Contingency and its two indices within conditional probability analysis

Four theoretical bases for detecting a contingency between behavior and consequent stimuli are considered: contiguity, correlation, conditional probability, and logical implication. It is argued that conditional probability analysis is statistically the most powerful of these options, in part due to its provision of two indices of contingency: a forward time probability that reinforcement follows behavior and a backward time probability that behavior precedes reinforcement. Evidence is cited that both indices appear to bear on the learning of a variety of animals, although they are unequally salient to human adults and to artificial neural networks designed to solve time-series functions. It is hypothesized that humans may acquire the capacity to detect contingency in the progressive sequence: contiguity, correlation, forward time conditional probability, backward time conditional probability, and ultimately logical implication.