The global neuronal workspace (GNW) model has inspired over two decades of hypothesis-driven research on the neural basis of consciousness. However, recent studies have reported findings that are at odds with empirical predictions of the model. Further, the macro-anatomical focus of current GNW research has limited the specificity of predictions afforded by the model. In this paper we present a neurocomputational model – based on Active Inference – that captures central architectural elements of the GNW and is able to address these limitations. The resulting ‘predictive global workspace’ casts neuronal dynamics as approximating Bayesian inference, allowing precise, testable predictions at both the behavioural and neural levels of description. We report simulations demonstrating the model’s ability to reproduce: 1) the electrophysiological and behavioural results observed in previous studies of inattentional blindness; and 2) the previously introduced four-way taxonomy predicted by the GNW, which describes the relationship between consciousness, attention, and sensory signal strength. We then illustrate how our model can reconcile/explain (apparently) conflicting findings, extend the GNW taxonomy to include the influence of prior expectations, and inspire novel paradigms to test associated behavioural and neural predictions.

全球神经元工作空间 (GNW) 模型启发了超过 20 年的关于意识神经基础的假设驱动研究。然而，最近的研究报告的结果与模型的经验预测不一致。此外，当前 GNW 研究的宏观解剖学重点限制了模型提供的预测的特异性。在本文中，我们提出了一个基于主动推理的神经计算模型，该模型捕获了 GNW 的核心架构元素并能够解决这些限制。由此产生的“预测性全局工作空间”将神经元动力学视为近似贝叶斯推理，允许在描述的行为和神经水平上进行精确、可测试的预测。我们报告的模拟证明了该模型的重现能力：1) 在先前的失明研究中观察到的电生理学和行为学结果；2) 之前介绍的 GNW 预测的四向分类法，描述了意识、注意力和感觉信号强度之间的关系。然后，我们说明我们的模型如何协调/解释（显然）相互矛盾的发现，扩展 GNW 分类以包括先前预期的影响，并激发新的范式来测试相关的行为和神经预测。