Classical conditioning as a nonstationary,multivariate time series analysis: A spreadsheet model

The implementation of the Gallistel (1990) model of classical conditioning on a spreadsheet with matrix operations is described. The model estimates the Poisson rate of unconditioned stimulus (US) occurrence in the presence of each conditioned stimulus (CS). The computations embody three implicit principles:additivity (of the rates predicted by each CS),provisional stationarity (the rate predicted by a given CS has been constant over all the intervals when that CS was present), andpredictor minimization (when more than one solution is possible, the model minimizes the number of CSs with a nonzero effect on US rate). The Kolmogorov-Smirnov statistic is used to test for non-stationarity. There are no free parameters in the learning model itself and only two parameters in the formally specified decision process, which translates what has been leamed into conditioned responding. The model predicts a wide range of conditioning phenomena, notably: blocking, overshadowing, overprediction, predictive sufficiency, inhibitory conditioning, latent inhibition, the invariance in the rate of conditioning under scalar transformation of CS-US and US-US intervals, and the effects of partial reinforcement on acquisition and extinction.