Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning

Human Neurocognition During Abstract Reasoning: EEG and Eye-Tracking Dataset Study Overview This dataset contains electroencephalography (EEG) recording, eye-tracking recording, and behavioral data from human participants performing an abstract reasoning task (visual analogy problem-solving). The original study investigated the alignment between Large Language Models (LLMs) and the human neural responses during abstract pattern completion and reasoning. Primary Research Question To what extent do LLM representations reflect the neural mechanisms underlying human abstract reasoning? Reference Publications Main preprint: Pinier, C., Vargas, S. A., Steeghs-Turchina, M., Matzke, D., Stevenson, C. E., & Nunez, M. D. (2025). Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning. arXiv preprint arXiv:2508.10057. Conference publications: Pinier, C., Vargas, S. A., Steeghs-Turchina, M., Matzke, D., Stevenson, C. E., & Nunez, M. (2026, March 6). Large language models show signs of alignment with human neurocognition during abstract reasoning [Poster session]. ICLR 2026 Workshop - From Human Cognition to AI Reasoning: Models, Methods, and Applications. PDF Pinier, C., Stevenson, C. E., & Nunez, M. D. (2025). Moderate evidence for large language models reflecting human neurocognition during abstract reasoning [Poster session]. Cognitive Computational Neuroscience (CCN) 2025, Amsterdam, Netherlands. Dataset Description Participants Number of participants: 25 Recruitment: University of Amsterdam community Inclusion criteria: Native or fluent English speakers, normal or corrected-to-normal vision Sessions: Multiple sessions per participant (1-5 sessions) Experimental Task Participants completed an abstract visual reasoning task involving pattern completion and analogy problems. Task Structure "Encoding phase" w/ individual icons of both pattern and response options flashing individually: 600 ms "Decision phase" w/ pattern and response options displayed w/ maximum response time: 12 seconds Number of sequences per session: 80 Stimuli Pattern types: 8 different abstract visual patterns representing varying levels of relational complexity: AAABAAAB, ABABCDCD, ABBAABBA, ABBACDDC, ABBCABBC, ABCAABCA, ABCDDCBA, ABCDEEDC Display: During the "Decision phase", the final icon in the sequence was replaced by a question mark. Four response icons were also displayed. Data Collection EEG Recording Electrode montage: BioSemi 64-channel standard montage Sampling rate: 2048 Hz Additional channels: EOG (4 channels): EOGL, EOGR, EOGT, EOGB Stimulus trigger channel: Status Recording type: Continuous Power line frequency: 50 Hz Eye-Tracking Recording Sampling rate: 2000 Hz Device: EyeLink 1000 Plus Behavioral Data Raw behavioral responses stored in TSV format including trial number, stimulus pattern ID, participant response, accuracy, and response time. Electrode Positions Note on electrode coordinates: The standard BioSemi 64-channel montage does not include recorded electrode positions, as this study used a standard template montage (not subject-specific digitization). Electrode locations follow the standard 10-20 positioning system for BioSemi 64-channel caps. Ethics and Study Approval This research project complies with the guidelines formulated by the Ethics Review Board (FMG-UvA), University of Amsterdam, The Netherlands, and has been approved by the aforementioned Ethics Review Board on 19-06-2024. License This dataset is made available under the Creative Commons Attribution 4.0 License (CC BY 4.0). How to Cite This Dataset Associated Publication: Pinier, C., Vargas, S. A., Steeghs-Turchina, M., Matzke, D., Stevenson, C. E., & Nunez, M. D. (2025). Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning. arXiv preprint arXiv:2508.10057. Preprocessing Notes This dataset contains minimally preprocessed raw EEG data: ✓ Bad channels identified based on visual inspection ✓ Trigger channel identified and annotated ✗ No rereferencing (e.g. not yet rereferenced to average) ✗ No filtering applied ✗ No ICA artifact correction applied ✗ No epoching applied Data Access and Use This dataset is intended for research purposes. Code Availability Code for data collection and analysis is available at https://github.com/chris-pinier/abstract_reasoning Contact For inquiries regarding this dataset, please contact: Michael D. NunezUniversity of Amsterdam m.d.nunez@uva.nl OR Christopher PinierUniversity of Amsterdam c.pinier@uva.nl