Identification Of Attributes Research Articles

Effects of attribute identification training on rule effects in an attribute identification transfer task

The hypothesis that attribute identification (AI) pretraining would reduce or eliminate rule effects in AI tasks was investigated. Ss were given 0, 4, or 8 AI problems based on the same rule prior to transfer to a final AI problem. Results indicated that familiarity with AI problems was not a key factor in eliminating rule effects in transfer. Implications for a two-component model of concept learning were discussed.

Effects of rule pretraining on rule effects in an attribute identification task

Rule learning (RL) training or familiarization as a means of reducing attribute identification (AI) rule effects was examined. Independent groups of college Ss were trained on 0, 2, 4, or 8 RL problems based on the conjunctive (Cj), disjunctive (Dj), conditional (Cd), or biconditional (Bd) rules prior to transfer to an AI task based on the same rule. Contrary to predictions, level of pretraining failed to interact with rule difficulty in AI [F(9,32) = 1.90, p <.25], while the main effect of rule obtained significance [F(3,32) = 10.12, p <.01]. Implications of these findings for research conceived within a model postulating independent processes for the RL and AI components of concept learning tasks were discussed.

Learning set formation and transfer in concept learning: A comparison between attribute identification and rule learning

With a selection paradigm, three experimental groups of 10 Ss each learned six conjunctive concepts successively under three learning conditions: attribute identification (AI), rule learning (RL), or their combination (CL). Only for AI did learning sets form and were strategies near optimum efficiency. Experimental Ss learned two additional disjunctive concepts under the three learning conditions, and three control groups learned only the disjunctive concepts. For a measure of response frequency, positive transfer occured for RL and CL, indicating the importance of rule learning for transfer. For a measure of strategy efficiency, negative transfer occurred for AI and positive transfer for RL.