Attention to a threat-related feature does not interfere with concurrent attentive feature selection

AbstractVisual features that are associated with a task and those that predict noxious events both prompt selectively heightened visuocortical responses. Conflicting views exist regarding how the competition between a task-related and a threat-related feature is resolved when they co-occur in time and space. Utilizing aversive differential Pavlovian conditioning, we investigated the visuocortical representation of two simultaneously presented, fully overlapping visual stimuli. Stimuli were isoluminant red and green random dot kinematograms (RDKs) which flickered at two tagging frequencies (8.57 Hz, 12 Hz) to elicit distinguishable steady-state visual evoked potentials (ssVEPs). Occasional coherent motion events prompted a motor response or predicted a noxious noise. These events occurred either in the green (task cue), the red (threat cue), or in both RDKs simultaneously. In an initial habituation phase, participants responded to coherent motion of the green RDK with a key press, but no loud noise was presented at any time. Here, selective amplification was seen for the task-relevant (green) RDK, but interference was observed when both RDKs simultaneously showed coherent motion. Upon pairing the threat cue with the noxious noise in the subsequent acquisition phase, the threat cue-evoked ssVEP (red RDK) was also amplified, but this amplification did not interact with amplification of the task cue, and did not alter the behavioral or visuocortical interference effect seen during simultaneous coherent motion. Results demonstrate that although competing feature conjunctions result in interference in visual cortex, the acquisition of a bias towards an individual threat-related feature does not result in additional cost effects.

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Spatial attention modulates steady state visually-evoked potentials in human visual cortex by multiplicative gain function.

NeuroImage ◽

10.1016/s1053-8119(09)70360-x ◽

2009 ◽

Vol 47 ◽

pp. S66

Author(s):

T.Z. Lauritzen ◽

A.R. Wade

Keyword(s):

Visual Cortex ◽

Steady State ◽

Evoked Potentials ◽

Spatial Attention ◽

Visually Evoked Potentials ◽

Gain Function ◽

Human Visual Cortex

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Enhancement of email spam detection using improved deep learning algorithms for cyber security

Journal of Computer Security ◽

10.3233/jcs-200111 ◽

2021 ◽

pp. 1-34

Author(s):

Kadam Vikas Samarthrao ◽

Vandana M. Rohokale

Keyword(s):

Feature Selection ◽

Deep Learning ◽

Visual Features ◽

Spam Detection ◽

Learning Approaches ◽

Learning Technique ◽

Text Features ◽

Optimal Feature Selection ◽

Optimal Feature ◽

Email Spam

Email has sustained to be an essential part of our lives and as a means for better communication on the internet. The challenge pertains to the spam emails residing a large amount of space and bandwidth. The defect of state-of-the-art spam filtering methods like misclassification of genuine emails as spam (false positives) is the rising challenge to the internet world. Depending on the classification techniques, literature provides various algorithms for the classification of email spam. This paper tactics to develop a novel spam detection model for improved cybersecurity. The proposed model involves several phases like dataset acquisition, feature extraction, optimal feature selection, and detection. Initially, the benchmark dataset of email is collected that involves both text and image datasets. Next, the feature extraction is performed using two sets of features like text features and visual features. In the text features, Term Frequency-Inverse Document Frequency (TF-IDF) is extracted. For the visual features, color correlogram and Gray-Level Co-occurrence Matrix (GLCM) are determined. Since the length of the extracted feature vector seems to the long, the optimal feature selection process is done. The optimal feature selection is performed by a new meta-heuristic algorithm called Fitness Oriented Levy Improvement-based Dragonfly Algorithm (FLI-DA). Once the optimal features are selected, the detection is performed by the hybrid learning technique that is composed of two deep learning approaches named Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). For improving the performance of existing deep learning approaches, the number of hidden neurons of RNN and CNN is optimized by the same FLI-DA. Finally, the optimized hybrid learning technique having CNN and RNN classifies the data into spam and ham. The experimental outcomes show the ability of the proposed method to perform the spam email classification based on improved deep learning.

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Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2020-0013 ◽

2020 ◽

Vol 16 (2) ◽

Author(s):

Stanisław Karkosz ◽

Marcin Jukiewicz

Keyword(s):

Genetic Algorithm ◽

Feature Selection ◽

Steady State ◽

Brain Computer Interface ◽

Morphological Features ◽

Computer Interface ◽

Visually Evoked Potentials ◽

Computer Interfaces ◽

Subject Variability ◽

Selection For

AbstractObjectivesOptimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.MethodsSystem of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI).ResultsThe designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects.ConclusionsIt is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

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Processing of the S-cone signals in the early visual cortex of primates

Visual Neuroscience ◽

10.1017/s0952523813000278 ◽

2013 ◽

Vol 31 (2) ◽

pp. 189-195 ◽

Cited By ~ 8

Author(s):

Youping Xiao

Keyword(s):

Visual Cortex ◽

Color Vision ◽

Short Wavelength ◽

Nonhuman Primates ◽

Striate Cortex ◽

Color Perception ◽

Visual Pathway ◽

Visual Features ◽

Area V2 ◽

Early Visual Cortex

AbstractThe short-wavelength-sensitive (S) cones play an important role in color vision of primates, and may also contribute to the coding of other visual features, such as luminance and motion. The color signals carried by the S cones and other cone types are largely separated in the subcortical visual pathway. Studies on nonhuman primates or humans have suggested that these signals are combined in the striate cortex (V1) following a substantial amplification of the S-cone signals in the same area. In addition to reviewing these studies, this review describes the circuitry in V1 that may underlie the processing of the S-cone signals and the dynamics of this processing. It also relates the interaction between various cone signals in V1 to the results of some psychophysical and physiological studies on color perception, which leads to a discussion of a previous model, in which color perception is produced by a multistage processing of the cone signals. Finally, I discuss the processing of the S-cone signals in the extrastriate area V2.

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Early Visual Cortex Dynamics during Top–Down Modulated Shifts of Feature-Selective Attention

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_00912 ◽

2016 ◽

Vol 28 (4) ◽

pp. 643-655 ◽

Cited By ~ 4

Author(s):

Matthias M. Müller ◽

Mireille Trautmann ◽

Christian Keitel

Keyword(s):

Visual Cortex ◽

Selective Attention ◽

Time Course ◽

Temporal Dynamics ◽

Behavioral Data ◽

Coherent Motion ◽

Neural Dynamics ◽

Early Visual Cortex ◽

Time Courses ◽

Feature Dimension

Shifting attention from one color to another color or from color to another feature dimension such as shape or orientation is imperative when searching for a certain object in a cluttered scene. Most attention models that emphasize feature-based selection implicitly assume that all shifts in feature-selective attention underlie identical temporal dynamics. Here, we recorded time courses of behavioral data and steady-state visual evoked potentials (SSVEPs), an objective electrophysiological measure of neural dynamics in early visual cortex to investigate temporal dynamics when participants shifted attention from color or orientation toward color or orientation, respectively. SSVEPs were elicited by four random dot kinematograms that flickered at different frequencies. Each random dot kinematogram was composed of dashes that uniquely combined two features from the dimensions color (red or blue) and orientation (slash or backslash). Participants were cued to attend to one feature (such as color or orientation) and respond to coherent motion targets of the to-be-attended feature. We found that shifts toward color occurred earlier after the shifting cue compared with shifts toward orientation, regardless of the original feature (i.e., color or orientation). This was paralleled in SSVEP amplitude modulations as well as in the time course of behavioral data. Overall, our results suggest different neural dynamics during shifts of attention from color and orientation and the respective shifting destinations, namely, either toward color or toward orientation.

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Steady-state visually evoked potentials reveal partial size constancy in early visual cortex

Journal of Vision ◽

10.1167/19.6.8 ◽

2019 ◽

Vol 19 (6) ◽

pp. 8 ◽

Cited By ~ 1

Author(s):

Jing Chen ◽

Meaghan McManus ◽

Matteo Valsecchi ◽

Laurence R. Harris ◽

Karl R. Gegenfurtner

Keyword(s):

Visual Cortex ◽

Steady State ◽

Evoked Potentials ◽

Size Constancy ◽

Visually Evoked Potentials ◽

Early Visual Cortex

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Steady-State Visual Evoked Potentials Elicited from Early Visual Cortex Reflect Both Perceptual Color Space and Cone-Opponent Mechanisms

Cerebral Cortex Communications ◽

10.1093/texcom/tgaa059 ◽

2020 ◽

Vol 1 (1) ◽

Author(s):

Sae Kaneko ◽

Ichiro Kuriki ◽

Søren K Andersen

Keyword(s):

Visual Cortex ◽

Steady State ◽

Evoked Potentials ◽

Visual Evoked Potentials ◽

Color Space ◽

Color Contrast ◽

Response Latencies ◽

Opponent Color ◽

Early Visual Cortex ◽

Visual Evoked

Abstract Colors are represented in the cone-opponent signals, L-M versus S cones, at least up to the level of inputs to the primary visual cortex. We explored the hue selective responses in early cortical visual areas through recordings of steady-state visual evoked potentials (SSVEPs), elicited by a flickering checkerboard whose color smoothly swept around the hue circle defined in a cone-opponent color space. If cone opponency dominates hue representation in the source of SSVEP signals, SSVEP amplitudes as a function of hue should form a profile that is line-symmetric along the cardinal axes of the cone-opponent color space. Observed SSVEP responses were clearly chromatic ones with increased SSVEP amplitudes and reduced response latencies for higher contrast conditions. The overall elliptic amplitude profile was significantly tilted away from the cardinal axes to have the highest amplitudes in the “lime-magenta” direction, indicating that the hue representation in question is not dominated by cone-opponency. The observed SSVEP amplitude hue profile was better described as a summation of a perceptual response and cone-opponent responses with a larger weight to the former. These results indicate that hue representations in the early visual cortex, measured by the SSVEP technique, are possibly related to perceptual color contrast.

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