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Gottlieb et al. found that the most salient and the most task-relevant visual stimuli evoke the greatest response in LIP.

Mysore and Knudsen say that deep SC neurons respond to relative saliency of a stimulus, i.e. to the saliency of stimuli in their receptive fields compared to the saliency of stimuli outside their receptive fields.

The superficial SC is modeled by Casey et al.'s system by two populations of center-on and center-off cells (whose receptive fields are modeled by a difference of Gaussians) and four populations of direction-sensitive cells.

Saliency of a stimulus might say something about its likelihood of offering reward if attended to.

The probability of reward of attending a stimuli is influenced by two factors:

  • the probability of selecting the right thing,
    Thus, highly distinctive things have great bottom-up saliency.
  • the probability of reward given that the right thing is selected,
    • Features that are associated with high reward salient
    • Goals can affect which features promise reward in a situation.

Visual feature combinations become more salient if they are learned to be associated with reward.

Targets which are selected in one trial tend to be more salient in subsequent trials—they are selected faster and rejected slower.

The extent of this effect is modulated by whether or not the selection was rewarded.

Anderson argues that it is not the selection process that is influenced by reward but saliency evaluation (ie. attentional priority of a stimulus).

Verschure summarizes version VII of his distributed adaptive control model as "a unifying theory" of perception cognition, and action. He states that it uses a learned world model in its contextual layer which biases perception processing (top-down) on the one hand, and saliency (bottom-up) on the other. Between these to appears to be what he calls the validation gate which defines matching and mismatch between world model and percepts.

If there are a number of stimuli, many of which share the same low-level features, then that stimulus that does not "pops out". Local saliency-based models like the one due to Itty and Koch fail to explain this effect.

De Kamps and van der Velde argue for combinatorial productivity and systematicity as fundamental concepts for cognitive representations. They introduce a neural blackboard architecture which implements these principles for visual processing and in particular for object-based attention.

An image is highly salient where

  • there is high contrast,
  • there is high variance,
  • it has distinctive higher-order statistics,
  • there is high local symmetry.

Some authors see the lower stages of visual processing as implementing an inverse model of optics—a model deriving causes from sensations and higher stages as implementing a forward model—a model generating expected sensations from assumed causes.

LIP has been suggested to contain a saliency map of the visual field, to guide visual attention, and to decide about saccades.

Saccade targets tend to be the centers of objects.

When reading, preferred viewing locations (PVL)—the centers of the distributions of fixation targets---are typically located slightly left of the center of words.

Contrast sensitivity is an important feature of early visual processing.

Spatial frequency carries a lot of information about a visual image.

In the pop-out condition of a visual search task, Buschman and Miller found that neurons in the posterior parietal cortex region LIP found the search target earlier than neurons in frontal cortex regions FEF and LPFC.

In the pure visual search condition of a visual search task, Buschman and Miller found that neurons in frontal cortex regions FEF and LPFC found the search target earlier than neurons in the posterior parietal cortex region LIP.

Visual attention is influenced both by local and global saliency, ie. bottom-up processes, and by semantics, ie. top-down processes.

According to Spratling's model, saliency arises from unexpected features in a scene.

Predictive coding and biased competition are closely related concepts. Spratling combines them in his model and uses it to explain visual saliency.