Recruiting New Graduate Students For Fall 2021

Posted on Sep 2, 2020

I am looking to recruit new graduate students to join my lab in Fall 2021. Check the web pages for Psychological Sciences for details on our graduate program and how to apply for admission; doctoral students are provided five years (12 months per year) of guaranteed support (stipend, tuition, health insurance).

My laboratory currently focuses on two interrelated lines of research.

One line of work examines visual object recognition, categorization, and the development of perceptual expertise in humans using behavioral experiments (laboratory and online), computational modeling, and cognitive neuroscience techniques; some of this work has been in collaboration with Isabel Gauthier and her laboratory. Some of my current work uses a combination of cognitive models and deep learning convolutional neural network models.

The other line of work develops and tests cognitive and neural models of visual attention, selection, categorization, and decision making that explain the dynamics of behavior in human and monkeys, electrophysiology in humans and monkeys, and neurophysiology in monkeys; much of this work has been in collaboration with Jeffrey Schall and Gordon Logan.

Our NIH/NEI grant “Stochastic Models of Visual Decision Making and Visual Search” is Renewed

Posted on Sep 2, 2020

We just received the official award notice that our NIH/NEI grant R01 EY021833 Stochastic Models of Visual Decision Making and Visual Search has been renewed for $1,583,958 for four years.

Project Summary: Support is requested to advance an innovative, productive collaboration aimed at linking mind, brain, and behavior using performance, neurophysiological, and electrophysiological measures from monkeys and humans performing visual search and visual decision making tasks. The general goal is to derive the connections from spike trains in monkeys to behavior in humans using computational models that specify mental states mathematically, link them to brain states in particular neurons, and explain how the neural computations produces behavior. Our Gated Accumulator Model (GAM) assumes a stochastic accumulation of evidence to threshold for alternative responses. Model assessment involves quantitatively testing alternative model architectures on predictions of behavioral measures, response probabilities and distributions of correct and error response times, as well as neural measures and how these change with set size and target-distractor discriminability in previously collected data from monkeys performing visual search. While our previously funded research aimed to understand the architecture of evidence accumulation in GAM and the relationship of model accumulators to the observed dynamics of movement-related neurons in FEF, our newly proposed research aims to understand computationally the nature of the evidence that drives that accumulation and its relationship to the measured dynamics of visually-responsive neurons in FEF. Aim 1 compares the quality of salience evidence in lateralized EEG signals and neural discharges from visually-responsive neurons in monkeys performing visual search as input evidence to a network of stochastic accumulators to predict behavior. Aim 2 addresses a major challenge to the neural accumulator framework by determining whether movement neuron dynamics in FEF actually ramp or step. Aim 3 evaluates alternative architectures for an abstract Visual Attention Model (VAM) of the evidence driving accumulation to jointly predict observed behavior and the measured dynamics of visually-responsive neurons. Aim 4 extends VAM to more complex visual tasks involving filtering and selection. The result will be a broader and deeper understanding of the visual processes that select targets and control eye movements. Computational models like VAM and GAM may be at the “just right” level of abstraction. They capture essential details of the computation in ways that explain neural activity and behavior in single participants, whether monkey or human. These models can be used to understand normal behavior as well as illness, disability, and disease; the best-fitting parameters can characterize individual differences in behavior and provide markers for brain measures. These models can also inform neurological conditions that have a biophysical basis at the level of individual neurons and neural circuits, offering insight into what neurons and circuits compute and how they do it.

Postdoctoral Fellowship in Model-based Cognitive Neuroscience at Vanderbilt

Posted on Aug 1, 2020

We eagerly seek postdoctoral fellows to join an ongoing collaboration between Thomas Palmeri, Jeffrey Schall, and Gordon Logan at Vanderbilt University using cognitive and neural models to understand visual cognition in humans and monkeys. Successful models predict details of observed behavior and are constrained by and predict neurophysiological, electrophysiological, or brain imaging data. 

Research facilities include several high-end laboratory workstations, computerized behavioral testing stations, a web-based server infrastructure for online experiments, two eye trackers, a shared 10,000+ core CPU cluster and large-scale GPU cluster at Vanderbilt’s ACCRE, state-of-the art facilities for neurophysiology, electrophysiology, and brain imaging, as well as ample office and research space. Postdoctoral fellows will also take advantage of the collaborative environment, facilities, and support in the Department of Psychology (www.vanderbilt.edu/psychological_sciences/) and the Vanderbilt Vision Research Center (vvrc.vanderbilt.edu). And as Dave Grohl of the Foo Fighters said, “Everybody now thinks that Nashville is the coolest city in America”.

Candidates can hold a Ph.D. in psychology, neuroscience, computer science, mathematics, engineering, or related disciplines. Candidates should have demonstrated skills in computer programming and statistical analyses. Some demonstrated experience with computational modeling is required. Some knowledge of vision science and neuroscience is desired but not required. Start date is negotiable, but preference will be given to candidates who can begin this fall or winter. Applications will be reviewed on a rolling basis as they arrive. Salary will be based on the NIH postdoctoral scale. 

Please forward to potential interested applicants.

Applicants should send a cover letter with a brief research statement, a current CV, and names and email addresses of three references to:
Thomas Palmeri
Department of Psychology
Vanderbilt Vision Research Center
Vanderbilt University
Nashville, TN 37240
thomas.j.palmeri@vanderbilt.edu  
catlab.psy.vanderbilt.edu  

Data Science Institute Welcomes DSI-SRP 2020 Fellows

Posted on Jun 2, 2020

The Vanderbilt Data Science Institute welcomed its second cohort of summer research fellows on June 1. The DSI Summer Research Program engages students who are interested in carrying out data science-related research with a Vanderbilt faculty member and integrates them into the institute’s community of data science scholars. This year the program is expanding its mission, as students will be required to dedicate at least 30 percent of their time working on COVID-19 related projects.

As Director of Undergraduate Research for the Data Science Institute, Thomas Palmeri oversees the DSI-SRP program.

Welcome New Members to the CatLab

Posted on Jun 24, 2018

We welcome three new members to the CatLab this summer:

Greg Cox received his PhD from Indiana University in Psychological and Brain Sciences and Cognitive Science, where he worked with Rich Shiffrin. Greg completed a postdoctoral fellowship at Syracuse University and will be joining our lab as a postdoctoral fellow in July. He will be working with Palmeri, Logan, and Schall on model-based cognitive neuroscience. Greg is interested in the development of experimental techniques and mathematical/computational models that help us understand how neural and cognitive processes jointly unfold across time.

Craig Sanders is receiving his PhD from Indiana University in Psychological and Brain Sciences this summer, where he has been working with Rob Nosofsky. Craig will be joining our lab as a postdoctoral fellow next month. He will be working with Palmeri and Gauthier on project using cognitive and deep learning models to understand individual differences in visual cognition. Craig is broadly interested in combining machine learning (especially deep learning) with classic cognitive models to understand how people perceive, categorize, and mentally represent objects.

Jason Chow completed his undergraduate degree in May 2018 at the University of Toronto, he worked with CatLab alumnus, and now University of Toronto faculty member, Michael Mack on the development and validation of a printable 3D stimulus set for categorization experiments in visual and tactile modalities. As a PhD student at Vanderbilt, Jason will be working with Palmeri and Gauthier on combining computational modeling and neuroimaging to gain insights on perceptual expertise.