Although great progress has been made in the search for the neural correlates of consciousness, this endeavour has several blind spots. These relate to the fact that there are two, not one, concepts that the term ‘consciousness’ refers to. Conscious content, the subjective character of our experience, and conscious state, the fluctuations in the overall conscious condition, for example alert wakefulness versus sleep. Problematically, conscious content and conscious state are typically studied in isolation. In this proposal, we explore their dynamic interaction to unravel how ongoing, and unexplored, fluctuations in state affect sensory information processing and therefore our conscious experience.

With increasing age, people’s cognitive skills decline. How fast this occurs, differs among people and depends on a variety of factors. Whether having autism is a risk-factor for accelerated aging or a potential protective factor is unknown. In this project this will be tested and influential factors will be determined.

Autism is a lifelong, heterogeneous, and impairing developmental disorder. To fully understand the neurodevelopmental nature of this disorder it is important to broaden the developmental perspective by including old age. The course of autism during later life has remained exempt of empirical scrutiny, but the possibility for such research is emerging now, as the first cohorts formally diagnosed with autism are currently approaching old age. This presents a unique opportunity for a new line of research. People with autism show deficits in cognitive control in childhood and adulthood. These deficits are associated with structural and functional abnormalities in the underlying fronto-striatal network. In healthy people this brain network and the related cognitive control abilities deteriorate when aging. If and how aging has an impact on the behavioral and brain anomalies in individuals with autism is unknown. Based on what is known about autism and normal aging, I hypothesize that age-related compensatory brain mechanisms cannot be recruited in people with autism, which leads to a faster decrease in cognitive control in people with autism as compared to controls. To test this novel hypothesis, I propose a series of studies across adulthood (20-30, 40-50, & 65-75 years) focusing on central aspects of cognitive control and the integrity of its underlying brain circuitries. Analyses will focus on individual differences between groups, but also within groups to capture the substantial heterogeneity characterizing both autism and healthy aging. The integrative approach of using newly developed tasks and various brain imaging techniques enables us to study the relationship between behavioral and symptomatic age-related changes and alterations in brain circuitries in autism. Results from the proposed studies will advance the understanding of (1) neurodevelopmental aspect of this lifelong disorder; (2) relationship of cognitive control to the underlying brain networks in autism; (3) heterogeneity across people with autism.

Deep neural networks (DNNs) are AI models that are good at recognizing objects. DNNs and the part of the brain that deals with visual information processing are remarkable similar. In this thesis it was investigated whether DNNs can be used to relate cognitive theories to (more) biologically plausible mechanisms that are important for these behaviors. Looking at three different forms of behavior (arousal, selective attention, and image recognition is a sequence of images) we show that DNNs can be used to bridge cognitive theories and brain mechanisms.

The brain probably works differently in autism, but the exact nature of these differences is not fully understood. Most studies focused on understanding which brain differences are common across all autistic people, while ignoring the pronounced differences between them. In contrast, our study focuses on understanding what makes each autistic brain unique. We assess how brain function in autism differs over time and across different situations, like when watching people interact or when making sense of dense information. This research hopes to shed light on the complexities of the autistic brain and potentially inform future therapeutic strategies.