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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
Virtual V1sion: A Collaborative Coding Project, Cheryl Olman
Virtual V1sion: A Collaborative Coding Project, Cheryl Olman
MODVIS Workshop
Virtual V1sion is a new idea for fostering modeling collaborations and data sharing. While still in its infancy, the ultimate goal is a website that hosts repositories for (1) interchangeable model elements, (2) datasets that can be fit/predicted by those models, and (3) educational modules that explain the background for both the models and the datasets. The scope of the modeling is limited to predictions of V1 responses, although not all computations represented by model elements in Virtual V1sion are required to be V1-intrinsic: a goal of the project is to provide a framework in which predictions for modulation by …
Parametrically Constrained Lightness Model Incorporating Edge Classification And Increment-Decrement Neural Response Asymmetries, Michael E. Rudd
Parametrically Constrained Lightness Model Incorporating Edge Classification And Increment-Decrement Neural Response Asymmetries, Michael E. Rudd
MODVIS Workshop
Lightness matching data from disk-annulus experiments has the form of a parabolic (2nd-order polynomial) function when matches are plotted against annulus luminance on log-log axes. Rudd (2010) has proposed a computational cortical model to account for this fact and has subsequently (Rudd, 2013, 2014, 2015) extended the model to explain data from other lightness paradigms, including staircase-Gelb and luminance gradient illusions (Galmonte, Soranzo, Rudd, & Agostini, 2015). Here, I re-analyze parametric lightness matching data from disk-annulus experiments by Rudd and Zemach (2007) and Rudd (2010) for the purpose of further testing the model and to try to constrain …
Learning Object Representations For Modeling Attention In Real World Scenes, Alex Schwarz, Frederik Beuth, Fred H. Hamker
Learning Object Representations For Modeling Attention In Real World Scenes, Alex Schwarz, Frederik Beuth, Fred H. Hamker
MODVIS Workshop
Models of visual attention have been rarely used in real world tasks as they have been typically developed for psychophysical setups using simple stimuli. Thus, the question remains how objects must be represented to allow such models an operation in real world scenarios. We have previously presented an attention model capable of operating on real-world scenes (Beuth, F., and Hamker, F. H. 2015, NCNC, which is a successor of Hamker, F. H., 2005, Cerebral Cortex), and show here how its object representations have been learned. We have used a learning rule based on temporal continuity (Földiák, P., 1991, Neural Computation) …