Extracting summary statistics of rapid numerical sequences

We examine the ability of observers to extract summary statistics (such as the mean and the relative-variance) from rapid numerical sequences (two digit numbers presented at a rate of 4/sec). In four experiments, we find that the participants show a remarkable ability to extract such summary statistics and that their precision in the estimation of the sequence-mean improves with the sequence-length (subject to individual differences). Using model selection for individual participants we find that, when they only estimate the sequence average, most participants rely on a holistic process of frequency based estimation and there is a minority who rely on a rule-based and capacity limited mid-range strategy. When both the sequence-average and the relative variance is estimated about half of the participants rely on these two strategies. Importantly, the holistic strategy appears more efficient in terms of its precision. We discuss implications for the domains of two pathways numerical processes and decision-making.