Implicit learning of conjunctive rule sets: An alternative to artificial grammars

A single experiment is reported that investigated implicit learning using a conjunctive rule set applied to natural words. Participants memorized a training list consisting of words that were either rare-concrete and common-abstract or common-concrete and rare-abstract. At test, they were told of the rule set, but not told what it was. Instead, they were shown all four word types and asked to classify words as rule-consistent words or not. Participants classified the items above chance, but were unable to verbalize the rules, even when shown a list that included the categories that made up the conjunctive rule and asked to select them. Most participants identified familiarity as the reason for classifying the items as they did. An analysis of the materials demonstrated that conscious micro-rules (i.e., chunk knowledge) could not have driven performance. We propose that such materials offer an alternative to artificial grammar for studies of implicit learning.

► We explore implicit learning with a conjunctive rule set using natural words. ► Participants identified rule consistent words at above chance levels in a 2AFC task. ► The basis of this performance was non-verbalisable. ► Chunk knowledge (bigrams or trigrams) was not the basis of this performance. ► Such stimuli provide an alternative to artificial grammar strings.