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Dynamic Pupil Behavior

Many complex tasks, including those in military command and control settings, involve the use of visual displays, especially those requiring real-time interaction between a human and a computer. Individuals use the visual displays to search and locate task-relevant information in a timely and accurate manner. In order to assess a person’s performance using the visual displays, and/or to assess the effectiveness of the displays, it is often useful to know whether or not cognitive activity occurs when the individual focuses on certain features of the display. It has been shown that pupillary response is a physiological correlate of cognitive activity, and more specifically of mental workload (e.g., Backs & Walrath, 1992; Lowenfeld, 1999; Marshall, Pleydell-Pearce, & Dickson, 2002; Hyönö, Tommola, & Alaja, 1995; Andreassi, 2000), in that the pupil dilates as a person gazes at a point on a display that stimulates cognition, and the amount of dilation is related to the amount of mental demand imposed by the information contained at that point on the display. Similarly, visual displays that are employed in less complex task situations, but are being used by people whose visual abilities are impaired in some manner, impose mental demands during use that may also be informed by pupillary response. The challenge in studying the relationship between cognitive activity, mental demand, and pupillary response resides with identifying and removing noise in the pupillary response data, and through appropriate statistical treatment of the data.

The research assesses and validates the use of pupillary response as a reliable and robust mechanism for assessing mental workload in the performance of tasks involving visual displays under conditions of visual impairment due to disability-induced impairment and situationally-induced impairments (SII), through the development of fast, inexpensive, and reliable statistical classification procedures that are able to discern and discriminate pupillary response measurements from healthy individuals and to detect and classify various pathologies of the eye in people with visual impairment. This study constitutes the first ever, systematically derived, fast, inexpensive, and reliable statistical classification procedures that are able to discern and discriminate pupillary response measurements from healthy individuals and to detect and classify various pathologies of the eye in people with visual impairment. The study’s outcomes have the capability to dramatically transform the ways in which visual displays (simple and complex displays including those typical of command and control operations) are designed for, assessed for, and used by the millions of Americans afflicted with visual impairments (including veterans of the U.S. armed forces), as well as by visually healthy people. The theoretical contributions relate to the statistical analysis and modeling innovations resulting from this research, including a phenomena-driven wavelet based filtering scheme, fast algorithms that assess the scaling behavior during the measurement process, and the derivation and use of novel wavelet techniques and state-of-the-art simulational procedures. An additional theoretical contribution includes merging high frequency models explaining scaling behavior with traditional time series models, which model behavior at low frequencies and overall shape.

For additional information on this collaborative research project, visit: http://www.isye.gatech.edu/news-events/news/article.php?id=125.

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