Conditional independence as a statistical assessment of evidence integration processes

Intuitively, combining multiple sources of evidence should lead to more accurate decisions than considering single sources of evidence individually. In practice, however, the proper computation may be difficult, or may require additional data that are inaccessible. Here, based on the concept of conditional independence, we consider expressions that can serve either as recipes for integrating evidence based on limited data, or as statistical benchmarks for characterizing evidence integration processes. Consider three events, [Formula: see text] , [Formula: see text] , and [Formula: see text]. We find that, if [Formula: see text] and [Formula: see text] are conditionally independent with respect to [Formula: see text] , then the probability that [Formula: see text] occurs given that both [Formula: see text] and [Formula: see text] are known, [Formula: see text] , can be easily calculated without the need to measure the full three-way dependency between [Formula: see text] , [Formula: see text] , and [Formula: see text]. This simplified approach can be used in two general ways: to generate predictions by combining multiple (conditionally independent) sources of evidence, or to test whether separate sources of evidence are functionally independent of each other. These applications are demonstrated with four computer-simulated examples, which include detecting a disease based on repeated diagnostic testing, inferring biological age based on multiple biomarkers of aging, discriminating two spatial locations based on multiple cue stimuli (multisensory integration), and examining how behavioral performance in a visual search task depends on selection histories. Besides providing a sound prescription for predicting outcomes, this methodology may be useful for analyzing experimental data of many types.