Hegarty et al. (2010) demonstrated that, in a map-based task, top-down task demands influenced where participants looked on the page, and then salience influenced what information they attended to in the region of interest.
This pattern suggests that attention shifts elicited by spatially uninformative feature singleton cues are contingent upon the stimulus parameters specified in the top-down task set [cf. 61].
Therefore, while both child groups exhibited similar network structures, no evidence was found for top-down task influences.
A formidable "top-down" task is to reconstruct the tentative biochemistry of the Zn world using bioinformatics approaches.
This paper presents an approach for saliency detection able to emulate the integration of the top-down (task-controlled) and bottom-up (sensory information) processes involved in human visual attention.
These findings are inconsistent with the majority of previous work looking at top-down task-based effects on scene viewing (Foulsham & Underwood, 2007; Henderson et al., 2007; Henderson et al., 2013; Smith & Mital, 2013; Yarbus, 1967).
The optimization of synaptic gain by attention can come about by (Jamesian) voluntary direction of attention through top-down task-relevant cues or bottom-up 'pop-out' mechanisms that occur outside conscious control (Hommel et al., 2001), and even in spite of it (Munoz and Everling, 2004).
ξ i = w T x i (15) Figure 4 Top-down multi-task (TDMT) training procedure.
The idea of top-down multi-task (TDMT) learning is to subsequently train models for each node of the given taxonomy in a top-down manner, obtaining more specialized models while descending the taxonomy.
In the traditional top-down system, the task of learning technology is to help enact the decisions made at higher levels in the system.
Nevertheless, it has been suggested that at least part of the improvement in auditory discrimination with age could be due to developing use of top-down skills affecting task performance [2],[5].
