Dataset set up

• to quickstart, just download the responses / wordsequences for 3 subjects from the encoding scaling laws paper
  • this is all the data you need if you only want to analyze 3 subjects and don't want to make flatmaps
• to run eng1000, need to grab em_data directory from here and move its contents to {root_dir}/em_data
• for more, download data with python experiments/00_load_dataset.py
  • create a data dir under wherever you run it and will use datalad to download the preprocessed data as well as feature spaces needed for fitting semantic encoding models
• to make flatmaps, need to set [pycortex filestore] to {root_dir}/ds003020/derivative/pycortex-db/

Code install

• pip install ridge_utils (for full control, could alternatively pip install -e ridge_utils_frozen from the repo directory)
• pip install -e . from the repo directory to locally install the neuro package
• set neuro.config.root_dir/data to where you put all the data
  • loading responses
    • neuro.data.response_utils function load_response
    • loads responses from at {neuro.config.root_dir}/ds003020/derivative/preprocessed_data/{subject}, where they are stored in an h5 file for each story, e.g. wheretheressmoke.h5
  • loading stimulus
    • ridge_utils.features.stim_utils function load_story_wordseqs
    • loads textgrids from {root_dir}/ds003020/derivative/TextGrids, where each story has a TextGrid file, e.g. wheretheressmoke.TextGrid
    • uses {root_dir}/ds003020/derivative/respdict.json to get the length of each story
• python experiments/02_fit_encoding.py
  • This script takes many relevant arguments through argparse

Reference

• builds on the dataset repo from the huth lab. See that wonderful repo!
• uses data from openneuro.
• builds on code from encoding-model-scaling-laws, which is the repo for the paper "Scaling laws for language encoding models in fMRI" (antonello, vaidya, & huth, 2023). See the cool results there!
• it also copies a lot of code from the repo for SASC