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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: https://psyarxiv.com/rkh8g/

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.

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.

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.

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.

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.

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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