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BEGIN:VEVENT
DTSTAMP:20261012T210905Z
LOCATION:Summit Pavilion
DTSTART;TZID=America/Los_Angeles:20261021T173000
DTEND;TZID=America/Los_Angeles:20261021T183000
UID:HFESAM_ASPIRE 2026_sess257_POST185@linklings.com
SUMMARY:A Computational Modeling Approach to Vigilance Performance in a Tw
 o-Boundary Detection Task
DESCRIPTION:Fernando Munoz Gomez Andrade (Oregon State University), Kayla 
 Hankey and Yusuke Yamani (Old Dominion University), and Jason McCarley (Or
 egon State University)\n\nThe ability to respond to rare but critical even
 ts is known to decline over time, a phenomenon termed the vigilance decrem
 ent. Although the vigilance decrement has been studied for more than 75 ye
 ars in a series of controlled experiments, methodological and analytical c
 hallenges have so far presented researchers from reaching consensus on the
  causes of the effect. We fit a generative model to data from a two-bounda
 ry detection task in which participants judged whether the spatial locatio
 n of a visual probe was sampled from a probability distribution with a lar
 ger or smaller standard deviation. The generative cognitive model assumed 
 either an attentive or inattentive state, each characterized by four param
 eters: sensitivity, response bias, attentional lapse rate, and guess rate.
  The model further assumed that participants render a "yes" response by co
 mparing the estimated probe position to decision cutoffs on either side of
  the midpoint. Results provide clear evidence of a vigilance decrement, ch
 aracterized by conservative shifts in bias, increased attentional lapses, 
 and a decreased guess rate. The generative cognitive modeling approach can
  yield useful, and potentially practical, insights into the mechanisms und
 erlying the vigilance decrement.\n\n
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