THE LEAST MEAN SQUARES NETWORK WITH INFORMATION CODING - A MODEL OF CUE LEARNING

Information Coding, a method for coding variables in the least mean squares (LMS) network, is proposed as an associative model of human cue learning. According to this model, participants in cue learning experiments assume the linear hypothesis and implement this hypothesis by updating their estimate of cue-target correlation on each trial. Because absence of a stimulus may give information about correlation, this model codes an absent stimulus as a nonzero value under some conditions. Thus, Information Coding differs from R. A. Rescorla and A. R. Wagner's (1972) model, a version of the LMS network in which absence is always coded as 0. In 5 experiments, participants estimated the likelihood that a hypothetical patient had a disease given the patient's symptoms. The results support Information Coding, whereas the Rescorla-Wagner model, G. B. Chapman's (1991) variant of the Rescorla-Wagner model, and A. B. Markman's (1989) coding of the LMS network all failed to account for at least one of these results.