A specific deep neural network SADE is used to extract the content features of the items.
The attributes in each case would be those features of the items that are of interest for the purpose of the study [1].
"Our technology is crawling the websites of brands and retailers and we extract the unique features of the items — either from the image or the description.
These familiar, real-world items inevitably came with semantic and associative 'baggage' making it difficult to achieve precise experimental control over numerous different features of the items which are often correlated with one another (Troiani et al., 2014) and could potentially confound any conclusions drawn (Sugiura et al., 2005).
In the NSEEV model, the salience of the target is influenced by both static features (contrast of the item of interest and the environment; see Itti & Koch, 2000) and dynamic or motion features (moment-to-moment changes of static salience; see Loy, Xiang, & Gong, 2012; Yantis & Jonides, 1990).
Thus, the visual features of the memory item, and not the semantic association between the memory item and the second exemplar, may have been what biased attention.
Unfortunately, it is not possible to determine the technical features of this item as CRT3 has not been released.
Consequently, participants in Experiment 2A, who had to remember the exact object image, would be encouraged to encode the visual features of the memory item.
Table 2 describes the psychometric features of the 13 items included in the PAM13-I.> Percentage of missing values per item was low and it ranged between 0.8%% (items 1, 3, 5, 13) and 2.1 % (item 8), as well as percentage of "Not Applicable" (ranging between 0.4 % and 2.6 %).
The main approach for overcoming some of the limitations of one-dimensional ordering of recorded information relies on extended description of its content and, for analog-form information, of some features of the physical items.
