CATLAB

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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.

News

New paper in Journal of Experimental Psychology: General

Posted on Mar 24, 2015

We have a new paper in press:

Mack, M.L., & Palmeri, T.J. (in press). The dynamics of categorization: Unraveling rapid categorization. Journal of Experimental Psychology: General. [PDF]

We explore a puzzle of visual object categorization: Under normal viewing conditions, you spot something as a dog fastest, but at a glance, you spot it faster as an animal. During speeded category verification, a classic basic-level advantage is commonly observed (Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976), with categorization as a dog faster than as an animal (superordinate) or Golden Retriever (subordinate). A different story emerges during ultra-rapid categorization with limited exposure duration (<30ms), with superordinate categorization faster than basic or subordinate categorization (Thorpe, Fize, & Marlot, 1996). These two widely cited findings paint contrary theoretical pictures about the time course of object categorization, yet no study has previously investigated them together. Over five experiments, we systematically examined two experimental factors that could explain the qualitative difference in categorization across the two paradigms: exposure duration and category trial context. Mapping out the time course of object categorization by manipulating exposure duration and the timing of a post-stimulus mask revealed that brief exposure durations favor superordinate-level categorization, but with more time a basic-level advantage emerges. But this superordinate advantage was modulated significantly by target category trial context. With randomized target categories, the superordinate advantage was eliminated; and with “blocks” of only four repetitions of superordinate categorization within an otherwise randomized context, the advantage for the basic-level was eliminated. Contrary to some theoretical accounts that dictate a fixed priority for certain levels of abstraction in visual processing and access to semantic knowledge, the dynamics of object categorization are flexible, depending jointly on the level of abstraction, time for perceptual encoding, and category context.

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Research Experience for Undergraduates (REU)

Posted on Feb 9, 2015

We are looking for outstanding students interested in a Research Experience for Undergraduates (REU) at the CatLab at Vanderbilt University this summer 2015. 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. Undergraduates who are students at Vanderbilt may have the opportunity to continue the REU into the 2015-16 academic year. Click on READ MORE for further details on the REU and how to apply.

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New Papers

Posted on Jan 28, 2015

New papers in press:

Palmeri, T.J., & Mack, M.L. (in press). How experimental trial context affects perceptual categorization. Frontiers in Psychology.

Logan, G.D., Yamaguchi, M., & Schall, G.D., & Palmeri, T.J. (in press). Inhibitory control in mind and brain 2.0: A blocked-input model of saccadic countermanding. Psychological Review.

Richler, J.J., Palmeri, T.J., & Gauthier, I. (in press). Holistic processing does not require configural variability. Psychonomic Bulletin & Review.

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Special Issue on Model-Based Cognitive Neuroscience

Posted on Dec 5, 2014

Thomas Palmeri from Vanderbilt, Brad Love from University College London, and Brandon Turner from The Ohio State University are co-editing a special issue of the Journal of Mathematical Psychology on Model-Based Cognitive Neuroscience. This special issue aims to explore the growing intersection between cognitive modeling and cognitive neuroscience. Cognitive modeling has a rich history of formalizing and testing hypotheses about cognitive mechanisms within a mathematical and computational language, making exquisite predictions of how people perceive, learn, remember, and decide. Cognitive neuroscience aims to identify neural mechanisms associated with key aspects of cognition, using techniques like neurophysiology, electrophysiology, and structural and functional brain imaging. These two come together in a powerful new approach called model-based cognitive neuroscience, which can both inform model selection and help interpret neural measures. Cognitive models decompose complex behavior into representations and processes and these latent model states are used to explain the modulation of brain states under different experimental conditions. Reciprocally, neural measures provide data that help constrain cognitive models and adjudicate between competing cognitive models that make similar predictions of behavior. For example, brain measures are related to cognitive model parameters fitted to individual participant data, measures of brain dynamics are related to measures of model dynamics, model parameters are constrained by neural measures, model parameters are used in statistical analyses of neural data, or neural and behavioral data are analyzed jointly within hierarchical modeling framework.

Click Here for more details.

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PEN XXX Reunion Meeting

Posted on Dec 1, 2014

The Perceptual Expertise Network will be celebrating our 30th workshop by inviting current PEN members, previous PEN members, and PEN friends to join us for a day of talks as well as a reunion dinner following the talks. This will be held on May 14th, 2015 at the TradeWinds Island Grand Resort in St. Pete Beach, Florida, as a satellite to the annual VSS conference.

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NEI Grant Renewed

Posted on Nov 4, 2014

Our NEI Grant Stochastic Models of Visual Decision Making and Visual Search was just renewed for another four years, through end of 2018.

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New papers from the CatLab

Posted on Aug 27, 2014

Shen, J., & Palmeri, T.J. (in press). The perception of a face is greater than the sum of its parts. Psychonomic Bulletin & Review.

Folstein, J., Palmeri, T.J., Van Gulick, A.B., & Gauthier, I. (in press). Category learning stretches neural representations in visual cortex. Current Directions in Psychological Science.

Shen, J., Mack, M.L., & Palmeri, T.J. (2014). Studying real-world perceptual expertise. Frontiers in Cognition.

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Braden Purcell wins honorable mention for James McKeen Cattell Award

Posted on Aug 14, 2014

Braden’s PhD dissertation, Neural Mechanisms of Perceptual Decision Making, has been chosen to receive honorable mention in the 2013-2014 James McKeen Cattell Dissertation competition sponsored by the Psychology Section of the New York Academy of Sciences. The field was highly competitive, with many excellent candidates for the award. His work was highly regarded by the reviewers and by the Steering Committee. The Academy further commended the work of his mentors, Thomas Palmeri and Jeffrey Schall, and the graduate program in Psychological Sciences at Vanderbilt University. In recognition of his noteworthy achievement, he will receive a certificate from the New York Academy of Sciences; his mentors will be similarly recognized. Congratulations Braden!

http://www.nyas.org/awards/13Cattell.aspx

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NSF REU Supplement awarded

Posted on Jul 15, 2014

The CatLab has just been awarded a $19,500 supplementary grant from the National Science Foundation for a Research Experience for Undergraduates. Academic year REU students will receive a $3000 stipend per semester. Summer REU students will receive a $5000 stipend, a $1500 housing and meal allowance, and

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$250 travel allowance. Interested undergraduates should contact Professor Palmeri at thomas.j.palmeri@vanderbilt.edu for information on how to apply.

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Braden Purcell wins Jum Nunnally Dissertation Award

Posted on Apr 22, 2014

Congratulations to Braden Purcell for winning the 2014 Jum Nunnally Dissertation Award! This makes the third CatLab PhD student to win this prestigious award, along with Mike Mack in 2011 and Jenn Richler in 2010.

The Jum Nunnally Dissertation Award recognizes a recent outstanding doctoral dissertation in the Department of Psychology. The recipient receives a certificate and a $500 award. Jum Nunnally came to Vanderbilt in 1960. In 1961, he became the second chair of the department. He served as chair from 1961-1964 and again from 1967-1970. Under Jum’s leadership, the department grew substantially in stature, including significant increases in both the number and quality of the faculty. A memorial fund to support student awards was established in 1982 by his friends and family. Proceeds from this fund were used to establish the Jum Nunnally Dissertation Award in 2010.

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Two new postdocs join the CatLab

Posted on Apr 1, 2014

Brent Miller joins to lab as a postdoctoral fellow this month with a background in computer engineering and psychology. He comes from the University of California, Irvine, where he received his PhD in 2014 with Mark Steyvers. Brent is broadly interested in how the mechanisms of information storage, retrieval, and encoding affect judgment and decision making. Previously, he used computational modeling to show how certain decision behavior necessarily arises from probabilistic information representation in the brain. As a postdoctoral fellow at Vanderbilt, he will work on developing and testing computational models of behavior and neurophysiology in our collaboration with Jeff Schall and Gordon Logan.

Jeff Annis will receive his PhD from the University of South Florida this summer, where he has been studying the relationship between memory and perception via sequential dependencies with Ken Malmberg. Jeff is interested in the mechanisms and representations involved in memory and categorization. He will join the lab as postdoctoral fellow this summer to use computational models and empirical investigations to understand the dynamics of perceptual expertise.

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Paper to appear in PNAS

Posted on Jan 8, 2014

Zandbelt, B.B., Purcell, B.A., Palmeri, T.J., Logan, G.D., Schall, J.D. (2014). Response times from ensembles of accumulators. Proceedings of the National Academy of Sciences. [PDF]

Decision making is explained by psychologists through stochastic accumulator models and by neurophysiologists through the activity of neurons believed to instantiate these models. This paper investigated an overlooked scaling problem: How does a response time (RT) that can be explained by a single model accumulator arise from numerous, redundant accumulator neurons, each of which individually appears to explain the variability of RT? 

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Three new contributions to edited volumes

Posted on Jan 8, 2014

Three new papers. Two in the Oxford Handbook of Computational and Mathematical Psychology and one in An Introduction to Model-Based Cognitive Neuroscience.

Palmeri, T.J., Schall, J.D. & Logan, G.D. (in press). Neurocognitive modeling of perceptual decision making. To appear in J.R. Busemeyer, J. Townsend, Z.J. Wang, & A. Eidels (Eds.), Oxford Handbook of Computational and Mathematical Psychology, Oxford University Press. [PDF]

Nosofsky, R.M., & Palmeri, T.J. (in press). Exemplar-based random walk model. To appear in J.R. Busemeyer, J. Townsend, Z.J. Wang, & A. Eidels (Eds.), Oxford Handbook of Computational and Mathematical Psychology, Oxford University Press. [PDF]

Logan, G.D., Schall, J.D., & Palmeri, T.J. (in press). Inhibitory control in mind and brain: The mathematics and neurophysiology of the underlying computation. To appear in B. Forstmann & E.J. Wagenmakers (Eds.), An Introduction to Model-Based Cognitive Neuroscience, Springer Neuroscience. [PDF]

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New work from the CatLab

Posted on Oct 24, 2013

New Papers

Palmeri, T.J. (in press). An exemplar of model-based cognitive neuroscience. Trends in Cognitive Science. [PDF]

Richler, J.J., & Palmeri, T.J. (in press). Visual category learning. To appear in Wiley Interdisciplinary Reviews in Cognitive Science. [PDF]

Upcoming Conference Presentations

Palmeri, T.J. (2013). Neurocognitive modeling of perceptual decision making. To be presented at Interfacing Models with Brain Signals to Investigate Cognition, UC Irvine, CA.

Richler, J.J., Palmeri, T.J., & Gauthier, I. (2013). The effects of varying configuration in the composite task support an attentional account of holistic processing. To be presented at Object Perception and Memory (OPAM), Toronto, Ontario, Canada.

Middlebrooks, P.G., Schall, J.D., Palmeri, T.J., & Logan, G.D. (2013). Modeling response time and accuracy of perceptual choice during a stop-signal task. To be presented at the Society for Neuroscience meeting, San Diego, CA.

Zandbelt, B.B., Schall, J.D., Palmeri, T.J., & Logan, G.D. (2013). Modeling response time and accuracy during a stop-signal task: Stimulus-response choice. To be presented at the Society for Neuroscience meeting, San Diego, CA.

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