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

C. Branton Shearer1 and Jessica M. Karanian2

1 MI Research and Consulting

2 Department of Psychology, Boston College

Corresponding Author:C. Branton Shearer1316 S. Lincoln St. Kent, OH 44240Tel.: (330) 687-1735E-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

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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.

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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].

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Among neuroscientists, the predominant view on intelligence is that there is either one

general intelligence (g) or two types of intelligence (fluid and crystallized). However, there is a

debate regarding the possible sub-divisions of intelligence and each sub-division’s relationship to

“g”. Numerous other theories that deviate from the unitary intelligence theory – including

triarchic [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)

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The following literature review organizes 30 years of cognitive neuroscience research on

human cognition into core cognitive units that are each associated with a particular intelligence.

We compared the neuroscientific evidence for each intelligence to the cortical areas outlined by

Gardner [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).

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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).

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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,

Talairach Atlas or the MNI Coordinate system. This variety of nomenclatures required 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

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important sub-regions within a sub-region of a top primary neural region (e.g., frontal cortex

prefrontal cortex 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

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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

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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%).

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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 literature included 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

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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

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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),

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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.

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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.

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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.

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General intelligence has four primary regions that account for 93% of its citations –

frontal is cortex (33 citations), tied with parietal cortex (33 citations), and temporal cortex (15

citations) and cingulate cortex (12 citations) are also close. There are very few citations for the

remaining four corticeswithin 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. Sub-regions accounting for 28% of the citations – inferior parietal lobule,

prefrontal cortex, inferior frontal gyrus and supramarginal gyrus – are also the highest cited for

logical-mathematical. 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 acts as a kind of gateway between the frontal and parietal cortices and is has been

associated with early learning, decision making, empathy, and managing the effort required for

dealing with difficult problems.

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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

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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

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investigated. First, the review revealed a strong congruence 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 configurations for 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

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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 318 neuroscience studies it appears there is robust

evidence 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 commonalities provides 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

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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

analogous to 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

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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) by Sadaghiani [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

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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.

24

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 plausibility3- core set of operations4- meaning encoded in a symbol system 5- a distinct developmental history & mastery 6- savants, prodigies and exceptional people7- 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 the frontal 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 brain regions within the temporal and occipital lobes are critical to early processing of sensory information: 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

25

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Tables

Table 1. The Neural Correlates of the Multiple Intelligences Originally Identified by Gardner Intelligences Neural RegionsInterpersonal Frontal lobes as integrating station, limbic systemIntrapersonal Frontal lobe systemLogical-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 lobeNaturalist Left parietal lobe for discriminating living from non-living entitiesMusical Right anterior temporal and frontal lobesKinesthetic Cerebral motor strip, thalamus, basal ganglia, cerebellum

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

30

Table 2. Details of Neuroscience Literature Review for Multiple Intelligences.Intelligence Search terms N Years Citations Original Core

Cognitive UnitsRevised Core Cognitive Units

Linguistic Verbal skillReadingWritingSpeakingRhetoric

28 1998–2015 362 -Language comprehension-Spoken language-Writing-Reading

-Speech-Reading-Writing-Multimodal Communication of Meaning

Logical-mathematical

ReasoningCalculationsMath skillAbstractionMeaning making

19 2000–2013 177 -Calculations-Logical reasoning-Problem Solving

-Mathematical Reasoning-Logical Reasoning

Musical Vocal / SingingInstrumental abilityMusical appreciationImprovisationMusic 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 movementFine motorDexterityTool useEye Hand coordinationDanceAthletics

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 visualizationImaginationSpatial orientation

37 1978–2015 385 -Spatial-Awareness-Working w/Objects-Art Perception-Art Production

-Spatial Cognition-Working with Objects-Visual Arts-Spatial Navigation

Interpersonal EmpathyTheory of mindInterpersonal perspective takingLeadership

53 1989–2013 294 -Empathy-Understanding Others-Leadership-Facilitator / Caregiver

-Social Perception-Interpersonal Understanding-Social Effectiveness-Leadership

Intrapersonal MetacognitionEmotional intelligenceSelf-managementImpulse control

73 1998-2014 627 -Self Understanding-Metacognition-Emotional Management

-Self-Awareness-Self-Regulation-Executive Functions-Self-Other Management

31

Naturalist Understanding animalsPlant careScienceClassification

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

32

Table 3. Interpersonal: Analysis of Primary Neural RegionsInterpersonalPrimary Neural Region Citations (N=111) % of CitationsFrontal Cortex 43 38.74Temporal Cortex 31 27.93Cingulate Cortex 12 10.81Parietal Cortex 10 9.01Insular Cortex 6 5.41Occipital Cortex 4 3.60Subcortical Structures 4 3.60Cerebellum 1 0.90

33

Table 4. Intrapersonal: Analysis of Primary Neural Regions IntrapersonalPrimary Neural Regions Citations (N=219) % of CitationsFrontal Cortex 90 41.10Cingulate Cortex 37 16.89Temporal Cortex 36 16.44Parietal Cortex 25 11.42Subcortical Structures 20 9.13Insular Cortex 9 4.11Cerebellum 2 0.91Occipital Cortex 0 0.00

34

Table 5. Visual-spatial: Analysis of Primary Neural RegionsSpatialPrimary Neural Regions Citations (N=143) % of CitationsFrontal Cortex 56 39.16Parietal Cortex 29 20.28Temporal Cortex 23 16.08Occipital Cortex 14 9.79Subcortical Structures 12 8.39Cerebellum 5 3.50Cingulate Cortex 3 2.10Insular Cortex 1 0.70

35

Table 6. Naturalist: Analysis of Primary Neural RegionsNaturalistPrimary Neural Regions Citations (N=58) % of CitationsTemporal Cortex 19 32.76Subcortical Structures 16 27.59Frontal Cortex 7 12.07Occipital Cortex 7 12.07Parietal Cortex 7 12.07Cerebellum 1 1.72Insular Cortex 1 1.72Cingulate Cortex 0 0.00

36

Table 7. Musical: Analysis of Primary Neural RegionsMusicalPrimary Neural Regions Citations (N=103) % of CitationsFrontal Cortex 42 40.78Temporal Cortex 28 27.18Subcortical Structures 16 15.53Cerebellum 10 9.71Parietal Cortex 5 4.85Insular Cortex 2 1.94Cingulate Cortex 0 0.00Occipital Cortex 0 0.00

37

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

38

Table 9. Linguistic: Analysis of Primary Neural Regions

39

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

Table 10. Logical-Mathematical: Analysis of Primary Neural RegionsLogical/MathPrimary Neural Regions Citations (N=71) % of CitationsFrontal Cortex 25 35.21Parietal Cortex 24 33.80Temporal Cortex 15 21.13Cingulate Cortex 5 7.04Insular Cortex 1 1.41Occipital Cortex 1 1.41Cerebellum 0 0.00Subcortical Structures 0 0.00

40

Table 11. Neural Highlights for General IntelligenceGeneral Intelligence Neural HighlightsMain % Sub-regions % Frontal Structures Ct.Frontal 33 Inferior Parietal Lobule 10 Prefrontal Cortex 12Parietal 33 Prefrontal Cortex 9 Inferior Frontal Gyrus 6Temporal 15 Anterior Cingulate 6 Posterior Inferior Frontal Gyrus 4Cingulate 12 Inferior Frontal Gyrus 5 Broca’s Area 4

Supramarginal Gyrus (Angular Gyrus)

4

Total 100 Total 132 Total 47

41

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

Interpersonal Intrapersonal Logical-Math Linguistic Spatial Naturalist Musical Kinesthetic

Ran

k

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 CortexParietal CortexOccipital Cortex

Subcortical Subcortical

4 Parietal Cortex Parietal Cortex Cingulate Cortex

Occipital CortexSubcortical

Occipital Cortex - Cerebellum Cerebellum

5 Insular Cortex Subcortical Occipital CortexInsular Cortex - Subcortical - Parietal Cortex Temporal

Cortex

6 Occipital CortexSubcortical Insular Cortex - Cerebellum Cerebellum Insular Cortex

Cerebellum Insular Cortex Cingulate Cortex

7 - Cerebellum SubcorticalCerebellum

Cingulate CortexInsular Cortex

Cingulate Cortex -

OccipitalCingulate

CortexInsular Cortex

8 Cerebellum - - - Insular Cortex Cingulate Cortex - Occipital Cortex

42

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

Primary Sub-regions Primary Sub-regions

Ran

k

1 Frontal Cortex PFC Frontal Cortex PFC

2 Temporal CortexMedial Temporal Lobe

AmygdalaSuperior Temporal Sulcus

Cingulate Cortex ACC

3 Cingulate Cortex ACC Temporal CortexMedial Temporal Lobe

Anterior Temporal LobeAmygdala

4 Parietal Cortex Parietal Cortex Medial Parietal CortexInferior Parietal Cortex

5 Subcortical Basal GangliaBrainstem

43

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

Primary Sub-regions Primary Sub-regionsR

ank

1 Frontal Cortex PFCInferior Frontal Gyrus Temporal Cortex Superior Temporal

Gyrus

2 ParietalIntraparietal Sulcus

Inferior Parietal LobuleAngular Gyrus

Frontal Cortex Broca’s AreaMotor Cortex

3 Temporal Cortex Medial Temporal Lobe ParietalInferior Parietal LobuleSupramarginal Gyrus

Angular Gyrus

44

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

Primary Sub-regions Primary Sub-regions

Ran

k

1 Frontal Cortex Motor CortexPFC Temporal Cortex

Superior Temporal Sulcus

Amygdala

2 Parietal Cortex Intraparietal SulcusSuperior Parietal Lobe

Subcortical Structures

BrainstemThalamus

Basal Ganglia

3 Temporal Cortex Medial Temporal Lobe Frontal Cortex -

4 Occipital Cortex - Occipital Cortex -

5 - - Parietal Cortex -

45

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

Primary Sub-regions Primary Sub-regions

Ran

k

1 Frontal Motor Cortex Frontal Cortex

Motor CortexPrimary Motor

PremotorSupplementary Motor

2 Temporal Cortex Superior Temporal SulcusPrimary Auditory Cortex Parietal Cortex Posterior Parietal Cortex

3 Subcortical Structures Basal Ganglia Subcortical

Basal GangliaThalamus

4 - - Cerebellum -

46