<commit_before>% Project Kappa Progress Report
% <NAME>, <NAME>, <NAME>, <NAME>, 
% November 12, 2015

# Background

## The Paper
- "Distributed and overlapping representations of faces and objects in ventral temporal cortex" Haxby et al.
- "Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001). revisited: is there a face area?" Hanson et al.
- "Partially distributed representations of objects and faces in ventral temporal cortex" O'Toole et al.
- from OpenFMRI.org
- ds105 

## The Data

- 6 subjects (sub001, sub002, .., sub006)
- 1 task (object viewing)
- 8 conditions per task (house, scrambled, cat, shoe, bottle, scissors, chair, face)
- 12 runs per subject (72 bold.nii files)

## The Method

-Haxby Similarity Method
	-Data for each subject were split into two sets, even and odd runs
	-Within category correlation was compared to between category correlation
	-Excluded maximally responsive voxels and again compared within category correlation to between category correlation
	
# Initial work

## EDA / Preprocessing
<commit_msg>Fixed more stuff<commit_after>% Project Kappa Progress Report
% <NAME>, <NAME>, <NAME>, <NAME>, 
% November 12, 2015

# Background

## The Paper
- "Distributed and overlapping representations of faces and objects in ventral temporal cortex" Haxby et al.
- "Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001). revisited: is there a face area?" Hanson et al.
- "Partially distributed representations of objects and faces in ventral temporal cortex" O'Toole et al.
- from OpenFMRI.org
- ds105 

## The Data

- 6 subjects (sub001, sub002, .., sub006)
- 1 task (object viewing)
- 8 conditions per task (house, scrambled, cat, shoe, bottle, scissors, chair, face)
- 12 runs per subject (72 bold.nii files)

## The Method

- Haxby Similarity Method
    - Data for each subject were split into two sets, even and odd runs
    - Within category correlation was compared to between category correlation
    - Excluded maximally responsive voxels and again compared within category correlation to between category correlation
	
# Initial work

## EDA / Preprocessing

{'fraction_non_alphanumeric': 0.07599629286376275, 'fraction_numerical': 0.026876737720111215, 'mean_word_length': 4.265853658536585, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 11, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '6494979', 'n_tokens_mistral': 644, 'n_tokens_neox': 566, 'n_words': 286}