Noncooperative Game Theory

Hespanha, João P.

doi:10.2307/j.ctt1vwmgbh

Cited by 8 publications

(1 citation statement)

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“…Having established an experimental platform for co-adaptive interfaces and validated tools to analyze user-decoder adaptation, we next aimed to build tools to predict how users and decoders will interact. We leveraged game theory to model the co-adaptive interactions that arise between these two agents optimizing their individual objectives (Başar and Olsder, 1999; Hespanha, J., 2017) (Fig. 3A).…”

Section: Resultsmentioning

confidence: 99%

Predicting and Shaping Human-Machine Interactions in Closed-loop, Co-adaptive Neural Interfaces

Madduri,

Yamagami,

et al. 2024

Preprint

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Neural interfaces can restore or augment human sensorimotor capabilities by converting high-bandwidth biological signals into control signals for an external device via a decoder algorithm. Leveraging user and decoder adaptation to create co-adaptive interfaces presents opportunities to improve usability and personalize devices. However, we lack principled methods to model and optimize the complex two-learner dynamics that arise in co-adaptive interfaces. Here, we present new computational methods based on control theory and game theory to analyze and generate predictions for user-decoder co-adaptive outcomes in continuous interactions. We tested these computational methods using an experimental platform where human participants (N=14) learn to control a cursor using an adaptive myoelectric interface to track a target on a computer display. Our framework predicted the outcome of co-adaptive interface interactions and revealed how interface properties can shape user behavior. These findings contribute new tools to design personalized, closed-loop, co-adaptive neural interfaces.

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Section: Resultsmentioning

confidence: 99%