Predicting Stimulus-Driven Attentional Selection Within Mobile Interfaces
被引:0
作者:
Still, Jeremiah D.
论文数: 0引用数: 0
h-index: 0
机构:
Old Dominion Univ, Dept Psychol, Norfolk, VA 23529 USAOld Dominion Univ, Dept Psychol, Norfolk, VA 23529 USA
Still, Jeremiah D.
[1
]
Hicks, John
论文数: 0引用数: 0
h-index: 0
机构:
Old Dominion Univ, Dept Psychol, Norfolk, VA 23529 USAOld Dominion Univ, Dept Psychol, Norfolk, VA 23529 USA
Hicks, John
[1
]
Cain, Ashley
论文数: 0引用数: 0
h-index: 0
机构:
Old Dominion Univ, Dept Psychol, Norfolk, VA 23529 USAOld Dominion Univ, Dept Psychol, Norfolk, VA 23529 USA
Cain, Ashley
[1
]
Billman, Dorrit
论文数: 0引用数: 0
h-index: 0
机构:
San Jose State Univ, Res Fdn, San Jose, CA 95192 USAOld Dominion Univ, Dept Psychol, Norfolk, VA 23529 USA
Billman, Dorrit
[2
]
机构:
[1] Old Dominion Univ, Dept Psychol, Norfolk, VA 23529 USA
[2] San Jose State Univ, Res Fdn, San Jose, CA 95192 USA
来源:
ADVANCES IN NEUROERGONOMICS AND COGNITIVE ENGINEERING (AHFE 2017)
|
2018年
/
586卷
关键词:
Human-computer interaction;
Mobile interface;
Cognitive engineering;
Saliency model;
Visual search;
D O I:
10.1007/978-3-319-60642-2_24
中图分类号:
TP18 [人工智能理论];
学科分类号:
081104 ;
0812 ;
0835 ;
1405 ;
摘要:
Masciocchi and Still [1] suggested that biologically inspired computational saliency models could predict attentional deployment within webpages. Their stimuli were presented on a large desktop monitor. We explored whether a saliency model's predictive performance can be applied to small mobile interface displays. We asked participants to free-view screenshots of NASA's mobile application Playbook. The Itti et al. [2] saliency model was employed to produce the predictive stimulus-driven maps. The first six fixations were used to select values to form the saliency maps' bins, which formed the observed distribution. This was compared to the shuffled distribution, which offers a very conservative chance comparison as it includes predictable spatial biases by using a within-subjects bootstrapping technique. The observed distribution values were higher than the shuffled distribution. This suggests that a saliency model was able to predict the deployment of attention within small mobile application interfaces.