Signal recognition models compared for random and Markov presentation sequences

Models of two-category signal recognition are compared to data from a variety of experimental conditions. For recognition, one of two signals (S₁, S₂) which vary slightly on some simple physical dimension is presented on each trial, and 0 is to identify (I₁, I₂ ) which signal was presented. In general, Os show a decrease in both Pr(I₁ /S₂) and Pr(I₂/S₁) for either greater Pr(S₂) or, sequentially, for greater S₂ recency. These effects are described as probability and sequential contrast, respectively. The memory state model (MS) describes a three-state threshold process with responses determined by a simple first-order Markov process which depends on the sensory state and response on the immediately preceding trial. The memory trace regression model (MTR) assumes that O compares the observed signal event with the memory trace of the previous signal event. When the difference is large, the sign of the difference determines the response; when it is small, the response depends on the preceding response. The memory trace is assumed to regress toward the mean signal value. Both models accurately predict the observed bias changes as a function of signal probabilities and of the subsequence of events on the previous trial. The response axioms of the MTR model are modified to predict the results for individual Os for Markov chains of signal events. The response axioms of the MS model are modified to predict responding when information is given to O concerning signal probabilities. Although both models do well under all conditions, the MS model uses fewer parameters and correctly predicts the direction of higher order sequential dependencies.