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Category Laboratory at Vanderbilt

supported by NSF, NEI, and Vanderbilt University

In the CatLab, we study visual cognition, including visual categorization, visual memory, and visual decision making. We study how objects are perceived and represented by the visual system, how visual knowledge is represented and learned, and how visual decisions are made. We approach these questions using a combination of behavioral experiments, cognitive neuroscience techniques, and computational and neural modeling. One line of work, funded by the National Science Foundation, investigates the temporal dynamics of visual object categorization and perceptual expertise for objects and faces. Another line of work, funded by the National Eye Institute, uses computational modeling of visual decision making to predict behavioral dynamics and neural dynamics.


New Psychological Review article: Salience by Competitive and Recurrent Interactions

Posted on Feb 22, 2022

Our major new theoretical paper has been accepted for publication in Psychological Review: Cox, G.E., Palmeri, T.J., Logan, G.D., Smith, P.L., Schall, J.D. (in press). Salience by competitive and recurrent interactions: Bridging neural spiking and computation in visual attention. Psychological Review. PsyArXiv:

Decisions about where to move the eyes depend on neurons in Frontal Eye Field (FEF). Movement neurons in FEF accumulate salience evidence derived from FEF visual neurons to select the location of a saccade target among distractors. How visual neurons achieve this salience representation is unknown. We present a neuro-computational model of target selection called Salience by Competitive and Recurrent Interactions (SCRI), based on the Competitive Interaction model of attentional selection and decision making (Smith & Sewell, 2013). SCRI selects targets by synthesizing localization and identification information to yield a dynamically evolving representation of salience across the visual field. SCRI accounts for neural spiking of individual FEF visual neurons, explaining idiosyncratic differences in neural dynamics with specific parameters. Many visual neurons resolve the competition between search items through feedforward inhibition between signals representing different search items, some also require lateral inhibition, and many act as recurrent gates to modulate the incoming flow of information about stimulus identity. SCRI was tested further by using simulated spiking representations of visual salience as input to the Gated Accumulator Model of FEF movement neurons (Purcell et al., 2010; Purcell, Schall, Logan, & Palmeri, 2012). Predicted saccade response times fit those observed for search arrays of different set size and different target-distractor similarity, and accumulator trajectories replicated movement neuron discharge rates. These findings offer new insights into visual decision making through converging neuro-computational constraints and provide a novel computational account of the diversity of FEF visual neurons.

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New Papers

Posted on Feb 16, 2022

Cox, G.E., Palmeri, T.J., Logan, G.D., Smith, P.L., & Schall, J.D. (in press). Spiking, salience, and saccades: Using cognitive models to bridge the gap between “how” and “why”. In B. Forstmann & B.M. Turner (Eds.), An Introduction to Model-Based Cognitive Neuroscience (2nd Ed.), Springer Neuroscience.

Chow, J.K., Palmeri, T.J., Mack, M.L. (in press). Revealing a competitive dynamic in rapid categorization with object substitution masking. Attention, Perception, & Performance.

Carrigan, A.J., Charlton, A., Wiggins, M.W., Georgiou, A., Palmeri, T.J., & Curby, K.M. (in press). Cue utilisation reduces the impact of response bias in histopathology. Applied Ergonomics.

Chow, J.K., Palmeri, T.J., Gauthier, I. (in press). Haptic object recognition based on shape relates to visual object recognition ability. Psychological Research.

Chow, J.K., Palmeri, T.J., Gauthier, I. (in press). Visual object recognition ability is not related to experience with visual arts. Journal of Vision.

Carrigan, A.J., Charlton, A., Foucar, E., Wiggins, M., Georgiou, A., Palmeri, T.J., & Curby, K. (in press). The role of cue based strategies in skilled diagnosis amongst pathologists. Human Factors.

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Welcome Giwon!

Posted on Jan 20, 2022

Giwon Bahg joined the lab, working as part of the collaboration with Palmeri, Logan, and Schall on their NEI-funded grant. Giwon received his Ph.D. from The Ohio State University in 2021 under the supervision of Dr. Brandon Turner, where he studied how category learning interacts with attention, information search, and other higher-order cognitive processes. His particular interest lies in understanding how such processes evolve over time in a closed-loop, interactive environment. His research also involves joint modeling of multimodal data using computational cognitive modeling, Bayesian methods, and machine learning approaches. His current research project involves linking our computational models of visual selection, attention, and decision making (SCRI+GAM) with behavioral, EEG, and neural spiking data.

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Welcome Jin!

Posted on Sep 22, 2021

We welcomed Jinhyeok Jeong to the lab this Fall 2021. Jin received his bachelor’s degree in Psychology and master’s degree in Cognitive Science at Yonsei University. Under the supervision of Professor Sang Chul Chong, he studied ensemble perception, especially the variability perception of multiple visual items. As a graduate student at Vanderbilt, he is interested in the computational mechanisms of ensemble perception and how it relates to object categorization. Jinhyeok also has great interests in cognitive models of perception, memory, and decision-making processes, and how deep neural networks can be combined with these models. His current first year research project involves developing and testing computational models of ensemble processing and looking at relations with models of categorization, memory, and decision making.

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Congratulations Claire!

Posted on Jun 12, 2021

Congratulations to Claire Hanson on graduating with Highest Honors in Neuroscience in May 2021.

Claire began working in the CatLab after being awarded a DSI-SRP fellowship for the summer of 2020. Her project, How the brain makes decisions: Modeling the dynamics of neurons that drive choice, became her senior honors thesis.

Claire’s DSI-SRP and honors project aimed to understand how the brain makes decisions. Historically, the canonical model of firing rates of decision-making neurons has been the accumulation of evidence model; accumulation of evidence is also the canonical model used to explain human and non-human primate decision-making behavior. This model assumes that neural activity gradually ramps up until a threshold is reached and then a decision is made. A relatively recent publication challenged this notion, introducing evidence for the possibility that an immediate transition in firing rates – a step rather than a ramp – is a better model to describe the dynamics of decision-making neurons when those dynamics are measured on a trial-to-trial basis rather than averaged across trials. The question of whether the firing rates of decision-making neurons are better characterized by ramping vs. stepping dynamics is foundational for our theoretical understanding how the brain makes decisions. Claire’s project involved conducting Monte Carlo simulations of model neurons with known dynamics and using a Bayesian statistical analysis program testing for ramping vs. stepping dynamics (an adaptation of same analysis program used by the authors who proposed stepping dynamics as the preferred model). These simulated model neurons included those with simple steps or with simple ramps, as well as the diffusion model of decision making and the leaky competing accumulator (LCA) model of decision making (both of which are members of the class of accumulator models); each of these models produced simulated spike trains across multiple trials that could be analyzed in the same way that real neural data is analyzed. Claire’s simulations have shown that while simple steps are classified as simple steps by the analysis program and that simple ramps are characterized as simple ramps by the analysis program, the more complex dynamics of the diffusion and LCA are actually characterized as steps by the analysis program. Even though models like the diffusion model and LCA are clearly accumulating evidence over time, the analysis program characterizes them as steps. While real neurons might look “step-like” on a trial-by-trial basis, the computations being performed by these neurons may still be best characterized as an accumulation of evidence over time.

Claire joined the NIH baccalaureate program in the summer of 2021 in the Section on Developmental Neurogenomics, where she will use computational techniques to better understand childhood-onset neuropsychiatric disorders. Claire then plans to go on to an MD-PhD program.

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Welcome Simon!

Posted on Feb 13, 2021

We welcome Simon Lilburn to the CatLab, Vanderbilt, Nashville, and the United States as a new postdoctoral fellow. Simon comes from the land of sunny beaches, large marsupials, and disgustingly salty spreads which Australians pretend to like to fool foreigners. He received his Ph.D degree from the University of Melbourne investigating the dynamics of visual short-term memory in the Vision and Attention Lab under the supervision of Prof. Philip Smith and Dr David Sewell. His work has used a blend of traditional psychophysical experimentation with computational models of memory and decision-making to understand some of the fundamental limits on perception. His research centers on coupling intensive and individualized experiments with formal theories of basic cognitive processes with a particular emphasis on the link between perception (what we see, hear, feel) and action (what we do).

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Vanderbilt University announces launch of new undergraduate data science minor

Posted on Jan 29, 2021

Vanderbilt University has announced the addition of an undergraduate minor in data science beginning with the fall 2021 term.

Driven by the growing interest on the part of the data science community at Vanderbilt for an undergraduate program in data science, the Data Science Minor Working Group was established in March 2020 by Provost and Vice Chancellor for Academic Affairs Susan R. Wente and the deans of the College of Arts and Science, Blair School of Music, Peabody College and School of Engineering to develop and propose a trans-institutional undergraduate minor in data science. Palmeri chaired the working group that developed and proposed the minor and will serve as Director of the minor.

Click below for a MyVU story:

Click below for a Vanderbilt Hustler story:

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Mike Mack Wins 2021 Randolph Blake Early Career Award

Posted on Oct 29, 2020

Congratulations to Mike for being the 2021 winner of the Randolph Blake Early Career Award from the program in Psychological Sciences at Vanderbilt. Mike is an alumnus of the CatLab. Mike came to Vanderbilt after earning a B.S. and M.S. in Computer Science from Michigan State, where he worked with Aude Oliva before she moved to MIT. After earning his PhD from our graduate program 2011, he went on to complete a postdoctoral fellowship at UT Austin with Alison Preston and Brad Love (continuing after Brad left for UCL). Since 2016 Mike has been an Assistant Professor of Psychology at the main campus of the University of Toronto.

The program in Psychological Sciences at Vanderbilt established The Randolph Blake Early Career Award to recognize exemplary alumni of our program in the early stages of their career. The recipient receives a plaque, a $500 award, and an invitation to give a research colloquium at Vanderbilt. This award honors Randolph Blake as a distinguished Vanderbilt alumnus, as an outstanding researcher and mentor, and as a former chair of the Department of Psychology who served in that role during some of the most important years of its growth.

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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.

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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.

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New Papers

Posted on Sep 2, 2020

Annis, J., Gauthier, I., & Palmeri, T.J. (in press). Combining convolutional neural networks and cognitive models to predict novel object recognition in humans. Journal of Experimental Psychology: Learning, Memory, and Cognition.

Carrigan, A.J., Magnussen, J., Georgiou, A., Curby, K.M., Palmeri, T.J., & Wiggins, M.W. (in press). Differentiating experience from cue utilization in radiological assessments. Human Factors.

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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 ( and the Vanderbilt Vision Research Center ( 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  

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Recent Papers

Posted on Jul 14, 2020

Middlebrooks, P.G., Zandbelt, B.B., Logan, G.D., Palmeri, T.J., Schall, J.D. (in press). Unification of countermanding and perceptual decision-making. iScience.

Mack, M.L., & Palmeri, T.J. (in press). Discrimination, recognition, and classification. To appear in M.J. Kahana & A. Wagner (Eds.), Handbook on Human Memory, Oxford University Press.

Benear, S., Sunday, M.A., Palmeri, T.J., & Gauthier, I. (in press). Can art change the way we see? Psychology of Aesthetics, Creativity, and the Arts.

Palmeri, T.J. (2019). On developing and testing cognitive models. Computational Brain & Behavioral.

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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.

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