Integrating the global neuronal workspace into the framework of predictive processing: Towards a working hypothesis.

Area of Science:

• Cognitive Neuroscience
• Philosophy of Mind

Background:

• The global neuronal workspace (GNW) theory explains conscious access.
• Predictive processing offers a framework for understanding brain function.
• Hohwy's (2013) predictive global neuronal workspace (PGNW) integrates these concepts but requires empirical validation.

Purpose of the Study:

• To delineate empirical predictions distinguishing the PGNW from other workspace models.
• To assess the current empirical support for the PGNW.
• To evaluate the PGNW against alternative theories like the limbic workspace.

Main Methods:

• Placing the PGNW in direct relation to experimental findings.
• Deriving specific, testable predictions differentiating PGNW from standard GNW.
• Examining evidence from bistable perception and auditory regularity processing.
• Comparing PGNW with the Chanes and Barrett (2016) limbic workspace model.

Main Results:

• The PGNW generates distinct empirical predictions compared to the standard GNW.
• Evidence from bistable perception and auditory processing aligns with PGNW predictions.
• The PGNW framework is shown to be both empirically testable and currently supported.

Conclusions:

• The predictive global neuronal workspace (PGNW) presents a theoretically sound and empirically testable model of conscious access.
• Current experimental data supports the PGNW, suggesting its viability as an advancement over traditional GNW.
• Further research can refine and validate the PGNW's predictions across diverse cognitive domains.