Abstract
There is a long history of interest in looking behavior during human interaction. With the advance of (wearable) video-based eye trackers, it has become possible to measure gaze during many different interactions. We outline the different types of eye-tracking setups that currently exist to investigate gaze during interaction. The setups differ mainly with regard to the nature of the eye-tracking signal (head- or world-centered) and the freedom of movement allowed for the participants. These features place constraints on the research questions that can be answered about human interaction. We end with a decision tree to help researchers judge the appropriateness of specific setups.
Original language | English |
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Pages (from-to) | 1592-1608 |
Number of pages | 17 |
Journal | Behavior Research Methods |
Volume | 53 |
Issue number | 4 |
DOIs | |
Publication status | Published - Aug 2021 |
Bibliographical note
Funding Information:This research was supported by the EU-MSCA Initial Training Network (814302; SAPIENS) and the Consortium on Individual Development (CID). CID is funded through the Gravitation program of the Dutch Ministry of Education, Culture, and Science and the NWO (Grant No. 024.001.003). The work of TFY was supported by the Swedish Collegium for Advanced Study (in collaboration with Riksbankens Jubileumsfond) and the Knut and Alice Wallenberg Foundation. We thank Gijs Holleman for proofreading the manuscript and providing valuable feedback.
Publisher Copyright:
© 2021, The Author(s).
Funding
This research was supported by the EU-MSCA Initial Training Network (814302; SAPIENS) and the Consortium on Individual Development (CID). CID is funded through the Gravitation program of the Dutch Ministry of Education, Culture, and Science and the NWO (Grant No. 024.001.003). The work of TFY was supported by the Swedish Collegium for Advanced Study (in collaboration with Riksbankens Jubileumsfond) and the Knut and Alice Wallenberg Foundation. We thank Gijs Holleman for proofreading the manuscript and providing valuable feedback.
Keywords
- Data analysis
- Data quality
- Eye tracking
- Human interaction
- Wearable