Some evidence on additive learning models

Abstract

Deterministic and probabilistic additive learning models forsignal detection/recognition replace the fixed criterion of classical detection models with one which shifts from trial to trial in the light of the preceding trial events. Data from a sinusoid in noise detection task without feedback and an auditory amplitude recognition task with feedback are used to test these models with respect to their predictions about asymptotic response frequency, and, where possible, by likelihood ratio tests. These and some previous experiments show that whether or not feedback is given subjects do not universally probability match, overmatch, undermatch, or keep response probability constant over discriminability, so that none of the testable special models can fit more than a proportion of subjects. The likelihood ratio tests confirm this conclusion for the special deterministic models. The six-parameter general deterministic model does nonsignificantly better than an ad hoc six-parameter response runs model in fitting the recognition data and significantly better than the five-parameter memory recognition model of Tanner, Rauk, andAtkinson (1970). Monte Carlo methods are used to confirm the applicability of asymptotic response frequency results to practically feasible sample sizes.