How to keep drivers engaged while supervising driving automation? A literature survey and categorization of six solution areas

This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation.submittedVersio

Real-Time Monitoring of High-Level States in Smart Environments

Modern smart environments are equipped with a multitude of devices and sensors aimed at intelligent services. The presence of these diverse devices has raised a major problem of managing complex environments. A rising solution to the problem is the modeling of user goals and intentions, and then interacting with the respective smart environments using user defined goals. Generally, the solution advocates that the user goal(s) can be represented by combining devices (smart appliances and sensor/actuators) in particular states. `Domotic Effects' is a high level modeling approach, which provides Ambient Intelligence (AmI) designers and integrators with a high level abstract layer that enables the definition of user goals in a smart environment, in a declarative way, which can be used to design and develop intelligent applications. This paper describes an approach for the automatic evaluation of domotic effects combined through Boolean expressions, that can provide efficient and intelligent monitoring of the domotic structure of the environment. ``Effects Evaluation'' addresses the problem of finding the new values of all the domotic effects defined for the environment when one or more devices change their state or one or more sensor value is recorded in the environment, hence determining a new overall state of the environment. The paper also presents an architecture to implement the evaluation of domotic effects. Results obtained from carried out experiments prove the feasibility of the approach and highlight responsiveness of the implemented effect evaluation

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Sense of agency and its disturbances: A systematic review targeting the intentional binding effect in neuropsychiatric disorders

Sense of agency (SoA) indicates a person's ability to perceive her/his own motor acts as actually being her/his and, through them, to exert control over the course of external events. Disruptions in SoA may profoundly affect the individual's functioning, as observed in several neuropsychiatric disorders. This is the first article to systematically review studies that investigated intentional binding (IB), a quantitative proxy for SoA measurement, in neurological and psychiatric patients. Eligible were studies of IB involving patients with neurological and/or psychiatric disorders. We included 15 studies involving 692 individuals. Risk of bias was low throughout studies. Abnormally increased action-outcome binding was found in schizophrenia and in patients with Parkinson's disease taking dopaminergic medications or reporting impulsive-compulsive behaviors. A decreased IB effect was observed in Tourette's disorder and functional movement disorders, whereas increased action-outcome binding was found in patients with the cortico-basal syndrome. The extent of IB deviation from healthy control values correlated with the severity of symptoms in several disorders. Inconsistent effects were found for autism spectrum disorders, anorexia nervosa, and borderline personality disorder. Findings pave the way for treatments specifically targeting SoA in neuropsychiatric disorders where IB is altered

Changing our brains and minds: exploring the effects of individual differences in bilingual language experience on brain structure, function, and cognition

Current research suggests that bilingualism affects both the structure of the brain and several cognitive processes. However, few studies have specifically examined effects of individual differences in bilingual language use on domain-general control processes and their neural correlates. This thesis project assesses the hypothesis that specific language use factors within the bilingual experience will alter neural activity and plasticity in regions implicated in language/executive control. Three studies are run. The first study is longitudinal, examining effects of long-term linguistic immersion on neural plasticity in highly proficient non-native (L2) speakers of English. Data from this study shows adaptations in brain structure related to increased efficiency of language processing and control and modulated by the length of L2 use prior to the study. The second and third studies are cross-sectional, examining effects of specific language use factors on 1) neural structure and intrinsic functional connectivity and 2) performance and neural activation patterns on executive function tasks. Factors related to duration of L2 use correlate to neurocognitive adaptations suggesting increased efficiency in language control. Factors related to extent of L2 use correlate to neurocognitive adaptations suggesting increased language control demands. Considered together, the data suggest that the brain constantly strives to be maximally effective and efficient in language processing control, which in turn affects domain-general cognitive processes. Crucially, the data highlight the necessity of considering specific, individual language experiences in assessing neurocognitive effects of bilingualism

Software agents & human behavior

People make important decisions in emergencies. Often these decisions involve high stakes in terms of lives and property. Bhopal disaster (1984), Piper Alpha disaster (1988), Montara blowout (2009), and explosion on Deepwater Horizon (2010) are a few examples among many industrial incidents. In these incidents, those who were in-charge took critical decisions under various ental stressors such as time, fatigue, and panic. This thesis presents an application of naturalistic decision-making (NDM), which is a recent decision-making theory inspired by experts making decisions in real emergencies. This study develops an intelligent agent model that can be programed to make human-like decisions in emergencies. The agent model has three major components: (1) A spatial learning module, which the agent uses to learn escape routes that are designated routes in a facility for emergency evacuation, (2) a situation recognition module, which is used to recognize or distinguish among evolving emergency situations, and (3) a decision-support module, which exploits modules in (1) and (2), and implements an NDM based decision-logic for producing human-like decisions in emergencies. The spatial learning module comprises a generalized stochastic Petri net-based model of spatial learning. The model classifies routes into five classes based on landmarks, which are objects with salient spatial features. These classes deal with the question of how difficult a landmark turns out to be when an agent observes it the first time during a route traversal. An extension to the spatial learning model is also proposed where the question of how successive route traversals may impact retention of a route in the agent’s memory is investigated. The situation awareness module uses Markov logic network (MLN) to define different offshore emergency situations using First-order Logic (FOL) rules. The purpose of this module is to give the agent the necessary experience of dealing with emergencies. The potential of this module lies in the fact that different training samples can be used to produce agents having different experience or capability to deal with an emergency situation. To demonstrate this fact, two agents were developed and trained using two different sets of empirical observations. The two are found to be different in recognizing the prepare-to-abandon-platform alarm (PAPA ), and similar to each other in recognition of an emergency using other cues. Finally, the decision-support module is proposed as a union of spatial-learning module, situation awareness module, and NDM based decision-logic. The NDM-based decision-logic is inspired by Klein’s (1998) recognition primed decision-making (RPDM) model. The agent’s attitudes related to decision-making as per the RPDM are represented in the form of belief, desire, and intention (BDI). The decision-logic involves recognition of situations based on experience (as proposed in situation-recognition module), and recognition of situations based on classification, where ontological classification is used to guide the agent in cases where the agent’s experience about confronting a situation is inadequate. At the planning stage, the decision-logic exploits the agent’s spatial knowledge (as proposed in spatial-learning module) about the layout of the environment to make adjustments in the course of actions relevant to a decision that has already been made as a by-product of situation recognition. The proposed agent model has potential to be used to improve virtual training environment’s fidelity by adding agents that exhibit human-like intelligence in performing tasks related to emergency evacuation. Notwithstanding, the potential to exploit the basis provided here, in the form of an agent representing human fallibility, should not be ignored for fields like human reliability analysis

Nonlinear Dynamical Systems for Theory And Research In Ergonomics

Nonlinear dynamical systems (NDS) theory offers new constructs, methods and explanations for phenomena that have in turn produced new paradigms of thinking within several disciplines of the behavioural sciences. This article explores the recent developments of NDS as a paradigm in ergonomics. The exposition includes its basic axioms, the primary constructs from elementary dynamics and so-called complexity theory, an overview of its methods, and growing areas of application within ergonomics. The applications considered here include: psychophysics, iconic displays, control theory, cognitive workload and fatigue, occupational accidents, resilience of systems, team coordination and synchronisation in systems. Although these applications make use of different subsets of NDS constructs, several of them share the general principles of the complex adaptive system