AI tools for analysis of empirical experiments on economic behavior
Abstract
This paper introduces a methodological framework designed to democratize neuroeconomics by integrating 4-channel EEG hardware (Muse 2) with advanced computational workflows. The central thesis argues for a transition from static, linear feature extraction to dynamic, spatio-temporal modeling to capture the evolving cognitive processes of economic decision-making. By leveraging the "NeuroSense" pipeline—utilizing the meegkit library for robust ringing artifact reduction—researchers can maintain high signal integrity from portable, dry-electrode devices.
The core of the framework utilizes the "NeuCube" Spiking Neural Network (SNN) architecture, which employs biologically plausible learning rules like Spike-Timing-Dependent Plasticity (STDP). This architecture effectively upgrades sparse surface data into a 3D evolving brain model, allowing for the reconstruction of complex neural states. The utility of this unified pipeline is demonstrated through three canonical economic paradigms: Willingness to Pay (WTP), the Ultimatum Game (UG), and Frontal Alpha Asymmetry (FAA). This approach enhances the ecological validity of neuroeconomic research, enabling rigorous "in-the-wild" experiments in real-world economics.
References
2. Cann, R., Tenison, C. and McCarty, C. (2024) 'Exploring the Utility of the Muse Headset for Capturing the N400: Dependability and Single-Trial Analysis', Sensors, 24(24), p. 7961.
3. Colafiglio, T., Lombardi, A., Sorino, P., Brattico, E., Lofù, D., Danese, D., Di Sciascio, E., Di Noia, T. and Narducci, F. (2024) 'NeuroSense: A Novel EEG Dataset Utilizing Low-Cost, Sparse Electrode Devices for Emotion Exploration', IEEE Access, 12, pp. 159296-159315. doi: 10.1109/ACCESS.2024.3487932.
4. de Cheveigné, A. and Arzounian, D. (2018) 'Robust detrending, rereferencing, outlier detection, and inpainting for multichannel data', NeuroImage, 172, pp. 903-912.
5. Doborjeh, M.G., Capecci, E. and Kasabov, N. (2017) 'Classification and segmentation of fMRI Spatio-Temporal Brain Data with a NeuCube evolving Spiking Neural Network model', IEEE Symposium on Evolving and Autonomous Learning Systems.
6. Gramfort, A., Luessi, M., Larson, E., Engemann, D.A., Strohmeier, D., Brodbeck, C., Goj, R., Jas, M., Brooks, T., Parkkonen, L. and Hämäläinen, M. (2013) 'MEG and EEG data analysis with MNE-Python', Frontiers in Neuroscience, 7(267).
7. Harmon-Jones, E. (2003) 'Clarifying the emotive functions of asymmetrical frontal cortical activity', Psychophysiology, 40(6), pp. 838-848.
8. InteraXon (2023) Muse 2 Technical Specifications. Available at: https://choosemuse.com (Accessed: 24 May 2024).
9. Kasabov, N.K. (2014) 'NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data', Neural Networks, 52, pp. 62–76. doi: 10.1016/j.neunet.2014.01.006.
10. KEDRI (2021) NeuCube-Py: Python implementation of the NeuCube architecture. Available at: https://github.com/KEDRI-AUT/NeuCube-Py (Accessed: 24 May 2024).
11. Krigolson, O.E., Williams, C.C., Norton, A., Hassall, C.D. and Colino, F.L. (2017) 'Choosing Muse: Validation of a Low-Cost, Portable EEG System for ERP Research', Frontiers in Neuroscience, 11, p. 109.
12. Ramsøy, T.Z., Skov, M., Christensen, M.K. and Stahlhut, C. (2018) 'Frontal Brain Asymmetry and Willingness to Pay', Frontiers in Neuroscience, 12, p. 138.
13. Smith, E.E., Reznik, S.J., Stewart, J.L. and Allen, J.J.B. (2017) 'Assessing and conceptualizing frontal EEG asymmetry: An updated primer on recording, processing, analyzing, and interpreting', International Journal of Psychophysiology, 111, pp. 98-114.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
By submitting a paper for publishing the authors hereby comply with the following provisions: 1. The authors retain the copyrights and only give the journal the right for first publication while licensing the work under Creative Commons Attribution License, which grants permissions to others to share the contribution citing this journal as first publication of the text. 2. The authors may enter separate, additional contractual relations for non-exclusive distribution of the published version of the work in this journal (e.g. to upload it in an institutional depository, or to be published in a book), given that they cite the first publication in this journal. 3. The authors are allowed and are encouraged to publish their works online (e.g. to upload it in an institutional depository, personal websites, social networks, etc.) before, during, and after the submission of the paper here, because this may lead to productive exchange, as well as earlier and larger referencing of the published works (see The Effect of Open Access).
