Components of Complex Decision Making- How Simple Decisions Determine Large Outcomes
Abstract
Expert behavior often seems effortless- an experienced pilot does no longer seem to think about how to operate a plane. To understand such behavior, it would be great if we could measure the pilot’s brain activity, and based on this activity automatically construct a model of the cognitive operations involved in flying a plane. The proposed research presents a first step towards this goal. The main objective of the current proposal is to understand how latent, simple sequential decisions interact to determine behavioral actions. Simple decision processes are one of the most important building blocks of behavior, if we define a decision process as any cognitive process that develops over time and results in a conceptual representation. This includes actual decisions that result in a course of action, but also decisions on the content of a perceptual object or the meaning of a perceived word. Even memory retrieval has been described as a decision between all potentially relevant memory traces (Ratcliff et al., 2016). Simple decision processes are very well understood through the application of Evidence Accumulation Models (EAMs). However, these models cannot be used to investigate multiple sequential decisions. 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 expert behavior. To be able to use EAMs to investigate multiple sequential decisions, we first need to identify such decision processes in ongoing cognitive processing. To this end, we will use a novel machine-learning analysis called Hidden semi-Markov Model Multivariate Pattern Analysis (HsMM-MVPA; Anderson et al., 2016; Borst and Anderson, 2015, 2021). By integrating information from all trials of all participants, this method can identify cognitive stages – and thus decision processes – in single trials. Here, we propose a new analysis framework, which combines EAMs with the HsMM-MVPA method to investigate how latent, simple sequential decisions interact and result in behavioral actions.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 04, 2023
- Source ID
- FA86552217003
Entities
People
- Jelmer P Borst
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Groningen