| Interest in sequence learning has exploded in the last decade and it has become one of the most popular paradigms in implicit learning since Nissen and Bullemer's serial reaction time task research (1987) and Lewicki, Czyzewska and Hoffman's contingent reaction time research (1987). The sequence-learning paradigm can broadly be defined as consisting of several types of situations that share the common features of presenting participants with a speeded task during which (a) they have to respond to the location of a target stimulus that may appear at one of several possible locations on a computer screen on each trial, and (b) in which the series of locations follows a regularity that is not revealed to participants (Cleeremans & Jimenez, 1997). Through a comparison between corresponding direct and indirect measure (Frensch & Miner, 1994) or manipulating participants' orientation to learn (Jimenez et al, 1996; Reed & Johnstone, 1994), the results have showed that people can obtain some tacit knowledge in sequence learning. And more and more studies have suggested that people can benefit from the environment and this is not accompanied by conscious awareness of the relevant sequential constraints.Various effects of implicit learning have been found and the exploring of the feature and mechanism of implicit learning has been started in sequence learning. But implicit learning of sequence is so complex that different authors have drawn different conclusions, for example there are four inconsistent opinions on what on earth implicit learning brings. First, Howard, Mutter and Howard argued that people learned the relations between the stimuli (S-S learning). Second, Willingham showed that learning could be based on the regularities in the sequence of responses (R-R learning). Willingham, Nissen and Bullemer proved that learning could be based on the learning of sequences of S-R pairs. Ziessler put forward the idea that learning the contingencies between responses and stimuli (R-S learning) was a crucial factor in implicit learning of sequence.Through an analysis of the data, we found that there were virtually some regularities in them though they seemingly contradicted with each other. First, thelearning of each relation needed different learning systems such as S-S learning was mostly performed by perceptual system, R-R learning mostly by motor system and R-S or S-R by both systems. Thus different systems processed information differently, which might lead to diverse results. Second, the significance of each relation was not same and this also could result in different results. Furthermore, almost all researchers believed that based on different situations participants had obtained the knowledge to predict the future stimulus or response in implicit learning of sequence though they were disparate on what implicit learning brought. Only because of the different methods they employed, some authors argued that participants predicted the future stimulus or response by the stimulus features (e.g. S-S learning or S-R learning) while others considered that they predicted it by the response features (e.g. R-R learning or R-S learning). It was obvious that the former belonged to the learning of perceptual knowledge and the latter belonged to the learning of motor knowledge. Then, what participants exposed to sequence learning situations that involved both perceptual knowledge and motor knowledge would learn about the material? Would the type and feature of knowledge influence what they learn?In this study, stimuli were color figures that included the regularities of figure locations and figure sequences. We used the complex stimuli because the study of implicit learning of sequence was often questioned due to their simple stimuli (Lewicki et al, 1988). The main goal of experiment 1 was to demonstrate effects of implicit learning and the influence of regularity features on effects of implicit learning through a comparison between corresponding direct and indirect measure and between relevant implicit and explicit... |
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