A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

Today, intelligent machines \emph{interact and collaborate} with humans in a way that demands a greater level of trust between human and machine. A first step towards building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real-time. In this paper, two approaches for developing classifier-based empirical trust sensor models are presented that specifically use electroencephalography (EEG) and galvanic skin response (GSR) measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust sensor model based on the general feature set (i.e., a "general trust sensor model"). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.Comment: 20 page

Improving Human-Machine Collaboration Through Transparency-based Feedback – Part II: Control Design and Synthesis

To attain improved human-machine collaboration, it is necessary for autonomous systems to infer human trust and workload and respond accordingly. In turn, autonomous systems require models that capture both human trust and workload dynamics. In a companion paper, we developed a trust-workload partially observable Markov decision process (POMDP) model framework that captured changes in human trust and workload for contexts that involve interaction between a human and an intelligent decision-aid system. In this paper, we defne intuitive reward functions and show that these can be readily transformed for integration with the proposed POMDP model. We synthesize a near-optimal control policy using transparency as the feedback variable based on solutions for two cases: 1) increasing human trust and reducing workload, and 2) improving overall performance along with the aforementioned objectives for trust and workload. We implement these solutions in a reconnaissance mission study in which human subjects are aided by a virtual robotic assistant in completing a series of missions. We show that it is not always benefcial to aim to improve trust; instead, the control objective should be to optimize a context-specifc performance objective when designing intelligent decision-aid systems that infuence trust-workload behavior

Quantifying Perception-Based Attributes in Design: A Case Study on the Perceived Environmental Friendliness of Vehicle Silhouettes.

Design optimization problems have traditionally used engineering functionality attributes to inform the design of products and systems. However, the quantification and inclusion of subjective attributes has become a necessary part of the product design process. Previous research has assessed the aesthetics, emotional appeal, and expressiveness of "concept-based" product attributes (such as luxury or sportiness) in products. Environmental friendliness is a new product attribute that has emerged in prominence as consumers and manufacturers become more concerned with issuesof sustainability and the "footprint" on the environment. In the automotive industry, there is an increased interest not only in making more fuel efficient vehicles, but also in making them visually appealing in a way that conveys environmental consciousness. Present day trends already show an increase in the number of fuel efficient and alternative powertrain vehicles being introduced in the market; it is expected that in the next few years there will be a large number of different hybrid powertrain vehicles on the market. Depending on market trends and government regulations, fuel economy may not be the only driver for the purchase of fuel efficient vehicles when the price premium paid for the new technology does not result in a timely payback in fuel cost savings. Previous research has shown that people used subjective reasoning, including styling, as a determining factor for purchasing hybrid vehicles. Using methods from psychology and engineering, this dissertation presents a methodology to quantify subjective attributes for inclusion in design optimization models. A demonstration case study addresses the quantification of a perceptual design attribute named perceived environmental friendliness (PEF). A modeling framework that consists of stimuli development using design of experiments, survey design, and statistical analysis of data is presented. The model derived is included in a design optimization framework that considers how variables that influence PEF tradeoff with those that impact fuel economy. Results indicate that under certain conditions, there is a tradeoff between PEF and fuel economy; as PEF increases, the fuel economy decreases.Ph.D.Design ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75934/1/tnreid_1.pd

Human Trust-based Feedback Control: Dynamically varying automation transparency to optimize human-machine interactions

Human trust in automation plays an essential role in interactions between humans and automation. While a lack of trust can lead to a human's disuse of automation, over-trust can result in a human trusting a faulty autonomous system which could have negative consequences for the human. Therefore, human trust should be calibrated to optimize human-machine interactions with respect to context-specific performance objectives. In this article, we present a probabilistic framework to model and calibrate a human's trust and workload dynamics during his/her interaction with an intelligent decision-aid system. This calibration is achieved by varying the automation's transparency---the amount and utility of information provided to the human. The parameterization of the model is conducted using behavioral data collected through human-subject experiments, and three feedback control policies are experimentally validated and compared against a non-adaptive decision-aid system. The results show that human-automation team performance can be optimized when the transparency is dynamically updated based on the proposed control policy. This framework is a first step toward widespread design and implementation of real-time adaptive automation for use in human-machine interactions.Comment: 21 page

Real-Time Sensing of Trust in Human-Machine Interactions

Human trust in automation plays an important role in successful interactions between humans and machines. To design intelligent machines that can respond to changes in human trust, real-time sensing of trust level is needed. In this paper, we describe an empirical trust sensor model that maps psychophysiological measurements to human trust level. The use of psychophysiological measurements is motivated by their ability to capture a human\u27s response in real time. An exhaustive feature set is considered, and a rigorous statistical approach is used to determine a reduced set of ten features. Multiple classification methods are considered for mapping the reduced feature set to the categorical trust level. The results show that psychophysiological measurements can be used to sense trust in real-time. Moreover, a mean accuracy of 71.57% is achieved using a combination of classifiers to model trust level in each human subject. Future work will consider the effect of human demographics on feature selection and modeling

The Interaction Gap: A Step Toward Understanding Trust in Autonomous Vehicles Between Encounters

Shared autonomous vehicles (SAVs) will be introduced in greater numbers over the coming decade. Due to rapid advances in shared mobility and the slower development of fully autonomous vehicles (AVs), SAVs will likely be deployed before privately-owned AVs. Moreover, existing shared mobility services are transitioning their vehicle fleets toward those with increasingly higher levels of driving automation. Consequently, people who use shared vehicles on an "as needed" basis will have infrequent interactions with automated driving, thereby experiencing interaction gaps. Using human trust data of 25 participants, we show that interaction gaps can affect human trust in automated driving. Participants engaged in a simulator study consisting of two interactions separated by a one-week interaction gap. A moderate, inverse correlation was found between the change in trust during the initial interaction and the interaction gap, suggesting people "forget" some of their gained trust or distrust in automation during an interaction gap.Comment: 5 pages, 3 figure

Broadening Participation: A Report on a Series of Workshops Aimed at Building Community and Increasing the Number of Women and Minorities in Engineering Design

Despite some progress in increasing the numbers of women and minorities in engineering over the past 30 years, their full participation in the discipline has yet to be achieved, particularly in engineering academia. One cause is the leaky pipeline ; even after women and minorities choose to major in engineering, they drop out at rates higher than their counterparts along all career stages (undergraduate school, graduate school, tenure-track, etc.). Their small numbers creates isolation that has the unfortunate risks of struggle, less professional success, less sense of personal belonging, and less retention. Our hypothesis is that building a community that provides networking and support, opportunities for collaboration, and professional development, will lead to greater career success, personal fulfillment and professional happiness, retention, and greater participation/contribution from women and minorities. The authors have been conducting a series of workshops aimed at broadening participation of women and other minorities within the American Society of Mechanical Engineers (ASME) Design Engineering Division (DED). This paper reports on the activities and results of the workshop series. Pre-workshop survey data indicated a clear opportunity to address the unmet needs of underrepresented groups in the ASME DED. Post-workshop survey data showed success in attendee satisfaction with feelings of inclusion and community, professional skill building, and the prospect of future workshops held by the committee. A follow-up impact assessment survey showed that the workshops have led to greater participation in DED activities, new positive connections within the DED community, and positive feelings regarding their communication/collaboration abilities, self confidence, level of comfort, feelings of inclusion, professional goals, leadership abilities, and skill sets. While these results are encouraging, the committee feels strongly that greater success in broadening the participation of underrepresented groups in engineering would be possible by sharing our strategies and successes, and learning from others with similar experience creating communities within the many engineering disciplines represented in ASEE

Comparing functional analysis methods for product dissection tasks

The purpose of this study is to begin to explore which function identification methods work best for specific design tasks. We use a 3-level within-subject study (n=78) to compare three strategies for identifying functions: energy-flow, top-down, and enumeration. These are tested in a product dissection task with student engineers who have minimal prior experience. Participants were asked to dissect a hair dryer, power drill, and toy dart gun and generate function trees to describe how these work. The function trees were evaluated with several metrics including the total number of functions generated, the number of syntactical errors, and the number of unique (relevant and non-redundant) functions. We found no statistical, practical, or qualitative difference between the trees produced for each method. We also found some generalized findings through surveys that the most difficult aspects of using functional decomposition include identifying functions, choosing function verbs, and drawing the diagram. Together, this may also mean that for novice engineers, simpler methods, such as enumeration, should be taught prior to more complicated methods so students can grasp core concepts such as identifying functions and structuring function diagrams