To increase fitness for survival, organisms not only passively react to environmental changes but also actively predict future events to prepare for potential hazards within their environment. Accumulating evidence indicates that the human brain is a remarkable predictive machine which constantly models causal relationships and predicts future events. This ‘predictive processing’ framework, a prediction-based form of Bayesian inference, states that the brain continuously generates and updates predictions about incoming sensory signals. This framework has been showing notable explanatory power in understanding the mechanisms behind both human behaviour and neurophysiological data and elegantly specifies the underlying computational principles of the neural system. However, even though predictive processing has the potential to provide a unified theory of the brain (Karl Friston, 2010), we still have a limited understanding about fundamental aspects of this model, such as how it deals with different types of information, learns statistical regularities and perhaps most fundamentally of all what its relationship to conscious experience is. This thesis aims to investigate the major gaps in our current understanding of the predictive processing framework via a series of studies. Study 1 investigated the fundamental relationship between unconscious statistical inference reflected by predictive processing and conscious access. It demonstrated that predictions that are in line with sensory evidence accelerate conscious access. Study 2 investigated how low level information within the sensory hierarchy is dealt with by predictive processing and regularity learning mechanisms through “perceptual echo” in which the cross-correlation between a sequence of randomly fluctuating luminance values and occipital electrophysiological (EEG) signals exhibits a long-lasting periodic (~100ms cycle) reverberation of the input stimulus. This study identified a new form of regularity learning and the results demonstrate that the perceptual echo may reflect an iterative learning process, governed by predictive processing. Study 3 investigated how supra-modal predictive processing is capable of
learning regularities of temporal duration and also temporal predictions about future events. This study revealed a supramodal temporal prediction mechanism which processes auditory and visual temporal information and integrates information from the duration and rhythmic structures of events. Together these studies provide a global picture of predictive processing and regularity learning across differing types of predictive information
