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“An Unscented Hound forWorking Memory” and the Cognitive Adaptation of User Interfaces

Abstract : We introduce An Unscented Hound for Working Memory (AUHWM), a new framework for the real-time tracking of human Working Memory (WM) that can be used to adapt computer interfaces to users' available cognitive resources. WM is the part of human cog-nition responsible for the short term storing and handling of information ; it can, in stressful situations, under information overload, or when suffering from dementia-like diseases, become severely limited, possibly leading to poor decision making. Our preliminary results suggest that AUHWM can provide a precise and timely assessment of WM capacity, so that the cognitive load a specific task imposes on users can be adapted, e.g., at the User Interface (UI) level. AUHWM is based on a low-level stochastic discrete model of human WM dynamics, implemented as a Gradient-Boosting-derived deterministic algorithm that simulates users' oblivion. AUHWM also performs Unscented Kalman filtering to track users' WM-specific parameters in real time, thus providing a dynamic assessment of their cognitive resources. Our approach has been tested and validated using data collected from Match 2 s, a visual memory game played by 18 users in another study. Going beyond real-time WM tracking, AUHWM is intended to also be used for WM prediction, paving the way to the adaptation of tasks and their UIs in real time as a function of users' cognitive abilities; we detail an example of such an adapted system, and provide experimental evidence this approach has the potential to lead to enhanced WM-adapted UIs in the future.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-02011002
Contributor : Claire Medrala <>
Submitted on : Tuesday, February 26, 2019 - 10:34:55 AM
Last modification on : Wednesday, October 14, 2020 - 3:52:25 AM
Long-term archiving on: : Monday, May 27, 2019 - 12:33:01 PM

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  • HAL Id : hal-02011002, version 1

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Bruno Sguerra, Pierre Jouvelot. “An Unscented Hound forWorking Memory” and the Cognitive Adaptation of User Interfaces. 2019. ⟨hal-02011002⟩

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