Empirical Support for the Theory of Multiple Intelligences

The Neuroscience of Intelligence: Empirical Support for the Theory of Multiple Intelligences?

C. Branton Shearer¹ and Jessica M. Karanian²

¹MI Research and Consulting

² Department of Psychology, Boston College

Corresponding Author:

C. Branton Shearer

1316 S. Lincoln St.

Kent, OH 44240

Tel.: (330) 687-1735

E-mail: sbranton@kent.edu

C. Branton Shearer is the creator of the Multiple Intelligences Developmental Assessment Scales.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Key Words:  intelligence, multiple intelligences, cognition, general intelligence, neural correlates

Abstract

The concept of intelligence has been strongly debated since introduction of IQ tests in the 1900s. Numerous alternatives to unitary intelligence have achieved limited acceptance by both psychologists and educators. Multiple intelligences theory (Gardner, H. (1983,1993). Frames of mind: The theory of multiple intelligences. New York: Basic Books), despite criticism that it lacks empirical validity, has had sustained interest by educators worldwide. MI theory was one of the first to be based on neuroscience evidence. This investigation reviewed 318 neuroscience reports to conclude that there is robust evidence that each intelligence possesses neural coherence comparable with general intelligence. Implications for using MI theory as a bridge between instruction and cognitive neuroscience are discussed.

The concept of intelligence has a checkered history in the minds of many scientists and educational theorists. Many have abandoned the concept in part or entirely, and instead investigate cognitive abilities, problem-solving, or information processing capacities. However, many scientists have also investigated the functional neural systems that underlie intellectual achievement. The reason for this has been summed up succinctly by Jung and Haier [1, p. 171] “...there is no more important concept in education than the concept of intelligence”They assert that not all brains think the same way, thus “this simple fact could be revolutionary for education because it demands a neuroscience approach that recognizes the importance of individual differences and the necessity to evaluate each student as an individual” [2, p. 174].

The theory of multiple intelligences (MI) is of primary interest to the present investigation. Howard Gardner [3,4]redefined intelligence as the ability to solve problems or create products of value in a culture or community.Using this broad, common sense definition and eight criteria* that cover a range of evidence (e.g., neuroscience, workplace behaviors, great cultural achievements), Gardner identified eight distinct forms of intelligence that are possessed by all people, but in varying degrees. The eight intelligences identified are linguistic, logical-mathematical, spatial, kinesthetic, musical, interpersonal, intrapersonal and naturalist (for detailed descriptions, see Appendix A).

Traditional psychologists criticize MI theory for a number of reasons. One criticism is that MI theory lacks support from large scale studies [4,5] or experimental research[7,8,9. It has also been proposed that the eight intelligences are simply different manifestations of general intelligence [10,11]. An important practical criticism is that educators should not base instructional and curricular decisions upon a theory that lacks support from neuroscience evidence [12,13] and is unsubstantiated and unproven[14,15,16].

Amongneuroscientists, the predominant view on intelligence is that there iseither one general intelligence (g) or two types of intelligence (fluid and crystallized). However, there is a debate regardingthepossible sub-divisions of intelligence and each sub-division’s relationship to “g”. Numerous other theories that deviate from the unitary intelligence theory – includingtriarchic[17], emotional intelligence [18,19], structure of intellect [20], faculties of mind [21], and cognitive styles [22] – have had noteworthy, but limited, influence. Many have been recognized by the field of psychology, but not embraced by educators. Few have had the lasting and profound impact on education as multiple intelligences theory which is still of interest world-wide more than 30 years after its introduction [3, 4, 23]. Despite this broad appeal to educators, MI remains more of an inspirational educational framework rather than a fully developed scientific theory [24, 25, 2].

The practical critiques are of particular importance as the emerging field of educational cognitive neuroscience strives to establish a foundation for neuroscientific evidence-based instructional approaches. This new field has struggled to build practical connections between brain activity and instruction/curriculum. In its early years, there was widespread skepticism that brain-based education could develop without an explicit use of psycho-educational theory to bridge between neuronal activity and instruction [26]. This situation has improved more recently [27, 28, 29, 30], but the field continues to struggle to make a distinction between “pop psychology” of brain-based teaching and the science of educational cognitive neuroscience that can be systematically applied.

(Table 1 here)

The following literature review organizes 30 years of cognitive neuroscienceresearch on human cognition into core cognitive units that are each associated with a particular intelligence. We compared theneuroscientific evidence for each intelligence to the cortical areas outlined byGardner [3 ,4](Table 1) to address the following inter-related questions: (1) do these neural functional structures and networks display shared coherence while being conceptually unique and distinct from other functions, (2) taken together, do these data describe a solid conceptual framework for the “neural architecture” underlying each of the eight intelligences, and (3) how well do these neural architectures compare to what is known about the neural basis for general intelligence (i.e., g theory)? It should be underscored that this review of the cognitive neuroscience literature in relation to MI theory is intended to provide a foundation rather than a definitive examination of the constantly evolving literature on the neural underpinnings of human cognition.

Methods

Procedures

This investigation began with a detailed review of the various cognitive units and specific skills associated with each intelligence. For example, musical intelligence includes instrumental, vocal, composing and appreciation. Each of these ability sets includes technical skill as well as creative performance (e.g., singing on key and jazz improvisation) so the review of musical neuroscience studies would ideally be inclusive of this range of abilities. Charts were constructed for each intelligence with rows for MI Cognitive Units and columns for matched Neural Structures and Cognitive Skills (linguistic sample in Appendix B. All data is available upon request).

Using the terms related to each Cognitive Unit or specific skill (Table 2), PubMed or Google Scholar were used to search for published peer-reviewed empirical neuroscience studies (neural organization Appendix C and journals list in Appendix D). The goal was to identify a minimum of three to five studies per major skill area. Surprisingly, a great many more studies were obtained. Studies of personality characteristics or dispositions were not included (e.g., introversion, diligence, etc.). Theoretical articles or books were used mainly for background information. Several extensive meta-analysis and topic reviews served as guides to finding pertinent studies in the target area. Over 318 articles were referenced for the eight intelligences. The minimum number of studies was 19 for Logical-mathematical with a maximum of 73 for Intrapersonal (Table 2).

(Table 2 here)

From this wealth of knowledge excerpts from each text describing neural activations associated with carefully defined cognitive skills were entered into the charts per Cognitive Unit(see linguistic sample in Appendix B and E). As the investigation proceeded, the labels and defining characteristics for various Cognitive Units were adjusted to better align the neuroscience evidence with MI theory (Table 2, columns 6 and 7). This became a dialectical process between compatible perspectives. The next step was for an objective neuroscience doctoral student to review the data charts and harmonize the various neural descriptors according to standard neural anatomical terminology. All neural regions were then put into an Excel spreadsheet and reorganized based on neural hierarchy (Appendices C and E).

It became a challenge to manage the varieties of neural terminology. Neuroscientific researchers have used a wide variety of terms and labels and specificity over the years as the technology has evolved. Some researchers identified broad regions with a single label while others used multiple terms to identify sub-regions. Still others used Brodmann numbering,TalairachAtlas or the MNI Coordinate system. This variety of nomenclaturesrequired a careful translation and mapping onto the three-level hierarchy (Primary, sub-regions and particular structures) described below.

Our analysis of this data employed both qualitative and quantitative methods to determine if a three-dimensional view of the neural structures associated with each intelligence could be created. This hybrid approach – qualitative and quantitative – reflects both the evolution of the field as well as how the brain processes information – from very specific to diffuse patterns of activation.Studies were included in this analysis regardless of the type of the subjects employed to better reflect a wide variety of abilities. Some studies used undifferentiated subjects while others included those with brain damage and still others required the use of subjects with specifically defined skills.

Analyses

First, we assessed the frequency of cited primary neural regions, which included the frontal cortex, temporal cortex, parietal cortex, occipital cortex, cingulate cortex, insular cortex, subcortical regions, and the cerebellum. We also ran a secondary analysis on the primary regions that were most associated with each of the intelligences (i.e., primary regions that represented at least 20% of the primary neural citations). Within the top cited primary regions, we identified the top sub-regions. All sub-regions that represented at least 20% of a top primary neural regions were reported. Lastly, in some instances, a third-level analysis was conducted to identify the important sub-regions within a sub-region of a top primary neural region (e.g., frontal cortex à prefrontal cortex à dorsomedial prefrontal cortex; Appendix E). These second-level and third-level analyses are highlighted in the text.

Results

The following descriptions are highlights from an extensive dataset (see Appendix F). Complete data and interpretations are available as supplemental material.

Interpersonal

The interpersonal literature review identified 53 studies, including 111 citations of primary neural regions. The core cognitive units of interpersonal intelligence include social perception, interpersonal understanding, social effectiveness, and leadership. Results from the analysis of the primary neural regions can be found in Table 3 and Figure 1.

The analysis of primary neural regions revealed that interpersonal intelligence was most associated with the frontal cortex (43 citations). Secondary analyses more specifically identified that the prefrontal cortex (PFC) accounted for the large majority of frontal cortex citations (33/43 = 76.74%). A third-level analysis revealed that the dorsolateral PFC was the dominant sub-region within the PFC (8/33 = 24%).

Interpersonal intelligence was also associated with the temporal cortex as revealed by 31 citations. Within the temporal cortex, the medial temporal lobe (9/31 = 29%), amygdala (8/31 = 26%), and the superior temporal sulcus (7/31 = 23%) were the predominantly cited sub-regions. Other notable regions associated with Interpersonal intelligence included the cingulate cortex (12 citations), particularly the anterior cingulate cortex (ACC; 8/12 = 75%), and the parietal cortex (10 citations).

(Table 3 here)

(Figure 1 here)

Intrapersonal

The intrapersonal literature review identified 73 studies, including 219 citations of primary neural regions. The core cognitive units of intrapersonal intelligence include self-awareness, self-regulation, executive functions, and self-other management. Results from the analysis of the primary neural regions can be found in Table 4 and Figure 2.

The primary analysis revealed that Intrapersonal intelligence was most associated with the frontal cortex (90 citations) – the large majority of which were specific to the PFC (73/90 = 81%). A third-level analysis within the PFC revealed the dorsomedial PFC (18/73 = 25%) and the lateral PFC (15/73 = 21%) as major sub-regions.

The primary analysis also identified the cingulate cortex (37 citations), temporal cortex (36 citations), parietal cortex (25 citations), and subcortical regions (20 citations). Within the cingulate cortex, dominant sub-regions included the anterior cingulate cortex (27/37 = 73%). Within the temporal cortex, notable sub-regions included the medial temporal lobe (9/36 = 25%), amygdala (8/36 = 22%), and anterior temporal cortex (8/36 = 22%). Within the parietal cortex, the secondary analysis revealed that medial regions (10/25 = 40%) and inferior regions (5/25 = 20%) were dominant. Lastly, within the subcortical regions, the basal ganglia (10/20 = 50%) and brainstem (9/20 = 45%) were dominant. These structures are associated with cognition, learning, reward management, and unconscious memory (motor control).

(Table 4 here)

(Figure 2 here)

Visual-Spatial

The visual-spatial intelligence literature review identified 37 studies, including 143 citations of primary neural regions. The core cognitive units of visual-spatial intelligence include spatial cognition, working with objects, visual arts, and spatial navigation. Results from the analysis of the primary neural regions can be found in Table 5 and Figure 3.

The primary analysis revealed the frontal cortex as the most associated with visual-spatial intelligence (56 citations). Within the frontal cortex, secondary analyses identified the motor cortex (21/56 = 38%) and PFC (17/56 = 31%) as most important. A third-level analysis within the motor cortex highlighted the premotor cortex (12/21 = 57%) and the primary motor cortex (5/21 = 24%) as dominant. Within the PFC, the third-level analysis revealed the dorsolateral PC as most dominant (6/17 = 35%).

Furthermore, the primary analysis identified the parietal cortex (29 citations) as the second most dominant neural region for visual-spatial intelligence. Within the parietal cortex, the intraparietal sulcus (7/29 = 24%) and superior parietal lobule (7/29 = 24%) were notable sub-regions. A third-level analysis within the superior parietal lobule identified the precuneus as dominant (3/7 = 43%).

Other regions of interest included the temporal cortex (23 citations), including the medial temporal lobe (8/23 = 35%). A third-level analysis within the medial temporal lobe identified the hippocampus as the most dominant sub-region (4/8 = 50%). Furthermore, the primary analysis identified the occipital cortex (14 citations) as associated with visual-spatial intelligence, and a secondary analysis within the occipital cortex specifically identified the primary visual cortex as the most dominant sub-region (6/14 = 43%).

(Table 5 here)

(Figure 3 here)

Naturalist

The naturalist literature review identified 25 studies, including 58 citations of primary neural regions. The core cognitive units of naturalist intelligence derived from MI theory as well as the neuroscience literatureincluded pattern cognition, understanding living entities (including animals and plant life), and science. Typical behaviors that were studied include perceiving animal forms, motion, and vocalization; reading animal’s actions, intentions & emotions; biological life detection; and taxonomic thinking. No studies were found pertaining to understanding plant life. Results from the analysis of the primary neural regions can be found in Table 6 and Figure 4.

Analysis of the primary neural regions revealed that naturalist intelligence is most associated with the temporal cortex (19 citations). Within the temporal cortex, the secondary analysis identified the superior temporal sulcus (6/19 = 32%) and amygdala (5/19 = 26%) as notable.

The primary analysis also revealed subcortical neural regions (16 citations) as important for naturalist intelligence. Notable subcortical regions included regions of the brainstem (5/16 = 31%), the thalamus (5/16 = 31%), and the basal ganglia (4/16 = 25%).

(Table 6 here)

(Figure 4 here)

Musical

The musical literature review identified 42 studies, including 103 citations of primary neural regions. The core cognitive units of musical intelligence include music perception, music and emotions, and music production. Results from the analysis of the primary neural regions can be found in Table 7 and Figure 5.

Musical intelligence was most associated with the frontal cortex (42 citations). Within the frontal cortex, the motor cortex (31/42 = 74%) was the most dominant sub-region. A third-level analysis revealed the premotor cortex (12/31 = 39%) and the supplementary motor area (10/31 = 32%) as the most dominant sub-regions.

The next most frequently cited region was the temporal cortex (28 citations). A secondary analysis revealed the most notable sub-region was the superior temporal gyrus (23/28 = 82%), including the primary auditory cortex (19/23 = 83%, as revealed by a third-level analysis). Of other note, subcortical regions (16 citations) were also implicated, primarily accounted for by the basal ganglia (11/16 = 69%, as revealed by a third-level analysis).

(Table 7 here)

(Figure 5 here)

Kinesthetic

The kinesthetic literature review identified 41 studies, including 142 citations of primary neural regions. The core cognitive units of kinesthetic intelligence included body awareness and control, whole body movement, dexterity, and other types of movement (e.g., imitation, embodied cognition, gestures). Results from the analysis of the primary neural regions can be found in Table 8 and Figure 6.

The primary neural region analysis revealed the frontal cortex as most frequently cited (61 citations). A secondary analysis revealed that the dominant sub-region of the frontal cortex for kinesthetic intelligence was the motor cortex (46/61 = 75%). A third-level analysis further identified the primary motor cortex (19/46 = 41%), premotor cortex (15/46 = 33%), and supplementary motor area (9/46 = 20%) as dominant sub-regions.

Furthermore, the primary analysis identified the parietal cortex as the next most associated primary region (33 citations) within kinesthetic intelligence. Within the parietal cortex, the posterior parietal cortex was associated with the most citations (7/33 = 21%). Other regions of interest identified by the primary analysis included subcortical regions (15 citations), including the basal ganglia (11/15 = 73%, as indicated by secondary analysis) and thalamus (4/15 = 27%, as indicated by secondary analysis), as well as the cerebellum (13 citations).

(Table 8 here)

(Figure 6 here)

Linguistic

The linguistic literature review identified 28 studies, including 124 citations of primary neural regions. The core cognitive units of linguistic intelligence included speech, reading, writing, and communication. Results from the analysis of the primary neural regions can be found in Table 9 and Figure 7.

The primary analysis revealed the temporal cortex (49 citations) as the most dominant. Within the temporal cortex, the secondary analysis highlighted the superior temporal gyrus (15/49 = 31%). Within the superior temporal gyrus, a third-level analysis identified Wernicke’s Area as most prominent (5/15 = 33%). 

The primary analysis for linguistic intelligence also identified the frontal cortex (33 citations) as a dominant region. The secondary analysis revealed the inferior frontal gyrus (14/33 = 42%) as dominant within the frontal cortex. Furthermore, a third-level analysis identified Broca’s Area within the inferior frontal gyrus as dominant (13/14 = 93%). The secondary analysis with the frontal cortex also identified the motor cortex (10/33 = 31%). Of note, the dominant sub-regions of both the temporal cortex and frontal cortex have been identified as critical for language processing, speech control, and speech production.

The parietal cortex was also identified as an important region for Linguistic intelligence (15 citations). A secondary analysis identified the inferior parietal lobule (10/15 = 67%) as accounting for the most parietal cortex citations, and a third-level analysis further identified both the supramarginal gyrus (4/10 = 40%) and the angular gyrus (4/10 = 40%) as dominant sub-regions of the inferior parietal lobule.

(Table 9 here)

(Figure 7 here)

Logical-Mathematical

The logical-mathematical literature review identified 19 studies, including 71 citations of primary neural regions. The core cognitive units of logical-mathematical intelligence were calculations and logical reasoning. Results from the analysis of the primary neural regions can be found in Table 10 and Figure 8.

The primary analysis revealed that logical-mathematical intelligence was most associated with the frontal cortex (25 citations). Within the frontal cortex, logical-mathematical intelligence was most associated with the PFC (11/25 = 44%) and the inferior frontal gyrus (5/20 = 25%). A third-level analysis of PFC revealed the dorsolateral PFC as the dominant sub-region (3/11 = 27%), and a third-level analysis of the inferior frontal gyrus revealed Broca’s Area as the dominant sub-region (4/5 = 80%). These regions have been associated with planning complex behavior, judgment, decision-making, and language processing.

The primary analysis also revealed that the parietal cortex (24 citations) was highly associated with logical-mathematical intelligence. Within the parietal cortex, logical-mathematical intelligence was primarily associated with the intraparietal sulcus (7/24 = 42%) and inferior parietal lobule (7/24 = 42%). A third-level analysis of the inferior parietal lobule revealed the angular gyrus as the dominant sub-region (5/7 = 71%). Furthermore, the secondary-level analysis of the parietal cortex identified the superior parietal lobule as a dominant sub-region (5/24 = 21%). Within the superior parietal lobule, the precuneus was most dominant (3/5 = 60%). These regions have been associated with planning, working memory, numerical operations, attention, language, and sensory interpretation.

To a lesser extent, logical-mathematical intelligence was also associated with the temporal cortex (15 citations), with the medial temporal lobe as a notable sub-region (4/15 = 27%). It is noteworthy that neural structures associated with logical-mathematical intelligence are also identified with general intelligence.

(Table 10 here)

(Figure 8 here)

General Intelligence

The general intelligence literature review identified 24 studies for two cognitive units: analytical thinking and verbal intelligence. From these studies, there were 100 citations for primary regions, 132 for sub-regions and 47 for specific frontal structures. 

General intelligence has four primary regions that account for 93% of its citations –frontal cortex (33 citations), parietal cortex (33 citations), temporal cortex (15 citations) and cingulate cortex (12 citations). There are very few citations within the occipital cortex (4 citations), subcortical regions (1 citation) and the cerebellum (1 citation). Interestingly, these dominant regions are the same four primary regions in the same order and nearly the same magnitude as cited for logical-mathematical intelligence. This indicates that both general intelligence and logical-math depend upon planning, complex reasoning, mental visualization, verbal comprehension, and judgment (see Table 11).

Second-level analyses revealed that the prefrontal cortex (12/33 = 36%) and the inferior frontal gyrus (6/33 = 18%) were the most dominant sub-regions of the frontal cortex, while the inferior parietal lobule (13/33 = 40%) was the most cited sub-region of the parietal cortex. Within the temporal cortex, the superior temporal gyrus was the most cited sub-region (3/15 = 20%), while the anterior cingulate cortex (8/12 = 67%) was the most cited sub-region within the cingulate cortex.These are sub-regions are largely associated with language, mathematical operations, complex problem-solving, judgment, and impulse control. The only exception is the anterior cingulate which is cited for general intelligence but not logical-math. This region is thought to act as a gateway between the frontal and parietal cortices and has beenassociated with early learning, decision making, empathy, and managing the effort required for dealing with difficult problems.

The top three frontal structures cited for general intelligence are also among the strongest for logical-mathematical – prefrontal cortex, inferior frontal gyrus and posterior inferior frontal gyrus. It is obvious that the frontal cortex is of fundamental importance to doing both math and logical thinking. An interesting distinction is that the intraparietal sulcus (IPS) is associated with logical-math but not general intelligence. IPS appears to have a particular role in the understanding and processing of numbers and numerosity. Additionally, it has been cited as a key structure for processing symbolic numerical information, visuospatial working memory,and theory of mind.

Taken together this constellation of neural regions appears to be a primary processing system for abstracting information and meaning from various kinds of sensory input requiring logical reasoning, verbal comprehension and multi-step planning and execution(P-FIT) [2].Meta-analysis of neural research on general intelligence conducted by [49, p. 24] extended the P-FIT model to“...propose an updated neurocognitive model for the brain bases of intelligence that includes insular cortex, posterior cingulate cortex and subcortical structures...”

(Table 11 here)

(Figure 9 here)

Summary of Results

Table 12 highlights the neural similarities and differences revealed by the primary neural regional analysis. For each intelligence, the primary neural regions are ranked based on the raw number of citations revealed by the literature review. The columns display the eight intelligences, while the rows represent the rank of each neural associate based on the frequency of citations associated with each intelligence. In some cells, multiple neural regions are listed – this simply reflects that those neural regions had identical citation frequencies. Highlights of the sub-regional activation pattern per intelligence are presented in Tables 13 – 16.

(Tables 12-16 here)

Discussion

A variety of models have been proposed as to the neural underpinnings of intelligence. One of the most accepted neural models for general intelligence (g) is called P-FIT** [1] which describes g as being comprised primarily of the parietal, frontal, and temporal regions. Other models have been offered for g as well [31, 32, 33, 33, 34 and others). Despite the significant influence of MI theory on the field of education, no study has directly and/or comprehensively assessed MI theory using neuroscientific techniques. However, since the arrival of functional neuroimaging in the 1990s,neuroscientists have extensively studied the neural underpinnings of human cognition.

Of present interest, such studies can be mapped onto each of the multiple intelligences first outlined by Gardner [3,4] (see Table 1).For example, aspects of cognition assessed within the neuroscience literature include linguistic [35, 36], logical-mathematical [37, 38], musical [39, 40], kinesthetic [41, 42], visual-spatial [43, 44], interpersonal [45, 46], and intrapersonal [47, 48].

Several inter-related questions regarding the neuroscientific evidence pertaining to eight hypothesized forms of intelligence and their relationship with general intelligence were investigated. First, the review revealed astrongcongruence among regions described by Gardner [3 ,4]and the cognitive neuroscience literature that has accumulated since the advent of functional neuroimaging.Such evidence provides support for MI theory.

A detailed examination of three levels of neural analysis was employed in this review: primary, sub-regions and particular structures within sub-regions. The primary neural region analysis divided the brain into eight large neural regions (i.e., frontal cortex, parietal cortex, temporal cortex, occipital cortex, cingulate cortex, insular cortex, cerebellum, and subcortical structures) most frequently cited in the literature. Six of the eight intelligences were most associated with the frontal cortex, while the other two intelligences revealed the temporal cortex as most dominant (see Table 12). The parietal and cingulate cortices were the next most frequently associated with the intelligences. Alternatively, the cerebellum and insular cortex were never ranked within the top three most associated neural regions for any of the eight intelligences.

These data highlight the commonalities among the eight intelligences. However, the primary region analysis largely identified distinct neural configurationsfor each intelligence (see Table 12). For example, none of intelligences shared the same top three ranked regions. Furthermore, the frequency of citations for each of the primary neural regions cited for each intelligence varies a great deal. The figures depicting the distribution of citation frequency are compelling evidence for these distinct regional patterns.

Secondary and tertiary neural sub-region analyses were conducted to identify the specific neural structures within the primary neural regions associated with each intelligence. Secondary sub-region analyses reveal which particular regions are most associated with each of the intelligences. For example, the frontal cortex accounted for approximately 40% of citations for both musical and intrapersonal, which may suggest a neural similarity. However, secondary analysis revealed that approximately 75% of the frontal cortex citations were specific to the motor cortex for musical intelligence, while approximately 81% of the frontal cortex citations were specific to the prefrontal cortex for intrapersonal intelligence. Critically, these two sub-regions of the PFC are quite distinct in function.

A third-level examination of specific structures within sub-regions describes a distinct configuration of structures responsible for processing each of the eight intelligences. For example, the visual-spatial intelligence is associated with the parietal cortex (primary level) and intraparietal and superior parietal lobule (sub-regions) and also the precuneus (third-level).  This example, and many others, highlights the necessity for including neural sub-region analyses to fully describe the neural substrates for each intelligence. For more extensive data on sub-region level differences, readers should refer to Appendix F and to the supplemental dataset.

Based on the detailed analysis of over 318neuroscience studies it appears there is robustevidence that each of the eight intelligences possesses its own unique neural architecture. There are also theoretically consistent commonalities among related intelligences. Understanding these unique configurations and commonalitiesprovides insight into how the brain processes a full range of intellectual products and performances. 

Finally, how well do these neural architectures compare to the neural correlates for general intelligence?As predicted by MI theory, the neural correlates for general intelligence are nearly identical to those responsible for processing the logical-mathematical and linguistic intelligences. The association is stronger for logical-mathematical than it is for linguistic. This may be because most neuroscientists use logical problem-solving tasks (e.g., Raven’s Progressive Matrices) as measures for g. Likewise, measures of verbal ability emphasize convergent problem-solving.

Limitations and Future Directions

            Several limitations to this analysis should be noted. First, by necessity the interpretation of the data from over 318 studies had to be conducted with broad-brush strokes that accentuate the frequency of neural citations for a specified class of cognitive behaviors. This approach can neglect or minimize the importance of a particular structure or even multi-region activation patterns and conductivity efficiencies. Also, instances of neural inhibition were missing from these accounts, which can play a crucial role in cognition (e.g., reduced critical thinking in the service of divergent thinking). A review of the neural data for each intelligence by an expert review panel would go a long way toward evaluating and clarifying the neural architecture for the intelligences.

            Second, this analysis has concentrated on the eight broad MI constructs, but perhaps of equal importance in the formulation of a robust scientific theory are the core cognitive units within each intelligence. These core units represent specific instances of skill and ability that require a fine-grained neural analysis within an overarching theoretical framework. This is analogousto the identification of working memory, attentional control and language processing as components of general intelligence. Both statistical and expert reviews will serve to clarify the neural and specific characteristics of these cognitive units.

            Third, an essential feature of any theory of intelligence is that it helps us to understand the differences among ability group levels[49]. A challenging next step for this investigation would be to describe key neural differences among impaired, typical and expert individuals for each intelligence (or components and combinations of intelligences).

            Fourth, the relationship among the eight intelligences and various information processing capacities (e.g., attention, concentration, cognitive control and memory, etc.) needs further clarification.This could also provide an opportunity to determine how logical problem-solving is related to all eight intelligences. This study has also revealed the possibility that there are several general cognitive abilities that are essential elements of MI theory – Insight / Intuition, Aesthetic Judgment and Creativity – that may be comparable to general intelligence. These capacities have neural correlates described in the literature, e.g., Qui, et al. [50], Fink, et al. [51] and Calvo-Merino, B. et al. [52]. A preliminary analysis is forthcoming.

This investigation focused on data that describes the localization of regions in the brain that are activated by intelligent performances in each area. As advocated by Basten, et al. [49, p. 27] such an analysis  “...can only be a first step in understanding how intelligence evolves from the brain...Only the integration of the current localization-focused results with neural network-based investigations of dynamic interactions in the brain may finally enable us to understand how the brain supports intelligent performance.”

Studies of inter-regional resting-state functional connectivity (rsFC) bySadaghiani [53] and many others have highlighted the importance of recognizing the influence of individual differences on task performance. A future review of rsFC research may shed light on questions regarding the influence of individual differences on academic achievement and life success. Furthermore, the neural overlap among intelligences needs further clarification as possible focus points for leveraging achievement in a particular skill by using a strength to enhance development. These findings could provide valuable information for guiding instructional interventions that are “personalized” to take into account each learner’s unique strengths for the direct improvement of deficits [13, 54].

Conclusions

This investigation uncovered a wealth of neuroscience evidence that describes in great detail the neural underpinnings of skills associated with both general intelligence and the eight multiple intelligences. To describe MI and g as mutually incompatible entities seems to be more of a cultural preference rather than a conclusion derived from the neuroscientific evidence. There are important points of confluence that might serve as a basis for a comprehensive theory of educational cognitive neuroscience. Due to theoretical disagreements and cultural biases, whether MI theory can serve as an effective interface between neuroscience and education remains an open question.Describing how the brain works is scientifically challenging but neuroscience is making great strides. It may prove to be an even harder task to create a Y-shaped bridge that merges IQ with MI to channel our energies into the “art of teaching” so that all students can develop their unique potential along with their academic skills.

Notes:

*To qualify as an intelligence, each set of abilities has to fair reasonably well in meeting eight criteria as specified in Frames of Mind ([3, p. 62 – 67]:

1-  identifiable cerebral systems

2-  evolutionary history and plausibility

3-  core set of operations

4-  meaning encoded in a symbol system

5-  a distinct developmental history & mastery

6-  savants, prodigies and exceptional people

7-  evidence from experimental psychology

8-  psychometric findings

Definition:  Intelligence is a biopsychological potential to process information that can be activated  in a cultural setting to solve problems or create products that are of value in a culture. Intelligence Reframed[4]

** Haier and Jung [2, p. 173]describe a widely distributed neural network model that underpins intelligence called the Parieto-Frontal Integration Theory (P-FIT)involving thefrontal lobes, parietal, temporal and occipital cortices.

“The P-FIT recognizes that our species gathers and processes information predominantly through auditory and/or visual means, usually in combination; thus, particular brainregions within the temporal and occipital lobes are critical to early processing of sensoryinformation: the extrastriate cortex (BAs 18, 19) and fusiform gyrus (BA 37), involving recognition and subsequent imagery and/or elaboration of visual input, and Wernicke’s area (BA 22), involving analysis and/or elaboration of syntax of auditory information. This basic sensory processing is then fed forward to the parietal cortex, predominantly the supramarginal (BA 40), inferior parietal (BA 7), and angular (BA 39) gyri, wherein structural symbolism and/or abstraction of the current set to alternative cognitive sets are generated and elaborated. The parietal cortex interacts with frontal regions (i.e., BAs 6, 9, 10, 45–47), which serve to hypothesis test various solutions to a given problem. Once the best solution emerges, the anterior cingulate (BA 32) is engaged to constrain response selection as well as inhibition of other competing responses. This process is critically dependent upon the fidelity of underlying white matter needed to facilitate rapid and error free transmission of data from posterior to frontal brain regions

References

[1] Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of

intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences,30,

135–187.

[2] Haier, R. & Jung, R. (2008) Brain imaging studies of intelligence and creativity: What is the

            picture for education?Roeper Review, 30:3, 171-180, DOI:10.1080/02783190802199347

[3] Gardner, H. (1983,1993). Frames of mind: The theory of multiple intelligences.

            New York:Basic Books.

[4] Gardner, H. (1999). Intelligence reframed: Multiple intelligences for the 21^st century. New

            York:Basic Books.

[5] Herrnstein, R., & Murray, C. (1994). The bell curve. New York: Free Press.

[6] Waterhouse, L. (2006b). Inadequate evidence for multiple intelligences, Mozart effect, and

emotional intelligence theories. Educational Psychologist, 41, 247–255.

[7] Sternberg, R. J. (1996). Successful intelligence. New York: Simon & Schuster.

[8] Traub, J. & Gardner, H. (1999). A debate on multiple intelligences. The Dana Foundation,

            downloaded 4-20-16 http://www.dana.org/Cerebrum/Default.aspx?id=39332

[9] Waterhouse, L. (2006a). Multiple intelligences, the Mozart effect, and emotional intelligence:

A critical review.Educational Psychologist, 41, 207–225.

[10] Jensen, A. (1999). The g factor: the science of mental ability. Psycoloquy. American

            Psychological Assn. http://psychprints.ecs.soton.ac.uk/archive/00000658/

[11] Visser, B., Ashton, M. & Vernon, P. (2006). Beyond g: Puttingmultiple intelligences theory

tothe test. Intelligence, 34, 487–502.

[12] Gerin, B. and Fien, H. (2016). Translating the neuroscience of physical activity to

education. Trends in Neuroscience and Education. 5, 12–19.

[13] Hale, J.B., et al. (2016). Reconciling individual differences with collective needs: The

juxtaposition of socialpolitical and neuroscience perspectives on remdiation and compensation of student skill deficits. Trends in Neuroscience and Education. 5, 41–51.

[14] Gottfredson, L. (Summer, 2004). Schools and the g factor.Wilson Quarterly. Washington,

            DC.The Woodrow Wilson International Center for Scholars.

[15] White, J. (1988). Do Howard Gardner’s multiple intelligences add up? London: Institute

            OfEducation, University of London.

[16] Willingham, D.T. (2004) Reframing the mind. Retrieved 10-1-04 from

http://educationnext.org/20043/18.html

[17]Sternberg, R.J. (1988). The triarchic mind. New York: Viking.

[18]Goleman, D. (1995). Emotional intelligence. New York: Bantam.

[19] Salovey, P., & Mayer, J. D. (1990). Emotional intelligence.Imagination, Cognition, and

            Personality, 9, 185–211.

[20] Guilford, J. L. (1954). The nature of human intelligence.New York: McGraw-Hill.

[21] Thurstone, L.L. (1938). Primary mental abilities. Chicago: University of Chicago Press.

[22]Kolb, A. Y., & Kolb, D. A. (2005). The Kolb learning style inventory – version 3.1: 2005

technical specifications. Boston: Hay Transforming Learning. www.​haygroup.​com/​tl.

[23]Chen, J., Moran, S. & Gardner, H. (Ed.) (2009). Multiple intelligences around the world.

            San Francisco, CA: Jossey-Bass.

[24] Brody, N. (1992). Intelligence. New York: Academic Press.

[25] Chomsky, N. (2009).  A conversation about multiple intelligences / an interview with Noam

            Chomsky. inMI at 25: Assessing the impact and future of multiple intelligences

for teaching and learning. New York: Teachers College Press.

[26] Bruer, J. T. (1997). Education and the brain: A bridge too far. EducationalResearcher,

            26, 4-16.

[27] Goswami, U., &Szucs, D. (2011). Educational neuroscience: Developmentalmechanisms:

            Towards a conceptual framework.NeuroImage, 57, 651–658.

[28] Immordino-Yang, M.H. &Gotlieb, R. (2016). Embodied brains, social minds, cultural

            meaning: Integrating neuroscientific and educational research on social-affective

            development (Manuscript accepted). American Educational Research Journal:

            Centennial Issue.

[29] Tokuhama-Espinosa, T. (2010). Mind, Brain, and Education Science: A Comprehensive

            Guide to the New Brain-Based Teaching. New York: WW Norton.

[30] Schwartz, M. (June, 2015). Mind, brain and education: A decade of evolution. Mind,

            Brain and Education.Vol. 9, 2.

[31] Barbey, K., Colom, R., Solomon, J.,Krueger, F., Forbes, C. Grafman, J., (2012). An

integrativearchitecture for general intelligence and executive function revealed by lesion

mapping. Brain, DOI: 10.1093/brain/aws021

[32] Duncan, J.et al. (2000).A neural basis for general intelligence.Science 289, 457.

            DOI: 10.1126/science.289.5478.457

[33] Grabner, R. et al. (2006).  Superior performance and neural efficiency: The impact of

intelligence and expertise. Brain Research Bulletin 69, 422–439.

[34] Neubauer, A. et al. (2005). Intelligence and neural efficiency: Further evidence of the

influence of task content and sex on the brain–IQ relationship. Cognitive Brain

Research, 25, 217–225.

[35] McCandliss, B.  andNoble, K.,  ( 2003). The development of reading impairment:

Acognitive neuroscience model. Mental Retardation And Developmental Disabilities.

Research Reviews9: 196–205.

[36]Price, C. (2012).  A review and synthesis of the first 20 years of PET and fMRI studies of

heard speech,spoken language and reading. NeuroImage62.816–847.

[37] Barbey, AK and Barsalou,LW. (2009). Reasoning and problem solving: Models.

            Encyclopedia of Neuroscience (2009), vol. 8, pp. 35-43.

[38] Dehaene, S., Molko, M.,Cohen, L. and Wilson, A. (2004).Arithmetic and the brain.Current Opinion in Neurobiology, 14:218–224. 

[39] Gaser, C. &Schlaug, G. (2003).Brain structures differ betweenmusicians and non-

musicians. J. Neurosci. 23, 9240–9245.

[40] Zatorre, R., and  Salimpoor, V. (2013). From perception to pleasure: Music and its neural

            substrates. PNAS,  vol. 110,  suppl.

[41] Brown S, Martinez MJ, Parsons LM (2006): The neural basis ofhuman dance. Cereb Cortex

            16:1157–1167.

[42] Jantzen, K., Steinberg, F. and  Kelso, J. (2008).Coordination dynamics of large-scale

neuralcircuitry underlying rhythmic sensorimotor behavior Journal of Cognitive

Neuroscience 21:12, pp. 2420–2433.

[43] Aziz-Zadeh,L.,  Liew,S., and Dandekar, F.  (2013). Exploring the neural correlates of visual creativitySCAN 8, 475 – 480.

[44] Cui, X. (2007). Vividness of mental imagery: Individual variability can be measured

            objectively. Vision Research 47 474–478.

[45] Decety,  J, Skelly, L., Kiehl, K.A.  (2013).    Brain response to empathy-eliciting

scenarios involving pain in incarcerated individuals with psychopathy.JAMA

Psychiatry.70(6):638-645. doi:10.1001/jamapsychiatry.2013.27.

[46] Adolphs, R. (2009). The social brain: Neural basis of social knowledge.Annu Rev Psychol.

            2009 ; 60: 693–716. doi:10.1146/annurev.psych.60.110707.163514

[47] Damasio, A.(2003). Mental self: The person within. Nature 423 (6937), 227.

[48] Northoff, G.,Heinzel, A., de Greck, M.,Bermpohl, F.,Dobrowolny, H.,Panksepp, J.

            (2006), Self-referential processing in our brain—A meta-analysis of imaging studies on

            the self. NeuroImage. Vol. 31 Issue 1, p440-457. 18p.

[49] Basten, U., Hilger, K. &Fiebach, C. J. (2015). Where smart brains are different: A

            quantitative meta-analysis of functional and structural brain imaging studies on

            intelligence. Intelligence, 51, 10–27.  http://dx.doi.org/10.1016/j.intell.2015.04.009

[50] Qiu, J. et al. (2008) The neural basis of insight problem solving: An event-related potential

            study. Brain and Cognition 68 (100–106).

[51] Fink, A. et al. (2007). Creativity meets neuroscience: Experimental tasks for the

            neuroscientific study of creative thinking. ScienceDirect.42, 68 – 76.

[52] Calvo-Merino, B. et al. (2007). Towards a sensorimotor aesthetics of performing art.

            Consciousness and Cognition 17, 911- 922.

[53]Sadaghiani, S. et al. (2010). The relation of ongoing brain activity, evoked neural responses,

and cognition. Front. Syst Neuroscience 4, 20.

[54] Chen, J., Krechevsky, M. &Viens, J. (1998). Building on children's strengths: The

            experience of project spectrum. New York: Teachers College Press

Tables

Table 1. The Neural Correlates of the Multiple Intelligences Originally Identified by Gardner 

Intelligences	Neural Regions
Interpersonal	Frontal lobes as integrating station, limbic system
Intrapersonal	Frontal lobe system
Logical-Mathematical	Left parietal lobes & adjacent temporal & occipital association areas, left hemisphere for verbal naming, right hemisphere for spatial organization, frontal system for planning and goal setting
Linguistic	Broca’s area in left inferior frontal cortex, Wernicke’s area in the left temporal lobe, lateral sulcus loop inferior parietal lobule
Spatial	Right parietal posterior, occipital lobe
Naturalist	Left parietal lobe for discriminating living from non-living entities
Musical	Right anterior temporal and frontal lobes
Kinesthetic	Cerebral motor strip, thalamus, basal ganglia, cerebellum

Source.[3] Frames of Mind (1983, 1993), [4] Intelligence Reframed (1999).

Table 2.Details of Neuroscience Literature Review for Multiple Intelligences.

Intelligence	Search terms	N	Years	Citations	Original Core Cognitive Units	Revised Core Cognitive Units
Linguistic	Verbal skill Reading Writing Speaking Rhetoric	28	1998–2015	362	-Language comprehension -Spoken language -Writing -Reading	-Speech -Reading -Writing -Multimodal Communication of Meaning
Logical-mathematical	Reasoning Calculations Math skill Abstraction Meaning making	19	2000–2013	177	-Calculations -Logical reasoning -Problem Solving	-Mathematical Reasoning -Logical Reasoning
Musical	Vocal / Singing Instrumental ability Musical appreciation Improvisation Music  emotions	42	1985-2013	288	-Perceiving pitch, melody, harmony, timbre and rhythm -Vocal singing -Emotional aspects of music -Instrumental music -Perception of both music and the sounds of human language	-Music Perception -Music and Emotions -Music Production
Kinesthetic	Large motor movement Fine motor Dexterity Tool use Eye Hand coordination Dance Athletics	41	1977-2015	349	-Fine motor movements -Large motor movements -Expressive Movements -Motor memory	-Body Awareness/Control -Whole Body Movement -Dexterity -Symbolic Movement
Spatial	Mental visualization Imagination Spatial orientation	37	1978–2015	385	-Spatial -Awareness -Working w/Objects -Art Perception -Art Production	-Spatial Cognition -Working with Objects -Visual Arts -Spatial Navigation
Interpersonal	Empathy Theory of mind Interpersonal perspective taking Leadership	53	1989–2013	294	-Empathy -Understanding Others -Leadership -Facilitator / Caregiver	-Social Perception -Interpersonal Understanding -Social Effectiveness -Leadership
Intrapersonal	Metacognition Emotional intelligence Self-management Impulse control	73	1998-2014	627	-Self Understanding -Metacognition -Emotional Management	-Self-Awareness -Self-Regulation -Executive Functions -Self-Other Management
Naturalist	Understanding animals Plant care Science Classification	25	1969–2015	172	-Understanding Animals -Understanding Plants -Pattern recognition -Science	-Pattern Cognition -Understanding Living Entities -Understanding Animals -Understanding Plant Life -Science
	Totals	318		2,654

Table 3. Interpersonal: Analysis of Primary Neural Regions

Interpersonal
Primary Neural Region	Citations (N=111)	% of Citations
Frontal Cortex	43	38.74
Temporal Cortex	31	27.93
Cingulate Cortex	12	10.81
Parietal Cortex	10	9.01
Insular Cortex	6	5.41
Occipital Cortex	4	3.60
Subcortical Structures	4	3.60
Cerebellum	1	0.90

Table 4. Intrapersonal: Analysis of Primary Neural Regions

Intrapersonal
Primary Neural Regions	Citations (N=219)	% of Citations
Frontal Cortex	90	41.10
Cingulate Cortex	37	16.89
Temporal Cortex	36	16.44
Parietal Cortex	25	11.42
Subcortical Structures	20	9.13
Insular Cortex	9	4.11
Cerebellum	2	0.91
Occipital Cortex	0	0.00

Table 5. Visual-spatial: Analysis of Primary Neural Regions

Spatial
Primary Neural Regions	Citations (N=143)	% of Citations
Frontal Cortex	56	39.16
Parietal Cortex	29	20.28
Temporal Cortex	23	16.08
Occipital Cortex	14	9.79
Subcortical Structures	12	8.39
Cerebellum	5	3.50
Cingulate Cortex	3	2.10
Insular Cortex	1	0.70

Table 6. Naturalist: Analysis of Primary Neural Regions

Naturalist
Primary Neural Regions	Citations (N=58)	% of Citations
Temporal Cortex	19	32.76
Subcortical Structures	16	27.59
Frontal Cortex	7	12.07
Occipital Cortex	7	12.07
Parietal Cortex	7	12.07
Cerebellum	1	1.72
Insular Cortex	1	1.72
Cingulate Cortex	0	0.00

Table 7. Musical: Analysis of Primary Neural Regions

Musical
Primary Neural Regions	Citations (N=103)	% of Citations
Frontal Cortex	42	40.78
Temporal Cortex	28	27.18
Subcortical Structures	16	15.53
Cerebellum	10	9.71
Parietal Cortex	5	4.85
Insular Cortex	2	1.94
Cingulate Cortex	0	0.00
Occipital Cortex	0	0.00

Table 8. Kinesthetic:Analysis of Primary Neural Regions.

Kinesthetic
Primary Neural Regions	Citations (N=142)	% of Citations
Frontal Cortex	61	42.96
Parietal Cortex	33	23.24
Subcortical Structures	15	10.56
Cerebellum	13	9.15
Temporal Cortex	8	5.63
Cingulate Cortex	6	4.23
Insular Cortex	5	3.52
Occipital Cortex	1	0.70

Table 9.Linguistic: Analysis of Primary Neural Regions

Linguistic
Primary Neural Regions	Citations (N=124)	% of Citations
Temporal Cortex	49	39.52
Frontal Cortex	33	26.61
Parietal Cortex	15	12.10
Occipital Cortex	9	7.26
Subcortical Structures	9	7.26
Cerebellum	5	4.03
Cingulate Cortex	2	1.61
Insular Cortex	2	1.61

Table 10. Logical-Mathematical: Analysis of Primary Neural Regions

Logical/Math
Primary Neural Regions	Citations (N=71)	% of Citations
Frontal Cortex	25	35.21
Parietal Cortex	24	33.80
Temporal Cortex	15	21.13
Cingulate Cortex	5	7.04
Insular Cortex	1	1.41
Occipital Cortex	1	1.41
Cerebellum	0	0.00
Subcortical Structures	0	0.00

Table 11. Neural Highlights for General Intelligence

General Intelligence Neural Highlights
Main	%	Sub-regions	%	Frontal Structures	Ct.
Frontal	33	Inferior Parietal Lobule	10	Prefrontal Cortex	12
Parietal	33	Prefrontal Cortex	9	Inferior Frontal Gyrus	6
Temporal	15	Anterior Cingulate	6	Posterior Inferior Frontal Gyrus	4
Cingulate	12	Inferior Frontal Gyrus	5	Broca’s Area	4
		Supramarginal Gyrus (Angular Gyrus)	4
Total	100	Total	132	Total	47

Table 12.Analysis of Primary Neural Regions: Summary of Relative Citation Frequencies.

		Intelligences
		Interpersonal	Intrapersonal	Logical-Math	Linguistic	Spatial	Naturalist	Musical	Kinesthetic
Rank	1	Frontal Cortex	Frontal Cortex	Frontal Cortex	Temporal Cortex	Frontal Cortex	Temporal Cortex	Frontal Cortex	Frontal Cortex
	2	Temporal Cortex	Cingulate Cortex	Parietal Cortex	Frontal Cortex	Parietal Cortex	Subcortical	Temporal Cortex	Parietal Cortex
	3	Cingulate Cortex	Temporal Cortex	Temporal Cortex	Parietal Cortex	Temporal Cortex	Frontal Cortex Parietal Cortex Occipital Cortex	Subcortical	Subcortical
	4	Parietal Cortex	Parietal Cortex	Cingulate Cortex	Occipital Cortex Subcortical	Occipital Cortex	-	Cerebellum	Cerebellum
	5	Insular Cortex	Subcortical	Occipital Cortex Insular Cortex	-	Subcortical	-	Parietal Cortex	Temporal Cortex
	6	Occipital Cortex Subcortical	Insular Cortex	-	Cerebellum	Cerebellum	Insular Cortex Cerebellum	Insular Cortex	Cingulate Cortex
	7	-	Cerebellum	Subcortical Cerebellum	Cingulate Cortex Insular Cortex	Cingulate Cortex	-	Occipital Cingulate Cortex	Insular Cortex
	8	Cerebellum	-	-	-	Insular Cortex	Cingulate Cortex	-	Occipital Cortex

Table 13.Interpersonal and Intrapersonal: A review of top neural structures

		Interpersonal		Intrapersonal
		Primary	Sub-regions	Primary	Sub-regions
Rank	1	Frontal Cortex	PFC	Frontal Cortex	PFC
	2	Temporal Cortex	Medial Temporal Lobe Amygdala Superior Temporal Sulcus	Cingulate Cortex	ACC
	3	Cingulate Cortex	ACC	Temporal Cortex	Medial Temporal Lobe Anterior Temporal Lobe Amygdala
	4	Parietal Cortex		Parietal Cortex	Medial Parietal Cortex Inferior Parietal Cortex
	5			Subcortical	Basal Ganglia Brainstem

Table 14.Logical-Mathematical and Linguistic: A review of top neural structures

		Logical-Mathematical		Linguistic
		Primary	Sub-regions	Primary	Sub-regions
Rank	1	Frontal Cortex	PFC Inferior Frontal Gyrus	Temporal Cortex	Superior Temporal Gyrus
	2	Parietal	Intraparietal Sulcus Inferior Parietal Lobule Angular Gyrus	Frontal Cortex	Broca’s Area Motor Cortex
	3	Temporal Cortex	Medial Temporal Lobe	Parietal	Inferior Parietal Lobule Supramarginal Gyrus Angular Gyrus

Table 15.Spatial and Naturalist: A review of top neural structures

		Spatial		Naturalist
		Primary	Sub-regions	Primary	Sub-regions
Rank	1	Frontal Cortex	Motor Cortex PFC	Temporal Cortex	Superior Temporal Sulcus Amygdala
	2	Parietal Cortex	Intraparietal Sulcus Superior Parietal Lobe	Subcortical Structures	Brainstem Thalamus Basal Ganglia
	3	Temporal Cortex	Medial Temporal Lobe	Frontal Cortex	-
	4	Occipital Cortex	-	Occipital Cortex	-
	5	-	-	Parietal Cortex	-

Table 16.Musical and Kinesthetic: A review of top neural structures

		Musical		Kinesthetic
		Primary	Sub-regions	Primary	Sub-regions
Rank	1	Frontal	Motor Cortex	Frontal Cortex	Motor Cortex Primary Motor Premotor Supplementary Motor
	2	Temporal Cortex	Superior Temporal Sulcus Primary Auditory Cortex	Parietal Cortex	Posterior Parietal Cortex
	3	Subcortical Structures	Basal Ganglia	Subcortical	Basal Ganglia Thalamus
	4	-	-	Cerebellum	-