apply same analysis to mouse data

[anne-urai]

install https://int-brain-lab.github.io/iblenv/notebooks_external/one_quickstart.html

[anne-urai]

• get eids from elife paper initially, then use larger query with more mice (3 sessions before trained)
• Compute lapses from average mouse data: gamma+lambda

rename columns, to allow specifying the same models independent of dataset

coherence / contrast -> stim_strength stimulus -> stim_idx

[anne-urai]

Now at https://github.com/kiante-fernandez/2023_choicehistory_HSSM/tree/get_ibl_data

[anne-urai]

ToDo

• [x] figure out negative / NaN RTs
• [ ] use firstMove vs respTime
• [ ] only trials where is_final_move or is_ballistic
• [x] get 3 sessions per mouse to boost trial count
• [x] some dataviz of individual RT distributions

Later

• [ ] connect with Felicia @ Churchland lab UCLA
• [ ] compare with internal database / include my ageing data