Supporting Attention Allocation in Multitask Environments : Effects of Likelihood Alarm Systems on Trust, Behavior, and Performance

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Objective: The aim of the current study was to investigate potential benefits of likelihood alarm systems (LASs) over binary alarm systems (BASs) in a multitask environment. Background: Several problems are associated with the use of BASs, because most of them generate high numbers of false alarms. Operators lose trust in the systems and ignore alarms or cross-check all of them when other information is available. The first behavior harms safety, whereas the latter one reduces productivity. LASs represent an alternative, which is supposed to improve operators’ attention allocation. Method: We investigated LASs and BASs in a dual-task paradigm with and without the possibility to cross-check alerts with raw data information. Participants’ trust in the system, their behavior, and their performance in the alert and the concurrent task were assessed. Results: Reported trust, compliance with alarms, and performance in the alert and the concurrent task were higher for the LAS than for the BAS. The cross-check option led to an increase in alert task performance for both systems and a decrease in concurrent task performance for the BAS, which did not occur in the LAS condition. Conclusion: LASs improve participants’ attention allocation between two different tasks and therefore lead to an increase in alert task and concurrent task performance. The performance maximum is achieved when LAS is combined with a cross-check option for validating alerts with additional information. Application: The use of LASs instead of BASs in safety-related multitask environments has the potential to increase safety and productivity likewise

Why Are People's Decisions Sometimes Worse with Computer Support?

In many applications of computerised decision support, a recognised source of undesired outcomes is operators' apparent over-reliance on automation. For instance, an operator may fail to react to a potentially dangerous situation because a computer fails to generate an alarm. However, the very use of terms like "over-reliance" betrays possible misunderstandings of these phenomena and their causes, which may lead to ineffective corrective action (e.g. training or procedures that do not counteract all the causes of the apparently "over-reliant" behaviour). We review relevant literature in the area of "automation bias" and describe the diverse mechanisms that may be involved in human errors when using computer support. We discuss these mechanisms, with reference to errors of omission when using "alerting systems", with the help of examples of novel counterintuitive findings we obtained from a case study in a health care application, as well as other examples from the literature

Assessing the human factors risks in extending the use of AWS

The project reported in this paper was conducted on behalf of the Rail Safety and Standards Board, and formed part of the SPAD reduction and mitigation research theme. It sought to assess the Human Factors risks associated with extending the use of the in-cab Automatic Warning System (AWS). The term “Extended AWS” refers to any situation where AWS is used other than to warn of the state of upcoming signals. This includes uses for permanent, temporary and emergency speed restrictions, certain level crossings, and, potentially, multi-SPAD signals. The paper summarises the work performed in the study. It considers new areas of psychological investigation believed to be important for driver related research, the methods used to gather and analyse industry experience, and concludes by examining the risk of drivers failing to behave appropriately to AWS warnings

Maximal benefits and possible detrimental effects of binary decision aids

Binary decision aids, such as alerts, are a simple and widely used form of automation. The formal analysis of a user's task performance with an aid sees the process as the combination of information from two detectors who both receive input about an event and evaluate it. The user's decisions are based on the output of the aid and on the information, the user obtains independently. We present a simple method for computing the maximal benefits a user can derive from a binary aid as a function of the user's and the aid's sensitivities. Combining the user and the aid often adds little to the performance the better detector could achieve alone. Also, if users assign non-optimal weights to the aid, performance may drop dramatically. Thus, the introduction of a valid aid can actually lower detection performance, compared to a more sensitive user working alone. Similarly, adding a user to a system with high sensitivity may lower its performance. System designers need to consider the potential adverse effects of introducing users or aids into systems

Cross-Border Information Transfers: Evidence from Profit Warnings Issued by European Firms

This paper reports evidence on cross-border accounting information transfers associated with profit warning announcements. Using a sample of firms from 29 European countries, we find that negative earnings surprises disclosed by firms in one country affect investors’ perceptions of comparable nonannouncing firms in other countries. The form and magnitude of cross-border effects is consistent with domestic transfers. Tests explaining variation in cross-border information transfers provide some (albeit rather limited) evidence that effects vary according to a range of firm-, industry- and country-level characteristics.Information transfers; Profit warnings

Flight-deck automation: Promises and problems

The state of the art in human factors in flight-deck automation is presented. A number of critical problem areas are identified and broad design guidelines are offered. Automation-related aircraft accidents and incidents are discussed as examples of human factors problems in automated flight

Designing informative warning signals: Effects of indicator type, modality, and task demand on recognition speed and accuracy

An experiment investigated the assumption that natural indicators which exploit existing learned associations between a signal and an event make more effective warnings than previously unlearned symbolic indicators. Signal modality (visual, auditory) and task demand (low, high) were also manipulated. Warning effectiveness was indexed by accuracy and reaction time (RT) recorded during training and dual task test phases. Thirty-six participants were trained to recognize 4 natural and 4 symbolic indicators, either visual or auditory, paired with critical incidents from an aviation context. As hypothesized, accuracy was greater and RT was faster in response to natural indicators during the training phase. This pattern of responding was upheld in test phase conditions with respect to accuracy but observed in RT only in test phase conditions involving high demand and the auditory modality. Using the experiment as a specific example, we argue for the importance of considering the cognitive contribution of the user (viz., prior learned associations) in the warning design process. Drawing on semiotics and cognitive psychology, we highlight the indexical nature of so-called auditory icons or natural indicators and argue that the cogniser is an indispensable element in the tripartite nature of signification

Scientific knowledge and scientific uncertainty in bushfire and flood risk mitigation: literature review

EXECUTIVE SUMMARY The Scientific Diversity, Scientific Uncertainty and Risk Mitigation Policy and Planning (RMPP) project aims to investigate the diversity and uncertainty of bushfire and flood science, and its contribution to risk mitigation policy and planning. The project investigates how policy makers, practitioners, courts, inquiries and the community differentiate, understand and use scientific knowledge in relation to bushfire and flood risk. It uses qualitative social science methods and case studies to analyse how diverse types of knowledge are ordered and judged as salient, credible and authoritative, and the pragmatic meaning this holds for emergency management across the PPRR spectrum. This research report is the second literature review of the RMPP project and was written before any of the case studies had been completed. It synthesises approximately 250 academic sources on bushfire and flood risk science, including research on hazard modelling, prescribed burning, hydrological engineering, development planning, meteorology, climatology and evacuation planning. The report also incorporates theoretical insights from the fields of risk studies and science and technology studies (STS), as well as indicative research regarding the public understandings of science, risk communication and deliberative planning. This report outlines the key scientific practices (methods and knowledge) and scientific uncertainties in bushfire and flood risk mitigation in Australia. Scientific uncertainties are those ‘known unknowns’ and ‘unknown unknowns’ that emerge from the development and utilisation of scientific knowledge. Risk mitigation involves those processes through which agencies attempt to limit the vulnerability of assets and values to a given hazard. The focus of this report is the uncertainties encountered and managed by risk mitigation professionals in regards to these two hazards, though literature regarding natural sciences and the scientific method more generally are also included where appropriate. It is important to note that while this report excludes professional experience and local knowledge from its consideration of uncertainties and knowledge, these are also very important aspects of risk mitigation which will be addressed in the RMPP project’s case studies. Key findings of this report include: Risk and scientific knowledge are both constructed categories, indicating that attempts to understand any individual instance of risk or scientific knowledge should be understood in light of the social, political, economic, and ecological context in which they emerge. Uncertainty is a necessary element of scientific methods, and as such risk mitigation practitioners and researchers alike should seek to ‘embrace uncertainty’ (Moore et al., 2005) as part of navigating bushfire and flood risk mitigation

Effects of Television Weather Broadcasters on Viewers During Severe Weather: To Be or Not To Be On-Screen

An association was tested between the presence of a television weather broadcaster on-screen and viewers’ likelihood to seek shelter, measured via risk perception and preventative behavior. Social networking websites were used to recruit respondents. Four clips of archived severe weather videos, one pair (on-screen and off-screen broadcaster) using the reflectivity product and another pair (on-screen and off-screen broadcaster) using velocity product, were presented to participants. Viewers’ trust and weather salience were also quantified for additional interactions. A relationship between viewers’ risk perception (preflectivity = 0.821, pvelocity = 0.625) and preventative behavior (preflectivity = 0.217, pvelocity = 0.236) and the presence of the broadcaster on-screen was not found. The reflectivity product was associated with higher risk perception and preventative behavior scores than the velocity product (prp = 0.000, ppb = 0.000)