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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

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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

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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.

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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/