Inattention and Uncertainty in the Predictive Brain

Modeling road user response timing in naturalistic traffic conflicts: A surprise-based framework

There is currently no established method for evaluating human response timing across a range of naturalistic traffic conflict types. Traditional notions derived from controlled experiments, such as perception-response time, fail to account for the situation-dependency of human responses and offer no clear way to define the stimulus in many common traffic conflict scenarios. As a result, they are not well suited for application in naturalistic settings. We present a novel framework for measuring and modeling response times in naturalistic traffic conflicts applicable to automated driving systems as well as other traffic safety domains. The framework suggests that response timing must be understood relative to the subject's current (prior) belief and is always embedded in, and dependent on, the dynamically evolving situation. The response process is modeled as a belief update process driven by perceived violations to this prior belief, that is, by surprising stimuli. The framework resolves two key limitations with traditional notions of response time when applied in naturalistic scenarios: (1) The strong situation dependence of response timing and (2) how to unambiguously define the stimulus. Resolving these issues is a challenge that must be addressed by any response timing model intended to be applied in naturalistic traffic conflicts. We show how the framework can be implemented by means of a relatively simple heuristic model fit to naturalistic human response data from real crashes and near crashes from the SHRP2 dataset and discuss how it is, in principle, generalizable to any traffic conflict scenario. We also discuss how the response timing framework can be implemented computationally based on evidence accumulation enhanced by machine learning-based generative models and the information-theoretic concept of surprise.

On the relationship between occlusion times and in-car glance durations in simulated driving

Drivers have spare visual capacity in driving, and often this capacity is used for engaging in secondary in-car tasks. Previous research has suggested that the spare visual capacity could be estimated with the occlusion method. However, the relationship between drivers' occlusion times and in-car glance duration preferences has not been sufficiently investigated for granting occlusion times the role of an estimate of spare visual capacity. We conducted a driving simulator experiment (N = 30) and investigated if there is an association between drivers' occlusion times and in-car glance durations in a given driving scenario. Furthermore, we explored which factors and variables could explain the strength of the association. The findings suggest an association between occlusion time preferences and in-car glance durations in visually and cognitively low demanding unstructured tasks but that this association is lost if the in-car task is more demanding. The findings might be explained by the inability to utilize peripheral vision for lane-keeping when conducting in-car tasks and/or by in-car task structures that override drivers' preferences for the in-car glance durations. It seems that the occlusion technique could be utilized as an estimate of drivers' spare visual capacity in research - but with caution. It is strongly recommended to use occlusion times in combination with driving performance metrics. There is less spare visual capacity if this capacity is used for secondary tasks that interfere with the driver's ability to utilize peripheral vision for driving or preferences for the in-car glance durations. However, we suggest that the occlusion method can be a valid method to control for inter-individual differences in in-car glance duration preferences when investigating the visual distraction potential of, for instance, in-vehicle infotainment systems.

Egocentric Chunking in the Predictive Brain: A Cognitive Basis of Expert Performance in High-Speed Sports

What principles and mechanisms allow humans to encode complex 3D information, and how can it be so fast, so accurately and so flexibly transformed into coordinated action? How do these processes work when developed to the limit of human physiological and cognitive capacity—as they are in high-speed sports, such as alpine skiing or motor racing? High-speed sports present not only physical challenges, but present some of the biggest perceptual-cognitive demands for the brain. The skill of these elite athletes is in many ways an attractive model for studying human performance “in the wild”, and its neurocognitive basis. This article presents a framework theory for how these abilities may be realized in high-speed sports. It draws on a careful analysis of the case of the motorsport athlete, as well as theoretical concepts from: (1) cognitive neuroscience of wayfinding, steering, and driving; (2) cognitive psychology of expertise; (3) cognitive modeling and machine learning; (4) human-in-the loop modellling in vehicle system dynamics and human performance engineering; (5) experimental research (in the laboratory and in the field) on human visual guidance. The distinctive contribution is the way these are integrated, and the concept of chunking is used in a novel way to analyze a high-speed sport. The mechanisms invoked are domain-general, and not specific to motorsport or the use of a particular type of vehicle (or any vehicle for that matter); the egocentric chunking hypothesis should therefore apply to any dynamic task that requires similar core skills. It offers a framework for neuroscientists, psychologists, engineers, and computer scientists working in the field of expert sports performance, and may be useful in translating fundamental research into theory-based insight and recommendations for improving real-world elite performance. Specific experimental predictions and applicability of the hypotheses to other sports are discussed.