Constraint Redundancy as a Determiner of Attribute- and Rule-Learning Aspects of Sequential Pattern Recognition

The purpose of this experiment was to assess the manner in which attribute-learning and rule-formation components of schema learning vary as a function of constraint redundancy magnitude. Twenty-four Ss participated in a pattern recognition task involving 70%, 50%, 40%, or 20% redundant Markov sequences sampled from three different schema families. Ss were required to work through the 90 sequences in a manner similar to that encountered in a probability learning paradigm. Results showed that decreasing magnitude of redundancy reduced the degree to which both attribute- and rule-learning occurred. The supposition that constraint redundancy is a necessary condition for and one of the main determiners of the schema-learning process was thus confirmed.