COMPOSITION DURING SERIAL-LEARNING - A SERIAL POSITION EFFECT

Composition is a computational learning mechanism that merges serially performed elementary processes into hierarchically organized knowledge structures. The main goals of this research were to explore (a) the role of serial position in composition and (b) the relation between degree of composition and explicit serial recall in serial learning. In 3 experiments, Ss performed a rule-based serial reaction time task in which they had to categorize a sequence of 12 stimuli shown simultaneously on a video monitor. A procedure based on the comparison of reaction times to random sequences and a repeating sequence identified a serial position effect of composition that was, however, moderated by Ss' explicit, postexperimental recall of the repeating sequence. A production-system-based computational model of composition is described that qualitatively reproduces the empirical findings. Implications for the mechanisms governing serial learning are discussed.