The relationship between consciousness, understanding, and rationality

An active inference model of conscious access: How cognitive action selection reconciles the results of report and no-report paradigms

Cognitive theories of consciousness, such as global workspace theory and higher-order theories, posit that frontoparietal circuits play a crucial role in conscious access. However, recent studies using no-report paradigms have posed a challenge to cognitive theories by demonstrating conscious accessibility in the apparent absence of prefrontal cortex (PFC) activation. To address this challenge, this paper presents a computational model of conscious access, based upon active inference, that treats working memory gating as a cognitive action. We simulate a visual masking task and show that late P3b-like event-related potentials (ERPs), and increased PFC activity, are induced by the working memory demands of self-report generation. When reporting demands are removed, these late ERPs vanish and PFC activity is reduced. These results therefore reproduce, and potentially explain, results from no-report paradigms. However, even without reporting demands, our model shows that simulated PFC activity on visible stimulus trials still crosses the threshold for reportability - maintaining the link between PFC and conscious access. Therefore, our simulations show that evidence provided by no-report paradigms does not necessarily contradict cognitive theories of consciousness.

The predictive global neuronal workspace: A formal active inference model of visual consciousness

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.