How the inference of hierarchical rules unfolds over time

Eckstein, Maria K.; Starr, Ariel und Bunge, Silvia A. (2019): How the inference of hierarchical rules unfolds over time. In: Cognition, Bd. 185: S. 151-162

Abstract

Inductive reasoning, which entails reaching conclusions that are based on but go beyond available evidence, has long been of interest in cognitive science. Nevertheless, knowledge is still lacking as to the specific cognitive processes that underlie inductive reasoning. Here, we shed light on these processes in two ways. First, we characterized the timecourse of inductive reasoning in a rule induction task, using pupil dilation as a momentby-moment measure of cognitive load. Participants' patterns of behavior and pupillary responses indicated that they engaged in rule inference on-line, and were surprised when additional evidence violated their inferred rules. Second, we sought to gain insight into how participants represented rules on this task - specifically, whether they would structure the rules hierarchically when possible. We predicted the cognitive load imposed by hierarchical representations, as well as by non-hierarchical, flat ones. We used task-evoked pupil dilation as a metric of cognitive load to infer, based on these predictions, which participants represented rules with flat or hierarchical structures. Participants categorized as representing the rules hierarchically or flat differed in task performance and self-reports of strategy. Hierarchical rule representation was associated with more efficient performance and more pronounced pupillary responses to rule violations on trials that afford a higher-order regularity, but with less efficient performance on trials that do not. Thus, differences in rule representation can be inferred from a physiological measure of cognitive load, and are associated with differences in performance. These results illustrate how pupillometry can provide a window into reasoning as it unfolds over time.

• Dokumententyp: Zeitschriftenartikel
• Fakultätsübergreifende Einrichtungen: Graduate School of Systemic Neurosciences (GSN)
• Themengebiete: 500 Naturwissenschaften und Mathematik > 500 Naturwissenschaften
• ISSN: 0010-0277
• Sprache: Englisch