EAGER: Using Learning Algorithms to Morph Product Behavior for Specific Task Contexts and Cognitive Styles of Users

Stanford University, Stanford CA

Investigators

Abstract

People have different ways of learning and thinking, termed cognitive styles. Past research in website design has shown that there is a link between cognitive style and user behavior. This project takes this promising foundation and applies it to the design of physical products. This EArly-concept Grant for Exploratory Research (EAGER) project investigates whether or not it is possible to use sensor data and morphing algorithms, a type of learning algorithm, to design a faucet that can "know" what a person wants to do, and how they prefer to do it, via an underlying relationship between cognitive style and behavior. If so, can the faucet be designed in a way that its behavior is adaptable and pleasing to distinct cognitive styles, while also reducing water consumption. Faucets and showers account for 20% of household water usage, yet have received no "smart" design improvements to curtail water use. On the contrary, research shows that current automatic on/off faucets use more water than conventional faucets. If successful, this research will advance the design of household appliances that decrease water consumption. The project objective is to create a design method that uses morphing algorithms to design generative, customized product behavior that responds to the user's cognitive style and the task they are performing. This involves: (1) Reworking existing morphing/learning algorithms to make them generate a customized product behavior, instead of serving-up predetermined design permutations; (2) Creating a protocol to identify meaningful independent variables (sensor data) that serve as the parameters for controling morphing; (3) Incorporating feedback from users, in the form of faucet manual adjustments, to the behavior updating process; and (4) Balancing exploration of the behavior space and exploitation of knowledge gained. The sensor data used in this initial research will be simulated based on a pilot study. The research advances the state of the art in learning algorithms, increasing their usefulness in design by allowing for continuous-space design exploration in response to manual human-in-the-loop user interaction behavior. If successful, it will result in a physical product that is capable of testing the relationship between cognitive style and user interaction. This product will be used in future human-subject experiments, potentially building new cognitive models of user/product interaction.