Navidi Parisa, Saeedpour Sepehr, Ershadmanesh Sara, Hossein Mostafa Miandari, Bahrami Bahador
Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran.
Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
PLoS One. 2023 Jun 23;18(6):e0287563. doi: 10.1371/journal.pone.0287563. eCollection 2023.
Prosocial learning involves the acquisition of knowledge and skills necessary for making decisions that benefit others. We asked if, in the context of value-based decision-making, there is any difference between learning strategies for oneself vs. for others. We implemented a 2-step reinforcement learning paradigm in which participants learned, in separate blocks, to make decisions for themselves or for a present other confederate who evaluated their performance. We replicated the canonical features of the model-based and model-free reinforcement learning in our results. The behaviour of the majority of participants was best explained by a mixture of the model-based and model-free control, while most participants relied more heavily on MB control, and this strategy enhanced their learning success. Regarding our key self-other hypothesis, we did not find any significant difference between the behavioural performances nor in the model-based parameters of learning when comparing self and other conditions.
亲社会学习涉及获取做出有利于他人的决策所需的知识和技能。我们询问，在基于价值的决策背景下，为自己学习与为他人学习的策略之间是否存在差异。我们实施了一个两步强化学习范式，参与者在不同的模块中分别学习为自己或为评价其表现的当前其他同盟者做出决策。我们在结果中重现了基于模型和无模型强化学习的典型特征。大多数参与者的行为最好用基于模型和无模型控制的混合来解释，而大多数参与者更依赖基于模型的控制，并且这种策略提高了他们的学习成功率。关于我们关键的自我与他人假设，在比较自我和他人条件时，我们没有发现行为表现或基于模型的学习参数之间存在任何显著差异。
