Digital Companions for Goal-Setting, Goal-Achievement, and Self-Improvement
Computing Optimal Sub-Goals
A Gamified App that Helps People Overcome Self-Limiting Beliefs by Promoting Metacognition
Helping People Choose Their Values
A Digital Companion That Helps People Achieve Their Goals
Development of Measures for Goal Setting and Pursuit
Value-Driven Hierarchical Goal-Setting
Effective Goal-Setting
AI for Productivity
Solve Education
Intelligent Cognitive Prostheses
Computing Optimal Incentive Structures
Executive Functions Training
Computing Optimal Incentive Structures

Today's rapid advances in artificial intelligence present an unprecedented opportunity to augment the human mind with the help of technology. Our goal is to leverage insights from cognitive science to develop technologies that help humanity overcome its cognitive limitations and enable people to make better decisions, learn faster, become more productive, and achieve their goals.
We are currently working on a cognitive prosthesis for helping people achieve their goals on time. The basic idea is to align each action's immediate reward with its long-term value so as to make good decisions easier (Lieder & Griffiths, 2006). We have developed a mathematical framework for designing incentive structures that can be used to guarantee that the rewards won't accidentally incentivize counter-productive behaviors and a computational method for computing incentives that make it as easy as possible for people to choose the course of action that is best in the long run. We are working on instantiating this approach in a to-do list gamification app. Initial results suggest that this is a promising approach to helping people overcome procrastination (Lieder, Chen, Krueger, & Griffiths, 2019). We are currently scaling up this approach and integrating it into a digital companion that helps people achieve their goals. We are also working towards incentivizing planning and goal-setting and computing personalized incentives based on inverse-reinforcement learning. In another project, we successfully applied this approach to incentivize self-directed learning (Xu, Wirzberger, & Lieder, 2019).
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