01 SEEV

• Salience

• Effort

• Expectancy

• Value

• attention-attracting properties of a display

• aufmerksamkeitserregende Eigenschaften eines Displays

• effort required to move attention between displays

• benötigter Aufwand um die Aufmerksamkeit zwischen mehreren Anzeigen zu bewegen/switchen

• expectancy that information (change) will appear within a display

• Erwartungshaltung, ob Information(-sveränderung) auf einem Display erscheinen wird

• events appearing within a display may have value in their own right

• relative importance of the task(s) served by that display

• Events auf einer Anzeige können intrinsischen Wert (von sich aus) haben

• auch relative Wichtigkeit von Aufgaben, die von einem spezifischen Display bereitgestellt werden

• Attentional attractiveness of a displayP =

  sS - efEF + exEX + vV

• Kleinbuchstaben sind Gewichte

• s and ef can be set to 0 -> creating a model that should guide attention in absence of potentially distracting salience and effort

• static: overall percentage dwell time on each display, but does not track the process of executing the scan path

• dynamic: captures the sequence of fixations and not just their overall frequency (first-order Markov model)

• salience: of the window scene is set larger than that of either the speed or heading displays (cause larger, color)

• effort: is rated lower for the window and the speed than for heading (distance from center NOT CENTROID)

• expectancy: assumptions that a) the window, depicting both heading and speed changes, will have a bandwidth that is the sum of the speed and heading displays and b) heading higher than speed (cause heading can be changed easier)

• value parameters involve the mediating variable of task importance and display for a task relevance

• relevance = Frequenz

• value = importance * relevance

-> macht Sinn, wenn man davon ausgeht dass für window display effort niedrig und salience hoch ist

-> alles recht hoch

• Level 1: Perception of data and the elements of the environment

• Level 2: Comprehension of the meaning and significance of the situation

• Level 3: Projection of future states and events

a specific decision

the performance of the chosen action

changes of the state of the environment/system, which in turn starts the situation awareness cycle again with the perception of the changed data/elements in the environment

• influence situation awareness, decision-making and the performance of action

• workload

• stressors

• system design

• complexity

• influence situation awareness, decision-making and the performance of action

• goals

• preconceptions / assumptions

• knowledge

• experience

• training

• abilities

• only visual attention

• mainly focal attention (absichtlich gesteuerte Aufmerksamkeit)

• effort and salience difficult to investigate (coding eye tracking data, modeling -> tedious)

• parameters need to be assigned for specific/situations (kein generalisierbares Modellieren möglich)

• real cases weniger gute Predictions als in simulated cases

• junior/senior nimmt sich nicht viel

• “Messy real cases in the OR compared to “tidy” simulated case in a more sheltered environment”

• support user during the actual execution of a task

• zB Checklisten, AR, Apps…

• simulation is limited

• enough monitoring of patient vital signs in real settings?

• Unterschiede zwischen Simulation und realer Umgebung in Bezug auf die visual attention distribution

• including effort and salience did not improve model fit -> EV ausreichend

• EV seems like a valid measure for situation awareness level 1 (perception)

• EV model not comprehensive enough to predict higher situation awareness levels or decision/performance

• EV model möglich als Tool um Technologie zu evaluieren

• EV parameters estimaed by 20 experienced drivers vs 9 HF experts

• 20 VP (experienced drivers)

• 257 Manöver in SILAB (simulation software)

• 16 AOI classes

• von Experten bestimmt

• Individual, experienced car drivers are bad in identifying the regions that they are looking at during an overtaking maneuver and also in modelling how they divide attention between these regions

• bad in predicting their own monitoring behavior

• r = 0.318 (wenig Übereinstimmung)

• >75% der Blicke waren nicht Teil der AOIs

• aggregating the models of multiple experienced car drivers improves attention prediction results compared to an average individual experienced car driver

• r=0.719

• 19% der Blicke waren nicht Teil der AOIs

• HF Experten r=.394

  -> aggregierte Fahrer sagen besser voraus

• 16 AOIs

• zeitlich effiziente Parameterschätzung (1h15Min)

• Automatisiertes Data Processing!

• average model scores of multiple domain experts for best predictions

• Fragestellung: is it enough to model human attentional behavior in public display?

• Display auf Sportmesse

• salience = positiv, konstant (extreme sports sind spannend)

• expectancy = positiv, konstant (public displays sind bekannt für viel Infos)

• value = positiv, konstant (Sport, freiwillige Anwesenheit -> Motivation intrinsisch)

• effort??

• handlabeled / kodiert

• 0: No perception (display out of fov)

• 1: Selective perception (display in peripheral visual range)

• 2: Switched Attention (display actively in wandering gaze area)

• 3: Focused attention (conscious perception, distinct behavior change)

• 4: Sustained attention (lasting perception, fundamental behavior change)

Averaging over single VP:

• average 148frames/VP

• bias for shorter samples (less frames means worse model fit)

• MW=n.a., Md r=0.263

Overall correlation:

• Annahme: 28.000 Frames = EIN User

• Dann Korrelation zwischen 5 Klassen und Effort Model: r=0.749

• kein safety-critical context

• kein Kontrolltask zur Überwachung

• Effort hat einen Unterschied gemacht

nur Expectancy und Value (EV)

Alle vier Komponenten