Proceedings of the Annual Meeting of the Cognitive Science Society

The question of whether people store absolute magnitude information or relative local comparisons of magnitudeshas remained unanswered despite persistent efforts over the last three decades to resolve it. Absolute identification is one of themost rigorous experimental benchmarks for evaluating theories of magnitude representation. We characterize difficulties withboth absolute and relative accounts of magnitude representation and propose an alternative account that potentially resolvesthese difficulties. We postulate that people store neither long-term internal referents for stimuli, not binary comparisons ofsize between successive stimuli. Rather, they obtain probabilistic judgments of size differences between successive stimuliand encode these for future use, within the course of identification trials. We set up a Bayesian ideal observer model for theidentification task using this representation of magnitude and propose a memory-sampling based approximation for solving it.Simulations suggest that the model adequately captures human behavior patterns in absolute identification.