scholarly journals Visual exploration dynamics are low-dimensional and driven by intrinsic factors

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andrea Zangrossi ◽  
Giorgia Cona ◽  
Miriam Celli ◽  
Marco Zorzi ◽  
Maurizio Corbetta
Keyword(s):  
Eye Movement ◽  
Latent Variables ◽  
Visual Exploration ◽  
Visual Images ◽  
Power Law Distribution ◽  
Intrinsic Factors ◽  
Image Saliency ◽  
Movement Dynamics ◽  
Low Dimensional ◽  
Intrinsic Dynamic

AbstractWhen looking at visual images, the eyes move to the most salient and behaviourally relevant objects. Saliency and semantic information significantly explain where people look. Less is known about the spatiotemporal properties of eye movements (i.e., how people look). We show that three latent variables explain 60% of eye movement dynamics of more than a hundred observers looking at hundreds of different natural images. The first component explaining 30% of variability loads on fixation duration, and it does not relate to image saliency or semantics; it approximates a power-law distribution of gaze steps, an intrinsic dynamic measure, and identifies observers with two viewing styles: static and dynamic. Notably, these viewing styles were also identified when observers look at a blank screen. These results support the importance of endogenous processes such as intrinsic dynamics to explain eye movement spatiotemporal properties.

When style matters: visual exploration is low dimensional and driven by intrinsic dynamics.

2021 ◽  
Author(s):  
Andrea Zangrossi ◽  
Giorgia Cona ◽  
Miriam Celli ◽  
Marco Zorzi ◽  
Maurizio Corbetta
Keyword(s):  
Eye Movements ◽  
Interindividual Variability ◽  
Visual Exploration ◽  
Specific Information ◽  
Power Law Distribution ◽  
Visual Content ◽  
Blank Screen ◽  
High Interindividual Variability ◽  
Low Dimensional ◽  
Degree Of Similarity

Abstract It is often assumed that we look at objects that are salient and behaviorally relevant, and that we pay attention differently depending on individual genetics, development, and experience. This view should imply high interindividual variability in eye movements. Conversely, we show that 60% of eye movements variance of more than a hundred observers looking at hundreds of different visual scenes could be summarized by a few components. The first component was not related to image-specific information and identified two kinds of observers during visual exploration: "static" and "dynamic". These viewing styles were accurately identifiable even when observers looked at a blank screen and were described by the degree of similarity to a power-law distribution of eye movements, which is thought to be a measure of intrinsic dynamics. This suggests that eye movements during visual exploration of real-world scenes are relatively independent of the visual content and may underlie intrinsic dynamics.

scholarly journals Interpretable Variational Graph Autoencoder with Noninformative Prior

2021 ◽  
Vol 13 (2) ◽  
pp. 51
Author(s):  
Lili Sun ◽  
Xueyan Liu ◽  
Min Zhao ◽  
Bo Yang
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Expert Knowledge ◽  
Structural Information ◽  
Standard Normal Distribution ◽  
Noninformative Prior ◽  
Latent Space ◽  
Distribution Parameters ◽  
Standard Normal ◽  
Low Dimensional

Variational graph autoencoder, which can encode structural information and attribute information in the graph into low-dimensional representations, has become a powerful method for studying graph-structured data. However, most existing methods based on variational (graph) autoencoder assume that the prior of latent variables obeys the standard normal distribution which encourages all nodes to gather around 0. That leads to the inability to fully utilize the latent space. Therefore, it becomes a challenge on how to choose a suitable prior without incorporating additional expert knowledge. Given this, we propose a novel noninformative prior-based interpretable variational graph autoencoder (NPIVGAE). Specifically, we exploit the noninformative prior as the prior distribution of latent variables. This prior enables the posterior distribution parameters to be almost learned from the sample data. Furthermore, we regard each dimension of a latent variable as the probability that the node belongs to each block, thereby improving the interpretability of the model. The correlation within and between blocks is described by a block–block correlation matrix. We compare our model with state-of-the-art methods on three real datasets, verifying its effectiveness and superiority.

scholarly journals Predictive learning as a network mechanism for extracting low-dimensional latent space representations

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Stefano Recanatesi ◽  
Matthew Farrell ◽  
Guillaume Lajoie ◽  
Sophie Deneve ◽  
Mattia Rigotti ◽  
...  
Keyword(s):  
Latent Variables ◽  
Latent Structure ◽  
Network Activity ◽  
Semantic Organization ◽  
Sequential Processing ◽  
Predictive Learning ◽  
Neural Representations ◽  
Latent Space ◽  
Low Dimensional ◽  
Sensory Prediction

AbstractArtificial neural networks have recently achieved many successes in solving sequential processing and planning tasks. Their success is often ascribed to the emergence of the task’s low-dimensional latent structure in the network activity – i.e., in the learned neural representations. Here, we investigate the hypothesis that a means for generating representations with easily accessed low-dimensional latent structure, possibly reflecting an underlying semantic organization, is through learning to predict observations about the world. Specifically, we ask whether and when network mechanisms for sensory prediction coincide with those for extracting the underlying latent variables. Using a recurrent neural network model trained to predict a sequence of observations we show that network dynamics exhibit low-dimensional but nonlinearly transformed representations of sensory inputs that map the latent structure of the sensory environment. We quantify these results using nonlinear measures of intrinsic dimensionality and linear decodability of latent variables, and provide mathematical arguments for why such useful predictive representations emerge. We focus throughout on how our results can aid the analysis and interpretation of experimental data.

Eye Movements and Mirror Neurons

2020 ◽  
pp. 89-103
Author(s):  
Giuseppe Iurato
Keyword(s):  
Eye Movements ◽  
Eye Movement ◽  
Universal Algebra ◽  
Natural Science ◽  
Mirror Neurons ◽  
Mirror Neuron System ◽  
Mirror Neuron ◽  
Phenomenological Theory ◽  
Neuron System ◽  
Movement Dynamics

Denotational mathematics, in the context of universal algebra, may provide algebraic structures that are able to formalize human eye movement dynamics with respect to Husserlian phenomenological theory, from which it is then possible to make briefly reference to some further relations with mirror neuron system and related topics. In this way, the authors have provided a first instance of fruitful application of socio-humanities (to be precise, philosophy and sociology) in exact/natural science used in formalizing processes.

scholarly journals Data Augmentation for Electricity Theft Detection Using Conditional Variational Auto-Encoder

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4291
Author(s):  
Xuejiao Gong ◽  
Bo Tang ◽  
Ruijin Zhu ◽  
Wenlong Liao ◽  
Like Song
Keyword(s):  
Latent Variables ◽  
Data Augmentation ◽  
Sampling Technique ◽  
Smart Meters ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Electricity Theft ◽  
Adversarial Network ◽  
Low Dimensional ◽  
Power Curves

Due to the strong concealment of electricity theft and the limitation of inspection resources, the number of power theft samples mastered by the power department is insufficient, which limits the accuracy of power theft detection. Therefore, a data augmentation method for electricity theft detection based on the conditional variational auto-encoder (CVAE) is proposed. Firstly, the stealing power curves are mapped into low dimensional latent variables by using the encoder composed of convolutional layers, and the new stealing power curves are reconstructed by the decoder composed of deconvolutional layers. Then, five typical attack models are proposed, and the convolutional neural network is constructed as a classifier according to the data characteristics of stealing power curves. Finally, the effectiveness and adaptability of the proposed method is verified by a smart meters’ data set from London. The simulation results show that the CVAE can take into account the shapes and distribution characteristics of samples at the same time, and the generated stealing power curves have the best effect on the performance improvement of the classifier than the traditional augmentation methods such as the random oversampling method, synthetic minority over-sampling technique, and conditional generative adversarial network. Moreover, it is suitable for different classifiers.

scholarly journals Collective dynamics of repeated inference in variational autoencoder rapidly find cluster structure

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yoshihiro Nagano ◽  
Ryo Karakida ◽  
Masato Okada
Keyword(s):  
Activity Pattern ◽  
Latent Variables ◽  
Cluster Structure ◽  
Generative Models ◽  
Cluster Center ◽  
Specific Data ◽  
Global Cluster ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Low Dimensional

Abstract Deep neural networks are good at extracting low-dimensional subspaces (latent spaces) that represent the essential features inside a high-dimensional dataset. Deep generative models represented by variational autoencoders (VAEs) can generate and infer high-quality datasets, such as images. In particular, VAEs can eliminate the noise contained in an image by repeating the mapping between latent and data space. To clarify the mechanism of such denoising, we numerically analyzed how the activity pattern of trained networks changes in the latent space during inference. We considered the time development of the activity pattern for specific data as one trajectory in the latent space and investigated the collective behavior of these inference trajectories for many data. Our study revealed that when a cluster structure exists in the dataset, the trajectory rapidly approaches the center of the cluster. This behavior was qualitatively consistent with the concept retrieval reported in associative memory models. Additionally, the larger the noise contained in the data, the closer the trajectory was to a more global cluster. It was demonstrated that by increasing the number of the latent variables, the trend of the approach a cluster center can be enhanced, and the generalization ability of the VAE can be improved.

scholarly journals Eye Movement Dynamics of Good and Poor Readers: Then and Now

1979 ◽  
Vol 11 (4) ◽  
pp. 319-328 ◽  
Author(s):  
Lester A. Lefton ◽  
Richard J. Nagle ◽  
Gwendolyn Johnson ◽  
Dennis F. Fisher
Keyword(s):  
Eye Movements ◽  
Eye Movement ◽  
Fifth Graders ◽  
Poor Readers ◽  
Movement Behavior ◽  
Poor Reading ◽  
Movement Dynamics ◽  
Reading Text ◽  
One Year ◽  
Good And Poor Readers

While reading text, the eye movements of good and poor reading fifth graders, third graders and adults were assessed. Subjects were tested in two sessions one year apart. Dependent variables included the duration and frequency of forward going fixations and regressions; an analysis of individual differences was also made. Results showed that poor reading fifth graders have relatively unsystematic eye movement behavior with many more fixations of longer duration than other fifth graders and adults. The eye movements of poor readers are quantitatively and qualitatively different than those of normal readers.

scholarly journals Application of Time-Scale Decomposition of Entropy for Eye Movement Analysis

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 168 ◽  
Author(s):  
Katarzyna Harezlak ◽  
Pawel Kasprowski
Keyword(s):  
Eye Movement ◽  
Time Scale ◽  
Movement Analysis ◽  
Approximate Entropy ◽  
Largest Lyapunov Exponent ◽  
Eye Movement Analysis ◽  
Movement Dynamics ◽  
Signal Characteristics ◽  
Time Scale Decomposition ◽  
Events Detection

The methods for nonlinear time series analysis were used in the presented research to reveal eye movement signal characteristics. Three measures were used: approximate entropy, fuzzy entropy, and the Largest Lyapunov Exponent, for which the multilevel maps (MMs), being their time-scale decomposition, were defined. To check whether the estimated characteristics might be useful in eye movement events detection, these structures were applied in the classification process conducted with the usage of the kNN method. The elements of three MMs were used to define feature vectors for this process. They consisted of differently combined MM segments, belonging either to one or several selected levels, as well as included values either of one or all the analysed measures. Such a classification produced an improvement in the accuracy for saccadic latency and saccade, when compared with the previously conducted studies using eye movement dynamics.

scholarly journals Effects of Multisine Signal Bandwidth on Eye Movement Dynamics

2019 ◽  
Vol 52 (19) ◽  
pp. 282-287
Author(s):  
Jasmijn Büskens ◽  
Johan J.M. Pel ◽  
Daan M. Pool
Keyword(s):  
Eye Movement ◽  
Movement Dynamics
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