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2020


Sweat softens the outermost layer of the human finger pad: evidence from simulations and experiments
Sweat softens the outermost layer of the human finger pad: evidence from simulations and experiments

Nam, S., Kuchenbecker, K. J.

Work-in-progress poster presented at EuroHaptics, Leiden, The Netherlands, September 2020, Award for best poster in 2020 (poster)

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Project Page [BibTex]

2020


Project Page [BibTex]


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Haptify: a Comprehensive Benchmarking System for Grounded Force-Feedback Haptic Devices

Fazlollahi, F., Kuchenbecker, K. J.

Work-in-progress poster presented at EuroHaptics, Leiden, The Netherlands, September 2020 (poster)

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[BibTex]

[BibTex]


Optimal To-Do List Gamification
Optimal To-Do List Gamification

Stojcheski, J., Felso, V., Lieder, F.

arXiv, August 2020 (techreport)

Abstract
What should I work on first? What can wait until later? Which projects should I prioritize and which tasks are not worth my time? These are challenging questions that many people face every day. People’s intuitive strategy is to prioritize their immediate experience over the long-term consequences. This leads to procrastination and the neglect of important long-term projects in favor of seemingly urgent tasks that are less important. Optimal gamification strives to help people overcome these problems by incentivizing each task by a number of points that communicates how valuable it is in the long-run. Unfortunately, computing the optimal number of points with standard dynamic programming methods quickly becomes intractable as the number of a person’s projects and the number of tasks required by each project increase. Here, we introduce and evaluate a scalable method for identifying which tasks are most important in the long run and incentivizing each task according to its long-term value. Our method makes it possible to create to-do list gamification apps that can handle the size and complexity of people’s to-do lists in the real world.

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link (url) Project Page [BibTex]


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Measuring the Costs of Planning

Felso, V., Jain, Y. R., Lieder, F.

CogSci 2020, July 2020 (poster) Accepted

Abstract
Which information is worth considering depends on how much effort it would take to acquire and process it. From this perspective people’s tendency to neglect considering the long-term consequences of their actions (present bias) might reflect that looking further into the future becomes increasingly more effortful. In this work, we introduce and validate the use of Bayesian Inverse Reinforcement Learning (BIRL) for measuring individual differences in the subjective costs of planning. We extend the resource-rational model of human planning introduced by Callaway, Lieder, et al. (2018) by parameterizing the cost of planning. Using BIRL, we show that increased subjective cost for considering future outcomes may be associated with both the present bias and acting without planning. Our results highlight testing the causal effects of the cost of planning on both present bias and mental effort avoidance as a promising direction for future work.

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[BibTex]

[BibTex]