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:
The Perceptual Expertise Network celebrated its 30th workshop by inviting current PEN members, previous PEN members, and PEN friends to a day of talks as well as a reunion dinner following the talks. This was held on May 14th, 2015 at the TradeWinds Island Grand Resort in St. Pete Beach, Florida, as a satellite to the annual VSS conference.
Speakers at PEN XXX were:
Thomas Palmeri (Vanderbilt), Opening remarks
Marlene Berhmann (Carnegie Mellon), Never the twain shall meet
Kim Curby (Macquarie University), Are faces super objects? Object-based benefits support holistic perception
Lisa Scott (UMass Amherst), How learning during infancy enhances and constrains brain and behavioral development
Bruno Rossion (University of Louvain), Understanding expertise in face perception with fast periodic visual stimulation
Suzy Scherf (Penn State), Puberty makes us different kinds of face experts
Jim Tanaka (Victoria), Bridging the expertise gap: From the laboratory to the real world
Mike Mack (UT Austin), The evolution of category knowledge: Linking learning models to the dynamics of neural representations
Jennifer Richler (Vanderbilt), Measuring individual differences in high-level vision in a latent-variables framework
Ben Cipollini (UCSD), Exploring anatomy and genetics of cortical asymmetries
Isabel Gauthier (Vanderbilt), 15 years of fMRI studies of expertise
Michael Tarr (Carnegie Mellon), Closing talk
Photos from the workshop can be found here:
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.
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