitive strategies that promote lifelong learning, cognitive flexibility, and intellectual growth.

Additionally, the connection between Factor 2 and nonverbal reasoning tasks, including those featured in the JCTI, highlights the importance of abstract reasoning in processing and interpreting visual information, as well as its influence on fluid intelligence. This relationship emphasizes the critical role abstract reasoning plays in tasks that require the mental manipulation of intricate visual patterns, which is essential for multiple dimensions of cognitive performance.

The capacity to reason abstractly with nonverbal stimuli facilitates the comprehension of complex patterns, relationships, and structures, even in the absence of explicit verbal cues. This skill is instrumental in many real-world situations, such as interpreting graphs, maps, and diagrams, or navigating unfamiliar environments. Additionally, the strong association between abstract reasoning and fluid intelligence suggests that this cognitive ability contributes to an individual's capacity to think logically, solve novel problems, and adapt to new situations. By examining the relationship between abstract reasoning and nonverbal reasoning tasks, researchers can better understand the development of these cognitive skills and devise strategies to enhance overall cognitive functioning.

Implications for Theory, Practice, and Future Research

The results of this study significantly contribute to the existing body of knowledge on the role of spatial-temporal reasoning and abstract reasoning in cognitive functioning. These findings have the potential to reshape our understanding of the complex interplay between these two cognitive abilities, helping to refine existing theoretical frameworks and inspire the creation of new ones. This deeper understanding may lead to a more nuanced appreciation of the intricacies of human cognition, ultimately fostering more effective and targeted interventions for individuals with varying cognitive profiles.

In practical terms, these findings can have critical applications in educational settings, workplace training, and cognitive assessments. By acknowledging the distinct roles played by spatial-temporal reasoning and abstract reasoning in cognitive functioning, educators, trainers, and test developers can create more tailored programs to address specific cognitive needs. This could lead to improved learning outcomes, more effective problem-solving strategies, and better career trajectories for individuals across diverse populations.

Furthermore, this study can serve as a catalyst for future research in various domains. The identification of these two factors can guide researchers in exploring the neural correlates of spatial-temporal and abstract reasoning, as well as the role of genetic and environmental factors in shaping these cognitive abilities. Additionally, future studies could investigate the potential for cognitive training programs to enhance these skills, and the impact of such interventions on academic achievement, creativity, and other cognitive domains.

Moreover, future research may benefit from exploring the potential moderating or mediating roles of other cognitive processes, such as working memory, attention, and executive functions, in the relationship between spatial-temporal reasoning, abstract reasoning, and cognitive performance. Understanding these interconnections could provide valuable insights into how these cognitive abilities complement and interact with one another, further enhancing our knowledge of the human mind.

The findings of this study hold considerable practical implications for the design and implementation of cognitive assessments and interventions. By elucidating the factors underlying various cognitive tests, researchers and practitioners gain valuable insights into the unique contributions of spatial-temporal reasoning and abstract reasoning to cognitive performance. This deeper understanding enables the development of more targeted interventions aimed at enhancing specific cognitive abilities, ultimately leading to more personalized and effective approaches to cognitive improvement.

For example, in educational settings, tailored curricula can be designed to focus on strengthening spatial-temporal reasoning and abstract reasoning skills, depending on the specific needs of individual students. Teachers could employ evidence-based strategies to foster these abilities, leading to improved academic outcomes and a better foundation for future learning.

In the context of older adults, targeted interventions could help mitigate age-related cognitive decline, thus improving quality of life and maintaining independent functioning for a longer period. By focusing on the enhancement of spatial-temporal reasoning and abstract reasoning abilities, older adults may experience tremendous success in everyday tasks that require problem-solving, planning, and decision-making skills.

For individuals with cognitive impairments or those recovering from brain injuries, personalized interventions based on these findings may prove particularly beneficial. By concentrating on the specific cognitive abilities that require improvement, rehabilitation programs could become more efficient and effective, ultimately promoting faster and more complete recovery.

Moreover, these practical implications extend to the development of cognitive assessments themselves. By incorporating a greater understanding of the factors underlying cognitive tests, assessment developers can create more accurate and reliable measures of spatial-temporal reasoning and abstract reasoning abilities. This could lead to better diagnostic tools for identifying cognitive strengths and weaknesses, and subsequently inform more targeted and individualized interventions.

The findings of this study pave the way for numerous avenues of future research in the field of cognitive abilities. Firstly, addressing the limitations of the current study will be crucial for obtaining more reliable and generalizable results. Future research should endeavor to employ larger and more diverse samples, encompassing various age groups, educational backgrounds, and cultural contexts. This would enhance the external validity of the findings and provide a more comprehensive understanding of the factors underlying cognitive abilities.

Moreover, future studies should explore alternative testing methods, such as computerized assessments and innovative experimental paradigms, to assess the identified factors more accurately and minimize potential biases. By utilizing cutting-edge technology and novel approaches, researchers can obtain a more nuanced understanding of spatial-temporal reasoning and abstract reasoning.

Another promising direction for future research involves investigating the relationships between the identified factors—spatial-temporal reasoning and abstract reasoning—and other cognitive abilities, such as verbal reasoning, attention, and executive functions. By examining these associations, researchers can gain insights into the intricate interplay of cognitive abilities and further refine theoretical models of cognitive functioning.

Longitudinal studies represent another essential avenue for future research. By tracking the developmental trajectories of spatial-temporal reasoning and abstract reasoning over time, researchers can explore how these factors evolve and potentially interact throughout an individual's life. This could help elucidate the mechanisms driving cognitive development and inform targeted interventions aimed at fostering cognitive growth in various populations.

Limitations

Despite its contributions, the present study has several limitations that should be acknowledged. First, the sample size of 118 participants may not be large enough to provide robust estimates of factor loadings, and the participants may not represent a diverse range of abilities or backgrounds. Second, selection bias may have influenced the results, as participants were not randomly selected. Third, methodological limitations, such as the use of multiple-choice, timed, and untimed tests, may have introduced measurement errors, as these test formats may not equally capture the different abilities under investigation. Future research could explore the use of alternative testing methods to assess the underlying factors more accurately.

Directions for Future Research

Given the limitations and findings of the current study, future research should focus on the following areas:

1. Using larger and more diverse samples, to increase the generalizability and robustness of the findings.
2. Employing alternative testing methods to assess the underlying cognitive abilities more accurately.
3. Investigating the relationships among spatial-temporal reasoning, abstract reasoning, and cognitive tasks in various contexts and settings.
4. Examining the potential for training and improvement in spatial-temporal and abstract reasoning skills.
5. Exploring the implications of these cognitive abilities for academic achievement, problem-solving, creativity, and other domains.

By addressing these areas, future research can continue to expand our understanding of the complex relationships among spatial-temporal reasoning, abstract reasoning, and various cognitive tasks. This will, in turn, contribute to the development of more effective educational and training interventions, as well as a deeper understanding of how these cognitive abilities impact academic achievement, problem-solving, creativity, and other critical domains. Ultimately, such research can help to create a more comprehensive picture of human cognition and its applications, leading to improved educational practices and outcomes for learners of all ages and backgrounds.

Conclusion

This study aimed to identify underlying factors that could explain the relationships among various cognitive tests: Jouve Cerebrals Test of Induction (JCTI), General Ability Measure for Adults (GAMA) Matching (MAT), Analogies (ANA), Sequences (SEQ), and Construction (CON). Results revealed two factors: Factor 1, Spatial-temporal reasoning, which is strongly related to sequences and construction tasks, and Factor 2, Abstract Reasoning, which is more strongly related to matching tasks, analogies, and nonverbal reasoning tasks. These findings align with the existing literature on the significance of spatial-temporal reasoning and abstract reasoning in various aspects of cognition.

The study contributes to the understanding of the relationships among different cognitive tasks and highlights the importance of these underlying factors in cognitive development, working memory, and various other cognitive domains. However, limitations, such as sample size and selection bias, need to be acknowledged, and future research should consider larger, more diverse samples and alternative testing methods to further explore and validate these factors.

This research offers valuable insights into the structure of cognitive abilities and has implications for educational, clinical, and workplace settings. By better understanding the underlying factors of cognitive tasks, we can develop more effective interventions, assessments, and training programs tailored to individual needs. Future research should continue to investigate these factors and their broader implications for cognition, learning, and overall human development.

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