Jenn Richler is accepting a new position as Senior Editor at Nature Publishing Group. In her new role, she will be covering psychology and social sciences for interdisciplinary Nature titles including Nature Climate Change, Nature Energy, and Nature Nanotechnology among others. The job will cover all aspects of the editorial process, including manuscript selection, commissioning and editing of Reviews and News & Views, and writing for the journals.
Jenn received her PhD in Psychology at Vanderbilt in 2010 working with Palmeri and Gauthier. Since then, she has continued at Vanderbilt as a postdoctoral fellow, worked as Editorial Associate and Associate Editor for the Journal of Experimental Psychology: General, and created and curated the APA PeePs (Particularly Exciting Experiments in Psychology).
Our research group has been awarded a new three-year grant from the National Science Foundation on Measuring, Mapping, and Modeling Perceptual Expertise; PI is Isabel Gauthier, co-PI is Thomas Palmeri, and Senior Investigators are Sun-Joo Cho from Vanderbilt, Gary Cottrell from UCSD, and Mike Tarr and Deva Ramanan from Carnegie Mellon.
Our project investigates how and why people differ in their ability to recognize, remember, and categorize faces and objects. Many important real-world problems, such as forensics, medical imaging, and homeland security, demand precise visual understanding from human experts. Understanding individual differences in high-level visual cognition has received little attention compared to other aspects of human performance. Recent studies indicate that there likely is far greater variability than commonly acknowledged and that the ability to learn high-level visual skills is poorly predicted by general intelligence. Not everybody who receives training in a visual domain like matching fingerprints or detecting tumors in chest x-rays may be able to reach expert levels. Visual object recognition is a new domain in which understanding and characterizing individual differences can have real-world predictive power, adding to the contributions that psychology has made in other areas, such as clinical psychology, personality, and general intelligence. This project supports a collaborative interdisciplinary research network that aims to develop measures of individual differences in visual recognition, relate behavioral and neural markers of individual differences, develop models that explain individual differences, and relate models with neural data. Because outcomes in many real-world domains depend on decisions based on visual information, developing measures, markers, and models of individual differences in high-level visual cognition can lead to substantial improvements in identifying real-world visual talent, in real-world visual performance and training. Moreover, identifying individuals with talents at visual recognition and learning will help guide individuals into fields that demand high levels of precision. Finally, understanding how people vary in visual recognition can inform individualized training at all learning levels (not just experts). For example, recognizing cases of disability in high-level vision and learning can inform rehabilitation and remediation. The collaborative team of scientists working on this project will capitalize on their individual successes and continue training female scientists and under-represented minorities. All students conducting research as part of this collaborative network, including female scientists and minorities, will be mentored by scientists from multiple disciplines, providing them with an understanding far deeper than that achievable by one discipline or method.
The project will support the activities of a collaborative research network on the study of individual differences in visual recognition. The scientists involved in these interdisciplinary efforts will include experts in brain imaging at ultra-high field, cutting-edge methods in the development of psychological tests and the development of “deep” convolutional neural network models, which are very powerful computer models that are biologically inspired. The project will investigate how brain activity and brain structure, such as the thickness of the cortex in visual areas, can predict the quality and time-course of visual learning. The team will develop and validate tests of visual ability that can be used to make precise predictions about brain activity and behavioral performance. These brain measures and behavioral tests will be related to inform deep convolutional neural models of vision. Deep convolutional neural models are the most successful computer models to date, and the higher layers of these hierarchical networks provide outstanding models of the brain’s areas critical to object recognition, but so far they have not been used to understand individual differences. Instead of the typical approach seeking to achieve the best performance possible, the team will seek models that can mirror human variability, making errors when people make errors, being slow when people are slow, and displaying a range of visual abilities and learning as observed in humans. These powerful models will help bridge between variability in people both in behavior and in the brain.
Thomas Palmeri and Brandon Turner from The Ohio State University will be chairing a symposium on Model-based Cognitive Neuroscience at the 2016 Annual Meeting of the Psychonomics Society. After an Introduction to Model-based Cognitive Neuroscience, Thomas Palmeri will present Approaches to Model-Based Cognitive Neuroscience: Bridging Levels of Understanding of Perceptual Decision Making, Brandon Turner will present Joint Models of Neural and Behavioral Data, Birte Forstmann from the University of Amsterdam will present Decision Threshold Dynamics in the Human Subcortex Measured with Ultra-high Resolution Magnetic Resonance Imaging, John Anderson from Carnegie Mellon will present Combining Space and Time in the Mind, Michael Mack from the University of Toronto will present Tracking the Neural Dynamics of Conceptual Knowledge During Category Learning with Computational Model-based Neuroimaging, and Sean Polyn from Vanderbilt University will present The Neurocognitive Dynamics of Memory Search.
Full abstracts can be found on the Psychonomics Society web site:
Purcell, B.A., & Palmeri, T.J. (in press). Relating accumulator model parameters and neural dynamics. Journal of Mathematical Psychology. [PDF]
Turner, B.M., Forstmann, B.U., Love, B., Palmeri, T.J., & Van Maanen, L. (in press). Approaches to analysis in model-based cognitive neuroscience. Journal of Mathematical Psychology. [PDF]
Annis, J., Miller, B.J., & Palmeri, T.J. (in press). Bayesian inference with Stan: A tutorial on adding custom distributions. Behavioral Research Methods. [PDF]
Ross, D.A., & Palmeri, T.J. (in press). The importance of formalizing computational models of face adaptation aftereffects. Frontiers in Psychology. [PDF]
Our recent review paper:
Richler, J.J., & Palmeri, T.J. (2014). Visual category learning. Wiley Interdisciplinary Reviews in Cognitive Science, 5, 75-94. [PDF]
was recognized as a Top Ten Cited Articles in the journal.
Mike will begin this fall as an assistant professor in the Department of Psychology at the University of Toronto. The University of Toronto is one of the oldest and most distinguished departments of psychology in the world.
Mike earned his PhD from our lab and has been a postdoctoral fellow at the University of Texas for the past several years. At Vanderbilt, Mike won the Jum Nunnally Dissertation Award, a Vanderbilt Dissertation Enhancement Grant, the Pat Burns Memorial Student Research Award, the William F. Hodges Teaching Assistant Award, and was a Learning Sciences Institute Fellow. During his postdoctoral fellowship, he has been funded by an NIH NRSA grant, he was an OPAM conference organizer, and a Memory Disorders Research Society organizer. Mike has published papers in JEP:General, JEP:HPP, Current Biology, Psychonomic Bulletin & Review, Journal of Vision, Vision Research, and several other journals and other publication outlets. His research combines behavioral experiments, functional brain imaging, and computational modeling to study human learning, memory, and categorization.
We all wish Mike the best of success in his new faculty position.
We congratulate Jianhong (May) Shen as the 2016 winner of The Lisa M. Quesenberry Foundation Award. This was established by Irvin and Mary Ann Quesenberry and Kathryn Quesenberry to memorialize the accomplishments of their daughter and sister, Lisa M. Quesenberry. It is designed to provide research or study awards to motivated graduate students. Preferably, the awards will be made to female graduate students who are studying the field of psychology and who have overcome significant personal challenges to pursue their education. Congratulations May!
Today, Julie Schnur received her Bachelor’s of Engineering in Biomedical Engineering with a minor in Scientific Computing. Julie has worked for the past year as an undergraduate research assistant in the lab under the direct supervision of postdoctoral fellow Brent Miller. At today’s graduation ceremonies, Julie was honored with the Founder’s Medal for First Honors in Engineering, the highest honor bestowed on a graduate of Vanderbilt. A link to the details can be found at http://news.vanderbilt.edu/2016/05/founders-medalists-honored-at-vanderbilt-commencement/.
This is the second year in a row that an undergraduate researcher in the lab has been so honored; last year’s winner of the Founder’s Medal in Engineering was our own Akash Umakantha.
Congratulations to May and Jeff on each being awarded 2016 Young Scientist Travel Awards to the Annual Meeting of the Society for Mathematical Psychology. The award provides $1000 in travel allowance to the society meeting this summer at Rutgers University.
We are looking for outstanding students interested in a Research Experience for Undergraduates (REU) at the CatLab at Vanderbilt University this summer 2016. Our REU is part of an NSF-funded project entitled Perceptual Categorization in Real-World Expertise. This project uses online behavioral experiments to understand the temporal dynamics of perceptual expertise, measuring and manipulating the dynamics of object recognition and categorization at different levels of abstraction and assessing how those dynamics vary over measured levels of expertise, using computational models to test hypotheses about expertise mechanisms. Students have opportunities to work on projects ranging from the development of online experiments, development of analysis routines, and development and testing of computational models. This REU is especially appropriate for students interested in applying to graduate programs in psychology, vision science, cognitive science, or neuroscience. The REU provides a $5000 summer stipend, $500 per week for ten weeks; an additional $150 per week helps offsets the cost of housing and meals; a $250 travel allowance is also provided. REUs are restricted to undergraduate students currently enrolled in a degree program and must be U.S. citizens, U.S. nationals, or permanent residents of the United States.
People with perceptual expertise are skilled at making rapid identifications of specialized objects at a glance, often in poor light and camouflage. Forensic experts can accurately match exemplars to latent fingerprints that may be small, distorted, or smudged. Expert radiologists can quickly categorize medical images as normal or cancerous. Bird experts can identify species at long distances and in poor light. This project examines perception, categorization, and identification along the continuum from novice to expert performance in two real-world perceptual domains: analysis of latent fingerprints and zoological identification of birds. Forensic expertise was chosen because of its real-world importance in criminal and civil investigations and homeland security. Bird expertise was chosen not because it is important to understand bird identification per se, but because it is an excellent domain for studying a broad continuum of real-world expertise with a large and willing subject population. The overall aim is to understand how fundamental perceptual and cognitive mechanisms are tuned and modified by experience and expertise. The models arising from this project will enable us to understand the development of real-world perceptual expertise and to validate theoretically-grounded measures of expert performance.
Why study perceptual expertise? Just as gifted athletes push the limits of their bodies, or prize-winning mathematicians push the limits of their minds, perceptual experts push the limits of their perceptual systems. Perhaps with better markers of perceptual expertise and a better understanding of how people become perceptual experts, we could identify potential perceptual experts more effectively, train new perceptual experts more efficiently, and evaluate existing perceptual experts more thoroughly. Studying perceptual expertise can also help inform our understanding of the kinds of everyday expertise that we all have, such as recognizing faces or reading words. This can yield new insights into education and workforce training along with new insights into how the ravages of brain damage or disease might lead to perceptual and learning deficits and potentially inform future breakthroughs in evaluation, intervention, or treatment.
Following standard practice in my lab for the past two decades, undergraduates will be paired with a senior graduate student or postdoctoral fellow and work with them on a specific concrete project. They will read relevant research pertaining to the project underway and they will attend lab meetings. They will meet regularly with the graduate student or postdoctoral fellow as well as myself to discuss goals, achievements, and challenges. Undergraduates in my lab typically begin by working on an ongoing project but as their skills develop and their interests blossom, they often end up working on new projects more independently. Lab meetings often offer opportunities to discuss broader issues related to topics like responsible conduct of research and professional development.
Please send the following to Professor Thomas Palmeri at firstname.lastname@example.org; we will begin reviewing applications February 15, 2016.
- 1-2 page cover letter describing your educational experience and research background, your interest in the research in the CatLab, and your future goals; please also describe your computer programming experience.
- Resume or vita.
- Recommendation letters from 2 individuals who could comment on your educational background, experience, and potential for research. Recommenders should send letters directly to Professor Palmeri.
Undergraduate student participants supported with NSF funds in REU programs must be U.S. citizens, U.S. nationals, or permanent residents of the United States. An undergraduate student is a student who is enrolled in a degree program (part-time or full-time) leading to a baccalaureate or associate degree. Students who are transferring from one college or university to another and are enrolled at neither institution during the intervening summer may participate. High school graduates who have been accepted at an undergraduate institution but who have not yet started their undergraduate study are also eligible to participate. Students who have received their bachelor’s degrees and are no longer enrolled as undergraduates are generally not eligible to participate.