The predictive power of heuristic portfolios in human syllogistic reasoning

Abstract

A core method of cognitive science is to investigate cognition by approaching human behavior through model implementations. Recent literature has seen a surge of models which can broadly be classified into detailed theoretical accounts, and fast and frugal heuristics. Being based on simple but general computational principles, these heuristics produce results independent of assumed mental processes. This paper investigates the potential of heuristic approaches in accounting for behavioral data by adopting a perspective focused on predictive precision. Multiple heuristic accounts are combined to create a portfolio, i.e., a meta-heuristic, capable of achieving state-of-the-art performance in prediction settings. The insights gained from analyzing the portfolio are discussed with respect to the general potential of heuristic approaches.