Dissociations of grammaticality and specific similarity effects in artificial grammar learning.

Higham, Philip A.

doi:10.1037/0278-7393.23.4.1029

Cited by 34 publications

(62 citation statements)

References 51 publications

(146 reference statements)

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“…By far the most common experimental paradigm used to investigate implicit learning is artificial grammar learning (AGL; e.g., Dienes & Scott, 2005;Higham, 1997aHigham, , 1997bReber, 1967Reber, , 1969see Pothos, 2007 for a review). In typical AGL experiments, participants first observe or attempt to memorize a set of letter strings (e.g., MVXRT) that conform to an underlying rule set (finite-state grammar).…”

Section: Introductionmentioning

confidence: 99%

Implicit learning of conjunctive rule sets: An alternative to artificial grammars

Neil

Higham

2012

Consciousness and Cognition

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A single experiment is reported that investigated implicit learning using a conjunctive rule set applied to natural words. Participants memorized a training list consisting of words that were either rare-concrete and common-abstract or common-concrete and rare-abstract. At test, they were told of the rule set, but not told what it was. Instead, they were shown all four word types and asked to classify words as rule-consistent words or not. Participants classified the items above chance, but were unable to verbalize the rules, even when shown a list that included the categories that made up the conjunctive rule and asked to select them. Most participants identified familiarity as the reason for classifying the items as they did. An analysis of the materials demonstrated that conscious micro rules (i.e., chunk knowledge) could not have driven performance. We propose that such materials offer an alternative to artificial grammar for studies of implicit learning.

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Section: Introductionmentioning

confidence: 99%

Implicit learning of conjunctive rule sets: An alternative to artificial grammars

Neil

Higham

2012

Consciousness and Cognition

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“…Each of the allowed transitions is associated with the addition of a symbol, so in going from the entry state to any of the exit states on the diagram, different sequences of symbols can be constructed. For example, M-S-V would be a grammatical sequence, but V-V-X would not be because once a V has been added to a sequence, there is no transition that could lead to the second V. In this example, the symbols associated with each allowed transition in the finite state grammar are letters, but there is no restriction as to what they could be replicated in later research (Brooks & Vokey, 1991;Dulany et al, 1984;Gomez & Schvaneveldt, 1994;Higham, 1997;Higham, Vokey, & Pritchard, 2000;Knowlton & Squire, 1994;Mathews et al, 1989;Meulemans & van der Linden, 1997;Mathews et al, 1989;Perruchet & Pacteau, 1990;Pothos & Bailey, 2000;Shanks, Johnstone, & Staggs, 1997;Whittlesea & Dorken, 1993; for discussions of AGL results see Dienes, 1992;Reber, 1989;Redington & Chater, 1996).…”

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Does Stimulus Appearance Affect Learning?

Pothos¹,

Chater²,

Ziori³

2006

The American Journal of Psychology

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We examined the learning process with 3 sets of stimuli that have identical symbolic structure but differ in appearance (meaningless letter strings, arrangements of geometric shapes, and sequences of cities). One hypothesis is that the learning process aims to encode symbolic regularity in the same way, largely regardless of appearance. Another is that different types of stimuli bias the learning process to operate in different ways. Using the experimental paradigm of artificial grammar learning, we provided a preliminary test of these hypotheses. In Experiments 1 and 2 we measured performance in terms of grammaticality and found no difference across the 3 sets of stimuli. In Experiment 3 we analyzed performance in terms of both grammaticality and chunk strength. Again we found no differences in performance. Our tentative conclusion is that the learning process aims to encode symbolic regularity independent of stimulus appearance.

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“…This raises the possibility that participants learn some grammatical rules and remember some specific items. Higham (1997b) showed that memory-and rule-based processes could be dissociated by dividing attention at test. Dividing attention reduced the contribution of similarity, but not the contribution of grammaticality, to classification performance.…”

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Sources of confidence judgments in implicit cognition

Tunney

2005

Psychonomic Bulletin & Review

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Subjective reports of confidence are frequently used as a measure of awareness in a variety of fields, including artificial grammar learning. However, little is known about what information is used to make confidence judgments and whether there are any possible sources of information used to discriminate between items that are unrelated to confidence. The data reported here replicate an earlier experiment by Vokey and Brooks (1992) and show that grammaticality decisions are based on both the grammatical status of items and their similarity to study exemplars. The key finding is that confidence ratings made on a continuous scale (50%-100%) are closely related to grammaticality but are unrelated to all of the measures of similarity that were tested. By contrast, confidence ratings made on a binary scale (high vs. low) are related to both grammaticality and similarity. The data confirm an earlier finding (Tunney & Shanks, 2003) that binary confidence ratings are more sensitive to low levels of awareness than continuous ratings are and suggest that participants are conscious of all the information acquired in artificial grammar learning.

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Dissociations of grammaticality and specific similarity effects in artificial grammar learning.

Cited by 34 publications

References 51 publications

Implicit learning of conjunctive rule sets: An alternative to artificial grammars

Implicit learning of conjunctive rule sets: An alternative to artificial grammars

Does Stimulus Appearance Affect Learning?

Sources of confidence judgments in implicit cognition

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