Department of Psychology, Emory University, Atlanta, USA.
J Exp Anal Behav. 2019 Sep;112(2):128-143. doi: 10.1002/jeab.543. Epub 2019 Aug 5.
An implementation of punishment in the evolutionary theory of behavior dynamics is proposed, and is applied to responding on concurrent schedules of reinforcement with superimposed punishment. In this implementation, punishment causes behaviors to mutate, and to do so with a higher probability in a lean reinforcement context than in a rich one. Computational experiments were conducted in an attempt to replicate three findings from experiments with live organisms. These are (1) when punishment is superimposed on one component of a concurrent schedule, response rate decreases in the punished component and increases in the unpunished component, (2) when punishment is superimposed on both components at equal scheduled rates, preference increases over its no-punishment baseline, and (3) when punishment is superimposed on both components at rates that are proportional to the scheduled rates of reinforcement, preference remains unchanged from the baseline preference. Artificial organisms animated by the theory, and working on concurrent schedules with superimposed punishment, reproduced all of these findings. Given this outcome, it may be possible to discover a steady-state mathematical description of punished choice in live organisms by studying the punished choice behavior of artificial organisms animated by the evolutionary theory.
提出了一种在行为动力学进化理论中实施惩罚的方法，并将其应用于具有叠加惩罚的同时强化时间表上的反应。在这种实现方式中，惩罚会导致行为发生突变，并且在强化不足的情况下比在强化充足的情况下更有可能发生突变。进行了计算实验，试图复制在活体实验中发现的三个结果。这些结果是：（1）当在同时性时间表的一个组成部分上叠加惩罚时，受惩罚部分的反应率降低，未受惩罚部分的反应率增加；（2）当以相等的预定速率在两个部分上叠加惩罚时，与无惩罚基线相比，偏好增加；（3）当以与预定强化速率成比例的速率在两个部分上叠加惩罚时，偏好与基线偏好保持不变。由该理论驱动的人工生物在具有叠加惩罚的同时性时间表上工作，再现了所有这些发现。鉴于这一结果，通过研究由进化理论驱动的人工生物的受惩罚选择行为，可能有可能发现活体生物中受惩罚选择的稳态数学描述。
