View source for Visual salience

===Top-down modulation by task demands===

The basic mechanism just described can be strongly influenced
top-down. Behaviorally, this is manifested by the ability to more
efficiently ''guide search'' towards visual targets whose appearance
is known in advance (Wolfe, 1994; note the weights applied to each
feature map in Wolfe's figure below; also see Niebur & Koch, 1996), and to more efficiently ignore
irrelevant distractors. At the single-unit level and in functional
neuroimaging studies, top-down modulation of salience is apparent from
so-called ''feature-based attention'' whereby the neural
representation of neurons encoding for specific visual features of
current behavioral interest to the animal show enhanced activity
compared to baseline (Motter, 1994; Treue & Martinez-Trujillo, 1999; Saenz et al.,
2002).

<center>
{|border="1" cellpadding="4" cellspacing="0"
|- valign="top"
! [[Image:VisualSalience_GuidedSearch.png|Guided search.]]
|- valign="top"
! Guided Search model of J. Wolfe (1994). Note the weights applied to each feature map and determined top-down by task demands.
|}
</center>

One question which has remained outstanding until recently is how exactly to set the weights so as to promote the salience of arbitrarily complex targets in complex backgrounds. Computationally, Navalpakkam & Itti (2007) have proposed that the gain
of different neuronal populations sensitive to different features
(e.g., different edge orientations) will be relatively modulated
depending on the features of the desired targets and of the
distractors, so as to maximize the overall signal-to-noise ratio at
the level of the entire population. In simple cases, where targets and
distractors are differentiated in very obvious ways (e.g., a vertical
target among horizontal distractors), this optimal biasing theory
correlates well with intuition and single-unit observations (e.g.,
neurons responsive to vertical orientations should be boosted while
neurons responsive to horizontal should be suppressed). In more
complicated cases (e.g., with multimodal distributions of target
and/or distractor features, or when significant overlap exists between
the features of the targets and of the distractors), the theory
generalizes these results in a manner which has been shown to be
compatible with human behavior (Navalpakkam & Itti, 2007).