Summary of Gazing at Rewards: Eye Movements As a Lens Into Human and Ai Decision-making in Hybrid Visual Foraging, by Bo Wang et al.
Gazing at Rewards: Eye Movements as a Lens into Human and AI Decision-Making in Hybrid Visual Foraging
by Bo Wang, Dingwei Tan, Yen-Ling Kuo, Zhaowei Sun, Jeremy M. Wolfe, Tat-Jen Cham, Mengmi Zhang
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores how people make decisions when searching for multiple types of items, such as coins, in a hybrid foraging task. The authors conducted human psychophysics experiments to understand how target values and their prevalence influence foraging behaviors, revealing that humans are proficient reward foragers. They developed a transformer-based Visual Forager (VF) model trained via reinforcement learning to simulate human decision-making processes. The VF model outperforms baselines, achieves cumulative rewards comparable to those of humans, and approximates human foraging behavior in eye movements and foraging biases within time-limited environments. The model also demonstrates effective generalization in stress tests with novel targets, unseen values, and varying set sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine searching a collection of coins for different types, like quarters, dimes, nickels, and pennies. How do the values and frequencies of these coins affect how we search for them? To find out, scientists studied how people make decisions when searching for multiple items at once. They found that people are good at finding valuable items, and their eye movements and decision-making processes can be simulated using a special computer model called the Visual Forager (VF) model. The VF model is trained to make decisions like humans do, and it performs well in different situations. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning » Transformer
