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The salience (also called saliency) of an item – be it an object, a person, a pixel, etc. – is the state or quality by which it stands out relative to its neighbors. Saliency detection is considered to be a key attentional mechanism that facilitates learning and survival by enabling organisms to focus their limited perceptual and cognitive resources on the most pertinent subset of the available sensory data.
Saliency typically arises from contrasts between items and their neighborhood, such as a red dot surrounded by white dots, a flickering message indicator of an answering machine, or a loud noise in an otherwise quiet environment. Saliency detection is often studied in the context of the visual system, but similar mechanisms operate in other sensory systems. What is salient can be influenced by training: for example, for human subjects particular letters can become salient by training.
When attention deployment is driven by salient stimuli, it is considered to be bottom-up, memory-free, and reactive. Conversely, attention can also be guided by top-down, memory-dependent, or anticipatory mechanisms, such as when looking ahead of moving objects or sideways before crossing streets. Humans and other animals have difficulty paying attention to more than one item simultaneously, so they are faced with the challenge of continuously integrating and prioritizing different bottom-up and top-down influences.
The brain component named the hippocampus helps with the assessment of salience and context by using past memories to filter new incoming stimuli, and placing those that are most important into long term memory. The entorhinal cortex is the pathway into and out of the hippocampus, and is an important part of the brain's memory network; research shows that it is a brain region that suffers damage early on in Alzheimer's disease, ine of the effects of which is altered (diminished) salience. 
One group of neurons (i.e., D1-type medium spiny neurons) within the nucleus accumbens shell (NAcc shell) assigns appetitive motivational salience ("want" and "desire", which includes a motivational component), aka incentive salience, to rewarding stimuli, while another group of neurons (i.e., D2-type medium spiny neurons) within the NAcc shell assigns aversive motivational salience to aversive stimuli.
The term is widely used in the study of perception and cognition to refer to any aspect of a stimulus that, for any of many reasons, stands out from the rest. Salience may be the result of emotional, motivational or cognitive factors and is not necessarily associated with physical factors such as intensity, clarity or size. Although salience is thought to determine attentional selection, salience associated with physical factors does not necessarily influence selection of a stimulus.
Aberrant salience hypothesis of schizophrenia
Kapur (2003) proposed that a hyperdopaminergic state, at a "brain" level of description, leads to an aberrant assignment of salience to the elements of one's experience, at a "mind" level. These aberrant salience attributions have been associated with altered activities in the mesolimbic system, including the striatum, the amygdala, the hippocampus, and the parahippocampal gyrus.  Dopamine mediates the conversion of the neural representation of an external stimulus from a neutral bit of information into an attractive or aversive entity, i.e. a salient event. Symptoms of schizophrenia may arise out of 'the aberrant assignment of salience to external objects and internal representations', and antipsychotic medications reduce positive symptoms, by attenuating aberrant motivational salience, via blockade of the dopamine D2 receptors (Kapur, 2003).
Visual saliency modeling
In the domain of psychology, efforts have been made in modeling the mechanism of human attention, including the learning of prioritizing the different bottom-up and top-down influences.
In the domain of computer vision, efforts have been made in modeling the mechanism of human attention, especially the bottom-up attentional mechanism. Such a process is also called visual saliency detection.
Generally speaking, there are two kinds of models to mimic the bottom-up saliency mechanism. One way is based on the spatial contrast analysis. For example, a center-surround mechanism is used to define saliency across scales, which is inspired by the putative neural mechanism.  The other way is based on the frequency domain analysis. This method was first proposed by Hou et al. While they used the amplitude spectrum to assign saliency to rarely occurring magnitudes, Guo et al. use the phase spectrum instead. Recently, Li et al. introduced a system that uses both the amplitude and the phase information.
A key limitation in many such approaches is their computational complexity which produces less than real-time performance, even on modern computer hardware. Some recent work attempts to overcome these issues but at the expense of saliency detection quality under some conditions. Other work suggests that saliency and associated speed-accuracy phenomena may be a fundamental mechanisms of recognition determined during recognition through gradient descent and does not have to be spatial in nature.
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VTA DA neurons play a critical role in motivation, reward-related behavior (Chapter 15), attention, and multiple forms of memory. This organization of the DA system, wide projection from a limited number of cell bodies, permits coordinated responses to potent new rewards. Thus, acting in diverse terminal fields, dopamine confers motivational salience (“wanting”) on the reward itself or associated cues (nucleus accumbens shell region), updates the value placed on different goals in light of this new experience (orbital prefrontal cortex), helps consolidate multiple forms of memory (amygdala and hippocampus), and encodes new motor programs that will facilitate obtaining this reward in the future (nucleus accumbens core region and dorsal striatum). In this example, dopamine modulates the processing of sensorimotor information in diverse neural circuits to maximize the ability of the organism to obtain future rewards. ...
The brain reward circuitry that is targeted by addictive drugs normally mediates the pleasure and strengthening of behaviors associated with natural reinforcers, such as food, water, and sexual contact. Dopamine neurons in the VTA are activated by food and water, and dopamine release in the NAc is stimulated by the presence of natural reinforcers, such as food, water, or a sexual partner. ...
The NAc and VTA are central components of the circuitry underlying reward and memory of reward. As previously mentioned, the activity of dopaminergic neurons in the VTA appears to be linked to reward prediction. The NAc is involved in learning associated with reinforcement and the modulation of motoric responses to stimuli that satisfy internal homeostatic needs. The shell of the NAc appears to be particularly important to initial drug actions within reward circuitry; addictive drugs appear to have a greater effect on dopamine release in the shell than in the core of the NAc.
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Recent evidence indicates that inactivation of D2 receptors, in the indirect striatopallidal pathway in rodents, is necessary for both acquisition and expression of aversive behavior, and direct pathway D1 receptor activation controls reward-based learning (Hikida et al., 2010; Hikida et al., 2013). It seems we can conclude that direct and indirect pathways of the NAc, via D1 and D2 receptors, subserve distinct anticipation and valuation roles in the shell and core of NAc, which is consistent with observations regarding spatial segregation and diversity of responses of midbrain dopaminergic neurons for rewarding and aversive conditions, some encoding motivational value, others motivational salience, each connected with distinct brain networks and having distinct roles in motivational control (Bromberg-Martin et al., 2010; Cohen et al., 2012; Lammel et al., 2013). ... Thus, the previous results, coupled with the current observations, imply that the NAc pshell response reflects a prediction/anticipation or salience signal, and the NAc pcore response is a valuation response (reward predictive signal) that signals the negative reinforcement value of cessation of pain (i.e., anticipated analgesia).
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