I am a Research Psychologst with the Air Force Research Laboratory. I use behavioral and computational methodologies to measure and understand memory and cognition.
Adults are unable to hold their attention to demanding cognitive tasks for long periods of time. Sometimes, these increases in fatigue across time-on-task can lead to errors. Along with other researchers at the 711th Human Performance Wing at the Air Force Research Laboratory (AFRL), I use computational models of cognition to understand and develop empirical hypotheses about fatigue and errors in vigilant attention tasks, such as the Psychomotor Vigilance Test (PVT). Our models capture individual differences in vigilant task performance using both behavioral and neural (EEG) data. [M28] [M29]
Research in cognitive psychology indicates that individuals do not have direct (or "privileged") access to the contents of their own memory. Cues diagnostic of information of interest, however, are available, though these cues may or may not be accessible at the time of inference. Furthermore, inferential cues can be overwhelmed by cues that are not diagnostic of a true memory state, such as fluency of retrieval or item associativeness. In order to understand this "multiple-cue utilization" (MCU) hypothesis of metacognition, the Adult Cognition Lab, along with Drs. Rick Thomas and David Illingworth, modified existing cognitive architectures (MINERVA2 and HyGene) to model how memory decisions are made based on the integration of multiple memory cues. [M24] [M30]
Infants face great acoustic developmental challenges - not only must they learn to distinguish speech tokens (i.e., individual vowel and consonant sounds), but they must do so while encountering high variability in these tokens between speakers. A large source of speaker invariance, for example, comes from differences between genders, with female speaker tokens registering at higher levels on the acoustic spectrum than male speakers. Despite this lack of invariance, infants readily learn differences between vowel sounds through observing their stochastic properties. Led by Dr. Joe Toscano at Villanova University, we developed a computational model of vowel category learning in infants that both capitalizes on statistical learning and accounts for speaker variability. [M5]
When we think about aging, we commonly hark upon declines in declarative episodic memory, specifically in explicit recall. While it is true that normally-aging older adults have greater difficulty with explicit recall than do younger adults, they are still able to provide accurate decisions about their memory that allow them to function normally in their daily lives. With Dr. Chris Hertzog and the Adult Cognition Lab (ACL), I study the accuracy of memory judgments across the lifespan and the strategies that can aid individuals in making decisions about their own knowledge, even when specific information cannot be directly recalled. Recently, we have been examining feelings-of-knowing (FOKs), which scale confidence that an individual will be able to recognize an item that they cannot currently recall and, in turn, allows researchers to make inferences about that individual's access to diagnostic information in the absense of explicit memory. [P2]
A major focus of the research during my graduate studies was understanding metacognitive monitoring (monitoring the status of the cognitive system) during experimental tasks. Specifically, how well can individuals judge their current level of knowing of to-be-remembered information? How accurate are their predictions of remembering that information in the future? With the Adult Cognition Lab, I use predictive judgments of if an item will be recognized in the event that it cannot be recalled (called feeling-of-knowing [FOK] judgments) to assess metacognitive accuracy in both young and older adults. [P5] [M24] [M30]
Although they largely escape our awareness, we use metacognitive monitoring constantly during learning to determine how to study information that needs to be remembered at a later time as well as how much time we should spend studying that information. A large focus of my research on the relationship between monitoring and control is on the decisions that learners make during studying when introduced to certain task manipulations, such as the difficulty of the items being studied or the wording of the instructions that are presented before learning even begins. [P4]
As a Research Psyhologist (DR-02), I integrate behavioral and neural data with computational techniques to understand human performance. I work in the Airmen Systems (RH) directorate within the 711th Human Performance Wing at the Air Force Research Laboratory (AFRL) at Wright-Patterson AFB.
I worked with researchers at the 711th Human Performance Wing at the Air Force Research Laboratory (AFRL) to examine behavioral and computational accounts of human cognition, such as the effects of fatigue during sustained vigilance tasks. I used a wide variety of modeling techniques, such as ACT-R and multilevel modeling, to capture individual differences in memory and attention.
As a graduate student at Georgia Tech, my primary research focus was age differences in episodic memory and memory awareness (metamemory). I helped design, run, and examine the results of behavioral studies that ranged from simple paired-associates learning to large-scale word norms using a number of statistical and computational techniques. Along with my research, I designed a graduate-level statistics lab curriculum that focused on the use of the R programming language.
I researched dynamics of cognitive performance and fatigue in the battlefield and implemented models of soldier performance using Python, R, Clojure, and in-house programming languages. These projects were funded by SBiRs with support from the U.S. Army Natick Soldier Systems Center.
Advisor: Chris Hertzog, PhD
Adult Cognition Lab (ACL)
Thesis Advisor: Thomas Toppino, PhD
Memory and Cognition Lab
Word Recognition and Auditory Perception (WRAP) Lab
Thesis Advisor: Jeffrey Toth, PhD
Aging and Cognitive Training (ACT) Lab
When I am not working, I enjoy spending my free time outside playing tennis. I also enjoy hiking in the Appalachian Mountains and competing in adventure races with my brother.
In the rare moments in which I'm neither working nor playing tennis, I enjoy reading, learning about new data analysis/programming technologies, watching Netflix, and (unsuccessfully) modifying simple robots.