Quantitative effects of overlay clutter and information access effort: Examining the scan-clutter trade-off in displays with geospatial maps.

Overlaying images from multiple geospatial databases increases clutter and imposes attentional costs by disrupting focusing attention on each database and dividing attention when comparing databases. Costs of overlay clutter may offset the benefits of reduced scanning between two images displayed separately. In two experiments, we examine these attention issues using computational metrics to quantify clutter. We also examine how the scan-clutter trade-off is modified by different levels of clutter, display separation, and task attentional requirements. Participants viewed information from a geographical terrain database and a schematic map database and made judgments that required focusing attention on either database or integrating information across both. In Experiment 1, databases were presented as either overlaid or adjacent displays, and in Experiment 2, as either overlay, adjacent, or more separated displays. Results showed that response time was modulated by the magnitude of clutter, spatial separation, and task type. Results also revealed that clutter costs dominated those of spatial separation, particularly in tasks requiring focused attention. A computational feature congestion metric of clutter effectively predicted performance but could be improved by incorporating an overlay component, which amplified the costs of clutter. The results provide design guidelines for overlay displays (e.g., head-mounted displays) that will minimize the scan-clutter trade-off. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Beyond the Wizard of Oz: Negative Effects of Imperfect Machine Learning to Examine the Impact of Reliability of Augmented Reality Cues on Visual Search Performance.

Despite knowing exactly what an object looks like, searching for it in a person's visual field is a time-consuming and error-prone experience. In Augmented Reality systems, new algorithms are proposed to speed up search time and reduce human errors. However, these algorithms might not always provide 100% accurate visual cues, which might affect users' perceived reliability of the algorithm and, thus, search performance. Here, we examined the detrimental effects of automation bias caused by imperfect cues presented in the Augmented Reality head-mounted display using the YOLOv5 machine learning model. 53 participants in the two groups received either 100% accurate visual cues or 88.9% accurate visual cues. Their performance was compared with the control condition, which did not include any additional cues. The results show how cueing may increase performance and shorten search times. The results also showed that performance with imperfect automation was much worse than perfect automation and that, consistent with automation bias, participants were frequently enticed by incorrect cues.

Supporting detection of hostile intentions: automated assistance in a dynamic decision-making context.

In a dynamic decision-making task simulating basic ship movements, participants attempted, through a series of actions, to elicit and identify which one of six other ships was exhibiting either of two hostile behaviors. A high-performing, although imperfect, automated attention aid was introduced. It visually highlighted the ship categorized by an algorithm as the most likely to be hostile. Half of participants also received automation transparency in the form of a statement about why the hostile ship was highlighted. Results indicated that while the aid's advice was often complied with and hence led to higher accuracy with a shorter response time, detection was still suboptimal. Additionally, transparency had limited impacts on all aspects of performance. Implications for detection of hostile intentions and the challenges of supporting dynamic decision making are discussed.

Information Access Effort: The Role of Head Movements for Information Presented at Increasing Eccentricity on Flat Panel and Head-Mounted Displays.

OBJECTIVE: This experiment examined performance costs when processing two sources of information positioned at increasing distances using a flat panel display and an augmented reality head-mounted display (AR-HMD). BACKGROUND: The AR-HMD enables positioning virtual information at various distances in space. However, the proximity compatibility principle suggests that closer separation when two sources of information require mental integration assists performance, whereas increased separation between two sources hurts integration performance more than when a single source requires focused attention. Previous studies have provided inconsistent findings regarding costs associated with increased separation. Few of these experiments have examined separation for both focused and integration tasks, compared vertical and lateral separation, or measured head movements. METHOD: Three experiments collectively examined these issues using a flat panel display and a virtual display presented with an HMD, where the separation of information varied laterally or vertically during a focused attention (digit reading) task and an information integration (mental subtraction) task. RESULTS: There was no performance cost for either display when information was increasingly separated. However, head movements mitigated performance costs by preserving accuracy at larger separations without increasing response time. CONCLUSION: Head movements appear to mitigate performance costs associated with presenting information increasingly far apart on flat panel displays and HMDs. Both eye scanning and head movements appear to be less effortful than expected. APPLICATION: These findings have important implications for design guidelines regarding the placement of information presented on flat panel displays and, more specifically, HMDs, which can present information 360° around the user.

Rendezvous Under Temporal Uncertainty.

OBJECTIVE: Three experiments sought to understand performance limitations in controlling a ship attempting to meet another moving ship that approached from various trajectories. The influence of uncertainty, resulting from occasional unpredictable delays in one's own movement, was examined. BACKGROUND: Cognitive elements of rendezvous have been little studied. Related work such as the planning fallacy and bias toward underestimating time-to-contact imply a tendency toward late arrival at a rendezvous. METHODS: In a simplified simulation, participants controlled the speed and/or heading of their own ship once per scenario to try to rendezvous with another ship. Forty-five scenarios of approximately 30 s were conducted with different starting geometries and, in two of three experiments, with different frequencies and lengths of the unexpected delays. RESULTS: Perfect rendezvous were hard to obtain, with a general tendency to arrive late and pass behind the target vessel, although this was dependent on the angle of approach and relative speed. When occasional delays were introduced, less frequent but longer delays disrupted performance more than shorter but more frequent delays. Where delays were possible, but no delay occurred, there was no longer evidence of a general tendency to more frequently pass behind the target ship. Additionally, people did not wait to see if the unpredictable delays would occur before executing a course of action. Different control strategies were deployed and dual axis control was preferred. CONCLUSIONS: The tendency to arrive late and the influence of the possibility of uncertain delays are discussed in relationship to control strategies.

Detection of Hostile Intent by Spatial Movements.

OBJECTIVE: The ability of people to infer intentions from movement of other vessels was investigated. Across three levels of variability in movements in the path of computer-controlled ships, participants attempted to determine which entity was hostile. BACKGROUND: Detection of hostile intentions through spatial movements of vessels is important in an array of real-world scenarios. This experiment sought to determine baseline abilities of humans to do so. METHODS: Participants selected a discrete movement direction of their ship. Six other ships' locations then updated. A single entity displayed one of two hostile behaviors: shadowing, which involved mirroring the participant's vessel's movements; and hunting, which involved closing in on the participant's vessel. Trials allowed up to 35 moves before identifying the hostile ship and its behavior. Uncertainty was introduced through adding variability to ships' movements such that their path was 0%, 25%, or 50% random. RESULTS: Even with no variability in the ships' movements, accurate detection was low, identifying the hostile entity about 60% of the time. Variability in the paths decreased detection. Detection of hunting was strongly degraded by distance between ownship and the hostile ship, but shadowing was not. Strategies employing different directions of movement across the trial, but also featuring some runs of consecutive movements, facilitated detection. CONCLUSIONS: Early identification of threats based on movement characteristics alone is likely to be difficult, but particularly so when adversaries employ some level of uncertainty to mask their intentions. These findings highlight the need to develop decision aids to support human performance.

How history trails and set size influence detection of hostile intentions.

Previous research suggests people struggle to detect a series of movements that might imply hostile intentions of a vessel, yet this ability is crucial in many real world Naval scenarios. To investigate possible mechanisms for improving performance, participants engaged in a simple, simulated ship movement task. One of two hostile behaviors were present in one of the vessels: Shadowing-mirroring the participant's vessel's movements; and Hunting-closing in on the participant's vessel. In the first experiment, history trails, showing the previous nine positions of each ship connected by a line, were introduced as a potential diagnostic aid. In a second experiment, the number of computer-controlled ships on the screen also varied. Smaller set size improved detection performance. History trails also consistently improved detection performance for both behaviors, although still falling well short of optimal, even with the smaller set size. These findings suggest that working memory plays a critical role in performance on this dynamic decision making task, and the constraints of working memory capacity can be decreased through a simple visual aid and an overall reduction in the number of objects being tracked. The implications for the detection of hostile intentions are discussed.

Dynamic ensemble visualizations to support understanding for uncertain trajectories.

When making decisions about uncertain spatial trajectories, such as storm forecasts, people rely on visualizations to support their understanding. Four experiments explored novel visualizations-dynamic ensembles. Nonexperts used visualizations to interpret probabilistic information about potential paths of a hurricane. Experiment 1 focused on global properties of the distribution, and showed dynamic ensembles imply a larger area at risk than traditional cones of uncertainty. Experiment 2 compared decisions with cones versus dynamic ensembles at specific individual locations. Dynamic ensembles offer more appreciation of risk outside the center of the distribution, and less abrupt in transitions from evacuation to nonevacuation choices. Experiment 3 compared decisions for dynamic ensembles versus static line ensembles. Similar evacuation rates across the two conditions suggest ensembles, rather than dynamics, are the more critical feature. Experiment 4 examined whether an additional dimension can be included in dynamic ensembles using color coding. Decisions reacted to this ancillary feature, with higher evacuation rates for locations threatened by more severe outcomes. Outcomes highlight the ability to systematically vary the level of risk communicated through the ensembles while also communicating the continuous nature of the risk. The overall findings show the viability of presenting uncertain spatial information using dynamic ensembles. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Nautical Collision Avoidance : The Cognitive Challenges of Balancing Safety, Efficiency, and Procedures.

OBJECTIVE: Experimentally investigate maneuver decision preferences in navigating ships to avoid a collision. How is safety (collision avoidance) balanced against efficiency (deviation from path and delay) and rules of the road under conditions of both trajectory certainty and uncertainty. BACKGROUND: Human decision error is a prominent factor in nautical collisions, but the multiple factors of geometry of collisions and role of uncertainty have been little studied in empirical human factors literature. APPROACH: Eighty-seven Mechanical Turk participants performed in a lower fidelity ship control simulation, depicting ownship and a cargo ship hazard on collision or near-collision trajectories of various conflict geometries, while controlling heading and speed with the sluggish relative dynamics. Experiment 1 involved the hazard on a straight trajectory. In Experiment 2, the hazard could turn on unpredictable trials. Participants were rewarded for efficiency and penalized for collisions or close passes. RESULTS: Participants made few collisions, but did so more often when on a collision path. They sometimes violated the instructed rules of the road by maneuvering in front of the hazard ship's path. They preferred speed control to heading control. Performance degraded in conditions of uncertainty. CONCLUSION: Data reveal an understanding of maneuver decisions and conditions that affect the balance between safety and efficiency. APPLICATION: The simulation and data highlight the degrading role of uncertainty and provide a foundation upon which more complex questions can be asked, asked of more trained navigators, and decision support tools examined.

Anchoring and Adjustment in Uncertain Spatial Trajectory Prediction.

OBJECTIVE: The aim of this study was to explore the impact of prior information on spatial prediction and understanding of variability. BACKGROUND: In uncertain spatial prediction tasks, such as hurricane forecasting or planning search-and-rescue operations, decision makers must consider the most likely case and the distribution of possible outcomes. Base performance on these tasks is varied (and in the case of understanding the distribution, often poor). Humans must update mental models and predictions with new information, sometimes under cognitive workload. METHOD: In a spatial-trajectory prediction task, participants were anchored on accurate or inaccurate information, or not anchored, regarding the future behavior of an object (both average behavior and the variability). Subsequently, they predicted an object's future location and estimated its likelihood at multiple locations. In a second experiment, participants repeated the process under varying levels of external cognitive workload. RESULTS: Anchoring influenced understanding of most likely predicted location, with fairly rapid adjustment following inaccurate anchors. Increasing workload resulted in decreased overall performance and an impact on the adjustment component of the task. Overconfidence was present in all conditions. CONCLUSION: Prior information exerted short-term influence on spatial predictions. Cognitive load impaired users' ability to effectively adjust to new information. Accurate graphical anchors did not improve user understanding of variability. APPLICATION: Prior briefings or forecasts about spatiotemporal trajectories affect decisions even in the face of initial contradictory information. To best support spatial prediction tasks, efforts also need to be made to separate extraneous load-causing tasks from the process of integrating new information. Implications are discussed.

Effect of Visualization Training on Uncertain Spatial Trajectory Predictions.

OBJECTIVE: The goal of this study was to explore the ways in which visualizations influence the prediction of uncertain spatial trajectories (e.g., the unknown path of a downed aircraft or future path of a hurricane) and participant overconfidence in such prediction. BACKGROUND: Previous research indicated that spatial predictions of uncertain trajectories are challenging and are often associated with overconfidence. Introducing a visualization aid during training may improve the understanding of uncertainty and reduce overconfidence. METHOD: Two experiments asked participants to predict the location of various trajectories at a future time. Mean and variance estimates were compared for participants who were provided with a visualization and those who were not. RESULTS: In Experiment 1, participants exhibited less error in mean estimations when a linear visualization was present but performed worse than controls once the visualization was removed. Similar results were shown in Experiment 2, with a nonlinear visualization. However, in both experiments, participants who were provided with a visualization did not retain any advantage in their variance estimations once the visualization was removed. CONCLUSIONS: Visualizations may support spatial predictions under uncertainty, but they are associated with benefits and costs for the underlying knowledge being developed. APPLICATION: Visualizations have the potential to influence how people make spatial predictions in the presence of uncertainty. Properly designed and implemented visualizations may help mitigate the cognitive biases related to such predictions.

Overconfidence in Projecting Uncertain Spatial Trajectories.

OBJECTIVE: The aim of this study was to understand factors that influence the prediction of uncertain spatial trajectories (e.g., the future path of a hurricane or ship) and the role of human overconfidence in such prediction. BACKGROUND: Research has indicated that human prediction of uncertain trajectories is difficult and may well be subject to overconfidence in the accuracy of forecasts as is found in event prediction, a finding that indicates that humans insufficiently appreciate the contributions of variance in nature to their predictions. METHOD: In two experiments, our paradigm required participants to observe a starting point, a position at time T, and then make a prediction of the location of the trajectory at time NT. They experienced several trajectories from the same underlying model but perturbed by random variance in heading and speed. RESULTS: In Experiment 1A, people predicted linear paths well and were better in heading predictions than in speed predictions. However, participants greatly underestimated the variance in predicted location, indicating overconfidence. In Experiment 1B, the effect was replicated with frequencies rather than probabilities used in variance estimates. In Experiment 2, people predicted nonlinear trajectories poorly, and overconfidence was again observed. Overconfidence was reduced on the more difficult predictions. In both main experiments, those better at predicting the mean were not better at predicting the variance. CONCLUSIONS: Predicting the level of uncertainty in spatial trajectories is not well done and may involve qualitatively different abilities than prediction of the mean. APPLICATION: Improving real-world performance at prediction demands developing better understanding of variability, not just the average case. Biases in prediction of uncertainty may be addressed through debiasing training and/or visualization tools that could assist in more calibrated action planning.

Time Sharing Between Robotics and Process Control: Validating a Model of Attention Switching.

OBJECTIVE: The aim of this study was to validate the strategic task overload management (STOM) model that predicts task switching when concurrence is impossible. BACKGROUND: The STOM model predicts that in overload, tasks will be switched to, to the extent that they are attractive on task attributes of high priority, interest, and salience and low difficulty. But more-difficult tasks are less likely to be switched away from once they are being performed. METHOD: In Experiment 1, participants performed four tasks of the Multi-Attribute Task Battery and provided task-switching data to inform the role of difficulty and priority. In Experiment 2, participants concurrently performed an environmental control task and a robotic arm simulation. Workload was varied by automation of arm movement and both the phases of environmental control and existence of decision support for fault management. Attention to the two tasks was measured using a head tracker. RESULTS: Experiment 1 revealed the lack of influence of task priority and confirmed the differing roles of task difficulty. In Experiment 2, the percentage attention allocation across the eight conditions was predicted by the STOM model when participants rated the four attributes. Model predictions were compared against empirical data and accounted for over 95% of variance in task allocation. More-difficult tasks were performed longer than easier tasks. Task priority does not influence allocation. CONCLUSIONS: The multiattribute decision model provided a good fit to the data. APPLICATIONS: The STOM model is useful for predicting cognitive tunneling given that human-in-the-loop simulation is time-consuming and expensive.

Complacency and Automation Bias in the Use of Imperfect Automation.

OBJECTIVE: We examine the effects of two different kinds of decision-aiding automation errors on human-automation interaction (HAI), occurring at the first failure following repeated exposure to correctly functioning automation. The two errors are incorrect advice, triggering the automation bias, and missing advice, reflecting complacency. BACKGROUND: Contrasts between analogous automation errors in alerting systems, rather than decision aiding, have revealed that alerting false alarms are more problematic to HAI than alerting misses are. Prior research in decision aiding, although contrasting the two aiding errors (incorrect vs. missing), has confounded error expectancy. METHOD: Participants performed an environmental process control simulation with and without decision aiding. For those with the aid, automation dependence was created through several trials of perfect aiding performance, and an unexpected automation error was then imposed in which automation was either gone (one group) or wrong (a second group). A control group received no automation support. RESULTS: The correct aid supported faster and more accurate diagnosis and lower workload. The aid failure degraded all three variables, but "automation wrong" had a much greater effect on accuracy, reflecting the automation bias, than did "automation gone," reflecting the impact of complacency. Some complacency was manifested for automation gone, by a longer latency and more modest reduction in accuracy. CONCLUSIONS: Automation wrong, creating the automation bias, appears to be a more problematic form of automation error than automation gone, reflecting complacency. IMPLICATIONS: Decision-aiding automation should indicate its lower degree of confidence in uncertain environments to avoid the automation bias.

Part-task training in the context of automation: current and future directions.

Automation often elicits a divide-and-conquer outlook. By definition, automation has been suggested to assume control over a part or whole task that was previously performed by a human (Parasuraman & Riley, 1997). When such notions of automation are taken as grounds for training, they readily invoke a part-task training (PTT) approach. This article outlines broad functions of automation as a source of PTT and reviews the PTT literature, focusing on the potential benefits and costs related to using automation as a mechanism for PTT. The article reviews some past work in this area and suggests a path to move beyond the type of work captured by the "automation as PTT" framework. An illustrative experiment shows how automation in training and PTT are actually separable issues. PTT with automation has some utility but ultimately remains an unsatisfactory framework for the future broad potential of automation during training, and we suggest that a new conceptualization is needed.

Evidence of automatic processing in sequence learning using process-dissociation.

This paper proposes a way to apply process-dissociation to sequence learning in addition and extension to the approach used by Destrebecqz and Cleeremans (2001). Participants were trained on two sequences separated from each other by a short break. Following training, participants self-reported their knowledge of the sequences. A recognition test was then performed which required discrimination of two trained sequences, either under the instructions to call any sequence encountered in the experiment "old" (the inclusion condition), or only sequence fragments from one half of the experiment "old" (the exclusion condition). The recognition test elicited automatic and controlled process estimates using the process dissociation procedure, and suggested both processes were involved. Examining the underlying processes supporting performance may provide more information on the fundamental aspects of the implicit and explicit constructs than has been attainable through awareness testing.

Representing serial action and perception.

This article presents a review on the representational base of sequence learning in the serial reaction time task. The first part of the article addresses the major questions and challenges that underlie the debate on implicit and explicit learning. In the second part, the informational content that underlies sequence representations is reviewed. The latter issue has produced a rich and equivocal literature. A taxonomy illustrates that substantial support exists for associations between successive stimulus features, between successive response features, and between successive response-to-stimulus compounds. We suggest that sequence learning is not predetermined with respect to one particular type of information but, rather, develops according to an overall principle of activation contingent on task characteristics. Moreover, substantiating such an integrative approach is proposed by a synthesis with the dual-system model (Keele, Ivry, Mayr, Hazeltine, & Heuer, 2003).

Encoding and representation of simultaneous and sequential arrays in visuospatial working memory.

The effect of presentation type on organization in visuospatial working memory (VSWM) was examined. Stimuli were presented sequentially or simultaneously at study, and participants made same/different judgements at test. The test array varied in four different spatial configuration conditions: one featuring no changes from study, one in which two items switched, one in which the same array repeated but in a different location, and one in which a completely novel test stimulus appeared. Results indicated the use of a global configuration for both simultaneous and sequential presentations and showed increased impairment of item-level knowledge with sequential presentations. Overall, these results support the use of a global configuration organization as a fundamental aspect of VSWM processing.

Implicit motor sequence learning is not represented purely in response locations.

This study employed a novel variant of the serial reaction time task, focused on sequencing one element of movement-direction. During the task a repeated pattern of alternating directions (right-left-right, etc.) was embedded in the stimuli, and there was no series of response locations. Responses were made via two effector systems: single-finger responding (necessitates lateral arm movements between response keys), and four-fingered responding (4 individual fingers on 4 individual keys; requires no lateral arm movement). The sequence of directions was only learned by participants who performed lateral movements during training, indicating that learning was contingent on the particular motor effector used. Participants with low levels of sequence awareness displayed the same pattern of results.

Incidental learning of abstract rules for non-dominant word orders.

One way in which adult second language learners may acquire a word order that differs from their native language word order is through exposure-based incidental learning, but little is known about that process and what constrains it. The current studies examine whether a non-dominant word order can be learned incidentally, and if so, whether the rule can be generalized to new words not previously seen in the non-dominant order. Two studies examined the incidental learning of rules underlying the order of nouns and verbs in three-word strings. The self-timed reading speeds of native English speakers decreased as a result of practice with a non-dominant rule (words ordered either as "verb noun noun" or "noun noun verb"). The same pattern of results was also found for new words ordered according to the previously encountered rule, suggesting learning generalized beyond the specific instances encountered. A second experiment showed such rule learning could also occur when the nouns were replaced with pronounceable pseudowords. Learning was therefore possible in the absence of any pre-existing relationships between the items. Theoretical and educational implications are discussed.