The role of predictive processing in conscious access and regularity learning across sensory domains

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

Visual perceptual echo reflects learning of regularities in rapid luminance sequences

A novel neural signature of active visual processing has recently been described in the form of the ‘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 (VanRullen & Macdonald, 2012). As yet, however, the mechanisms underlying the perceptual echo and its function remain unknown. Reasoning that natural visual signals often contain temporally predictable, though non-periodic features, we hypothesized that the perceptual echo may reflect a periodic process associated with regularity learning. To test this hypothesis, we presented subjects with successive repetitions of a rapid non-periodic luminance sequence, and examined the effects on the perceptual echo, finding that echo amplitude linearly increased with the number of presentations of a given luminance sequence. These data suggest that the perceptual echo reflects a neural signature of regularity learning. Furthermore, when a set of repeated sequences was followed by a sequence with inverted luminance polarities, the echo amplitude decreased to the same level evoked by a novel stimulus sequence. Crucially, when the original stimulus sequence was re-presented, the echo amplitude returned to a level consistent with the number of presentations of this sequence, indicating that the visual system retained sequence specific information, for many seconds, even in the presence of intervening visual input. Altogether, our results reveal a previously undiscovered regularity learning mechanism within the human visual system, reflected by the perceptual echo

Boredom-Driven Curious Learning by Homeo-Heterostatic Value Gradients

This paper presents the Homeo-Heterostatic Value Gradients (HHVG) algorithm as a formal account on the constructive interplay between boredom and curiosity which gives rise to effective exploration and superior forward model learning. We offer an instrumental view of action selection, in which an action serves to disclose outcomes that have intrinsic meaningfulness to an agent itself. This motivated two central algorithmic ingredients: devaluation and devaluation progress, both underpin agent's cognition concerning intrinsically generated rewards. The two serve as an instantiation of homeostatic and heterostatic intrinsic motivation. A key insight from our algorithm is that the two seemingly opposite motivations can be reconciled—without which exploration and information-gathering cannot be effectively carried out. We supported this claim with empirical evidence, showing that boredom-enabled agents consistently outperformed other curious or explorative agent variants in model building benchmarks based on self-assisted experience accumulation

The structure of inter-individual differences in visual ability: evidence from the general population and synaesthesia

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

Fibroblast heterogeneity in the cancer wound

Fibroblasts regulate the structure and function of healthy tissues, participate transiently in tissue repair after acute inflammation, and assume an aberrant stimulatory role during chronic inflammatory states including cancer. Such cancer-associated fibroblasts (CAFs) modulate the tumor microenvironment and influence the behavior of neoplastic cells in either a tumor-promoting or tumor-inhibiting manner. These pleiotropic functions highlight the inherent plasticity of fibroblasts and may provide new avenues to understand and therapeutically intervene in malignancies. We discuss the emerging themes of CAF biology in the context of tumorigenesis and therapy

Cross-modal prediction changes the timing of conscious access during motion-induced blindness

Despite accumulating evidence that perceptual predictions influence perceptual content, the relations between these predictions and conscious contents remain unclear, especially for cross-modal predictions. We examined whether predictions of visual events by auditory cues can facilitate conscious access to the visual stimuli. We trained participants to learn associations between auditory cues and colour changes. We then asked whether congruency between auditory cues and target colours would speed access to consciousness. We did this by rendering a visual target subjectively invisible using motion-induced blindness and then gradually changing its colour while presenting congruent or incongruent auditory cues. Results showed that the visual target gained access to consciousness faster in congruent than in incongruent trials; control experiments excluded potentially confounding effects of attention and motor response. The expectation effect was gradually established over blocks suggesting a role for extensive training. Overall, our findings show that predictions learned through cross-modal training can facilitate conscious access to visual stimuli

Incorporating data warehouse technology into asset information management systems for large assets

Large sized engineering assets such as power transformers are critical parts of the power supply networks. Therefore, focusing on early fault diagnosis to maintain large transformers in good condition is an important operational task to the power companies. In order to manage and fully utilize the big data generated from the large number of transformers, this paper incorporates data warehouse technology to a fault diagnosis system for the entire transformer fleet. The research includes two major parts. First, a data warehouse (DW) is designed for the large assets information management. Then, the DW-based intelligent fault diagnosis system is developed and implemented. The DW stores the complete transformers’ data and then different data cubes are defined according to various applications. The fault diagnosis system for power transformers consists of the condition monitoring module, failure diagnosis module, and intelligent decision supports module. The research methodology and prototype system are verified with real data from a series of 161 kV transformers in operations.</p

Control over self and others’ face: Exploitation and exploration

The sense of agency, defined as the subjective experience of controlling one’s actions and their consequences, is a fundamental aspect of human cognition. This study introduces a dual-mode theory that posits two underlying modes: Exploitation and exploration. Exploitation aligns with the comparator model, relying on prediction errors and a strong sense of self-agency, whereas exploration involves accounting for others’ potential influence and a more flexible self-model. We employed a face-motion mixing paradigm using a deep generative model to test our theory, manipulating the belief in control by having participants interact with their own face or someone else’s face, with full or partial control. The results supported our hypothesis, showing that controlling one’s own face, linked to stronger control beliefs, was associated with less movement diversity and sharper drop in agency rating when small discrepancies were presented, compared to controlling someone else’s face, which engaged exploratory behavior and yielded higher agency ratings and more varied movements. These findings contribute to understanding how beliefs in control influence action policies and perceptual sensitivities. The proposed dual-mode theory offers a comprehensive understanding of the dynamic interplay between exploitation and exploration modes of agency, providing a useful framework to predict and interpret the nuanced ways in which individuals experience and exert control in varying contexts