Compressed Representations and Attentional Competition in Numeric Integration for Average Estimations

The ability to gauge the average of a number stream is a fundamental aspect of numeric cognition, information processing, and value-based decisions. Research on this ability has primarily focused on the integration of numerical information from a single source. Here, we examined the estimation of averages when competing sources of information are presented. We tested two theories of numeric value integration: the Compressed Mental Number Line (CMNL) predicts underestimation of averages independent of competing information; Selective Integration (SI) predicts that competing information interferes with the target information. Across four experiments, we found significant underestimation of averages in both single- and dual-stream conditions, and a limited impact of competing information on estimation. Computational modeling showed that the CMNL provides the overall better account than SI to describe estimation behavior in our data. However, about one-third of our participants were best described by SI. We also modeled the integration noise of the CMNL and found that this noise increased in the dual- compared to the single-stream conditions without affecting the representational compression. Overall, our findings clarify the role of competing information in average estimations, discover limitations in processing multiple streams, and shed light on the cognitive processes underlying sequential information integration.