• International Development Grant
• Senior Student Presenter Grant
• Innovative Student Research Grant
• Innovative Student Research Grant in Diversity, Equity, and Inclusion
• Sidney W. & Janet R. Bijou Grant
• Public Awareness Grant
• Microcredentials Grant

University of Guadalajara

Project Title: How Does Punishment work?: Using Quantitative Models to Explore the Mechanisms of Behavioral Suppression

Julian C. Velasquez is a second-year doctoral student in Behavioral Science at the Centro de Estudios e Investigaciones en Comportamiento (CEIC) of the University of Guadalajara, Mexico, where he is supervised by Dr. Carlos Flores and Dr. Cristiano Valerio dos Santos, with external advisory support from Dr. Rafaela Fontes. Originally from Colombia, Julian completed his undergraduate training at Konrad Lorenz University under the mentorship of Dr. Camilo Hurtado-Parrado, focusing on choice behavior and aversive control across multiple species. His research has earned him the 2026 SEAB Rising Star Grant, the APA Division 25 Innovate Student Research Award, and twice receiving the SQAB Tony Nevin Award.

Julian’s doctoral dissertation addresses a profound theoretical and empirical gap in the science of behavior: the general misunderstanding of punishment mechanisms and its interactions with reinforcement. While contemporary behavior analysis has reconceptualized reinforcement in terms of stimulus value, quantitative models for punishment have lagged sharply behind since their peak in the 1960s and 1970s. Understanding punishment is critical not only for basic science, but also for conceptualizing clinical disorders like addiction.

Supported by the 2026 SABA Student Innovation Grant, Julian’s project utilizes quantitative modeling to elucidate the underlying mechanisms of punishment. Specifically, his research extends the historical framework of Herrnstein's matching law by providing a rigorous test of the contemporary Concatenated Generalized Matching Law (cGML; Klapes & McDowell, 2025) punishment model, which mathematically integrates multiple consequence parameters into a unitary mechanism for behavioral control. Moving beyond traditional frameworks that focus primarily on punishment frequency, Julian’s work expands the cGML framework by exploring the independent effects of punishment magnitude (manipulated as the number of shocks delivered) within a dynamic choice procedure. Furthermore, the project evaluates choice allocations under conditions of equal punishment density arranged through different dimensions across alternatives. Ultimately, this research aims to establish a more precise, predictive, and theoretically coherent account of punishment.

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