1126-6715-3-PB.pdf

Neuroscience Communications 2016; 2: e1126. doi: 10.14800/nc.1126; © 2016 by Kazuo Nishimura, et al.

http://www.smartscitech.com/index.php/nc

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Individual differences in mental imagery tasks: a study of

visual thinkers and verbal thinkers

Kazuo Nishimura1, Takaaki Aoki2, Michiyo Inagawa3, Yoshikazu Tobinaga4, Sunao Iwaki5

1RIEB, Kobe University, Kobe, 657-8501, Japan; Santa Fe Institute, Santa Fe, New Mexico, 87501, USA 2Institute of Economic Research, Kyoto University, Kyoto, 606-8501, Japan 3Medical Welfare Center, St. Joseph Hospital, Kyoto, 603-8323, Japan 4Elegaphy, Inc., Otsu, 520-0225, Japan 5Human Technology Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, 305-8566, Japan

Correspondence: Kazuo Nishimura

E-mail: [email protected]

Received: November 17, 2015

Published online: January 11, 2016

In the context of individual differences in human thinking patterns, the present study examined differences

between visual thinking and verbal thinking, specifically with regard to brain activity associated with each. In a

recent study, we divided subjects into visual thinkers and non-visual thinkers according to their responses to a

questionnaire survey, and took magnetoencephalogram (MEG) measurements while they underwent verbal and

visual tasks [1]. This revealed significant differences in brain activation patterns near both the primary visual

area and frontal language area, particularly during the verbal task [1]. From this, we surmised that human

original thinking can be divided into visual and verbal types. Our neurological analyses of the diversity of

individual thinking characteristics can likely be applied to the fields of economics and education.

Keywords: Visual thinkers and verbal thinkers; Mental imagery; Beta-band and gamma-band activities; Group

differences; Magnetoencephalography

To cite this article: Kazuo Nishimura, et al. Individual differences in mental imagery tasks: a study of visual thinkers and

verbal thinkers. Neurosci Commun 2016; 2: e1126. doi: 10.14800/nc.1126.

Copyright: © 2016 The Authors. Licensed under a Creative Commons Attribution 4.0 International License which allows

users including authors of articles to copy and redistribute the material in any medium or format, in addition to remix,

transform, and build upon the material for any purpose, even commercially, as long as the author and original source are

properly cited or credited.

Introduction

In the field of economics, neuroeconomics has received a

great deal of interest. This field examines economic behavior

and its association with accompanying brain activity by

integrating research in experimental economics and

behavioral economics, fusing them with that of neuroscience.

For example, one study of neuroeconomics found that the

prefrontal cortex is active when choices are made about the

distant future, while the limbic system of the cerebrum is

active when choices are made about the near future [2]. In

other words, different regions of the brain are used when

making choices pertaining to the near versus distant future.

These studies seem to take the approach of obtaining the

overall perspective concerning the average human being.

However, conventional economics places a strong

emphasis on conducting studies that address resource

distribution in a society comprising heterogeneous agents [3].

Individuals can be divided into two completely different

groups of risk lovers and risk averters based on attitudes

toward danger [4]. In game theory, some have studied

RESEARCH HIGHLIGHT



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interdependent relationships such as conflicts of interest or

collaboration between different individuals, as well as

decisions on strategy [5].

In general, comprehension of another individual’s thought

process is difficult. The psychological state that would allow

one to stand in another’s shoes and understand him or her is

called the “theory of mind” in psychology [6]. The theory of

mind has been studied extensively in young children in their

formative stages, and in individuals with developmental

disorders such as autism spectrum disorder [7]. However,

given the diversity of individual personalities and demeanors,

even healthy individuals in the general population require

high level communication skills to grasp another’s

personality or sensitivities and interpret their emotional

dynamics. For example, we must collect information related

to another’s personality and actions, or predict long-term

their personality and sensitivities as the relationship develops

with time.

While the importance of distinguishing between

individuals based on differences in thinking patterns

(regardless of whether they are congenital or acquired) is

widely acknowledged, only a few studies have addressed

economic behaviors among different individuals. In

particular, studies that examine how differences in individual

thinking characteristics affect decision-making are incredibly

scarce. Against this backdrop, our research has focused on

understanding why different individuals end up making

different choices and how brain function differs in these

situations. We anticipate that research on thinking

characteristics and decision-making will form the foundation

for understanding judgments and decisions made by

consumers and investors regarding economic challenges.

Thinking comprises verbal thinking (using words to think)

and visual thinking (using images, rather than words, to

think), and some have studied differences between the two

using brain measurements [8]. In the same way that some

people can remember the faces of those they have just met,

while others cannot, individuals also vary in their capacity to

evoke images of certain things. It is known that individuals

who can remember a large amount of information in a short

period do so using visual images. When playing chess, a

professional chess player can evoke images of the patterns of

chess pieces and move their chess pieces accordingly without

verbalizing these thoughts. In general, these individuals use

more dominantly the right hemisphere of their brain over the

left hemisphere, as well as the visual area over the verbal

area. In addition to professional chess players, many who

work in design and imaging occupations share this tendency

toward visual dominance.

Individuals whose thinking is dominated by verbal

Table 1. Questionnaire and Tasks: Five-item questionnaire (a) was used to assess subjects’ ability to form

mental images. Tasks (b) were used in the experiment



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thinking tend to comprehend things in order. They have

difficulty starting a novel halfway through, or listening to a

mathematical lecture halfway through. These individuals

must read from the beginning, or listen from the start, in

order to understand the content. When they think, they think

while talking to themselves. This differs from visually

dominant thinkers, who think while moving images around.

In a recent study, we divided subjects into two groups

based on whether they had strong or weak visualization

ability to form mental imagery. We then proceeded to

examine how this ability at the time of thinking affected

global brain activation patterns, as well as differences in

local activation patterns that emerged in a region of interest

(ROI) [1]. With the prediction that the strength of

visualization ability is associated with a tendency toward

visual or verbal thinking, we hypothesized that “when

undergoing a visual or verbal task, those with strong imaging

capacities will have more activation in the visual area, while

those with weak imaging capacities will show more

activation in the frontal language area, as assessed with

high-β and low-γ bands.” To test this hypothesis, we

administered block design tasks to our subjects and

conducted magnetoencephalography (MEG) using SQUID.

This experimental task has been used in previous studies [9-11].

Subjects were divided into groups according to whether they

had strong or weak visualization abilities using a survey

developed in a previous study that uses visual and verbal

factors to classify individuals in this manner [8]. The survey

we used was created independently, but was based on

essentially the same principle [9-11]. Comparison of brain

activity in the two groups revealed that those with strong

visualization ability tended to be visual thinkers, while those

with weak visualization ability tended to be verbal thinkers.

Below, we will discuss the research in light of results from

our recent paper, together with some complementary findings [1].

Materials and methods

Experiments were conducted on four dates from August 2

through September 13, 2011, at the National Institute of

Advanced Industrial Science and Technology in Ikeda City

(Osaka, Japan). Individuals subject to analysis totaled 13 (11

males, 2 females), and were divided ahead of time into two

groups according to their dominant thinking pattern as

determined by the survey. Actual question items used to

divide subjects are shown in Table 1(a). For each “A”

selected for any of the questions, the respondent received 1

point, and each “B” received 0 points. Those who chose

more “A”s than “B”s (i.e., had a total score of 3 or higher)

were classified as Group I (strong visualizers). Those with a

total score of 2 or lower were classified as Group L (weak

visualizers).

Each experimental session comprised 2 back-to-back

repeated sequences in which a subject was asked to do the

following: envision Kiyomizu-dera (a famous temple in

Kyoto), envision the Japanese House of Parliament, recall the

12 signs of the Chinese zodiac, recall a conversation they had

with someone that day, and cease thinking at rest (Table 1b).

Ten seconds was allotted to each task, with no breaks in

between. The reasoning behind each task was as follows:

envisioning Kiyomizu-dera and the Japanese House of

Parliament required them to imagine a book or photograph

(1, 2). Recalling the 12 signs of the Chinese zodiac led them

to chant nouns in their head (3). Recalling a personal

conversation challenged them to remember an encounter with

someone (4). Tasks 5 and 6 were done to have them rest. Our

intent was to measure neurological activity while subjects

were thinking as they performed tasks 1 and 2 for visual

imagery (hereafter, visual conditions) and 3 and 4 for verbal

recollection (hereafter, verbal conditions), and then compare

these measurements with those taken while subjects were

still and rest during tasks 5 and 6 (rest conditions) in order to

Figure. 1. SQUID sensor locations. Sensor Nos. v1 and v2 monitored visual areas. Sensor Nos. b1, b2 and b3 monitored frontal language areas. More specifically, Nos. v1 and v2 designate primary visual and early visual areas, respectively. Nos. b1, b2 and b3 designate frontal language areas in

the middle frontal gyri (b1) and in the left inferior frontal gyri (b2 and b3). No. b3 corresponds to so-called Broca’s area.



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identify any increases in measurements relative to those at

baseline (tasks 5 and 6).

A whole-cortex-type 122-channel direct current SQUID

system (Neuromag 122, Elekta-Neuromag, Helsinki,

Finland) was used for MEG measurements. Several groups

have published protocols, theorems, and spectrum analyses

according to MEG [12-19]. For each channel, the MEG signal

was measured, and a short-time Fourier transform was

applied to each 1/5 second interval. We thereby derived an

estimated spectral density for all 61 sensor locations in 5 Hz

widths. For each condition (visual, verbal, and rest), means

for the two groups were determined. In order to examine

activation patterns under visual conditions and verbal

conditions relative to baseline, we calculated the mean ratios

of these measurements relative to the mean from the rest

conditions for each sensor.

Results

In this section, we will report some of our findings, with

new results on additional frequency bands and sensors [1].

Figure 2. Plots of the sensor-specific spectral densities under the visual condition (upper

row) and the verbal condition (lower row), for 27.5–32.5 Hz (central frequency: 30 Hz). Fig. 2 plots the sensor-specific spectral densities (the ratios) under the visual condition (upper row) and the verbal condition (lower row).They are relative to the resting condition. All figures represent the results

of nonparametric regression smoothing on the layout map as shown in Fig. 1. In order to improve the MEG measurement sensitivity for detecting global changes in neural activity, the spectral densities are normalized by the mean of densities of all sensors measured under the corresponding conditions. In each figure, the left-most images present the data for Group I, the middle images present the data for Group L, and the right-most images present the ratios of the Group I data relative to Group L data.



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Figure 1 shows the distribution of the 122 sensors made into

61 pairs. Figure 2 is a color-coded display of the low-γ band

(27.5 Hz-32.5 Hz, central frequency of 30 Hz) spectral

density for each sensor under visual conditions (upper panel)

and verbal conditions (lower panel) in each group, presented

as ratios to that for rest conditions. Group I is in the first row,

Group L is in the second row, and the ratio of Group I to

Group L is in the third row. For each of these figures,

smoothing by nonparametric regression was performed

according to the sensor layout map shown in Figure 1.

In rows 1 and 2 of Figure 2, Group I and Group L both

show activation from the parietal region to the frontal lobe

under both visual and verbal conditions, and represent

“thinking” characteristics. In addition, as shown in row 3 (far

right), the left side near the visual area of Group I shows

more activation (reddish tint) relative to that of Group L.

This suggests that relative to Group L, those in Group I

(visual thinkers as defined by the survey) had more neural

activity near the visual area. Observations of the frontal

language area, which is located within the middle frontal gyri

and left inferior frontal gyri, revealed more activity in Group

L (bluish tint). The high-β bands (22.5 Hz-27.5 Hz, central

frequency of 25 Hz) showed a similar tendency to that of

low-γ bands (central frequency 30 Hz). For more on this,

refer to Figure 1 of our recent study [1].

From among the sensors thought to be near the visual

area and frontal language area, we selected some from each

area (Figure 1): v1 and v2 from the visual area and b1, b2,

and b3 from the frontal language area. The primary visual

area and early visual area were indicated by v1 and v2,

respectively. Frontal language areas in the middle frontal

gyri were indicated by b1, while those in the left inferior

frontal gyri were indicated by b2 and b3. In particular, b3 in

the left inferior frontal gyri corresponded to Broca’s area.

Figure 3 shows the Group I spectrogram (ratio against

Group L) for sensors v1, b1, and b3 for visual and verbal

conditions. The y-axis of the spectrogram shows the

frequency (9 frequency bands, central frequency of 10-50

Hz), while the x-axis shows the measurement time (0-20/3

sec). The y-axis is divided into 5 Hz intervals, and the x-axis

into 1/15 sec intervals, presenting a color-coded version of

spectrum density (ratio). Information on sensors v2 and b2

can be found in Figure 3a of our recent study [1].

Figure 3. Temporal variations of spectral densities in the ROI’s (visual and frontal language areas). Spectrograms represent the spectral intensities (the ratios) of the major regions of interest (v1: primary visual area, b1: frontal language area in the middle frontal gyri, and b3: Broca’s area) for Group I relative to Group L. The horizontal axis designates the measurement time (interval 1/15 second, total duration 20/3 seconds). The vertical axis designates the central frequency of the frequency bands (overall range of the central frequency 10–50 Hz, with steps of each 5 Hz wide). The left column presents the data under the visual condition, and the right column presents the

data under the verbal condition.



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Sensors in the visual area show an overall reddish tint, and

are consistent with the results of Figure 2, as they indicate

more activity in Group I. Particularly for v1, β bands with

central frequencies of 20 and 25 Hz (y axis) show a more

highly activated band (reddish) in the direction of the x-axis

(time). This corresponds to our previous observation of the

presence of low-γ band activity (around 30 Hz) during image

processing [20]. On the other hand, compared to sensors in the

visual area, those in the frontal language areas show an

overall bluish tint, with Group L showing markedly higher

activation, particularly under verbal conditions. Sensors b1

and b3 in particular show band-like areas of higher activation

(more blue) in the direction of the x-axis (time), specifically

for high-β/low-γ bands (y-axis) with central frequencies of

25 and 30 Hz.

Next, for sensors v1 (primary visual area) and b3 (Broca’s

area), we evaluated group-dependent differences in activation

patterns of the high-β band (25 Hz) and low-γ band (30 Hz)

under both conditions, testing for both fixed and random

effects. We also conducted a 3-D current source estimation

of brain activity using a spatial filter, and assessed

group-dependent differences in the primary visual area

(BA17) (MNI coordinates: [6-75-6] (mm)) and Broca’s area

(BA44) ([-51 27 18] (mm)) using fixed effects analysis.

Many previous studies have published on source localization

using MEG data [21-25].

Statistical analysis revealed that for both the sensor-based

and spatial filter-based approaches, consistent findings

obtained are as follows. Group I showed more activation in

the visual area compared to Group L, markedly so for the

high-β band (25 Hz). Group L showed more activation in the

frontal language area than Group I, markedly so for the low-γ

band (central frequency, 30 Hz).

Discussion

Group-dependent differences in global activation patterns

The present study aimed to demonstrate that individuals

who use more imaging in their spontaneous thinking show

more activity in the visual area when undergoing tasks,

relative to those who use less imaging. Research using fMRI

has found a significant correlation between an individual’s

subjective “vividness” of visual imagery and activity in the

visual area [26]. In addition, visual imagery activities involve

not only the visual area but associated areas from the frontal

lobe to the parietal area, involving activation of these areas as

well [27]. Our analysis revealed activation, not only in the

low-γ bands (central frequency 30 Hz; Figure 2), but for α, β,

and γ bands in the region spanning the frontal lobe to parietal

area. This was observed in both groups, regardless of visual

or verbal conditions (Figure 2, rows 1, 2). On the other hand,

both groups showed reduced activation near the visual area

(areas centered around sensor pairs v1 and v2) under both

conditions [1]. For both visual and verbal conditions, the ratio

of Group I to Group L revealed more activation in Group I

(Figure 2, row 3). We therefore conclude that our hypothesis

was supported, as Group I had higher activity than Group L

in the occipital area including the visual area while

performing the tasks. This result was consistent with that of

our previous study [9].

Importance of β and γ bands for spontaneous imagery

One study examined α (8-14 Hz) and β (14-24 Hz) band

brain activity during visual imagery [28]. During visual

imagery resulting from external stimulation, increased

activity is evident for θ waves and β waves in the frontal lobe

(particularly the medial superior frontal gyrus), as well as α

waves and β waves of the parietal area (particularly the

superior parietal lobe) [27]. In addition, γ bands play an

important role in higher brain function, especially in visual

cognition [29, 30]. Other research studies using EEG/MEG

have demonstrated that high-β bands and low-γ bands are

appropriate indicators that can be used to evaluate the degree

of individual competency with regard to visual and verbal

task processing [20, 31-33].

The present study analyzed α waves (central frequency 10

Hz), β waves (central frequency 15, 20, 25 Hz), and γ waves

(central frequency 30, 35, 40, 45, 50 Hz) (see spectrogram in

Figure 3a and Supplementary Figures from our recent study [1]). This revealed that Group I had higher activation with

regard to α waves, but particularly the high-β bands (central

frequency, 25 Hz) and low-γ bands (central frequency, 30

Hz) in the occipital area including the visual area.

Visual condition for visual imagery and verbal condition for

verbal recollection

While it is certainly necessary to distinguish between

perception of external stimuli and spontaneous imagery, the

two share similarities (in particular, activity near the visual

area) as well as differ with regard to brain activity [28, 34-40].

We focused on spontaneously occurring thinking, and

divided this into visual thinking and verbal thinking. The

visual thinking considered here is that which creates images,

while verbal thinking is that comprising self-talk. Some

studies have analyzed visual thinking and verbal thinking as

they relate to the strength of an individual’s ability for

imagery [1, 8, 41-43]. One such study examined the interaction

between activity in the frontal cortical area and that in

Wernicke’s area during verbal thinking [41]. We found,



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especially under verbal condition, that Group I showed

higher activation in the visual area associated with visual

cognition, while Group L had higher activation in the frontal

language area associated with verbal expression in the

middle frontal gyri and left inferior frontal gyri. This

indicates that strong visualizers are visual thinkers, while

weak visualizers are verbal thinkers. In other words, this

study presents evidence for an association between one’s

ability for mental imagery and brain activity.

Conclusions

Compared to measurement of neural activity evoked by

external stimuli of the visual or auditory organs, measuring

spontaneous neural activity has been considered difficult.

However, the present study used a SQUID device that

enables highly sensitive and non-invasive measurements of

spontaneous brain activity, which allowed for successful

examination of individual differences.

Specifically, global activation patterns for spontaneous

thinking activities revealed that strong visualizers showed

marked differences in brain activity from weak visualizers.

When the analysis was limited to a region of interest (ROI),

the group with strong visualization showed activation near

the visual area, while the group with weak visualization

showed activation near the frontal language area.

It is easy to imagine how an individual’s thinking pattern

(visual or verbal) might greatly affect that individual’s

thinking in everyday life. The present study demonstrates

that these individual characteristics would significantly

correlate with changes in global brain wave activity as well

as local activation patterns. We anticipate that these findings

will be applicable to fields related to economic activity and

education.

Conflicting interests

The authors have declared that no competing interests

exist.

Acknowledgements

We thank the editors of the journal for useful suggestions.

This work was supported by Japan Society for the Promotion

of Science, Grant-in-Aid for Research #23000001 and

#15H05729. This article contains Table 1 and some figures

with legends of the article and supplementary materials of

our paper in Neuroscience Letters, although figures are

slightly modified [1].

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