The Nature of Human Errors: An Emerging Interdisciplinary Perspective
Bibliographic record
Abstract
Cognitive Science Symposium Proposal The Nature of Human Errors: An Emerging Interdisciplinary Perspective Jiajie Zhang, Ph.D. (Chair) Associate Professor, Dept of Health Informatics University of Texas at Houston Edward H. Shortliffe, M.D., Ph.D. Professor and Chair, Department of Medical Informatics Columbia College of Physicians and Surgeons Vimla L. Patel, Ph.D., DSc Professor, Departments of Medicine and Psychology Director, Center for Medical Education, McGill University Michael Freed 1 , Ph.D. & Roger Remington 2 , Ph.D. Research Associate 1 , Director 2 , Cognition Lab NASA Ames Research Center cognitive theory of slips (based on Norman’s schema the- ory) that attempts to explain why slips occur and predict when they occur. The second part will be about the design of systems that minimize human errors. The cognitive theory of slips points out the causes and predicts what types of slips will happen under what circumstances. With such a theoretical guideline, we can design systems that have properties that can make certain types of slips impossible to occur or minimize the factors that can cause errors (e.g., a good user interface that minimizes mental workload). Introduction In light of a growing awareness of the role of human errors in widely publicized incidents such as airline accidents and complications of medical procedures, now is the right time for cognitive science to make a contribution to the study and prevention of human errors. As shown in Figure 1, human errors account for more than half of accidents in most indus- tries. In air traffic control, the rate is over 90%. Human er- rors occur primarily due to inadequate information process- ing. As an interdisciplinary field for the study of informa- tion processing in humans and machines, cognitive science can make a significant contribution to human error studies. In this symposium, the four presentations will address human errors from four different perspectives. Conceptual and Procedural Errors in Medical Decision Making Vimla L. Patel Cognitive studies of errors in medical decision making have traditionally focused on biases and faulty heuristics that lead health professionals to fail to attend to, or prop- erly consider, relevant data. The error is sometimes attrib- uted to physicians' lack of competency in probabilistic rea- soning. In our view, decision making is an inherently com- plex cognitive and social process and errors can have mul- tiple etiologies. It is convenient to partition sources of error into three categories: 1) individual/cognitive, 2) so- cial/communicative and 3) systemic/institutional. Errors can arise due to actions (or neglect) of a single individual. Decision making critically depends on the availability of current information, a level of understanding, and the use of appropriate decision strategies. The most serious cognitive errors are those that arise for reasons other than simple neglect or oversight (e.g., unintended slips). Possible causes include procedural er- rors and faulty conceptual knowledge. In addition, several studies have documented errors due to dissociations be- tween subjects' conceptual understanding and their applica- tion of knowledge in solving patient problems. For exam- ple, a subject may understand that certain levels of serum cholesterol coupled with other symptoms necessitate pharmaceutical intervention, but may fail to incorporate this knowledge into an action plan. Similarly, an individual may know how to carry out an effective procedure, but lack the prerequisite conceptual knowledge required to determine its suitability or to cope with problems that arise when it is being performed. This can lead to errors of over- generalization or contribute to use of an overly narrow perspective (violation of constraints). Accidents Due To Human Errors Human Error Others Petrochemical plants Medicine Worldwide Jet cargo US nuclear power plants Automobiles Air traffic control Figure 1. Accidents due to human errors Human Errors: Cognitive Theory & Interface Design Jiajie Zhang There are two major types of human errors (Reason, 1990): planning and execution errors. Slips are errors of execution in which the correct action does not proceed as intended. Mistakes are errors of planning in which the original in- tended action is not correct. This presentation will focus on four types of slips (Norman, 1981). Caption slips result from automatic activation of a well-learned routine that overrides the current intended activity (e.g., driving home directly instead of picking up a prescription on the home way). Description slips are due to incomplete or ambiguous specification of intention that is similar to a familiar inten- tion (e.g., inserting a Zip disk to a floppy drive). Associative activation slips are due to activation of similar but incorrect schemas (e.g., picking up the desktop phone when the cell phone rings). Loss-of-activation slips are due to loss of the activation of current intention (e.g., forgetting an idea for this symposium proposal after answering an interruptive phone call). The first part of this presentation will describe a
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".