Neurocognitive Impact of Icon Styles and Semantics on Memory and Recall Speed: An N-Back Study Using EEG
This research investigates the effectiveness of icons in memory-related tasks, focusing on two icon styles (Solid and Outline) and two semantic classifications (Resemblance and Abstract). Through the N-Back task and electroencephalogram (EEG) measurements, the study aims to provide quantitative insights into memory load and recall speed. Twelve University of Kent students participated in an experiment involving 1-Back and 2-Back tests, using four icon combinations: Solid Resemblance, Solid Abstract, Outline Resemblance, and Outline Abstract. Each participant completed eight tests, during which N-Back reaction times, accuracy, and EEG data were recorded using a 32-channel wireless brain-computer interface (BCI). Statistical analysis was performed using the Wilcoxon signed rank test to evaluate reaction times and event-related potentials (ERPs). The results revealed no significant differences in recall speed between the Solid and Outline icon styles. However, contrary to previous research favoring Resemblance icons, Abstract icons were recalled significantly faster, particularly in the Outline style. However, Outline Abstract icons exhibited a significantly smaller P300 amplitude compared to Outline Resemblance icons, indicating a higher memory load. These findings suggest that Outline Resemblance icons may offer better usability due to their lower memory load but Outline Abstract icons if recall speed is the key. The study also employed N400 analysis to further explore the semantic processing of the icon combinations. These findings underscore the need for further research into the various dimensions of icons to better understand their impact on memory performance.
Authors
- Ramaswamy Palaniappan (ORCID: https://orcid.org/0000-0001-5296-8396)
- Daniel Thomas-Hercules
Institutions
- Medway School of Pharmacy (GB)
- University of Kent (GB)
Publication Details
- Published
- 2024-11-18
- DOI
- https://doi.org/10.1109/icdici62993.2024.10810917
- Citations
- 1
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.34