Structural complexity as a determinant of serial pattern learning

Abstract

A new methodology extends the study of serial pattern learning to nonhuman organisms by constructing patterns using elements that are both familiar and motivationally meaningful to animals. Two experiments examine the ability of rats to anticipate various quantities of food as measured by running times in a runway when the quantities occur in a serial order. In Experiment 1, a serial monotonic pattern (14-7-3-1-0 food pellets) produced more rapid learning and more accurate anticipation of the various quantities (“tracking”) than a serial nonmonotonic pattern (14-1-3-7-0 food pellets), particularly with respect to the final 0-pellet element. In Experiment 2, the same monotonic pattern generated faster learning and more accurate anticipation of pattern elements than a weakly monotonic pattern (14-5-5-1-0 pellets). Associative explanations including simple excitatory or inhibitory effects, temporal anticipation, reinforcement contrast, and the number and discriminability of pairwise associations are not adequate to account for the data. Rather, the formally defined structural complexity of each pattern adequately predicts its relative difficulty.