Semantic and associative factors in probability learning with words

Abstract

Using a probability-learning technique with a single word as the cue and with the probability of a given event following this word fixed at .80, it was found (1) that neither high nor low associates to the original word and (2) that neither synonyms nor antonyms showed differential learning curves subsequent to original learning when the probability for the following event was shifted to .20. In a second study when feedback, in the form of knowledge of results, was withheld, there was a clear-cut similarity of predictions to the originally trained word and the synonyms of both high and low association value and a dissimilarity of these words to a set of antonyms of both high and low association value. Two additional studies confirmed the importance of the semantic dimension as compared with association value as traditionally measured.