Programa de Pós-Graduação em Psicologia, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.
Instituto de Ciências Humanas e da Informação, Universidade Federal do Rio Grande (FURG), Rio Grande, Brazil.
Behav Res Methods. 2023 Oct;55(7):3645-3657. doi: 10.3758/s13428-022-01982-6. Epub 2022 Oct 11.
Online experiments are an alternative for researchers interested in conducting behavioral research outside the laboratory. However, an online assessment might become a challenge when long and complex experiments need to be conducted in a specific order or with supervision from a researcher. The aim of this study was to test the computational validity and the feasibility of a remote and synchronous reinforcement learning (RL) experiment conducted during the social-distancing measures imposed by the pandemic. An additional feature of this study was to describe how a behavioral experiment originally created to be conducted in-person was transformed into an online supervised remote experiment. Open-source software was used to collect data, conduct statistical analysis, and do computational modeling. Python codes were created to replicate computational models that simulate the effect of working memory (WM) load over RL performance. Our behavioral results indicated that we were able to replicate remotely and with a modified behavioral task the effects of working memory (WM) load over RL performance observed in previous studies with in-person assessments. Our computational analyses using Python code also captured the effects of WM load over RL as expected, which suggests that the algorithms and optimization methods were reliable in their ability to reproduce behavior. The behavioral and computational validation shown in this study and the detailed description of the supervised remote testing may be useful for researchers interested in conducting long and complex experiments online.
在线实验是对实验室外进行行为研究感兴趣的研究人员的一种替代方法。然而，当需要按照特定顺序或在研究人员的监督下进行长而复杂的实验时，在线评估可能会成为一个挑战。本研究的目的是测试在大流行期间实施的社交距离措施期间进行远程和同步强化学习（RL）实验的计算有效性和可行性。本研究的一个额外特点是描述如何将原本设计用于现场进行的行为实验转变为在线监督远程实验。我们使用开源软件来收集数据、进行统计分析和进行计算建模。创建了 Python 代码来复制模拟工作记忆（WM）负荷对 RL 性能影响的计算模型。我们的行为结果表明，我们能够远程复制并使用修改后的行为任务，复制以前在现场评估中观察到的工作记忆（WM）负荷对 RL 性能的影响。我们使用 Python 代码进行的计算分析也如预期那样捕获了 WM 负荷对 RL 的影响，这表明算法和优化方法在复制行为方面是可靠的。本研究中显示的行为和计算验证以及对监督远程测试的详细描述可能对有兴趣在线进行长而复杂实验的研究人员有用。
