From Causal Models to Sound Heuristic Inference

Ana Sofia Morais, Lael J. Schooler, Henrik Olsson, Björn Meder

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We investigate whether people rely on their causal intuitions to determine the predictive value or importance of cues. Our real-world data set consists of one criterion variable (child mortality) and nine cues (e.g., GDP per capita). We elicited people's intuitive causal models about the domain. In a second task, we asked them to rank the cues according to their beliefs about the cues' predictive value. Alternative cue importance rankings were derived directly from their causal models using measures of causal centrality. The results show that people's judgments of cue importance corresponded more closely to the causal-based cue orders than to the statistical associations between the cues and the criterion. Using computer simulations, we show that people's causal-based cue orders form a sound basis for making inferences, even when information about the statistical structure of the environment is scarce or unavailable. Central to the simulations is take-the-best (TTB)-a simple decision strategy that makes inferences by considering cues sequentially. The simulations show that causal-based cue orders can be as accurate as individuals' judged orders. Causal-based cue orders allow TTB to perform as would be expected from estimating the weights of a linear model using about 35% of the available data. These findings suggest that people can rely on their causal intuitions to determine the importance of cues, thereby reducing the computational complexity involved in finding useful cue orders.

Original languageEnglish (US)
Title of host publicationProceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014
PublisherThe Cognitive Science Society
Pages1036-1041
Number of pages6
ISBN (Electronic)9780991196708
StatePublished - 2014
Externally publishedYes
Event36th Annual Meeting of the Cognitive Science Society, CogSci 2014 - Quebec City, Canada
Duration: Jul 23 2014Jul 26 2014

Publication series

NameProceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014

Conference

Conference36th Annual Meeting of the Cognitive Science Society, CogSci 2014
Country/TerritoryCanada
CityQuebec City
Period7/23/147/26/14

Keywords

  • Causal models
  • cue orders
  • inductive inference
  • information search
  • simple heuristics
  • take-the-best

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

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