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Factors Affecting Perception

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Perception is influenced by perceptual set, context, motivation, and emotion. Perceptual set, or perceptual expectancy, refers to the tendency to perceive things in a particular way, influenced by previous experiences and expectations. This phenomenon affects the interpretation of stimuli, creating a set of mental tendencies and assumptions that impact sensory perceptions of sound, taste, touch, and sight.
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Related Experiment Video

Updated: Aug 7, 2025

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Published on: January 16, 2020

Occupant privacy perception, awareness, and preferences in smart office environments.

Beatrice Li¹, Arash Tavakoli², Arsalan Heydarian^3,4

¹Department of Systems Engineering, University of Virginia, Charlottesville, VA, 22904, USA.

Scientific Reports

|March 11, 2023

Summary

Smart office buildings collect sensor data, potentially revealing personal information. Occupant privacy preferences depend on data context and personal factors, guiding better privacy designs.

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Area of Science:

Human-Computer Interaction
Building Science
Privacy Studies

Background:

Building management systems (BMS) offer efficiency and comfort but collect extensive sensor data.
Machine learning can infer personal information from BMS data, exceeding intended use.
Occupant privacy concerns are understudied in smart offices compared to smart homes.

Purpose of the Study:

To investigate smart office occupants' perceptions and privacy preferences.
To identify factors influencing privacy preferences in smart office environments.

Main Methods:

Conducted 24 semi-structured interviews with smart office building occupants.
Analyzed interview data to understand privacy perceptions and influencing factors.

Main Results:

Privacy preferences are shaped by data modality features (spatial, security, temporal context).
Personal features, including awareness, definitions of privacy, and perceived utility, also influence preferences.
A model was developed to explain privacy preferences in smart office settings.

Conclusions:

Understanding data and personal features is crucial for designing effective privacy measures.
The proposed model can inform the development of privacy-preserving smart office technologies.
Addressing occupant privacy is essential for the ethical deployment of smart building systems.