Detecting multiple sequential decisions within a single trial using EEG

Simple decision processes can be regarded as one of the most important building blocks of behavior. A decision process is here defined as any cognitive process that develops over time and results in a conceptual representation. This includes – but is not limited to – a decision that results in the representation of a course of action, a decision on the content of a perceptual object, and a decision on the meaning of a perceived word. Although current state-of-the-art evidence accumulation models (EAMs) are excellent predictors of behavior that is determined by single decisions, they cannot be used to investigate multiple sequential decisions. It therefore remains unclear how latent decision processes influence subsequent cognitive processes and decisions, and ultimately overt behavior. This has resulted in a lack of understanding of behavior that involves a sequence of decisions – which is imperative, as this is a situation that occurs almost immediately when addressing slightly more complex laboratory tasks, let alone when leaving the lab to investigate real-life situations. In this presentation, we discuss a novel approach in which we first apply machine learning to discover sequential processing stages in EEG data (called Hidden semi-Markov Modeling and Multivariate Pattern Analysis or HMP) and then characterize the duration effects in such identified stages using EAMs. This approach leads to a more fine-grained understanding of decision processes by demarcating the relevant processing stage. Moreover, it allows for understanding multiple decision processes in a single trial that are convoluted in behavior.