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Department of Psychology, University of California, Los Angeles
Summer A 2013
Table of Contents
Perceptual Learning…………………………………………………………………………………….. 2-7
Erika Der Sarkissian, Faculty Mentor: Philip Kellman, Ph.D.
Dual TMS and the Modulation of Cortical Connectivity ………………………………………… 8-14
Marc Feldman, Faculty Mentor: Marco Iacoboni, Ph.D.
Examining Procedural Memory Using the Mirror-Tracing Task………………………….........15-21
Scott Korchinski, Faculty Mentor: Donald Mackay, Ph.D.
How Preschoolers Use Casual Inferences to Make Predictions……………………………….. 22-27
Tanya Zamorano, Faculty Mentor: Patricia Cheng, Ph.D.
Summer C 2013
Paired Associative Stimulation and Synaptic Plasticity…………………………………………28-33
Marc Feldman, Faculty Mentor: Allan Wu, Ph.D.
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Perceptual Learning
Erika Der Sarkissian
This session, I ran a study under Carolyn Bufford in the Human Resources Lab. The
study focused on how experts differ from novices through perceiving the world differently due to
experience, which is called perceptual learning. In this study, we used technology to imitate this
type of learning to study how it can change the participant’s perception and performance on
simple algebra problems. I will begin by reviewing three similar studies that have been done
before on this subject, all by Professor Phillip J. Kellman, the faculty sponsor of my research. I
will then compare them to our study on perceptual learning. I will conclude with what further
research could be done to better understand this phenomenon.
In 2011, Khanh-Phoung Thai, Everett Mettler, and Phillip J. Kellman did a study called
“Basic Information Processing Effects from Perceptual Learning in Complex Real-World
Domains”. In this study, they looked at the effects of perceptual learning interventions on the
recognition of Chinese characters. Specifically, they noted the previous research on expertise and
the ability of experts to attend to relevant features and intake larger chunks of information with
less attentional load. Thai, Mettler, and Kellman attempted to imitate this type of result in their
study by using perceptual learning to improve the extraction of information from complex
patterns in Chinese characters. The participants were undergraduate college students with no
prior experience or background in reading Chinese characters.
They began by testing their participants using a visual search task, which was a basic
transfer task. It tested the efficiencies of searches for stimuli that were not presented in the
perceptual learning phase. The participants were given four kinds of distracter characters in the
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visual search task: characters that had the same structure and component with the target
character, characters that had the same structure but different components than the target,
characters that had the same components but a different structure than the target, and characters
that had a different structure and components. They then trained the participants based on three
features: stroke, component, and structure. There were two perceptual learning conditions, where
participants had to match Chinese characters either by component or by overall structure. In the
control condition, participants judged the Chinese character’s stroke count, which served as a
non-structural control task. After the perceptual learning training, they then tested the
participants using a visual search task again.
The results showed that the error rates were low at the pretest and the posttest visual
search tasks, and that there was no speed-accuracy trade-off. The perceptual learning training
showed a significant effect on the post-test visual search performance. Specifically, perceptual
learning training that was based on relational configurations and specific components led to
significantly higher improvements in the visual search task than the perceptual learning based on
strokes.
In 2009, Phillip J. Kellman, Christine M. Massey and Ji Y. Son ran three studies called
“Perceptual Learning Modules in Mathematics: Enhancing Students’ Pattern Recognition,
Structure Extraction, and Fluency”. Each of these studies tested a perceptual learning module
involving mathematics: MultiRep, Algebraic Transformations, and Linear Measurement.
In the MultiRep perceptual learning module, participants were presented with an
equation, a graph, or a word problem of a linear function and were asked to pick the equivalent
function in a different representation out of three possible options. This practice led to an
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improvement in the participants’ abilities to generate the right graphs and equations out of given
word problems.
In the Algebraic Transformations perceptual learning module, participants were shown a
target equation and had to decide which equation, out of four options, was a legal algebraic
transformation of the target equation. The results showed that this practice dramatically
increased the participants’ improvements in the speed of equation solving.
In the Linear Measurement perceptual learning module, participants were presented with
a graphic display of a ball on top of a ruler with a billiard cue aimed at it. They were then given
four different types of trials that varied on the information given (starting point, ending point, or
distance traveled), and the information that was to be entered. Animated feedback was given
after each trial was carried out on the screen. This practice resulted in a successful transfer to
performance on measurement problems and fraction problem solving. The results of these three
studies exhibit the potential of perceptual learning modules in producing fast and enduring
advances in learning.
Philip J. Kellman and Mary K. Kaiser ran earlier studies in 1994 called “Perceptual
Learning Modules in Flight Training”. In these studies, they examined how perceptual learning
could determine the differences in the piloting skills between experts and novices. For
participants in the studies, they used experienced civil aviators for experts and non-pilots for
novices. The two perceptual learning modules that they used for these studies were the Visual
Navigation perception learning module and the Instrument Relationship perceptual learning
module.
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During the Visual Navigation perceptual learning module, the participants were given
instructions on aeronautical chart symbology and shown 20 seconds of a video segment of terrain
from filmed from an aircraft. Participants then had to quickly decide on the aircraft’s location
out of three possible choices of grid locations on the aeronautical chart. In the Instrument
Relationships perceptual learning module, participants were shown pictures of primary flight
instruments and had to quickly classify the flight attitude shown. Although expert pilots initially
performed much better than the novice pilots, the end results showed that both of the perceptual
learning modules led to significant improvements in accuracy and in speed for both novice and
expert pilots.
In this session, I had the opportunity to help run a study called “Experts, Patterns,
Learning, and Technology”. In this study, we focused on high-level perceptual learning using
algebra problems. All of our participants were undergraduate students that participated in
exchange for course credit. They were randomly assigned to either the control group or the
experimental/PLM group.
All groups began with a pretest in the form of a short algebra assessment, a like-terms
task, and a match-mismatch task. For the algebra assessment, participants were shown algebra
problems, and they were instructed to solve for the variable and enter their answer in a box. In
the like-terms task, participants were presented with one equation per trial and had to indicate
whether or not there were like terms in the equation. During the match-mismatch task, the
participants were shown two equations simultaneously and were instructed to indicate whether
they were identical or not.
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If the participant was in the experimental/PLM group, they would then go on to do the
learning phase of the experiment. For each trail, they were shown one equation at the top of the
screen, and four options below it. They were told to choose the option that is an allowable
transformation of the equation at the top. The first part of the study was over after this phase, and
the participant returned the next day to complete the second part. Part two of this study consisted
of a post-test that was very similar to the pre-test assessment.
We hypothesized that the learning phase of this study would give participants meaningful
practice in algebra that would lead to perceptual learning. We expected the participants that had
the learning phase to perform much better on the post-test assessment than their pre-test
assessment. We also expected the participants that did not have the learning phase to have the
same accuracy on both days.
Overall, there have not been enough studies done on the effect of high-level perceptual
learning on human perception and performance. There is clearly a link between perceptual
learning training and higher performance, and I think it is an important phenomenon to explore
further. Future studies can focus on other domains of high-level perceptual learning, such as
language or science. In the study that I will participate in next session, I will be working on a
study about perceptual learning and music.
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References
Kellman, P. J., Mettler, E., & Thai, K. (2011). Basic information processing effects from
perceptual learning in complex, real-world domains. Journal of Vision, 11(11),
555-560. doi:10.1167/11.11.1028
Kellman, P. J., Massey, C. M., & Son, J. Y. (2009). Perceptual learning modules in
mathematics: Enhancing students’ pattern recognition, structure extraction, and
fluency. Topics in Cognitive Science, 2(2), 285-305.
doi:10.1111/j.1756-8765.2009.01053.x
Kellman, P. J., & Kaiser M. K. (1994). Perceptual learning modules in flight training.
Proceedings of the Human Factors and Ergonomics Society Annual Meeting,
38(18), 1193-1187. doi:10.1177/154193129403801808.
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Dual TMS and the Modulation of Cortical Connectivity
Marc Feldman
Transcranial Magnetic Stimulation (TMS) is a method of inducing neuronal
depolarization by using a magnetic field to non-invasively induce electrical current in the cortex
(Lisanby et al., 2000). TMS is a safe and useful means of studying the brain, due to the low
incidence of side effects, as well as its relatively painless application. TMS can be applied at
different frequencies in order to receive different effects, and the effects are generally transient,
lasting up to an hour after stimulation; it can also be applied to two locations of the cortex,
allowing researchers to study cortical connectivity. High frequency “repetitive” TMS (rTMS),
low frequency rTMS, and single pulse TMS can all produce either inhibition or facilitation of
neuronal functioning, depending on the specific application, however there is no general rule
describing when these effects will be attained. TMS can also be combined with imaging
techniques, such as fMRI, to provide new and powerful insights into the functioning and
connectivity of the human brain.
Stimulating motor regions of the cortex induces contraction on peripheral muscles,
creating a motor evoked potential (MEP) (Chawla, 2012). MEPs are important in many aspects
of TMS; they are monitored during thresholding, when the researchers determine what strength
of stimulation is the lowest that will produce as MEP in a given subject. Thresholding is
performed because it is a normalization factor between subjects. MEPs are also used as measures
before and after trials in order to indirectly measure cortical excitability: if application of TMS
during the study either increased or decreased cortical excitability, the strength of MEPs should
have increased or decreased as well.
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Cortico-cortical connections exist between a vast number of cortical regions, pertinent to
the current study are those connections involved in various aspects of motor control such as
initiating movements, planning movements, and movement execution (Torres et al., 2013;
Grafton et al., 1997). One circuit of particular interest is the connection between the dorsal
premotor cortex (PMd) and the posterior parietal cortex (PPC) (Rizzolatti et al., 1998). Both of
these areas project to the primary motor cortex (M1) and can be easily found and stimulated
using TMS.
The intention behind the study to be described was to modulate the functional
connectivity of the PMd and PPC by stimulating them simultaneously. The hypothesis, derived
from physiological and dynamical systems observations, was that by stimulating these two
anatomically connected regions, the functional connectivity could be modulated. Since the
experiment is complicated and difficult to carry out on large numbers of subjects, it is still in the
pilot stages. Four naïve subjects participated in the pilot, all of who gave their informed consent
to be studied. They all sat on a computer chair, with their hands resting on a portable writing
desk with a cushion on the bottom side.
An EMG electrode was placed on the subject’s right hand in correspondence with the
first dorsal interosseous (FDI) muscle. A swim cap was placed on the subject’s head, and M1
(the anatomical region, found in the human precentral gyrus), PMd (the systemic region anterior
to M1) and the cP4 area of the PPC (based on the 10-20 EEG electrode map, located between
areas C4 and P4) were marked on the swim cap using a permanent marker (Purves et al., 2001;
Koch et al., 2013; Niedermeyer and Da Silva, 2005). The vertex of the cranium was found by
measuring the distance between the nasion and the inion, as well as the distances between the
two tragi. M1 was marked as the region 5 cm lateral to the vertex and 2 cm rostral, PMd was
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marked as the area 2 cm anterior and 1 cm medial to M1, and cP4 was found using the 10-20
EEG system. After all of the points were marked, they were registered on a computer using the
neuronavigation system “Brainsight.”
The subjects’ TMS thresholds were calculated by stimulating the left motor cortex in
order to elicit MEPs from the FDI. Once a location was found that would produce strong, regular
MEPs, the researchers would determine if 5 of the next 10 pulses produced MEPs. If, within the
ten pulses, five desirable MEPs were found, the intensity of stimulation was reduced by 2% of
the stimulator’s maximum output (MSO). The intensity of stimulation continued to be reduced
until no further MEPs were produced; the intensity was then increased slightly to the lowermost
level that would still produce MEPs. The lowest intensity at which MEPs were produced by 5 of
10 pulses was determined to be the subject’s threshold. After threshold was established, the pre-
study measure of MEPs was taken. The TMS devices were adjusted to an intensity that would
produce approximately .75 mV MEPs from the FDI. 20 of these MEPs were recorded using the
EMG recording program Signal and the average intensity was noted. After a period of five
minutes, another 20 MEPs were recorded at the same intensity as the previous 20. Twenty MEPs
were collected during each baseline trial because MEP voltage can vary between each pulse. The
average strength of the MEPs for each trial, therefore, was the relevant value.
Next, the modulation was performed by positioning one TMS coil over the area
determined to be the left PMd and another coil over the right cP4. One pulse of TMS of an
intensity equal to 110% of the subject’s motor threshold was sent to the subject’s head every ten
seconds for 15 minutes for a total of 90 pulses. Right after the 15 minutes of stimulation, a coil
was returned to M1 and 20 MEPs were recorded using the same intensity of stimulation that was
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used prior to the modulation. MEPs were collected in this manner every ten minutes thereafter
for a total of 9 sets if 20 MEPs including the 2 baselines.
After the study, the voltages of the MEPs were analyzed using the dataWizard plugin for
Matlab. The data from the pilot study were ambiguous: one subject showed motor facilitation
relative to baseline, two subjects showed motor suppression relative to baseline, and one subject
showed neither suppression nor facilitation. In addition to dual stimulation, one subject was also
stimulated at just PMd and just cP4 on separate days. No significant difference between the three
modes of stimulation was found. The lack of significant findings in this study isn’t discouraging
because this particular study is one pilot of a few and we only tested two specific areas of two
larger brain regions.
Despite the ambiguity of the findings, we will continue piloting this study using naïve
subjects and we will stimulate various other areas of the posterior parietal cortex. We have tried
dual TMS using other locations of the parietal and premotor cortices in the past and have gotten
different results. In the future, we will look at different sites, attempting to find combinations
that, when stimulated together, produce a more consistent facilitation or suppression of MEPs.
We also plan to combine TMS with fMRI and specific analytic techniques to better measure
changes in connectivity. We will attempt to take subjects to receive MRI before and after being
stimulated in order to see the differences in connectivity induced by dual TMS.
Combining TMS with methods of imaging, such as fMRI, and neuronavigation will allow
us to better and more accurately select regions of interest and localize the application of TMS to
subjects. The power of combining techniques to study the brain’s activity, structure, and function
with a technique like TMS, which allows for selective neural modulation, cannot be overstated.
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The ability to stimulate and modulate areas of the brain and subsequently study and track the
effects opens up a whole world of new possibilities for discoveries and new studies. Integrating
these techniques into our experimentation will be no less significant.
As the knowledge base of the effects of different forms of TMS grows, even more doors
will continue to open. TMS is a powerful and exciting method of studying changes in the brain,
as well as relationships, such as those between physiology and behavior, sensation and
physiology, and connections between cortical regions. It has already been, and continues to be,
used to study aspects of cognitive, psychological, and neurologic functioning, such as motor
control, working memory, language, mood, neuroplasticity, sensation, and even neurologic and
psychiatric disorders (Lisanby et al., 2000). The possibilities for future applications of TMS are
limitless, and as it and future techniques are further understood and developed, there is potential
for much greater understanding of the brain and development of new treatments for disorders of
the brain.
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References
Chawla, J., MD. (2012, January 26). Motor Evoked Potentials . Motor Evoked Potentials.
Retrieved from http://emedicine.medscape.com/article/1139085-overview
Grafton, S., Fagg, A., & Arbib, M. (1998). Dorsal premotor cortex and conditional movement
selection: A PET functional mapping study. Journal of Neurophysiology, 79(2), 1092-
1097.
Koch, G., Franca, M., Fernandez Del Olmo, M., Cheeran, B., Milton, R., Alvarez Sauco, M., &
Rothwell, J. C. (2006). Time course of functional connectivity between dorsal premotor
and contralateral motor cortex during movement selection. The Journal of
Neuroscience, 26(28), 7452-7459.
Lisanby, S. H., Luber, B., Perera, T., & Sackeim, H. A. (2000). Transcranial magnetic
stimulation: Applications in basic neuroscience and
neuropsychopharmacology. International Journal of Neuropsychopharmacology, 3, 259-
273.
Lopes, D. S., & Niedermeyer, E. (2005). Electroencephalography: Basic principles, clinical
applications, and related fields (p. 140). Philadelphia, PA: Lippincott Williams &
Wilkins.
Purves, D., & Williams, S. M. (2001). The Primary Motor Cortex: Upper Motor Neurons That
Initiate Complex Voluntary Movements. In Neuroscience. Sunderland, MA: Sinauer
Associates.
Rizzolatti, G., Luppino, G., & Matelli, M. (1998). The organization of the cortical motor system:
New concepts. Electroenephalography and Clinical Neurophysiology, 106, 283-296.
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Torres, E., Quiroga, R. Q., Cui, H., & Bueno, C. (2013). Neural correlates of learning and
trajectory planning in the posterior parietal cortex. Frontiers in Integrative
Neuroscience, 7(39), 1-20. doi: 10.3389
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Examining Procedural Memory Using the Mirror-Tracing Task
Scott Korchinski
My research project this quarter primarily consisted of developing a tool to be used in
studies that examine procedural memory. Procedural memory is one type of long-term memory
that involves forming skills through repetition and experience. The tool I developed is a
computer program that functions like a game, in which the participant must trace the inside of a
square path. The participant clicks the mouse to begin drawing a path inside the square, and must
not release the mouse until they have drawn a path all the way around the square and returned to
the beginning location. The difficult aspect of the game is that the vertical mouse movement is
flipped, so that in order to draw a downwards path, the participant must move the mouse
upwards. This is an effective replication of the mirror-tracing task that has been used by
psychologists for decades to examine the faculties of memory. Traditional mirror-tracing tasks,
such as the one used by our lab in previous research, require the participant to trace a complex
shape such as a five-pointed star. The application I created uses a simple square, because
previous results have indicated that a significant amount of participants do not accurately learn
the rule for mouse movement, even after up to 30 trials. My simplified version of the mirror-
tracing task will help our lab test participants under easier conditions in order to potentially
create a playing environment in which a significant amount of participants learn the rule. The
mirror-tracing task is a significant endeavor in psychology because it helps pinpoints the
components of procedural memory, which can be further examined to aid in memory damage
treatment for people with memory loss conditions such as Alzheimer’s and amnesia.
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The mirror-tracing task was most famously used by Milner in her 1962 study of the
patient H.M., a man with severe global and anterograde amnesia. In these first trials of the
mirror-tracing task, H.M. was asked to trace a five-pointed star with a pencil while his vision of
his hand, pencil, and paper were blocked except for a reflection from a mirror placed at the top of
the paper. H.M. was asked to stay inside the lines and trace the shape as quickly and accurately
as possible. Gabrieli et al. (1993) stated that the mirror-tracing task:
requires subjects to inhibit and reverse powerful associations between vision and motor
control of hand and arm movements. Initially, subjects perform the task slowly and make
frequent errors by departing from the pattern, but with practice they gain a skill for mirror
tracing. Subjects can then trace the star more quickly with fewer errors. (p. 900)
Due to his severe amnesia, H.M. stated that he had never done the task at the beginning of each
session. However, over three contiguous days, H.M. was able to significantly improve his
performance on the mirror-tracing task. This indicates that there are memory faculties for
sensorimotor skill learning that function separately from the memory faculties that are
compromised by amnesia.
The main function that must be used to improve one’s ability on the mirror-tracing task is
procedural memory. Procedural memory is a result of procedural learning, which consists of
repeating a complex task until the skill to complete the task has been instantiated and
consequently improves. Repetition alone of the mirror-tracing task however is not sufficient for
improvement on the task, as is illustrated by Squire (2004), who states that “what is important is
not only the task that is to be learned but also what strategy is implemented during learning,
which in turn reflects what memory system is engaged” (p. 174). Procedural memory can be
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drawn upon without conscious decision in tasks ranging in complexity, from teeth brushing to
driving. The skill is acquired through repeated practice, and improves as a function of
experience. Procedural skill acquisition can be observed when, following repeated practice trials
of the task, a change in behavior occurs that clearly illustrates improved completion of the task.
In the case of the mirror-tracing task, procedural skill has clearly improved when the participant
is able to complete the task with fewer mistakes and in a shorter amount of time.
The mirror-tracing task program I created for this research project is in the form of
JavaScript functions run by and displayed through an HTML document. The HTML document
consists of a header, an instruction set, a status bar – including not started, in progress, and
completed – a time display for the previous trial, a canvas containing the mirror-tracing task
itself, and buttons to show and hide cumulative results. Instructions for how to use this program
to run participants are as follows: first, the researcher opens the HTML file, and reads the
appropriate background information and instructions for the task aloud to the participant. Next,
the participant clicks on the gray circle inside the larger shape to be traced – in this case a square
– and holds down the mouse. This initiates the drawing phase, and the thick border around the
game area turns from black to green. From here, the participant must trace the shape as quickly
as possible, while staying inside the track (the inner and outer squares) as closely as possible.
Each time the participant goes outside the boundaries of the track, the outer border turns from
green to red, returning to green once the participant moves the mouse back within the
boundaries. Once the participant has successfully traced the whole shape and returned to the
initial gray circle, he or she releases the mouse. This concludes the first trial. Once the trial is
completed, the participant begins a new trial by simply clicking in the gray circle, holding down
the mouse, and following the exact same instructions. After each trial, a time is displayed above
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the game area which states the length of the previous trial. To view the complete set of results for
multiple trials completed by a single participant, the researcher can click the “Show Results”
button below the bottom border of the game. This will open a display window in the upper left of
the window, which displays each trial time in seconds and the corresponding number of times the
participant went out of bounds. The data in this window can be copied, pasted into a word
processor, saved as a .csv (comma-separated values) file, and opened in a statistical analysis
program such as Microsoft Excel. To close the results window, the researcher can click the
“Hide Results” button directly to the left of the “Show Results” button. To reset the results and
run a new participant, the researcher can either refresh or close and reopen the page.
The new mirror-tracing program I developed this quarter, although more simple than the
five-pointed star shape previously used by our lab, is an improvement upon the old design.
Previous research conducted by our lab indicates that the five-pointed star shape proved to be too
difficult for the majority of participants to learn the mouse movement. On a quiz after the
training phase that asked participants how to move a dot from one point on a star shape to
another using the mouse movement rule from the training, participants correctly answered
significantly more questions than the control group that did not undergo training. However, the
training group correctly answered only slightly more questions than would be expected by
chance, averaging 2 out of 5 correct answers. On a similar quiz with a new shape – a two-sided
arrow – used as the basis for the questions, the participants answered slightly worse on average
than they did on the star shape quiz. Finally, in response to a quiz that asked the participants to
describe the mouse movement rule, only one third of the trained participants got this correct.
Thus, a simpler shape will allow our lab to refine the mirror-tracing task to its basic elements, in
order to more closely examine behavior on this task. This will improve upon existing research on
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procedural memory, thus enhancing knowledge about how procedural learning occurs, and how
it is distinct from other forms of memory. Cavaco et al. (2004) state that:
Since some procedural memory systems are anatomically distinct from the declarative
memory system, amnesic patients should be able to acquire skills with major implications
for daily functioning. To date, however, this notion has had little impact on
neurorehabilitation. One reason for this may be the limited number of experimental tasks
that have been used to study procedural memory. (p. 1854).
Thus, this new version of the mirror-tracing task can be quite useful, as it may add new
information to the study of procedural learning, which can consequently improve rehabilitation
for patients with memory damage disorders. This program can be used in experiments by
running groups of participants in varying numbers of trials, then using the results from their
performance on a final mirror-tracing task using a novel shape – such as a circle – to compare to
a control group that received no training.
Through doing this project, I learned how to create a functioning game-like program from
scratch. This involved improving my computer programming and debugging skills, my ability to
reverse engineer a program from existing similar programs, and my cumulative knowledge of
how HTML and JavaScript work. The most difficult task for the development of this program
was improving my debugging skills. Problem solving for computer programming can be, and
often is, quite frustrating; when one miniscule thing is not working correctly, the whole program
can be rendered useless. This project gave me the opportunity to work through these periods of
adversity, and taught me how to reach out to the appropriate resources to get help. My research
leader, Laura Johnson, was useful in repeatedly examining the program and giving me advice
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and instructions on what existing aspects to improve upon and the next steps to take towards
completion. A computer game developer gave me invaluable input on how to use the
programming languages appropriately, as well as help with making the program’s functions run
smoothly. Online communities served as bountiful resources, and I learned that many people had
already run into the exact same problems I had and gave assistance on how to work through
them. Upon completion of the mirror-tracing task project, I felt a strong sense of satisfaction in
being able to assist in our lab’s continuing efforts to research the complex nature of human
mental behavior.
The mirror-tracing task is an undoubtedly significant experimental task that has led to
innovative additions towards the understanding of memory. However, as previous results from
our lab have demonstrated, the traditional mirror-tracing task leaves room for improvement.
Thus, a simplification of the task is a key step towards a better understanding of procedural
learning and memory. The skills I have learned through developing this program for our lab will
be useful for future applications, both personally and professionally. My involvement in this lab
has been exciting and rewarding, and I look forward to seeing how this mirror-tracing program is
put to use. This research can have valuable effects as it may aid in the treatment of people with
reduced memory functions. Striving to improve the conditions of patients suffering from
memory loss is a virtuous and worthwhile cause, as memory is one of the most significant
functions of the human brain and an integral part of the human experience.
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References
Cavaco, S. "The Scope of Preserved Procedural Memory in Amnesia." Brain 127.8 (2004):
1853-1867. Print.
Gabrieli, John D. E., Suzanne Corkin, Susan F. Mickel, and John H. Growdon. "Intact
Acquisition and Long-term Retention of Mirror-tracing Skill in Alzheimer's Disease and
in Global Amnesia." Behavioral Neuroscience 107.6 (1993): 899-910. Print.
Squire, L. "Memory Systems of the Brain: A Brief History and Current
Perspective."Neurobiology of Learning and Memory 82.3 (2004): 171-177. Print.
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How Preschoolers Use Causal Inferences to Make Predictions
Tanya Zamorano
Causal learning is important to make new predictions and form new theories. Making a
prediction involves taking into account the causal relationships between past instances.
According to numerous studies, adults are experts at learning causal relations and by the time
children are five years old they understand causal relations between events (Schulz, Bonawitz, &
Griffiths, 2007). However, even though they may make correct causal inferences, they may not
actually understand the evidence that they are using to make their prediction. So how exactly do
children learn causal relations? They may use their prior knowledge or new examples to make
inferences about what caused an event to happen. In this study, I am presenting children with
pictures of sick dogs (pictured as having red dots on their face) and the treats (starfruit or
mangostein) that they ate, which may or may not cure them. I am testing whether the children
can make a prediction based on the presence or absence of the red dots and how that is correlated
with the treat the dog ate. The results of this study are important to teachers and educators, who
may find it more beneficial to use causal inferences, instead of repetition or memorization, to get
their students to learn.
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Method
Participants
Preschool aged children between two to four years old (male or female) will participate in
this study. All of the children’s native language must be English. Any participant who does not
pay attention during the study or does not understand the instructions will not be used in the final
results.
Materials
The script contains the story and instructions for the visual cues. The visual cues are
cartoons used to portray the story. There is a picture of a farmer, red dots, starfruit, mangostein,
and dogs. The first two dog pictures are of the white dog before and after it ate the fruit. The next
two dog pictures are of the brown dog before and after it ate the fruit. The last dog picture is of
the gray dog with red dots on its face.
Design
There are two conditions in this study: preventative and contingency. Both conditions are
placed in two trials that involve either of the two treats: the starfruit or the mangostein. This is a
double blind study where I do not know the hypothesis, which prevents me from leading the
child to the correct answer. After I gather enough data, my faculty advisor will discuss the
hypothesis with me to analyze.
Procedure
First, the study must be done in a quiet corner of the classroom away from distractions. I
start by telling the child that I am going to read him a story called “How to Get Rid of Red
24
Dots.” The story is about a farmer who has pet dogs, but realizes that some of them have red dots
on their faces. The farmer’s friend tells him that two treats called a mangostein and starfruit may
make the red dots go away. The child is then told that it is up to them to figure out if the starfruit
or if the mangostein makes the red dots go away. The child is then presented with scenarios that
differ in how the treats affect the red dots on the dog’s faces. Each child views a starfruit trial
and a mangostein trial, but only views the contingency or preventive condition. In the starfruit
trial of the contingency condition, the farmer’s white dog has red dots on its face, but after it eats
a starfruit the red dots disappear. In the mangostein trial of the contingency conditions, the
farmer’s brown dog does not have red dots on its face before or after it eats the mangostein. In
the starfruit trial of the preventive condition, the farmer’s white dog has red dots on its face and
the red dots remain there even after it eats the starfruit. In the mangostein trial of the preventive
condition, the farmer’s brown dog does not have red dots on its face before or after it eats the
mangostein. After they have listened to and viewed the two example scenarios, they are
presented with a new scenario. The farmer gets a new dog that has red dots on its face. The child
is then asked which treat they would pick to make the red dots go away. I place a picture of the
treats in front of the child and they must choose one to feed to the dog. If the child attempts to
pick both fruits at the same time, they have to try again and I must tell them that they can only
pick one. If the child picks one treat and then another, then only their first answer is recorded.
The study is finished as soon as the child makes a decision and I record their answer.
Results
I am in the process of contacting local preschools for permission to come into their
classroom to run the study. Even though I do not have actual data, I can explain what I am
looking for. As an adult, it is easy to choose the correct answer of which treat will cure the red
25
dots. This is because we have developed cognitive logic that helps us reach solutions to problems
we may face in our every day lives. So, this simple decision of choosing which treat works best
to cure red dots after been given two example scenarios is extremely easy for us. For example in
the contingency condition, we see that the white dog has red dots but they disappear after it eats
the starfruit and the brown dog does not have red dots before or after it eats the mangostein.
Therefore it is more likely that the starfruit cures red dots. In the preventive condition, the brown
dog still does not have red dots, however the white dog has red dots before and after it eats the
starfruit. We can tell that the starfruit obviously did not cure red dots so it is more likely that the
mangostein will cure red dots. Choosing the right answer is easy for adults, but what do children
do? Will they follow the same logic cognitive process as adults? The results to this study will
answer that question.
Discussion
It is important to note a few small changes that must be made in this study before I take it to the
preschools. In a small pilot study, one child insisted that the “white dog” I was showing him was
in fact a light gray color. He was so focused on the color of the dog that he did not pay attention
to the story. I am going to fix this problem by renaming the pictures as Dog 1, Dog 2, and Dog 3.
After I analyze the results, I will be able to comment on the impact causal relationships
have on children’s decision-making skills. Given that the children are only provided with
limited examples, how do they decide which treat works best to cure red dots? I want to
determine whether the children understand how they are using the examples I provide to them to
make their causal inference.
26
This is applicable to education in the real world. If we can determine how they based
their decision, educators can structure their material in a way that students can actually
understand. Educators can focus on a new way of teaching that involves getting students to learn
through causal inferences. This differs from the traditional method of teaching that focuses on
repetition and memorization, which usually causes students to not truly understand what they are
learning. Causal relations and inferences may become the new method of learning
27
References
Muentener P., Bonawitz E., Horowitz A., Schulz L.E. (2012). Mind the Gap: Investigating
Toddlers’ Sensitivity to Contact Relations in Prediective Events. PLoS ONE. 7(4).
Schulz L.E., Bonawitz E.B., & Griffiths T.L. (2007). Can Being Scared Cause Tummy Aches?
Naïve Theories, Ambiguous Evidence, and Preschooler’s Causal Inferences.
Developmental Psychology, 43, 1124-1139.
28
Paired Associative Stimulation and Synaptic Plasticity
Marc Feldman
The study of neurophysiology in a clinical setting can be achieved by the application of
transcranial magnetic stimulation (TMS), which can be used to externally and non-invasively to
induce electrical current and then neuronal depolarization in the cortex (Lisanby et al., 2000).
Various combinations of TMS at different frequencies, different intensities, different sites, and
with other techniques allow researchers to easily study aspects of neural function and physiology
that was previously difficult or impossible. Paired associative stimulation (PAS) is a particular
application of TMS in which an area of the motor cortex is magnetically stimulated and each
pulse is paired with contralateral electrical stimulation of a peripheral nerve (Quartarone et al.,
2009). The current study is a collaboration between Cornell and UCLA examining a potential
method of inducing and modulating neuroplasticity which, combined with pharmacologic and
behavioral interventions, could prove to be an effective measure of the efficacy of treatments for
patients with strokes and other neurological damage and disorders. The idea is that PAS can be
used as a measurement tool for the changes that occur with various treatments and doses and can
help determine how plastic the involved connections are in patients and healthy subjects.
Hebbian Plasticity is a theoretical explanation of the neural changes that occur during
learning. The theory states that continuing and repetitive activation of a postsynaptic neuron by
the presynaptic neuron leads to an increase in synaptic strength and efficiency and thus neural
plasticity at the level of an individual cell (Ljaschenko et al., 2013). These synaptic changes, if
long lasting, are known as long-term potentiation. Plasticity, as described by Quartarone et al., is
the optimization of “neuronal activity at a cellular and system level according to the needs
imposed by the environment” (2009). Neuroplasticity is important to the functioning of the
29
human brain, and in the sensorimotor cortex, plasticity is “crucial to enhance function to increase
skillfulness.” Another theory that more specifically addresses certain changes in neuroplasticity
and conditions in which such changes occur is spike timing dependent plasticity (STDP). In
STDP, the “precise timing of spikes affects the sign and magnitude of changes in synaptic
strength” (Shouval et al., 2010). In other words, the frequency at which neuronal depolarization
occurs in the presynaptic cell determines whether the change in plasticity induces increased or
decreased sensitivity between the two neurons and also the strength of the connection between
the cells.
The induction of stimulation of neurons using TMS on the motor cortex and paired
electrical peripheral nerve stimulation can produce effects of long-term potentiation or
depression depending on the frequency and the order at which the stimulation is applied
(Quartarone et al., 2008). The pairing of pulses to the motor cortex and peripheral nerve can
cause an increase in excitation within the pathway being stimulated and motor evoked potentials
(MEPs) induced by TMS are facilitated by the plasticity and are larger than MEPs before PAS.
Shouval et al. noted that many researchers and scientists take a reductionist stance
towards STDP, perhaps supposing that it is the “comprehensive learning rule for a synapse”
while overlooking the fact that the pairing of depolarization alone doesn’t explain the huge
variability in plasticity that can occur. Furthermore, in addition to electrical impulses, many other
biological processes occur during neural depolarization (2010). They pointed out that, as stated
above, the number and frequency of pre and postsynaptic spikes both have considerable
influence on the strength and direction of the changes in plasticity. Neuronal activity, in general,
and plasticity, more specifically, involves multiple neurotransmitters, ions, “receptor-generated
second messengers,” and enzymes in depolarization and intercellular communication which can’t
30
be separated from the rest of the activity that goes on in plasticity and potentiation (Shouval et
al., 2010). NMDA receptors are involved and play a primary role in the control of synaptic
plasticity, and they control the flow of certain of the previously mentioned ions into and out of
neurons. An uncontrolled or poorly controlled influx of calcium ions through the NMDA
receptor can cause excitotoxicity causing damage to both the specific cell in question as well as
cytotoxic damage to neighboring tissue (Cacabelos et al., 1999). The damage to neighboring
tissue can cause some of the learning and memory deficits seen in patients with certain
neurological diseases or damage. The Cornell group identified an NMDA receptor that is
particularly overactive after stroke and can damage nearby cells through its cytotoxicity. Using
an animal model that studied the modulators of plasticity depend on a glutamate\NMDA
receptor, it was determined that a potentially useful pharmacologic intervention in the present
study would be Memantine, an NMDA antagonist that encourages plasticity at lower doses and
blocks it at high doses.
While plasticity plays an important role in motor learning and adaptation to the
environment, it can also occur during neuronal repair. When “pushed to an extreme,” the usually
beneficial process of neuroplasticity can produce “maladaptive sensorimotor reorganization”
which can be counterproductive and inhibit motor control instead of improving it (Quartarone et
al., 2009). Dystonia is a disorder that can be described as a loss of inhibition by the central
nervous system, which results in excessive, uncontrollable movement. In certain individuals,
“during the acquisition of new motor skills, the mechanisms of neuroplasticity are subtly
abnormal.” After injury to the peripheral nervous system or during extensive, repeated practice
of a motor skill, such as playing piano, “abnormal maladaptive plasticity” can occur which can
lead to dystonia (Quartarone et al., 2008). Dystonia is an interesting case in studying the effects
31
of PAS because, unlike normal subjects whose motor facilitation, potentiated by PAS, occurs in
the specific region innervated by the stimulated cortical-peripheral nerve connection, subjects
with dystonia experience a less special specificity in the muscles with increased plasticity.
Furthermore, dystonic subjects experience a much larger change in facilitory or inhibitory effects
provoked by PAS.
In the current study, in conjunction with the lab in Cornell, the TMS lab at UCLA’s
ALBMC will test spike timing plasticity. The collaboration will benefit both groups of
researchers based on the capabilities of the individual labs. The lab in Cornell is involved with a
rehabilitation clinic and will be able to recruit more patients who are recovering from a stroke.
Both facilities have TMS labs, but the UCLA lab has more readily available access to fMRI. One
goal of the study is to develop one of the first practical and predictive methods to help assess the
efficacy of drugs and treatments in pharmaceutical trials. This will be done by testing whether or
not a drug helps to increase and improve plasticity in learning and memory, assess the response
of these drugs at varied doses and in combination with specific training, and develop methods of
determining physiological biomarkers in human subjects. These biomarkers can help determine
the drugs’ specific utilities for learning and memory rehabilitation as well as other treatments
which can lessen impairments and disabilities.
The first pilot study of many that will be conducted as part of the overall study has been
performed on healthy subjects and has shown encouraging results. Each of the ten healthy
subjects received 200 pulses of PAS and their motor excitability was assessed before and after
the modulation by gathering MEPs generated using TMS. Three baselines were gathered prior to
PAS, and MEPs were gathered directly after PAS modulation and for every ten minutes
thereafter for an hour. Interestingly, across the experiment, the subjects’ MEPs and thus
32
excitability was decreased relative to baseline immediately after modulation but over the
following 20 minutes, MEP size increased by an average of 67%. The latency in MEP
facilitation was not unexpected because the modulatory effects of TMS generally don’t occur
immediately. The results of the pilot study show that we can indeed induce excitatory motor
facilitation and plasticity using PAS, and given these results, we hope to be able to generate a
similar facilitation in patients with neurological damage or disease.
In upcoming elements of the study, we will use resting state functional connectivity
(cMRI), an fMRI technique that can be used to analyze the co-functioning of neural circuits
across the brain. The cMRI can help to better analyze the results and efficacy of experimental
interventions in addition to behavioral outcomes. We will use cMRI and TMS assessments of
cortical excitability to test the effectiveness of pharmacologic intervention at different doses. We
will also use the PAS method, which has shown to have an excitatory effect on plasticity, on a
patient population with and without the addition pharmaceutical compounds, to assess the ability
of the pharmacologic intervention to affect plasticity. Patients will be given increasing doses of
Memantine over time and be tested for plasticity using the protocol developed in the healthy
subjects. PAS, fMRI, TMS, and resting state MRI provide physiological and quantitative
measures in which to test the scientific principles of plasticity and potentiation. These methods
could be used to help determine the effectiveness of treatment in patients with damage or disease
that causes deficits in plasticity. PAS could be used to not only test current methods of treatment
in various neurological diseases, such as deep brain stimulation and current drugs on the market,
but also to assess future treatments before they even make it to the market. Although the current
study is focused on studying and eventually helping stroke patients, the findings could be applied
in the future to help patients with dystonia and many other disorders.
33
References
Cacabelos, R., Takeda, M., & Winblad, B. (1999). The glutamatergic system and
neurodegeneration in dementia: Preventive strategies in Alzheimer's disease.International
Journal of Geriatric Psychiatry, 14(1), 3-47. doi: 10.1002/(SICI)1099-
1166(199901)14:13.0.CO;2-7
Lisanby, S. H., Luber, B., Perera, T., & Sackeim, H. A. (2000). Transcranial magnetic
stimulation: Applications in basic neuroscience and
neuropsychopharmacology. International Journal of Neuropsychopharmacology, 3, 259-
273.
Ljaschenko, Dmitrij, Nadine Ehmann, Robert J. Kittel, Hebbian Plasticity Guides Maturation of
Glutamate Receptor Fields In Vivo, Cell Reports, Volume 3, Issue 5, 30 May 2013,
Pages 1407-1413, ISSN 2211-1247, http://dx.doi.org/10.1016/j.celrep.2013.04.003.
(http://www.sciencedirect.com/science/article/pii/S2211124713001708)
Quartarone, A., Classen, J., Morgante, F., Rosenkranz, K., & Hallett, M. (2009). Consensus
paper: Use of transcranial magnetic stimulation to probe motor cortex plasticity in
dystonia and levodopa-induced dyskinesia. Brain Stimulation, 2(2), 108-117. doi:
10.1016/j.brs.2008.09.010
Quartarone, A., Rizzo, V., & Morgante, F. (2008). Clinical features of dystonia: A
pathophysiological revisitation. Current Opinion in Neurology, 24(4), 484-490. doi:
10.1097/WCO.0b013e328307bf07
Shouval, H. Z., Wang, S. S., & Wittenberg, G. M. (2010, July 01). Abstract. National Center for
Biotechnology Information. Retrieved from
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2922937/