Correlation Analysis to Investigate Unconscious Mental Processes, 2018-2021

Data and code for Malejka et al. (2021), "Correlation analysis to investigate unconscious mental processes". The present project focused on a particular domain of this literature, implicit learning. Studies conducted in this area try to determine whether we are able to detect regularities in our environment without awareness of those regularities. Finding evidence of awareness in these domains is important because it suggests that some degree of control may be available as well. In the present project we propose new methods for the study of unconscious learning. Many of the problems that we have detected in our previous research can be ameliorated by employing cutting-edge statistical analysis, including Bayesian and meta-analytic methods and model fitting. However, the validity of these approaches in the domain of implicit cognition remains untested.

本数据集及配套代码源自Malejka等人2021年发表的《用于探究无意识心理过程的相关分析》（Correlation analysis to investigate unconscious mental processes）研究。本项目聚焦于该研究领域中的特定分支——内隐学习（implicit learning）。该领域的相关研究旨在探明：人类能否在未意识到环境中存在特定规律的前提下，察觉这些规律本身。在该领域中发现意识参与的相关证据具有重要学术价值，因为这意味着人类或许可对相关规律实现一定程度的掌控。本项目针对无意识学习的研究提出了全新的分析方法。我们在过往研究中发现的诸多问题，可通过采用前沿统计分析手段得到改善，其中涵盖贝叶斯（Bayesian）分析、元分析（meta-analytic）方法以及模型拟合（model fitting）技术。但目前这些方法在内隐认知领域的有效性仍未得到实证验证。