Learning and memory Journal Article Review
A critical review of Shanks, D. R., & St. John, M. F. (1994). Characteristics of dissociable human learning systems. Behavioral and Brain Sciences, 17, 367–447.
Shanks & St. John (1994) claimed that the proposal of dissociable human learning systems – explicit and implicit learning systems is based on the following – explicit learning takes place with concurrent awareness and involves encoding of instances or fragments; implicit learning takes place without concurrent awareness and involves unconscious rule learning. In their review article, they concluded that generally implicit learning was unsupported with consideration to instrumental learning tasks, Pavlovian and evaluative conditioning models, sequential reaction time tasks and so on. In this review, we would concentrate on the domains of sequential reaction time tasks and Pavlovian and evaluative conditioning models, with close reference to whether relevant empirical evidence would support the view of implicit learning or not.
As implicit learning is classified as unaware, Shanks & St. John (1994) proposed two criteria where tests of awareness for implicit learning should meet. The first one was named the ‘information criterion’ - before concluding that subjects are unaware of the learned information that influences their behavior, the experimenter must be able to establish that the information he or she is looking for in the awareness test is indeed the information responsible for changes in performance. They also devised a second criterion - the sensitivity criterion, which stated that unaware learning must achieve an adequate level of sensitivity. In detail, in order to show that two dependent variables say tests of conscious knowledge and task performance relate to dissociable underlying systems, we must be able to show that our test of awareness is sensitive to all of the relevant conscious knowledge. Unless this criterion is met, the fact that subjects are able to transmit more information in their task performance than in a test of awareness may simply be due to the greater sensitivity of the performance test to whatever conscious information the subject has encoded. It was based on these criteria that Shanks & St. John (1994) concluded that there was not any reliable evidence available to support implicit learning.
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Moreover, in the domain of conditioning, Shanks & St. John (1994) argued that there was no formal establishment of dissociation between learning of reinforcement contingence and presence of awareness. They outlined an experiment by Lovibond (1992), where galvanic skin responses (GSR) of participants were stronger to A than to B. For tests measuring the awareness of subjects, Shanks & St. John (1994) suggested that a verbal report test was inadequate because it involved a different retrieval context compared with performance test, therefore they were uncertain whether the amount of the conscious information picked up for both tests matched or not. As there were other tests like recognition and prediction tests which were capable of detecting information that verbal report tests were unable to detect, Shanks & St. John (1994) also doubted that a verbal report test could exhaustively extract conscious information from subjects. They argued that when subjects cannot report the "implicitly learned" rules that govern stimulus selection, it was often because their knowledge consists of instances or fragments of the training stimuli rather than rules. Concerning Pavlovian Conditioning, a well-established paradigm in learning, three studies had lent support that learning can occur without conscious awareness (Esteves et al., 1994; Wong et al., 1997; OÈ hman and Soares, 1998). They all had demonstrated that both skin conductance response (SCR) and event-related brain potentials (ERPs) could be conditioned without being consciously aware of the contingent relationship between the conditioned stimulus (CS) and the unconditioned stimulus (US). Thus, there were various neurological evidence that disputed the claim by Shanks & St. John (1994) that concurrent awareness was a prerequisite for Pavlovian Conditioning.
Accurately measuring unconscious learning processes had been difficult could be explained by the process-purity problem (Curran, 2001). When we learn, an inter-play of explicit and implicit knowledge would usually be involved, making process-pure assessment tasks for implicit learning difficult to conduct. The ‘method of opposition’ suggested by Jacoby and colleagues (1991, 1998) reasoned that conscious and unconscious processes might be separated if they were placed in opposition such that they would influence performance in opposite ways. The method of opposition’ assumed that there are variations in intentional control between conscious and unconscious processes. People can manage the way to use information when it can be accessed consciously, for instance responding ‘non-famous’ to names that are recollected from a study list. However, as people lack control over using unconscious information, a person’s behaviour may conflict with his or her true intentions, say responding ‘famous’ to a name that is merely familiar because it was on the study list.
In sequence learning tasks, there was recent evidence by Destrebecqz and Cleeremans (2001) has provided compelling evidence for implicit sequence learning without awareness by using the ‘method of opposition’, reliably suggesting the possibility of implicit learning. Destrebecqz and Cleeremans (2001) applied the method of opposition in a Serial Reaction Time experiment. There were two conditions in their SRT task which placed implicit and explicit knowledge in opposition. In the ‘inclusion’ condition, participants were asked to press response keys in an order following the sequence in the SRT task. On the contrary, participants were asked to press response keys in an order that mismatched the sequence in the ‘exclusion’ condition. It was expected that participants having good explicit knowledge of the material would regularly follow the sequence in the inclusion condition but not under the exclusion condition. However, people having no explicit knowledge about the material tend to generate the sequence equally often on inclusion and exclusion trials.
In their experiment, two groups – the ‘RSI’ and ‘non-RSI’ groups of participants were tested in conditions that led to different levels of explicit knowledge. The ‘RSI’ group, was given a brief pause between each response and the appearance of the next stimulus while the non-RSI group was not given any pauses. The RSI group showed a large difference between sequence and random SRT trials as well as generating the sequence significantly more often for inclusion than exclusion trials. Thus, the RSI group learned the sequence, but that learning was at least partially attributable to
explicit knowledge (inclusion > exclusion). The no-RSI group (RSI = 0) also was faster for sequence than random SRT trials, but their generation performance suggested an implicit learning system was operating. The no-RSI group generated the sequences in the inclusion as often as in exclusion condition. Moreover, participants’ ability to discriminate between parts of the sequence in a final recognition test was consistent with their generation performance. Therefore, Destrebecqz and Cleeremans (2001) had produced a compelling procedure that could satisfy both the sensitivity and information criteria by Shanks (1994) and could demonstrate implicit learning.
In conclusion, the claim by Shanks & St. John (1994) that there’s no reliable evidence of implicit learning is questionable. It might due to the fact that methodology options for carrying out different tasks were limited at their time. To shed light on the issue, more sensitive tests should be conducted. Different domains of human learning should also be contained, for instance motor learning, in order to form a comprehensive view of the nature of learning – a combination of explicit and implicit learning. With reference to the outlined research in the review, it is suggestive that learning can be achieved through both non-associative, propositional hypothesis testing – deduction of rules and an associative, link-formation mechanism - memorization of instances instead of a one-way fashion.
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