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Security surveillance entails many cognitive challenges (e.g., task interruption, vigilance decrements, cognitive overload). To help surveillance operators overcome these difficulties and perform more efficient visual search, gaze-based intelligent systems can be developed. The present study aimed at testing the impact of the Scantracker system—which pinpointed neglected cameras while detecting and correcting attentional tunneling and vigilance decrease—on human scanning behavior and surveillance performance. Participants took part in a surveillance simulation, monitoring cameras and searching for ongoing incidents, and half of them was supported by the Scantracker. Although behavioral surveillance performance was not improved, participants supported by the Scantracker showed more efficient gaze-based measures of surveillance. Moreover, some of these measures were associated with performance, suggesting that scan pattern improvements might lead indirectly to more efficient incident detection. Overall, these results speak to the potential of using gaze- aware intelligent systems to support surveillance operators.

The Cognitive Shadow is a prototype tool intended to support decision making by autonomously modeling human operators’ response pattern and providing online notifications to the operators about the decision they are expected to make in new situations. Since the system can be configured either in a reactive “shadowing” or a proactive “recommendation” mode, this study aimed to determine its most effective mode in terms of human and model accuracy, workload, and trust. Subjects participated in an aircraft threat evaluation simulation without decision support or while using either mode of the Cognitive Shadow. Whereas the recommendation mode had no advantage over the control condition, the shadowing mode led to higher human and model accuracy. These benefits were maintained even when the tool was unexpectedly removed. Neither mode influenced workload, and the initial lower trust rating in the shadowing mode faded quickly, making it the best overall configuration for the cognitive assistant.

The Cognitive Shadow is a decision-support system that uses policy capturing to model human operators’ judgment policies and provide online predictions of their decisions. The system can provide support in reaction to a decision mismatch (shadowing mode) or proactively (recommendation mode). The goal of this study was to compare these two modes of operation in their ability to effectively model and support decision-making and to examine impacts on information processing, workload, and trust. Participants took part in an aircraft threat evaluation simulation without decision support or with the Cognitive Shadow (either shadowing or recommendation mode). Dwell time was collected over different areas of the user interface. While the recommendation mode had no advantage over the control group, the shadowing mode resulted in greater human and model accuracy. This mode led to longer dwell time over the parameters zone presenting key information for decision-making. These benefits were maintained even after the tool was removed. Workload was unaffected by the mode, and while trust was initially higher in the recommendation mode, it quickly became equivalent between both modes, overall supporting shadowing as the better configuration for cognitive assistance. Results are discussed in terms of decision processes, operators support, and automation bias.

Decision making can be modeled in various ways for the design of decision-support systems. One strategy privileged for this purpose is policy capturing, i.e. using statistical techniques (and more recently machine learning) to model judgement policies. The Cognitive Shadow is a prototype tool suited for frugal learning that automatically learns a user’s decision pattern in real time based on an ensemble of seven supervised learning algorithms. This tool can provide advisory warnings when the user decision is inconsistent with the predicted outcome. Evolutionary computation methods could reinforce the system’s efficiency because of their ability to deal with computational complexity via evolution-inspired optimization mechanisms. The goal of this study was to assess the potential of evolutionary algorithms for frugal learning in an online policy capturing context. To do so, we tested three evolutionary algorithms on three different datasets (each split in three sizes), and compared both their prediction performance and training time with that of the other modeling techniques already implemented in the Cognitive Shadow system. Although all three evolutionary models were generally outperformed by non-evolutionary learning algorithms, one genetic programming method showed good prediction performance for the more complex use cases with the smaller datasets.

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/.

Mental workload assessment is of great importance for safety critical applications, especially in situations that involve physical demands, such as with first responders (e.g., paramedics, firefighters, or police officers). Advancements in physiological signal monitoring with wearable sensors have made way for real-time mental workload assessment using physiological signals. However, these models have typically been conducted in controlled laboratory settings and rely on a single physiological modality. As a result, such models often experience a drop in performance due to movement artifacts introduced in real-life conditions. In this paper, we demonstrate that a multi-modal mental workload model not only improves measurement accuracy, but can also increase robustness against physical activity artifacts. To this end, an experiment was conducted where mental workload and physical activity levels were modulated simultaneously while physiological data was collected from 48 participants using off-the-shelf wearable devices. Results show improved mental workload assessment with multi-modal fusion under varying physical activity conditions.

Heart rate variability (HRV) has been studied in the context of human behavior analysis and many features have been extracted from the inter-beat interval (RR) time series and tested as correlates of constructs such as mental workload, stress and anxiety. Such constructs are crucial in assessing quality-of-life of individuals, as well as their overall performance when doing critical tasks. Most studies, however, have been conducted in controlled laboratory environments with artificially-induced psychological responses. While this assures that high quality data are collected, the amount of data is limited and the transferability of the findings to more ecologically-appropriate settings remains unknown. Additionally, it is desirable for such mental state monitoring systems to have high temporal resolution, thus allowing for quick feedback and adaptive decision making. In this article, we explore the use of features computed from time windows much shorter than typically reported in the literature. More specifically, we evaluate the potential of HRV and breathing features computed over so-called ultra-short-term segments (i.e., < 5 minutes) for stress and mental workload prediction. Experiments with 27 police academy trainees show that short time windows as low as 60 seconds can provide useful insights, in particular for mental workload assessment. Moreover, the fusion of HRV and breathing features showed to be an important aspect for reliable behavioural assessment in highly ecological settings.

BACKGROUND Future long-duration space exploration missions, such as traveling to Mars, will create an increase in communication time delays and disruptions and remove the viability of emergency returns to Earth for timely medical treatment. Thus, higher levels of medical autonomy are necessary. Crew selection is proposed as the first line of defense to minimize medical risk for future missions; however, the second proposed line of defense is medical preparedness and crew member autonomy. In an effort to develop a decision support system, the Canadian Space Agency mandated a team of scientists from Thales Research and Technology Canada (Québec, QC) and Université Laval (Québec, QC) to create an evidence-based medical condition database linking mission-critical human conditions with key causal factors, diagnostic and treatment information, and probable outcomes. OBJECTIVE To complement this database, we are currently conducting a scoping review to better understand the depth and breadth of evidence about managing medical conditions in space. METHODS This scoping review will adhere to quality standards for scoping reviews, employing Levac, Colquhoun, and O’Brien's 6-stage methodology; the reported results will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. In stage 1, we identified the research question in collaboration with the Canadian Space Agency (CSA), the main knowledge user. We prioritized 10 medical conditions: (1) acute coronary syndrome, (2) atrial fibrillation, (3) eye penetration, (4) herniated disk, (5) nephrolithiasis, (6) pulmonary embolism, (7) retinal detachment, (8) sepsis, (9) stroke, and (10) spaceflight associated neuro-ocular syndrome. In stage 2, with the help of an information specialist from Cochrane Canada Francophone, papers were identified through searches of the following databases: ARC, Embase, IeeeXplore, Medline Ovid, PsychINFO, and Web of Science. In stage 3, studies will be selected and assessed using a 3-step process and emerging, refined exclusion criteria. In stage 4, the data will be charted in a table based on parameters required by the CSA and developed using Google spreadsheets for shared access. In stage 5, evidence-based descriptive summaries will be produced for each condition, as well as descriptive analyses of collected data. Finally, in stage 6, the findings will be shared with the CSA to guide the completion of this project. RESULTS This study was planned in December 2018. Stage 1 has been completed. The initial database search strategy with all target conditions combined identified a total of 10,403 citations to review through title and abstract screening and after duplicate removal. We plan to complete stages 2-6 by the beginning of 2021. CONCLUSIONS This scoping review will map the literature on the management of 10 priority medical conditions in space. It will also enable us to identify knowledge gaps that must be addressed in future research, ensuring successful and medically safe future missions as humankind embarks upon new frontiers of space exploration. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/24323

Providing decision support to operators in command and control contexts requires careful assessment of its impacts on task performance. Here we describe a human-in-the-loop experiment using a naval air defense testbed to compare three conditions: 1) a baseline interface (DSSBASE; 2) one that displays the temporal proximity of radar aircraft (DSSTEMP); and 3) one which adds a change history panel (DSSCHEX). Threatevaluation accuracy and cognitive models of participants’ judgments did not significantly differ across conditions. Eye-tracking data showed that DSSTEMP lead to a reduced verification of track attributes. Confusion matrices also differed across conditions: DSSTEMP lead to more errors that are “two-categoriesaway” when erroneously classifying hostile aircraft as non-hostile or uncertain. We conclude that in addition to looking at standard metrics of task accuracy and response times, critical insights can be obtained by assessing how different designs for decision support may alter strategies and cognitive processes.

Assessment of mental workload is crucial in safety-critical applications. Often, such applications require the user to be ambulant, such as first responders (e.g., paramedics, firefighters, or police officers). Typically, mental workload models have relied on electroencephalography (EEG) signals. EEGs, however, are known to be highly sensitive to movement artefacts, thus limited applications exist for ambulant users and studies have mostly occurred in controlled laboratory settings. In this paper, we explore the robustness of new non-linear features against movement artefacts and test their effectiveness in monitoring mental workload for ambulant users with the end goal of developing mitigation measures based on mental state of operators. To this end, an EEG experiment was conducted where mental workload and physical activity levels were modulated simultaneously and data was collected from 48 participants. Classical EEG features used for workload assessment, such as spectral power and amplitude/phase coherence, were used as benchmarks and compared against the proposed non-linear multi-scale permutation entropy features. Experimental results show the proposed features consistently outperforming the benchmark ones, thus high-lighting their robustness to movement artefacts.

Stress is well known to have negative effects on health and workplace performance. Physiological sensing using wearables shows in turn great potential for realtime stress monitoring. While some off-the-shelf consumer products (e.g. smartwatches) already feature stress detection, there is still a pressing need to improve the robustness of these models in ecological settings where physical activity can hamper detection accuracy. In this paper, we show that using a multimodal physiological stress model can not only improve model accuracy, but can increase robustness to physical activity inference. To do so, we propose a video game based method to elicit emotional responses. More specifically, 48 participants played video games in which psychological stress and physical activity were jointly modulated. Physiological features showing robustness to stress are analyzed in order to guide further research.

Interest in stress recognition has notably increased over the past few years. In this work, we focus on recognizing stress from speech. We propose the use of modulation spectral features as input to a convolutional neural network (CNN) for classifying stress. As benchmark, the OpenSMILE features used in the INTERSPEECH 2010 Paralinguistic Challenge is adopted and evaluated with a support vector machine (SVM) and a deep neural network (DNN) based backends. Experiments are performed with the well-known Speech Under Simulated and Actual Stress (SUSAS) database. Performances are investigated considering 2-class, 4-class and 9-class classification problems. Results show that the proposed approach outperforms the benchmark on a challenging 9-class classification task with accuracy as high as 70% representing gains of roughly 18% over the benchmark.

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.

Workers such as police officers are exposed to many hazardous situations while performing traffic duties, which renders them vulnerable to work accidents. Such a dangerous work context can lead to high stress levels. This is conducive to performance impairment and, consequently, to high-risk situations in which safety, for both workers and road users, may be jeopardized. It then seems important to better understand the stress experienced by traffic workers to ultimately improve road safety. Hence, the current study aims at investigating whether work context could be associated with the stress levels of police officers doing traffic duties on foot. Nineteen police officers in Quebec City and Montreal, Canada, performed traffic duties on several sites (including constructions ones) while their physiological state was measured by a wearable sensor harness. Change in subjective stress was measured every 15 min. Characteristics of the environment were also noted for every working site, which permitted to perform hierarchical cluster classifications to assess each site's complexity. Results show that stress levels were significantly lower for less complex sites. Interestingly, traffic and safety measures—identified through automated computer vision—differ depending on the site complexity and were generally associated with measures of stress. Taken together, these results suggest that the context in which pedestrian workers perform traffic duties, that is the environment and the higher-risk events to which they are exposed, can lead to higher levels of stress. Implications for road safety are discussed in light of these results.

Background Future long-duration space exploration missions, such as traveling to Mars, will create an increase in communication time delays and disruptions and remove the viability of emergency returns to Earth for timely medical treatment. Thus, higher levels of medical autonomy are necessary. Crew selection is proposed as the first line of defense to minimize medical risk for future missions; however, the second proposed line of defense is medical preparedness and crew member autonomy. In an effort to develop a decision support system, the Canadian Space Agency mandated a team of scientists from Thales Research and Technology Canada (Québec, QC) and Université Laval (Québec, QC) to create an evidence-based medical condition database linking mission-critical human conditions with key causal factors, diagnostic and treatment information, and probable outcomes. Objective To complement this database, we are currently conducting a scoping review to better understand the depth and breadth of evidence about managing medical conditions in space. Methods This scoping review will adhere to quality standards for scoping reviews, employing Levac, Colquhoun, and O’Brien's 6-stage methodology; the reported results will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. In stage 1, we identified the research question in collaboration with the Canadian Space Agency (CSA), the main knowledge user. We prioritized 10 medical conditions: (1) acute coronary syndrome, (2) atrial fibrillation, (3) eye penetration, (4) herniated disk, (5) nephrolithiasis, (6) pulmonary embolism, (7) retinal detachment, (8) sepsis, (9) stroke, and (10) spaceflight associated neuro-ocular syndrome. In stage 2, with the help of an information specialist from Cochrane Canada Francophone, papers were identified through searches of the following databases: ARC, Embase, IeeeXplore, Medline Ovid, PsychINFO, and Web of Science. In stage 3, studies will be selected and assessed using a 3-step process and emerging, refined exclusion criteria. In stage 4, the data will be charted in a table based on parameters required by the CSA and developed using Google spreadsheets for shared access. In stage 5, evidence-based descriptive summaries will be produced for each condition, as well as descriptive analyses of collected data. Finally, in stage 6, the findings will be shared with the CSA to guide the completion of this project. Results This study was planned in December 2018. Stage 1 has been completed. The initial database search strategy with all target conditions combined identified a total of 10,403 citations to review through title and abstract screening and after duplicate removal. We plan to complete stages 2-6 by the beginning of 2021. Conclusions This scoping review will map the literature on the management of 10 priority medical conditions in space. It will also enable us to identify knowledge gaps that must be addressed in future research, ensuring successful and medically safe future missions as humankind embarks upon new frontiers of space exploration. International Registered Report Identifier (IRRID) DERR1-10.2196/24323

While simple heuristics can be ecologically rational and effective in naturalistic decision making contexts, complex situations require analytical decision making strategies, hypothesis-testing and learning. Sub-optimal decision strategies – using simplified as opposed to analytic decision rules – have been reported in domains such as healthcare, military operational planning, and government policy making. We investigate the potential of a computational toolkit called “IMAGE” to improve decision-making by developing structural knowledge and increasing understanding of complex situations. IMAGE is tested within the context of a complex military convoy management task through (a) interactive simulations, and (b) visualization and knowledge representation capabilities. We assess the usefulness of two versions of IMAGE (desktop and immersive) compared to a baseline. Results suggest that the prosthesis helped analysts in making better decisions, but failed to increase their structural knowledge about the situation once the cognitive prosthesis is removed.

Policy capturing is a judgment analysis method that typically uses linear statistical modeling to estimate expert judgments. A variant to this technique is to capture decision policies using data-mining algorithms designed to handle nonlinear decision rules, missing attributes, and noisy data. In the current study, we tested the effectiveness of a decision-tree induction algorithm and an instance-based classification method for policy capturing in comparison to the standard linear approach. Decision trees are relevant in naturalistic decision-making contexts since they can be used to represent “fast-and-frugal” judgment heuristics, which are well suited to describe human cognition under time pressure. We examined human classification behavior using a simulated naval air defense task in order to empirically compare the C4.5 decision-tree algorithm, the k-nearest neighbors algorithm, and linear regression on their ability to capture individual decision policies. Results show that C4.5 outperformed the other methods in terms of goodness of fit and cross-validation accuracy. Decision-tree models of individuals’ judgment policies actually classified contacts more accurately than their human counterparts, resulting in a threefold reduction in error rates. We conclude that a decision-tree induction algorithm can yield useful models for training and decision support applications, and we discuss the application of judgmental bootstrapping in real time in dynamic environments.

Assessment of mental workload is crucial for applications which require constant attention and where conditions such as mental fatigue and drowsiness must be avoided. As such, electroencephalography (EEG) based mental workload models have been developed in the past. The majority of these models, however, have assumed individuals are not ambulant, thus bypassing the issue of movement-related EEG artefacts. While such models may be useful for a number of applications (e.g., operators are sitting), they may not apply in situations in which operators are performing their task under different physical activity levels. Representative examples can include first responders, such as paramedics, firefighters, or police officers. In this work, we take the first steps towards overcoming this limitation and present results of an experiment simultaneously eliciting increasing mental workload states at varying physical activity levels. EEG data from forty-seven participants was collected while they performed the NASA Revised Multi-Attribute Task Battery II (MATB-II) under three different activity level conditions (no, medium, high). In this study, we report the effects of activity on the noise-robustness and distribution of several spectral, amplitude/phase coherence, and amplitude modulation features, with the ultimate goal of deriving a feature set tailored towards automated workload assessment during physical activity. Preliminary results show spectral features acquired from the frontal area of the cortex as the most promising and that activity aware mental workload models should be developed.

Policy capturing is a decision analysis method that typically uses linear statistical modeling to estimate the basis of expert judgments. Using more flexible data mining algorithms may yield more accurate models or instead result in poor functional estimations. The objective of this study is to test the effectiveness of a decision tree induction algorithm for policy capturing in comparison to the standard linear approach. We examined human classification behavior using a simulated naval air-defense task in order to empirically compare the C4.5 decision tree algorithm to linear regression on their ability to capture individual decision policies. The pattern of results shows that C4.5 outperformed linear regression in terms of goodness-of-fit and cross-validation accuracy. Results also show that the decision tree models of individuals’ judgment policies actually classified contacts more accurately than their human counterparts. We conclude that non-linear policy capturing can yield useful models for training and decision support applications.

There has been substantial investment in the field of closed-circuit television (CCTV) technology to increase the level of surveillance efficiency and system performance for the security of citizens and the protection of public infrastructures. In control rooms, operators have to actively monitor a number of camera feeds that generally surpass the number of displays available. Operators tend to prioritize some visual scenes to cope with the sheer amount of visual content to monitor. The objective of the present study is to assess the impact on surveillance performance of different monitoring strategies adopted by (nonexpert) operators using a highly realistic CCTV operation simulation.

Automated mental workload measurement is particularly important in safety-critical settings, such as in nuclear plants, aviation, air traffic control, shipping, and transportation, to name a few. As an example, recent statistics have suggested that 90% of the accidents in the transport industry are due to human factors. In this paper, we explore the potential of off-the-shelf wearable technologies in monitoring mental workload in real-time, thus potentially reducing the number of accidents due to human errors. Wearable technologies, while providing the user with ease-of-use, comfort, and portability, have several limitations, such as lower quality signal readings (e.g., due to dry electrodes) and smaller number of recording sites. Such limitations place a burden on the accuracy of existing mental workload models. To overcome this limitation, we propose the use of phase-amplitude and amplitude-amplitude coupling features computed from a portable commercial electroencephalography (EEG) device. Experiments with three different tasks, namely N-back, mental rotation and visual search, show the proposed features being significantly correlated with multiple dimensions of the widely-used NASA task load index test and providing complementary information to other conventional features.

The assessment of an operator's functional state (i.e., the multidimensional pattern of human psychophysiological conditions that mediates performance) has great potential for increasing safety and reliability of critical systems. However, live monitoring of functional state using physiological and behavioral data still faces several challenges before achieving the level of precision required in many operational contexts. One open question is the level of granularity of the models. Is a general model sufficient or should subject-specific models be trained to ensure high accuracy? Another challenge concerns the formalization of a valid ground truth for training classifiers. This is critical in order to train models that are operationally relevant. This paper introduces the Decontextualized Dynamic Performance (DDP) metric which allows models to be trained simultaneously on different tasks using machine learning algorithms. This paper reports the performance of various classification algorithms at different levels of granularity. We compare a general model, task-specific models, and subject-specific models. Results show that the classification methods do not lead to statistically different performance, and that the predictive accuracy of subject-specific and task-specific models was actually comparable to a general model. We also compared various time-window sizes for the new DDP metric and found that results were degrading with a larger time window size.

The authors report two experiments studying the requirements for effective decision making in a complex environment. The focus lies on three components of individual cognitive readiness: situation awareness (SA), problem solving, and decision making. Participants performed a simulated society management task in which they could allocate resources to stabilize a national crisis involving multiple interrelated factors (political, economic, environmental, and social). A striking aspect of this simulation is that even though information about the causes and effects within the system is available, most individuals fail to bring the system to the targeted state because of unintended consequences of their decisions. The experiments test the impact of two cognitive support tools designed to improve anticipation of future outcomes. Results show that supporting short-term anticipation (with perfectly accurate projections) was insufficient to improve effectiveness, but supporting long-term anticipation (with approximate projections) successfully improved performance in this complex environment. We conclude with a review of requirements that training and technological support should address to augment individual cognitive readiness for operations in complex environments and propose an extension to SA theory by conceptualizing a Level 4 SA (long-term projection) that may be particularly important to overcome the “wall of complexity.”

This article reports an experiment using the C3Fire microworld—a functional simulation of command and control in a complex and dynamic environment—in which 24 three-person teams were organized according to either a functional or multifunctional allocation of roles. We proposed a quantitative approach for estimating teamwork requirements and comparing them across team structures. Two multiple linear regression models were derived from the experimental data, one for each team structure. Both models provided excellent fits to the data. The regression coefficients revealed key similarities and some major differences across team structures. The two most important predictors were monitoring effectiveness and coordination effectiveness regardless of team structure. Communication frequency was a positive predictor of performance in the functional structure but a negative predictor in the multifunctional structure. In regard to communication content, the proportion of goal-oriented communications was found to be a positive predictor of team performance in functional teams and a weak negative predictor of team performance in multifunctional teams. Mental load was a useful predictor in functional teams but not in multifunctional teams. Results show that this method is useful for estimating teamwork requirements and support the claim that teamwork requirements can vary as a function of team structure.

Omnipresent in everyday multitasking environments task interruptions are usually detrimental to individual performance. Here, we examined whether teaming up renders an individual less vulnerable to interruptions in complex and dynamic situations. We employed a microworld to simulate command and control in a crisis management situation and to examine the relative impact of interruptions on operators working in a functional dyad versus operators working alone. While task interruption was detrimental to efficacy in supervisory control of both single and team interrupted operators, the latter were less vulnerable than the former. However, teaming up did not translate into faster resumption time, a consequence of the overhead attributable to coordination and communication requirements of collaborative work. These findings suggest that in complex and dynamic environments working in a small team confers more resistance to task interruption than working alone and speed of interruption recovery is no guarantee of quality of recovery.

Sequence learning is essential in cognition and underpins activities such as language and skill acquisition. One classical demonstration of sequence learning is that of the Hebb repetition effect, whereby serial recall improves over repetitions on a repeated list relative to random lists. When addressing the question of which mechanism underlies the effect, the traditional approach is to prevent the action of processes thought to be responsible for sequence learning: If the typical Hebb repetition effect is reduced, these processes are key to the effect, researchers claim. By reanalyzing the data of F. B. R. Parmentier, M. T. Maybery, M. Huitson, and D. M. Jones (2008)-who reported no Hebb effect for sequences of auditory-spatial stimuli-we revealed that error learning can be mistaken for the absence of sequence learning. Indeed, incorrect responses are reproduced increasingly over repetitions. Our findings suggest that the Hebb repetition effect can be associated with response learning as well as stimulus processing.

Research in cognitive systems engineering (CSE) and decision support requires an understanding of the psychological processes involved in a given task. The purpose of the present study is to investigate how policy capturing and process tracing may help understand the decision mechanisms involved in a naval air-defense task and characterize how human decision making effectiveness can be improved. We report results from a study in which participants performed a threat evaluation and weapons assignment task within a naval air-defense microworld. Policy capturing and process tracing techniques provide both converging perspectives and complementary insights into complex decision making processes.

The authors present 3 decision-tree models of categorization adapted from T. Trabasso, H. Rollins, and E. Shaughnessy (1971) and use them to provide a quantitative account of categorization response times, choice proportions, and typicality judgments at the individual-participant level. In Experiment 1, the decision-tree models were fit to reaction time and choice proportion data from a study reported by A. L. Cohen and R. M. Nosofsky (2003). In Experiment 2, participants were also asked to provide typicality ratings for each stimulus. A process-tracing method called the "4-questions game" (Y. Sayeki, 1969) was used in a posttest phase to identify a decision tree for each participant. In both experiments, the decision-tree models explained a very high proportion of variance in the data and compared favorably with 2 leading exemplar models.

Temporal awareness is key to successful decision making in a wide range of command and control situations, yet little explicit support to maintaining temporal awareness is provided by Decision Support Systems (DSS) for time-critical decisions. In the context of simulated weapon-target scheduling, the present study compared the decision support gained from two display formats: typical geospatial display and temporal display. The results demonstrated that the temporal display facilitates scheduling performance though its beneficial impact seems to require greater familiarization.

Feldman [(2000). Minimization of Boolean complexity in human concept learning. Nature, 407, 630–633] has provided evidence that the complexity (subjective difficulty) of a Boolean concept is related to the length of its minimum algebraic description. The catalog of Boolean concepts [Feldman, J. (2003a). A catalog of Boolean concepts. Journal of Mathematical Psychology, 47, 98–112] provides an extensive list of basic categorization problems along with their minimal algebraic descriptions and complexity levels. In the present study, we demonstrated that the minimum algebraic description is very useful for describing the most economical categorization rule that can be used, but does not always correspond to the category definition used by a participant in a concept-learning task. We developed a new minimization procedure, reanalyzed Feldman's catalog and revised the complexity of 148 out of 253 basic concept types. Among these cases, two new solutions that correspond to concept types used in the classic study on categorization by Shepard, Hovland, and Jenkins [(1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75, 1–42] predicted a new difficulty rank ordering (for Types I–VI) that did not agree with experimental results reported in the literature. New experimental results reported here show that participants used similar non-minimal classification rules that can be explained by the hypothesis-testing sequence in the RULEX model [Nosofsky, R. M., Palmeri, T. J., & McKinley, S. C. (1994). Rule-plus-exception model of classification learning. Psychological Review, 101, 53–79].

The present work describes the application of a multi-criteria assessment tool called MYRIAD to the evaluation of a methodology that was developed to improve team understanding of a complex situation. The methodology aims to support a multi-disciplinary team working collaboratively on the development of a mission plan during an expeditionary stability operation. We conducted a case study assessing collaborative understanding and team performance while subject matter experts employed the methodology as a complement to the standard planning process. Results were analyzed using MYRIAD, a preference modeling system that allows combining disparate measures into a coherent assessment capturing several key logical relationships between metrics – ones that may not be modeled using the traditional weighted sum approach. MYRIAD was also used to perform a sensitivity analysis in order to derive from the preference model which aspects of the planning methodology would lead to the greatest overall improvement. Results helped identifying priority areas for future development.

Objective: This study examined whether teaming up mitigates individual vulnerability to task interruptions in complex dynamic situations. Background: Omnipresent in everyday multitasking environments, task interruptions are usually detrimental to individual performance. This is particularly crucial in dynamic command and control (C2) safety-critical contexts because of the additional challenge imposed by the continually evolving situation during the interruption. Method: We employed a firefighting microworld to simulate C2 in the context of supervisory control to examine the relative impact of interruptions on participants working in a functional dyad versus operators working alone. Results: Although task interruption was detrimental to participants’ efficacy of monitoring resources, the negative impact of interruption was reduced for those working in teams. Teaming up translated into faster resumption time, but only if both teammates were interrupted simultaneously. Interrupting only one team member was associated with increased postinterruption communications and slower resumption time. Conclusion: These findings suggest that in complex dynamic situations working in a small team confers more resistance to task interruption than working alone by virtue of the reduced individual workload typical of teamwork. The benefit of collaborative work seems nevertheless mediated by the coordination and communication overhead associated with teamwork. Application: The present findings have practical implications for operators dealing with unexpected events such as task interruptions in C2 environments.

We report a study that investigates the role of situation awareness acquisition behaviors and decisional heuristics on mission success in a complex strategic decision making simulation. Thirty-two subjects completed a turn-based counter-insurgency scenario against a simulated adversary. Subjects played the role of a commander whose goal was to stabilize a failing state by allocating resources in various spheres of intervention like “Infrastructure development” and “Security operations”. We measured goal attainment, subjects' behaviors in terms of situation awareness, and decisional heuristics. Results show that overall performance was far from optimal. Two behaviors were positively associated with performance: system exploration behaviors at the first turn of the simulation and outcome assessment behaviors for subsequent turns. Other results suggest that the nature of the information that is sought, and the nature of the decision heuristics used, played a critical role in goal attainment. Results are discussed in terms of training and cognitive support requirements.

Interactive learning environments are increasingly used to help people better deal with complex situations. One way to improve decision making effectiveness is to train systems thinking skills using interactive simulations in order to reduce the occurrence of “unintended side-effects” of interventions and catastrophic failures. We present a prototype training procedure intended for military officers and civilian personnel engaged in “full spectrum” operations. The Complex Decision Making simulation environment (CODEM) is the core of the proposed training procedure. CODEM aims to improve systems thinking skills, adaptability and other abilities associated with the integrative concept of cognitive readiness. Four training scenarios are designed to reproduce key properties of complex decision making situations. An intelligent tutor providing corrective feedback on decision making behaviors is integrated into each scenario. The tutor interventions help avoid tunnel vision (i.e., the opposite of systems thinking) by discouraging the use of overly simple heuristics. Nine behavioral metrics are monitored by the intelligent tutor—five are related to information seeking behaviors, and four are related to specific decision patterns. The effectiveness of this prototype training procedure is currently being assessed experimentally.

The complex decision making experimental platform (CODEM) is intended as a shareable research tool to stimulate multidisciplinary research on complex dynamic situation management and as an environment for training and testing cognitive readiness. The experimenter can set general parameters, configure the interface, specify the model, insert events and define the resources and capabilities of each player using the scenario development tool. No programming skills are required. Task complexity can be varied by introducing feedback loops, delayed effects, time pressure, situational uncertainty, adjusting model transparency and changing the relationships between system elements. CODEM creates detailed logs of events and actions essential for cognitive process tracing and the evaluation of decision making effectiveness. The first task designed with CODEM is a counter-insurgency scenario in which a coalition force seeks to stabilize a failing state. A genetic algorithm is used to estimate the best strategy in that scenario for comparison with human results. An adversarial version also allows insurgents to be controlled by a human opponent (or a red team) rather than an artificial agent. CODEM can be used as a cognitive engineering testbed and as a training environment for improving decision making and adaptation skills in complex situations.

The prevalence of surveillance and information collection technologies provides decision-makers with greater volume and complexity of information to monitor and on which to base decisions than ever before. In this ever increasing dynamic and information rich environment, the role for decision support systems (DSS) to augment cognition and situation awareness (SA) is becoming crucial. However, unless a better understanding is gained of the factors that promote SA without interfering with other critical cognitive functions, the design and development of such technology may serve only to exacerbate rather than enhance the desired effect. The present study investigates how DSS designed to support particular aspects of SA may affect task performance. In the context of a functional computer-controlled simulation of single ship naval anti-air warfare, a baseline condition was compared to two conditions in which a DSS was integrated to the original interface to support different facets of SA. Participants in the two DSS conditions showed an increased SA level, as measured by the QUASA technique, compared to those in the control condition. Despite this benefit, the two DSS actually lead to a reduced performance, as indexed by defense effectiveness. These findings suggest that the benefits of DSS in terms of SA may be accompanied by an overhead with adverse effects on task performance, particularly in situations of high cognitive load and time constraints. This calls for more holistic evaluations of the cognitive impacts of decision support technologies and the development of methods to simultaneously address competing constraints when designing DSS.

Motivation - Computational models are increasingly used to gain insights into complex phenomena. However, better tools are needed to analyze and understand them. Research approach - We describe the IMAGE technology and an ongoing empirical evaluation of IMAGE compared to a baseline condition. Findings/Design - IMAGE provides interactive visualizations to facilitate the management and comparison of multiple simulations, the exploration of data, and the explicit representation of knowledge. Research limitations/Implications - While it may not be possible to fully understand nor predict the behaviour of complex systems, analysts and decision makers would benefit from technologies designed to facilitate their analysis. Originality/Value - IMAGE is a set of advanced technologies working in synergy that seeks to augment comprehension of complex systems. Take away message - IMAGE provides powerful innovative tools for simulation, exploration, and representation--three functions that the human mind cannot perform effectively in complex settings without the support of cognitive artifacts.

In a serial recall task, the Hebb repetition effect occurs when recall performance improves for a sequence repeated throughout the experimental session. This phenomenon has been replicated many times. Nevertheless, such cumulative learning seldom leads to perfect recall of the whole sequence, and errors persist. Here the authors report evidence that there is another side to the Hebb repetition effect that involves learning errors produced in a repeated sequence. A learning measure based on past recalls (correct or incorrect) shows that the probability of a given response increases with the number of prior occurrences of that response. The pattern of results reveals another manifestation of the Hebb repetition effect and speaks to the nature of implicit learning.

Traffic workers are vulnerable to accidents and must make critical decisions to avoid conflicts between road users. This can lead to high stress levels, which may hinder their capacity to mitigate the occurrence of hazards. Measuring stress on the field could represent an efficient solution to help pinpoint risky situations and identify factors that increase risk. The goal of this study was to verify whether stress among traffic workers could be predicted using physiological measures and characteristics of the work situation. Nineteen police officers in Quebec City and Montreal, Canada, performed traffic duties while their physiological activity was assessed by a wearable physiological harness. Every 15 minutes, change in subjective stress was also measured. Results showed that decision-tree models outperformed multifactorial logistic regressions for predicting subjective stress based on both situational factors and physiological measures. This demonstrated the potential of using such measures to monitor stress among traffic workers.