Swammerdam Institute for Life Sciences, Center for Neuroscience, Faculty of Science, University of Amsterdam, the Netherlands; Research Priority Program Brain and Cognition, University of Amsterdam, the Netherlands.
Behav Brain Res. 2022 Aug 26;432:113969. doi: 10.1016/j.bbr.2022.113969. Epub 2022 Jun 16.
This review provides an update on Neurorepresentationalism, a theoretical framework that defines conscious experience as multimodal, situational survey and explains its neural basis from brain systems constructing best-guess representations of sensations originating in our environment and body (Pennartz, 2015). It posits that conscious experience is characterized by five essential hallmarks: (i) multimodal richness, (ii) situatedness and immersion, (iii) unity and integration, (iv) dynamics and stability, and (v) intentionality. Consciousness is furthermore proposed to have a biological function, framed by the contrast between reflexes and habits (not requiring consciousness) versus goal-directed, planned behavior (requiring multimodal, situational survey). Conscious experience is therefore understood as a sensorily rich, spatially encompassing representation of body and environment, while we nevertheless have the impression of experiencing external reality directly. Contributions to understanding neural mechanisms underlying consciousness are derived from models for predictive processing, which are trained in an unsupervised manner, do not necessarily require overt action, and have been extended to deep neural networks. Even with predictive processing in place, however, the question remains why this type of neural network activity would give rise to phenomenal experience. Here, I propose to tackle the Hard Problem with the concept of multi-level representations which emergently give rise to multimodal, spatially wide superinferences corresponding to phenomenal experiences. Finally, Neurorepresentationalism is compared to other neural theories of consciousness, and its implications for defining indicators of consciousness in animals, artificial intelligence devices and immobile or unresponsive patients with disorders of consciousness are discussed.
这篇综述更新了神经表象论，这一理论框架将意识经验定义为多模态、情境调查，并从构建源自我们环境和身体的感觉的最佳猜测表象的大脑系统中解释其神经基础（Pennartz，2015）。它假设意识经验具有五个基本特征：（i）多模态丰富性，（ii）情境性和沉浸感，（iii）统一性和整体性，（iv）动态性和稳定性，以及（v）意向性。意识还被认为具有生物学功能，这是由反射和习惯（不需要意识）与目标导向、计划行为（需要多模态、情境调查）之间的对比来定义的。因此，意识经验被理解为身体和环境的丰富感觉、空间上全面的表象，而我们却有一种直接体验外部现实的印象。对理解意识神经机制的贡献来自于预测处理模型，这些模型是在无监督的情况下进行训练的，不一定需要明显的行动，并已扩展到深度神经网络。然而，即使有了预测处理，问题仍然是为什么这种类型的神经网络活动会产生现象体验。在这里，我提议用多层次表象的概念来解决“难题”，这些表象会突然产生与现象体验相对应的多模态、空间广泛的超推断。最后，将神经表象论与其他意识的神经理论进行了比较，并讨论了其对定义动物、人工智能设备和意识障碍患者的意识指标的影响。
