Katti Harish, Peelen Marius V, Arun S P
Centre for Neuroscience, Indian Institute of Science, Bangalore, 560012, India.
Center for Mind/Brain Sciences, University of Trento, 38068, Rovereto, Italy.
Atten Percept Psychophys. 2017 Oct;79(7):2021-2036. doi: 10.3758/s13414-017-1359-9.
Humans excel at finding objects in complex natural scenes, but the features that guide this behaviour have proved elusive. We used computational modeling to measure the contributions of target, nontarget, and coarse scene features towards object detection in humans. In separate experiments, participants detected cars or people in a large set of natural scenes. For each scene, we extracted target-associated features, annotated the presence of nontarget objects (e.g., parking meter, traffic light), and extracted coarse scene structure from the blurred image. These scene-specific values were then used to model human reaction times for each novel scene. As expected, target features were the strongest predictor of detection times in both tasks. Interestingly, target detection time was additionally facilitated by coarse scene features but not by nontarget objects. In contrast, nontarget objects predicted target-absent responses in both person and car tasks, with contributions from target features in the person task. In most cases, features that speeded up detection tended to slow down rejection. Taken together, these findings demonstrate that humans show systematic variations in object detection that can be understood using computational modeling.
人类擅长在复杂的自然场景中找到物体，但引导这种行为的特征却难以捉摸。我们使用计算模型来衡量目标、非目标和粗略场景特征对人类物体检测的贡献。在单独的实验中，参与者在大量自然场景中检测汽车或人物。对于每个场景，我们提取与目标相关的特征，标注非目标物体（如停车计时器、交通信号灯）的存在，并从模糊图像中提取粗略的场景结构。然后，这些特定于场景的值被用于模拟每个新场景中人类的反应时间。正如预期的那样，目标特征是两项任务中检测时间的最强预测指标。有趣的是，粗略的场景特征会额外促进目标检测时间，但非目标物体则不会。相比之下，非目标物体在人物和汽车任务中都预测了目标不存在的反应，在人物任务中还有目标特征的贡献。在大多数情况下，加速检测的特征往往会减慢排除反应。综上所述，这些发现表明，人类在物体检测中表现出系统的变化，可以通过计算模型来理解。
