1. Anthropomorphic Interfaces on Automation Trust, Dependence, and Performance in Younger and Older Adults (FA9550-12-1-0385) PI: Richard Chong Pak (Clemson Univ.; Psychology) AFOSR Program Review: T rust & Influence May 11-15, 2015, USAF Academy, CO.

2. Overview Human-automation trust calibration Age stereotypes and anthropomorphic automation (2014) - Three follow-up questions, three studies Optimizing trust calibration for different users

3. Our perceptions of the reliability of the system (trust) are just as important as actual reliability (e.g., Dzindolet, et al., 2003, Cohen, Parasuraman, & Freeman, 1998; Lee & Moray, 1992) How do we convey trust when warranted, distrust when not? Trust Calibration Perceived reliability Actual reliability Proper calibration Gempler & Wickens, 1998

4. Conveying Trust / Distrust Users apply gender stereotypes to computer agents (E. Lee, 2003) Activation of stereotypes is automatic (Greenwald & Banaji, 1995) and are applied in situations of uncertainty Individual differences in attitudes about technology affect trust (Merritt et al., 2012) - Anthropomorphic elements in automation affects reliance, trust (Pak, et al., 2012)

5. Stereotype Effects on Automation How is trust in anthro automation affected by users’ stereotypes of age, gender? - Age is one of the most salient features we notice of others How does this interact with automation reliability? Pak, R., McLaughlin. A. C., & Bass, B. (2014). A Multi-level Analysis of the Effects of Age and Gender Stereotypes on Trust in Anthropomorphic Technology by Younger and Older Adults. Ergonomics

6. Task, Design, Procedure 107 younger and older participants given a diabetes dilemma Exposed to young / old / male / female automation of varying reliability

7. 7 Trust is stereotype-consistent only at low/med automation reliabilities (shown below) Age difference: Younger adults tended to trust older aids (stereotype-consistent), older adults showed no differences (graph not shown) Male aid Female aid Male aid Female aid

8. Interim Discussion Replicability of unique age stereotype effect? - Disentangling age/occupation stereotype Task context matters; culture/person effects on automation - Is health automation generalizable to aviation automation? - Do different “user groups” perceive automation similarly? Individual differences account for variance - Individuals vary in cognitive abilities; how do these differences interact with types/levels to affect trust/performance?

9. Three Follow-up Studies Study 1: Age stereotypes and trust in robots How will pervasive age stereotypes influence: trust perceived capability attribution of fault Younger and older adults view a Baxter robot completing various cognitive and physical tasks in the context of a future home aid Branyon & Pak (data collection in progress) Change a light bulb (older aid, physical task)

10. Three Follow-up Studies Study 2: Trust perceptions of automation by context Younger (civilian and cadets) & older adults view automation success/failures in 4 contexts and judge trust. Civilian ugrad mean age = 18 Cadet mean age = 19 Older adult age = 76 Pak, McLaughlin, Rovira, & Baldwin, (finished data collection; manuscript in progress) Study 3: How do individual differences affect trust & performance in automation of varying type/levels? Rovira, Pak, & McLaughlin (under review)

11. Individual Differences in Abilities Individuals differ in stereotypes and beliefs but also cognitive abilities To what extent do individual differences in working memory affect trust/performance with automation? How do ID in WM interact with types/levels of automation?

12. Task Design, Procedure Eighty-six USMA cadets engaged in a time-limited, workload-intensive low-fidelity UAV targeting task 320 trials of: - No automation - Information automation (low automation) - Low-decision automation (medium automation) - Medium-decision automation (high automation) Individual differences in working memory capacity, complacency potential assessed

13. Information automation (low automation) Medium-decision automation (higher automation)

14. Decision Accuracy by WM Complacency (overtrust) Reliable TrialsUnreliable Trials Decision Accuracy (proportion) Amount of automation

15. Conclusions All users, but especially low-WM users, benefit from more automation if it is reliable Low-WM users hurt more when automation is unreliable - WM negatively correlated to trust in automation Can we reduce apparent complacency of: - Older users? - Cadets? - Low-WM users?

16. Individual Differences & Anthropomorphic Automation Users are different: can we accommodate individual and age differences in automation trust & behavior with anthropomorphism? Large scale, web-based individual and age differences study examining trust with anthropomorphic automation - Age, org culture, anthropomorphic design (visual appearance, etiquette), level of automation, cognitive abilities, personality Web-based complex WM span measure Pak, McLaughlin, Leidheiser, & Branyon (validation study in progress) Web-based anthropomorphic decision automation task

17. Project Summary Research Objectives: Understand the role of visual anthropomorphism on automation trust Examine what individual differences interact with anthropomorphic automation to affect trust and performance Key Findings: Pre-existing stereotypes interact with automation design to influence trust in automation regardless of machine-factors Individual differences in working memory predict who benefits/is hurt by automation Context of automation use alters trust in automation Trust differences by age group and organizational culture (civilian vs. military) Technical Approach: Empirical (lab, web) experiments examining perceptions and performance with automation Testing of civilian college, older adult (age 65+), and USMA cadets Benefits to the wider academic or DoD community: Automation is ubiquitous in DoD operations Understanding and improving human- automation trust calibration is critical to effective operator performance in complex situations. Project Start Date: 4/1/12 Project End Date: 3/31/15 17

18. Publications, Awards, Patents, or Transitions Attributed to the Grant Publications Pak, R., McLaughlin, A. C., & Bass, B. (2014). A Multilevel Analysis of the Effects of Age and Gender Stereotypes on Trust in Anthropomorphic Technology by Younger and Older Adults. Ergonomics. Rovira, E., Pak, R., & McLaughlin, A. C. (under review). Low Memory, Mo' Problems: Effects of individual differences on types and levels of automation. Human Factors. Pak, R., McLaughlin, A. C., Rovira, E., & Baldwin, N. (manuscript in progress). Younger and older adults’ trust in automation is affected by task context and reliability. Pak, R., McLaughlin, A. C., Leidheiser, W., & Branyon, J. (manuscript in progress). Validation of a web-based measure of complex working memory span. Conference Proceedings Branyon, J., & Pak, R. (2015). Investigating older adults’ trust, causal attributions, and perception of capabilities in robots as a function of robot appearance, task, and reliability. To be presented at the 2015 Annual Human Factors and Ergonomics Society Meeting. Leidheiser, W., Branyon, J., Baldwin, N., Pak, R., McLaughlin, A. C. (2015). Lessons learned in adapting a lab-based measure of working memory capacity for the web. To be presented at the 2015 Annual Human Factors and Ergonomics Society Meeting. Baldwin, N., Branyon, J., Pak, R., Sethumadhavan, A., (2015). Social facilitation of a virtual agent for younger and older adults. To be presented at the 2015 Annual Human Factors and Ergonomics Society Meeting. Leidheiser, W., & Pak, R. (2014). The Effects of Age and Working Memory Demands on Automation-Induced Complacency. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 58(1), 1919–1923. Bass, B. M., Goodwin, M., Brennan, K., Pak, R., & McLaughlin, A. C. (2013). Effects of age and gender stereotypes on trust in an anthropomorphic decision aid. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 57(1), 1575-1579. Bass, B. M., & Pak, R. (2012). Faces as Ambient Displays: Assessing the attention-demanding characteristics of facial expressions. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 56(1), 2142–2146. Grant Submissions National Robotics Initiative: Mutual Trust-based Human-Robot Collaborations in Flexible Light Assembly in Manufacturing; Sponsored by National Science Foundation from 9/1/2015 to 8/31/2018. Yue Wang, PI; Richard Pak Co-Investigator, 20%, $1,440,672 18