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Design Evaluations in Educational Settings: A Neuroscientific Study of Incentivized Test Retest on Student Performance


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To understand the impact of incentivized test/retest scenarios – where students are afforded an opportunity to retest for an incentive – in design education settings, this study examines participants brain activity using electroencephalography (EEG) during stressful retest situation. This study mimics educational scenarios where students are afforded an opportunity to retest after a first attempt. Twenty-three student participants were randomly divided into two cohorts: control and experimental. Participants were asked to complete a preliminary questionnaire self-assessing their ability to handle stressful situations. Both cohorts were subsequently asked to complete the typing test and complete an Emotional Stress Reaction Questionnaire (ESRQ), indicating their emotional response during the typing test. The participants were subsequently asked to complete the typing test and accompanying ESRQ a second time. However, prior to the second test, the participants in the experimental cohort were incentivized with a monetary reward for improving their typing speed. This stimulus is used to increase the already stressful situation for the experimental cohort and examine changes in brain activity when the “retest” is incentivized. The results indicate no significant changes in brain activity, emotions, or typing performance for the control group. However, the experimental group showed an increase in EEG sensor activity; specifically, the sensors that control vision and emotion. The experimental group's performance was correlated to their emotional responses, rather than their EEG sensor data. Additionally, the experimental groups' positive emotions were increased for the incentivized typing test. The findings provide recommendations for educational retests practices.
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Devanshi Shah
College of Engineering,
University of Georgia,
2036 ISTEM I Building,
302 East, Campus Road,
Athens, GA 30602
Elisabeth Kames
Department of Mechanical Engineering,
Florida Polytechnic University,
4700 Research Way,
Lakeland, FL 33805
Jaslynn Pelham
University of South Florida,
4202 East, Fowler Avenue, ENG 030,
Tampa, FL 33620
Beshoy Morkos
College of Engineering,
University of Georgia,
2040J ISTEM I Building,
302 East, Campus Road,
Athens, GA 30602
Design Evaluations
in Educational Settings:
A Neuroscientific Study
of Incentivized Test/Retest
on Student Performance
To understand the impact of incentivized test/retest scenarioswhere students are afforded
an opportunity to retest for an incentivein design education settings, this study examines
participantsbrain activity using electroencephalography (EEG) during stressful retest
situations. This study mimics educational scenarios where students are allowed to retest
after a rst attempt. Twenty-three student participants were randomly divided into two
cohorts: control and experimental. Participants were asked to complete a preliminary ques-
tionnaire self-assessing their ability to handle stressful situations. Both cohorts were sub-
sequently asked to complete the typing test and complete an Emotional Stress Reaction
Questionnaire (ESRQ), indicating their emotional response during the typing test. The par-
ticipants were subsequently asked to complete the typing test and accompanying ESRQ a
second time. However, prior to the second test, the participants in the experimental
cohort were incentivized with a monetary reward for improving their typing speed. This sti-
mulus is used to increase the already stressful situation for the experimental cohort and
examine changes in brain activity when the retestis incentivized. The results indicate
no signicant changes in brain activity, emotions, or typing performance for the control
group. However, the experimental group showed an increase in EEG sensor activity; spe-
cically, the sensors that control vision and emotion. The experimental groups perfor-
mance was correlated to their emotional responses, rather than their EEG sensor data.
Additionally, the experimental groupspositive emotions were increased for the incentivized
typing test. The ndings provide recommendations for educational retests practices.
[DOI: 10.1115/1.4053801]
Keywords: reward-performance, cognitive neuroscience, electroencephalogramc, data-
driven engineering, engineering informatics
A goal of design research today is understanding human design
behavior [1]. Efforts from educators and researchers in learning
the potentials of a designers cognitive and creative thinking have
opened the proverbial door for more intervention studies to
further understand the complex nature of human behavior and
thinking. Intervention studies are conducted in classrooms, occupa-
tional epidemiology, and educational institutes aimed at improving
individual health and achieving gender equality, diversity, and
motivation. The objective of intervention studies is to understand
human behavior and understanding of complex brain functions, to
develop training modules for improving motivation and increasing
performance. Research in human brain plasticity provides evidence
on rapid adaptability to complex tasks involving decision-making,
memory strength, and cognition. There is a growing need for the
application of cognitive neuroscience ndings in real-world, educa-
tional, and workplace environments. The study of cognitive neuro-
science in design education settings may provide insight into the
formation of design engineers and benet educators.
Neuroscience studies on development training are aimed at
increasing the understanding of brain functions and performance [2].
Popular studies on training and development, such as London Taxi
drivers memory training, leading to modulation of hippocampuss
gray volume [3], and increment in brain activities in the prefrontal
lobe among meditation practitioners have positively supported the
importance of training [4,5]. Training is a well-organized and sys-
tematic approach toward improving performance in any controlled
environment setting by ethically increasing the potential of the indi-
vidual or group in said environment. Classroom techniques, such as
training students in collaborative learning, project-based activities,
group work, etc., have shown a positive impact on learning and
Engineering design is synonymous with problem-solving [6], as
it is regarded as a twofold activity comprising of identifying the
problem and generating the solution for said problem. Engineering
design involves conceptual development [7], collaboration [8],
computer-aided tool development [9], optimization [10], sustain-
ability [11], human factors, and many other facets. Designers cre-
ativity in problem-solving is the protagonist of the innovation, and
yet more than often, it fails to meet the greater demand of empathy
[12,13] from the stakeholders. Manufacturing with digital twin
technology [14] aims to bridge the gap between product design
and emotional response. Electroencephalogram (EEG) data in addi-
tion to physical data and emotional feedback aims to benet smart
design and manufacturing. Yet, there exists a gap in understanding
how students learn designs and their thinking throughout the learn-
ing process. Acquiring a fundamental understanding of the various
Corresponding author.
Contributed by the Computers and Information Division of ASME for publication
received July 30, 2021; nal manuscript received January 6, 2022; published online
March 4, 2022. Assoc. Editor: Paul Witherell.
Journal of Computing and Information Science in Engineering JUNE 2022, Vol. 22 / 031013-1
Copyright © 2022 by ASME; reuse license CC-BY 4.0
facets of design including designers thinking [15,16] will benet
the design educators by providing insight into how students
respond to various design learning stimuli. The setting for current
formal design education is senior capstone design, a culminating
design experience for students enrolled in engineering programs.
Educators have used the principle of providing students with
extrinsic motivation to impact intrinsic motivation for a more
engaging learning environment. Intervention studies in project-
based learning environments, such as the senior capstone design
course in engineering, targeted at increasing student motivation
have shown the importance of training and incentives. In a previous
intervention study, the researchers built channels to increase extrin-
sic motivation (praise and extra-credit) in senior capstone design
classroom presentations [17]. The aim of this intervention study
was to decrease anxiety, one driving factor of motivation. The
instructor of the course and graduate student assistants guided
senior capstone design students in developing intrinsic motivation
throughout the course by providing opportunities through test/
retest training and thus hoping to regulate anxiety and self-efcacy.
Test/retest training is a method of providing individuals with an
additional opportunity to repeat the activity by incentivizing the
repeated task. For example, if the student design team missed the
opportunity of understanding how to formulate a problem statement
correctly, the instructor would subsequently provide the team a
second chance at presenting that information, incentivizing the
second opportunity with a bonus point for meeting the requirements
before the second, immediately repeated, deadline. This technique
not only improves student motivation but also provides opportuni-
ties for learning. This paper takes this previous study a step further
in examining the impact of the test/retest training from a neurocog-
nition perspective. Using the existing work to understand the impact
of intervention studies through cognitive neuroscience provides an
added dimension and clarity on the complex workings of human
Cognitive neuroscience, a branch of neuroscience, offers nd-
ings on brain functions to support ethical interventions of altering
brain functions and relationships in the betterment of a larger pop-
ulation [18]. EEGs are one of the most effective techniques used to
examine and study neural/electrical activity in the human brain.
This is achieved by placing electrodes on the human scalp. The
electrodes capture micro-electrical charges that take place
between brain cells. EEGs have been used for decades in cognitive
research for diagnosing epilepsy and sleeping disorders. Further,
EEGs are used to study correlations between brain activity and
working memory performance in infants, [19,20] and memory per-
formance in toddlerhood [21]. EEGs are used to study overall
brain activity to monitor impacts of trauma, addiction, and brain
In this paper, we present an amalgamation of neuroscience, test/
retest training, and a reward-based training case study. The content
of this paper is based on an initial study presented at the IDETC-
CIE 2021 conference [22]. The study was performed in a university
setting to understand student performance during a typing test with
a control group and an experimental group, from a neurocognitive
lens. The experimental group is presented with a reward for best
performance. Each of the groups are examined with respect to
their neural activity during the typing tests, using an EEG. In addi-
tion, immediately following each of the two typing tests, the partic-
ipants are required to self-evaluate their stress levels during the
typing test, using an Emotional Stress Reaction Questionnaire
Since individual EEGs could not be placed on students during
retest design presentation scenarios, we develop a test that is anal-
ogous to the stress-induced environment where students are
rewarded for making improvements over their original test. We
investigate differences in neural activity among participants in the
control and the experimental groups and examine the emotional
response under a stress-induced environment. This is subsequently
examined with respect to the participants cohort to determine if the
test/retest and reward incentives impacted the participants neural
activity or emotions during the second, immediately repeated,
Cognitive psychology is a branch of psychology focused on
understanding brain functions and aid in analyzing human brain
activities such as language, typing, memory, and abnormalities
[23]. In a clinical setting, cognitive psychologists diagnose abnormal
brain functions and provide valuable information in the treatment
of conditions such as Alzheimers[24], trauma, memory loss, and
other motor disabilities. Advances in neuroscience research have
guided advances in the bio-design industry such as the invention
of biologically inspired prosthetic hands/legs that use electro-
encephalography signals to output the triggered command from
neurons [25], which impacts roughly 3 million people across the
globe [26]. Provided the ndings from research, it helps medical
practitioners in developing training modules to improve those func-
tions [27].
In experimental settings, cognitive psychologists study the rela-
tionship between brain and mind and explain the different functions
of the brain in event-related potentials. The two settings serve as the
foundations of all neuroscience discoveries and applications. EEG
studies are recommended in understanding designersdecision-
making and performance. EEGs provide high-resolution data and
have the ability to capture cognitive processes in specic brain
functions. Traditionally, researchers in engineering have studied
engineering behavior in design settings for concept generation,
motivation, and decision-making through surveys, interviews,
observation, and intervention studies. The addition of neuroscience
in design research will provide insights into the cognitive processes
of students and engineers at every stage of decision-making [28],
hence assisting educators and instructors in increasing student moti-
vation in classroom and performance. Advances in design research
among engineers and architects lead to more discoveries in neuro-
cognition and performance [29]. Educators across all disciplines
highlight the importance of motivation in engaging classroom envi-
ronment [30]. Engineering students have reported the value of class-
room performance in the overall preparedness for industry and
persistence in the program [31]. Studies have shown a 4060% of
retention rate in engineering, [32] which makes this study a small
step in contributing to the larger goal of targeting student retention
and preparedness for future challenges.
Cognitive training is popularly referred toward improv-
ing cognitive functions by training/interventions [2]. The two
major approaches to cognitive training can be classied as
strategy-based and process-based training paradigms [3335]. Strat-
egy-based training involves task instruction and applications.
Process-based training is more focused on practice/repetition. In
this study, we focus on process-based training, where the participants
are asked to complete a test in under one minute and subsequently
repeat the same test. This is termed as test/retest training in this
study. The control group were administered two typing tests with
no incentive to improve during the repetition; the experimental
group is incentivized to repeat the typing test with improved perfor-
mance for a reward of $100.
The rst typing test can be linked to the preparatory practices that
are used in research and real-world settings to prepare candidates for
optimal performance under high-stress conditions. Research shows
negative effects including increased anxiety and decreased perfor-
mance in stressful environments [3638]. Preparatory practices
have long been used in medical and clinical settings to reduce the
negative effects of stress and indicated promising outcomes [39].
For example, medical doctors practice verbal preparation with
their patients before important surgeries and operations to reduce
anxiety. Studies have also shown the positive impact of written
and verbal preparations used in psychotherapy groups [40].
However, there is not enough evidence of the same results
031013-2 / Vol. 22, JUNE 2022 Transactions of the ASME
outside of medical settings. A study by Olson examined the impact
of preparatory information on speech anxiety outside a clinical
setting; however, the results indicated no overall signicant
change [41]. All high-stress environments share similar characteris-
tics. Some examples include muscle tension, anxiety, increased
heart rate, and so on. experienced by individuals under stressful
conditions [42]. Therefore, while stressful situations may differ in
nature, they produce similar responses and characteristics. The
adaptability to those environments may change from person to
person. The routine stressful tasks response is different from emer-
gency stressful tasks. Huey and Wickens coined this change in
stressor as a transition event [43].
Our previous research has shown that senior capstone design pre-
sentations can be regarded as one such stress-inducing environment
for students, with the requirement of biweekly presentations to
industry clients and instructors. It has also shown a decrease in pre-
sentation anxiety and an increase in performance at the end of the
course, inferring the impact of repetition and preparedness garnered
throughout the course [44,45]. Incentives can be intrinsic and
extrinsic, both considered motivation-specic processes leading to
desired behavior/outcome of the task. This type of cognitive inter-
vention can be studied from a neuroscience perspective to gain
insights into the event-related brain function.
With performance incentive systems, rewards can be conditional
to the standards of performance [46]. With the adamant of research
in reward-based performances, studies highlight two important
topics: improving performance [47] and changes in intrinsic motiva-
tion [48]. The authors of this study have previously contributed to
understanding student motivation in mechanical design courses
[49]. The idea of improving motivation and student performance
with rewards is controversial [48]. Educators have expressed
mixed opinions on the use of rewards in educational settings to
increase motivation and performance. Studies have also shown the
negative impact on performance when the reward is larger than the
task [50]. Important research in rewards and behaviors has shown
the change in adult behavior, and the change in value motivation
can be correlated to the monetary reward and its impact on perfor-
mance [5153]. It has also been identied that an adult tends to
spend more physical and mental resources on a task given a bigger
monetary reward [54]. Adults can optimize and strategize their
actions toward the reward, thus making this study important in under-
standing the complex working reward-based performance.
Electroencephalography. The human nervous system is made
up of the brain, spinal cord, nerves, and ganglia. The nervous
system controls the bodys response to internal and external
stimuli. It is comprised of two main types of cells: neurons and
glia. The basic function of neurons is to convey information
across various parts of the nervous system. Neurons transfer this
information in the form of electrical and chemical processes. Elec-
troencephalogram or EEG is an imaging technique administered to
read electrical activity within the brain, commonly referred to as
brain waves. EEGs can be categorized under a safe experimental
procedure with minimal discomfort caused to the participants.
Brain waves are categorized under four main groups depending
on their frequencies: beta, alpha, delta, and theta. Beta (>13 Hz)
waves are the most commonly and frequently observed among
adults and children [55]. Beta activities are relatively fast and pre-
dominate the wave frequencies in the brain [56]. Alpha (813 Hz)
is commonly observed in the normal awake EEG recordings. It is
associated with memory functions [57]. Theta (48 Hz) rhythms
are associated with drowsiness and early stages of sleep. Delta
(0.54 Hz) is dominant in deep sleep state [58].
The four categorized lobes in the brain are occipital, temporal,
parietal, and frontal. The occipital lobe is responsible for visual pro-
cessing in the brain. It determines the concept of color, depth,
height, facial recognition, and formation of memory visuals [59].
The temporal lobe is associated with the main functions in the
brain: emotions, visual recognition, and audio processes. The
parietal lobe plays three important roles: integration of information
from sensory modalities, integration of memory and information
from the sensory world, and integration of the individual internal
state with sensory information. The role of this integration is to
provide feedback to the muscles, eyes, limbs, head, etc. The
frontal lobe is divided into three main regions: the primary motor
region, the premotor region, and the prefrontal region. Frontal
regions are associated with planning, guidance, and evaluation of
behavior. Frontal regions are also associated with emotional func-
tioning, decision-making, and judgment. The frontal lobe is respon-
sible for a varied range of activities including motivation, regulation
of dopamine, and personality.
In this paper, we examine participantsneural activity during a
typing test. Research has shown the activation of brain regions
such as the left superior parietal lobule, the left supramarginal
gyrus, and the left premotor cortex in experiments measuring
typing and writing [60]. It has been found that motor skills, such
as typing, involve numerous cognitive processes in clinical
studies [6163]. Researchers have extensively studied and analyzed
the human brain under chronic stress conditions. Advances in neu-
roscience research help in training individuals to overcome stress
and brain dysfunctions.
Research Method
Twenty-three students, aged 1622 years old, participated in the
EEG experiment study. The students had varied educational back-
grounds in STEM-based majors; however, none of the participants
had formal typing training. Out of 23 study subjects, 12 were ran-
domly assigned control group and 11 under the experimental group
without prior information on the participants background. The
experiment takes place in a design laboratory setting at the university.
An EEG device is administered to collect data on event-related poten-
tial activities. Additionally, participants take a stress survey after each
trial of the typing test. None of the participants had received formal
typing training, nor are they provided any information about the
study prior to it being conducted. The experiment is conducted in a
controlled environment; there is no change in the lab setting through-
out the duration of the experiment. Each participant completes the
typing test individually, to eliminate additional distractions.
Further, participants are advised not to consume any stimulants
(coffee/tea/energy drinks) across the duration of the study. Natural
light in a lab setting was used instead of articial light.
This quantitative study is an amalgamation of EEG data and
survey responses collected from participants. In this section, we
introduce the EEG device and the experimental setup for perform-
ing the typing test for the control and experimental cohort partici-
pants. We chose the use of a typing test for this study, as it
provides a combination of multiple motor and cognitive functions.
This preliminary study aims to provide a foundation for future,
design specic, and neuroscience studies.
Experiment Design. The EEG experiment takes place in two
groups: the control group and the experimental group. None of
Fig. 1 EEG Experiment
Journal of Computing and Information Science in Engineering JUNE 2022, Vol. 22 / 031013-3
the participants were trained specically beforehand. The randomly
selected participants follow the sequence of events as shown in
Fig. 1. This is completed in a single, individual session, to eliminate
distractions during the typing activity. Once the sequence starts, the
participant does not leave the room until all tests and surveys are
completed and data are collected. The study begins with a human
consent form, followed by a pre-experiment survey. Upon comple-
tion, the participants are briefed about the typing test and EEG setup
administered by the researchers (shown in Fig. 2). Each participant
subsequently completes the rst typing test (T1) while EEG and
heart rate data are collected. Upon completion of the rst typing
test, each participant completes a stress response (ESRQ) survey
self-assessing their stress levels during T1. Here is where the
sequence of events diverge between the control and the experimen-
tal group: (a) the participants in the control group subsequently take
the second typing test (T2), with EEG and heart rate data being
recorded, and complete a second ESRQ survey thereafter to end
their participation; (b) prior to a participants second typing test in
the experimental group, the researchers inform them that the partic-
ipant that increases their typing speed the most will receive an
award at the end of the test. The experimental group is subsequently
administered the second typing test, followed by the ESRQ stress
response survey, similar to the control group. For both groups,
the second typing test is administered immediately following the
completion of the rst test and ESRQ survey.
An Institutional Review Board (IRB)-approved human consent
form was given to participants upon arrival. The consent form
signed by participants stated no health risk, however, indicated the
possibility of experiencing minimum stress throughout the
experiment, which varied based on the individual. The consent
form was followed by a pre-experiment questionnaire. This pre-
experiment questionnaire was administered to collect demographic
information of the participants. The questionnaire consisted of ques-
tions on gender, age, and a Likert scale rating on daily stress levels.
The participants scored on a scale of 15, poor to excellent, on items
such as how well they handle stress, how often they experience stress
and the impact of their daily stressors on their wellbeing.
Typing Test. In an ideal research setting, students would be
equipped with an EEG during a design test/retest situation.
However, due to the intrusive nature of EEGs and their immobility,
we devised an incentivized test/retest scenario that mimics the stress
of design presentations. Further, presentations involving the partici-
pant speaking could not be performed as the movement of muscles in
the human face could alter the EEG readings. Thus, the authors
devised a stressful environment that afforded a test/retest opportunity
without requiring participants to speak or use their facial muscles.
The participants take part in a one-minute typing test twice during
the experiment. The typing test is available online on TypingTest.-
com [64]. For the one-minute typing test, the website offers eight
options for typing: Easy Text, Medium Text, Difcult Text, Tricky
Spelling, Blind Spelling, Story Typing, Themed, and Professional.
From the eight options, the researchers selected the story typing
option beforehand for the participants. The story typing category
further has seven options to select from. The researcher chose two
of those options for the participants: Aesops Fable and Tigers in
the Wild. The control group and the experiment group participants
complete both the typing tests. If a participantsrst typing test is
Tigers in the Wild, the second test is Aesops Fable, or vice-versa.
This is specically designed so that the participants cannot recall
their rst typing test during the second test and do not know what
they will be expected to type prior to either of the two tests.
Electroencephalogram Measurements. The EEG device was
positioned on all participants before starting the EEG test. The
researchers in this study had received prior formal training in
administering the EEG device. The EEG device used in this study
is a mobile EMOTIV Epoc+. This device has 14 channels recording
brain neural activities. The channels are spaced with a standard
International 1020 location method with a sampling frequency
of 256 Hz. The channels targeting the designated hemispheres are
AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, and
AF4 [65]. This device used a sequential sampling method with a
single ADC. The alternating current (AC) coupled device has a
built-in digital fth-order sinc lter. The motion sampling capacity
of the EMOTI Epoc+is 128 Hz. The EMOTIV EEG is shown in
Fig. 3(a), the spatial position of the electrodes is shown in
Fig. 3(b), and a heatmap (one type of output) of the neuroactivity
Fig. 2 Study subject setup
Fig. 3 (a) EMOTIV EEG headset, (b) spatial mapping of electrodes, and (c) heatmap of neuroactivity [65]
031013-4 / Vol. 22, JUNE 2022 Transactions of the ASME
during stimuli In Fig. 3(c). Odd numbers refer to the left hemisphere
and even numbers refer to the right hemisphere. O, P, T, C, and F
denote the occipital, parietal, temporal, central, and frontal lobe,
F8 surrounding area is associated with emotion regulation. F7 and
F8 are considered important for impulse control and metabolic activ-
ities, cognition, and self-control. They are also referred to as control
centers [66]. Working memory is associated with the prefrontal
cortex area. F7 is associated with verbal and behavior, F8 with
social inhibitions. F8 regulates emotions such as anger, joy, and hap-
piness. Amygdala is responsible for communication with this region
of the brain. F4 and F3 are associated with motor planning [67]. F4 is
typically associated with left hemisphere motor planning activities.
O1 and O2 are associated with visual processing in the brain. P7
and P8 of the parietal lobe are associated with somatosensory percep-
tion, spatial representation, and tactile perceptions. This refers to the
physical sensory movements with a conscious perception such as
touch, pressure, pain, etc. AF3 and AF4 are associated with the man-
agement of cognitive and executive decision-making. Temporal
lobes, T7 and T8, are associated with the perception of biological
motions. FC5 and FC6 are also associated with executive tasks and
emotional resources on a given task. Common Mode Sense (CMS)
and Driven Right Leg (DRL) are the reference channels primarily
used to provide high-quality readings by reducing noise. External
factors such as low blood sugar, ashing lights, caffeine, and hair
products may interfere with the EEG test results.
Emotional Stress Reaction Questionnaire. The Emotional
Stress Reaction Questionnaire [68] is used in this study to record
the emotional responses of the participants at two instances
during the study: after the rst and after the second typing tests.
The ERSQ questionnaire was designed for the participants to
answer the emotional response in less than 60 s, therefore not
extending the time between the two trials of the test (to ensure a
test/retest situation). The ESRQ consists of 14 emotional words:
indifferent, relaxed, pleased, glad, alert, focused, concentrated,
energetic, concerned, uncertain, heated, mad, and angry. Student
participants were asked to rate each emotion on a Likert scale of
14. T-tests analysis is performed on the recorded responses to
determine differences between the two tests, as well as differences
between the two groups of participants.
Analysis. The analysis performed will investigate the impact a
test/retestsituation has on the participants brain activity and the
impact of incentivizing the second test for the experimental cohort.
The goal of the study is to determine, through the use of an EEG,
the change in brain activity and performance (measured in words
per minute) between a participantsrst attempt and second
attempt of a one-minute typing test. The typing test is intended to
induce a stressful situation for the students as they attempt to
perform well. Two statistical analyses are conducted to correlate
and compare the typing performance and brain activity data:
T-tests and regression analyses. T-tests are conducted between the
two groups of participants (control versus experimental), as well as
within cohorts (T1 versus T2), to determine if statistically signicant
differences existed in their brain activity during the typing test.
Linear regression is used to determine the impact of the participants
negative emotions on their typing speed for each of the tests. The
analysis utilizes Akaikes Information Criterion (AIC) to nd
the best t model since correlations may be multilevel [69]. In
this study, statistical signicance is considered at α< 0.05;
however, α< 0.10 is maintained for discussion purposes.
The overarching goal of the study is to determine, if differences in
brain activity exist in the test/retest situation, what part of the brain
this activity occurred in and what information this could tell us
about the participants reaction to the ability to retest immediately.
The goal of the study is to determine the impact of a test/retest
policy when participants are exposed to a stressful situation to
mimic design test/retest scenarios. An EEG is used to monitor the
participants brain activity during two trials of a one-minute
typing test.
First, it was necessary to determine if inherent differences existed
between the two cohorts of participants. A t-test was performed to
examine both cohortstyping speed on the rst test, to ensure that
one cohort was not naturally superior at typing to the other.
Increased typing ability in one of the cohorts could result in natu-
rally lower anxiety in our simulated stressful situation.Table 1
shows the results of the t-test exhibiting that there was not a differ-
ence between the two groups (p=0.20).
Additionally, it is necessary to view the differences in the two
cohortspositive and negative emotion scores (using the ESRQ)
during the rst typing test to determine how each of the participants
reacted to the initial test. Similarly, a t-test was used to examine the
participantsreactions in the rst stressful situation. Tables 2and 3
show the positive and negative emotional scores, respectively.
Table 1 T1 Typing speed t-test results
Typing speed Control Experimental
Mean 40.25 32.17
Variance 337.5 117.9
Pooled variance 227.7
tStat 1.312
p-Value 0.203
tCritical 2.074
Table 2 T1 Comparison of positive emotional score
Positive emotions Control Experimental
Mean 17.42 18.33
Variance 12.27 9.333
Pooled variance 10.80
tStat 0.683
p-Value 0.502
tCritical 2.074
Table 3 T1 Comparison of negative emotional score
Negative emotions Control Experimental
Mean 11.58 12.42
Variance 19.90 8.447
Pooled variance 14.17
tStat 0.542
p-Value 0.593
tCritical 2.074
Table 4 t-Test comparison of F4 (T1)
F4 Probe Control Experimental
Mean 4222 4206
Variance 316.1 626.7
Pooled variance 464.0
tStat 1.884
p-Value 0.073
tCritical 2.080
Journal of Computing and Information Science in Engineering JUNE 2022, Vol. 22 / 031013-5
Table 5 Experimental O2 sensor t-test
O2 Sensor T1 T2
Mean 4150 4153
Variance 17.02 37.67
Pearson correlation 0.252
tStat 1.485
p-Value 0.084
tCritical 1.812
Table 6 Experimental FC6 sensor t-test
FC6 Sensor T1 T2
Mean 4149 4155
Variance 308.4 201.9
Pearson correlation 0.741
tStat 1.704
p-Value 0.059
tCritical 1.812
Fig. 4 Experimental group emotion sum values versus adjusted typing speed
031013-6 / Vol. 22, JUNE 2022 Transactions of the ASME
As shown in the tables, neither the positive (p=0.50) or negative (p
=0.59) emotional scores differed between the control and experi-
mental cohorts during the rst test. This indicates that neither of
the two groups were well suited for the situation emotionally.
Provided that the data showed no signicant difference in the par-
ticipantsnatural typing ability or emotional scores, a t-test assuming
equal variance was performed between the two groupsEEG data
during the initial typing test (T1) to determine if signicant differ-
ences in brain activity occurred between the two groups during
their rst attempt at the typing test. Of the 14 different probes,
none of the differences between the two groups were found to be sig-
nicant; however, one sensor was maintained for discussionF4
Right Hemisphere Frontal Lobeproducing a p-value =0.073.
Additionally, as previously outlined, signicance was not found
between the two groupspositive, negative, or total ESRQ scores
during the rst typing test. Table 4shows the t-test comparison of
F4 for the rst test. Therefore, the groups of participants were not sig-
nicantly different in their abilities, emotions, or performance during
the rst typing test. This indicates that all of the participants
responded similarly to the initial test, providing a baseline for the
retest scenario.
Paired t-tests were completed on each individual group to deter-
mine if signicant differences existed between the two typing tests
(T1 to T2). It is interesting to note that none of the probes produced
a statistically signicant difference in the control group. Recall,
the control group was exposed to a test/retest scenario without
an incentive. However, the experimental group produced two
p-values (p< 0.1) that were maintained for discussion purposes.
The O2 and FC6 sensors produced p-values of 0.084 and 0.059,
respectively. As shown in Tables 5and 6, both the O2 and FC6
mean values increased between the two tests for the incentivized,
experimental group. Linear regression was also performed to deter-
mine which (if any) of the sensors correlated to the participants per-
formance on the typing test. Further, an AIC analysis was also
performed using to determine the best t model. However, for the
experimental group, it was found that the participants adjusted
typing speed was not correlated to any of the sensor data; rather,
the participants performance on the test was dictated by their emo-
tional scores. This information is shown in Fig. 4.
Therefore, the participants typing data were further examined to
determine if there were any statistically signicant changes between
tests for the two groups of participants. Figure 5shows a compari-
son between the two groupschange in typing errors and typing
speed between the rst and second typing tests.
In the control group, the typing errors, adjusted typing time (with
errors considered), and emotion scores (positive, negative, and
overall) were found to be insignicant between typing trials.
However, the control groupstyping speed (overall speed not con-
sidering errors made) was found to increase, with signicance. This
is shown in Table 7. This same phenomenon was found to be true
for the experimental group of participants, as well. This is shown
in Table 8.
However, both groups of participantsadjusted typing speed
(considering errors and accuracy) were not found to have a signi-
cant change between trials. This indicates that, while both groups of
participants did signicantly increase their typing speeds, they were
also making more errors and did not signicantly improve between
The analysis of the ERSQ questionnaire indicates the neutral
response from the two groups upon completion of typing test 1
(T1), with no signicant differences between the control and exper-
imental groupspositive or negative emotions. However, one inter-
esting nding was that the overall emotional score (determined via
the survey given after the typing test) increased signicantly during
the second typing test for the experimental group that were offered a
reward. The overall change for the control and experimental groups
is shown in Fig. 6. It is important to note that this is the overall
change in emotion, examining the change in both positive and neg-
ative emotion scores.
To further investigate this information, the control and experi-
mental groups change in positive and negative emotion scores
were viewed individually. This is shown in Fig. 7.
Fig. 5 Change in typing data between trials
Table 7 Control group typing speed (WPM)
Typing speed T1 T2
Mean 46.33 49.25
Variance 426.9 425.1
Pearson correlation 0.975
tStat 2.224
p-Value 0.048
tCritical 2.200
Table 8 Experimental group typing speed (WPM)
Typing speed T1 T2
Mean 36.64 40.55
Variance 157.9 170.1
Pearson correlation 0.942
tStat 2.952
p-Value 0.014
tCritical 2.228
Fig. 6 Average change in ESRQ
Journal of Computing and Information Science in Engineering JUNE 2022, Vol. 22 / 031013-7
As Fig. 7shows, the experimental group that was offered a
reward before the second typing test experienced a sizable increase
in their positive emotions (also a small increase in their negative
emotions) during the second typing compared to the rst. The pos-
itive emotion was found to increase signicantly on the second
typing test (p=0.017) for the experimental group, shown in
Table 9. Their negative emotion did increase slightly as well, but
this was not found to be signicant.
In this paper, we analyze the neural activity comparison between
a control group and experimental group of participants during a
one-minute typing test. We infer that the designing and typing
tasks share some common attributes such as precision, decision-
making, planning, efciency, and many more. Hence, typing task
was chosen for this study to mimic design presentation test/retests
scenarios. The control groups participants were administered
two, one-minute typing tests with no reward for improvement.
Conversely, the experimental group was also administered two,
one-minute typing tests with a reward of $100 gift card for best per-
formance at the end of the second typing test.
There were no signicant differences in EEG data between the two
typing tests for the control group of participants. This indicates that
similar brain activity occurred during the rst and the second test for
the group that was not offered an incentive to retest. However, in the
experimental group, there was an increase in activity in the O2 and
FC6 sensors, at p< 0.1, which is maintained for discussion purposes.
This interesting nding aligns with other research [70] that suggests
senior college students who are given incentives to retest will
outperform those who are not incentivized. The ability to observe
this difference through neuroactivities is unique and provides
insight into the potential source for the difference.
The O2 sensor showed an increase between the two tests for the
experimental group, upon announcement of a reward for the retest.
The O2 region of the brain is responsible for visual processing in the
brain. Therefore, this suggests the participants increased their atten-
tion in an effort to increase their accuracy and performance on the
second, incentivized test. This is backed by ndings that increased
saccadic eye movement has been observed upon reward expectation
in neuroscience studies [71]. The same can be inferred in the nd-
ings of this paper, where the typing test involves vision accuracy,
and announcement of a reward therefore increases activation in
the O2 sensor.
The FC6 sensor showed an increase between the two tests for the
experimental group. FC6 is in the frontal/central lobe and associated
with the executive and emotional resources. This suggests that the
participants have an increased physical and emotional response
when the test is incentivized. Other studies have also reported an
extra expense of emotional and physical resources among adults
when incentives are presented; this emotional and physical expen-
diture is also shown to increase when the monetary incentive is
increased [54]. The results showing changes in the FC6 stressors
in the experimental group indicate the impact of a reward on their
emotions in the second typing test.
From a design education standpoint, the results suggest that when
students are incentivized to retest their design demonstration, typi-
cally in the form of a presentation, they perform better than their
unincentivized counterparts. This nding suggests that students
may not make concentrated efforts (as indicated by the brain activ-
ities observed in the O2 and FC6 regions in the incentivized results)
to improve if not incentivized to do so. If retests are only benecial
when incentives are offered, there may be value in intentionally
evaluating original submissions meticulously with the intention of
students attempting a retest to improve their grades. While this
may seem like an aggressive approach to student evaluation, the
experimental groupsparticipants positive emotions increased
with signicance (p=0.017) on the second typing test. This
implies that students that participated in the incentivized retest
felt better during the retest than during their initial typing test.
Thus, students may benet from performing a retest but will
require an incentive to do so.
Fig. 7 Overall change in ESRQ data
Table 9 Paired t-test for positive emotion of experimental group
between tests
Positive emotion T1 T2
Mean 17.91 20.18
Variance 7.891 15.36
Pearson correlation 0.737
tStat 2.845
p-Value 0.017
tCritical 2.228
031013-8 / Vol. 22, JUNE 2022 Transactions of the ASME
The design education recommendations of this study suggest that
students should be implored to perform test/retests as part of their
formal evaluation process. While students should not be forced to
perform a retest, the option should be provided with an incentive
(for instance, earning back missed points). By doing so, students
place a greater concentrated effort toward improved performance
which leads to positive emotions toward their activity.
The goal of the study is to determine the neuroscientic impact of
providing students with a incentivized test/retest scenario in design
presentations. Due to challenges in performing EEG studies during
design presentations, a typing test is used to examine the effective-
ness of using an incentivized test/retestscenario to improve par-
ticipantsperformance during stressful situations. The participants
were divided into a control group and an experimental group,
where the experimental groupssecond trial was incentivized. The
participantsbrain activity was detected using an EEG and com-
pared to their emotional response surveys and typing performance
during each of the typing tests.
The results show that there were no signicant changes in brain
activity, emotions, or typing performance for the control group of
participants (no reward offered). However, the experimental
group showed an increase in O2 and FC6 sensor activity, control-
ling vision and emotion, respectively. The experimental partici-
pants performance was also found to be correlated to their
emotional responses, where their positive emotions were increased
signicantly during the second typing test, even though their perfor-
mance did not increase.
Overall, this indicates that an incentivized retest has a positive
impact on a participants emotions, which shows promise for
future design education intervention studies where students are
allowed to complete a retest of a design activity. The recommenda-
tions of this study for design educators are to provide students with
an option to exercise a test/retest with an incentive. Students should
not be forced to perform a retest, but incentivized to do so as part of
their formal evaluation process. To accomplish this, students must
see value in attempting a retest, so educators must develop evalua-
tion rubrics with the intention of a retest in mind.
Conict of Interest
There are no conicts of interest.
Data Availability Statement
The data and information that support the ndings of this article
are freely available online.
Data under embargo.
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031013-10 / Vol. 22, JUNE 2022 Transactions of the ASME
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Full-text available
The present paper describes the development of a prosthetic hand based on human hand anatomy. The hand phalanges are printed with 3D printing with Polylactic Acid material. One of the main contributions is the investigation on the prosthetic hand joins; the proposed design enables one to create personalized joins that provide the prosthetic hand a high level of movement by increasing the degrees of freedom of the fingers. Moreover, the driven wire tendons show a progressive grasping movement, being the friction of the tendons with the phalanges very low. Another important point is the use of force sensitive resistors (FSR) for simulating the hand touch pressure. These are used for the grasping stop simulating touch pressure of the fingers. Surface Electromyogram (EMG) sensors allow the user to control the prosthetic hand-grasping start. Their use may provide the prosthetic hand the possibility of the classification of the hand movements. The practical results included in the paper prove the importance of the soft joins for the object manipulation and to get adapted to the object surface. Finally, the force sensitive sensors allow the prosthesis to actuate more naturally by adding conditions and classifications to the Electromyogram sensor.
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Cognitive neuroscience research has traditionally focused on understanding the brain mechanisms that enable cognition by means of experimental laboratory tasks. With a budding literature, there is growing interest in the application of the related methods and findings to real-world settings. In this opinion paper we explore the potential and promise of employing current cognitive neuroscience methodologies in the field of design. We review recent evidence from preliminary studies that have employed such methods toward identifying the neural bases of design thinking and discuss their impact and limitations. Further, we highlight the importance of pairing neuroscience methods with well-established behavioral paradigms during ecologically-valid, real-world design tasks. Experimental investigations that meet these requirements can generate powerful datasets of neurocognitive measures that can offer new insights into the complex cognitive and brain systems enabling design thinking. We argue that this new knowledge can lead to the development and implementation of new techniques toward cultivating and improving design thinking in design education and professional practice.
Conference Paper
The goal of the study is to examine the effectiveness of using an incentivized “test/retest” scenario to improve participants’ performance during stressful situations. The study makes use of an electroencephalography (EEG) machine to detect participants’ stress levels during a one-minute typing test. The typing test administered was a standard, “story-typing” test. A total of 23 student participants were randomly divided into two cohorts: the control cohort and the experimental cohort. Participants were asked to complete a preliminary questionnaire self-assessing their ability to handle stressful situations. Both cohorts were then asked to complete the typing test (hereafter referred to as T1) and fill out an Emotional Stress Reaction Questionnaire (ESRQ), indicating their emotions during the typing test. The participants were then asked to complete the typing test and accompanying ESRQ a second time (hereafter referred to as T2). However, prior to the second test, the participants in the experimental cohort were told that the participant that shows the most improvement in their typing speed (measured in words per minute) will receive a $100 gift card. This stimulus is used to increase the already stressful situation for the experimental cohort and examine whether participants’ brain activity changes when the “retest” is incentivized. Each participant’s EEG data and heartrate were measured through the duration of the experiment and t-tests and regression analyses were used to determine if a statistically significant difference existed between cohorts (control vs. experimental) or within cohorts (T1 vs. T2). The results show that there were no significant changes in brain activity, emotions, or typing performance for the control group of participants (no reward offered). However, the experimental group showed an increase in EEG sensor activity; specifically, the sensors that control vision and emotion. Interestingly, the participant’s performance was found to be correlated to their emotional responses, rather than their EEG sensor data. Additionally, the experimental groups’ positive emotions were increased for the second typing test, which is incentivized. The findings lay a foundation for design settings scenarios where preparatory practices can be incorporated.
Digital twin, a new emerging and fast-growing technology which is one of the most promising technologies for smart design and manufacturing, has attracted much attention worldwide recently. With the application of digital twin, product performance evaluation has entered the data-driven era. However, traditional methods for evaluation mainly place emphasis on structure analysis in the stage of manufacturing and service in digital twin. They cannot synthesize multi-source information and take the high-level emotional response into consideration in the design stage. To overcome these disadvantages, a digital twin-driven method is proposed evaluating product design schemes in this study. It enables the acquisition of EEG data, physical data, and emotional feedback. Human factors are systematically considered in the evaluation process to establish the information association between EEG and performance levels. Moreover, intelligent psycho-physiological analysis that incorporates EEG into the fuzzy comprehensive evaluation and machine learning methods is adopted within the proposed method. It synthesizes human factors such as psychological requirements, subjective and objective assessment indicators to realize a novel machine learning-based EEG analysis. Taking advantage of the Binary Particle Swarm Optimization (BPSO) improved Riemannian manifold mapping, Riemann Geometry features are extracted and selected from EEG signals. Differences of implicit psychological states while using the product produced by different design schemes can be more easily detected and classified. A case study of high-speed elevator is conducted to verify the feasibility and effectiveness of the proposed method. The accuracy of EEG classification for performance evaluation reaches 92%.
Objective: To assess the role of brief neuropsychological assessments in prediction and identification of Alzheimer's disease (AD) pathology and progression to AD dementia. Method: Adults (N = 255; range = 40-81 years) with self-reported cognitive decline underwent baseline and 2-year follow-up clinical assessment, including a brief neuropsychological screening and lumbar puncture. Five different mild cognitive impairment (MCI) algorithms were applied on baseline cognitive test results: one conventional, three amnestic (lenient, stringent, multidomain), and one comprehensive criterion. We compared predictive and diagnostic accuracy of these MCI criteria by performing logistic regression analyses and calculating diagnostic accuracy measures for 2-year outcomes of (1) clinical diagnosis of AD dementia and (2) cerebrospinal fluid biomarkers in the Alzheimer's continuum. Results: The lenient amnestic MCI criterion showed the largest effect size for predicting progression to AD dementia (OR = 13.762, 99% CI = 1.969-96.194, p = .001) and AD biomarkers (OR = 4.855, 99% CI = 1.974-11.924, p < .001) after 2 years. This criterion was sensitive for progression to dementia (sensitivity = 92.0%, specificity = 54.8%, positive likelihood ratio [LR+] = 2.03, LR- = 0.15) and showed the highest overall diagnostic accuracy for AD biomarkers (sensitivity = 72.7%, specificity = 59.1%, LR+ = 1.78, negative likelihood ratio [LR-] = 0.46). The multidomain amnestic MCI criterion produced the highest specificity for dementia (sensitivity = 76.0%, specificity = 73.0%, LR+ = 2.82, LR- = 0.33) and AD biomarkers (sensitivity = 46.8% specificity = 70.9% LR+ = 1.61, LR- = 0.75). Conclusions: Defining MCI using a brief neuropsychological battery provided limited accuracy for progression to AD dementia and cerebrospinal fluid Aβ. The lenient amnestic MCI criterion identified the highest number of individuals who progressed to clinical AD or showed biomarker pathology, but this approach included a substantial number of false positives. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
We present results from an EEG experiment EEG to measure neurophysiological activation to study novice and experienced designers when designing and problem-solving. We adopted and extended the tasks described in a previous fMRI study. The block experiment consists of 3 tasks: problem-solving, basic design, and open layout design. The block is preceded by a familiarizing pre-task and extended to an open design sketching task. Results from 36 sessions of mechanical engineers and industrial designers indicate significant differences in activations between the problem-solving and the design tasks.