The Temporal Dynamics of Learning Center’s (TDLC) research activities are quite broad. We have a number of ongoing projects at UC San Diego aimed at understanding the dynamics of teaching and learning in multiple domains. There are ongoing behavioral studies, neuroimaging studies, social robotics for teaching, brain dynamics during learning, analysis of dynamic signals such as audio and EEG (brain waves), modeling and behavioral experiments in eye movements during visual search, and many others.

Here is the current list of projects available to our program trainees. This list will be updated as new projects are added.

Rhythm, music, and cognition
Theme(s): Rhythm, music, and cognition
Mentor: Andrea A. Chiba, PhD; Associate Professor of Cognitive Science, Program in Neuroscience, Computational Neuroscience Program, and Interdisciplinary Program in Cognitive Science. Science Director: Temporal Dynamics of Learning Center.
Rationale/Motivation: As all of our actions are performed in time, it should be expected that the ability to process temporal informaion might exert an effect on various aspects of our cognition. One instance of time-processing is keeping rhythmic synchrony. Consistent with this we have found that the ability of children to synchronize rhythmically in the context of a music class is correlated with their attention.
Objective: The objective of this project is to understand the relationship between music, time processing, and general aspects of cognition such as attention, memory, and language processing. This will be done by carrying out randomized interventions, with “music lessons” as a treatment group. We will also perform cognitive tests of memory and language processing under different rhythmic conditions.
Specific Role of the REU Student: The student will help in the design of the experiments and will carry out cognitive testing on the subjects. They might also help organize the randomized intervention.

Using designer receptors exclusively activated by designer drugs to study memory reorganization in a rodent model of human memory impairment
Theme(s): Memory Modulation
Mentor: Robert E. Clark, Ph.D., Professor of Psychiatry
Rationale / Motivation: A fundamental question about memory is how it is organized and stored in the brain. Life experiences are not formed and stored instantaneously in a form that persists as long-term memory. Instead, new memories are transformed gradually from a labile state, where they are vulnerable to disruption, to a more permanent state in which they can persist indefinitely and are resistant to disruption. This transformation is often referred to as consolidation and involves reorganization at both synaptic and brain system levels. System consolidation, the focus of the Clark laboratory, is a prolonged process that can take days, weeks, or longer and involves a gradual reorganization of multiple brain circuits that support long-term memory; this process appears to involve time-dependent modifications in circuits that support memory storage and recall.

Designer receptors exclusively activated by designer drugs (DREADDS) work by mutating neuronal receptors so they do not respond to their agonist, but are instead activated by the otherwise biologically inert ligand clozapine-N-oxide (CNO). This mutation is accomplished using viral vectors carrying a promoter and the receptor plasmid. Expression of DREADD receptors in neurons enables the control of their activity simply by delivering CNO to the rodent systemically (CNO can cross the blood-brain barrier). The Clark laboratory is currently using DREADDs to study systems consolidation in rats. 

Rats are used as a model system for human memory function. DREADDs/viruses are infused directly into different brain structures during brain surgery.  Later, after the new receptors are expressed, rats are trained on various tasks to measure memory. Memory retention is tested under conditions were the brain structure is functioning normally and when the brain structure is inactivated with CNO. The brains are then analyzed. 
Objective: We are seeking to understand the dynamic nature of memory and memory reorganization across time.
Specific Role of the REU Student: Students will be engaged in all aspects of these tasks, including neurosurgery, histology, and behavioral testing.

Learning New Visual Object Classes
Theme(s): Computational Cognitive Neuroscience
Mentor: Garrison W. Cottrell Ph.D., Professor of Computer Science and Engineering, and Director, The Temporal Dynamics of Learning Center
Rationale/Motivation: We have developed a neural network model of human visual object recognition that includes two visual areas: one for "basic level" object recognition (e.g., recognizing a chair as a chair) and one for fine-level discrimination of objects (e.g., George Bush as George Bush, not just as a human). We have used this model to explain what we call "the Visual Expertise Mystery": why does the Fusiform Face Area (FFA, the fine-level discrimination area), an area of cortex that is initially specialized for face processing, become recruited for dogs by dog show judges, birds for bird experts, and cars for car experts. In other words, the FFA is an area specialized for fine-level discrimination of homogeneous classes - it is good at telling similar things apart, and this ability generalizes to new domains of expertise. However, it seems important to also show why the rest of visual cortex gets recruited for new object classes for which all we have to do is recognize the object (e.g., a chair as a chair, not as a particular kind of chair). The goal of this project is to test that hypothesis - will our model's object recognition area be faster at learning new categories of objects than the model of the FFA?
Objective: Our goal is to answer the complementary question to the visual expertise mystery - does the object category recognition area become recruited for categories of object where we *don't* need to differentiate objects within the category?
Specific Role of the REU Student: A computationally-oriented student will learn how to build and analyze computational models of visual processes. A non-computationally-oriented student will gather images from the web (using existing databases), read papers on the neurobiology of human object recognition, and help write the paper.

User Context Awareness From Smartphone Sensors
Theme(s): Human-Computer Interaction, Applied Statistics
Mentor: Gert R.G. Lanckriet, Ph.D., Assistant Professor of Electrical and Computer Engineering
Rationale/Motivation: Smartphones are becoming an intimate extension of many users. The sensing abilities and computational capabilities of smartphones keep improving. This naturally gives rise to context awareness applications, where the smartphone-program is aware of the current activity, physical state, mental state or mood of the user, and can adjust its behavior accordingly. Possible directions for such applications can be in public health (physical activity monitoring), emergency aid (fall detection, emergency detection and alert), entertainment (multimedia recommendation) and e-commerce (product recommendation).
Objective: In the current stage of the project we are developing a smartphone application (starting with an iPhone app) that collects measurements from various sensors (accelerometer, GPS, etc.) and activity/context labels from the user. This data can then be used to train automatic context-aware systems.
Specific Role of the REU Student: The REU student will assist with the application development, both on the phone side (iOS application – objective C code) and on the server side (python code, apache server).

The Developing Social Brain: Measuring EEG and Action During Child-Parent Play
Themes: Social development , EEG imaging, social neuroscience, infant learning
Mentor: Gedeon Deák, Ph.D.,  Professor of Cognitive Science, UCSD
Rationale/Motivation: Little is known about how infants' brains develop to process social information. This project uses electroencephalogram (EEG) and motion capture of toddlers and parents playing games to examine how infants' brain patterns change when they act, and when they watch their parent act.
Objective: To test theories of social development through brain and behavioral measures in a uniquely natural and unscripted social situation.
Specific Role of the REU Student: The REU student will be expected to learn about EEG methods and theory, motion tracking methods, and about scientific studies of infant social development. The student will work with the PI and a team of graduate and undergraduate students to recruit families, run testing sessions, and analyze EEG and behavioral data. Also, the REU student will attend weekly journal club and project meetings in the Cognitive Development Lab.

DNA Methylation and Postnatal Brain Development
Themes: Systems neuroscience, neural mechanisms of behavior.
Mentor: Terrence Sejnowski, Ph.D., Professor of Biology, UCSD; Professor, Salk Institute, and Margarita Behrens, Ph.D., Staff Scientist, Salk Institute.
Rationale/Motivation: DNA methylation is a stable covalent modification that persists in post-mitotic neurons throughout their lifetime, defining their cellular identity. However, the methylation status at each of the ~1 billion cytosines in the genome is potentially an information-rich and flexible substrate for epigenetic modification that can be altered by cellular activity. Indeed, our recent data shows that neuronal methylation patterns change profoundly during the period between birth and adolescence in mouse and humans (Lister et al., 2013. Science 341:1237905). It is conceivable then that alterations in these methylation patterns may lead to profound perturbation in brain postnatal development, and may be at the origins of many neuropsychiatric disorders. 
Objective: Characterize the dynamic changes in DNA methylation patterns during postnatal brain development at the region and cell-type level.
Specific Role of the REU Student: The student will participate in the preparation of brain samples, cell-type isolations, and methylome and transcriptome analyses.

Development of Novel Brain Computer Interface spellers and games for high school students
Mentor: Virginia de Sa, Ph.D., Associate Professor of Cognitive Science and Member of the Graduate Program in Neurosciences.
Themes: Bioengineering, Machine Learning, Electrophysiology
Rationale/Motivation: Brain-computer interfaces (BCIs) allow locked in users to communicate with the world without moving their muscles. In our lab we are developing two novel BCI spellers to try to make them easier for more people to use. We are also developing applications for consumer-grade cheap EEG systems that we can use for STEM outreach to middle and high school students.
Objective: We propose to investigate the EEG responses during the use of our new spellers, analyze the signals, and create machine learning algorithms to determine the desired letter the user wants to spell.
Specific Role of the REU Student:The student will assist graduate students in designing the experiments, preparing subjects (placing and testing electrodes), recording electrophysiological data, and analyzing the data. Students with a good mathematical and computer
programming background will benefit most from this opportunity. Or the student could help design applications for the consumer-grade EEG systems and work on outreach lessons for high school students. Students with a good engineering or computer programming background would benefit most from this opportunity. Other projects consistent with the lab interests in machine learning, brain-computer interfaces, and multi-sensory perception that are compatible with the mission of the TDLC are also possible.

Developing Mind and Brain
Mentor: Terry L. Jernigan, Professor of Cognitive Science, Psychiatry, and Radiology, and Director, UCSD Center for Human Development
Theme(s): Brain Development, Learning, Cognition
Rationale/Motivation: The Center for Human Development is a research organization at the University of California, San Diego. CHD researchers ask questions like: What are the factors that influence developing minds and personalities? How and why do we become individuals? What role is played by our experiences? By our genes? How does developing behavior relate to brain development? The goals of CHD research are discoveries and innovation that may ultimately lead to increased personal resilience and enhanced creativity of the people in our society.
Objective:To understand these factors, the Center develops cognitive tasks, interventions, and surveys to understand how development changes behavior over time. These tasks and surveys are currently delivered in the lab, but the Center is now developing capabilities to allow these to be completed via the Internet to allow for more frequent and convenient data collection for participants.
Specific Role of the REU Student: Student researchers will have the opportunity to develop these tasks and surveys for use in both the lab setting and online. In addition, interventions will be developed for use in classrooms as part of a larger longitudinal study. Student researchers will have opportunities to learn to program in Python, and using web technologies such as Django, Javascript/Coffeescript, and Backbone.
Requirements: Previous exposure to programming (COGS 8, COGS 18, COGS 119, or equivalent CSE Intro to Programming); cumulative GPA of 3.0 or higher; experience contributing to teams, and working independently to find solutions to problems.

The Role of Eye Movement Behavior in Cognitive and Social Development
Theme: Cognitive and behavioral neuroscience
Mentor: Leanne Chukoskie, Ph.D., Assistant Research Scientist, Institute for Neural Computation, UCSD
Rationale/motivation: Studies of early motor development suggest the inter-relation of motor skills with cognitive and social development, such that motor development may be foundational to higher-level social and cognitive skills. We wish to refine these perspectives to include the role of eye movement behavior in the development of social and cognitive skills.
Objective: Eye movement behavior tasks, ranging from simple to complex, have been designed for children of a range of ages from both typically- and atypically- developing groups. Data from the eye tracking experiments will be related to performance on social and cognitive tasks.
Specific role of the REU student: The student will assist in collecting eye-tracking data, analyzing and interpreting the data. Students with relevant experience might also assist in modeling the data and the design and implementation of the next phase of experiments.

SEED: Socio­Emotional Early Development ­ Social Robots in Early Childhood Education 
Theme: Interactive and Adaptive Behavioral and Computational tools for learning environments outside the Laboratory.  
Ethnography, Automated Facial Expression, Object Recognition, Prototyping & Fabrication Lab. 
Mentor: Deborah Forster, Ph.D., Assistant Project Scientist, Qualcomm Institute of Calit2-UCSD
Rationale/motivation: Project RUBI (Robot Using Bayesian Inference) started a decade ago (now building RUBI-6 and RUBI-7), in close association and collaboration with the Early Childhood Education Center (ECEC) at UCSD. We believe that building relevant education technology requires immersive iterative design - immersion in the field where the technology is (and will continue to be) used, and an interdisciplinary team that can collaborate between and across divergent specializations. Most importantly, the children and staff of ECEC are our most important co-designers. This year's effort is motivated equally by ECEC objectives and RUBI Project goals. Objectives: The goal is to deploy RUBI­6 and RUBI­7 for field testing and pilot data collection and analysis, while developing design ideas / goals for future RUBIs. While previously research assistants specialized in hardware, software or behavioral observations, this year the REU students will work as members of the RUBI team, and will be exposed to a variety of tools and methods, both in the lab and in the field. The team will decide on a division of labor that most effectively delivers the milestones during the year, yet will also make it possible for students to learn a divergent skill set and will provide an opportunity to shift focus along the way.  
Specific role of REU students: Participating REU students will participate in three main activities - field observations and testing (at ECEC); data collection, processing and analysis (video annotation, facial expressions recognition toolkits, machine learning) in the Machine Perception Laboratory; and human-robot interaction 'tinkering' - which happens everywhere - in the lab, in the field, and in the prototyping shop. We expect 2-3 REU students that would coordinate their schedules to overlap at least 3-5 hours a week.

Human Memory, Awareness, and the Brain
Theme: Cognitive and Behavioral Neuroscience
Mentor: Christine N. Smith, Ph.D., Assistant Research Scientist, Department of Psychiatry, UC San Diego
Rational/Motivation: Drawing on the traditions of neuroscience, neuropsychology, and cognitive science, our work in the laboratory of Dr. Larry Squire, Ph.D. involves patients with amnesia who have lesions of the medial temporal lobe. The analysis of such cases provides useful information about the structure and organization of normal memory. We study memory in healthy individuals and in memory-impaired patient populations using neuropsychological tests, eye tracking, and neuroimaging.
Objective: We seek to understand the role of awareness for what has been learned in healthy humans and in memory-impaired patients with damage to the hippocampus or larger lesions of the medial temporal lobe for tasks where behavior changes as a function of experience, particularly eye movements.
Specific Role of the REU student: Students will assist in the development of new tests of memory and other cognitive processes; establish study files and organize and maintain recordkeeping for the project; score test results, carry out statistical analysis, and prepare summaries of research results; carry out literature searches concerning these investigations.
Requirements: Knowledge of Windows operating system, basic data analysis and graphing of data stored in spreadsheets, and word-processing (e.g., Word, Excel, and Powerpoint); principles of basic experimental design, and of software for designing and administering experiments (e.g., E-Prime, MATLAB, and Photoshop); basic statistical analyses (e.g., descriptive summaries, t-tests, ANOVAs) and the ability to perform these analyses using statistical software (e.g. SPSS, Excel, Systat); familiarity with eye tracking systems and software. Programming skills are desirable.