人类用简单规则做视觉搜索，竟比理论最优还准。
Optimal Visual Search with Highly Heuristic Decision Rules
- 用固定启发式规则就能接近最优搜索表现
- 中央目标位置受视网膜中心忽视影响最大
- 神经噪声的空间相关性让表现超预期
视觉搜索是人类和其他动物的基本认知任务。本研究考察了在短暂呈现、目标位置明显分离的隐性（单注视）搜索中，人类的决策过程。将人类表现与假设各潜在目标位置信息独立时的贝叶斯最优决策过程进行比较，结果令人惊讶：尽管人类在中央视野敏感度显著下降（视网膜中心忽视），且大脑几乎不可能完成最优计算，但其表现仍略优于最优水平。我们证明三个因素可定量解释这一看似矛盾的结果：最重要的是，简单的固定启发式决策规则即可达到近似最优性能；其次，视网膜中心忽视主要影响中央潜在目标位置；最后，空间相关的神经噪声会导致搜索表现超过独立噪声预测值。这些发现对理解人类及其他动物的视觉搜索和识别任务具有广泛意义。
原文摘要 · Abstract (English)
Visual search is a fundamental natural task for humans and other animals. We investigated the decision processes humans use in covert (single-fixation) search with briefly presented displays having well-separated potential target locations. Performance was compared with the Bayesian-optimal decision process under the assumption that the information from the different potential target locations is statistically independent. Surprisingly, humans performed slightly better than optimal, despite humans' substantial loss of sensitivity in the fovea (foveal neglect), and the implausibility of the human brain replicating the optimal computations. We show that three factors can quantitatively explain these seemingly paradoxical results. Most importantly, simple and fixed heuristic decision rules reach near optimal search performance. Secondly, foveal neglect primarily affects only the central potential target location. Finally, spatially correlated neural noise can cause search performance to exceed that predicted for independent noise. These findings have broad implications for understanding visual search tasks and other identification tasks in humans and other animals.
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