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Mind Meets Machine: Towards a Cognitive Science of Human-Machine Interactions
Emily S Cross1, Richard Ramsey2
1Department of Cognitive Science, Macquarie University, Sydney, Australia; Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, Scotland, UK.
Trends in Cognitive Sciences
|January 1, 2021
Summary
This study introduces a framework to understand the cognitive and brain mechanisms behind human-machine interactions as robots become more social. It emphasizes a comprehensive approach to studying these complex interactions in various settings.
Area of Science:
- Cognitive Neuroscience
- Human-Computer Interaction
- Robotics
Background:
- Robots are increasingly integrated into social environments like homes and hospitals.
- Understanding human-machine interaction is crucial as robots take on social roles.
Purpose of the Study:
- To introduce a novel framework for studying the cognitive and brain mechanisms of human-machine interactions.
- To bridge different levels of description using cognitive neuroscience principles.
Main Methods:
- Leveraging advances in cognitive neuroscience.
- Integrating theory and methods from brain and behavioral sciences.
- Developing a framework adaptable to diverse social machines and user expectations.
Main Results:
- A structured approach to investigate the cognitive science of human-machine interactions.
- Identification of unique challenges and rewards for researchers in this field.
- Emphasis on a multi-system perspective considering cognitive and brain functions.
Conclusions:
- The proposed framework facilitates a deeper understanding of human-robot social interactions.
- It accommodates the variability in social robots, user experiences, and individual differences.
- It provides a robust methodology for future research in human-machine cognition.
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