Realism in confidence judgements of performance based on implicit learning

In two experiments we employed calibration methods to investigate the realism of participants' confidence ratings of their own classification performance based on knowledge acquired after training on an artificial grammar. In Experiment 1 participants showed good realism (but overconfidence) for grammatical strings but very poor realism for non-grammatical strings. Method of training (string repetition in writing or mere exposure) did not affect the realism. Furthermore, the participants underestimated their overall performance. In Experiment 2, using a more complex grammar and controlling for two types of associative chunk-strength, participants showed good realism (but still overconfidence) for both letter and symbol strings, irrespective of grammaticality. Together, these experiments show that implicit learning can give rise to knowledge products that are associated with fairly realistic meta-knowledge. It is argued that both the zero-correlation criterion and the guessing criterion are misplaced when used to define implicit knowledge; two reasons being that confidence judgements may be affected both by implicit knowledge and by inferences.     

Prior familiarity with components enhances unconscious learning of relations

The influence of prior familiarity with components on the implicit learning of relations was examined using artificial grammar learning. Prior to training on grammar strings, participants were familiarised with either the novel symbols used to construct the strings or with irrelevant geometric shapes. Participants familiarised with the relevant symbols showed greater accuracy when judging the correctness of new grammar strings. Familiarity with elemental components did not increase conscious awareness of the basis for discriminations (structural knowledge) but increased accuracy even in its absence. The subjective familiarity of test strings predicted grammaticality judgments. However, prior exposure to relevant symbols did not increase overall test string familiarity or reliance on familiarity when making grammaticality judgments. Familiarity with the symbols increased the learning of relations between them (bigrams and trigrams) thus resulting in greater familiarity for grammatical versus ungrammatical strings. The results have important implications for models of implicit learning.     

Fluency does not express implicit knowledge of artificial grammars

It is commonly held that implicit knowledge expresses itself as fluency. A perceptual clarification task was used to examine the relationship between perceptual processing fluency, subjective familiarity, and grammaticality judgments in a task frequently used to produce implicit knowledge, artificial grammar learning (AGL). Four experiments examined the effects of naturally occurring differences and manipulated differences in perceptual fluency, where decisions were based on a brief exposure to test-strings (during the clarification task only) or normal exposure. When perceptual fluency was not manipulated, it was weakly related to familiarity and grammaticality judgments, but unrelated to grammatical status and hence not a source of accuracy. Counterbalanced grammatical and ungrammatical strings did not differ in perceptual fluency but differed substantially in subjective familiarity. When fluency was manipulated, faster clarifying strings were rated as more familiar and were more often endorsed as grammatical but only where exposure was brief. Results indicate that subjective familiarity derived from a source other than perceptual fluency, is the primary basis for accuracy in AGL. Perceptual fluency is found to be a dumb heuristic influencing responding only in the absence of actual implicit knowledge.     

Syntactic transfer in artificial grammar learning

In an artificial grammar learning (AGL) experiment, participants were trained with instances of one grammatical structure before completing a test phase in which they were required to discriminate grammatical from randomly created strings. Importantly, the underlying structure used to generate test strings was different from that used to generate the training strings. Despite the fact that grammatical training strings were more similar to nongrammatical test strings than they were to grammatical test strings, this manipulation resulted in a positive transfer effect, as compared with controls trained with nongrammatical strings. It is suggested that training with grammatical strings leads to an appreciation of set variance that aids the detection of grammatical test strings in AGL tasks. The analysis presented demonstrates that it is useful to conceptualize test performance in AGL as a form of unsupervised category learning.     

Information theory and artificial grammar learning: inferring grammaticality from redundancy

In artificial grammar learning experiments, participants study strings of letters constructed using a grammar and then sort novel grammatical test exemplars from novel ungrammatical ones. The ability to distinguish grammatical from ungrammatical strings is often taken as evidence that the participants have induced the rules of the grammar. We show that judgements of grammaticality are predicted by the local redundancy of the test strings, not by grammaticality itself. The prediction holds in a transfer test in which test strings involve different letters than the training strings. Local redundancy is usually confounded with grammaticality in stimuli widely used in the literature. The confounding explains why the ability to distinguish grammatical from ungrammatical strings has popularized the idea that participants have induced the rules of the grammar, when they have not. We discuss the judgement of grammaticality task in terms of attribute substitution and pattern goodness. When asked to judge grammaticality (an inaccessible attribute), participants answer an easier question about pattern goodness (an accessible attribute).     

Implicit preferences: the role(s) of familiarity in the structural mere exposure effect

In four experiments using an artificial grammar (AG) learning procedure, the authors examined the links between the "classic" mere exposure effect [heightened affect for previously encountered stimulus items (Bornstein, 1989; Zajonc, 1968)] and the "structural" mere exposure effect [greater hedonic appreciation for novel stimuli that conform to an implicitly acquired underlying rule system (Gordon & Holyoak, 1983)]. After learning, participants: (a) classified stimuli according to whether they conformed to the principles of the grammar and, (b) rated them in terms of how much they liked them. In some experiments unusual and unfamiliar symbols were used to instantiate the AG, in others highly familiar characters were used. In all cases participants showed standard AG learning. However, whether the two exposure effects emerged was dependent on symbol familiarity. Symbols with high a priori familiarity produced a structural mere exposure effect. Moderately familiar symbols produced only the classic, but not the structural, mere exposure effect. Highly unfamiliar symbols produced neither exposure effect. Results are discussed in the context of implicit learning theory and implications for a general theory of aesthetics are presented.     

Implicit Learning and Concept Learning

In Experiments 1 and 2, subjects were exposed to letter strings that followed a pattern—the second letter was always the same. This exposure was disguised as a test of immediate memory. Following this training, subjects could discriminate new letter strings following the pattern from letter strings not following the pattern more often than would be expected by chance, which is the traditional evidence for concept learning. Discrimination was also better than would be predicted from subjects' explicit report of the pattern, demonstrating the co-occurrence of concept learning and implicit learning. In Experiment 3, rules were learned explicitly. Discrimination was worse than would be predicted from subjects' explicit report, validating the implicit learning paradigm. In Experiment 4, deviations from a prototypical pattern were presented during training. In the test of discrimination, prototypes were as familiar as old deviations and more familiar than new deviations, even when considering only implicit knowledge. Experiment 5 found implicit knowledge of a familiar concept. These results are consistent with the hypothesis that the distinguishing features of a concept can be learned implicitly, and that one type of implicit learning is concept learning.     

The relationship between the structural mere exposure effect and the implicit learning process

Three experiments are reported that investigate the relationship between the structural mere exposure effect (SMEE) and implicit learning in an artificial grammar task. Subjects were presented with stimuli generated from a finite-state grammar and were asked to memorize them. In a subsequent test phase subjects were required first to rate how much they liked novel items, and second whether or not they thought items conformed to the rules of the grammar. A small but consistent effect of grammaticality was found on subjects' liking ratings (a "structural mere exposure effect") in all three experiments, but only when encoding and testing conditions were consistent. A change in the surface representation of stimuli between encoding and test (Experiment 1), memorizing fragments of items and being tested on whole items (Experiment 2), and a mismatch of processing operations between encoding and test (Experiment 3) all removed the SMEE. In contrast, the effect of grammaticality on rule judgements remained intact in the face of all three manipulations. It is suggested that rule judgements reflect attempts to explicitly recall information about training items, whereas the SMEE can be explained in terms of an attribution of processing fluency.     

Surface features can deeply affect artificial grammar learning

Three experiments explored the extent to which surface features explain discrimination between grammatical and non-grammatical strings in artificial grammar learning (AGL). Experiment 1 replicated Knowlton and Squire’s (1996) paradigm using either letter strings as in the original study, or an analogous set of color strings to further explore if learning was affected by type of stimuli. Learning arose only with letter strings, but the results were mostly due to the discrimination of non-grammatical strings containing highly salient illegal features. Experiments 2 and 3 tested a new grammar devised to control for those features. Experiment 2 showed reduced grammar learning effects, and again only for letter materials. Experiment 3 explored the effect of additional practice with letter stimuli, and found increased learning only in the spaced practice condition, though additional practice also produced more explicit knowledge. These findings call for further research on the boundary conditions of learning in AGL paradigms.     

Multiple context mere exposure: Examining the limits of liking

Recent evidence suggests that increased liking of exposed stimuli—a phenomenon known as the mere exposure effect—is dependent on experiencing the stimuli in the same context at exposure and test. Three experiments extended this work by examining the effect of presenting target stimuli in single and multiple exposure contexts. Target face stimuli were repeatedly paired with nonsense words, which took the role of contexts, across exposure. On test, the mere exposure effect was found only when the target face stimuli were presented with nonsense word cues (contexts) with which they had been repeatedly paired. The mere exposure effect was eliminated when exposure to target face stimuli with the nonsense word cues (contexts) was minimal, despite the overall number of exposures to the target face being equated across single- and multiple-context exposure conditions. The results suggest that familiarity of the relationship between stimuli and their context, not simply familiarity of the stimuli themselves, leads to liking. The finding supports a broader framework, which suggests that liking is partly a function of the consistency between past and present experiences with a target stimulus.     

Individual behavior in learning of an artificial grammar

Artificial grammar learning (AGL) is a widely used experimental paradigm that investigates how syntactic structures are processed. After a familiarization phase, participants have to distinguish strings consistent with a set of grammatical rules from strings that violate these rules. Many experiments report performance solely at a group level and as the total number of correct judgments. This report describes a systematic approach for investigating individual performance and a range of different behaviors. Participants were exposed to strings of the nonfinite grammar A ⁿ B ⁿ . To distinguish grammatical from ungrammatical strings, participants had to pay attention to local dependencies while comparing the number of stimuli from each class. Individual participants showed substantially different behavioral patterns despite exposure to the same stimuli. The results were replicated across auditory and visual sensory modalities. It is suggested that an analysis that looks at individual differences grants new insights into the processes involved in AGL. It also provides a solid basis from which to investigate sequence-processing abilities in special populations, such as patients with neurological lesions.     

Abstractionist and processing accounts of implicit learning

Five experiments evaluated the contributions of rule, exemplar, fragment, and episodic knowledge in artificial grammar learning using memorization versus hypothesis-testing training tasks. Strings of letters were generated from a biconditional grammar that allows different sources of responding to be unconfounded. There was no evidence that memorization led to passive abstraction of rules or encoding of whole training exemplars. Memorizers instead used explicit fragment knowledge to identify the grammatical status of test items, although this led to chance performance. Successful hypothesis-testers classified at near-perfect levels by processing training and test stimuli according to their rule structure. The results support the episodic-processing account of implicit and explicit learning.     

人工语法范式下的无意识知识探析

人工语法范式不仅是内隐学习领域广泛运用的范式,同时也是研究无意识知识的主要方法。文章通过对人工语法范式组成材料中的刺激频率、组块等方面进行深入探讨,不仅使人们对无意识知识有了更全面的认识,而且也有利于对内隐学习获得规则知识的假设提出更确定的回答;并通过对无意识知识的神经机制研究以及考察镜射规则学习过程的脑神经激活状况,获得了规则学习对应更为准确的脑神经活动,推动了人工语法范式下无意识知识的深入研究。     

The P600 in Implicit Artificial Grammar Learning

The suitability of the artificial grammar learning (AGL) paradigm to capture relevant aspects of the acquisition of linguistic structures has been empirically tested in a number of EEG studies. Some have shown a syntax‐related P600 component, but it has not been ruled out that the AGL P600 effect is a response to surface features (e.g., subsequence familiarity) rather than the underlying syntax structure. Therefore, in this study, we controlled for the surface characteristics of the test sequences (associative chunk strength) and recorded the EEG before (baseline preference classification) and after (preference and grammaticality classification) exposure to a grammar. After exposure, a typical, centroparietal P600 effect was elicited by grammatical violations and not by unfamiliar subsequences, suggesting that the AGL P600 effect signals a response to structural irregularities. Moreover, preference and grammaticality classification showed a qualitatively similar ERP profile, strengthening the idea that the implicit structural mere‐exposure paradigm in combination with preference classification is a suitable alternative to the traditional grammaticality classification test.     

Beyond dissociation logic: evidence for controlled and automatic influences in artificial grammar learning

Evidence for unconscious learning has typically been based on dissociations between direct and indirect tests of learning. Because of some inherent problems with dissociation logic, we applied the logic of opposition to 2 artificial grammar learning experiments. In Experiment 1, participants were exposed to 2 different sets of letter strings, generated from 2 different grammars, and later rated test strings for grammaticality with either in-concert (rate grammatical strings consistent with either structure) or opposition (rate grammatical only strings from 1 of the structures) instructions. Manipulating response deadline affected controlled, but not automatic influences. In Experiment 2, after similar training, a source-monitoring test was administered from which the in-concert and opposition conditions were derived. The test indicated that varying the retention interval affected controlled, but not automatic, influences. The results are discussed in terms of awareness, knowledge representation, and metacognitive processing.     

Implicit and explicit knowledge bases in artificial grammar learning.

Two experiments examined the claim for distinct implicit and explicit learning modes in the artificial grammar-learning task (Reber, 1967, 1989). Subjects initially attempted to memorize strings of letters generated by a finite-state grammar and then classified new grammatical and nongrammatical strings. Experiment 1 showed that subjects' assessment of isolated parts of strings was sufficient to account for their classification performance but that the rules elicited in free report were not sufficient. Experiment 2 showed that performing a concurrent random number generation task under different priorities interfered with free report and classification performance equally. Furthermore, giving different groups of subjects incidental or intentional learning instructions did not affect classification or free report.     

Implicit partisanship: taking sides for no reason

After spending 45 s studying the names of 4 members of a hypothetical group, subjects showed both implicit liking and implicit identification with the group. These effects of studying names were much larger than the mere exposure (R. B. Zajonc, 1968) effects of either 6 (Experiment 2) or 10 (Experiment 3) extra exposures to each name. This implicit partisanship effect differs from the minimal group effect (H. Tajfel, 1970) because its procedure involves no membership in the target group. It also differs from the mere exposure effect because the target stimuli are presented once as members of a group rather than multiple times as unrelated individuals. A plausible (but not established) interpretation is that the attitude and identification effects are consequences of mere categorization.     

Implicit learning is order dependent

Psychological Research - We report two experiments using the artificial-grammar task that demonstrate order dependence in implicit learning. Studying grammatical training strings in different...     

Artificial grammar learning by 1-year-olds leads to specific and abstract knowledge.

Four experiments used the head-turn preference procedure to assess whether infants could extract and remember information from auditory strings produced by a miniature artificial grammar. In all four experiments, infants generalized to new structure by discriminating new grammatical strings from ungrammatical ones after less than 2 min exposure to the grammar. Infants acquired specific information about the grammar as demonstrated by the ability to discriminate new grammatical strings from those with illegal endpoints (Experiment 1). Infants also discriminated new grammatical strings from those with string-internal pairwise violations (Experiments 2 and 3). Infants in Experiment 4 abstracted beyond specific word order as demonstrated by the ability to discriminate new strings produced by their training grammar from strings produced by another grammar despite a change in vocabulary between training and test. We discuss the implications of these findings for the study of language acquisition.     

AGL StimSelect: Software for automated selection of stimuli for artificial grammar learning

Artificial grammar learning (AGL) is an experimental paradigm that has been used extensively incognitive research for many years to study implicit learning, associative learning, and generalization on the basis of either similarity or rules. Without computer assistance, it is virtually impossible to generate appropriate grammatical training stimuli along with grammatical or nongrammatical test stimuli that control relevant psychological variables. We present the first flexible, fully automated software for selecting AGL stimuli. The software allows users to specify a grammar of interest, and to manipulate characteristics of training and test sequences, and their relationship to each other. The user therefore has direct control over stimulus features that may influence learning and generalization in AGL tasks. The software, AGL StimSelect, enables researchers to develop AGL designs that would not be feasible without automatic stimulus selection. It is implemented in MATLAB.