Child ASRT eye-tracking

This script was used for testing statistical learning in an oculomotor based ASRT experiment recorded with children.

Prerequisites

Running ASRT script requires all of the following and their dependencies. In parentheses you can find the version which was used for the specific experiment.

• Python (3.6)
• Tobii Pro SDK (1.7.0)
• PsychoPy (3.2.3)
• pyglet (1.3.2)

After Python 3.6 and pip is intalled, you can install psychopy, tobii_research and pyglet packages using pip install. For example:

pip install pyglet==1.3.2

Setup

After all prerequisites are installed you need to download the content of this repository.

You can run the script by python asrt.py command or by running the asrt.py file from PsychoPy.

Credits

This code was forked from this github repository: https://github.com/tzolnai/project_ET_zero.

Orsolya Pesthy (OrsPesthy) adapted the code for the specific experiment and added a possibility for more frequent calibration.

Tamás Zolnai (tzolnai) reworked the original code and extended it with eye-tracking capabilities.

Emese Szegedi-Hallgató (hallgatoemese) wrote the initial ASRT script used for registering reaction times based on keyboard input.

References

ASRT (alternating SRT, alternating serial reaction time task)

• Howard Jr, J. H., & Howard, D. V. (1997). Age differences in implicit learning of higher order dependencies in serial patterns. Psychology and aging, 12(4), 634.

• Kóbor, A., Janacsek, K., Takács, Á., & Nemeth, D. (2017). Statistical learning leads to persistent memory: Evidence for one-year consolidation. Scientific reports, 7(1), 1-10.

• Simor, P., Zavecz, Z., Horvath, K., Éltető, N., Török, C., Pesthy, O., ... & Nemeth, D. (2019). Deconstructing procedural memory: Different learning trajectories and consolidation of sequence and statistical learning. Frontiers in psychology, 9, 2708.

Oculomotor version of ASRT

• Zolnai, T., Dávid, D., Pesthy, O., Nemeth, M., Kiss, M., Nagy, M., & Nemeth, D. (2022). Measuring statistical learning by eye-tracking. Experimental Results, 3, E10. doi:10.1017/exp.2022.8

I-DT: Dispersion-Threshold identification of fixations

• Salvucci, D. D., & Goldberg, J. H. (2000, November). Identifying fixations and saccades in eye-tracking protocols. In Proceedings of the 2000 symposium on Eye tracking research & applications (pp. 71-78).

Dispersion threshold

• Blignaut, P. (2009). Fixation identification: The optimum threshold for a dispersion algorithm. Attention, Perception, & Psychophysics, 71(4), 881-895.

• Blignaut, P., & Beelders, T. (2009). The effect of fixational eye movements on fixation identification with a dispersion-based fixation detection algorithm.

Fixation duration threshold

• Manor, B. R., & Gordon, E. (2003). Defining the temporal threshold for ocular fixation in free-viewing visuocognitive tasks. Journal of neuroscience methods, 128(1-2), 85-93.

Linear interpolation of missing data

• Olsen, A. (2012). The Tobii I-VT fixation filter. Tobii Technology, 1-21.

Technical documentation

• Tobii Pro Python SDK reference