Contingency (the learned relative salience of environmental features) and latency (the learned timing of response to stimuli) are central phenomena of learning and memory. This paper provides a computational analysis of, and algorithms for, a set of empirical data on contingency and latency in classical and instrumental conditioning. These analyses are presented within the framework of an information-processing architecture that describes a set of modules which operate in parallel and asynchronously to store, retrieve and modify experiential information. The architecture (called 'CEL', for 'Components of Experiential Learning') provides a way of making explicit the interactions among a number of otherwise separate algorithms for related phenomena. The modules comprising the architecture each emerge from the operation of an indexed network memory. The algorithms presented are also implemented in working computer programs that interact with a simulated environment to produce contingent associative learning and differential response latencies that correspond to the relevant behavioral data. The model makes a number of specific empirical predictions that can be experimentally tested.