scispace - formally typeset

Topic

Salience (neuroscience)

About: Salience (neuroscience) is a(n) research topic. Over the lifetime, 3549 publication(s) have been published within this topic receiving 151206 citation(s). The topic is also known as: saliency & salient.
Papers
More filters

Journal ArticleDOI
Laurent Itti1, Christof Koch1, Ernst Niebur2Institutions (2)
Abstract: A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail.

9,639 citations


Journal ArticleDOI
TL;DR: Two distinct networks typically coactivated during functional MRI tasks are identified, anchored by dorsal anterior cingulate and orbital frontoinsular cortices with robust connectivity to subcortical and limbic structures, and an “executive-control network” that links dorsolateral frontal and parietal neocortices.
Abstract: Variations in neural circuitry, inherited or acquired, may underlie important individual differences in thought, feeling, and action patterns. Here, we used task-free connectivity analyses to isolate and characterize two distinct networks typically coactivated during functional MRI tasks. We identified a "salience network," anchored by dorsal anterior cingulate (dACC) and orbital frontoinsular cortices with robust connectivity to subcortical and limbic structures, and an "executive-control network" that links dorsolateral frontal and parietal neocortices. These intrinsic connectivity networks showed dissociable correlations with functions measured outside the scanner. Prescan anxiety ratings correlated with intrinsic functional connectivity of the dACC node of the salience network, but with no region in the executive-control network, whereas executive task performance correlated with lateral parietal nodes of the executive-control network, but with no region in the salience network. Our findings suggest that task-free analysis of intrinsic connectivity networks may help elucidate the neural architectures that support fundamental aspects of human behavior.

5,244 citations


Proceedings ArticleDOI
Xiaodi Hou1, Liqing Zhang1Institutions (1)
17 Jun 2007-
TL;DR: A simple method for the visual saliency detection is presented, independent of features, categories, or other forms of prior knowledge of the objects, and a fast method to construct the corresponding saliency map in spatial domain is proposed.
Abstract: The ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational modeling of this basic intelligent behavior still remains a challenge. This paper presents a simple method for the visual saliency detection. Our model is independent of features, categories, or other forms of prior knowledge of the objects. By analyzing the log-spectrum of an input image, we extract the spectral residual of an image in spectral domain, and propose a fast method to construct the corresponding saliency map in spatial domain. We test this model on both natural pictures and artificial images such as psychological patterns. The result indicate fast and robust saliency detection of our method.

3,204 citations


Journal ArticleDOI
TL;DR: A heuristic framework for linking the psychological and biological in psychosis is provided and it is proposed that a dysregulated, hyperdopaminergic state, at a "brain" level of description and analysis, leads to an aberrant assignment of salience to the elements of one's experience, at an "mind" level.
Abstract: OBJECTIVE: The clinical hallmark of schizophrenia is psychosis. The objective of this overview is to link the neurobiology (brain), the phenomenological experience (mind), and pharmacological aspects of psychosis-in-schizophrenia into a unitary framework. METHOD: Current ideas regarding the neurobiology and phenomenology of psychosis and schizophrenia, the role of dopamine, and the mechanism of action of antipsychotic medication were integrated to develop this framework. RESULTS: A central role of dopamine is to mediate the “salience” of environmental events and internal representations. It is proposed that a dysregulated, hyperdopaminergic state, at a “brain” level of description and analysis, leads to an aberrant assignment of salience to the elements of one’s experience, at a “mind” level. Delusions are a cognitive effort by the patient to make sense of these aberrantly salient experiences, whereas hallucinations reflect a direct experience of the aberrant salience of internal representations. Antipsyc...

2,159 citations


Proceedings ArticleDOI
01 Sep 2009-
TL;DR: This paper collects eye tracking data of 15 viewers on 1003 images and uses this database as training and testing examples to learn a model of saliency based on low, middle and high-level image features.
Abstract: For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene Where eye tracking devices are not a viable option, models of saliency can be used to predict fixation locations Most saliency approaches are based on bottom-up computation that does not consider top-down image semantics and often does not match actual eye movements To address this problem, we collected eye tracking data of 15 viewers on 1003 images and use this database as training and testing examples to learn a model of saliency based on low, middle and high-level image features This large database of eye tracking data is publicly available with this paper

1,903 citations


Network Information
Related Topics (5)
Feature (computer vision)

128.2K papers, 1.7M citations

87% related
Convolutional neural network

74.7K papers, 2M citations

87% related
Feature extraction

111.8K papers, 2.1M citations

87% related
Image segmentation

79.6K papers, 1.8M citations

86% related
Deep learning

79.8K papers, 2.1M citations

85% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2021175
2020282
2019312
2018309
2017306
2016251