Inferring text comprehension from eye movements in reading using deep neural sequence models

Masters Thesis by Laura Stahlhut, University of Zurich, Institute for Computational Linguistics (2023), Grade: 6

This repo contains the code belonging to my masters thesis "Inferring text comprehension from eye movements in reading using deep neural sequence models".

LinguisticEyeLSTM is a sequential neural architecture that processes the sequence of fixations during reading and infers the level of text comprehension.

Repo Structure

project
│   README.md
│   requirements.txt
│      
└───data
│   └───SB-SAT
│   │   ...
│   │
│   └───InDiCo
│       └───external
│       │    
│       └───raw
│       │    └───stimuli
│       │         ...
│       │    └───fixation
│       │         ...
│       └───interim
│       │   ...
│       │   
│       └───processed
│           indico_fix_lexfeats_final.csv
│   
└───src
│   annotate_texts.py
│   encode_data.py
│   extract_label.py
│   map_fix_to_roi.py
│   preprocess_fixations.py
│   reading_measures.py
│   reformat_lexical_surprisal_SBSAT.py
│   reformat__SBSAT_texts.py
│   txt_to_csv.py
│   
└───nn
│   └───sbsat_splits
│   └───indico_splits
│   │   └───data_s1_rm1_lf1
│   │   └───data_s1_rm1_lf0
│   │   └───data_s1_rm0_lf0_pos_cont
│   │   └───data_s1_rm0_lf0
│   │   └───data_s0_rm1_lf0
│   │   └───data_s0_rm0_lf1
│   │        └───book
│   │        └───subject
│   │        └───book-page
│   │  
│   └───model.py
│   └───results
│       └───InDiCo
│       │    └───data_s1_rm1_lf1
│       │    └───...
│       └───SBSAT
│
└───notebooks

Datasets

• SB-SAT: Stony Brook SAT reading fixation dataset (English stimuli). This dataset is publicly available data here. The expermients can be reproduced with the SB-SAT dataset.

https://github.com/aeye-lab/etra-reading-comprehension

• InDiCo: Individual Differences Corpus, compiled at the University of Zurich (German stimuli). This dataset is not publicly available. I had access to the edf files and exported the fixation report with the same columns that are in the SB-SAT dataset via SR Research's Dataviewer.

How to reproduce the experiments with the SB-SAT dataset:

Clone this repository

git clone https://[email protected]:l-stahlhut/ET-Reading-Comprehension.git

Install packages

pip install -r requirements.txt

Follow the instructions below to preprocess the data and perform classification.
The following image gives an overview over the general workflow which includes data preprocessing (fixation data, stimulus texts and target variable), data encoding and classification.

1. Data preprocessing

Stimulus texts

Preprocessing

1. Download the stimulus texts and the surprisal file. Save both in /data/SBSAT/raw/stimuli.
2. reformat the file for lexical surprisal and the SB-SAT texts. Reason: The text annotation code was initially written for InDiCo so we first have to bring the SBSAT texts into the same format. The reformated files are saved in /data/SBSAT/raw/stimuli/lexical_surprisal and /data/SBSAT/raw/stimuli/sbsat_sentences_screens.

python3 src/reformat_lexical_surprisal_SBSAT.py
python3 src/reformat_SBSAT_texts.py --reformat

Linguistic annotation

Annotate the texts from this dataset:

python3 src/annotate_texts.py --SBSAT

The annotated texts are saved in /data/SB-SAT/interim/stimuli/annotated_texts.

Fixation Data

The raw fixation data for SBSAT is publicly available (18sat_fixfinal.csv). I extracted the raw fixation data from InDiCo directly from SR Research Dataviewer.

Preprocessing the raw fixation data:

SB-SAT: Preprocessing involves dropping unnecessary rows and columns and deleting fixations outside of interest areas.

python3 preprocess_fixations.py --SBSAT

InDiCo: Preprocessing involves data cleaning, choosing relevant columns and mapping interest areas from the level of characters to the level of words.

python3 preprocess_fixations.py --InDiCo

Calculating Reading measures and merging fixation reports with lexical features

The following script calculates reading measures and merges the fixation report and the lexical features after fixing alignment problems.

python3 reading_measures.py --InDiCo
python3 reading_measures.py --SBSAT

Target variable

The label for the SB-SAT dataset gets extracted from the fixation report while the label for the InDiCo dataset gets extracted from the results folder of the experiment.

python3 src/extract_label.py --SBSAT
python3 src/extract_label.py --InDiCo

2. Encoding the data

Encode the data by specifying the dataset and the desired features, for example: --s1_rm1_lf1

python3 src/encode_data.py --SBSAT --s1_rm1_lf1 
python3 src/encode_data.py --InDiCo --s1_rm1_lf0

Please refer to the code to see which flag refers to which version of the features.

3. Classification

Train the different model variations, e.g. for the SB-SAT dataset with all features:

python3 nn/model.py --GPU 0 --s1_rm1_lf1 --SBSAT --save_dir nn/results --weights_dir  nn/weights

This saves the results and model weights. The flags for the data variations of the dataset can be seen in the script. The evaluation of the model (Ahn (2020) baseline implemented by Reich et al.(2022)) is included in this script.

Sources:

• SB-SAT data: Ahn et al. (2020): https://github.com/ahnchive/SB-SAT
• Transforming interest areas: https://github.com/l-stahlhut/indiff-preprocessing/blob/main/parsing/map_fix_to_roi.py
• The preprocessing code is based on the InDiCo Preprocessing code (2022): https://github.com/l-stahlhut/individual-differences/tree/main/src (annotate_texts.py, reading_measures.py)
• The code for the data encoding and the model is based on Reich et al. (2022): https://github.com/aeye-lab/etra-reading-comprehension (model.py, generate_text_sequence_splits.py)