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

Abstract

The global neuronal workspace (GNW) model has inspired over two decades of hypothesis driven research on the neural basis consciousness. However, recent studies have reported findings that appear inconsistent with the predictions of the model. Further, the macroanatomical 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 the active inference framework -that captures central architectural elements of the GNW and that can 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 behaviour results observed in previous studies of inattentional blindness, and 2) the previously described 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..

An Integrated World Modeling Theory (IWMT) of Consciousness: Combining Integrated Information and Global Neuronal Workspace Theories With the Free Energy Principle and Active Inference Framework; Toward Solving the Hard Problem and Characterizing Agentic Causation

“…From an FEP-AI (and IWMT) perspective, these phase transitions may correspond to discrete updating and Bayesian model selection with respect to perception and action (Friston et al, 2012a ; Hohwy, 2012 ; Parr and Friston, 2018b ). GNWT has been increasingly described in terms of Bayesian inference (Dehaene, 2020 ; Mashour et al, 2020 ), including in a recently proposed Predictive Global Neuronal Workspace model (Whyte and Smith, 2020 ).…”

Cognitive Robots and the Conscious Mind: A Review of the Global Workspace Theory

“…In fact, it should be pointed out that this selection contains the models inspired by Baars' GWT, which are the most relevant in terms of the results obtained, their popularity, and future potential. Among these, however, the GNW also represents a theoretical model in its own right: its architecture has inspired subsequent theories such as those of Shanahan [24,25] and those of Whyte related to the predictive global neuronal workspace (PGNW) [26,27]. In light of this, it is possible to argue that, although Baars is the founder of GWT which is today the predominant theory in this field, Dehaene's architecture of GNW, being more specific [28] and less high-level, would be a proper variation of GWT.…”

A Step-by-Step Tutorial on Active Inference and its Application to Empirical Data

“…Now that we have a clear idea of how to specify a generative model of a behavioral task, and how to interpret the relevant outputs, we will now extend this foundation to build a hierarchical or 'deep temporal' model; for examples, see (Friston et al, 2018;Parr & Friston, 2017b;Smith, Lane, Parr, & Friston, 2019;Whyte & Smith, 2020). The steps are quite similar to what we've already covered, because this primarily just involves building two models, and then placing one below the other.…”

“…One could also specify several time points in each lower-level trial, such that higher-level states generate sequences or trajectories of state transitions at the lower level (i.e., within-trial). In previous work, hierarchical models have been used to model working memory, reading, visual consciousness, and emotional awareness, among other phenomena (Friston, Parr, et al, 2017;Friston et al, 2018;Hesp et al, 2020;Parr & Friston, 2017b, 2018bSmith, Lane, et al, 2019;Whyte & Smith, 2020). Hierarchical POMDPs also afford further opportunities for simulating neuronal processes.…”