Reliance on haptic assistance reflected in haptic cue weighting

Journal article (2018)

Authors

T.L. Gibo Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
M.A. Plaisier Biomechanical Engineering
W. Mugge Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
David Abbink Human-Robot Interaction - Mechanical, Maritime and Materials Engineering
Research Group
Biomechatronics & Human-Machine Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Reliance Trust Automation Haptic shared control Visualization Force Standards Haptic interfaces Supervisory control Cue weighting Task analysis Haptic assistance Trial-by-trial variability
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Published Date

2018

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Biomechanical Engineering

Research Group

Biomechatronics & Human-Machine Control

Abstract

When using an automated system, user trust in the automation is an important factor influencing performance. Prior studies have analyzed trust during supervisory control of automation, and how trust influences reliance: the behavioral correlate of trust. Here, we investigated how reliance on haptic assistance affects performance during shared control with an automated system. Subjects made reaches towards a hidden target using a visual cue and haptic cue (assistance from the automation). We sought to influence reliance by changing the variability of trial-by-trial random errors in the haptic assistance. Reliance was quantified in terms of the subject's position at the end of the reach relative to the two cues. Our results show that subjects aimed more towards the visual cue when the variability of the haptic cue errors increased, resembling cue weighting behavior. Similar behavior was observed both when subjects had explicit knowledge about the haptic cue error variability, as well as when they had only implicit knowledge (from experience). However, the group with explicit knowledge was able to more quickly adapt their reliance on the haptic assistance. The method we introduce here provides a quantitative way to study user reliance on the information provided by automated systems with shared control.

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