Measurable Decision Making with GSR and Pupillary Analysis for Intelligent User Interface

Abstract

This article presents a framework of adaptive, measurable decision making for Multiple Attribute Decision Making (MADM) by varying decision factors in their types, numbers, and values. Under this framework, decision making is measured using physiological sensors such as Galvanic Skin Response (GSR) and eyetracking while users are subjected to varying decision quality and difficulty levels. Following this quantifiable decision making, users are allowed to refine several decision factors in order to make decisions of high quality and with low difficulty levels. A case study of driving route selection is used to set up an experiment to test our hypotheses. In this study, GSR features exhibit the best performance in indexing decision quality. These results can be used to guide the design of intelligent user interfaces for decision-related applications in HCI that can adapt to user behavior and decision-making performance.

Using Galvanic Skin Response (GSR) to Measure Trust and Cognitive Load in the Text-Chat Environment

“…Galvanic Skin Response (GSR) is a physiological signal captured easily and cost effectively via the skin [15]. These signals reflect changes in the skin's ability to conduct electricity and are used to indicate the extent of nerve responses [7].…”

Be Informed and Be Involved

“…Such investigation could help develop more effective user interface for ML-based intelligent systems. Our previous research found that the type, number, and values of decision factors from ML output affect decision difficulties as well as decision qualities [10]. This paper is a natural extension of previous research.…”

Real-time Prediction of User Performance based on Pupillary Assessment via Eye-Tracking

“…In addition, Jiang, Atkins, Tien, Bednarik, and Zheng, (2014), Weber et al (2015), and Zhou et al (2015) also investigated mental effort based on pupillary responses using realistic experimental setups. Jiang et al (2014) found a relationship between pupil dilations and the level of task requirements (n = 12) in a surgical tele-operation setting.…”

“…Weber et al (2015) applied a pupillary-based mental effort assessment to assess individuals when creating process models (n = 3). Zhou et al (2015) used an experimental travelroute decision making setup with 40 participants to demonstrate that one can classify subjective decision difficulty levels ex post by jointly using three task time-based factors (task time length, number of responses of pupil diameter signal, and sum of duration of pupil diameter signal).…”