BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Telerik Inc.//Sitefinity CMS 14.4//EN BEGIN:VTIMEZONE TZID:Central Standard Time BEGIN:STANDARD DTSTART:20251102T020000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYHOUR=2;BYMINUTE=0;BYMONTH=11 TZNAME:Central Standard Time TZOFFSETFROM:-0500 TZOFFSETTO:-0600 END:STANDARD BEGIN:DAYLIGHT DTSTART:20250301T020000 RRULE:FREQ=YEARLY;BYDAY=2SU;BYHOUR=2;BYMINUTE=0;BYMONTH=3 TZNAME:Central Daylight Time TZOFFSETFROM:-0600 TZOFFSETTO:-0500 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT DESCRIPTION:Speaker Information:Laura A. McNamaraSandia National Laboratori esIn this talk\, I will discuss how and why Sandia National Laboratories e mploys cognitive neuroscientists\, human factors psychologists\, and even an anthropologist (me!) to study visual cognition\, visual inspection\, an d information foraging problems in the RF-heavy world of Synthetic Apertur e Radar. \; Using observations from interdisciplinary studies with int elligence groups\, I’ll provide an introduction to the world of imagery an alysis. These workflows are complicated amalgamations of visual inspection and information foraging behaviors\, supported by a wide range of image p roducts\, viewing platforms\, and tools. Of particular importance is the s hift over the past couple of decades from so-called “hardcopy” to “softcop y” image analysis workflows: not only have the tools and techniques of ima ge analysis changed\, but analysts can access an increasingly diverse set of highly specialized image types. \; \; As any of our team member s can attest\, expert imagery analysts are extremely good – and very fast – in detecting\, evaluating\, and extracting meaningful signatures from ve ry large\, diverse sets of imagery\nOver the past few years\, we’ve come t o appreciate the potential for eye tracking data to help us understand the chains of micro-decisions that describe an imagery analyst’s path through a geospatial information space. \; The ability to associate gaze even ts with image features in dynamic\, user-driven workflows could reveal how imagery analysts acquire the skills necessary to extract information effi ciently and accurately from geospatial datasets. \; In practice\, howe ver\, it is extremely difficult to study gaze-contingent decision-making i n realistic\, user-driven workflows. \; Therefore\, we’ve recently emb arked on a methodological/software development project to create tools tha t will enable us to integrate gaze data with complementary behavioral indi cators of analytic decision-making. Ultimately\, we’d like to enable resea rchers to establish a theoretically sound\, well-characterized empirical f oundation for the design of visual analytic systems\, workflows\, and trai ning protocols. \;Contact:Liz Stine-Morroweals@illinois.eduSponsor:Edu cational Psychology CSTL Division DTEND:20160126T195000Z DTSTAMP:20260310T215233Z DTSTART:20160126T183000Z LOCATION:IL\,USA\,Champaign SEQUENCE:0 SUMMARY:“Gaze-Informed Information Foraging Models for Imagery Analysis ” UID:RFCALITEM639087583534765135 X-ALT-DESC;FMTTYPE=text/html:< /tr>

Speaker Information:< /p>

Laura A. McNamara
Sandia National Laboratories

In this talk\, I will discuss how and why Sandia Na tional Laboratories employs cognitive neuroscientists\, human factors psyc hologists\, and even an anthropologist (me!) to study visual cognition\, v isual inspection\, and information foraging problems in the RF-heavy world of Synthetic Aperture Radar. \; Using observations from interdiscipli nary studies with intelligence groups\, I’ll provide an introduction to th e world of imagery analysis. These workflows are complicated amalgamations of visual inspection and information foraging behaviors\, supported by a wide range of image products\, viewing platforms\, and tools. Of particula r importance is the shift over the past couple of decades from so-called “ hardcopy” to “softcopy” image analysis workflows: not only have the tools and techniques of image analysis changed\, but analysts can access an incr easingly diverse set of highly specialized image types. \; \; As a ny of our team members can attest\, expert imagery analysts are extremely good – and very fast – in detecting\, evaluating\, and extracting meaningf ul signatures from very large\, diverse sets of imagery

\n

Over the p ast few years\, we’ve come to appreciate the potential for eye tracking da ta to help us understand the chains of micro-decisions that describe an im agery analyst’s path through a geospatial information space. \; The ab ility to associate gaze events with image features in dynamic\, user-drive n workflows could reveal how imagery analysts acquire the skills necessary to extract information efficiently and accurately from geospatial dataset s. \; In practice\, however\, it is extremely difficult to study gaze- contingent decision-making in realistic\, user-driven workflows. \; Th erefore\, we’ve recently embarked on a methodological/software development project to create tools that will enable us to integrate gaze data with c omplementary behavioral indicators of analytic decision-making. Ultimately \, we’d like to enable researchers to establish a theoretically sound\, we ll-characterized empirical foundation for the design of visual analytic sy stems\, workflows\, and training protocols. \;

Contact:

Liz Stine-Morrow

eals@illinois.edu

Sponsor:

Educational Psychology CSTL Division

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