Figure 1 - uploaded by Daniel N. Cassenti
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Hypothesized workload curve based on the data from Cassenti and Kelley (2006). The letters A, B, C, and D represent bounded regions of workload where the qualitative description of the performance function change.
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Human factors research is often focused on the mental workload that is required to perform a task or set of tasks with the goal of reducing workload to make systems easier to manage. The Improved Performance Research Integration Tool (IMPRINT) includes an algorithm to predict mental workload. The algorithm was developed using subject matter expert...
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... finding presumably resulted from increased distraction as the participant filled remaining mental resources with non-task-related thought. In agreement with North et al. (1979), Cassenti and Kelley found steadily decreasing performance (Section C of Figure 1) and that performance decreased until it reached an asymptote where an increase in tasks results in no greater proportion of errors per number of tasks (Section D of Figure 1). Figure 1 displays the revised model based on the Cassenti and Kelley data. ...
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... finding presumably resulted from increased distraction as the participant filled remaining mental resources with non-task-related thought. In agreement with North et al. (1979), Cassenti and Kelley found steadily decreasing performance (Section C of Figure 1) and that performance decreased until it reached an asymptote where an increase in tasks results in no greater proportion of errors per number of tasks (Section D of Figure 1). Figure 1 displays the revised model based on the Cassenti and Kelley data. ...
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... agreement with North et al. (1979), Cassenti and Kelley found steadily decreasing performance (Section C of Figure 1) and that performance decreased until it reached an asymptote where an increase in tasks results in no greater proportion of errors per number of tasks (Section D of Figure 1). Figure 1 displays the revised model based on the Cassenti and Kelley data. The goal of this paper is to extend the Cassenti and Kelley (2006) studies by continuing to study how workload affects performance across a variety of task types. ...
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... two more difficult rules are linear. These findings resemble two regions of Figure 1 (i.e., B and C). First the functions for the easiest task types resemble Region B. Although the curves are not flat, the increase in errors is relatively flat and could be considered ceiling performance. ...
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... we found that task types with lower workload values (i.e., workload values established by Bierbaum et al., 1989) roughly corresponded with Region B of Figure 1. The difference between the data for Experiments 1 and 2 and the hypothesized curve from Cassenti and Kelley (2006) was a slight increase in the number of errors with greater workload. ...
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... 2000 ms that participants had available to decide whether a target followed the rule should be changed in a new set of studies and the new results should guide the modeling function recommended to IMPRINT developers. The change in timing parameters should change performance and potentially shift the performance function into different regions of Figure 1. Together, the current studies and the future studies would help to also leverage a workload model described in Hancock and Caird (1993) that is also based on the time available to perform a task or set of tasks. ...
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The goal of each investigation in the computer area is to optimize the correlation " performance-cost ". The computer structures and processes could be formalized as a collection of different finite sets (system resource) and by stochastic flow for the workload. In this reason a hierarchical procedure for evaluation is discussed and its formal desc...
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... More specifically, we developed an experimental protocol using the Multi-Attribute Task Battery II (MATB-II) (Santiago-Espada et al., 2011) in which participants performed a cognitive task under two levels of MW (low, high) and under three levels of physical activity (no, medium, high) by either walking/running on a treadmill or riding a stationary bike. Recent works (e.g., Wilson and Russell, 2003b;Cassenti et al., 2010) have shown that the MATB-II better elicits MW than tasks typically reported in the literature, such as the N-back task (Milner, 1998), mental rotation (Johnson, 1990), and visual search (Shepard and Metzler, 1971). This experimental design allows investigating questions that remain elusive in the MW assessment literature, such as the interplay between different modalities, and the impact of increased physical activity and movement on MW correlates in terms of added artifacts, as well as what additional mental resources are drawn by the physical activity. ...
Assessment of mental workload is crucial for applications that require sustained attention and where conditions such as mental fatigue and drowsiness must be avoided. Previous work that attempted to devise objective methods to model mental workload were mainly based on neurological or physiological data collected when the participants performed tasks that did not involve physical activity. While such models may be useful for scenarios that involve static operators, they may not apply in real-world situations where operators are performing tasks under varying levels of physical activity, such as those faced by first responders, firefighters, and police officers. Here, we describe WAUC, a multimodal database of mental Workload Assessment Under physical aCtivity. The study involved 48 participants who performed the NASA Revised Multi-Attribute Task Battery II under three different activity level conditions. Physical activity was manipulated by changing the speed of a stationary bike or a treadmill. During data collection, six neural and physiological modalities were recorded, namely: electroencephalography, electrocardiography, breathing rate, skin temperature, galvanic skin response, and blood volume pulse, in addition to 3-axis accelerometry. Moreover, participants were asked to answer the NASA Task Load Index questionnaire after each experimental section, as well as rate their physical fatigue level on the Borg fatigue scale. In order to bring our experimental setup closer to real-world situations, all signals were monitored using wearable, off-the-shelf devices. In this paper, we describe the adopted experimental protocol, as well as validate the subjective, neural, and physiological data collected. The WAUC database, including the raw data and features, subjective ratings, and scripts to reproduce the experiments reported herein will be made available at: http://musaelab.ca/resources/.
... Two of the most commonly used questionnaires are the (1) Subjective Workload Assessment Technique (SWAT) [4] and (2) NASA Task Load Index (NASA-TLX) [5]. The latter requires subjects to rate multiple dimensions that can then be aggregated into a single general workload index [6][7][8]. However, subjective ratings have some key limitations. ...
Mental workload assessment is crucial in many real life applications which require constant attention and where imbalance of mental workload resources may cause safety hazards. As such, mental workload and its relationship with heart rate variability (HRV) have been well studied in the literature. However, the majority of the developed models have assumed individuals are not ambulant, thus bypassing the issue of movement-related electrocardiography (ECG) artifacts and changing heart beat dynamics due to physical activity. In this work, multi-scale features for mental workload assessment of ambulatory users is explored. ECG data was sampled from users while they performed different types and levels of physical activity while performing the multi-attribute test battery (MATB-II) task at varying difficulty levels. Proposed features are shown to outperform benchmark ones and further exhibit complementarity when used in combination. Indeed, results show gains over the benchmark HRV measures of 24 . 41 % in accuracy and of 27 . 97 % in F1 score can be achieved even at high activity levels.
... Task sets where this happens are not difficult to imagine. However Cassenti et al. [26] found that tasks do not typically follow a sudden-drop-off pattern. Instead, task performance gets more difficult over time in a largely steady manner. ...
The Multi-Level Cognitive Cybernetics (MLCC) [1] approach provides a methodological approach to studying adaptive automation and advancing its development across multiple levels of analysis. We follow up on the previous paper [1] by focusing on the cognitive modeling level of MLCC. Adaptive aids must only be triggered when the inclusion of the aid will boost performance relative to what it would be without the aid. Computational cognitive modeling provides a means to represent the cognitive sequence that completes a task. Using cognitive modeling and MLCC, we discuss two ways to provide optimal triggering for adaptive automation. First, models will provide a mapping of which cognitive stages caused the most difficulty for individuals and therefore aids may be designed to support those cognitive functions. Second, models may provide information about optimal thresholds for determining when a user is having difficulty, allowing more timely aid interventions than without the model.
... Kuntz et al. (2014) suggested a nonlinear relationship between hospital workload and mortality rates. The results presented in Cassenti et al. (2010) indicate that the human error rate increases close to linearly in workload. These results are more or less analogous to saying that if an employee has a heavier workload on any given day, then the total number of errors on that day increases convexly. ...
We present a framework to describe and analyze operational risk in financial services from an operations management perspective, focusing in particular on process design, process management and human behavior aspects. The financial services industry differs from other service industries in ways that affect the nature of the operational risks it is subject to. In recent decades, many books and papers have focused on operational risk in financial services; however, this literature has focused mainly on the conceptual and statistical aspects of operational risk management and not on its operational aspects. Operational risk in financial services has not received much attention from the operations management community. The framework presented here is based on the premise that operational risk in financial services can reap significant benefits from research done in the theory and practice of operations management in manufacturing industries as well as in other services industries. The objective of this paper is to propose particular challenges and questions raised in the practice of operational risk management that may stimulate future research in this particular area of operations management. This article is protected by copyright. All rights reserved.
... According to Sheridan [31], human error is defined as an action that fails to meet some implicit or explicit criterion. Errors of omission and commission in writing also reflect the individual's mental workload [32, 33]. Thus, mental workload may be reflected in the number of errors made and in accuracy of performance. ...
The aim of the study was to examine the application of a computerized handwriting model for characterizing complex figure-drawing performance. We posit that spatial, temporal, and pressure measures that reflect figure-drawing behavior will differ significantly under two mental workload conditions, and that both drawing and handwriting process measures will predict the quality of what is drawn and/or written. Thirty participants copied the Rey–Osterrieth Complex Figure Test (ROCFT). They then reproduced it from memory and finally copied a paragraph on a digitizer that is part of the Computerized Penmanship Evaluation Tool (ComPET) system. Results indicated that certain computerized measures of the ROCFT copying significantly correlated with those of the paragraph-copying behavior (r = .38–.75). Significant differences were found between the spatial and temporal computerized measures of performance in the ROCFT copying and drawing-from-memory tasks. Stepwise regressions indicated that mean pressure predicted 12 % of the variance of the ROCFT and paragraph-copying quality scores and 6 % of the ROCFT drawing-from-memory score. Furthermore, 52 % of the variance of the ROCFT drawing-from-memory score was predicted by the mean velocity. The benefits and significance of obtaining computerized measures of the drawing process for better insight about human performance characteristics are discussed, and applications are suggested.
... The research described here included four experiments each of which investigated a different task type with an independent variable of the number of tasks performed at once. The four experiments acted as follow-up experiments to those described in Cassenti, Kelley, and Carlson (2010) with changes in the time available to respond (TAR) to each task. The findings suggest that a modeling system should predict fewer errors with more TAR, less effortful tasks, and fewer tasks. ...
... Bierbaum et al.'s (1989) cognitive workload scale is reprinted in Table 1. Cassenti et al. (2010) study was to evaluate this workload scale by investigating four of the task types (i.e., alternative selection, signal recognition, simple evaluation, and complex evaluation) within the same experimental paradigm to evaluate the scaling. We chose these four because they were the most conducive to the same type of experimental paradigm. ...
... The same experimental paradigm was used for four more experiments with the same task types as in Cassenti et al. (2010). The accuracy data from Cassenti et al. (2010) indicated whether the new participants should have more (if accuracy was low) or less (if accuracy was high) TAR for each target that required a response. ...
Mental workload is the amount of mental effort necessary to perform a task or set of tasks and is an important factor for predicting performance. The research described here included four experiments each of which investigated a different task type with an independent variable of the number of tasks performed at once. The four experiments acted as follow-up experiments to those described in Cassenti, Kelley, and Carlson (2010) with changes in the time available to respond (TAR) to each task. The findings suggest that a modeling system should predict fewer errors with more TAR, less effortful tasks, and fewer tasks. Together, these experiments illuminate a way forward to constructing performance-prediction algorithms based on number of tasks, task types, and TAR. We propose that based on these results and others a plug-in could be added to a human performance modeling system such as the IMproved Performance Research INtegration Tool (IMPRINT) to estimate performance given the number, type, and TAR of tasks. An example of how algorithms that populate this plug-in may be implemented is illustrated and equations for a host of task types are provided.
... Cassenti and Kelly (2006) proposed the workload-performance function depicted in Figure 1, which is a modification and extension of North, Stackhouse, and Graffunder's (1979) classification. The depiction in Figure 1 was supported in several experiments (Cassenti & Kelley, 2006;Cassenti, Kelley, & Carlson, 2010). These studies found that when performance is plotted by an increasing number of identical tasks (a variable positively correlated with workload, though not necessarily linearly), the function moves through four distinct regions -undertax (slight performance decline when low attentional requirements make distractions more common; section A in Figure 1), ceiling performance (section B), steady decline (section C), and floor performance (section D). ...
... Pauses between each successive display could help participants recover between successive events and they could affect subjective ratings. Directly varying the time available to respond to the stimuli was identified as an important consideration for future studies (Cassenti & Kelley, 2006;Cassenti et al., 2010). ...
... In the present study, we aimed to expand on previous research including Cassenti and Kelley (2006) and Cassenti et al. (2010). Whereas, the previous studies focused on the performance changes as mental workload increased, the present study investigated both selfratings and performance. ...
Mental workload is the amount of demand on an individual’s limited mental resources and thus is an important consideration in human factors research. This research focuses on workload from two primary methods of measuring it - self-ratings of workload and performance. An experiment to test workload involved the manipulation of the number of tasks to be performed at once and the time available to respond to the task or tasks. The results show that performance changes by shifting between ceiling, linear decrease, and floor performance as workload increases. SWAT ratings of workload followed the same pattern. We conclude that the IMPRINT (Improved Performance Research Integration Tool; Archer & Adkins, 1999) modeling system should maintain its existing method of modeling self ratings of workload, but that they may make use of a new algorithm based on this data to model performance as workload changes.
This monograph is an attempt to establish a framework for Operations in Financial Services as a research area from an Operations Management perspective. Operations in Financial Services has not developed itself yet as a well-defined research area within the Operations Management community. It has been touched upon by researchers from various different disciplines, including Operations Management, Statistics, Information Technology, Finance, and Marketing. However, each discipline has a different perspective on what the important issues are and the various disciplines are often at odds with one another. This monograph has been written from an Operations Management perspective.
