Factors that affect younger and older adults' causal attributions of robot behaviour.

Stereotypes are cognitive shortcuts that facilitate efficient social judgments about others. Just as causal attributions affect perceptions of people, they may similarly affect perceptions of technology, particularly anthropomorphic technology such as robots. In a scenario-based study, younger and older adults judged the performance and capability of an anthropomorphised robot that appeared young or old. In some cases, the robot successfully performed a task while at other times it failed. Results showed that older adult participants were more susceptible to aging stereotypes as indicated by trust. In addition, both younger and older adult participants succumbed to aging stereotypes when measuring perceived capability of the robots. Finally, a summary of causal reasoning results showed that our participants may have applied aging stereotypes to older-appearing robots: they were most likely to give credit to a properly functioning robot when it appeared young and performed a cognitive task. Our results tentatively suggest that human theories of social cognition do not wholly translate to technology-based contexts and that future work may elaborate on these findings. Practitioner summary: Perception and expectations of the capabilities of robots may influence whether users accept and use them, especially older users. The current results suggest that care must be taken in the design of these robots as users may stereotype them.

Gender differences in spatial navigation: Characterizing wayfinding behaviors.

Men show a consistent spatial navigation advantage over women, which is often attributed to their increased use of survey spatial strategies. But what about men's navigation gives them an advantage? One possibility is that the way in which men explore environments is fundamentally different, leading to better navigational performance. To test this possibility, this study investigated whether there are gender differences in wayfinding behaviors during navigation that relate to navigational success in a real-world, large-scale, unconstrained navigation task. West Point cadets were given a masked GPS tracker and sent into a large-scale, natural environment to locate targets indicated on maps. We assessed how they explored the environment by computing three measures from the GPS tracks and related these measures to their ability to find the assigned target locations. We also tested whether their self-reported spatial ability related to navigational success. Results showed that males performed better than females, which replicates prior work. Further, traveling longer distances without changing course, pausing less, and fewer returns to previously visited locations were significantly related to the ability to locate the correct target. Consistent with full mediation, the significant relationship between gender and navigational success is fully accounted for by men and women producing different wayfinding behaviors, which in turn predict differences in navigational success. Further, there was no unique relationship between self-reported spatial skills and navigational success. This study is a first step toward showing the relationship between gender, wayfinding behaviors, and navigational success in a natural, real-world navigation task.

Detecting automation failures in a simulated supervisory control environment.

The goal of this research was to determine how individuals perform and allocate their visual attention when monitoring multiple automated displays that differ in automation reliability. Ninety-six participants completed a simulated supervisory control task where each automated display had a different level of reliability (namely 70%, 85% and 95%). In addition, participants completed a high and low workload condition. The performance data revealed that (1) participants' failed to detect automation misses approximately 2.5 times more than automation false alarms, (2) participants' had worse automation failure detection in the high workload condition and (3) participant automation failure detection remained mostly static across reliability. The eye tracking data revealed that participants spread their attention relatively equally across all three of the automated displays for the duration of the experiment. Together, these data support a system-wide trust approach as the default position of an individual monitoring multiple automated displays. Practitioner Summary: Given the rapid growth of automation throughout the workforce, there is an immediate need to better understand how humans monitor multiple automated displays concurrently. The data in this experiment support a system-wide trust approach as the default position of an individual monitoring multiple automated displays. Abbreviations: DoD: Department of Defense; UA: unmanned aircraft; SCOUT: Supervisory Control Operations User Testbed; UAV: unmanned aerial vehicle; AOI: areas of interest.

Looking for Age Differences in Self-Driving Vehicles: Examining the Effects of Automation Reliability, Driving Risk, and Physical Impairment on Trust.

PURPOSE: Self-driving cars are an extremely high level of autonomous technology and represent a promising technology that may help older adults safely maintain independence. However, human behavior with automation is complex and not straightforward (Parasuraman and Riley, 1997; Parasuraman, 2000; Rovira et al., 2007; Parasuraman and Wickens, 2008; Parasuraman and Manzey, 2010; Parasuraman et al., 2012). In addition, because no fully self-driving vehicles are yet available to the public, most research has been limited to subjective survey-based assessments that depend on the respondents' limited knowledge based on second-hand reports and do not reflect the complex situational and dispositional factors known to affect trust and technology adoption. METHODS: To address these issues, the current study examined the specific factors that affect younger and older adults' trust in self-driving vehicles. RESULTS: The results showed that trust in self-driving vehicles depended on multiple interacting variables, such as the age of the respondent, risk during travel, impairment level of the hypothesized driver, and whether the self-driving car was reliable. CONCLUSION: The primary contribution of this work is that, contrary to existing opinion surveys which suggest broad distrust in self-driving cars, the ratings of trust in self-driving cars varied with situational characteristics (reliability, driver impairment, risk level). Specifically, individuals reported less trust in the self-driving car when there was a failure with the car technology; and more trust in the technology in a low risk driving situation with an unimpaired driver when the automation was unreliable.

The effect of individual differences in working memory in older adults on performance with different degrees of automated technology.

A leading hypothesis to explain older adults' overdependence on automation is age-related declines in working memory. However, it has not been empirically examined. The purpose of the current experiment was to examine how working memory affected performance with different degrees of automation in older adults. In contrast to the well-supported idea that higher degrees of automation, when the automation is correct, benefits performance but higher degrees of automation, when the automation fails, increasingly harms performance, older adults benefited from higher degrees of automation when the automation was correct but were not differentially harmed by automation failures. Surprisingly, working memory did not interact with degree of automation but did interact with automation correctness or failure. When automation was correct, older adults with higher working memory ability had better performance than those with lower abilities. But when automation was incorrect, all older adults, regardless of working memory ability, performed poorly. Practitioner Summary: The design of automation intended for older adults should focus on ways of making the correctness of the automation apparent to the older user and suggest ways of helping them recover when it is malfunctioning.

A role for attention during wilderness navigation: Comparing effects of BDNF, KIBRA, and CHRNA4.

OBJECTIVE: To better understand what influences interindividual differences in ability to navigate in the wilderness, we hypothesized that better performance would be seen in (a) BDNF (rs6265) Val/Val homozygotes increased use of a spatial strategy, (b) KIBRA rs17070145 T/T homozygotes superior episodic memory, (c) CHRNA4 (rs1044396) T allele carriers better ability to focus visuospatial attention. METHOD: Military cadets (n = 382) genotyped for BDNF, KIBRA, and CHRNA4 SNPs used a map and compass to navigate in unmarked woods. Participants completed a morning course within 3.0 km and an afternoon course within 7.0 km. RESULTS: Success or failure in finding each point was analyzed in a logistic regression model with KIBRA, BDNF, and CHRNA4 genotypes as fixed effects. For the morning course, the adjusted odds ratio for the effect of KIBRA T/T over KIBRA C/C was 2.58 (95% CI of 1.31, 5.06) demonstrating a statistical benefit of the KIBRA T/T genotype over individuals with KIBRA C/C genotype. BDNF did not have an independent association with navigational success. For the afternoon course, the adjusted odds ratio for the effect of CHRNA4 C/T over C/C was 1.67 (95% CI of 1.24, 2.25) demonstrating a statistical benefit of CHRNA4 T allele carriers over the C/C genotype. CONCLUSIONS: Ability to navigate in the wilderness benefits less from sense of direction (BDNF and Santa Barbara Sense of Direction) and more from episodic memory (KIBRA) in the first course and heightened ability to focus attention (CHRNA4) after experience in the 2nd course. (PsycINFO Database Record

Displaying contextual information reduces the costs of imperfect decision automation in rapid retasking of ISR assets.

OBJECTIVE: The impact of a decision support tool designed to embed contextual mission factors was investigated. Contextual information may enable operators to infer the appropriateness of data underlying the automation's algorithm. BACKGROUND: Research has shown the costs of imperfect automation are more detrimental than perfectly reliable automation when operators are provided with decision support tools. Operators may trust and rely on the automation more appropriately if they understand the automation's algorithm. The need to develop decision support tools that are understandable to the operator provides the rationale for the current experiment. METHOD: A total of 17 participants performed a simulated rapid retasking of intelligence, surveillance, and reconnaissance (ISR) assets task with manual, decision automation, or contextual decision automation differing in two levels of task demand: low or high. Automation reliability was set at 80%, resulting in participants experiencing a mixture of reliable and automation failure trials. Dependent variables included ISR coverage and response time of replanning routes. RESULTS: Reliable automation significantly improved ISR coverage when compared with manual performance. Although performance suffered under imperfect automation, contextual decision automation helped to reduce some of the decrements in performance. CONCLUSION: Contextual information helps overcome the costs of imperfect decision automation. APPLICATION: Designers may mitigate some of the performance decrements experienced with imperfect automation by providing operators with interfaces that display contextual information, that is, the state of factors that affect the reliability of the automation's recommendation.

Understanding reliance on automation: effects of error type, error distribution, age and experience.

An obstacle detection task supported by "imperfect" automation was used with the goal of understanding the effects of automation error types and age on automation reliance. Sixty younger and sixty older adults interacted with a multi-task simulation of an agricultural vehicle (i.e. a virtual harvesting combine). The simulator included an obstacle detection task and a fully manual tracking task. A micro-level analysis provided insight into the way reliance patterns change over time. The results indicated that there are distinct patterns of reliance that develop as a function of error type. A prevalence of automation false alarms led participants to under-rely on the automation during alarm states while over relying on it during non-alarms states. Conversely, a prevalence of automation misses led participants to over-rely on automated alarms and under-rely on the automation during non-alarm states. Older adults adjusted their behavior according to the characteristics of the automation similarly to younger adults, although it took them longer to do so. The results of this study suggest the relationship between automation reliability and reliance depends on the prevalence of specific errors and on the state of the system. Understanding the effects of automation detection criterion settings on human-automation interaction can help designers of automated systems make predictions about human behavior and system performance as a function of the characteristics of the automation.

Transitioning to future air traffic management: effects of imperfect automation on controller attention and performance.

OBJECTIVE: This study examined whether benefits of conflict probe automation would occur in a future air traffic scenario in which air traffic service providers (ATSPs) are not directly responsible for freely maneuvering aircraft but are controlling other nonequipped aircraft (mixed-equipage environment). The objective was to examine how the type of automation imperfection (miss vs. false alarm) affects ATSP performance and attention allocation. BACKGROUND: Research has shown that the type of automation imperfection leads to differential human performance costs. METHOD: Participating in four 30-min scenarios were 12 full-performance-level ATSPs. Dependent variables included conflict detection and resolution performance, eye movements, and subjective ratings of trust and self confidence. RESULTS: ATSPs detected conflicts faster and more accurately with reliable automation, as compared with manual performance. When the conflict probe automation was unreliable, conflict detection performance declined with both miss (25% conflicts detected) and false alarm automation (50% conflicts detected). CONCLUSION: When the primary task of conflict detection was automated, even highly reliable yet imperfect automation (miss or false alarm) resulted in serious negative effects on operator performance. APPLICATION: The further in advance that conflict probe automation predicts a conflict, the greater the uncertainty of prediction; thus, designers should provide users with feedback on the state of the automation or other tools that allow for inspection and analysis of the data underlying the conflict probe algorithm.

Effects of imperfect automation on decision making in a simulated command and control task.

OBJECTIVE: Effects of four types of automation support and two levels of automation reliability were examined. The objective was to examine the differential impact of information and decision automation and to investigate the costs of automation unreliability. BACKGROUND: Research has shown that imperfect automation can lead to differential effects of stages and levels of automation on human performance. METHOD: Eighteen participants performed a "sensor to shooter" targeting simulation of command and control. Dependent variables included accuracy and response time of target engagement decisions, secondary task performance, and subjective ratings of mental work-load, trust, and self-confidence. RESULTS: Compared with manual performance, reliable automation significantly reduced decision times. Unreliable automation led to greater cost in decision-making accuracy under the higher automation reliability condition for three different forms of decision automation relative to information automation. At low automation reliability, however, there was a cost in performance for both information and decision automation. CONCLUSION: The results are consistent with a model of human-automation interaction that requires evaluation of the different stages of information processing to which automation support can be applied. APPLICATION: If fully reliable decision automation cannot be guaranteed, designers should provide users with information automation support or other tools that allow for inspection and analysis of raw data.