Abstract


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Click to download BibTeX data Clik to view abstract R. Carmi, P. Tseng, I. G. M. Cameron, D. P. Munoz, L. Itti, The impact of maturation and aging on mechanisms of attentional selection, In: Proc. Vision Science Society Annual Meeting (VSS07), May 2007.

Abstract: How do mechanisms of attentional selection change as people mature and age? To investigate this question, we tracked the eyes of 3 groups of human observers (children: 10-13, adults: 20-28, and elderly: 69-73) as they watched MTV-style video clips (30 s each) constructed from unrelated shots of natural scenes (2-4 s each). It was previously shown that jump cuts - abrupt transitions between shots - lead to stereotypical changes in the balance between bottom-up and top-down influences on attentional selection (http://journalofvision.org/6/9/4). Specifically, the impact of bottom-up influences peaks shortly after jump cuts, followed by monotonic decreases for up to 2.5 s. Here we investigated the effects of maturation and aging on the balance between bottom-up and top-down influences. We analyzed the input video clips with a bottom-up computer model of attentional selection, and probed the impact of bottom-up influences by quantifying the accuracy of the model in predicting saccade targets (>40,000 in total). We found that the overall impact of bottom-up influences increased monotonically as a function of age (>10 percent magnitude difference between adjacent age groups, p<<0.01). Temporal changes in the impact of bottom-up influences were highly conserved between the children and the adults, but differed substantially in the elderly. A straight-forward yet counter-intuitive interpretation of the results is that people become more bottom-up driven as they mature and age. Alternatively, jump cuts may affect attentional mechanisms differently in different ages, leading to more random selections by children and slower utilization of top-down information by the elderly.

Themes: Computational Modeling, Model of Bottom-Up Saliency-Based Visual Attention, Model of Top-Down Attentional Modulation, Human Eye-Tracking Research, Scene Understanding

 

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