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Social and Behavioral Sciences Commons™
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- Free-viewing (2)
- 3D motion (1)
- Active vision (1)
- Attention (1)
- Benchmarking (1)
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- Biased competition (1)
- Binding (1)
- Biologically-inspired eep learning (1)
- Computational Rationality (1)
- Computational modeling (1)
- Confidence ratings (1)
- Deep learning (1)
- Edge detection (1)
- FFA (1)
- Face recognition (1)
- Feedback (1)
- Fixational eye movements (1)
- Growth-cone (1)
- Information theory (1)
- Intuitive Physics (1)
- Maps (1)
- Material Perception (1)
- Model comparison (1)
- Motion mechanism (1)
- Object-based attention (1)
- Psychometric function (1)
- Psychophysics (1)
- Pyramidal cell (1)
- Real-world scenes (1)
- Rigidity and non-rigidity (1)
Articles 1 - 11 of 11
Full-Text Articles in Social and Behavioral Sciences
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.
A Signal Detection Model For The Analysis Of Continuous Response Gradients And An Application To Confidence Rating Data, Fabian A. Soto
A Signal Detection Model For The Analysis Of Continuous Response Gradients And An Application To Confidence Rating Data, Fabian A. Soto
MODVIS Workshop
see attached
Modelling Pairwise Comparisons For Thurstonian Scaling And Kendall Rank Correlation, Maarten Wijntjes
Modelling Pairwise Comparisons For Thurstonian Scaling And Kendall Rank Correlation, Maarten Wijntjes
MODVIS Workshop
Pairwise comparisons are a simple and effective way to measure the relative ordering of two samples. For more than two samples, a global ordering can be constructed and with sufficient data it is possible to construct a metric scale, for example by using Thurstonian scaling. We will discuss the concept of Number of Distinguishable Levels (NDLs) that emerges from a Thurstonian scaling procedure. The NDL is the attribute range in terms of Just Noticeable Differences (JNDs), for example the number of grayscale values. The NDL is either limited by the visual system or by the range of stimuli. The latter …
The Model 2.0 And Friends: An Interim Report, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni, Shashank Venkatramani, Yash Shah, Keyu Long, Xuzhe Zhi, Shivaank Agarwal, Cody Li, Jingyuan He, Thomas Fischer
The Model 2.0 And Friends: An Interim Report, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni, Shashank Venkatramani, Yash Shah, Keyu Long, Xuzhe Zhi, Shivaank Agarwal, Cody Li, Jingyuan He, Thomas Fischer
MODVIS Workshop
Last year, I reported on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. This year, I will report on new results and some variations on network architectures that we have explored, mainly as a way to generate discussion and get feedback. This is by no means a polished, final presentation!
We look forward to the group’s suggestions for these projects.
How Object Segmentation And Perceptual Grouping Emerge In Noisy Variational Autoencoders, Ben Lonnqvist, Zhengqing Wu, Michael H. Herzog
How Object Segmentation And Perceptual Grouping Emerge In Noisy Variational Autoencoders, Ben Lonnqvist, Zhengqing Wu, Michael H. Herzog
MODVIS Workshop
Many animals and humans can recognize and segment objects from their backgrounds. Whether object segmentation is necessary for object recognition has long been a topic of debate. Deep neural networks (DNNs) excel at object recognition, but not at segmentation tasks - this has led to the belief that object recognition and segmentation are separate mechanisms in visual processing. Here, however, we show evidence that in variational autoencoders (VAEs), segmentation and faithful representation of data can be interlinked. VAEs are encoder-decoder models that learn to represent independent generative factors of the data as a distribution in a very small bottleneck layer; …
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.
Evaluating Models Of Scanpath Prediction, Matthias Kümmerer, Matthias Bethge
Evaluating Models Of Scanpath Prediction, Matthias Kümmerer, Matthias Bethge
MODVIS Workshop
No abstract provided.
Modeling The Spread Of Object-Based Attention During Free Viewing, Nicolas Roth, Olga Shurygina, Flora Marleen Muscinelli, Klaus Obermayer, Martin Rolfs
Modeling The Spread Of Object-Based Attention During Free Viewing, Nicolas Roth, Olga Shurygina, Flora Marleen Muscinelli, Klaus Obermayer, Martin Rolfs
MODVIS Workshop
No abstract provided.
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 …
Object Rigidity: Competition And Cooperation Between Motion-Energy And Feature- Tracking Mechanisms And Shape-Based Priors, Akihito Maruya, Qasim Zaidi Dr.
Object Rigidity: Competition And Cooperation Between Motion-Energy And Feature- Tracking Mechanisms And Shape-Based Priors, Akihito Maruya, Qasim Zaidi Dr.
MODVIS Workshop
No abstract provided.
Efficient Perception Of Physical Object Properties With Visual Heuristics, Vivian C. Paulun, Florian S. Bayer, Joshua B. Tenenbaum, Roland W. Fleming
Efficient Perception Of Physical Object Properties With Visual Heuristics, Vivian C. Paulun, Florian S. Bayer, Joshua B. Tenenbaum, Roland W. Fleming
MODVIS Workshop
No abstract provided.