Compressed Representations and Attentional Competition in Numeric Integration for Average Estimations

The ability to estimate the average value of a number stream is a fundamental aspect of information processing and a building block of value-based decisions. Yet, research on average estimation has 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 three experiments, we found a significant underestimation of the averages, and a limited impact of competing information on estimation. Computational modeling shows that the CMNL (together with an explicit noise theory) 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. Among these participants, the computational mechanism of SI consisted of an underweighting of lower numbers in local sample comparisons. Overall, our findings clarify the role of competing information in average estimations, and shed light on the exact cognitive process and limitations of SI as a general theory of sequential information integration.