Factors affecting numerical typing performance of young adults in a hear-and-type task

Effects of monetary reward and punishment on information checking behaviour

Two experiments were conducted to examine whether checking one's own work can be motivated by monetary reward and punishment. Participants were randomly assigned to one of three conditions: a flat-rate payment for completing the task (Control); payment increased for error-free performance (Reward); payment decreased for error performance (Punishment). Experiment 1 (N = 90) was conducted with liberal arts students, using a general data-entry task. Experiment 2 (N = 90) replicated Experiment 1 with clinical students and a safety-critical 'cover story' for the task. In both studies, Reward and Punishment resulted in significantly fewer errors, more frequent and longer checking, than Control. No such differences were obtained between the Reward and Punishment conditions. It is concluded that error consequences in terms of monetary reward and punishment can result in more accurate task performance and more rigorous checking behaviour than errors without consequences. However, whether punishment is more effective than reward, or vice versa, remains inconclusive.

Residents' numeric inputting error in computerized physician order entry prescription

BACKGROUND

Computerized physician order entry (CPOE) system with embedded clinical decision support (CDS) can significantly reduce certain types of prescription error. However, prescription errors still occur. Various factors such as the numeric inputting methods in human computer interaction (HCI) produce different error rates and types, but has received relatively little attention.

OBJECTIVE

This study aimed to examine the effects of numeric inputting methods and urgency levels on numeric inputting errors of prescription, as well as categorize the types of errors.

METHODS

Thirty residents participated in four prescribing tasks in which two factors were manipulated: numeric inputting methods (numeric row in the main keyboard vs. numeric keypad) and urgency levels (urgent situation vs. non-urgent situation). Multiple aspects of participants' prescribing behavior were measured in sober prescribing situations.

RESULTS

The results revealed that in urgent situations, participants were prone to make mistakes when using the numeric row in the main keyboard. With control of performance in the sober prescribing situation, the effects of the input methods disappeared, and urgency was found to play a significant role in the generalized linear model. Most errors were either omission or substitution types, but the proportion of transposition and intrusion error types were significantly higher than that of the previous research. Among numbers 3, 8, and 9, which were the less common digits used in prescription, the error rate was higher, which was a great risk to patient safety.

CONCLUSIONS

Urgency played a more important role in CPOE numeric typing error-making than typing skills and typing habits. It was recommended that inputting with the numeric keypad had lower error rates in urgent situation. An alternative design could consider increasing the sensitivity of the keys with lower frequency of occurrence and decimals. To improve the usability of CPOE, numeric keyboard design and error detection could benefit from spatial incidence of errors found in this study.

Integrating Behavior Modeling with Data Mining to Improve Human Error Prediction in Numerical Data Entry

Human errors in numerical data entry can lead to serious consequence but it is difficult to predict those errors because mechanisms of human errors vary and no contextual clues are available. This study suggests integrating human behavior modeling and data mining as an advanced method to predict human errors. Human behavior modeling utilized top-down inference to transform interactions between task characteristics and conditions into general inclination of an average operator to make errors, while data mining parsed psychophysiological measurements into individual’s likeliness of making errors on a trial-by-trial basis through bottom-up analysis. Specifically, an enhanced Queuing Network-Model Human Processor (QN-MHP) generated modeling features to be combined with real-time EEG features that were collected in a realistic numerical typing experiment, and potential errors were predicted by detecting error-associated features by linear discriminant analysis (LDA) classifiers before responses. The detection could be made as early as 300 milliseconds beforehand, and the results showed that integration improved the LDA classifiers’ performance by 31.7% in keenness ( d') and by 12.5 % in area under ROC curve (AUC) from that of using EEG only. The integration may help implement future adaptive augmented system to prevent cognitive breakdown by determining appropriate automation/augmentation levels.

Reactions, accuracy and response complexity of numerical typing on touch screens

Touch screens are popular nowadays as seen on public kiosks, industrial control panels and personal mobile devices. Numerical typing is one frequent task performed on touch screens, but this task on touch screen is subject to human errors and slow responses. This study aims to find innate differences of touch screens from standard physical keypads in the context of numerical typing by eliminating confounding issues. Effects of precise visual feedback and urgency of numerical typing were also investigated. The results showed that touch screens were as accurate as physical keyboards, but reactions were indeed executed slowly on touch screens as signified by both pre-motor reaction time and reaction time. Provision of precise visual feedback caused more errors, and the interaction between devices and urgency was not found on reaction time. To improve usability of touch screens, designers should focus more on reducing response complexity and be cautious about the use of visual feedback.

PRACTITIONER SUMMARY

The study revealed that slower responses on touch screens involved more complex human cognition to formulate motor responses. Attention should be given to designing precise visual feedback appropriately so that distractions or visual resource competitions can be avoided to improve human performance on touch screens.