Trust influences sensory weighting

Motivating an extension of the Maximum Likelihood Estimation model with a factor trust

Master Thesis (2018)
Author(s)

B.C. Jongbloed (TU Delft - Mechanical Engineering)

Contributor(s)

A.C. Schouten – Mentor

W. Mugge – Mentor

H. Boessenkool – Coach

G Smit – Graduation committee member

Faculty
Mechanical Engineering
Copyright
© 2018 Barbara Jongbloed
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 Barbara Jongbloed
Graduation Date
18-04-2018
Awarding Institution
Delft University of Technology
Faculty
Mechanical Engineering
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Abstract

There is a distinction between environmental and social interactions as humans interpret the world. Environmental interactions are based on weighted sensory estimates formed on integrated redundant information. Trust is a heuristic used in social interactions. As social interactions and technology interrelate, social factors become influences on the environmental estimates. Hence trust could influence our sensory estimates.

The optimal sensory weights for the sensory estimates are determined with a Maximum Likelihood Estimate based on the reliability of the sensory information.

The objective of this research is to find evidence that trust influences sensory weighting on the level of sensory integration, believing that human-machine interaction will benefit from a better understanding of sensory weighting.

We hypothesised that information on the reliabilities of the sensory cues, while not changing these, will cause participants to reweigh the sensory information. Reweighing sensory information when the reliability of the sensory cue has not changed contradicts the Maximum Likelihood Estimation prediction.

An experiment was conducted in which participants performed a target-hitting task with a haptic device. The target was hidden, yet participants were presented with a visual and haptic cue to deduct the location of the target. Although both cues were not of equal reliability, the participants created the optimal sensory estimate by sensory integration.

The results showed that trust influenced sensory weighting.
Since the outcome of the Maximum Likelihood Estimation model was challenged by the occurrence of sensory reweighting because of trust, it is recommended to extend the model with a factor trust.

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