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.

When visual input has conflicting interpretations, conscious perception can alternate spontaneously between these possible interpretations. This is called bistable perception. Previous neuroimaging studies have indicated the involvement of two right parietal areas in resolving perceptual ambiguity (ant-SPLr and post-SPLr). Transcranial magnetic stimulation (TMS) studies that selectively interfered with the normal function of these regions suggest that they play opposing roles in this type of perceptual switch. In the present study, we investigated this fractionation of parietal function by use of combined TMS with electroencephalography (EEG). Specifically, while participants viewed either a bistable stimulus, a replay stimulus, or resting-state fixation, we applied single pulse TMS to either location independently while simultaneously recording EEG. Combined with participant’s individual structural magnetic resonance imaging (MRI) scans, this dataset allows for complex analyses of the effect of TMS on neural time series data, which may further elucidate the causal role of the parietal cortex in ambiguous perception.

This study considers how inter-individual differences in visual ability are structured. Visual ability could be a single entity (along the lines of general intelligence, or ‘g’), or could be structured according to major anatomical or physiological pathways (dorsal v. ventral streams; magno- v. parvo-cellular systems); or may be a finer-grained mosaic of abilities. To test this, we employed seven visual psychophysical tests (generating 16 measures) on a large (100+) sample of neurotypical participants. A Varimax-rotated PCA (Principal Component Analysis) revealed a two-factor solution that broadly corresponds to a high and low spatial frequency division (consistent with a magno/parvo distinction). Over and above this, two measures (temporal order judgments; gain in contrast sensitivity) correlated with most others, and loaded on both factors, suggesting that they tap broad visual processing demands. These analyses open up further possibilities for exploring the genetic and neuroscientific foundations of differences in visual ability. The tests were also run on a group of individuals with different types of visually-based synaesthesia, given that previous research have suggested they possess a distinct profile of visual abilities. Synaesthesia was linked to enhanced processing of colour and shape/curvature information (amongst others), that may relate to differences in V4 in this group. In conclusion, individual differences in vision are both striking and meaningful, despite our difficulty to imagine seeing the world any differently.