Invited talks

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 invited talk at the Society for Brain Mapping and Therapeutics 21st Annual World Congress in Los Angeles, CA, I gave a brief history of consciousness science, reviewed the current state of the neural correlates of consciousness and theories of consciousness, and provided two examples of my ongoing research projects related to integrated information theory.

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.