Decision prioritization and causal reasoning in decision hierarchies
Fig 9
Motion strength and tree level influence which node is blamed for an error.
Panels A-C show the predictions of the heuristic model and panels D-F correspond to the behavioral data. (A) Proportion of errors for which the blame was assigned to a node with the motion strength indicated in the abscissa, given that a node with that motion strength was in the error-path. Note that proportions do not need to add to 1 since the error-path can contain up to three different values of motion strength. (B) Proportion of errors that were followed by an on-path query to a node of level ℓ, given that level ℓ of the error-path had the motion strength indicated in the abscissa. For example, if the root node had the weakest possible motion strength, the probability that an on-path query is made at that node is ∼0.23. (C) Proportion of errors that were followed by an off-path query from a node of level ℓ, given that level ℓ of the error-path had the motion strength indicated in the abscissa. For example, if the root (level 1) node had the weakest possible motion strength, there is a ∼0.25 chance that the root node is blamed for the error and an off-path query is made to its child that was not on the error path. Proportions were first calculated per participant and then averaged across participants.