Assessment and Diagnosis
There are a number of known human conditions that can be detected or measured with eye tracking in controlled experiments. As a result, eye tracking is seeing growing use in clinical research. Eye movement analysis is studied for the identification of ocular disease, as well as mental and neural disorders such as autism spectrum disorder, ADHD, and Parkinson’s disease. In the fields of psychology and neuroscience, eye tracking can be used to understand how and why eye movements are made and how we gather information visually.
More and more, we’re finding neurological disorders to have associated and detectable eye movement patterns. Some reliable eye movement pattern distinctions have been found in autism1, Angelman syndrome2, dyslexia3, and schizophrenia4. These patterns are being used for diagnosis, as well as for understanding the important differences in informational processing between these differently-abled individuals.
As an example, in a study comparing autistic and non-autistic children, researchers found that eye contact affects cognition differently in the two groups. According to the researchers, eye tracking conducted during the cognitive test revealed strikingly similar patterns of eye movements, indicating that the results cannot be explained by differences in overt attention.1
Blink and saccade parameters can reliably indicate an increased level of fatigue. Detecting drowsiness can save fatal accidents from happening when driving, operating heavy machinery, monitoring critical systems, or any other profession where alertness is absolutely necessary.
A common application of eye-based tiredness detection is in the automotive industry. According to a 2008 study conducted by Daimler Chrysler and several German research institutes, blink duration, delay of lid reopening, blink interval and standardized lid closure speed were identified as the best indicators of subjective as well as objective sleepiness.5
Subconscious eye movements and pupil dilations can reveal a person’s intention. Under controlled circumstances, successful predictions can be made about what a person will choose to interact with, purchase, etc. Eye tracking is also commonly used to evaluate saliency models, which predict where a person will look.
A 2012 study, using machine learning on a large eye movement data set, explored the hypothesis that intents were observable in human gaze as specific patterns of eye movements, and therefore, these patterns could possibly be parametrized by a set of features in order to build a prediction model.6
The consumption of alcohol, some psychoactive drugs, and some prescription drugs results in detectable differences in eye movements and pupil size.
To keep drivers safe on the road, law enforcement often check a person’s eyes to determine if they are under the influence of drugs or alcohol. A 1986 study found that alcohol has been shown to have diverse effects, including reduction of the velocity of both saccadic and smooth pursuit eye movements, increased saccadic latency, impairment of convergence and induction of nystagmus. These effects probably contribute to impaired visual information processing, which reduces driving ability.7
Another interesting use of eye tracking was a study that found that the prescription drug Oxytocin has the unusual effect of increasing eye contact in social settings. This is proposed to have a therapeutic effect on people with autism spectrum disorders whose natural tendency is to avoid eye contact in these situations.8
Hong, Michael & Guilfoyle, Janna & Mooney, Lindsey & Wink, Logan & Pedapati, Ernest & Shaffer, Rebecca & Sweeney, John & Erickson, Craig. (2017). Eye gaze and pupillary response in Angelman syndrome. Research in Developmental Disabilities. 68. 88-94. 10.1016/j.ridd.2017.06.011. ↩︎
Al-Wabil, Areej & Sheaha, Maha. (2010). Towards an Interactive Screening Program for Developmental Dyslexia: Eye Movement Analysis in Reading Arabic Texts. 6180. 25-32. 10.1007/978-3-642-14100-3_5. ↩︎
Matsumoto, Yukiko & Takahashi, Hideyuki & Murai, Toshiya & Takahashi, Hidehiko. (2014). Visual Processing and Social Cognition in Schizophrenia: Relationships among Eye Movements, Biological Motion Perception, and Empathy. Neuroscience Research. 90. 10.1016/j.neures.2014.10.011. ↩︎
Schleicher, Robert & Galley, Niels & Briest, Susanne & Galley, Lars. (2008). Blinks and saccades as indicators of fatigue in sleepiness warnings: Looking tired?. Ergonomics. 51. 982-1010. 10.1080/00140130701817062. ↩︎
Bednarik, Roman & Vrzakova, Hana & Hradis, Michal. (2012). What do you want to do next: A novel approach for intent prediction in gaze-based interaction. Eye Tracking Research and Applications Symposium (ETRA). 10.1145/2168556.2168569. ↩︎
Auyeung, Bonnie & Lombardo, Michael & Heinrichs, Markus & Chakrabarti, Bhismadev & Sule, A & Deakin, Julia & Bethlehem, Richard A.I. & Dickens, L & Mooney, Natasha & Sipple, J & Thiemann, Pia & Baron-Cohen, Simon. (2015). Oxytocin increases eye contact during a real-time, naturalistic social interaction in males with and without autism. Translational Psychiatry. 5. e507. 10.1038/tp.2014.146. ↩︎