Vision is widely understood as an inference problem. However, two contrasting
conceptions of the inference process have each been influential in research on
biological vision as well as the engineering of machine vision. The first
emphasizes bottom-up signal flow, describing vision as a largely feedforward,
discriminative inference process that filters and transforms the visual
information to remove irrelevant variation and represent behaviorally relevant
information in a format suitable for downstream functions of cognition and
behavioral control. In this conception, vision is driven by the sensory data,
and perception is direct because the processing proceeds from the data to the
latent variables of interest. The notion of "inference" in this conception is
that of the engineering literature on neural networks, where feedforward
convolutional neural networks processing images are said to perform inference.
The alternative conception is that of vision as an inference process in
Helmholtz's sense, where the sensory evidence is evaluated in the context of a
generative model of the causal processes giving rise to it. In this conception,
vision inverts a generative model through an interrogation of the evidence in a
process often thought to involve top-down predictions of sensory data to
evaluate the likelihood of alternative hypotheses. The authors include
scientists rooted in roughly equal numbers in each of the conceptions and
motivated to overcome what might be a false dichotomy between them and engage
the other perspective in the realm of theory and experiment. The primate brain
employs an unknown algorithm that may combine the advantages of both
conceptions. We explain and clarify the terminology, review the key empirical
evidence, and propose an empirical research program that transcends the
dichotomy and sets the stage for revealing the mysterious hybrid algorithm of
primate vision.

视觉被广泛理解为一个推理问题。本文介绍了两种对推理过程的对立概念，分别对生物视觉和机器视觉工程有影响。视觉可以被描述为一种主要是前馈式、区分式的推理过程，通过滤除视觉信息中的无关变化并以适合于认知和行为控制下游功能的格式来表示行为上相关的信息，由感官数据驱动，并且感知是直接的。另一种推理的概念是赫姆霍兹的观点，即视觉是通过对引起视觉的因果过程的生成模型进行证据评估的推理过程。原始证据通过对感觉数据的自上而下的预测来调查其可信度。作者包括科学家根植于这两种概念，并积极尝试克服它们之间可能是错误的二元对立，并在理论和实验证据上探讨另一种视角。灵长类动物大脑采用一种未知的算法，可能结合了这两种概念的优势。我们解释和阐明术语，回顾关键的实验证据，并提出了一个超越二元对立的实证研究计划，为揭示灵长类动物视觉的神秘混合算法铺平了道路。