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Locomotive action affordances in brains, behavior and DNNs
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Description: To navigate the world, we must quickly determine which locomotive actions—such as walking, swimming, or cycling—are possible within a given environment. This project investigates how the brain processes these action affordances by combining functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and behavioral annotations with deep neural networks (DNNs). By examining how the brain encodes navigationally relevant information, we aim to uncover the neural mechanisms underlying affordance perception and evaluate the extent to which artificial models can replicate these processes.
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Components
Image set
This image set contains high-resolution color photographs of everyday visual environments, sourced from Flickr under open licenses (CC0 1.0, CC BY 2.0...
Behavioral data
Behavioral data collected from an online experiment in which participants labeled stimuli across seven distinct tasks. Designed using the Gorilla Expe...
fMRI data
BOLD functional magnetic resonance imaging (fMRI) data from 20 participants using a Philips Achieva 3T scanner. Separate localizer scans were conduct...
Raw EEG Data
We recorded Electroencephalography (EEG) for 19 participants using a Biosemi 64-channel ActiveTwo EEG system with a 10-20 layout at a sampling freque...
DNN representations
Extracted layer activations from several pre-trained deep neural network (DNN) models using the Net2Brain Python package.
Code
Github repositories with code to reproduce the analysis and results in our papers.
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