Computational Inference in Cognitive Science: Operational, Societal and Ethical Considerations

There is a research trend in cognitive science that shifts from a top-down direction (guided by hypothesis-driven testing of cognitive theories) towards a bottom-up approach (enabled by data-drivcen pattern discovery of cognition-related properties). The emergence of high-throughput data collection techniques provides cognitive scientists rich research substances of labelled behavioral data, from one's digital traces on a social media, to large-scale crowdsourcing of experimental responses to well-defined cognitive tasks [1]. Riding along the big data era of cognitive science is the advanced developments of artificial intelligence (AI) methods that is capable of performing components of cognitive functions at human-level or superhuman-level performances. With the new directions, comes new challenges. As the study of the essence, tasks and functions of cognition, how can we as cognitive scientists reshape the field using these new sources of data and new tools of analytical methods, such that it maintains a coherent core as the classical theory-driven studies of cognitive science? To better formulate this challenge, we categorizes the interactions between the concepts of AI and those of the human cognition into three main types (Figure 1). First, we have the computational inference, the process of utilizing machine learning models as a prediction or inference engines to map from measurable signals to the cognitive properties. The second direction is to use the cognitive theory as a prior to build AI. This approach can be dated as early as the symbolic cognitive architectures in 1970s [2, 3], where major cognitive processes such as knowledge representation, memory, learning and control are explicitly mapped into computational components.