At Psychoinformatics Lab, I have contributing to the development of Neuroscout, an end-to-end platform for the analysis of naturalistic fMRI data. You can read more about Neuroscout in our eLife paper: https://elifesciences.org/articles/79277.
I am focusing on expanding Neuroscout’s annotation set by implementing feature extraction pipelines that use pretrained deep learning models (e.g., from HuggingFace’s transformers and TensorflowHub) in pliers.
I contributed to validating the platform and showing its potential to increase the generalizability of neuroimaging findings through a series of large-scale meta-analyses presented in the paper, and available as a Jupyter book here.
Spatial demonstratives are words like ‘this’ and ‘that’ used to direct manipulate people’s attentional focus. They are extremely frequent, yet far from simple. Understanding what they refer to requires not only knowing language, but also the context in which they are pronounced.
As part of my PhD, I ran a naturalistic fMRI study combining synthesized dialogical narratives, fast multiband acquisition, and finite impulse response modeling to understand how the brain makes sense of them.
I found that spatial words engaged dorsal regions of the brain implicated not only in language, but in various aspects of visuospatial cognition, supporting distributed views of language processing.
This study has been published in NeuroImage, and it is available here https://www.sciencedirect.com/science/article/pii/S1053811919307190