Poster presentations

In this study, we measure participants’ neurophysiological (EEG), subjective, and behavioral responses in states of normal wakefulness to visual and auditory stimuli that vary in granularity of subjective characteristics, such as meaningfulness. This study advances our understanding of consciousness by clarifying the relationship between stimulus complexity and measures of brain complexity and phenomenology.

In this paper, we first review how IIT’s axiomatic framework has evolved through the latest version (4.0), and why the theory takes such an approach. We then analyze this approach through the lens of Husserlian phenomenology, finding support for both IIT’s axiomatic approach in general, and for all of IIT’s individual axioms.

In this paper, we provide a detailed analysis of IIT’s two silent neuron predictions, showing how they can in fact be tested, contra Bartlett (2022). In the final section of the paper, we also distinguish between two ways of explaining one of the predictions. By drawing on the work of Imre Lakatos, we conclude with a discussion of how the distinction between the two explanations sheds light on why it is so difficult to resolve theoretical disputes about consciousness. Despite these difficulties, we provide a framework that can lead to concrete progress.

In this proof-of-concept study, we analyzed three existing EEG datasets where 40 participants performed an active face perception task, an active visual oddball task, and a passive auditory oddball task. We computed the perturbational complexity index (PCIst) and Lempel-ziv complexity (LZc) for every trial and analyzed the results using Bayesian mixed-effects models. We found that i) PCIst was higher for meaningful visual stimuli but that LZc could be higher or lower; i) PCIst was higher for rare visual stimuli but LZc was lower; and ili) PCIst was higher for rare auditory stimuli but LZc did not discriminate rare vs. frequent auditory stimuli.