Abstract
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.
Highlights
Our immediate observations must be supplemented with contextual information to resolve ambiguities
As a clear signature of probabilistic inference over the context, we find that the sample size of previous observations is used by our participants to infer the reliability of the context
We designed two experiments to test whether humans can use the reliability of contextual information to guide decisions and confidence judgments about a latent variable at a lower hierarchical level
Summary
Our immediate observations must be supplemented with contextual information to resolve ambiguities. We would first need to infer the context (whether the event attracts more people of the red or blue type) by observing samples of passengers leaving several airplanes. Humans can infer the transition probability between two stimuli where the transition probability itself undergoes unexpected changes, defining a partially observable context[8] These results and other studies suggest that a refined form of uncertainty representation is held at several hierarchical levels by the brain[9,10,11,12,13,14]. By manipulating both the tendency and the sample size, we can control the reliability of previous observations upon which inference about the context should be based Overall, this task structure creates hierarchical dependencies among latent variables that should be resolved by bottom-up (inferring the context from previous observations) and top-down message passing (inferring the current state by combining current observations with the inferred context)[6]
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