Arthur Prevel
Axes de recherche
Conditioning Processes and Behavior Modification
Following a behaviorist approach, my primary research interest lies in understanding how human behavior is shaped by operant and Pavlovian conditioning. I investigate how individuals learn that outcomes result either from their own actions (operant conditioning) or from environmental cues (Pavlovian conditioning), and how they adapt their behavior in response to these learned contingencies.
In my research, I seek to understand the factors that influence the effects of operant and Pavlovian conditioning procedures on human behavior. I am also deeply interested in the application of these learning and motivation principles to applied and clinical contexts.
Keywords: AI, Autism Spectrum Disorder, Behavior Modification, (Moral) Decision-Making, Human Reinforcement Learning, Operant Conditioning, Pavlovian Conditioning.
Selected publications:
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Salatino, A., Prével, A., Caspar, E., & Bue, S. L. (2025). Influence of AI behavior on human moral decisions, agency, and responsibility. Scientific reports, 15(1), 12329. https://doi.org/10.1038/s41598-025-95587-6
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Urcelay, G. P., Symmons, K., Amos, B., Toutounji, H., & Prével, A. (2024). Renewal of instrumental avoidance in humans. Journal of experimental psychology. Animal learning and cognition, 50(3), 197–209. https://doi.org/10.1037/xan0000383
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Prével, A., Krebs, R. M., Kukkonen, N., & Braem, S. (2021). Selective reinforcement of conflict processing in the Stroop task. PloS one, 16(7), e0255430. https://doi.org/10.1371/journal.pone.0255430
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Prével, A., & Krebs, R. M. (2021). Higher-Order Conditioning With Simultaneous and Backward Conditioned Stimulus: Implications for Models of Pavlovian Conditioning. Frontiers in behavioral neuroscience, 15, 749517. https://doi.org/10.3389/fnbeh.2021.749517
The Effect of Operant Conditioning on (Moral) Decision-Making Supported by AI
Artificial intelligence and automation are now deeply integrated into various aspects of our lives. They assist drivers in navigation, students in proofreading texts, and medical professionals in evaluating diagnostic results. However, these systems are not without risks, and a phenomenon of over-reliance may occasionally emerge. This becomes especially concerning when AI is employed in military or security contexts. Drawing upon a behaviorist approach, my research seeks to understand the factors that influence human performance and decision-making assisted by AI. Specifically, I examine how operant reinforcement contingencies can influence the confidence placed in AI systems.
Project:
- Safe and Efficient Autonomous Systems for Military Operations (SEAS), 2023-2024, Directed by Pr. Lt. Col. Lo Bue (Royal Military Academy).
Digital Applications to Support Behavioral Interventions
The application of operant conditioning principles has led to significant advancements in supporting individuals with autism spectrum disorders. Numerous procedures have been developed to identify the function of problematic behaviors, teach appropriate alternatives, and promote the acquisition of skills related to autonomy, social interactions, academic abilities, and professional competencies, resulting in a richer behavioral repertoire. However, the implementation and monitoring of these procedures often entail significant costs for professionals and family caregivers, sometimes leading to partial abandonment of the procedures. In this context, recent years have seen the development of digital applications for tablets and smartphones designed to assist practitioners and caregivers in applying these procedures. In my research, I study the impact of these digital tools, both in terms of the quality of procedure implementation and the user experience.