SUGISAKAMASANORI

Journal of Robotics, Networking and Artificial Life
Vol. 9(3); December (2022), pp. 221–228
ISSN (Online):2352-6386; ISSN (Print):2405-9021
https://alife-robotics.org/jrnal.html

Research Article

Proposing Discussion Framework and Hypothesis for Neural
Underpinningsof
Human
Symbolic
andEmbodied
Communication from Synchronization Viewpoint
Masayuki Fujiwara, Takashi Hashimoto

School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1–1 Asahidai, Nomi, Ishikawa, 923–1211, Japan

ARTICLE INFO
Article History
Received 25 November 2021
Accepted 15 August 2022
Keywords
Communication
Framework
Synchronization
Electroencephalogram
Neurocognitive modeling

ABSTRACTA framework for discussing the neural underpinning of communication processes is proposedfrom the perspective of synchronization. This framework comprises four stages: (i) characterizing
the target communication in a two-dimensional space defined by symbolic/embodied (nonsymbolic) and voluntary/involuntary processes, (ii) focusing on the level of analysis of synchrony
on an ontological hierarchy, (iii) constructing a neurocognitive model to simulate neural dynamics,
and (iv) testing an empirical hypothesis on the neural underpinning of communication throughmodel-based electroencephalography (EEG) connectivity neurofeedback in communication
experiments with the cognitive neural mass/field model. We performed two EEG experiments,
implementing the former two stages: the formation of symbolic communication, in which
communication changed from voluntary to involuntary, and intentional switching in embodied
communication, which involves switching between voluntary and involuntary behavior. The
findings on communicative brain activities from these experiments culminated in the hypothesisthat three brain regions are involved in interpreting symbols and motor intentions as well as insocial coordination, in which one region might be shared by two modalities and the other two are
specific to each modality. As we could perform the experiments and their analyses and derive aworking hypothesis based on the framework, we claim that the proposed framework may be vitalfor investigating the neural underpinnings of communication in two different modalities in a
unified manner.
© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/)

between the symbolic and embodied modalities,
differences and similarities between these two modalities
remain unclear. With a comprehensive framework fordiscussing the relationships among communication
modalities, we may be able to understand their unified
neural underpinnings.
This study proposes a framework for discussing theneural underpinnings of symbolic and embodied
communication systems from the viewpoint of

1. Introduction
Human communication involves various modalities,
using symbols such as letters and icons (symbolic
communication) and body movements such as facial
expressions and gestures (embodied, and often, but notlimited to, non-symbolic communication). Although theneural underpinning of both symbolic and embodied
communication has been studied, mostly independently

Corresponding author’s E-mail: m-fujiw@jaist.ac.jp, hash@jaist.ac.jp, web: www.jaist.ac.jp221Stage 2

Voluntary
Symbolic
Communication

Organization
Organization
Organization

Embodied
Communication

Organization

Involuntary

Organization

3-1) Neural Mass/Field
Model3-2) Network of
Neural Mass/Field
Model

Body
Brain andNervesNeural Circuit
NeuronMolecule...

Stage 4

Stage 3Behavior

Constraints
Constraints
Constraints
Constraints
Constraints

Target Level in this paper

Symbolic

EmbodiedSocial...

Stage 1

Individual

4-2) Connectivity
Neurofeedback4-1) Estimation of
Neural Activity
Neurocognitive
Model

Participant

EEG Phase
Synchronization
Network4-3) Communication Experiment with
Cognitive Neural Mass/Field Model

Fig. 1. A framework for discussing the neural underpinning of symbolic and embodied communication. (Stage 1) A two-dimensional
space to characterize communication modality. The horizontal and vertical axes were symbolic-embodied and voluntary–involuntary,
respectively. The two communication experiments summarized in this paper are illustrated with red and blue arrows, respectively. (Stage
2) A conceptual diagram depicting a micro–macro loop chain with organizations and constraints. The upper levels focused on the social
phenomena, whereas the lower levels focused on the individual phenomena in this ontological hierarchy. The target in this paper is thelevel of neural circuit. (Stage 3) Steps to construct a neuro-dynamics model, using the neural mass/field model for the target level in thispaper, and (Stage 4) to empirically validate the neural underpinning and its causal mechanism for cognitive function, using anelectroencephalography connectivity neurofeedback, during communication with a cognitive neural mass/field model.

macro loop chains” from individual neuralactivities to social behavior, in which the upper
level is organized and constrains the lower levelin order to conduct empirical measurements
during communication,(iii) Constructing a neuro-dynamics model toexplain neural mechanisms, and(iv) Testing an empirical hypothesis using theelectroencephalography (EEG) connectivity
neurofeedback method based on the cognitive
neural mass/field model.
Investigating these four stages, we expect to deeply
understand the neural dynamics and mechanisms ofcommunication.
The remainder of this paper is organized as follows.
In Section 2, each stage of the proposed framework is
explained in each subsection. We provide an overview ofthe findings from two EEG experiments on symbolic andsynchronization. Our framework aims to treat different
communication modalities in a unified manner.
Synchronization phenomena are universally observed at
different scales and levels of hierarchy, such as
intercellular [1], intracerebral [2], interbrain [3], andcollective and social behaviors [4]. In particular, neuralsynchronizations within and between the brain insymbolic [5] and embodied communication [6] havebeen investigated. Therefore, by focusing on suchsynchronization phenomena, human and non-humancommunication can be treated in a unified manner.
The framework comprises the following four stages:(i) Characterizing the target communication in a
space
defined
with
two
axes:
symbolic/embodied(non-symbolic)andvoluntary/involuntary,(ii) Focusing on the level of synchronization
analysis on an ontological hierarchy of “micro–
222

neurons as a physical entity can take numerous states,
they must be organized into a specific neuronal
population or neural circuit in order to perform a certain
function. By contrast, the neural activity of neurons isconstrained by population and circuit. Furthermore, theorganization of the intracerebral and the whole brain bycoordination within the neural circuits and brain regions
achieves
higher-ordercognitivefunctions.
Simultaneously, coordination within neural circuits andbrain regions is constrained in achieving and maintaining
cognitive function. Epileptic seizures can be interpreted
as symptoms in which the constraint is defeated, and theentire cerebral nervous system is synchronized. We
assumed that organizations and constraints exist at eachlevel in the brain, and they work to realize various
cognitive functions.
The concept of a “micro–macro loop chain” isinspired by two related concepts. One is the hierarchy of
emergence in tacit knowing [8], in which elements form
the comprehensive whole as an emergence, which is tacit
knowing epistemologically and emergence ontologically,
and the whole becomes an element at the consecutive
emergence, which constitutes a hierarchical structure.
The other is the micro–macro loop in social science(organization theory [9] and economics [10]), in whichmicro information is connected to macro information,
which is then fed back to the micro level.
A critical issue in empirical research is the level offocus. Once the target level was determined, the upperand lower levels became apparent. Hence, empiricalmeasurements and analyses were performed to clarify the
self-organization and constraints between the target andupper/lower levels.

embodied communication, performed according to ourframework, in Sections 3.1 and 3.2, respectively. A
working hypothesis for the neural underpinning ofcommunication systems is proposed in Section 4. Finally,in Section 5, we summarize the proposed comprehensive
framework, describe its advantages and limitations, anddiscuss further operational validation methods for theneural underpinning hypothesis in the brain.
2. Framework for the neurological discussion ofcommunication
2.1. Two-dimensional space to characterize
human communication
First, we positioned the target communication as a
research subject in a two-dimensional space, according to
the target’s communication modality. The spacecomprises two axes: symbolic/embodied (non-symbolic)and voluntary/involuntary. The former axis represents a
difference in communication modality, whereas the latter
represents whether the behavior is intentional. In Fig. 1(Stage 1), the two EEG experiments that demonstratecommunication were placed in the space.
Positioning is either a point for a fixedcommunicative phenomenon or a path for adevelopmental/changing process in space. The targets inthis paper are represented as paths with directionalities
because we are interested in dynamic phenomena, suchas the formation of communication systems (Section 3.1)
and intentional switching during communication
processes (Section 3.2). This stage makes it possible tocharacterize the target communication clearly.
2.2. Micro–macro loop chain in cognitive
neuroscience

2.3. Computational modeling using a neural
mass/field model

In the second stage, we examined which ontologicallevel should be focused on to empirically investigate thetargeted communication in the first stage. Hence, we
propose a “micro–macro loop chain” [7] with feedback
loops of emergence and constraints among ontologicallevels of communication phenomena (Fig. 1, Stage 2).
An emergence ((self-)organization), where the hierarchyof levels in natural systems is assumed, should appear asa property at the upper level as a whole, and not merely
as the sum of the properties of its parts due to theinteraction among parts at the lower level; constraints
indicate that the property at the upper level governs theparts at the lower level [8]. For example, although

In the third stage, we construct a computational model ofphenomena at the target level (Fig. 1, Stage 3) in two
steps:3-1) building a model for the lower level and 3-2)
thereby creating a network of the lower-level models.
This model construction is a sort of constructive
approach [11,12,13] that is complementary to predictions
and inferences from laboratory experiments and iseffective in understanding complex phenomena and theirmechanisms. Following this approach, we construct amodel or system based on a specific prediction orinference of the targeted phenomena. Althoughvalidating the model in actual situations can be difficult,223

should be estimated (4-1). This can be achieved byconducting a communication experiment with a virtual
partner [18,19] using the computational model
constructed in the third stage. In this experiment, thecomputational model plays the role of thecommunication partner; therefore, communication canbe controlled in the experiment. This experiment is not
only a measurement, but also includes EEG connectivity
neurofeedback (4-2). A coupled neurofeedback method[20,21] was devised to train experimental participants tomanipulate global neural activity as a technique toimprove cognitive performance. The neural activity ofparticipants can be targeted (controlled to some extent)
using this technique, which allows us performing
operational validation of the neural mechanism of humancommunication (4-3).
Specifically, for discussing the neural underpinningof communication from the viewpoint of neuralsynchronization, functional connectivity in the humanbrain is estimated through quantifying neuralsynchronization processes. This quantification is fed to acomputational model to reflect the effect of functional
connectivity on the communicative behavior (e.g.,decision making) of the virtual partner. Manipulating thecomputational model and neural activities in theparticipants’ brain through connectivity neurofeedback
allows us to approach the empirical validation from theperspective of indirect neural causalities.

we run the model on a computer for verification bycomparing the computation results with real-world
phenomena. This constructive approach is beneficial forneuroscientific studies of communication, especiallywhen focusing on the human brain, where the problem ofinvasiveness arises.
Various types of neurodynamic models [14,15] havebeen explored in computational neuroscience, startingwith neuron models, neural mass models, such as the
Wilson–Cowan model [16], next-generation neural fieldmodels [17], and neurocognitive models. By exploring
the conditions necessary for neural networks and neural
underpinning of human communication with aconstructive approach using these models, anunderstanding of the (self-)organization and constraints
in the micro–macro loop chain will be achieved. Whilevalidating the constructed neurodynamic model bycorresponding it with actual phenomena is necessary,
more comprehensive models can also be investigatedbased on specific phenomena.
We mainly focused on a neural mass/field model
because the target level was set at the “neural circuit” inthis paper. However, for discussing and understanding
neural underpinnings, neural models that estimate theorganization and constraints in the phenomena at higher
and lower levels, respectively, need to be consideredrather than a neural mass/field model.
2.4. Model-based EEG connectivity
neurofeedback

3. EEG recording experiments
This section presents an overview of our two
communication experiments with EEG recordings using

The fourth stage, comprising three steps, involves theoperational validation of the neural mechanism of humanFig. 2. An overview of the symbolic communication task.

communication. Neural activity during communication

the proposed framework. For details on the experiments
224the frontal and right centro-parietal regions. These
differences may reflect the organization’s ability toachieve a higher cognitive function of communicative
understanding of symbol meaning.

and results, please refer to [22,23] for the former and [24]for the latter.
3.1. Formation of symbolic communication
We investigated the formation of symbolic
communication using symbolic communication task (redarrow in Fig. 1, Stage 1). The task was a coordination
game [25,26], in which two participants coordinated their
behavior through exchanges of symbols only. The
repetition of the task allowed us to observe theemergence of artificial language and communication
systems by deliberately restricting the means ofcommunication based on experimental semiotics [27,28].
This task was designed to observe the emergent
process of symbolic communication. Specifically, theparticipants were required to move their avatars, placed
in one of the four rooms, to the same room as theirpartners, only by exchanging predetermined andmeaningless figures (Fig. 2). At the beginning of the task
repetition, the participants interpreted the figuresintentionally. They agreed on the meaning of the symbols
as a code of figures and their meanings, and finally, thesymbols could be used without intentional interpretation.
The task was designed such that it was impossible tocompletely achieve the task without inferring implicit
intentions as well as the correspondence between figuresand rooms.
The EEG analysis revealed significant increases in
the amplitude and phase synchronization in the low(theta/alpha) and high-frequency (gamma) bands in the
success group, but not in the failure group, wheninterpreting the meaning of the symbols as the partner’s

3.2. Intentional switching in embodied
communication
Regarding embodied non-symbolic communication, we
proposed a new experimental paradigm called “LookThis Way!” game [24]. In this game, pairs of participants
played “janken” (rock–paper–scissors phase), followed
by a finger-wagging task (look this way phase) that wasa modified version of a traditional Japanese game
“Acchi-Muite-Hoi” (Fig. 3).
This task was designed to observe the representation
and understanding of dynamic motor intentions when thetwo participants switched between cooperation andcompetition (blue arrow in Fig. 1, Stage 1). Specifically,
during the rock–paper–scissors phase, the twoparticipants involuntarily synchronized their rhythms associal coordination while shaking their arms. By contrast,
the subsequent look this way phase required voluntarycompetitive motions, particularly pointing the finger in adifferent direction from that of the partner. By comparing
neural and physical activities in cooperative andcompetitive finger-pointing conditions, this experimental
design allowed us to observe intentional switching
involved in embodied communication.
As a result, the power of the alpha and gamma bands
significantly increased during cooperation comparedwith that during competition. This difference wasobserved in the left fronto-central and right centrolook this way
phase

Trial

rock-paper-scissors
phase

ResponderTime

0 sec.

Director

6 sec.

Fig. 3. An overview of the Look This Way! game.

parietal regions. Differences in neural activity may

message in this task. These differences were observed in225

Alpha Phase
Synchronization

symbolic and embodied communication processes, as
described in Fig. 4. As presented in the figure, thesimilarity of the neural underpinnings of the twomodalities of communication is the activity of the gamma
band in the right centro-parietal region, and thedifference is the neural activity of the low-frequency(theta/alpha) band in the frontal and left fronto-central
regions, which may reflect the difference between thetwo modalities. The long-range alpha band phase locking
in the anterior–posterior direction may represent thepossibility of coordination in the brain. Thus, thesesimilarities and differences between the two modalities
may be candidates for organizing and constraining theneural underpinnings of symbolic and embodied humancommunication. We will also perform stages 3 and 4 inthe proposed framework to validate this working
hypothesis.

Semantic Processingof Symbols

Theta and Alpha
Power/Amplitude

Understanding andExpressing Motor
Intentions

Alpha
Power/Amplitude

SocialCoordination

Gamma
Power/Amplitude

Perception

Fig. 4. A hypothesis of neural underpinning for symbolic andembodied (non-symbolic) communication processes. The
channel location, represented by letters such as “Fp1,” wasbased on the International 10–20 system.

reflect the organization of embodied communication
which requires intentional switching.

5. Conclusion

4. Working hypothesis of the neural
underpinning of symbolic and embodied
communication systems

We propose a framework comprising four stages todiscuss the neural underpinning of symbolic andembodied (non-symbolic) communication. One
advantage of this framework is the unified handling of
two different modalities of communication processes:
symbolic/embodied (non-symbolic). We performed andanalyzed symbolic and embodied (non-symbolic)communication experiments according to the framework.
We propose a new working hypothesis on the neural
underpinnings of symbolic and non-symbolic
communication based on the observed neural activities incommunication processes. As we could perform theexperiments and their analyses and could derive aworking hypothesis based on the framework, theproposed framework may be vital for investigating theneural underpinnings of communication in two different
modalities in a unified manner.
This framework aimed to understand the causal
mechanism of communication by proceeding to stages 3
and 4. Namely, we attempt to validate that the
hypothetical neural underpinning causally realizes the
cognitive function of communication. In order to achievethis, the neural activity of the communication participants
should be manipulated. This manipulation is possibleusing model-based connectivity neurofeedback. Morespecifically, in the case of symbolic communication,
long-range alpha band synchronization in the anterior–posterior direction is trained using EEG connectivity
neurofeedback [20,21] during a communication

The target level of the hierarchy in the experiments
described in this paper is the neural activity during EEG
recordings, where the upper level is the whole brain asfunctional connectivity between brain regions and thelower level is the hierarchy at the neuronal level. The
EEG recordings reflect a neural oscillation by electricalactivity, in which the sum of the action and synaptic
potentials of neurons appears with a certain rhythm.
Neural oscillations are used to observe self-organization
at the level of the functional network in the brain [29,30].
By contrast, neural oscillation is also maintained in acertain state owing to the constraints of the brain network,
thus being considered a constraint on neuronal activity.
For example, epilepsy can be considered a state in which
the constraints of the brain network (or neural oscillation)
are inadvertently broken, and neurons become
spontaneously and continuously active and synchronized.
We observed neural activation and synchronization
between the frontal and right centro-parietal regions fromthe former EEG experiment (Section 3.1) and activationof the left fronto-central and right centro-parietal regions
from the latter (Section 3.2). The results suggest that thethree brain regions were involved in interpreting symbols,
motor intentions, and social coordination processes.
Based on these findings on neural activity, we proposeda working hypothesis on the neural underpinnings of226experiment between participants and the cognitive neural
mass/field model. Furthermore, the cognitive neural
mass/field model is analyzed theoretically andcomputationally to determine how neural connectivity
modulates the cognitive function of communication for amechanistic understanding of causality.
To date, we have focused on human communication
processes to analyze the mechanisms of communication
with animals and machines and to understand theevolution of communication. However, the importantfactors to distinguish among humans, animals, andmachines need to be specified. Therefore, the proposed
framework will be helpful in comprehending such
universal communication processes.

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Acknowledgements

10

The authors are grateful to Jiro Okuda (Kyoto Sangyo
University), Guanhong Li (Kyoto University of Foreign
Studies), Takeshi Konno (Kanazawa Institute ofTechnology),
KazuyukiSamejima(Tamagawa
University), and Junya Morita (Shizuoka University) for
collaboration on the EEG experiment on the formation ofsymbolic communication systems. We also would like tothank Hiroaki Wagatsuma (Kyushu Institute ofTechnology), Maria R.V. Sanchez (Kyushu Institute ofTechnology, Japan), Satoru Mishima (Kyushu Instituteof Technology, Japan) for collaboration on the EEG
experiment on the intentional switching in embodied
communication. A gratitude we give Sotaro Kondoh(University of Tokyo) and Yukitoshi Sakaguchi(Doshisha University) for advancing our thoughts of“micro–macro loop chain”. This work was supported by
JSPS KAKENHI Grant Numbers JP17H06383,
JP17J06623, and JP26240037.

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Authors Introduction
Mr. Masayuki Fujiwara
He graduated from the Department ofInformation
andSystems
Engineering, Faculty of Information
Engineering, Fukuoka Institute ofTechnology (FIT) in 2015 andreceived his master's degree from the
School of Knowledge Science, Japan
Advanced Institute of Science andTechnology (JAIST) in 2017. He wasa Research Fellow (DC1) of the Japan Society for the
Promotion of Science (JSPS) until 2020. He has been
studying the EEG research and neural modeling ofsymbolic and embodied human communication from theviewpoint of complex systems and neural synchronization.
Dr. Takashi Hashimoto
He is a Professor at School of
Knowledge
Science,Japan
Advanced Institute of Science andTechnology (JAIST). He was given
his Ph.D. degree in 1996 from the
Graduate School of Arts andSciences, the University of Tokyo.
He has been studying the origin andevolution of language, the dynamics
of communication, and the design of social institution fromthe viewpoint of complex systems, with pursuing toconstruct a scientific field called "knowledge science" forcreation, sharing, and utilization of knowledge.

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