IISc develops novel computational model to predict blindness

Bengaluru, October 23, 2021:

Indian Institute of Science (IISc) Centre for Neuroscience and the Department of Computer Science and Automation has developed a novel computational model of eye movement that can predict a person’s ability to detect changes in their visual environment.

The insights into understanding change blindness provided by their model could help scientists better understand visual attention and its limitations. Some examples of areas where such insights can be applied include diagnosing neurodevelopmental disorders like autism, improving road safety while driving or enhancing the reliability of eyewitness testimonies.

The research team is of the view that successful change detection may be linked to enhanced visual attention of how some people are better at selectively focusing on specific objects. Initially, the team began with testing for change in blindness among 39 people by showing them an alternately flashing pair of images that have a minor difference between them. The findings of the research are published in PLoS Computational Biology.

Prof. Sridharan Devarajan, Associate Professor, Centre for Neuroscience, IISc and corresponding author of the paper said, “We expected some complex differences in eye movement patterns between subjects who could do the task well and those who could not. Instead, we found some very simple gaze-metrics that could predict the success of change detection.”

Now the change detection was found to be linked with two aspects. One was the duration of the subjects’ gaze which was fixated at a point. Second was the variability in the path taken by their gaze between two specific points known as ‘saccade amplitude’. Subjects who fixated for longer at a particular spot, and whose eye movements were less variable were found to detect changes more effectively, he added.

Following these insights, our team developed a computational model which predicts how well a person would be able to detect changes in a sequence of similar images shown to them. The model also takes into consideration various biological parameters, constraints and human bias. Since biological neurons are ‘noisy’, they do not encode the image precisely, said Prof. Sridharan.

There is a lot of variability in the way neurons encode related to processing and response to images in the brain, which can be captured by a mathematical representation called the Poisson process, he said.

The research team, which included Akshay Jagatap, Simran Purokayastha and Hritik Jain, have previously developed models that focus either only on eye movement or on change detection, but the model developed by the IISc team goes one step further and combines both. The researchers also tested their model against an advanced deep neural network called DeepGaze II, and found that their model performed better at predicting human gaze patterns in free-viewing conditions when the subjects were casually viewing the images.

While DeepGaze II could predict where a person will look if presented with an image, it did not work as well as the IISc-developed model at predicting the eye movement pattern of a person searching for a difference in the images, stated the researchers.

“It’s not enough to just predict where a subject will look, the model also has to take into consideration the goals of the subject when they view images. In the future, the researchers also plan to incorporate artificial neural networks with “memory” into the model – to more realistically mimic the way our brains retain recollections of past events to detect changes,” said Prof. Sridharan. PharmaBiz