Bayesian change-point analysis reveals developmental change in a classic theory of mind task

Sara T. Baker, Alan M. Leslie, C.R. Gallistel, Bruce M. Hood

Research output: Contribution to journalArticle (Academic Journal)peer-review

9 Citations (Scopus)
294 Downloads (Pure)

Abstract

Although learning and development reflect changes situated in an individual brain, most discussions of behavioral change are based on the evidence of group averages. Our reliance on group-averaged data creates a dilemma. On the one hand, we need to use traditional inferential statistics. On the other hand, group averages are highly ambiguous when we need to understand change in the individual; the average pattern of change may characterize all, some, or none of the individuals in the group. Here we present a new method for statistically characterizing developmental change in each individual child we study. Using false-belief tasks, fifty-two children in two cohorts were repeatedly tested for varying lengths of time between 3 and 5 years of age. Using a novel Bayesian change point analysis, we determined both the presence and—just as importantly—the absence of change in individual longitudinal cumulative records. Whenever the analysis supports a change conclusion, it identifies in that child’s record the most likely point at which change occurred. Results show striking variability in patterns of change and stability across individual children. We then group the individuals by their various patterns of change or no change. The resulting patterns provide scarce support for sudden changes in competence and shed new light on the concepts of “passing” and “failing” in developmental studies.
Original languageEnglish
Pages (from-to)124–149
Number of pages26
JournalCognitive Psychology
Volume91
Early online date20 Oct 2016
DOIs
Publication statusPublished - Dec 2016

Structured keywords

  • Developmental

Keywords

  • Change point analysis
  • Bayesian statistics
  • Theory of mind
  • Preschoolers

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