A model-driven tool for getting insights into car drivers’ monitoring behavior

Feuerstack, Sebastian; Wortelen, Bertram

doi:10.1109/ivs.2017.7995824

Cited by 5 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The best model prediction is obtained by averaging the scores of multiple subject matter experts (Feuerstack & Wortelen, 2017); therefore, we asked further two qualified emergency physicians to provide priority, expectancy, and relevance scores. The average correlation of the three parameter score sets from each expert was r = 0.541 (all p -value < .001).…”

Section: Methodsmentioning

confidence: 99%

Guiding Attention via a Cognitive Aid During a Simulated In-Hospital Cardiac Arrest Scenario: A Salience Effort Expectancy Value Model Analysis

et al. 2021

View full text Add to dashboard Cite

Objective To investigate the effect of a cognitive aid on the visual attention distribution of the operator using the Salience Effort Expectancy Value (SEEV) model. Background Cognitive aids aim to support an operator during the execution of a task. The effect of cognitive aids on performance is frequently evaluated but whether a cognitive aid improved, for example, attention distribution has not been considered. Method We built the Expectancy Value (EV) model version which can be considered to indicate optimal attention distribution for a given event. We analyzed the eye tracking data of emergency physicians while using a cognitive aid application versus no application during a simulated in-hospital cardiac arrest scenario. Results The EV model could fit the attention distribution in such a simulated emergency situation. Partially supporting our hypothesis, the cognitive aid application group showed a significantly better EV model fit than the no application group in the first phases of the event, but a worse fit in the last phase. Conclusion We demonstrated that a cognitive aid affected attention distribution and that the SEEV model provides the means of capturing these effects. We suggest that the aid supported and improved visual attention distribution in the stressful first phases of a cardiopulmonary resuscitation but may have focused attention on objects that are relevant for lower priority goals in the last phase. Application The SEEV model can provide insights into expected and unexpected effects of cognitive aids on visual attention distribution and may help to design better artifacts.

show abstract

Section: Methodsmentioning

confidence: 99%

Guiding Attention via a Cognitive Aid During a Simulated In-Hospital Cardiac Arrest Scenario: A Salience Effort Expectancy Value Model Analysis

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In addition, the method does not include any threshold level for mental overload as opposed to other methods such as GOMS. AIE (Wortelen et al, 2013a;Wortelen et al, 2013b;Wortelen et al, 2013c) and HEE (Feuerstack & Wortelen, 2017) are two separate dynamic simulation models of human attention, although they are both based on the SEEV model, which is the method that is typically used to predict visual attention allocation to multiple information sources and is a well-tested and widely used model of attention allocation (Wickens et al, 2003). SEEV classifies four factors that influence attention allocation including saliency, effort, expectancy, and value.…”

Section: Other Modeling Approachesmentioning

confidence: 99%

“…The method is easy to use for experts without human factors background or programming skills as it runs on a GUI-based modeling environment. Using photos of human-machine monitoring situations and operators in each task as inputs, HEE generates attention allocation responses (Feuerstack & Wortelen, 2017).…”

Section: Other Modeling Approachesmentioning

confidence: 99%

A Review of Human Performance Models for Prediction of Driver Behavior and Interactions With In-Vehicle Technology

Park

Zahabi

2022

Hum Factors

View full text Add to dashboard Cite

Objective This study investigated the use of human performance modeling (HPM) approach for prediction of driver behavior and interactions with in-vehicle technology. Background HPM has been applied in numerous human factors domains such as surface transportation as it can quantify and predict human performance; however, there has been no integrated literature review for predicting driver behavior and interactions with in-vehicle technology in terms of the characteristics of methods used and variables explored. Method A systematic literature review was conducted using Compendex, Web of Science, and Google Scholar. As a result, 100 studies met the inclusion criteria and were reviewed by the authors. Model characteristics and variables were summarized to identify the research gaps and to provide a lookup table to select an appropriate method. Results The findings provided information on how to select an appropriate HPM based on a combination of independent and dependent variables. The review also summarized the characteristics, limitations, applications, modeling tools, and theoretical bases of the major HPMs. Conclusion The study provided a summary of state-of-the-art on the use of HPM to model driver behavior and use of in-vehicle technology. We provided a table that can assist researchers to find an appropriate modeling approach based on the study independent and dependent variables. Application The findings of this study can facilitate the use of HPM in surface transportation and reduce the learning time for researchers especially those with limited modeling background.

show abstract

Semi-automatic Aggregation of Multiple Models of Visual Attention for Model-Based User Interface Evaluation

Knoop

Wortelen

Behrendt

2019

Engineering Psychology and Cognitive Ergonomics

View full text Add to dashboard Cite

A model-driven tool for getting insights into car drivers’ monitoring behavior

Cited by 5 publications

References 11 publications

Guiding Attention via a Cognitive Aid During a Simulated In-Hospital Cardiac Arrest Scenario: A Salience Effort Expectancy Value Model Analysis

Guiding Attention via a Cognitive Aid During a Simulated In-Hospital Cardiac Arrest Scenario: A Salience Effort Expectancy Value Model Analysis

A Review of Human Performance Models for Prediction of Driver Behavior and Interactions With In-Vehicle Technology

Semi-automatic Aggregation of Multiple Models of Visual Attention for Model-Based User Interface Evaluation

Contact Info

Product

Resources

About