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The Impact of Artificial Intelligent Tools on Decision Making Behavioral and Neural Dynamics

Author

Listed:
  • Edmundo Molina-Perez

    (School of Government and Public Transformation, Tecnológico de Monterrey)

  • Pedro Cortes

    (Tecnológico de Monterrey)

  • Isaac Molina

    (Tecnológico de Monterrey)

  • Fernanda Sobrino

    (Tecnológico de Monterrey)

  • Mario Tellez

    (Tecnológico de Monterrey)

  • Yessica Orozco

    (Tecnológico de Monterrey)

  • Mitzi Castellón

    (Tecnológico de Monterrey)

  • Steven Popper

    (Tecnológico de Monterrey)

  • Luis Serra

    (Tecnológico de Monterrey)

Abstract

Decision-making is a multifaceted cognitive process influenced by task complexity, information availability, individual cognitive strategies, and environmental settings. Yet, the neural mechanisms guiding everyday choices remain incompletely understood. This gap intensifies when integrating real-time aids, such as artificial intelligence tools (AIT), as cognitive decisionsupport especially for complex and ambiguous problems. This study explores the neural mechanisms of decision-making and examines how AIT influences these processes. Combining behavioral assessments and neurophysiological measurements, we investigate the dynamic interplay between human cognition and AIT through behavioral execution and electroencephalogram (EEG) activity. Experimental data from 54 participants suggest that in low-complexity decision-making, AIT is largely ignored in favor of heuristics. In high-complexity contexts, AIT positively influences decision-making outcomes while also increasing capacity for engagement with a challenging task as registered by EEG cortical activity. This suggests a non-linear effect of AIT in decision-making strategies highlighting its role as a complement to —rather than a replacement of—human cognitive processes.

Suggested Citation

  • Edmundo Molina-Perez & Pedro Cortes & Isaac Molina & Fernanda Sobrino & Mario Tellez & Yessica Orozco & Mitzi Castellón & Steven Popper & Luis Serra, 2025. "The Impact of Artificial Intelligent Tools on Decision Making Behavioral and Neural Dynamics," Working Paper Series of the School of Government and Public Transformation 8, School of Government and Public Transformation, Tecnológico de Monterrey.
    • Handle: RePEc:gnt:wpaper:8
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    References listed on IDEAS

    as
    1. Desouza, Kevin C. & Dawson, Gregory S. & Chenok, Daniel, 2020. "Designing, developing, and deploying artificial intelligence systems: Lessons from and for the public sector," Business Horizons, Elsevier, vol. 63(2), pages 205-213.
    2. Anthony Zador & Sean Escola & Blake Richards & Bence Ölveczky & Yoshua Bengio & Kwabena Boahen & Matthew Botvinick & Dmitri Chklovskii & Anne Churchland & Claudia Clopath & James DiCarlo & Surya Gangu, 2023. "Catalyzing next-generation Artificial Intelligence through NeuroAI," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
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    Keywords

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    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D89 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Other

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