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Articles 1 - 30 of 33
Full-Text Articles in Psychology
Extracting Edges In Space And Time During Visual Fixations, Lynn Schmittwilken, Marianne Maertens
Extracting Edges In Space And Time During Visual Fixations, Lynn Schmittwilken, Marianne Maertens
MODVIS Workshop
No abstract provided.
Constraining The Binding Problem Using Maps, Zhixian Han, Anne Sereno
Constraining The Binding Problem Using Maps, Zhixian Han, Anne Sereno
MODVIS Workshop
We constrained the binding problem by creating maps of different attributes. We compared the performance of different models with different maps in our current study. Our preliminary results showed that the performance of the model is the highest when location maps were used. These results suggest that the optimal way to constrain the binding problem is to create location maps of different attributes.
A Dynamical Model Of Binding In Visual Cortex During Incremental Grouping And Search, Daniel Schmid, Daniel A. Braun, Heiko Neumann
A Dynamical Model Of Binding In Visual Cortex During Incremental Grouping And Search, Daniel Schmid, Daniel A. Braun, Heiko Neumann
MODVIS Workshop
Binding of visual information is crucial for several perceptual tasks. To incrementally group an object, elements in a space-feature neighborhood need to be bound together starting from an attended location (Roelfsema, TICS, 2005). To perform visual search, candidate locations and cued features must be evaluated conjunctively to retrieve a target (Treisman&Gormican, Psychol Rev, 1988). Despite different requirements on binding, both tasks are solved by the same neural substrate. In a model of perceptual decision-making, we give a mechanistic explanation for how this can be achieved. The architecture consists of a visual cortex module and a higher-order thalamic module. While the …
Validity Of Neural Distance Measures In Representational Similarity Analysis, Fabian A. Soto, Emily R. Martin, Hyeonjeong Lee, Nafiz Ahmed, Juan Estepa, Kianoosh Hosseini, Olivia A. Stibolt, Valentina Roldan, Alycia Winters, Mohammadreza Bayat
Validity Of Neural Distance Measures In Representational Similarity Analysis, Fabian A. Soto, Emily R. Martin, Hyeonjeong Lee, Nafiz Ahmed, Juan Estepa, Kianoosh Hosseini, Olivia A. Stibolt, Valentina Roldan, Alycia Winters, Mohammadreza Bayat
MODVIS Workshop
No abstract provided.
Visual Expertise In An Anatomically-Inspired Model Of The Visual System, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni
Visual Expertise In An Anatomically-Inspired Model Of The Visual System, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni
MODVIS Workshop
We report on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. Contrary to the generally accepted wisdom, our hypothesis is that the face inversion effect can be accounted for by the representation in V1 combined with the reliance on the configuration of features due to face expertise. We take two features of the primate visual system into account: 1) The foveated retina; and 2) The log-polar mapping from retina to V1. We simulate acquisition of faces, etc., by gradually increasing the number of identities the network learns. …
Fixational Eye Movements, Perceptual Filling-In, And Perceptual Fading Of Grayscale Images, Michael E. Rudd
Fixational Eye Movements, Perceptual Filling-In, And Perceptual Fading Of Grayscale Images, Michael E. Rudd
MODVIS Workshop
No abstract provided.
Constraining Computational Models Of Brightness Perception: What’S The Right Psychophysical Data?, Guillermo Aguilar, Joris Vincent, Marianne Maertens
Constraining Computational Models Of Brightness Perception: What’S The Right Psychophysical Data?, Guillermo Aguilar, Joris Vincent, Marianne Maertens
MODVIS Workshop
No abstract provided.
Identifying And Localizing Multiple Objects Using Artificial Ventral And Dorsal Visual Cortical Pathways, Zhixian Han, Anne Sereno
Identifying And Localizing Multiple Objects Using Artificial Ventral And Dorsal Visual Cortical Pathways, Zhixian Han, Anne Sereno
MODVIS Workshop
We concluded in our previous study that model cortical visual pathways actively retained information differently according to the different goals of the training tasks. One limitation of our study was that there was only one object in each input image whereas in reality there may be multiple objects in a scene. In our current study, we try to find a brain-like algorithm that can recognize and localize multiple objects.
Is The Selective Tuning Model Of Visual Attention Still Relevant?, John K. Tsotsos
Is The Selective Tuning Model Of Visual Attention Still Relevant?, John K. Tsotsos
MODVIS Workshop
No abstract provided.
Differentiating Changes In Population Encoding Models With Psychophysics And Neuroimaging, Jason Hays, Fabian Soto Phd
Differentiating Changes In Population Encoding Models With Psychophysics And Neuroimaging, Jason Hays, Fabian Soto Phd
MODVIS Workshop
It is now common among visual scientists to make inferences about neural population coding of stimuli from indirect measures such as those provided by neuroimaging and psychophysics. The success of such studies depends strongly on simulation work using standard population encoding models extended with decoders (in psychophysics) and measurement models (in neuroimaging). However, not all studies are accompanied by simulation work, and those that are tend to vary widely in their assumptions about encoding, decoding, and measurement. To solve these issues, we designed a Python package (PEMGUIN) to assist computational modelling by providing simple ways to manage encoders' tuning functions, …
The Challenge For Vision Of Fluctuating Real-World Illumination, David H. Foster
The Challenge For Vision Of Fluctuating Real-World Illumination, David H. Foster
MODVIS Workshop
No abstract provided.
Linking Signal Detection Theory And Encoding Models To Reveal Independent Neural Representations From Neuroimaging Data, Fabian A. Soto
Linking Signal Detection Theory And Encoding Models To Reveal Independent Neural Representations From Neuroimaging Data, Fabian A. Soto
MODVIS Workshop
No abstract provided.
Texture Statistics Are Sufficient For Ensemble Perception, Sasen S. Cain, Matthew S. Cain
Texture Statistics Are Sufficient For Ensemble Perception, Sasen S. Cain, Matthew S. Cain
MODVIS Workshop
No abstract provided.
Michelson Contrast For Transparency Perception In Scenes With Multiple Luminances, Minjung Kim, Guillermo Aguilar, Marianne Maertens
Michelson Contrast For Transparency Perception In Scenes With Multiple Luminances, Minjung Kim, Guillermo Aguilar, Marianne Maertens
MODVIS Workshop
No abstract provided.
Modeling Neural Computations In Lgn And Visual Cortex That Underlie Contextual Modulation Of Lightness And Darkness Magnitudes In Simple And Complex Images, Michael E. Rudd
MODVIS Workshop
No abstract provided.
A Feature-Based Model Of Visually Perceiving Deformable Objects, Vivian C. Paulun, Filipp Schmidt, Roland W. Fleming
A Feature-Based Model Of Visually Perceiving Deformable Objects, Vivian C. Paulun, Filipp Schmidt, Roland W. Fleming
MODVIS Workshop
No abstract provided.
Global Estimation Of Signed 3d Surface Tilt From Natural Images, Seha Kim, Johannes Burge
Global Estimation Of Signed 3d Surface Tilt From Natural Images, Seha Kim, Johannes Burge
MODVIS Workshop
The ability of human visual systems to estimate 3D surface orientation from 2D retinal images is critical. But the computation to calculate 3D orientation in real-world scenes is not fully understood. A Bayes optimal model grounded in natural statistics has explained 3D surface tilt estimation of human observers in natural scenes (Kim and Burge, 2018). However, the model is limited because it estimates only unsigned tilt (tilt modulo 180deg). We extend the model to predict signed tilt estimates and compared with human signed estimates. The model takes image pixels as input and produces optimal estimates of tilt as output, using …
Inferring The Neural Representation Of Faces From Adaptation Aftereffects, Kara J. Emery, Michael A. Webster Ph.D.
Inferring The Neural Representation Of Faces From Adaptation Aftereffects, Kara J. Emery, Michael A. Webster Ph.D.
MODVIS Workshop
The aftereffects of adaptation to faces have been studied widely, in part to characterize the coding schemes for representing different facial attributes. Often these aftereffects have been interpreted in terms of two alternative models of face processing: 1) a norm-based or opponent code, in which the facial dimension is represented by relative activity in a pair of broadly-tuned mechanisms with opposing sensitivities; or 2) an exemplar code, in which the dimension is sampled by multiple channels narrowly-tuned to different levels of the stimulus. Evidence for or against these alternatives is based on the different patterns of aftereffects they predict (e.g. …
Effect Of Noise On Mutually Inhibiting Pyramidal Cells In Visual Cortex: Foundation Of Stochasticity In Bi-Stable Perception, Naoki Kogo, Felix Kern, Thomas Nowotny, Raymond Van Ee, Richard Van Wezel, Takeshi Aihara
Effect Of Noise On Mutually Inhibiting Pyramidal Cells In Visual Cortex: Foundation Of Stochasticity In Bi-Stable Perception, Naoki Kogo, Felix Kern, Thomas Nowotny, Raymond Van Ee, Richard Van Wezel, Takeshi Aihara
MODVIS Workshop
Bi-stable perception has been an important tool to investigate how visual input is interpreted and how it reaches consciousness. To explain the mechanisms of this phenomenon, it has been assumed that a mutual inhibition circuit plays a key role. It is possible that this circuit functions to resolve ambiguity of input image by quickly shifting the balance of competing signals in response to conflicting features. Recently we established an in vitro neural recording system combined with computerized connections mediated by model neurons and synapses (“dynamic clamp” system). With this system, mutual inhibition circuit between two pyramidal cells from primary visual …
Divisive Inhibition As A Solution To The Correspondence Problem In Perceptual Grouping, Chien-Chung Chen, Yi-Shiuan Lin, Li Lin
Divisive Inhibition As A Solution To The Correspondence Problem In Perceptual Grouping, Chien-Chung Chen, Yi-Shiuan Lin, Li Lin
MODVIS Workshop
No abstract provided.
Discovery Of Activities Via Statistical Clustering Of Fixation Patterns, Jeffrey B. Mulligan
Discovery Of Activities Via Statistical Clustering Of Fixation Patterns, Jeffrey B. Mulligan
MODVIS Workshop
No abstract provided.
Neural Computation Of Statistical Image Properties In Peripheral Vision, Christoph Zetzsche, Ruth Rosenholtz, Noshaba Cheema, Konrad Gadzicki, Lex Fridman
Neural Computation Of Statistical Image Properties In Peripheral Vision, Christoph Zetzsche, Ruth Rosenholtz, Noshaba Cheema, Konrad Gadzicki, Lex Fridman
MODVIS Workshop
No abstract provided.
Using Classification Images To Understand Models Of Lightness Perception, Minjung Kim, Jason M. Gold, Richard F. Murray
Using Classification Images To Understand Models Of Lightness Perception, Minjung Kim, Jason M. Gold, Richard F. Murray
MODVIS Workshop
No abstract provided.
Edge Integration And Image Segmentation In Lightness And Color: Computational And Neural Theory, Michael E. Rudd
Edge Integration And Image Segmentation In Lightness And Color: Computational And Neural Theory, Michael E. Rudd
MODVIS Workshop
No abstract provided.
Heuristics From Statistics—Modeling The Behavior And Perception Of Non-Rigid Materials, Vivian C. Paulun, Roland W. Fleming
Heuristics From Statistics—Modeling The Behavior And Perception Of Non-Rigid Materials, Vivian C. Paulun, Roland W. Fleming
MODVIS Workshop
No abstract provided.
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) …
‘Edge’ Integration Explains Contrast And Assimilation In A Gradient Lightness Illusion, Michael E. Rudd
‘Edge’ Integration Explains Contrast And Assimilation In A Gradient Lightness Illusion, Michael E. Rudd
MODVIS Workshop
In the ‘phantom’ illusion (Galmonte, Soranzo, Rudd, & Agostini, submitted), either an incremental or a decremental target, when surrounded by a luminance gradient, can to be made to appear as an increment or a decrement, depending on the gradient width. For wide gradients, incremental targets appear as increments and decremental targets appear as decrements. For narrow gradients, the reverse is true. Here, I model these phenomena with a two-stage neural lightness theory (Rudd, 2013, 2014) in which local steps in log luminance are first encoded by oriented spatial filters operating on a log-transformed version of the image; then the filter …
The Bounded Log-Odds Model Of Frequency And Probability Distortion, Hang Zhang, Laurence T. Maloney
The Bounded Log-Odds Model Of Frequency And Probability Distortion, Hang Zhang, Laurence T. Maloney
MODVIS Workshop
No abstract provided.