Browsing by Subject "pupillary response"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Multinomial Processing Models in Visual Cognitive Effort Diagnostics
    (IEEE, 2015-06) Elkins, Joshua D.; Hossain, Gahangir; Department of Electrical and Computer Engineering, School of Engineering and Technology
    The pupillary response has been used to measure mental workload because of its sensitivity to stimuli and high resolution. The goal of this study was to diagnose the cognitive effort involved with a task that was presented visually. A multinomial processing tree (MPT) was used as an analytical tool in order to disentangle and predict separate cognitive processes, with the resulting output being a change in pupil diameter. This model was fitted to previous test data related to the pupillary response when presented a mental multiplication task. An MPT model describes observed response frequencies from a set of response categories. The parameter values of an MPT model are the probabilities of moving from latent state to the next. An EM algorithm was used to estimate the parameter values based on the response frequency of each category. This results in a parsimonious, causal model that facilitates in the understanding the pupillary response to cognitive load. This model eventually could be instrumental in bridging the gap between human vision and computer vision.
  • Thumbnail Image
    Item
    Neural Mechanisms of Pupillary Dynamics and Cognitive Effort
    (Office of the Vice Chancellor for Research, 2015-04-17) Elkins, Joshua; Hossain, Gahangir; Yoshida, Ken
    The pupillary response has been used to measure mental workload because of its sensitivity to stimuli and high resolution. The goal of this study was to understand interconnections between the visual or auditory pathway and the resulting pupillary response relative to cognitive effort. A multinomial processing tree was used as a diagnostic tool in order to disentangle and measure separate cognitive processes, with the resulting output being a change in pupil diameter. This model was fitted to previous test data related to the pupillary response when presented a mental multiplication task. Two models were derived as a result: a subject response category tree and a pupil dilation response category tree. One tree compares the visual and aural pathways, while the other compares latent processes within the visual pathway. This results in a parsimonious model that facilitates in the understanding of the neural interconnections involved with the pupillary response to cognitive load.