Kermani Mojtaba, Fatahi Zahra, Sun Dechuan, Haghparast Abbas, French Chris
Department of Optometry and Vision Science, The University of Melbourne; Department of Medicine, The University of Melbourne;
Neuroscience Research Center, Shahid Beheshti University of Medical Science.
J Vis Exp. 2018 Sep 10(139):57907. doi: 10.3791/57907.
Reinforcement-guided decision making is the ability to choose between competing courses of action based on the relative value of the benefits and their consequences. This process is integral to the normal human behavior and has been shown to be disrupted by neurological and psychiatric disorders such as addiction, schizophrenia, and depression. Rodents have long been used to uncover the neurobiology of human cognition. To this end, several behavioral tasks have been developed; however, most are non-automated and are labor-intensive. The recent development of the open-source microcontroller has enabled researchers to automate operant-based tasks for assessing a variety of cognitive tasks, standardizing the stimulus presentation, improving the data recording and consequently, improving the research output. Here, we describe an automated delay-based reinforcement-guided decision-making task, using an operant T-maze controlled by custom-written software programs. Using these decision-making tasks, we show the changes in the local field potential activities in the anterior cingulate cortex of a rat whilst it performs a delay-based cost-and-benefit decision-making task.
强化引导的决策是指根据收益的相对价值及其后果，在相互竞争的行动方案之间进行选择的能力。这一过程是正常人类行为不可或缺的一部分，并且已被证明会受到成瘾、精神分裂症和抑郁症等神经和精神疾病的干扰。长期以来，啮齿动物一直被用于揭示人类认知的神经生物学机制。为此，已经开发了几种行为任务；然而，大多数任务是非自动化的，且劳动强度大。开源微控制器的最新发展使研究人员能够将基于操作性条件反射的任务自动化，以评估各种认知任务，规范刺激呈现，改进数据记录，从而提高研究产出。在此，我们描述了一种基于延迟的强化引导决策自动化任务，该任务使用由定制软件程序控制的操作性T迷宫。通过这些决策任务，我们展示了大鼠在执行基于延迟的成本效益决策任务时，前扣带回皮质局部场电位活动的变化。
