The roles of conceptual device models and user goals in avoiding device initialization errors

While mistakes, and approaches to design and training that reduce them, have been studied extensively, relatively little work in HCI studies 'slip' errors, which occur when one intends to do a certain action during a skilled task but unintentionally does another. In this article we examine approaches to training that might reduce the occurrence of a slip error referred to as a 'device initialization error'. This error occurs when skilled users of a device forget to perform some initialization action, such as positioning the cursor in a text entry box or setting the device into the correct mode, before entering data or performing some other significant activity. We report on an experiment studying the effects of two training interventions on this error, which aim to manipulate the salience of the error-prone action without making any physical changes to the device. In the first intervention participants were given a particular conceptual model of the device's operation, to evaluate whether having an improved understanding of the effect of each action would lead to fewer errors. In the second, participants were given a new device operation goal requiring them to 'test' the device, to evaluate whether attending to the outcome of initialization actions would lead to fewer errors. Only participants who were asked to 'test' the device and also given enhanced instructions to enter dummy data after completing initialization actions showed a statistically significant improvement in performance. Post-test interviews and evidence from existing literature suggest that when participants forgot the initialization step it was because they were attending to the subsequent data entry steps. This study highlights the central roles that user goals and attention play in the occurrence (or avoidance) of slip errors. (C) 2010 Elsevier B.V. All rights reserved

Making a Task Difficult: Evidence That Device-Oriented Steps Are Effortful and Error-Prone

Errors in the execution of procedural tasks can have severe consequences. Attempts to ameliorate these slip errors through increased training and motivation have been shown to be ineffective. Instead, we identified the steps in a task procedure on which errors are most likely to occur, so that these might be designed out of the task procedure in the first place. Specifically, we considered whether device-oriented steps (i.e., steps in the task procedure that do not directly contribute to the achievement of the task goal) are more error-prone than task-oriented steps (i.e., steps that do directly contribute to the task goal). Two experiments are reported in which participants were trained to perform a novel procedural task. Across conditions, we manipulated the extent to which each step in the task procedure appeared to contribute to the achievement of the task goal (i.e., alternating the assignment of a task step between device- and task-oriented), while keeping the interface and underlying task procedure the same. Results show that participants made more errors and took longer to complete a task step when it played a device-oriented role rather than a task-orientated role. These effects were exacerbated by the introduction of a secondary task designed to increase working memory load, suggesting that when a task step plays a device-oriented role it is more weakly represented in memory. We conclude that device-oriented task steps are inherently problematic and should be avoided where possible in the design of task procedures

Task Structure and Postcompletion Error in the Execution of a Routine Procedure

Objective: To replicate a successful laboratory slip-class error paradigm and, more importantly, to further understand the underlying causes of errors made in that paradigm. Background: Routine procedural errors are facts of everyday life but have received limited controlled empirical study, despite the sometimes severe consequences associated with such errors. This research concerns one such error, postcompletion error (M. D. Byrne & S. Bovair, 1997), which is a lapse that occurs after the main goal of a task has been satisfied. Method: In the two experiments conducted, participants were trained to criterion on a routine procedural task and were then brought back to the lab for a later session or sessions in which performance on task execution was measured. In the second experiment, a variety of motivational manipulations, retraining, and task redesign were compared. Results: Experiment 1 demonstrated a substantial reduction of error rate generated by a simple design change (alteration of when feedback about goal completion occurred). Furthermore, the reduction in error rate came with no penalty in terms of overall speed of performance. Experiment 2 showed that this more appropriate design is superior to motivationally oriented interventions, retraining, and even midtask redesign. As in Experiment 1, Experiment 2 revealed no speed-accuracy tradeoff. Conclusion: These experiments provide evidence that controlled laboratory studies of slip-class errors can be meaningful and highlight the centrality of cognitive factors (particularly goal structure) in such errors. Application: Potential applications include design of interfaces and their related procedures as well as error-mitigation techniques

Predictive models of procedural human supervisory control behavior

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2011.Page 150 blank. Cataloged from PDF version of thesis.Includes bibliographical references (p. 138-149).Human supervisory control systems are characterized by the computer-mediated nature of the interactions between one or more operators and a given task. Nuclear power plants, air traffic management and unmanned vehicles operations are examples of such systems. In this context, the role of the operators is typically highly proceduralized due to the time and mission-critical nature of the tasks. Therefore, the ability to continuously monitor operator behavior so as to detect and predict anomalous situations is a critical safeguard for proper system operation. In particular, such models can help support the decision making process of a supervisor of a team of operators by providing alerts when likely anomalous behaviors are detected. By exploiting the operator behavioral patterns which are typically reinforced through standard operating procedures, this thesis proposes a methodology that uses statistical learning techniques in order to detect and predict anomalous operator conditions. More specifically, the proposed methodology relies on hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) to generate predictive models of unmanned vehicle systems operators. Through the exploration of the resulting HMMs in two distinct single operator scenarios, the methodology presented in this thesis is validated and shown to provide models capable of reliably predicting operator behavior. In addition, the use of HSMMs on the same data scenarios provides the temporal component of the predictions missing from the HMMs. The final step of this work is to examine how the proposed methodology scales to more complex scenarios involving teams of operators. Adopting a holistic team modeling approach, both HMMs and HSMMs are learned based on two team-based data sets. The results show that the HSMMs can provide valuable timing information in the single operator case, whereas HMMs tend to be more robust to increased team complexity. In addition, this thesis discusses the methodological and practical limitations of the proposed approach notably in terms of input data requirements and model complexity. This thesis thus provides theoretical and practical contributions by exploring the validity of using statistical models of operators as the basis for detecting and predicting anomalous conditions.by Yves Boussemart.Ph.D

An empirical investigation of post-completion error: A cognitive perspective.

Forgetting to retrieve your original after photocopying, forgetting to collect your card after a withdrawal from a cash machine, are examples of a specific type of omission error termed post-completion error (Byrne & Bovair, 1997). A post-completion error (PCE) is the omission of a "clean-up" step after the main goal of a task is fulfilled. The error phenomenon has the property of being infrequent but persistent it does not occur very often and, yet, it continues to occur now and again. This thesis is an empirical investigation of PCE to examine factors that provoke or mitigate the error. The investigation consists of two series of experiments. The first series of experiments is an extension of Byrne & Bovair's finding of the effect of high working memory demand on the increased occurrences of PCE. A novel paradigm was designed and adopted in the experiments it was found that PCE also occurs in problem-solving tasks, which impose a high demand on working memory load. Results from the experiments also suggest that the use of static visual cues may reduce the error rate. The second series of experiments investigates the effect of interruption on PCE in a procedural task paradigm. Based on the activation-based goal memory model (Altmann & Trafton, 2002) predictions were made on the effect of interruption position and duration on the error. Results show that PCE is more likely to occur with interruption occurring just before the post-completion step. Interruption occurring earlier in the task has no effect on PCE rate it was found to be the same as having no interruptions at all. Moreover, interruption as brief as 15 seconds was found to be disruptive enough to increase PCE rate. The same disruptive effect was also obtained for other non-PCEs. The scarcity and disparate nature of the existing theoretical approaches to PCE motivated a meta-theoretical analysis of PCE. The analysis has resulted in the identification of the major criteria required for an adequate account of PCE. Although a complete cognitive model of PCE is beyond the scope of the current thesis, the meta-theoretical analysis offers new insights into the understanding of PCE and aids future theoretical development. The current thesis constitutes a methodological advance in studying PCE. New factors that provoke or mitigate the occurrence of the error were identified through empirical investigations. New insights into the understanding of the error were also possible through a meta-theoretical analysis within a coherent theoretical structure

Nuclear Power - Control, Reliability and Human Factors

Advances in reactor designs, materials and human-machine interfaces guarantee safety and reliability of emerging reactor technologies, eliminating possibilities for high-consequence human errors as those which have occurred in the past. New instrumentation and control technologies based in digital systems, novel sensors and measurement approaches facilitate safety, reliability and economic competitiveness of nuclear power options. Autonomous operation scenarios are becoming increasingly popular to consider for small modular systems. This book belongs to a series of books on nuclear power published by InTech. It consists of four major sections and contains twenty-one chapters on topics from key subject areas pertinent to instrumentation and control, operation reliability, system aging and human-machine interfaces. The book targets a broad potential readership group - students, researchers and specialists in the field - who are interested in learning about nuclear power