| Summary: | In a highly
uncertain world, individuals often have to make decisions in situations with
incomplete information. We investigated in three experiments how partial cue
information is treated in complex probabilistic inference tasks. Specifically,
we test a mechanism to infer missing cue values that is based on the
discrimination rate of cues (i.e., how often a cue makes distinct predictions
for choice options). We show analytically that inferring missing cue values
based on discrimination rate maximizes the probability for a correct inference
in many decision environments and that it is therefore adaptive to use it.
Results from three experiments show that individuals are sensitive to the
discrimination rate and use it when it is a valid inference mechanism but rely
on other inference mechanisms, such as the cues' base-rate of positive
information, when it is not. We find adaptive inferences for incomplete
information in environments in which participants are explicitly provided with
information concerning the base-rate and discrimination rate of cues (Exp. 1)
as well as in environments in which they learn these properties by experience
(Exp. 2). Results also hold in environments of further increased complexity
(Exp. 3). In all studies, participants show a high ability to adaptively infer
incomplete information and to integrate this inferred information with other
available cues to approximate the naive Bayesian solution.
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