elements, etc.). The contribution of the various components to performance depends on both the specific instruction in the acquisition phase and the requirements of the retrieval task. Therefore, the instructions on every phase are important in order to determine whether or not each stage will require automatic processing. Each phase should be evaluated for automaticity separately. One hypothesis that contradicts the automaticity of AGL is the "mere exposure effect". The mere exposure effect is increased affect towards a stimulus that is the result of nonreinforced, repeated exposure to the stimulus. Results from over 200 experiments on this effect indicate that there is a positive relationship between mean "goodness" rating and frequency of stimulus exposure. Stimuli for these experiments included line drawings, polygons and nonsense words (which are types of stimuli used in AGL research). These experiments exposed participants to each stimulus up to 25 times. Following each exposure participants were asked to rate the degree to which each stimulus suggested "good" vs. "bad" effect on a 7-point scale. In addition to the main pattern of results, it was also found in several experiments that participants rated higher positive affect for previously exposed items than for novel items. Since implicit cognition should not reference previous study episodes, the effects on affect ratings should not have been observed if processing of this stimuli is truly implicit. The results of these experiments suggests that different categorization of the strings may occur due to differences in affect associated with the strings and not due to implicitly learned grammar rules. Artificial intelligence Since the advent of computers and artificial intelligence, computer programs have been adapted that attempt to simulate the implicit learning process observed in the AGL paradigm. The AI programs first adapted to simulate both natural and artificial grammar learning used the following basic structure: Given A set of grammatical sentences from some language. Find A procedure for recognizing and/or generating all grammatical sentences in that language. An early model for AI grammar learning is Wolff's SNPR System. The program acquires a series of letters with no pauses or punctuation between words and sentences. The program then examines the string in subsets and looks for common sequences of symbols and defines "chunks" in terms of these sequences (these chunks are akin to the exemplar-specific chunks described for AGL). As the model acquires these chunks through exposure, the chunks begin to replace the sequences of