Next Article in Journal
Relationship between Language Dominance and Stimulus-Stimulus or Stimulus-Response Inhibition in Uyghur-Chinese Bilinguals with an Investigation of Speed-Accuracy Trade-Offs
Previous Article in Journal
Self-Concepts in Reading and Spelling among Mono- and Multilingual Children: Extending the Bilingual Advantage
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle

How to Understand Behavioral Patterns in Big Data: The Case of Human Collective Memory

Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697-2525, USA
Behav. Sci. 2019, 9(4), 40; https://doi.org/10.3390/bs9040040
Received: 6 March 2019 / Revised: 8 April 2019 / Accepted: 11 April 2019 / Published: 16 April 2019
  |  
PDF [657 KB, uploaded 16 April 2019]
  |  

Abstract

Simple patterns often arise from complex systems. For example, human perception of similarity decays exponentially with perceptual distance. The ranking of word usage versus the frequency at which the words are used has a log-log slope of minus one. Recent advances in big data provide an opportunity to characterize the commonly observed patterns of behavior. Those observed regularities set the challenge of understanding the mechanistic processes that generate common behaviors. This article illustrates the problem with the recent big data analysis of collective memory. Collective memory follows a simple biexponential pattern of decay over time. An initial rapid decay is followed by a slower, longer lasting decay. Candia et al. successfully fit a two stage model of mechanistic process to that pattern. Although that fit is useful, this article emphasizes the need, in big data analyses, to consider a broad set of alternative causal explanations. In this case, the method of signal frequency analysis yields several simple alternative models that generate exactly the same observed pattern of collective memory decay. This article concludes that the full potential of big data analyses in the behavioral sciences will require better methods for developing alternative, empirically testable causal models. View Full-Text
Keywords: collective behavior; cultural transmission; signal frequency analysis collective behavior; cultural transmission; signal frequency analysis
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Frank, S.A. How to Understand Behavioral Patterns in Big Data: The Case of Human Collective Memory. Behav. Sci. 2019, 9, 40.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Behav. Sci. EISSN 2076-328X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top