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Tuesday, November 16, 2021

Decision Prioritization and Causal Reasoning in Decision Hierarchies

Zylberberg, A. (2021, September 6). 


From cooking a meal to finding a route to a destination, many real life decisions can be decomposed into a hierarchy of sub-decisions. In a hierarchy, choosing which decision to think about requires planning over a potentially vast space of possible decision sequences. To gain insight into how people decide what to decide on, we studied a novel task that combines perceptual decision making, active sensing and hierarchical and counterfactual reasoning. Human participants had to find a target hidden at the lowest level of a decision tree. They could solicit information from the different nodes of the decision tree to gather noisy evidence about the target's location. Feedback was given only after errors at the leaf nodes and provided ambiguous evidence about the cause of the error. Despite the complexity of task (with 10 to 7th power latent states) participants were able to plan efficiently in the task. A computational model of this process identified a small number of heuristics of low computational complexity that accounted for human behavior. These heuristics include making categorical decisions at the branching points of the decision tree rather than carrying forward entire probability distributions, discarding sensory evidence deemed unreliable to make a choice, and using choice confidence to infer the cause of the error after an initial plan failed. Plans based on probabilistic inference or myopic sampling norms could not capture participants' behavior. Our results show that it is possible to identify hallmarks of heuristic planning with sensing in human behavior and that the use of tasks of intermediate complexity helps identify the rules underlying human ability to reason over decision hierarchies.


Adaptive behavior requires making accurate decisions, but also knowing what decisions are worth making. To study how people decide what to decide on, we investigated a novel task in which people had to find a target, hidden at the lowest level of a decision tree, by gathering stochastic information from the internal nodes of the decision tree. Our central finding is that a small number of heuristic rules explain the participant’s behavior in this complex decision-making task. The study extends the perceptual decision framework to more complex decisions that comprise a hierarchy of sub-decisions of varying levels of difficulty, and where the decision maker has to actively decide which decision to address at any given time.  

Our task can be conceived as a sequence of binary decisions, or as one decision with eight alternatives.  Participants’ behavior supports the former interpretation.  Participants often performed multiple queries on the same node before descending levels, and they rarely made a transition from an internal node to a higher-level one before reaching a leaf node.  This indicates that participants made categorical decisions about the direction of motion at the visited nodes before they decided to descend levels. This bias toward resolving uncertainty locally was not observed in an approximately optimal policy (Fig. 8), and thus may reflect more general cognitive constraints that limit participants’ performance in our task (Markant et al., 2016). A strong candidate is the limited capacity of working memory (Miller, 1956). By reaching a categorical decision at each internal node, participants avoid the need to operate with full probability distributions over all task-relevant variables, favoring instead a strategy in which only the confidence about the motion choices is carried forward to inform future choices (Zylberberg et al., 2011).