Change Blindness, Attention, and Driving Performance

Lees, Monica N.; Sparks, JonDavid; Lee, John D.; Rizzo, Matthew

doi:10.17077/drivingassessment.1211

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…In a follow-up study the authors examined the underlying features of two attention-related tasks, CB and UFOV, in relation to commonly used vision and cognitive test batteries, and they assessed driving performance measures using a simulator and an instrumented vehicle that measured real-world driver behavior (Lees, Sparks, Lee, & Rizzo, 2007). Driving performance was evaluated using simulated driving events (a police car located on the side of the road and a vehicle that ran a stop sign) and during an on-road drive where drivers were asked to perform a routefollowing task and a landmark and traffic sign identification task (see Uc, Rizzo, & Anderson, 2005;Uc, Rizzo, Anderson, Shi, & Dawson, 2004).…”

Section: Overview Of Neuroergonomics With Respect To Drivingmentioning

confidence: 99%

Translating cognitive neuroscience to the driver’s operational environment: A neuroergonomic approach

Lee¹

2011

The American Journal of Psychology

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Neuroergonomics provides a multidisciplinary translational approach that merges elements of neuroscience, human factors, cognitive psychology, and ergonomics to study brain structure and function in everyday environments. Driving safety, particularly that of older drivers with cognitive impairments, is a fruitful application domain for neuroergonomics. Driving makes demands on multiple cognitive processes that are often studied in isolation and so presents a useful challenge in generalizing findings from controlled laboratory tasks to predict safety outcomes. Neurology and the cognitive sciences help explain the mechanisms of cognitive breakdowns that undermine driving safety. Ergonomics complements this explanation with the tools for systematically exploring the various layers of complexity that define the activity of driving. A variety of tools, such as part task simulators, driving simulators, and instrumented vehicles, provide a window into cognition in the natural settings needed to assess the generalizability of laboratory findings and can provide an array of potential interventions to increase driving safety. Overview of neuroergonomics with respect to drivingNeuroergonomics is the study of brain and behavior at work (Parasuraman, 2003;Parasuraman & Rizzo, 2007). This multidisciplinary field merges the principles and practice of neuroscience and ergonomics to study brain structure and function in everyday environments. Whereas neuroscience and cognitive psychology have tended to focus on the neural structures and mental processes underlying cognition in controlled laboratory settings, ergonomics is more concerned with naturalistic behaviors. In each case, valuable information about how humans think and act in relation to the environment has been obtained. However, because cognition and behavior are often situational in context, laboratory findings-particularly with regard to decision making and related executive functions-may fail to predict behavior in complex and dynamic tasks that people confront in their daily lives. Thus, cognition must be considered in relation to actions and artifacts in the environment. Humans and computers make up joint cognitive systems that function in real-world settings. Consequently, neuro-

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Section: Overview Of Neuroergonomics With Respect To Drivingmentioning

confidence: 99%

Translating cognitive neuroscience to the driver’s operational environment: A neuroergonomic approach

Lee¹

2011

The American Journal of Psychology

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“…In a follow-up study the authors examined the underlying features of two attention related tasks, CB and UFOV, in relation to commonly used vision and cognitive test batteries, and driving performance measures assessed using a simulator and an instrumented vehicle that measured real world driver behavior (Lees, Sparks, Lee, & Rizzo, 2007). Driving performance was evaluated using simulated driving events (a police car located on the side of the road and a vehicle that ran a stop sign) and during an on-road drive where drivers were asked to perform a route following task and a landmark and traffic sign identification task (see Uc, Rizzo, & Anderson, 2005; Uc, Rizzo, Anderson, Shi, & Dawson, 2004).…”

Section: Using a Driving Context To Understand The Errors Drivers Makmentioning

confidence: 99%

“…(a) Change blindness task used by Rizzo et al (2009) and Lees et al (2007). The changes occurred over time with the change element being modified to either appear, disappear, change color or change location.…”

Section: Figurementioning

confidence: 99%

Translating cognitive neuroscience to the driver’s operational environment: A neuroergonomic approach

Lees

Cosman

Lee

et al. 2010

The American Journal of Psychology

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“…Attention recognition provides potential benefits in real-time applications. In manual driving, waiting at traffic lights requires selective attention [9], which can be recognized to provide warning to the driver if not attended. In automated driving, the vehicle should be able to access the sustained attention state of the driver, to check whether the driver is vigilant when the vehicle cannot handle the situation alone, paving the way to driver safety in automated driving.…”

Section: Research Scope and Questionsmentioning

confidence: 99%

“…It turned out that CSDF selected similar numbers of channels with other algorithms in terms of the average case. We also compared the number of selected channels with feature selection algorithms, as shown in Table 4.10, where SVM RFE selected the least channels (7) and CSDF selected the second least channels (9). In contrast, the feature selection algorithms which achieved similar accuracy with CSDF in the features are grouped into channels as much as possible.…”

Section: Comparison Of Selected Channels For the Tova Experimentsmentioning

confidence: 99%

EEG-based attention recognition using machine learning

Sun¹

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Attention is constantly required in many daily life tasks. Attention-related behavior, such as driving distraction, has been reported as a major reason in traffic accidents. Therefore, the recognition of attention can enhance task performance. Electroencephalogram (EEG) is used to study attention, since it provides a direct measure of the brain activity with high temporal resolution at 1-10ms. In this thesis, we study the recognition of selective attention and sustained attention (vigilance) using machine learning. We start by proposing an experiment which aims to recognize unattended and attended conditions induced by Test of Variables of Attention (TOVA), using EEG features supported by the event-related potential (ERP) literature. However, ERP does not work for real-time applications where the external stimuli (event) required by ERP cannot be controlled. In face of the low signal-to-noise ratio (SNR) in the non-ERP approach, we propose Channel Selection with Different Features (CSDF) algorithm, which selects channels with their own different feature sets, as well as restricts features to as few channels as possible. Using CSDF, 83 out of 868 features are selected to distinguish the unattended and attended conditions. The accuracy 94.3% (±5.6%) is the best compared to other feature selection and channel selection algorithms. Based on CSDF, we find that the first and second order difference in the left parietal and temporal lobes, as well as the Higuchi fractal dimension and mean signal amplitude in the right frontal lobe, are relevant to selective attention. Unlike selective attention which has discrete conditions such as attended/unattended, the vigilance stages cannot be easily observed. Analogous to sleep stages, we want to define the vigilance stages in open eye and situation-aware state in a subject-independent and vii SUMMARY SUMMARY data-driven way. In the literature, there are vigilance stage models defined under closed eye. However, the EEG signals are more complex in open eye and situation-aware state. Extreme learning machine autoencoder (ELMAE) is used to learn the EEG spectral features and define the vigilance stages during simulated driving. Results show that ELMAE is an efficient alternative to restricted Boltzmann machine (RBM) in vigilance recognition: ELMAE achieves root mean square error at 0.189 (±0.049), which is better than RBM at 0.195 (±0.046); and training speed significantly faster than RBM. Based on ELMAE, we define three vigilance stages in open eye and situation-aware state. Stage I is high vigilance, where the subject is attentive. Stage II is low vigilance, which is further divided into Stage II.1: drowsiness and difficulty in attention allocation; and Stage II.2: distraction instead of falling asleep. A possible explanation for stage II.2 is that, the environment contains not enough external stimuli to keep the open eye and situation-aware state, so that the brain performs vigilance regulation to seek external stimuli, and hence leading to distraction. A major limitation of ELMA...

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Change Blindness, Attention, and Driving Performance

Cited by 5 publications

References 17 publications

Translating cognitive neuroscience to the driver’s operational environment: A neuroergonomic approach

Translating cognitive neuroscience to the driver’s operational environment: A neuroergonomic approach

Translating cognitive neuroscience to the driver’s operational environment: A neuroergonomic approach

EEG-based attention recognition using machine learning

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