ORCID Profile
0000-0002-2253-1844
Current Organisations
CHU-Sainte Justine Research Center
,
Université de Montréal
,
Mila - Institut Québécois d'Intelligence Artificielle
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Publisher: Center for Open Science
Date: 23-02-2021
Abstract: This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. We call this approach computational phenomenology because it applies methods originally developed in computational modelling to phenomenology. The first section presents a brief review of the project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project, and situates our project with respect to these projects. The third section reviews the generative modelling framework. The following section presents our new approach to neurophenomenology based on generative modelling. We then discuss how this application of generative modelling differs from previous attempts to use it to explain consciousness. In summary, generative modelling allows us to construct a computational model of the inferential or interpretive process that best explain this or that kind of lived experience.
Publisher: Elsevier BV
Date: 11-2021
DOI: 10.1016/J.CORTEX.2021.03.013
Abstract: There is growing awareness across the neuroscience community that the replicability of findings about the relationship between brain activity and cognitive phenomena can be improved by conducting studies with high statistical power that adhere to well-defined and standardised analysis pipelines. Inspired by recent efforts from the psychological sciences, and with the desire to examine some of the foundational findings using electroencephalography (EEG), we have launched #EEGManyLabs, a large-scale international collaborative replication effort. Since its discovery in the early 20th century, EEG has had a profound influence on our understanding of human cognition, but there is limited evidence on the replicability of some of the most highly cited discoveries. After a systematic search and selection process, we have identified 27 of the most influential and continually cited studies in the field. We plan to directly test the replicability of key findings from 20 of these studies in teams of at least three independent laboratories. The design and protocol of each replication effort will be submitted as a Registered Report and peer-reviewed prior to data collection. Prediction markets, open to all EEG researchers, will be used as a forecasting tool to examine which findings the community expects to replicate. This project will update our confidence in some of the most influential EEG findings and generate a large open access database that can be used to inform future research practices. Finally, through this international effort, we hope to create a cultural shift towards inclusive, high-powered multi-laboratory collaborations.
Publisher: Center for Open Science
Date: 12-02-2021
Abstract: Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open and inclusive environment. Departing from the formats of typical scientific workshops, these events are based on grassroots projects and training, and foster open and reproducible scientific practices. We describe here the multifaceted, lasting benefits of Brainhacks for in idual participants, particularly early career researchers. We further highlight the unique contributions that Brainhacks can make to the research community, contributing to scientific progress by complementing opportunities available in conventional formats.
Publisher: Elsevier BV
Date: 06-2021
Publisher: Elsevier BV
Date: 10-2021
Location: Canada
No related grants have been discovered for Guillaume Dumas.