Abstract:
In many research contexts it is useful to group experimental subjects into behavioral “types.”
Usually, this is done by pre-specifying a set of candidate decision-making heuristics and assigning
each subject to a heuristic in that set. Such approaches might perform poorly when applied to subjects
with prefrontal cortex damage, because it can be hard to know what cognitive heuristics such
subjects might use. We suggest that the Houser, Keane and McCabe (HKM) robust classification algorithm
can be a useful tool in these cases. An important advantage of this classification approach is
that it does not require one to specify either the nature or number of subjects’ heuristics in advance.
Rather, both the number and nature of the heuristics are discerned directly from the data. To illustrate
the HKM approach, we draw inferences about heuristics used by subjects in the well-known
gambling task [Bechara, A., Damasio, A.R., Damasio, H., Anderson, S., 1994. Insensitivity to future
consequences following damage to human prefrontal cortex. Cognition 50, 7–12].
