With the start of 2018, I became an Associate Editor at Cognitive Psychology: Cognitive Psychology is concerned with advances in the study of attention, memory, language processing, perception, problem solving, and thinking. Cognitive Psychology specializes in extensive articles that have a major impact on cognitive theory and provide new theoretical advances.
Cognitive Psychology is one of the premier theoretical journals in the field, with an impact factor of 4.945.
Annis, J., & Palmeri, T.J. (in press). Bayesian statistical approaches to evaluating cognitive models. Wiley Interdisciplinary Reviews in Cognitive Science.
Dutilh, G., Annis, J., Brown, S.D., Cassey, P., Evans, N.J., Grasman, R.P.P.P., Hawkins, G.E., Heathcote, A., Holmes, W.R., Krypotos, A.-M., Kupitz, C.-N., Leite, F.P. Lerche, V., Lin, Y.S., Logan, G.D., Palmeri, T.J., Starns, J.J., Trueblood, J.S., van Maanen, L., van Ravenzwaaij, D., Vandekerckhove, J., Visser, I., Voss, A., White, C.N., Wiecki, T.V., Rieskamp, J., & Donkin, C. (in press). The quality of response time data inference: A blinded, collaborative approach to the validity of cognitive models. Psychonomic Bulletin & Review.
New papers from the CatLab:
Cheng, X.J., McCarthy, C., Wang, T.S.L., Palmeri, T.J., & Little, D.R. (in press). Composite faces are not (necessarily) processed coactively: A test using Systems Factorial Technology and logical-rule models. Journal of Experimental Psychology: Learning, Memory, and Cognition.
Vogelsang, M.D., Palmeri, T.J., Busey, T.A. (2017). Holistic processing of fingerprints by expert forensic examiners. Cognitive Research: Principles and Implications, 2: 15.
Vanderbilt has recently funded our TransInstitutional Programs (TIPs) proposal, Data Science Visions:
Modern society, medicine, business, science, engineering and even the humanities are awash in data. The amount of data being produced is growing so fast that a new interdisciplinary field called data science has emerged to process, analyze, visualize and ultimately extract knowledge from the data. This initiative seeks to take the first steps in positioning Vanderbilt to be a leader in this critical new field. The initiative will identify and connect all the disparate islands of data science activity at Vanderbilt to create a unified data science community and spark cross-campus research collaborations. The initiative will also support new educational tracks and establish active partnerships with on-campus research groups and off-campus industry to provide immersive real-world training for students. More ambitiously, this TIPs award hopes to seed a sustainable, visible and internationally impactful activity with the future creation of a trans-institutional data science institute at Vanderbilt.
Palmeri will also be part of the Provost’s Working Group on Data Science:
The Data Science Visions Working Group consists of 20 faculty members from a broad set of disciplines who will be engaged in this project over the next year and will report out to the provost and all the school and college deans, including Jeff Balser, president and CEO for Vanderbilt University Medical Center and dean of the School of Medicine.
Palmeri, T.J., Love, B.C., & Turner, B.M. (in press). Model-based cognitive neuroscience. Journal of Mathematical Psychology.
Schall, J.D. Palmeri, T.J., & Logan, G.D. (in press). Models of inhibitory control. Philosophical Transactions of the Royal Society B.
Shen, J., & Palmeri, T.J. (in press). Modeling individual differences in visual categorization. Visual Cognition. [PDF]
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. The full top 10 list can be found here: