Extracting Summary Statistics of Rapid Numerical Sequences

David Rosenbaum, Moshe Glickman, Marius Usher

Research output: Contribution to journalArticlepeer-review

Abstract

We examine the ability of observers to extract summary statistics (such as the mean and the relative-variance) from rapid numerical sequences of two digit numbers presented at a rate of 4/s. In four experiments (total N = 100), 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 only the sequence-average is estimated, most participants rely on a holistic process of frequency based estimation with a minority who rely on a (rule-based and capacity limited) mid-range strategy. When both the sequence-average and the relative variance are 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 processing and decision-making.

Original languageEnglish
Article number693575
JournalFrontiers in Psychology
Volume12
DOIs
StatePublished - 1 Oct 2021

Keywords

  • averaging
  • computational modeling
  • decision making
  • numerical cognition
  • population coding
  • summary statistics

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