August 9, 2017
July 24, 2017
April 27, 2017
Our new research has just been published. The full text PDF is available by clicking here.
Citation:
Pak, R., Rovira, E., McLaughlin, A. C., & Leidheiser, W. (2017, April 10). Evaluating Attitudes and Experience With Emerging Technology in Cadets and Civilian Undergraduates. Military Psychology. http://dx.doi.org/10.1037/mil0000175
Evaluating Attitudes and Experience With Emerging Technology in Cadets and Civilian Undergraduates.
Abstract: Existing research on the characteristics of digital natives, traditionally defined as those born after 1980, has shown subtle differences in how they approach technology compared with other cohorts. However, much of the existing research has focused on a limited set of conventional technologies, mostly related to learning. In addition, prior research has shown differences within this cohort in how they respond to autonomous technology (e.g., trust, reliance; Pak, Rovira, McLaughlin, & Baldwin, 2016). The purpose of this short report, representing the first wave of data collection in a larger study examining technology experience and attitude change, is to directly address 2 shortcomings in the literature on digital natives which tends to emphasize: (a) civilian students; and (b) conventional, often learning technologies. We addressed these 2 issues by recruiting 2 subgroups of digital natives (students and military cadets) and assessing attitudes and experience with a wide range of technology spanning from conventional (e.g., mobile) to emerging (e.g., robotics). The results showed that that both groups were surprisingly unfamiliar with emerging consumer technologies. Additionally, contrary to expectations, cadets were significantly, albeit only slightly, less experienced with mobile technologies, VR/augmented reality, social media, and entertainment technology as compared to civilian undergraduates.
April 12, 2017
November 21, 2016
Our latest article “Effects of individual differences in working memory on performance and trust with various degrees of automation” has been published on Taylor & Francis Online. It is available at: http://www.tandfonline.com/doi/full/10.1080/1463922X.2016.1252806.
ABSTRACT
Previous studies showed performance benefits with correct automation, but performance costs when the automation was incorrect (i.e. provided an incorrect course of action), particularly as degrees of automation increased. Automation researchers have examined individual differences, but have not investigated the relationship between working memory and performance with various degrees of automation that is both correct and incorrect. In the current study, working memory ability interacted with automation reliability and degree of automation. Higher degrees of correct automation helped performance while higher degrees of incorrect automation worsened performance, especially for those with lower working memory. Lower working memory was also associated with more trust in automation. Results illustrate the interaction between degree of automation and individual differences in working memory on performance with automation that is correct and automation that fails.
July 13, 2016
Our latest research is published and available here: http://www.tandfonline.com/doi/full/10.1080/00140139.2016.1189599
Pak, R., McLaughlin, A. C., Leidheiser, W., & Rovira, E. (2016). The effect of individual differences in working memory in older adults on performance with different degrees of automated technology. Ergonomics. http://doi.org/10.1080/00140139.2016.1189599
ABSTRACT
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.
June 1, 2016
Background: Technology gains have improved tools for evaluating complex tasks by providing environmental supports (ES) that increase ease of use and improve performance outcomes through the use of information visualizations (info-vis). Complex info-vis emphasize the need to understand individual differences in abilities of target users, the key cognitive abilities needed to execute a decision task, and the graphical elements that can serve as the most effective ES. Older adults may be one such target user group that would benefit from increased ES to mitigate specific declines in cognitive abilities. For example, choosing a prescription drug plan is a necessary and complex task that can impact quality of life if the wrong choice is made. The decision to enroll in one plan over another can involve comparing over 15 plans across many categories. Within this context, the large amount of complex information and reduced working memory capacity puts older adults’ decision making at a disadvantage. An intentionally designed ES, such as an info-vis that reduces working memory demand, may assist older adults in making the most effective decision among many options.
Objective: The objective of this study is to examine whether the use of an info-vis can lower working memory demands and positively affect complex decision-making performance of older adults in the context of choosing a Medicare prescription drug plan.
Methods: Participants performed a computerized decision-making task in the context of finding the best health care plan. Data included quantitative decision-making performance indicators and surveys examining previous history with purchasing insurance. Participants used a colored info-vis ES or a table (no ES) to perform the decision task. Task difficulty was manipulated by increasing the number of selection criteria used to make an accurate decision. A repeated measures analysis was performed to examine differences between the two table designs.
Results: Twenty-three older adults between the ages of 66 and 80 completed the study. There was a main effect for accuracy such that older adults made more accurate decisions in the color info-vis condition than the table condition. In the low difficulty condition, participants were more successful at choosing the correct answer when the question was about the gap coverage attribute in the info-vis condition. Participants also made significantly faster decisions in the info-vis condition than in the table condition.
Conclusions: Reducing the working memory demand of the task through the use of an ES can improve decision accuracy, especially when selection criteria is only focused on a single attribute of the insurance plan.
April 22, 2016
Our new paper can be downloaded at: http://www.tandfonline.com/eprint/HJrFr5ChDd6xvFjv5pjA/full
Pak, R., Rovira, E., McLaughlin, A. C., & Baldwin, N. (2016). Does the Domain of Technology Impact User Trust? Investigating trust in automation across different consumer-oriented domains in young adults, military, and older adults. Theoretical Issues in Ergonomics Science. doi:10.1080/1463922X.2016.1175523.
ABSTRACT
Trust has been shown to be a determinant of automation usage and reliance. Thus, understanding the factors that affect trust in automation has been a focus of much research. Despite the increased appearance of automation in consumer-oriented domains, the majority of research examining human-automation trust has occurred in highly specialised domains (e.g. flight management, military) and with specific user groups. We investigated trust in technology across three different groups (young adults, military, and older adults), four domains (consumer electronics, banking, transportation, and health), two stages of automation (information and decision automation), and two levels of automation reliability (low and high). Our findings suggest that trust varies on an interaction of domain of technology, reliability, stage, and user group.
March 28, 2016
The students recently presented their work at the Duke Robotics Student Symposium, held at Duke University on March 28th.
March 16, 2016
Title: Investigating Older Adults’ Trust, Causal Attributions, and Perception of Capabilities in Robots as a Function of Robot Appearance, Task, and Reliability
Committee: Dr. Richard Pak (Chair), Dr. Kelly Caine, and Dr. Patrick Rosopa
When: Thursday, March 24, 2016 at 10:30am
Where: Brackett Hall, Room 419
Abstract: The purpose of the current study was to examine the extent to which the appearance, task, and reliability of a robot is susceptible to stereotypic thinking. Stereotypes can influence the types of causal attributions that people make about the performance of others. Just as causal attributions may affect an individual’s perception of other people, it may similarly affect perceptions of technology. Stereotypes can also influence perceived capabilities of others. That is, in situations where stereotypes are activated, an individual’s perceived capabilities are typically diminished. The tendency to adjust perceptions of capabilities of others may translate into levels of trust placed in the individual’s abilities. A cross-sectional factorial survey using video vignettes was used to assess young adults’ and older adults’ attitudes toward a robot’s behavior and appearance. Trust and capability ratings of the robot were affected by participant age, reliability, and domain. Patterns of causal reasoning within the human-robot interaction (HRI) context differed from causal reasoning patterns found in human-human interaction.
