Pa. Frensch, COMPOSITION DURING SERIAL-LEARNING - A SERIAL POSITION EFFECT, Journal of experimental psychology. Learning, memory, and cognition, 20(2), 1994, pp. 423-442
Composition is a computational learning mechanism that merges serially
performed elementary processes into hierarchically organized knowledg
e structures. The main goals of this research were to explore (a) the
role of serial position in composition and (b) the relation between de
gree 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 simultaneo
usly on a video monitor. A procedure based on the comparison of reacti
on times to random sequences and a repeating sequence identified a ser
ial position effect of composition that was, however, moderated by Ss'
explicit, postexperimental recall of the repeating sequence. A produc
tion-system-based computational model of composition is described that
qualitatively reproduces the empirical findings. Implications for the
mechanisms governing serial learning are discussed.