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首页> 外文期刊>Vision Research: An International Journal in Visual Science >Automatic computation of an image's statistical surprise predicts performance of human observers on a natural image detection task.
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Automatic computation of an image's statistical surprise predicts performance of human observers on a natural image detection task.

机译：自动计算图像的统计惊喜预测人类观察者在自然图像检测任务上的表现。

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To understand the neural mechanisms underlying humans' exquisite ability at processing briefly flashed visual scenes, we present a computer model that predicts human performance in a Rapid Serial Visual Presentation (RSVP) task. The model processes streams of natural scene images presented at a rate of 20Hz to human observers, and attempts to predict when subjects will correctly detect if one of the presented images contains an animal (target). We find that metrics of Bayesian surprise, which models both spatial and temporal aspects of human attention, differ significantly between RSVP sequences on which subjects will detect the target (easy) and those on which subjects miss the target (hard). Extending beyond previous studies, we here assess the contribution of individual image features including color opponencies and Gabor edges. We also investigate the effects of the spatial location of surprise in the visual field, rather than only using a single aggregate measure. A physiologically plausible feed-forward system, which optimally combines spatial and temporal surprise metrics for all features, predicts performance in 79.5% of human trials correctly. This is significantly better than a baseline maximum likelihood Bayesian model (71.7%). We can see that attention as measured by surprise, accounts for a large proportion of observer performance in RSVP. The time course of surprise in different feature types (channels) provides additional quantitative insight in rapid bottom-up processes of human visual attention and recognition, and illuminates the phenomenon of attentional blink and lag-1 sparing. Surprise also reveals classical Type-B like masking effects intrinsic in natural image RSVP sequences. We summarize these with the discussion of a multistage model of visual attention.
机译：为了理解人类在短暂闪烁视觉场景的精致能力上的神经机制，我们提出了一个计算机模型，该模型可以在快速串行视觉呈现（RSVP）任务中预测人类性能。模型处理自然场景图像的流向人类观察者的速率为20Hz的速率，并试图预测受试者何时将正确检测出介绍的图像之一是否包含动物（目标）。我们发现，贝叶斯惊喜的指标是对人类注意力的空间和时间方面进行建模的指标，在​​RSVP序列之间存在显着差异，而RSVP序列将在该序列上检测到目标（容易）以及受试者错过目标的人（硬）。在以前的研究之外，我们在这里评估了各个图像特征在内的贡献，包括颜色对立和Gabor边缘。我们还研究了视野中惊喜空间位置的影响，而不仅仅是使用单个聚合度量。生理上合理的饲料前馈系统，最佳地结合了所有功能的空间和时间惊喜指标，可以正确地预测79.5％的人类试验中的性能。这比基线最大似然贝叶斯模型（71.7％）要好得多。我们可以将注意力视为令人惊讶的衡量，这是RSVP中观察者表现的很大比例。不同特征类型（频道）中惊喜的时间过程为人类视觉关注和识别的快速自下而上过程提供了更多的定量见解，并阐明了注意力眨眼和滞后1的现象。惊喜还揭示了自然图像RSVP序列中固有的固有的经典B型B。我们通过讨论视觉注意的多阶段模型来总结这些内容。

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