September 2021
Volume 21, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2021
Technological advances are the scaffold for propelling science forward in social neuroscience
Author Affiliations
  • Aina Puce
    Indiana University
Journal of Vision September 2021, Vol.21, 75. doi:https://doi.org/10.1167/jov.21.9.75
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      Aina Puce; Technological advances are the scaffold for propelling science forward in social neuroscience. Journal of Vision 2021;21(9):75. doi: https://doi.org/10.1167/jov.21.9.75.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Over the last 20 years, neuroimaging techniques [e.g. EEG/MEG, fMRI] were used to map neural activity within a core and extended brain network to study how we use social information from faces. By the 20th century’s end, neuroimaging methods had identified the building blocks of this network, but how these parts came together to make a whole was unknown. In 20 years, technological advances in data acquisition and analysis have occurred in a number of spheres. First, network neuroscience has progressed our understanding of which brain regions functionally connect with one another on a regular basis. Second, improvements in white matter tract tracing have allowed putative underlying white matter pathways to be identified for some functional networks. Third, [non-]invasive brain stimulation has allowed the identification of some causal relationships between brain activity and behavior. Fourth, technological developments in portable EEG and MEG systems propelled social neuroscience out of the laboratory and into the [ecologically valid] wide world. This is changing activation task design as well as data analysis. Potential advantages of these ‘wild type’ approaches include the increased signal-to-noise provided by a live interactive 3D visual stimulus e.g. another human being, instead of an isolated static face on a computer monitor. Fifth, work with machine learning algorithms has begun to differentiate brain/non-brain activity in these datasets. Finally, we are finally ‘putting the brain back into the body’ – whereby recordings of brain activity are made in conjunction with physiological signals including EKG, EMG, pupil dilation, and eye position.

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