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Diminishing Sensitivity and Absolute Difference in Value-driven Attention

Overview
Journal J Vis
Specialty Ophthalmology
Date 2022 Jan 20
PMID 35050309
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Abstract

Kim and Beck (2020b) demonstrated that value-driven attention is based on relative value rather than absolute value, suggesting that prospect theory is relevant to our understanding of value-driven attention. To further this understanding, the present study investigated the impacts of diminishing sensitivity on value-driven attention. According to diminishing sensitivity, changes in outcomes have greater impacts nearer the reference point of 0 than farther from the point. Thus, the difference between $1 and $100 looms larger than that between $901 and $1000, due to their different ratios (100/1 > 1000/901). However, according to the absolute difference hypothesis, the differences should have similar impacts due to the absolute differences being the same (100 - 1 = 1000 - 901). Experiment 1 investigated whether diminishing sensitivity operates in the modified value-driven attention paradigm while controlling the impact of absolute differences. In the training phase, 100-point and 1000-point color targets had references of 1-point and 901-point color targets, respectively. In the test phase, 100-point color distractors attracted attention more than 1000-point color distractors, supporting the diminishing sensitivity hypothesis. Experiment 2 examined the absolute difference hypothesis while controlling the impact of diminishing sensitivity. Contrary to the absolute difference hypothesis, the test phase showed that 1000-point color distractors (compared with 10-point colors for a 990 absolute difference in the training phase) failed to attract attention more than 100-point color distractors (compared with 1-point colors, for a 99 absolute difference). These results suggest that diminishing sensitivity rather than absolute difference influences value-driven attention, further supporting the relevance of prospect theory to value-driven attention.

Citing Articles

Value-driven attention and associative learning models: a computational simulation analysis.

Jeong J, Ju J, Kim S, Choi J, Cho Y Psychon Bull Rev. 2023; 30(5):1689-1706.

PMID: 37145388 DOI: 10.3758/s13423-023-02296-0.

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