CATLAB

category laboratory

Navigation Menu

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).

Read More


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:
https://news.vanderbilt.edu/2021/01/29/vanderbilt-university-announces-launch-of-new-data-science-minor-in-fall-2021/?utm_source=myvupreview&utm_medium=myvu_email&utm_campaign=myvupreview-2021-01-29

Click below for a Vanderbilt Hustler story:
https://vanderbilthustler.com/37760/featured/data-science-minor-to-be-offered-beginning-fall-2021/

Read More


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.

Read More


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.

Read More


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.

Read More


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.

Read More