Conscious Processing and the Global Neuronal Workspace Hypothesis

Abstract

We review the central tenets and neuroanatomical basis of the global neuronal workspace (GNW) hypothesis, which attempts to account for the main scientific observations regarding the elementary mechanisms of conscious processing in the human brain. The GNW hypothesis proposes that, in the conscious state, a non-linear network ignition associated with recurrent processing amplifies and sustains a neural representation, allowing the corresponding information to be globally accessed by local processors. We examine this hypothesis in light of recent data that contrast brain activity evoked by either conscious or non-conscious contents, as well as during conscious or non-conscious states, particularly general anesthesia. We also discuss the relationship between the intertwined concepts of conscious processing, attention, and working memory.

Figure 1.. The Global Neuronal Workspace (GNW) Hypothesis

(A–C) Original schemas from Dehaene et al. (1998) illustrating the main tenets of the GNW hypothesis: local, specialized cortical processors are linked, at a central level, by a core set of highly interconnected areas (A) containing a high density of large pyramidal neurons with long-distance axons (B). At any given moment, this architecture can select a piece of information within one or several processors, amplify it, and broadcast it to all other processors, thus rendering it consciously accessible and available for verbal report. Recent tracer studies of global feedforward and feedback cortical connectivity confirm a bow-tie architecture with a central core set mostly comprised of parietal and prefrontal areas and forming a structural bottleneck capable of routing information between other cortical processors (C) (Markov et al., 2013).

Figure 2.. Dynamics of Neural Ignition in the GNW

(A) Elementary simulations of networks with feedforward propagation and a higher set of areas with elevated recurrent excitation and feedback projections predict two dynamic states for an identical stimulus: either the incoming activity cascades upward in a self-amplified manner, ultimately igniting the entire network, thus corresponding to conscious access (A, right) or the propagating activity remains below the threshold for ignition and induces only a progressively decaying wave of activity in higher regions, corresponding to subliminal processing (A, left). (B and C) Electrophysiological test of those predictions in awake macaque monkeys. Recordings were performed in V1, V4, and PFC while monkeys attempted to detect a weak stimulus of variable contrast placed in the neurons’ receptive field (B). Monkeys reported target presence with an eye movement, thus resulting in four trial types: hits, misses, correct rejections, and false alarms (C). Depending on their strength, the missed stimuli could evoke strong early transients in V1 and V4, indicating that such firing was not sufficient for a consciously reportable representation. The main difference between conscious stimuli (hits and false alarms) versus non-conscious stimuli (misses and correct rejections) was late, sustained activity in PFC (green and blue curves) together with small but significant concomitant late sustained activation in V1 and V4 (see inset in middle panel). Missed stimuli evoked only transient, decaying PFC activity. See van Vugt et al., 2018.

Figure 3.. Late Feedback to V1 Reflects Conscious Figure/Ground Segregation

A square figure is composed of line elements of one orientation superimposed on a background with line elements of the opposite orientation. Initial feedforward activity is strictly identical whether the figure is placed within V1 neurons’ receptive field (thick curve) or when the receptive field falls on the background regions (thin curve). Only the later sustained activity, dependent on top-down cortical signals, discriminates figure from ground but only when the monkey detected the figure (hit), not when it failed (miss). See Supèr et al., 2001.

Figure 4.. Proposed Integration of Multiple Features of the Same Conscious Object in a Single GNW State

Many tasks require the interaction between different cortical processors with distinct functions. The GNW interconnects these processors and enables them to exchange information about the object that lies at the current focus of attention. The Raven’s progressive matrices test is one of many tasks that depends on such an information exchange. In this task, the observer forms hypotheses about the relations between the cells of the matrix and predicts the configuration in the empty cell that completes the matrix in a regular manner. It requires the analysis of simple and complex features, feature counts, and feature constellations and spatial locations. The observer may, for example, notice that there are three diamonds and three squares but only two circles in the matrix by successively directing feature-based attention to these shapes and counting their number (a form of visual search). The underlying attentional operations require interactions between the representations of features, spatial positions, and spatial configurations. According to the GNW theory, the attended information corresponds to what is in the observer’s awareness.

Figure 5.. General Anesthesia Suppresses the GNW

Schematic representations of functional connectivity across nodes of the GNW in the right hemisphere of the macaque brain, as derived from functional magnetic resonance imaging in awake and anesthetized monkeys (from Uhrig et al., 2018). The rich functional interactions across these nodes in the awake monkey are reduced due to the dose-dependent effects of the intravenous anesthetic propofol and the inhaled anesthetic sevoflurane. Importantly, the intravenous drug ketamine has a similar effect on the GNW, despite the molecular and neurophysiological differences of this anesthetic compared to propofol and sevoflurane. These data suggest that the functional connectome of the GNW might be a drug-invariant target of general anesthetics.