This episode introduces 2023 paper presented at the ACM SIGIR Conference on Human Information Interaction and Retrieval. This research was conducted by PhD students Li Shi, Nilavra Bhattacharya and Anubrata Das under supervision of Dr. Jacek Gwizdka and Dr. Matt Lease.

Shi, L., Bhattacharya, N., Das, A., & Gwizdka, J. (2023). True or false? Cognitive load when reading COVID-19 news headlines: an eye-tracking study. Proceedings of the 2023 Conference on Human Information Interaction and Retrieval, 107–116. https://doi.org/10.1145/3576840.3578290

This podcast discussion summarizes a study using eye-tracking to examine how people process online information. The research reveals position bias, where information presented first receives disproportionate attention. Pupil dilation indicated increased cognitive effort when encountering information contradicting personal beliefs or presenting incorrect evidence, especially when aligning with pre-existing biases. Interestingly, belief changes didn't significantly impact cognitive load. The study highlights how our brains prioritize information confirming existing beliefs, even if inaccurate, emphasizing the need for critical thinking and awareness of cognitive biases.

IX Lab website: https://ixlab.us/

Dr. Jacek Gwizdka website: https://jacekg.ischool.utexas.edu/

The audio for this conversation has been generated by AI using: https://notebooklm.google/

Music intro created by a human (C) Jacek Gwizdka

This episode discusses the 2024 paper: Mishra, A., Shukla, S., Torres, J., Gwizdka, J., & Roychowdhury, S. (2024). Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs) (No. arXiv:2410.07507). arXiv. https://doi.org/10.48550/arXiv.2410.07507

This research is conducted under Dr. Abhijit Mishra as the main Principal Investigator with students Shreya Shukla and Jose Torres and contributions from Dr. Jacek Gwizdka and Dr. Shounak Roychowdhury.

Researchers at the University of Texas at Austin are developing technology to translate brainwaves into text using electroencephalography (EEG) and large language models (LLMs). The system employs a three-stage process: training an EEG encoder to extract features, fine-tuning LLMs on multimodal data (images and text), and further refining the LLMs with EEG embeddings for text generation. Experiments using a public EEG dataset demonstrate the effectiveness of this approach, surpassing chance performance and showing promise for future applications in assistive technologies and neuroscience. While the technology shows promising results, it's still in its early stages and faces challenges such as noise in EEG data, limited spatial resolution of EEG, and ethical concerns about privacy and bias. Potential applications include assistive technology for communication impairments and advances in healthcare and education.

IX Lab website: https://ixlab.us/

Dr. Jacek Gwizdka website: https://jacekg.ischool.utexas.edu/

The audio for this conversation has been generated by AI using: https://notebooklm.google/

Music intro created by a human (C) Jacek Gwizdka

This episode introduces journal article originally published in Journal of the Association for Information Science and Technology. This is joint work with Rahilsadat Hosseini and Shouyi Wang from UT Arlington and Michael Cole from Rutgers Uni / Lexis-Nexis.

Gwizdka, J., Hosseini, R., Cole, M., & Wang, S. (2017). Temporal dynamics of eye-tracking and EEG during reading and relevance decisions. Journal of the Association for Information Science and Technology, 68(10), 2299–2312. https://doi.org/10.1002/asi.23904

This podcast discusses a research paper investigating how people determine online information relevance. Researchers used eye-tracking and EEG to monitor brain activity and eye movements while participants read news articles and answered questions. The study revealed that relevance isn't immediately apparent but develops over time, with more accurate predictions possible as reading progresses. Key findings included pupil dilation correlating with relevant information and identifiable patterns in eye movements during "aha" moments. The discussion also explores potential applications of this research in improving technology's interaction with human brains and the ethical implications of personalized information systems.

IX Lab website: https://ixlab.us/

Dr. Jacek Gwizdka website: https://jacekg.ischool.utexas.edu/

The audio for this conversation has been generated by AI using: https://notebooklm.google/

Music intro created by a human (C) Jacek Gwizdka