Skip to content

FibonacciDude/UnsupervisedLatentModelling

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

21 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Unsupervised Modelling of Latent Cognitive Characteristics

Approach

I created a sequence model (Gated Recurrent Unit, GRU) to predict the next gaze given all previous ones – auto-regression. After the model was trained, I took the hidden vector (64 dimensional) inside the GRU at each frame as the new data. I then trained a small neural network model to predict which task the user was performing in the sequence given only these hidden vectors. The network was given 32 total examples to generalize for all tasks and all people.

The model performed with ~87% accuracy in predicting from the 4 tasks only using the hidden vectors. The task of next-gaze prediction highly correlated with which task the user was performing. Furthermore, the trained model’s hidden vector was high-fidelity as it allowed generalization from the network using only 32 examples.

I wished to explore which latent factors contribute to the behavior and motion of a person’s eye gaze. After all, the eyes are the windows to the soul. So a model that predicts eye gaze could possibly predict certain characteristics of a person’s thoughts/intentions.

Data

The project uses a dataset of x,y coordinates of people’s eye gazes when 4 different computer tasks are being performed. The coordinates are compiled through time until the task ends. The dataset is called “GazeBase” https://www.nature.com/articles/s41597-021-00959-y.

To download data, I fetch from their website through the https://github.com/FibonacciDude/UnsupervisedLatentModelling repository (different from training repo) data/get_gazebase.sh and unzip by data/unzip_gazebase.sh.

Data is then converted to pytorch-compatible and extraneous data cleaned through running data_index.py. When fed into the model, the sequence data is properly batched.

model.py file

The GRU model object is created using the config.json file and the task prediction model as well. The task prediction is a simple feedforward network.

train.py file

Runs an automatic cross-validation of the GRU model and the task predictor model using the configurations specified in config.json. Data is batched and the model is trained using this data for prediction of next x,y gaze vectors using MSE loss (if GRU), or on task classification using the GRU hidden vectors (if predictor model).

Releases

No releases published

Packages

No packages published