Cognitive Science Commons^™

Using Neural Networks To Better Understand Static And Dynamic Components Of Facial Expression Recognition, Yi-Fan Li, Anne Bibiana Sereno

MODVIS Workshop

Facial expressions are crucial social information for human communication. In the real world, facial expressions are dynamic; however, much of existing research in facial expressions relies on static stimuli. This static approach may limit the ecological validity of our understanding of the emotional information on faces. Our study aims to investigate the dynamic and static information contained in different emotional categories of facial expressions (e.g., happy, sad). Four convolutional neural networks are introduced as models to be trained with a large-scale dynamic facial expression dataset (short videoclips of 16 frames of 7 different emotional categories) in four different ways: ordered …

Bilateral Symmetric 3d Reconstructor (Biased): A 3-Dimensional Object Re- Construction Model Demonstrating Human-Like Shape Constancy, Doreen Hii

MODVIS Workshop

No abstract provided.

Euclidean Coordinates Are The Wrong Prior For Models Of Primate Vision, Garrison W. Cottrell

MODVIS Workshop

Convolutional Neural Networks (CNNs) are currently the best models we have of the ventral temporal lobe – the part of cortex engaged in recognizing objects. They have been effective at predicting the firing rates of neurons in monkey cortex, as well as fMRI and MEG responses in human subjects. They are based on several observations concerning the visual world: 1) pixels are most correlated with nearby pixels, leading to local receptive fields; 2) stationary statistics – the statistics of image pixels are relatively invariant across the visual field, leading to replicated features 3) objects do not change identity depending on …

Perceptual Grouping With Latent Noise, Ben Lonnqvist, Zhengqing Wu, Michael H. Herzog

MODVIS Workshop

Humans effortlessly group elements into objects and segment them from the background and other objects without supervision. For example, the black and white stripes of a zebra are grouped together despite vastly different colors. A thorough theoretical and empirical account of perceptual grouping is still missing – Deep Neural Networks (DNNs), which are considered leading models of the visual system still regularly fail at simplistic perceptual grouping tasks. Here, we propose a counterintuitive unsupervised computational approach to perceptual grouping and segmentation: that they arise because of neural noise, rather than in spite of it. We show that adding noise in …

Anisotropy In Non-Rigidity Perception: The Role Of Anisotropies In Neural Populations, Akihito Maruya, Qasim Zaidi

MODVIS Workshop

No abstract provided.

Feature Integration And Spatial Localization For Attention Across The Visual Hierarchy, Joyce Tam, Chloe Callahan-Flintoft, Brad Wyble

MODVIS Workshop

The featural and spatial specificity of visual representations broadly decrease along the ventral visual stream. The selection of behaviorally relevant information, or attention, must therefore establish spatial correspondence across the visual hierarchy while maintaining behavioral guidance to relevant visual features. Moreover, selection is often accompanied by an inhibitory surround as well as selective inhibition of distractor items. Considering these key functions, we describe a biologically realistic computational theory of visual selection and inhibition through feedforward and feedback signals along the ventral visual stream, complemented by feature-agnostic spatial competition in the pulvinar nuclei. Our model simulates signature visual search behaviors and …

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

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

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.

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.

Understanding The Influence Of Perceptual Noise On Visual Flanker Effects Through Bayesian Model Fitting, Jordan Deakin, Dietmar Heinke

MODVIS Workshop

No abstract provided.

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. …

Monocular 3d Reconstruction Of Polyhedral Shapes Via Neural Network, Mark Beers, Zygmunt Pizlo

MODVIS Workshop

No abstract provided.