Temporal and statistical information in causal structure learning.

Teresa Mccormack,Fiona Patrick,David Lagnado,Caren Frosch

doi:10.1037/a0038385

Teresa Mccormack, Fiona Patrick + Show 2 more

Open Access

https://doi.org/10.1037/a0038385

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Abstract

Three experiments examined children's and adults' abilities to use statistical and temporal information to distinguish between common cause and causal chain structures. In Experiment 1, participants were provided with conditional probability information and/or temporal information and asked to infer the causal structure of a 3-variable mechanical system that operated probabilistically. Participants of all ages preferentially relied on the temporal pattern of events in their inferences, even if this conflicted with statistical information. In Experiments 2 and 3, participants observed a series of interventions on the system, which in these experiments operated deterministically. In Experiment 2, participants found it easier to use temporal pattern information than statistical information provided as a result of interventions. In Experiment 3, in which no temporal pattern information was provided, children from 6- to 7-years-old, but not younger children, were able to use intervention information to make causal chain judgments, although they had difficulty when the structure was a common cause. The findings suggest that participants, and children in particular, may find it more difficult to use statistical information than temporal pattern information because of its demands on information processing resources. However, there may also be an inherent preference for temporal information.

Highlights

An advantage of the causal Bayes net approach is that it can model updating that occurs not just on the basis of observing a system, and as a result of making such interventions
They provide evidence that adults will systematically use a simple temporal heuristic that assumes that correlated changes that occur after one event are likely to be caused by it
This sort of local computation does not require that participants bear in mind multiple hypotheses and calculate conditional probability information extracted from aggregating information over a variety of observations

Summary

Introduction

An advantage of the causal Bayes net approach is that it can model updating that occurs not just on the basis of observing a system, and as a result of making such interventions. Fernbach and Sloman argue that participants may instead use what they term a structurally local computation strategy: participants may focus on confirming or disconfirming individual causal links in a structure, which they combine together to infer a structure (for a related suggestion, see Waldmann, Cheng, Hagmayer, & Blaisdell, 2008) As they characterize it, this sort of local computation does not require that participants bear in mind multiple hypotheses and calculate conditional probability information extracted from aggregating information over a variety of observations. Participants seem to use “rules of thumb” or heuristics when they observe such temporal patterns, inferring a causal chain in the former instance and a common cause in the latter case This sort of heuristic, like other types of simple heuristics (Gigerenzer & Goldstein, 1996), places minimal demands on processing resources as participants do not have to track statistical information across observations or even combine information obtained from different observations

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