testing the efficacy of robot-to-human goal …faculty.uncfsu.edu/dmontoya/lab_files/thesisfinal _...

TESTING THE EFFICACY OF ROBOT-TO-HUMAN GOAL DIRECTED

ACTIONS

By

Alissa Baker-Oglesbee

A thesis submitted to the Graduate Faculty of

Fayetteville State University, North Carolina

In partial fulfillment of the

Requirements for the Degree of

Master of Arts in Psychology

DEPARTMENT OF PSYCHOLOGY

Fayetteville, North Carolina

July 2011

APPROVED BY:

___________________________________________________________

Dr. Daniel Cordoba-Montoya, Chair of Thesis Advisory Committee

__________________________________________

Dr. Matthew Lindberg, Professor of Psychology

_________________________________________

ii

Dr. Sambit Bhattacharya, Professor of Computer Science

ABSTRACT

Baker-Oglesbee, Alissa. Testing the Efficacy of Robot-to-Human Goal Directed

Actions (Under the direction of Daniel Cordoba-Montoya, PhD). Robotic technology

is rapidly being integrated into human life. One aspect of robotics is as an educational

tool to aide humans in learning skills. Prior research on the mirror neuron system has

posited that humans are able to discern the goal of motor actions enacted by humans even

when the action flow is incomplete. Theoretically, this effect is only observable when

humans see other humans perform tasks. If this is true, participants should have a more

difficult time accurately replicating motor tasks when demonstrated by a robot rather than

a human. Participants viewed either a human or a robot performing motor tasks. The

participants then attempted the motor task demonstrated while timed and tracked for

error. Measurements were analyzed revealing significant differences in error within group

but not between groups. Implications of research are beneficial in the novel combination

of the mirror neuron system with embodiment theory and in gauging a robot-human

interaction through a motor-teaching paradigm.

Keywords: robotics, mirror neuron system, goal understanding, embodiment

TESTING ROBOT ACTIONS

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ACKNOWLEDGEMENT

Foremost, I would like to formally thank Nathan Oglesbee, Sean Mobeley, Merei

Clouden, and Ashley Bofill for their assistance in various elements of my work. I would

like to express my thanks toward the educators in my life…my parents, grandparents,

nature, and great teachers. Of these teachers, there are a few that stand out and deserve

individual acknowledgement: Mr. John Spencer, Dr. Sharon Roberts, and Dr. David Scott.

Each in their own way exposed me to new ideas and ways of thinking. One cannot be

more grateful for the opportunity to experience that ultimate freedom of thought.


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DEDICATION

This labor, representing an immense amount of personal, professional, and

emotional growth, can be dedicated to no one but my sister Tara Davis and my husband

Nathan Oglesbee. You both are pivotal in my life and I am grateful for all your love and

support.


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TABLE OF CONTENTS

Page

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

Chapter I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Chapter II. Review of related literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Mirror neuron system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Embodiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

The current experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Chapter III. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33


vi

Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Chapter IV. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Chapter V. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Chapter VI. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Appendix B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Appendix C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70


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LIST OF TABLES

Page

Table 1. Descriptive statistics for robot-viewing group . . . . . . . . . . . . . . . . . . . . . 38

Table 2. Tests for normality by group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Table 3. Mean times and standard deviations for time variable

between groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Table 4. Mean difference in time between second and novel puzzles for

robot-viewing group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Table 5. Mean backtracking error differences between groups . . . . . . . . . . . . . . 45

Table 6. Mean differences between novel and exposed errors

between groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49


viii

LIST OF FIGURES

Page

Figure 1. Flow Chart of Experimental Procedure . . . . . . . . . . . . . . . . . . . . . . . . . 36

Figure 2. Bar Graph for Time Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Figure 3. Bar Graph for Error Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46


1

CHAPTER I

INTRODUCTION

This could be an overly optimistic opinion, but I believe that it is currently a

remarkable time to be a scientist. Within the past 50 years, we as a global society have

been witness to a dizzying amount of technological innovation and sophistication. These

advancements have allowed new insights and methods for scientific study. While some

inventions have undoubtedly had a dubious impact (the atomic bomb, for example), the

general purpose of technology is to provide people an easier and more efficient way of

life. The many implications of technological application can be convoluted though. It can

be argued that any technological advancement creates its own set of complications. For

instance, the instantiation of text messaging into our daily lives can make sending a quick

communiqué simple, yet the overwhelming usage has been tied to increased vehicular

accidents (Hosking & Young, 2009).

Whatever a person’s philosophical stance is on the matter, it is unlikely that the

expansion of technology is not going to stop anytime soon. Some of the latest

technological developments have brought humans and machines closer than ever before.

The development of nanotechnology, for example, makes possible the introduction of

very small robots into our bodies to perform medical procedures or provide sensory

enhancement (Roberts, 2011). New areas of social research have developed alongside the

field of robotics as machines have become increasingly integrated into the human social

sphere. The increasing social sophistication of robotics is prompting new questions


2

regarding a human’s relation to machines (Morana, Movellan, & Tanaka, 2011). This

study intends to delve into the area of human and machine interaction. In the face of

increasing incorporation of robotics into human lives, what are the possibilities of these

exchanges?

The goals of this study are to measure the capacity a human has for learning from

a robot in comparison to learning from another human; specifically whether a human is

able to ascertain a goal from a series of motor tasks from a robot as clearly as from

another human. The mirror neuron system has been indicated as the physiological area

for motor learning and goal understanding (Gallese, 2009), and for this reason will be

examined through existing literature to question its’ function in response to a robot or

human stimulus.

Additionally, the theory of embodiment will be examined as a potential theoretical

fit for future study of the human mirror neuron system. The mirror neuron system has

been indicated as the physiological area for motor learning (Gallese, 2009). Although

this element of the study is not directly tested in the design, it proposes a perspective that

may render deeper understanding of the function of the mirror neuron system while

underlining the philosophical problems of human-robotic interaction.


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CHAPTER II

REVIEW OF RELATED LITERATURE

The field of robotics is currently expanding into new applications through

personal robotics. This application of robotic technology finds uses for robots in more

intimate settings and is focused on creating a relationship between the human user and

the robot. These roles vary from small non-humanoid robots that vacuum a room or to

humanoid robots that can serve a user coffee or even teach a foreign language (Turner,

2006; “ASIMO”, 2010; Kanda, Hirano, Eaton, & Ishiguro, 2004). This type of personal

interaction has created a need for research on the ability for humans to relate socially to

robots. This study will examine the human ability to learn motor skills and derive goal

intentionality from robotic counterparts. An introduction to personal robotics and some of

the underlying theoretical assumptions of the cognitive scientists who pioneered the

development of digital computing will follow.

Physiologically, a human’s ability to learn motor actions and goals has been tied

to a particular region of the brain known as the mirror neuron system (MNS) (Rizzolatti

& Craighero, 2004). A brief overview of the history of study of the MNS and description

of its basic functions will be discussed, as well as some of the more controversial

proposed functions. The mirror neurons system’s role in learning motor actions, response


4

to different stimuli (biological and non-biological), developmental changes in response,

and the ability for goal understanding will be of primary focus.

Finally, to ground this study in a proper theoretical framework as necessitated by

the goal understanding function of the MNS, a brief review of embodiment theory will be

discussed. Evidence of the rehearsal of motor actions in the mirror neuron system

(Gallese, 2009) will be shown to be better understood theoretically through the framing

of embodiment theory than the framing of the mirror neuron systems function through the

computational assumptions of cognitive science (the predominant theoretical approach

within the field). Examples of brain function and perception will be highlighted to exhibit

embodiment within a cognitive framework as opposed to the classical philosophical

construct made popular by Merleau-Ponty and Husserl (Dolezal, 2009).

Robotics

By and large, the exploration of robotic technology within the field of psychology

occurs almost exclusively by researchers in the cognitive science area of study. The

adoption of the study of robotics and artificial intelligence by cognitive psychologists is

found in the very early history of the field of cognitive science itself. Originally dubbed

cybernetics by its founders, two key symposiums in Cambridge and Dartmouth during

1943 presented the original foundations for theoretical development in cognitive science

(Varela, Thompson, & Rosch, 1993). The primary concepts that emerged as unique to

cybernetics were computational theory and symbolic informational-processing.


5

At the time cybernetics was developing, the theoretical framework for psychology

was largely derived from behaviorism. The behavioralist foundations of objectivity and

quantifiable methodology produced great strides for the burgeoning field of psychology

by solidifying it as a natural science based on observable phenomenon as opposed to the

introspective techniques popular in the early history of American psychology. While

facilitating positive growth for psychology, the focus on overt behaviors also invariably

stifled any study that involved the mind or its processes. This lack of exploration of the

processes that occur between the administration of the stimulus to the organism and the

organisms’ resulting response is referred to as the “black box” approach (Varela et al.,

1993). The reason behaviorists chose not to engage in discussion or study of the mind lies

in their methodological philosophy. Observable behaviors were thought to be of utmost

importance, primarily because it is only observable behaviors that could be viewed in a

primarily objective sense and thus allow for more reliable and valid measures. The mind

and its processes are not observable and thereby not objectively reliable or easily

measured. The behaviorists did not believe that the mind was nonexistent or that it was

unimportant to explore, but they recognized that an objective methodology offered no aid

for such study.

Cognitive science found its way into field discussion by attempting to peer into

the “black box.” Cybernetics and later, cognitive psychology, found interest in trying to

define, measure, and specify the mental processes, or cognition, needed to process

information from stimulus to response. To approach these issues, a new theoretical

approach was devised that more allowed for a more effective look into the mind and its


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processes. Cyberneticists reframed psychological science by attempting to resolve a

classic philosophical quandary, the ‘mind-body problem’ (Anderson, 2001; Dolezal,

2009; Varela et al., 1993).

Most famously discussed by Descartes, but acknowledged even earlier in

philosophical history, the mind-body problem recognizes the apparent gap between

thoughts and the body or brain. The root of the problem lies in the observation of

Newtonian physics which states that only matter can affect matter. Thoughts have no

physical substance and, as it is generally accepted, one may not use thoughts in a way

that impacts the physical environment. The question arises, how does thought (which is

non-matter) cause change and movement in the body (which is matter)? Descartes’

solution, known as Cartesian dualism, states that the mind and body are two separate

properties, such as a gas and a solid. Descartes believed that the mind was housed within

the body and interacted with the body through the pineal gland in the brain. This premise

of mind and body being separate substances, properties, or sides of a spectrum formed an

ongoing philosophical perspective collectively termed dualism. Although dualistic

perspectives pervade both scientific and folk psychology, they have yet to effectively

solve the mind-body problem. Dualism does not provide an answer regarding how the

intangible mind can bridge the gap to communicate and guide the body (Anderson, 2001;

Dolezal, 2009; Papineau & Selina, 2002).

Cognitive scientists largely subscribe to a diametrically opposing perspective to

dualism. This perspective, termed monism, can be expressed in very different ways. The

primary division falls between idealism, which states that the physical world does not


7

exist, and materialism which states that the physical world is all that exists. Cognitive

scientists are generally material monists. Regarding the mind and brain, the fundamental

principles of cognitive science presume that the mind and brain are the same property or

that mind is an emergent property of the brain. Following this assumption the study of the

brain is ultimately study of the mind (Anderson, 2001).

Computational theory is a strong example of the implicit presence of the material

monist philosophy, and also illustrates how philosophy can guide physical research of the

mind and brain. It is no small coincidence that as cyberneticists were honing in on the

foundations of cognitive science, computer science was moving leaps and bounds away

from early versions of adding machines to what is more representative of modern

computer technology. Analog technology was becoming more refined and many scientists

of the mind saw computer models as a way to conceptualize brain and mind function

(Abraham, 2002; Rav, 2002).

During a symposium in 1948, John von Neumann discussed his conceptual design

for the analog computer. Briefly, this model outlined the input of data, the manipulation

of data by the central processing unit or executive control into short term and long-term

memory, and the resulting output. Immediately following von Neumann’s presentation

was a discussion led by McCulloch on his publication ‘Why the Mind is in the Head’,

during which he presented the theory that the brain functions in a manner similar to an

analog computer. In this analogy, the brain operates as the hardware and mental processes

operate as symbolic information software. Warren McCulloch is credited for creating the

foundation of the computational theory of mind, officially uniting computer science with


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psychology (Rav, 2002; Varela, et al, 1993). Many psychology texts today still include

Von Neumann’s model for information-processing, which, although was originally

intended to serve as a model for computers, was usurped by psychologists as a model of

how the brain itself processes information.

Computational theory posits that the brain functions in a specific manner, as

directed by algorithms, and performs as an information processor. Information processing

constitutes taking information from the environment, transcoding that information

symbolically in the brain to allow for different cognitive functions to act upon the

information, and when necessary produce some type of output. This system is regarded a

cohesive integration of mind, brain, and body according to the material monist

philosophical foundation. This early interdisciplinary marriage between computer science

and psychology has made possible the creation of artificial neural networks, artificial

intelligence, robotics, and telepresence technology; all currently expanding fields of

research and development in private, federal, and academic sectors (Abraham, 2002).

Within the past decade, society has watched on as robots have begun to vacuum

carpets (Turner, 2006) and enable people to interact with others remotely via the robot

itself (Sofge, 2009). More and more often robotic technology is being integrated into the

private sector and our personal lives. Personal robots are robots that are designed to assist

with household and office tasks (Wang, Miller, Fritz, Darrel & Abbeel, 2011). According

to ABI Research (2010), personal robotics is a growing industry with a projected market

of $19 billion in 2017. During the six years after the prominent personal robotics

company iRobot® (the maker of the Roomba®) first released their initial models in


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2000, 3 million robots were purchased (LaGeese, 2008). The International Federation of

Robotics states there were 2 million personal robots in use in 2004, with a projected

increase of 7 million by 2008 as cited by Bill Gates (2007). Willow Garage® is a

burgeoning personal robotics company that has been making popular news with its

premier open-source personal robot model, the PR2®. Many of the tasks these personal

robots are capable of allow some simple tasks to be left to the machine, freeing up the

owner to pursue other activities (Alsever, 2010). The modern advent of personal robotics

is akin to the revolution that transformed the 1950’s kitchen, freeing housewives from

day-to-day drudgery through use of modern electric appliances.

Japan is a country on the cutting edge in the personal robotics industry. Large

advancements in the field are proving to be of more use to the Japanese population than

merely providing careers and lending status to the country as a sophisticated player in the

robotics field. Japan has been experiencing a negative growth rate that is only increasing

since 2007 (“CIA World Fact Book”, 2010; Yamaguchi & Shen, 2008). The inverse

relationship between births and deaths in the country has brought difficulties to the

business sector and robots have been considered as potential clerical workers in the midst

of a dwindling population (Yamaguchi & Shen, 2008). These are positions that rely in no

small way upon rapport, trust, empathy, and other emotional capacities for effective

service.


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Perhaps one of the most advanced examples is the Honda Corporation’s ASIMO®

bipedal humanoid robot. The ASIMO® was designed to operate easily within human

living and work spaces and to perform services for owners (“ASIMO”, 2010). Another

example is the Mitsubishi Wakamaru® robot model. Touted as the first domestic robot

with communication capabilities, the Wakamaru® was created specifically to be a house

sitter, watch over ailing owners, and perform secretarial duties such as answering the

phone, taking messages, and reminding the owner of meetings (“Domestic Robot to

Debut”, 2005).

A slightly different approach to designing personal robots as office workers is

taken by The Kokoro Dreams Company®, who focuses on developing android models.

An android is a specialized type of robot that is designed to look and behave like a human

(Prucher, 2007). Therefore, the Kokoro® robots look very much like real human beings

and have had great attention paid toward facial expressions, voice pattern and diction,

and body language. The Kokoro Company® rents its android models to businesses for

use in many roles. Potential positions are as a fashion model or hostess, a ‘booth

bunny’ (a slang term for models who sell products at tradeshows) or as a remote access

device for an absent employee at a meeting (Kokoro Company Ltd., 2008). Other

suggestions for use include nurse duties, performing as an ambassador, or acting as a

cabin attendant in an airplane. Curiously, the company states that the robot even “takes a

spiritual trip from time to time [on] pilgrimages” (Kokoro Company Ltd., 2008). One can

only imagine the specifications of religiously symbolic usage, but we can infer the extent

to which robots could be or are currently accepted in various roles in Japanese society.


11

Robots have also been developed to participate as a player in multiple user

dungeons or MUDs, a role-playing game that relies on player interaction to advance.

Players explored a virtual world together with robots and collaborated in developing

strategies to defeat monsters, find success in the mock economy, and develop traits and

characteristics of the player’s avatar character. Good performance in these individual

tasks led to an overall goal of game success (Chung-Hsiang & Chuen-Tsai, 2007).

The use of robotics is also expanding into classrooms. Robots programmed to act

as a peer tutor and social partner to aid children in learning English were shown to

improve children’s English language skills significantly over children who did not

interact with a tutoring robot. A positive correlation between time spent with a tutor robot

and improved English language skills emerged (Kanda et al., 2004). Given these

examples, the potential for uses of robots in educational settings is intriguing. Certainly,

the above studies show how a robot can provide some elements of educational use to the

benefit of children and very likely, adults. There seems to be a foundation in which

humans are able to understand and develop skills in certain tasks gained initially through

interaction with a robot.

Language, a shared mode of information exchange, may enable robots to act as

effective teachers for human beings. The skills that are being developed and goals to be

attained are expressed through language, either spoken or written, and a large part of the

rapport is undoubtedly created by use of language as well. Language could perceivably

close the gap for a human in connecting with a robot that may not appear human-like or

is otherwise not engaging. Perhaps the ability for a robot to teach lies in its capacity for


12

language; however, is it actually possible for a human to learn skills and understand goals

from a robot on an even more basic level of observational learning?

There are some notable problems when considering relying on the creation of

rapport with a robot on a visual or observational basis. First, a preeminent problem in

creating a basis for contextual understanding of human communication by robots is their

incapacity to recognize facial expressions (Nikolaidis & Pitas, 2000). On the other side of

the conversation, humans have exhibited feelings of apprehensiveness and fear in

reaction to communicating or even looking at robots who appear too human, a

phenomenon known as the “Uncanny Valley effect” (Mori, 1970). These are some

problems that might arise when a robot or android approaches more sophisticated levels

of social exchange. An aesthetically simpler robot could obviate these issues without

sacrificing the attempt to test the ability for the robot to teach a human through

observation.

To circumvent potential robot-to-human language barriers, an examination of

learned motor skills might be useful. Motor skills are often learned through observation

and practice, so a motor task is an appropriate way to find whether a robot can

successfully engage a human and teach a skill without the use of language. Motor skills

must expand beyond mere mimicry to establish more sophisticated learning. Mastery of

motor learning can be exhibited through use of the skills initially learned in one task used

to complete another task, understanding that the skills may be used to reach a similar goal

in a different task. The overall goal understanding of motor actions can signal more

meaningful processing than merely visual attending to stimuli. These processes are


13

largely mediated by the motor cortex. Recently, a study has revealed that a specific area

of the motor cortex lends itself to processing the intended goals of motor actions. This

area has become known as the mirror neuron system (MNS) (Umiltà et al. 2008).

The Mirror Neuron System

To examine the capacity for a human to learn a motor task and achieve goal

understanding from a robot model, research on the mirror neuron system provides a

physiologically sound and theoretically expanding framework to guide hypotheses and

interpret results. The mirror neuron system is located in the motor cortex and is currently

being explored as a location for more than the delegation and execution of motor tasks, as

is the traditional functional view of the motor sensory area. Recent research presents

evidence that the mirror neuron system may be capable of actual learning and rehearsal of

motor tasks. Previously, learning and rehearsal tasks were considered too complex to be

performed in sensory areas. As prescribed by cognitive computational theory, information

that entered a sensory modality was thought to be transcoded and passed on to other areas

of the brain more suited for higher-order processing (Gallese, 2009).

The initial physiological evidence detailing the location and function of the MNS

was gathered from monkeys. Measurements collected from the mirror neuron system

demonstrated that the neurons fired at the same rate and time for a monkey observing

another monkey eat peanuts as if the observant monkeys had eaten peanuts themselves

(Rizzolatti & Craighero, 2004). While the original ‘discovery’ of the MNS was through

primate brain research, much of the evidence regarding primate’s MNS has carried over


14

to inform the function and location of the human MNS. The location of the mirror neuron

system in a monkey’s brain is the F5 area of the premotor cortex (Rizzolatti & Craighero,

2004). Although the evidence for the MNS in humans is indirect, brain imaging

techniques such as electroencephalography (EEG), transcranial magnetic stimulation

(TMS), and functional magnetic resonance imaging (fMRI) as well as neurophysiological

study have created a strong case for the its presence. Human participants have exhibited

strong activation of the neurons in the lower part of the precentral gyrus, the posterior of

the inferior frontal gyrus, and the rostral area of the inferior parietal lobe while

performing motor actions and also while observing other humans perform motor actions

(Grezes, Armony, Rowe, & Passingham, 2003; Jokisch, Daum, Suchan, & Troje, 2005;

Kilner, Paulignan, & Blakemore, 2003). These areas are widely considered to be the

primary area of the proposed mirror neuron system in humans (Gallese, 2009; Gallese et

al., 2009; Oberman, McCleery, Ramachandran, & Pineda, 2007).

Prior to this evidence, learning and deriving goal understanding from actions was

largely regarded as functions of executive cognitive capacities, and thereby primarily

mediated by the frontal lobe region of the brain (Barsalou, 2008). The apparent mental

rehearsal and understanding of goals through motor actions by the MNS opens the door

to considerations that intentionality in some instances may be derived earlier in

processing than previously believed. Other evidence gathered from the MNS also shows

that this system may be responsible for categorizations of perpetrators of action, such as

whether they are human or not human (Shimada & Hiraki, 2006). This automatic

recognition of a conspecific (a member of one’s own species) seems to affect the


15

rehearsal of the motor action being observed. In the event a motor action is performed by

an organism that the observer does not recognize as a conspecific, the MNS may not

activate (Calvo-Merino, Glaser, Grèzes, Passingham, & Haggard, 2005; Shimada &

Hiraki, 2006). It would be expected that if the motor action does not activate the MNS to

allow for automatic rehearsal that the motor action may not be performed as well as if the

action had been carried out by a conspecific (thereby increasing the probability of MNS

activation). As a robot would generally not be regarded as a conspecific, the MNS may

play a role in whether humans are able to fundamentally exchange, recognize, and learn

information from robots.

Although many accept the prevailing evidence for the existence of the human

MNS (Andric & Small, 2010; Engel, Burke, Fiehler, Bien, & Rosler, 2008; Gallese,

2009; Gallese et al., 2009; Iacoboni, 2009), the exact function of the system in humans

has created several hypotheses and respective counterarguments. A prime example of

contentious proposed MNS function (or more accurately, malfunction) is the role it may

play in the development of autism spectrum disorders (cf. Ferber, 2010; Hamilton et al.,

2007). Another lively area of debate is that of the phylogenetic development of language

as a coevolving process with the development of the mirror neuron system (Fisher, 2008;

Ramachandran & Hubbard, 2006). Regardless of the controversy involved in these areas,

there is almost ubiquitous agreement from researchers from all sides that the human MNS

is likely to exist. The present study will focus on the relatively accepted aspects and

functions of the MNS in the processing of observed motor actions and goal

understanding.


16

Although it is generally accepted that the MNS functions optimally when

receiving stimuli from conspecifics, questions have been raised about the type of

conspecific stimuli the MNS will respond to. Interestingly, it has been shown that the

human MNS will fire in response to a conspecific performing motor actions that are

impossible. The human MNS was shown to fire when the participant observed impossible

movements in an artificial human hand, meaning that the hand performed movements

that are outside of normal physiology (Costantini, Galati, Ferretti, Caulo, Tartaro,

Romani, et al., 2005). This shows that the MNS can and will respond to motor actions

outside a preexisting representational motor repertoire and also outside of movements

that the participant has or can engage in themselves. Such evidence qualifies the ‘direct-

matching hypothesis’ that proposes that only movements that are already a part of an

organisms’ motor repertoire will elicit a response from the MNS (Rizzolatti, Fogassi, &

Gallese, 2001). Evidence suggests that the MNS may develop to respond more to direct

matches through natural age development or specialization of expert skills (via long-term

potentiation).

The sensitivity of the mirror neuron system to different types of stimuli appears to

change over the development of an individual from child to adult. In exploring the mirror

neuron system’s response to mediated realities, differences between adult and child

mirror neuron systems’ firing were tracked while the participants watched live or

televised actions. They found that a child’s MNS activated in both the live action and

televised action conditions while an adult’s MNS activated only in the live condition.

While the children’s MNS seems to react to both mediated or non-mediated motor


17

stimuli, the adult’s MNS seems to be more parsimonious or specialized (Shimada &

Hiraki, 2006). Their evidence suggests that the MNS fires in response to different stimuli

over the developmental span of a human, although it is worthwhile to note that the

children used in the experiment may have had more exposure to mediated stimuli earlier

in life than the adult participants did. To highlight the potential of mirror neuron system

specialization through long-term potentiation processes, a separate study tracked a

dancer’s MNS response while the participant watched dance movements or capoeira (a

Brazilian martial art that can appear dance-like) movements and found that the dancer’s

MNS fired more readily during action observation of specialized action (dance) than

other action sequences (Calvo-Merino et al., 2005).

In conjunction with Shimada and Hiraki’s findings, this study suggests that an

adults’ MNS becomes specialized to activate for certain types of motor actions in certain

mediums, and fires more readily in a context that the motor system has more experience

with. This specialization seems to occur through both developmental and experiential

processes. This would suggest that while infant’s brains may behave like sponges,

tracking any activity to have the opportunity to learn, adult’s brains (or expert’s brains)

might be more selective about the stimuli it responds to. This evidence is important when

considering that much of research that focuses on a robot-to-human educational

interaction focuses on the use of child participants (Chung-Hsiang & Chuen-Tsai, 2007;

Kanda, Hirano, Eaton, & Ishiguro, 2004), and that any readiness of a child to learn motor

actions from a robot must be considered to be perhaps a more responsive example than if

an adult were the participant.


18

The role of the MNS in deriving the goal of a given sequence of motor actions, or

deriving motor intentionality, is one of the leading theories of proposed function in the

MNS. Neuroscientists have hypothesized that this function would assist an individual in

learning motor processes (Gallese, 2009; Gallese et al., 2009; Pineda, 2008; Oberman et

al., 2007b). Another popular term for this phenomenon is goal understanding.

If the MNS were deriving action understanding in response to the observation of

motor sequences, this would suggest that at least some motor goals could be understood

through bottom-up processing. This would render the processing of the goals as non-

symbolic and modal in representation. Non-symbolic modal processing is a jarring shift

from the traditional computational symbolic models of information-processing that

created the foundations of cognitive science and still dominate theory today.

Computationally based information-processing models provided the field of psychology

the means to conceive of covert cognitive functions (often capitulated by bottom-up

processing of environmental stimuli) that would activate mental models and

representations accessible by the conscious mind for general thinking processes. The

theoretical paradigm of symbolic amodal processing mediated by executive function was

the assumption of much of the cognitive framework and quickly informed other

experimental subfields of psychology, including developmental, industrial/organizational,

and social psychology.

The ability of the brain to perform what is usually considered higher-order mental

tasks, such as recognizing intentionality, through a bottom-up approach requires an

alternate theoretical approach to logical computationalism. One of the currently


19

expanding theoretical approaches that is able to account for such processing are the ideas

of embodiment or grounded cognition, which will be explored later in this paper.

Needless to say, it is likely this root issue of bottom-up versus top-down processing of

tasks that results in so much controversy and fascination concerning the study of the

MNS and its’ functions.

Yet, there would be no contest without evidence, and there exists ample evidence

to support the contention that the MNS derives goal understanding from motor actions. In

one such study, monkeys who had previously been trained to grasp objects were given

two different sets of pliers, one that opened with the normal squeezing action and another

pair that opened in reverse fashion by widening the hand’s spread. Monkeys operated the

pliers while the MNS was recorded. The different operations needed to open the sets of

pliers allowed researchers to dissociate the neural motor pattern from the neural goal

pattern (opening). The researchers found the same neural patterns in both cases of pliers

use, even though the motor operation of each set of pliers was different. This would

support a hypothesis that the neurons were firing in response to the goal of opening the

pliers, which is the same across the conditions, instead of merely firing to support the

differential movement of the hand in order to open the pliers, which would have resulted

in different firing patterns across the conditions (Umiltà et al. 2008).

Neural firing in response to goals has been demonstrated in the mirror neuron

systems of human participants as well. In one study, participants watched videos that

displayed a series of finger movements: in one condition the fingers moved toward and

touched red dots (a goal directed movement) while in the other condition the fingers


20

moved the same way without the presence of the red dots (no-goal condition). Increased

blood oxygenation was detected via fMRI in the MNS in the goal condition, whereas

there was no increase in blood oxygenation in the non-goal condition (Koski et al., 2002).

In another study focused on goal understanding, participants who were familiar, but not

experts, with basketball watched videos of basketball related actions (dribbling, shooting,

man-to-man plays), which constituted a goal directed condition. Another video showed

non-basketball related actions (rolling the ball, carrying to goal, players crossing in front

of others with no action) to constitute a no-goal condition. fMRI readings showed

significantly greater activation of the superior temporal sulcus and the inferior parietal

lobule (part of the MNS) in the goal-directed condition (Takahashi, Shibuya, Kato, Sassa,

Koeda, Yahata, et al., 2008). While there is evidence that some goal understanding is

occurring in the MNS, it also seems that the activation to goal conditions occurs early in

the processing of information and that later in processing the system begins discerning

whether the observation included a conspecific (Costantini et al., 2005). This would seem

to indicate that there is room for both top-down and bottom-up processing of goals and

intentionality.

Another study compared the relative activation of goal understanding in different

areas of the brain by having participants view videos that displayed a woman drinking

coffee. Participants would view either an expected flow of events, extraordinary events

(woman puts coffee cup to ear instead of bringing to mouth), or extraordinary means

(woman has a power grip on the dainty cup). During exposure to the video stimuli, the

MNS and the social cognition areas of the brain traditionally considered responsible for


21

‘mentalizing’ goals – the posterior cingulate cortex, frontal cortex, and superior temporal

sulcus, were monitored for activation. When the activation results were compared,

evidence for a complementary system of goal understanding emerged. Participants would

show more activation in the MNS area when observing of the extraordinary events

segment compared to the ordinary actions (ordinary means and events) segment. The

inferior frontal gyrus in the MNS activated whether participants were asked to focus on

intention or the manner of action. More activation was shown in social cognition areas

when participants were asked to selectively attend to the intentionality of the woman’s

actions (de Lange, Spronk, Willems, Toni, & Bekkering, 2008). This evidence shows that

the MNS consistently activates whether attention is directed to intention or actions (with

the enhanced activation to extraordinary events vs. ordinary actions showing selectivity

for intentions) during observation, but that a person must focus on intentions solely to

activate the social cognition areas. This suggests that these areas work together in a

complementary system of bottom-up and top-down processing.

To assess the role of context in deriving intentionality in the MNS, a study was

performed using a similar instrumental method as the previously discussed study. One

scene was created in which various sundries and containers were displayed as if in

preparation for a meal, all items untouched and organized. A separate scene was created

in which these objects and food items had been obviously consumed and were ready for

clean up; containers had been empty, crumbs and leftover food was left where untouched

food items had been previously, a jar was tipped over, and so on. A control scene was also

used wherein no object was displayed except for a single coffee mug. The mug was


22

included in every scene and was filmed being grasped in two different ways, either using

the whole hand to pick the cup up around the lip or using a precision grip using the

fingers at the handle. The whole hand grasp is more suggestive of picking up the cup for

something other than drinking, and when used in the scene in which everything is ready

to be cleaned the grasp fits well contextually. The precision grip is contextually fitting in

the scene where it appears that a meal is about to be enjoyed, and that the cup is being

picked up to be drank from. Blood oxygenation level-dependent (BOLD) readings of the

MNS revealed significantly more firing (by way of heightened blood oxygenation) in the

contextually congruent grip and scene pairing for the precision grip and drinking context.

This effect was not present in the whole-hand grasp and cleaning context, which is

possibly because a whole-hand grasp is not used solely for cleaning and could

conceivably be used to pick up a cup to drink from (Kaplan & Iacoboni, 2006). These

studies suggest that goal understanding is occurring in the MNS, at least in part, and is

likely contextually sensitive.

As mentioned previously, the MNS has been shown to anticipate the goal of a

series of motor actions. Much of this research has used biological stimuli to elicit

activation, primarily in the context of a human confederate performing motor actions

with objects. There has been some research performed testing the ability of the MNS to

activate in response to non-biological action, although much of this research focuses on

the apparent self-locomotion of objects such as toys, balls, or other common household

items (Engel et al., 2008; Eshuis, Coventry, & Vulehanova, 2009). In the face of

increasing and more personalized technology (especially in the robotics field) an


23

important question is if the mirror neuron system will derive intentionality from

observation of a robot as easily as from observation of a human.

To begin to address this inquiry, it is first important to find if the human mirror

neuron system will even fire in response to non-human movement. There has been a

range of experiments performed to see if the mirror neuron system will fire in response to

general non-biological movement, such as self-locomotion in shapes vs. human hand

movement. In studies such as this, the mirror neuron system does activate to non-

biological motion, but not as strongly as it does to conspecific actions. These results have

been interpreted as a side effect of the human tendency to personify moving objects

(Heider & Simmel, 1944) and also as a tendency to perceive movement as biological

simply because that is the type of movement encountered most often (Engel, Burke,

Fiehler, Bien, & Rosler, 2008).

The mirror neuron system has been shown to fire in response to robotic agents in

some cases (Gazzola, Rizzolatti, Wicker, & Keysers, 2007; Oberman et al., 2007a;

Oberman et al., 2007b), and not in others (Kilner et al., 2003; Tai, Scherfler, Brooks,

Sawamoto, & Castiello, 2004). Although the literature contains mixed results, it does

seem that more often than not a robot will activate the human mirror neuron system as

much as a human agent if the robot agent is more humanoid in appearance and is

performing an action that is similar to a human-like action, such as picking up a ball,

walking bipedally, or grasping a martini glass. Results have shown though that if one of

these actions is shown repeatedly, the mirror neuron system habituates to the action and

responds less aggressively to the robotic movement as compared to a human movement


24

(Gazzola et al., 2007). These results may tie in with the function of goal understanding. If

a robot performs a singular action, such as picking up a martini glass, this action matches

well with the observer’s own motor repertoire and also is matched with the potential goal

of drinking. Yet if the action is performed repeatedly with no conclusion of the action

(lifting the glass to a mouth, or throwing a ball after grasping) the goal is never actually

fulfilled and the action is no longer perceived as a goal oriented action, resulting in

habituation of the mirror neuron system.

It can be extrapolated from the literature that it is likely that the human mirror

neuron system seems to fire in response to any motion (non-biological, biological,

possible or impossible) initially, but that it fires most readily to motion that infers a goal.

Research on more complex goal understanding using adult participants has been focused

on conspecific observation primarily (de Lange et al., 2008; Kaplan & Iacoboni, 2006;

Koski et al., 2002; Takahashi et al., 2008); while goal conditions involving a robot have

involved moving blocks from one area to another (Erlhagen, Mukovsky, & Bicho, 2006)

or picking up a glass (Gazzola et al., 2006). The goal conditions in these studies are far

more simplistic than the basketball paradigm (Takahashi et al., 2008) or the contextually

rich cleanup or drinking scenario (de Lange et al., 2008). The current study design

addresses the need for data concerning the ability for a human to recognize and learn

complex goal understanding from a robot engaged in a motor task.

There has been some research performed in which robots would perform more

complex tasks than moving an object to a new location or pick an object up. In one such

study, children watched either a human or a robot sort cards according to a specific


25

conditional rule. The children were then asked to sort cards according to a different

conditional rule. It was found that the children who observed the human sort cards had a

more difficult time in the task than the children who observed a robot sort cards. The

results imply that the children’s mirror neuron systems were more engaged in simulating

the human’s actions than the robots, and that they were less able to move on to a new

condition because of the impact of the human rehearsal condition (Moriguchi, Kanda,

Ishiguro, & Itakura, 2010). A separate study showed that children were able to infer the

intended goal of a robot (‘intending’ to put beads in a cup, but ‘accidentally’ dropping

beads outside of the cup) better if the humanoid robot made eye contact with the children

during the task than if the robot made no eye contact (Itakura et al., 2008).

The increased rate of testing of human discernment of goal understanding in a

robot’s motor actions using children as compared to adult participants is undoubtedly a

result of the anticipation of the use of robots in primary education settings. Although

robots are being used primarily for educational interaction with children at the moment, it

is safe to assume that the growth of the robotics industry will eventually place robots in

roles through which adults will interact with robots. Also, considering the evidence that a

child’s mirror neuron system may react differently to alternatively mediated stimuli and

non-biological motion than an adult’s (Shimada & Hiraki, 2006), it would be unwise to

assume the data garnered using child participants would be directly replicated in with

adult participants. Current research is still unclear concerning evidence that of MNS

activation by robots in adults and very little complex goal-understanding research has

been studied using an adult population.


26

Embodiment Theory

Embodiment theory can be viewed as a new and radical perspective on current

psychological research, or as a philosophical construct with roots as far back as the

origins of Eastern spiritual and metaphysical thought (Varela, et al., 1993). Although

there are several iterations of embodiment, the crux of the philosophy of embodiment

purports that nothing in this physical world exists as a singular and isolated entity,

including human beings (Barsalou, 2008; Varela, et al., 1993). To illustrate this idea using

a popular adage; “No man is an island…” (Donne, 1839). Human beings are not simply

the bodies we observe in the grocery store or the classroom, nor are they the mind alone.

In fact, even the union of the body and mind would not be a complete human.

Embodiment states that we are a continuous union of mind, body, and environment,

including the social environment (Varela et al., 1993).

More to the point of interest for this work, one aspect of cognitive embodiment

theory provides that the mind and brain rehearses some external stimuli in modal

(sensory) areas, rather than transforming all stimuli into logical abstractions of

knowledge per the computational approach (Glenberg, 1999). This perspective lends

itself well to what researchers know about MNS function. The MNS is rehearsing

observed motor stimuli in the motor area of the brain rather than acting as simply an input

area (Gallese, 2009). I am proposing that the MNS be considered a hard example of


27

embodiment processing in humans. This proposal, to my knowledge, is novel in the field

of psychology. Rather than asking if the human MNS can learn motor actions from a

robot as well as a human, I feel it is more correct to ask whether the human MNS can

embody motor actions from a robot as well as a human.

The MNS is not the only example of modal processing in the brain. There have

been other modal processing areas and phenomena that highlight how embodiment can be

an attractive theoretical approach for further research and exploration. It has become

clear that the symbolic computational approach does not adequately describe or explain

information-processing in all instances.

Perhaps the most famous example of a cognitive process best understood through

embodiment theory is that of color vision. It has been understood for some time at this

point in the sciences that color does not exist in the natural world as we perceive it, but it

also does not reside solely within our brains. Color is actually a phenomenon, an

interaction between the environment and our visual perceptual faculties (Varela et al.

1993). This relationship highlights the dynamic exchange between the brain and the

environment, a contrast to the one-sided linear process operating within a static

environment as in symbolic processing theories.

Another example of a well-known psychological phenomenon that melds well

with embodiment theory is phi phenomenon, or apparent motion, as described by Gestalt

theorist Max Wertheimer in 1912. Phi phenomenon is the subjective perception that two

alternately flashing lights that are horizontally arranged in the field of vision appear to be


28

one light moving back and forth. Wertheimer successfully rejected other explanations of

the phenomenon by illustrating that the perception was a product of the stimuli

movement (environment) and our perceptual system (mind and body) (Ammons &

Weitz, 1951). Arguably, many of the observations made by the Gestalt psychologists

are intrinsically descriptions of embodied phenomena.

A more modern example of embodied processing is Shepard and Metzler’s visual

representation experiments in the 1970’s. Participants were presented with sets of two

three-dimensional figures and asked to determine whether the set contained the same or

different figures. The sets that contained the same figure would present that figure at

different degrees of rotation. The researchers found a direct linear relationship between

the time it took to determine whether the figure was indeed a rotated version of the

primary stimuli and the degree of rotation of the figure (Shepard & Metzler, 1988).

Similar findings were produced in a separate experiment (Kosslyn, 1973) in

which a participant viewed a picture of an object (ex: an airplane) and was then asked to

focus on one particular element of the object (a propeller). After viewing the object for a

set amount of time the picture would be removed from view and the participant would be

asked to recall if the object had an element that would have been out of the area of

directed focus for the participant (vertical or horizontal fins on the tail). Results showed

that the length of time it took for participants to answer was relative to the distance of the

parts of directed focus and parts questioned about but not focused on.


29

These experiments strongly suggest that the visual information available to

conscious processing was not represented in a symbolic manner as prescribed by

computational theorists, but were represented visually in their mind by actually

‘picturing’ the stimuli. These are strong examples of modal representation of information

and thereby embodied cognition. This evidence parallels the rehearsal process of the

MNS (Gallese, 2009).

Although the proposal for the MNS to be considered a structure that operates at

least partially in an embodied manner is novel, the potential for human embodiment of

robotic actions without consideration of the MNS has been studied at least once. In this

singular study, children played a drumming game with a humanoid robot. Both the child

and the robot had their own drums, and the game involved the children and robot taking

turns playing their respective drums. The children were asked to mimic the beats that the

robot would play. The first condition of the experiment would have the robot and child in

a room together (‘face-to-face’). The second condition was mediated, the children

watched a live video feed of the robot. In the final condition the children would play in

the same room as the robot, but the robot was behind a partition so they would not see the

robot but could hear the drumming. Children reported more enjoyment playing the game,

played longer, and made significantly fewer errors in the live condition than in either the

mediated or listening condition. It would appear that children embodied the live robot’s

actions better than the other conditions (Kose-Bagci, Ferrari, Dautenhahn, Syrdal, &

Nehaniv, 2009).


30

There are two important differences to be considered when comparing this study

design with the current design: first, this experiment used an extremely human-like robot

(the robot looked very much like a child) and one would expect a greater degree of

embodiment from what could essentially be considered a conspecific visually and by

motor movement. Secondly, the research as outlined previously in the robotics section of

this work has shown that children’s mirror neuron systems are more predisposed to react

to robotic or mediated agents easier than an adult’s MNS (Shimada & Hiraki, 2006). This

does not negate the importance of the studies findings, merely suggests that there is a

need for varied inquiry.

These differences highlight that the current study’s use of adult participants and a

non-humanoid robot addresses a neglected area of study in the embodiment literature.

The suggestion that the MNS functions in a manner that supports the growing literature

on embodied cognition is a novel proposition. There is no proposal from the embodiment

literature concerning goal understanding, or whether embodiment of a robot will or will

not enable goal understanding to occur more easily. My hopes are that this experiment

can lend itself to further experimental study and consideration of embodiment theory in

robotics and psychology.

The Current Experiment

The current study is focused on assessing the potential for human motor learning

from a robotic teacher. Human-robot interactions are becoming an important aspect of

human life, and the personal robotics area is rapidly growing due to consumer demand.


31

Personal robotics will play an increasing role in home service, and the use of robotics in

education is beginning to be explored. Motor learning and goal understanding will be an

important factor in building better interactions between humans and robots.

The capability for a human to learn motor actions and understand goals or

intentionality from a robotic counterpart is currently best assessed through findings on the

human mirror neuron system. Evidence has been gathered that shows that the human

mirror neuron system will fire most readily to conspecific movement, but will also fire in

response to non-biological motion such as a robotic arm movement. The mirror neuron

system will fire most readily to familiar movements, but will also fire in response to

impossible movements. The mirror neuron system also fires in response to goal related

sequences of movement more significantly than randomized series of movements. Some

study has been performed to assess the readiness of the human mirror neuron system to

fire in response to robotic movement, but no work has been done to assess the ability for

humans to perceive goal intentionality from a robot’s sequence of motor actions.

Determining whether humans are able to derive intentionality from a robot’s motor

sequences is the main point of question in this study. If humans are better able to

understand motor goals from conspecifics, the group that watches a robot perform motor

tasks should perform worse than the group who watches a human perform motor tasks.

Embodiment theory allows the current study to be viewed in a larger theoretical

perspective. The human mirror neuron system operates through automatic rehearsal of

motor actions when performed by a conspecific. This rehearsal is processed in the human

mirror neuron system as if the observer had performed the action his or her self. The


32

simulation process is performed modally in the motor system and does not require

transmission of the information to other cognitive areas for learning (Gallese, 2009). Goal

understanding of motor actions has also been proposed as a modal function of the mirror

neuron system (Umiltà et al. 2008). Both functions, but especially the goal understanding

function (Hans, Henk & Ruud, 2012), had been previously determined to be solely top-

down symbolic processes while the modal areas served as sensory inputs (Palmer, 1999).

Embodiment theory proposes that at least some forms of cognition are represented and

interpreted modally, making the proposed functions of the mirror neuron system a good

example of embodied cognition. Current embodiment literature has very little study on

whether a human can embody information originating from a robot, and no work on

whether goal understanding or intentionality can be embodied. These missing points in

the literature are the secondary focus of this study. If the mirror neuron system is able to

embody the motor actions and intentionality of the robots, there should be little difference

in the times between groups on tasks and the groups should show similar execution of the

tasks in terms of repetition of observed movements. This study questions whether a

human can embody robotic motor actions and goal understanding as competently as

human motor actions. I hypothesize that a human will not embody a robot’s motor actions

as effectively as they would embody a conspecific’s motor actions. I also hypothesize that

a human will have less overall goal understanding of a robot’s motor actions than a

human’s motor actions.


33

CHAPTER III

METHOD

The present experiment utilized a between-groups design (human-viewing x

robot-viewing), the first group was exposed to a human video stimulus and the second

group was exposed to a robot video stimulus. In the robot-viewing condition the

participant viewed two videos of a robot solving two different tangram puzzles. Tangram

puzzles consist of different geometric plastic shapes (ranging in size from 1 inch to 3

inches in area) that may be arranged to create large shape and figure silhouettes. In the

human-viewing condition the participant viewed two videos of a human solving the same

two tangram puzzles. After viewing each video, the participant was asked to solve the

puzzle they had just observed being solved and then solve a final novel tangram puzzle

they had not seen solved. The dependent variables are the amount of time it takes to


34

complete the puzzles (a measure for each of the three trials) and how many times tangram

pieces are removed from the puzzle form while the participant is solving. The removal of

tangram pieces from the puzzle form during a randomized selection of solving time

served as a count for error.

Participants

Forty-one students from Fayetteville State University were tested in total, with 22

participants in the human condition and 19 participants in the robot condition. Six

participants were excluded from data analysis; 1 participant because of researcher error in

administration of the experiment tasks, and the other 5 for failing to complete two of the

three puzzles within the time limit of 15 minutes. Group counts after exclusions were 16

participants in the human group and 15 participants in the robot group. Twenty-six

participants were female and 5 were male. No significant differences were found on any

performance measure between males and females. No other demographic data was

collected on the participants. Nearly all participants volunteered for participation through

the university Participant Pool website. Four participants volunteered for participation

with the researcher directly. Undergraduate students received grade points for

participation in the research.

Instruments

The original experimental design was to use a Hanoi Tower puzzle as one of the

tasks, but was later cut due to the inability of the robot or the grabber arm to complete the

task. This task could allow for time measurement, a move count, and an error count as it


35

requires the user to follow a specific pattern to correctly solve. Sets of tangram pieces

were chosen as an alternate puzzle to the Hanoi Tower puzzle, and puzzle forms were

drawn and laminated. For the human video, a female volunteer was filmed solving 5

different tangram puzzles. The camera aperture was focused on only her hands, the

tangram pieces and the puzzle form. Two of the puzzles with the shortest solution time

were chosen to serve as the human group instruments. After selecting the videos, they

were coded movement-by-movement and timed. To film the robot video stimulus, a long

arm grabber was purchased to simulate a robotic arm. The grabber arm was 30 inches

long with a gripping set of two arms with suction cups at the end which can be closed by

the pull of a trigger at the opposite end of the mechanism. Using the arm, the two puzzles

selected in the human condition were replicated for the robot video stimulus. The puzzles

were solved in an almost exact method to the human condition, although some elements

were unable to be replicated; such as picking up certain pieces and manipulating them in

the hand, or moving more than one piece simultaneously on the board. The stimulus films

were edited and copied to a DVD using iMovie™ and Microsoft Movie Maker™. The

puzzle solving was not edited aside from a frame rate manipulation and the insertion of a

blue screen at the movies’ starting and ending points.

As using the grabber arm slowed down the overall time to solve the puzzles in

comparison with the human condition films, the robot films had the frame rate increased

by 125%. This manipulation permitted the alignment of the different film conditions. It

was not readily apparent that any manipulation had been performed on the films. No

participant inquired or remarked about the speed of the robot condition.


36

Participants watched the video stimuli on a 27-inch television that played the

DVD through a Zenith® DVD player. Participants viewed the films from a chair and

table centered in front of the television from a distance of 5 feet. Participants were filmed

solving the puzzles with a Kodak® EasyShare® digital camera and timed with a

stopwatch application on a Samsung® cellular telephone. The camera aperture was

focused on only their hands, the tangram pieces and the puzzle form. Data analysis was

performed using SPSS® v. 19 and G*Power™ v. 3. Any and all data collected from

participants were secured in a room only accessible by the researcher and Fayetteville

State University psychology faculty.

Procedure

Experimentation was performed on an individual basis. The experiment was

approved by the Internal Revue Board of Fayetteville State University (Appendix C).

After signing the consent forms (Appendix B) the participants were read the oral script

(Appendix A) by the researcher describing what they would be doing, which contained

information as to whether they would be watching a human or a robot solving puzzles.

The robot-viewing participants were read an additional statement to prime expectations

for viewing a robot: “The arm you will see is being manipulated by a robotic software

program.”

The script also informed participants that they had 15 minutes to solve each

puzzle and that they could quit at any time. To try and ensure the participants were fully

paying attention to the movies, they were asked to count the number of moves used to


37

solve the puzzle, operationalized as how many times the tangram pieces were moved into

the black area of the puzzle. After the script was read participants had a chance to ask

questions and then participants watched the first film. Once the film had reached the blue

screen in between the first and second puzzles, the researcher paused the film and asked

the participant how many moves they had counted while watching the video. After

receiving an answer the researcher brought the participant the puzzle they had just

observed being solved from an adjoining room where the puzzles were kept out of sight

of the participant. The researcher filmed the participant working on the puzzle until it was

solved or the allotted time of 15 minutes had passed. The participant’s time was tracked

along with the filming.

After the first puzzle trial had been completed or timed out, the researcher cleared

the puzzle from the table and removed the puzzle to the adjoining room. The researcher

reminded the participant that they were to count the moves used to solve the puzzle and

started the film again, which played through the blue screen and the second puzzle. After

the film ended, the researcher asked the participant for the moves they had counted and

then retrieved the puzzle they had just observed being solved from the adjacent room.

Participants worked on the second puzzle trial while being filmed until the puzzle was

either complete or they ran past the allotted time of 15 minutes. The puzzle was then

cleared from the work area and the researcher brought out the final puzzle, which was

novel to the participants in that they had not seen the puzzle solved on a video or seen the

puzzle form before. Participants solved the novel puzzle under the same conditions as the

first two puzzle trials. After either solving or failing to solve within time, the researcher


38

indicated to the participant that they had completed the experiment and thanked them for

their participation. At that point the participant was offered a copy of the experiment’s

consent form for their own records. A visual representation of the procedure of the

experiment follows in Figure 1.

Watch first film

Solve first puzzle

Watch second

Solve second

Solve novel

Figure 1

CHAPTER IV

RESULTS

Time and error served as the dependent measures to constitute an overall measure

of learning. Time of completion was the elapsed time of the participant’s engagement

with the puzzle. Time was measured until the participant would lay their hands on the

table along the sides of the puzzle form indicating completion as directed, or until the

participant requested to end the puzzle trial before completion. Error was operationalized

as the number of times a participant would remove pieces from the puzzle form in a

randomized 1-minute video sample from each puzzle trial. The error variable is called


39

‘backtracking’, a term created by the author for this study. It should be noted that there

was one participant who did not backtrack during any puzzle trial (making them an

outlier on this variable), and another participant was missing a backtracking measure in

one of their three trials due to video failure during experimentation. These two

participants had group means placed into their missing values as an alternative to deletion

from the small sample size.

When assessing normality of the data the time measures were found to be

positively skewed. A log transformation was applied to the time measure, which

completely normalized the human-viewing group’s distribution, and nearly all of the

robot-viewing group’s distribution. After transformation, the robot-viewing group still

exhibited slight skewness in the first puzzle trial data as shown in Table 1. This

abnormality in the robot-viewing group’s first puzzle trial distribution is due to the high

number of participants that were unable to solve the puzzle before the 15-minute limit. As

the main considerations in analysis pertain to the second and third (novel) puzzle trials

there should not be grievous errors due to the first puzzle trial’s slightly skewed

distribution. The error variable satisfied assumptions of normality.

Table 1

Descriptive statistics for robot-viewing group

Table 1


Table 1


Table 1

Descriptive statistics for robot-viewing groupMeasures First Second Novel

Skewness 1.262 .352 -.469Std. Error of Skewness .550 .550 .550Kurtosis 1.829 -1.182 -.288Std. Error of Kurtosis 1.063 1.063 1.063


40

Table 1 Descriptive TableTable 1 Descriptive TableTable 1 Descriptive TableTable 1 Descriptive Table

To assess the significance of the violation of normality, a Shapiro-Wilk test was

performed on each group distribution. In the human-viewing group the time variable for

the first puzzle trial, W(16) = .82, p < .05, and the backtracking variable for the first

puzzle trial, W(16) = .88, p < .05 were both significantly non-normal. In the robot-

viewing group the time variable for the first puzzle trial W(16) = .87, p < .05, and the

backtracking variable for the second puzzle trial W(15) = .87, p < .05 were both

significantly non-normal. Full results of the Shapiro-Wilk and the Kolmogorov-Smirnov

may be found in Table 2. Due to the near equal sample size in the groups and the

independent t-test’s robustness to non-normality, statistical analysis of the data is

considered appropriate. Assumptions for homogeneity of variance were met

satisfactorily.

Table 2

Tests for normality by group

Table 2


Table 2


Table 2


Table 2


Table 2


Table 2


Table 2

Tests for normality by groupTime and Error

Measurements, by group

Time and Error


Kolmogorov-SmirnovKolmogorov-SmirnovKolmogorov-Smirnov Shapiro-WilkShapiro-WilkShapiro-WilkTime and Error


Time and Error

Measurements, by groupD df p W df p

First puzzle time Human-

viewing

.231 16 .022* .829 16 .007*First puzzle time

Robot-

viewing

.200 15 .109 .877 15 .043*

First puzzle errorHuman-

viewing

.230 16 .023* .883 16 .044*


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First puzzle error

Robot-

viewing

.209 15 .076 .908 15 .127

Second puzzle

time

Human-

viewing

.164 16 .200 .972 16 .874Second puzzle

timeRobot-

viewing

.163 15 .200 .934 15 .310

Second puzzle

error

Human-

viewing

.246 16 .010* .893 16 .062Second puzzle

errorRobot-

viewing

.200 15 .110 .876 15 .041*

Novel puzzle

time

Human-

viewing

.134 16 .200 .916 16 .148Novel puzzle

timeRobot-

viewing

.158 15 .200 .951 15 .536

Novel puzzle

error

Human-

viewing

.150 16 .200 .957 16 .607Novel puzzle

errorRobot-

viewing

.227 15 .036* .889 15 .065

Note: *Significant at p < .05Note: *Significant at p < .05Note: *Significant at p < .05Note: *Significant at p < .05Note: *Significant at p < .05Note: *Significant at p < .05Note: *Significant at p < .05Note: *Significant at p < .05Table 2 Descriptives TableTable 2 Descriptives TableTable 2 Descriptives TableTable 2 Descriptives TableTable 2 Descriptives TableTable 2 Descriptives TableTable 2 Descriptives TableTable 2 Descriptives Table

An independent t-test on the time variable between groups showed no significant

differences. Table 3 and Figure 2 depict the mean log transformed times for the human-

viewing (n = 16) and robot-viewing (n = 15) groups across puzzle trials. Although not

significant, the second puzzle trial times are approaching significance, t (29) = 1.72, p < .

05, with the human-viewing group (M = 2.10, SD = .415) completing the puzzle faster

than the robot-viewing group (M = 1.87, SD = .309).


42

Table 3

Mean times and standard deviations for time variable between groups

Table 3


Table 3


Table 3


Table 3


Puzzles and groups M SDFirst Puzzle Human-viewingHuman-viewing 2.49 .483

Robot-viewingRobot-viewing 2.30 .464Second Puzzle Human-viewingHuman-viewing 2.10* .415

Robot-viewingRobot-viewing 1.87* .309Novel Puzzle Human-viewingHuman-viewing 2.22 .281

Robot-viewingRobot-viewing 2.37 .357Note: *Mean values approaching significance at p < .05Note: *Mean values approaching significance at p < .05Note: *Mean values approaching significance at p < .05Note: *Mean values approaching significance at p < .05Note: *Mean values approaching significance at p < .05Table 3 Means and Standard Deviations TableTable 3 Means and Standard Deviations TableTable 3 Means and Standard Deviations TableTable 3 Means and Standard Deviations TableTable 3 Means and Standard Deviations Table


43

To further explore the difference between the groups on the second puzzle trial, a

within-subjects paired samples t-test was performed. The paired samples t-test failed to

reveal a significant difference, t (15) = -1.12, ns, between the mean time on the second

and novel puzzle trials for the human-viewing group (n = 16), (M = -.121, SD - .432), as

expected by the results shown in Table 3. The results for the robot-viewing group (n = 15)

were highly significant, t (14) = -4.72, p < .05, in the time difference between the second

and novel puzzle trials as seen in Table 4. A visual representation of this data may be seen

in Figure 2.

Table 4

Mean difference in time between second and novel puzzles for robot-viewing group

Table 4

Mean difference in time between second and novel puzzles for robot-viewing group

Table 4

Mean difference in time between second and novel puzzles for robot-viewing groupPaired Puzzles M SD

Second puzzle time – Novel puzzle time

-.497* .408

Note: *Significant at p = .05, 2-tailedNote: *Significant at p = .05, 2-tailedNote: *Significant at p = .05, 2-tailedTable 4 Within-Group Paired t-Test Table 4 Within-Group Paired t-Test Table 4 Within-Group Paired t-Test


44

Figure 2

Results of an independent t-test examining puzzle backtracking error between the

human-viewing group (n=16) and the robot-viewing group (n=15) found a significant

difference between groups in the novel puzzle trial, t (29) = 2.49, p < .05. The human-

viewing group (M = 4.31, SD = 1.778) committed significantly more errors than the

robot-viewing group (M = 2.73, SD = 1.751). This data is shown in Table 5 and

represented visually in Figure 3.


45

Table 5

Mean backtracking error differences between groups

Table 5


Table 5


Table 5

Mean backtracking error differences between groupsPuzzles and groupsPuzzles and groups M SDFirst Puzzle Error Human-viewing 2.81 1.759First Puzzle Error

Robot-viewing 2.53 1.922Second Puzzle Error Human-viewing 2.25 1.693Second Puzzle Error

Robot-viewing 1.80 1.859Novel Puzzle Error Human-viewing 4.31* 1.778Novel Puzzle Error

Robot-viewing 2.73* 1.751

Note: *Significant at p < .05Note: *Significant at p < .05Note: *Significant at p < .05Note: *Significant at p < .05Table 5 Means and Standard DeviationsTable 5 Means and Standard DeviationsTable 5 Means and Standard DeviationsTable 5 Means and Standard Deviations

Figure 3


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Exploratory variables

As the design of this study focused on measuring a behavior that is unable to be

directly measured (learning), several different methods were used in an attempt to

appropriately measure the implied behavior. While not all of the methods provided

significant results, consideration of the methods and their results are still descriptive of

learning processes in their own right. These results may be used to develop future

research as a means of narrowing the scope of measurement.

The initial working definition of error as a variable was the number of ‘board

clears’ a participant would make throughout the course of solving in each puzzle trial. A

board clear was operationalized as each time a participant would clear the puzzle form

testing the efficacy of robot-to-human goal …faculty.uncfsu.edu/dmontoya/lab_files/thesisfinal _...

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