Abstract Under numerous circumstances, humans recognize visual objects in their environment with remarkable response times and accuracy. Existing artificial visual object recognition systems have not yet surpassed human vision, especially in its universality of application. We argue that modeling the recognition process in an exclusive feedforward manner hinders those systems’ performance. To bridge that performance gap between them and human vision, we present a brief review of neuroscientific data, which suggests that considering an agent’s internal influences (from cognitive systems that peripherally interact with visual-perceptual processes) recognition can be improved. Then, we propose a model for visual object recognition which uses these systems’ information, such as affection, for generating expectation to prime the object recognition system, thus reducing its execution times. Later, an implementation of the model is described. Finally, we present and discuss an experiment and its results.

中文翻译：

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基于神经科学证据的使用期望启动的感知视觉单一对象识别系统的扩展模型

摘要 在许多情况下，人类以惊人的响应时间和准确度识别环境中的视觉对象。现有的人工视觉对象识别系统尚未超越人类视觉，特别是其应用的普遍性。我们认为，以独特的前馈方式对识别过程进行建模会阻碍这些系统的性能。为了弥合它们与人类视觉之间的性能差距，我们简要回顾了神经科学数据，这表明考虑到代理的内部影响（来自与视觉感知过程外围交互的认知系统），识别可以得到改善。然后，我们提出了一个视觉对象识别模型，该模型使用这些系统的信息，例如情感、用于生成期望以启动对象识别系统，从而减少其执行时间。稍后，将描述该模型的实现。最后，我们介绍并讨论了一个实验及其结果。