Nature Communications · 2019 · 14 citations · 45 references
ComputingBehavioral SciencesPsychologyCognitive PsychologyExperimental PsychologyUncertainty QuantificationHierarchical ModelingHuman ReliabilityProbabilistic ModelingStatistical InferenceUncertainty EstimationUncertainty RepresentationContext UncertaintyHeuristicsHidden Variable ModelsHierarchical Integration ModelsHierarchical State ModelingHierarchical Task ModelsReliability IntegrationState-space Hierarchical Models
Our immediate observations must be supplemented with contextual information to resolve ambiguities. However, the context is often ambiguous too, and thus it should be inferred itself to guide behavior. Here, we introduce a novel hierarchical task (airplane task) in which participants should infer a higher-level, contextual variable to inform probabilistic inference about a hidden dependent variable at a lower level. By controlling the reliability of past sensory evidence through varying the sample size of the observations, we find that humans estimate the reliability of the context and combine it with current sensory uncertainty to inform their confidence reports. Behavior closely follows inference by probabilistic message passing between latent variables across hierarchical state representations. Commonly reported inferential fallacies, such as sample size insensitivity, are not present, and neither did participants appear to rely on simple heuristics. Our results reveal uncertainty-sensitive integration of information at different hierarchical levels and temporal scales.
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