IISc team develops novel computational model to predict ‘change blindness’
A research group at the Centre for Neuroscience and the Department of Computer Science and Automation, Indian Institute of Science (IISc) have developed a novel computational model of eye movement that can predict a person’s ability to detect changes in their visual environment, in a study published in PLoS Computational Biology. The researchers believe that successful change detection may be linked to enhanced visual attention – how some people are better at selectively focusing on specific objects.
Based on the observation conducted in 39 persons wherein they checked for change blindness by showing them flashing pair of minor differences, they observed that subjects who fixated for longer at a particular spot, and whose eye movements were less variable were found to detect changes more effectively. The researchers developed a computational model that can predict how well a person might 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.
Other researchers 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 a state-of-the-art 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. 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. The authors say that 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.