Bengaluru: IISc’s computational eye movement model to understand ‘change blindness’ better

“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,” a statement from the premier science institute read.

What is change blindness?

In simple terms, ‘change blindness’ is a perceptual phenomenon that occurs when a change in a visual stimulus is introduced with the observer failing to notice it. This includes instances where someone fails to notice a key element of their surroundings once a change has taken place.

According to Sridharan Devarajan, associate professor at the Centre for Neuroscience, the team first checked for change blindness among 39 people by showing them alternately flashing pairs of images that have minor differences between them.

“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 (such as eye-tracking, fixations and gaze points) that could predict the success of change detection,” he explained.

Also the corresponding author of the paper published in PLOS Computational Biology (a peer-reviewed journal), Devarajan said that change detection was found to be linked to two metrics, particularly on how long the subjects’ gaze was fixated at a point and the variability in the path taken by their gaze between two specific points. Subjects who fixated for longer at a particular spot, and whose eye movements were less variable were found to detect changes more effectively.

This led the team of researchers towards developing a computational model that can predict how well a person might be able to detect changes in a sequence of similar images being shown.

Further, researchers claimed that the model also considers various biological parameters, constraints and human bias. Previously, other researchers have focussed either only on eye movement or change detection, but the model developed by the IISc team combines both, the institute added.

Meanwhile, Sridharan highlighted that the model should also consider the goals of the subject when they view images by not limiting itself to predict where a subject will look.

Researchers are hopeful of incorporating artificial neural networks with “memory” into the model, to realistically mimic how the brain retains recollections of past events, in a bid to detect changes.