21 ACS equations.
The mean intercept of .47, with confidence intervals of .43 to .51, and slope of .03, with confidence intervals o f - . 15 to .21, indicated that there was no relationship between grammatical accuracy and confidence. In contrast, the mean intercept o f .51, with confidence intervals of .41 to .61, and slope o f .64, with confidence intervals o f
.35 to .93, demonstrated a stronger relationship between confidence and sensitivity to ACS. Chance accuracy in relation to both rule and ACS knowledge accompanied 50% confidence ratings.
Discussion
Experiment 7 investigated the possibility that knowledge applied accurately in a classification test might be implicit in relation to subjective confidence as measured by guessing and zero correlation criteria. Two analyses of the mean proportion correct across participants for the 50% confidence category were unable to determine whether participants performed at chance when they said they were guessing. However,
regressions across all confidence categories (50-100%) supported the conclusion that there was no evidence for implicit knowledge according to either criterion.
The results o f the linear regressions support the unitary episodic-processing account (Whittlesea & Dorken, 1997) and prior studies that found no evidence for the guessing or zero correlation criteria (Redington, Chater, & Friend, 1996; Whittlesea, Brooks, & Westcott, 1994). Evidence that accuracy and confidence, in relation to ACS, are related across the 50-100% confidence range suggests that one type o f knowledge (i.e., fluency) can support both classification and confidence judgements.
While prior studies used finite-state grammars provide evidence o f implicit knowledge according to subjective criteria (Dienes & Altmann, 1997; Dienes et al.,
1995), Experiment 7 was based on a biconditional grammar that allowed the contributions of rule and ACS knowledge to be independently assessed. As in Experiments 1 to 6, unconfounding rule and ACS knowledge indicated that unaware memorisers use ACS rather than rule knowledge.
If ACS is the real basis o f classification performance, then measuring the relationship between accuracy and confidence on its true basis (i.e., ACS) should have resulted in greater accuracy in the 50% confidence category than the rule-based 63% calculated by Dienes and Altmann (1997, Experiment 2, same letters group). As the results o f Experiment 7 indicated ACS accuracy o f 54% in the 50% confidence category the existing data need not undermine the notion o f a subjective threshold.
Chapter 7: General Discussion
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Everyday experience suggests that we have an ability to store two types of knowledge independently. Episodic knowledge allows us to recall the context of specific experiences, such as what we did on our last holiday. In contrast, general knowledge o f the properties of classes of objects and events is not tied to specific experiences and enables us to make judgements about novel instances. Thus, we can judge the grammaticality of a sentence we have never heard before and read words in
unfamiliar handwriting. Moreover, as we are not normally intending to abstract underlying rules, it appears that we acquire general knowledge in an incidental and unconscious manner.
A good deal of evidence supporting this dual systems account has come from AGL studies. For example, Knowlton, Ramus, and Squire (1992) presented evidence that despite being selectively impaired in making judgements about specific items, amnesics had intact general knowledge of an artificial grammar. Evidence such as this seems to support the notion that we have separate learning systems that acquire implicit, general and explicit, specific knowledge.
However, despite 30 years of AGL research, there is still debate over the form of knowledge acquired in incidental learning situations. While the dual systems
account suggests that memorising letter strings, without realising that those letter strings were constructed according to a set of rules, leads to both implicit knowledge of the rules of the grammar and episodic knowledge of specific training examples (e.g., Cleeremans, 1993; Lewicki & Hill, 1989; Reber, 1967, 1989), it has also been suggested that behaviour that appears to be rule-based can also be explained by an
episodic system that acquires specific knowledge of a collection of training exemplars (Brooks, 1978; Brooks & Vokey, 1991; Neal & Hesketh, 1997; Vokey & Brooks,
1992), the frequency statistics o f letter fragments in training items (e.g., Dulany, Carlson, & Dewey, 1984; Perruchet & Pacteau, 1990), or processing training items in particular ways in order to meet the demands of the training task (Whittlesea, 1997a, b; Whittlesea & Dorken, 1993, 1997; Whittlesea & Williams, in press; Whittlesea & Wright, 1997; Wright & Whittlesea, 1998).
As well as varying in assumptions about the form of knowledge acquired and whether we have one or two learning systems, these four accounts also differ in other ways. While the implicit rule-abstraction, exemplar, and fragments accounts suggest that knowledge acquisition is stimulus-driven, the episodic-processing account suggests that knowledge acquisition is driven by the processing required to meet the demands of the training task. Finally, there is also debate over whether incidentally acquired knowledge is always applied implicitly (i.e., the abstraction account suggests that implicit rule knowledge is applied in classification tests), applied explicitly
(exemplar and letter-fragment knowledge), or applied implicitly or explicitly
depending on whether test instructions disguise or alert participants to the relationship between fluent processing of test items and the information acquired during training (episodic processing account).
In Chapter 2, a reanalysis of a study that supported the dual-systems account (Meulemans & Van der Linden, 1997) demonstrated that finite-state grammars, which dominate AGL research, do not allow us to unconfound the contributions of rule, exemplar and fragment knowledge in test performance. Because these grammars (e.g., Brooks & Vokey, 1991, see Figure 2 in Chapter 2) use transition rules that dictate
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