On the efficiency of instruction-based rule encoding

Instructions have long been considered a highly efficient route to knowledge acquisition especially compared to trial-and-error learning. We aimed at substantiating this claim by identifying boundary conditions for such an efficiency gain, including the influence of active learning intention, repeated instructions, and working memory load and span. Our experimental design allowed us to not only assess how well the instructed stimulus-response (S-R) rules were implemented later on, but also to directly measure prior instruction encoding processes. This revealed that instruction encoding was boosted by an active learning intention which in turn entailed better subsequent rule implementation. As should be expected, instruction-based learning took fewer trials than trial-and-error learning to reach a similar performance level. But more importantly, even when performance was measured relative to the identical number of preceding correct implementation trials, this efficiency gain persisted both in accuracy and in speed. This suggests that the naturally greater number of failed attempts in the initial phase of trial-and-error learning also negatively impacted learning in subsequent trials due to the persistence of erroneous memory traces established beforehand. A single instruction trial was sufficient to establish the advantage over trial-and-error learning but repeated instructions were better. Strategic factors and inter-individual differences in WM span - the latter exclusively affecting trial-and-error learning presumably due to the considerably more demanding working memory operations - could reduce or even abolish this advantage, but only in error rates. The same was not true for response time gains suggesting generally more efficient task automatization in instruction-based learning.