Implicit and explicit knowledge decay at different rates: a dissociation between priming and recognition in artificial grammar learning

An experiment tested the common assumption that implicit and explicit knowledge are forgotten at different rates. In a training phase participants responded to sequences of letters generated by a finite-state grammar by pressing corresponding letters on a keyboard. A control group responded to randomized sequences. Participants were tested immediately following training and after intervals of seven and fourteen days. During each test participants responded to the letters of old and new sequences, and performed a concurrent recognition test. Priming was indexed by the time taken to respond to the sequences. In the immediate test both priming and recognition were substantially greater than the control group. In the delayed tests the level of priming remained unchanged but recognition had declined. The data indicate that priming and recognition decay at different rates, and are discussed with reference to both single and dual process models of memory.     

Implicit learning of a recursive rule in an artificial grammar

Participants performed an artificial grammar learning task, in which the standard finite state grammar (J. Verb. Learn. Verb. Behavior 6 (1967) 855) was extended with a recursive rule generating self-embedded sequences. We studied the learnability of such a rule in two experiments. The results verify the general hypothesis that recursivity can be learned in an artificial grammar learning task. However this learning seems to be rather based on recognising chunks than on abstract rule induction. First, performance was better for strings with more than one level of self-embedding in the sequence, uncovering more clearly the self-embedding pattern. Second, the infinite repeatability of the recursive rule application was not spontaneously induced from the training, but it was when an additional cue about this possibility was given. Finally, participants were able to verbalise their knowledge of the fragments making up the sequences-especially in the crucial front and back positions-, whereas knowledge of the underlying structure, to the extent it was acquired, was not articulatable. The results are discussed in relation to previous studies on the implicit learnability of complex and abstract rules.     

Modes of knowledge acquisition and retrieval in artificial grammar learning

The aim of this study was to conceptualize artificial grammar learning (AGL) in terms of two orthogonal dimensions—the mode of knowledge acquisition and the mode of knowledge retrieval—as was done by Perlman and Tzelgov (2006) for sequence learning. Experiment 1 was carried out to validate our experimental task; Experiments 2–4 tested, respectively, performance in the intentional, incidental, and automatic retrieval modes, for each of the three modes of acquisition. Furthermore, signal detection theory (SDT) was used as an analytic tool, consistent with our assumption that the processing of legality-relevant information involves decisions along a continuous dimension of fluency. The results presented support the analysis of AGL in terms of the proposed dimensions. They also indicate that knowledge acquired during training may include many aspects of the presented stimuli (whole strings, relations among elements, etc.). The contribution of the various components to performance depends on both the specific instruction in the acquisition phase and the requirements of the retrieval task.     

Recollection, fluency, and the explicit/implicit distinction in artificial grammar learning

In this article, the authors propose that both implicit memory and implicit learning phenomena can be explained by a common set of principles, in particular via participants' strategic use of recollective and fluency heuristics. In a series of experiments, it was demonstrated that manipulating processing fluency had an impact on classification decisions in an artificial grammar learning task (Experiments 1, 2, 4, and 7), showing that participants were using a fluency heuristic. Under identical conditions, however, this manipulation had no effect on recognition decisions (Experiments 3 and 5), consistent with a greater default reliance on recollection. Most significant, the authors also showed that a fluency effect can be induced in recognition (Experiments 4-6) and can be eliminated in classification (Experiment 7).     

The relationship between strategic control and conscious structural knowledge in artificial grammar learning

We address Jacoby’s (1991) proposal that strategic control over knowledge requires conscious awareness of that knowledge. In a two-grammar artificial grammar learning experiment all participants were trained on two grammars, consisting of a regularity in letter sequences, while two other dimensions (colours and fonts) varied randomly. Strategic control was measured as the ability to selectively apply the grammars during classification. For each classification, participants also made a combined judgement of (a) decision strategy and (b) relevant stimulus dimension. Strategic control was found for all types of decision strategy, including trials where participants claimed to lack conscious structural knowledge. However, strong evidence of strategic control only occurred when participants knew or guessed that the letter dimension was relevant, suggesting that strategic control might be associated with – or even causally requires – global awareness of the nature of the rules even though it does not require detailed knowledge of their content.     

Role of prior knowledge in implicit and explicit learning of artificial grammars

Artificial grammar learning (AGL) performance reflects both implicit and explicit processes and has typically been modeled without incorporating any influence from general world knowledge. Our research provides a systematic investigation of the implicit vs. explicit nature of general knowledge and its interaction with knowledge types investigated by past AGL research (i.e., rule- and similarity-based knowledge). In an AGL experiment, a general knowledge manipulation involved expectations being either congruent or incongruent with training stimulus structure. Inconsistent observations paradoxically led to an advantage in structural knowledge and in the use of general world knowledge in both explicit (conscious) and implicit (unconscious) cases (as assessed by subjective measures). The above findings were obtained under conditions of reduced processing time and impaired executive resources. Key findings from our work are that implicit AGL can clearly be affected by general knowledge, and implicit learning can be enhanced by the violation of expectations.     

Implicit sequence learning with competing explicit cues.

Previous research has shown that the expression of implicit sequence learning is eliminated in a choice reaction time task when an explicit cue allows participants to accurately predict the next stimulus (Cleeremans, 1997), but that two contingencies predicting the same outcome can be learned and expressed simultaneously when both of them remain implicit (Jiménez & Méndez, 1999). Two experiments tested the hypothesis that it is the deliberate use of explicit knowledge that produces the inhibitory effects over the expression of implicit sequence learning. However, the results of these experiments do not support this hypothesis, rather showing that implicit learning is acquired and expressed regardless of the influence of explicit knowledge. These results are interpreted as reinforcing the thesis about the automatic nature of both the acquisition and the expression of implicit sequence learning. The contradictory results reported by Cleeremans are attributed to a floor effect derived from the use of a special type of explicit cue.     

Effects of grammar complexity on artificial grammar learning

The present study identified two aspects of complexity that have been manipulated in the implicit learning literature and investigated how they affect implicit and explicit learning of artificial grammars. Ten finite state grammars were used to vary complexity. The results indicated that dependency length is more relevant to the complexity of a structure than is the number of associations that have to be learned. Although implicit learning led to better performance on a grammaticality judgment test than did explicit learning, it was negatively affected by increasing complexity: Performance decreased as there was an increase in the number of previous letters that had to be taken into account to determine whether or not the next letter was a grammatical continuation. In particular, the results suggested that implicit learning of higher order dependencies is hampered by the presence of longer dependencies. Knowledge of first-order dependencies was acquired regardless of complexity and learning mode.     

Theories of artificial grammar learning

Artificial grammar learning (AGL) is one of the most commonly used paradigms for the study of implicit learning and the contrast between rules, similarity, and associative learning. Despite five decades of extensive research, however, a satisfactory theoretical consensus has not been forthcoming. Theoretical accounts of AGL are reviewed, together with relevant human experimental and neuroscience data. The author concludes that satisfactory understanding of AGL requires (a) an understanding of implicit knowledge as knowledge that is not consciously activated at the time of a cognitive operation; this could be because the corresponding representations are impoverished or they cannot be concurrently supported in working memory with other representations or operations, and (b) adopting a frequency-independent view of rule knowledge and contrasting rule knowledge with specific similarity and associative learning (co-occurrence) knowledge.     

Effects of divided attention and speeded responding on implicit and explicit retrieval of artificial grammar knowledge

The artificial grammar (AG) learning literature (see, e.g., Mathews et al., 1989; Reber, 1967) has relied heavily on a single measure of implicitly acquired knowledge. Recent work comparing this measure (string classification) with a more indirect measure in which participants make liking ratings of novel stimuli (e.g., Manza & Bornstein, 1995; Newell & Bright, 2001) has shown that string classification (which we argue can be thought of as an explicit, rather than an implicit, measure of memory) gives rise to more explicit knowledge of the grammatical structure in learning strings and is more resilient to changes in surface features and processing between encoding and retrieval. We report data from two experiments that extend these findings. In Experiment 1, we showed that a divided attention manipulation (at retrieval) interfered with explicit retrieval of AG knowledge but did not interfere with implicit retrieval. In Experiment 2, we showed that forcing participants to respond within a very tight deadline resulted in the same asymmetric interference pattern between the tasks. In both experiments, we also showed that the type of information being retrieved influenced whether interference was observed. The results are discussed in terms of the relatively automatic nature of implicit retrieval and also with respect to the differences between analytic and nonanalytic processing (Whittlesea & Price, 2001).     

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.     

Impaired artificial grammar learning in agrammatism

It is often assumed that language is supported by domain-specific neural mechanisms, in part based on neuropsychological data from aphasia. If, however, language relies on domain-general mechanisms, it would be expected that deficits in non-linguistic cognitive processing should co-occur with aphasia. In this paper, we report a study of sequential learning by agrammatic aphasic patients and control participants matched for age, socio-economic status and non-verbal intelligence. Participants were first exposed to strings derived from an artificial grammar after which they were asked to classify a set of new strings, some of which were generated by the same grammar whereas others were not. Although both groups of participants performed well in the training phase of the experiment, only the control participants were able to classify novel test items better than chance. The results show that breakdown of language in agrammatic aphasia is associated with an impairment in artificial grammar learning, indicating damage to domain-general neural mechanisms subserving both language and sequential learning.     

Recoding and representation in artificial grammar learning

We apply an exemplar model of memory to explain performance in the artificial grammar task. The model blends the convolution-based method for representation developed in Jones and Mewhort’s BEAGLE model of semantic memory (Psychological Review 114:1–37, 2007) with the storage and retrieval assumptions in Hintzman’s MINERVA 2 model of episodic memory (Behavior Research Methods, Instruments, and Computers, 16:96–101, 1984). The model captures differences in encoding to fit data from two experiments that document the influence of encoding on implicit learning. We provide code so that researchers can adapt the model and techniques to their own experiments.     

Goal relevance and artificial grammar learning

This investigation used a newly developed artificial grammar learning (AGL) paradigm in which participants were exposed to sequences of stimuli that varied in two dimensions (colours and letters) that were superimposed on each other. Variation within each dimension was determined by a different grammar. The results of two studies strongly suggest that implicit learning in AGL depends on the goal relevance of the to-be-learned dimension. Specifically, when only one of the two stimulus dimensions was relevant for their task (Experiment 1) participants learned the structure underlying the relevant, but not that of the irrelevant dimension. However, when both dimensions were relevant, both structures were learned (Experiment 2). These findings suggest that implicit learning occurs only in dimensions to which we are attuned. Based on the present results and on those of Eitam, Hassin, and Schul (2008) we suggest that focusing on goal relevance may provide new insights into the mechanisms underlying implicit learning.     

Abstract analogies and positive transfer in artificial grammar learning.

Following Brooks and Vokey (1991), we show that positive transfer to new items generated from an artificial grammar in which the vocabulary has been changed from training to test can be based on "abstract analogy" to specific training items (specific similarity) rather than abstraction of a grammar and symbol remapping rules, even with remapping unique to each test item. The results confirm that transcendence over symbols provides no support for the implicit learning of abstract structure. Ironically, they also show that the effect of specific similarity does not depend on surface characteristics of the items, but the residual effect of grammaticality does.     

Conscious and unconscious thought in artificial grammar learning

Unconscious Thought Theory posits that a period of distraction after information acquisition leads to unconscious processing which enhances decision making relative to conscious deliberation or immediate choice (Dijksterhuis, 2004). Support thus far has been mixed. In the present study, artificial grammar learning was used in order to produce measurable amounts of conscious and unconscious knowledge. Intermediate phases were introduced between training and testing. Participants engaged in conscious deliberation of grammar rules, were distracted for the same period of time, or progressed immediately from training to testing. No differences in accuracy were found between intermediate phase groups acting on decisions made with meta-cognitive awareness (either feeling-based intuitive responding or conscious rule- or recollection-based responding). However, the accuracy of guess responses was significantly higher after distraction relative to immediate progression or conscious deliberation. The results suggest any beneficial effects of 'unconscious thought' may not always transfer to conscious awareness.     

Measuring strategic control in artificial grammar learning

In response to concerns with existing procedures for measuring strategic control over implicit knowledge in artificial grammar learning (AGL), we introduce a more stringent measurement procedure. After two separate training blocks which each consisted of letter strings derived from a different grammar, participants either judged the grammaticality of novel letter strings with respect to only one of these two grammars (pure-block condition), or had the target grammar varying randomly from trial to trial (novel mixed-block condition) which required a higher degree of conscious flexible control. Random variation in the colour and font of letters was introduced to disguise the nature of the rule and reduce explicit learning. Strategic control was observed both in the pure-block and mixed-block conditions, and even among participants who did not realise the rule was based on letter identity. This indicated detailed strategic control in the absence of explicit learning.     

Rule-based and exemplar-based classification in artificial grammar learning

In this study, we examined the induction of syntactic rules, given the presentation of letter strings generated from a finite-state grammar. Our primary interest was whether application of abstracted syntax or analogy to remembered exemplars could serve as a basis for judgments of grammaticality of novel stimuli. The grammatical status of test items and their objective similarity to studied exemplars were manipulated independently to investigate whether rule-based or instance-based information was a more important determinant of classification performance. When group data were examined, the results indicated that both factors were equally important in influencing grammaticality judgments about novel letter strings. There were, however, large individual differences in the magnitude of grammatical status effects, with a subgroup of subjects clearly using a classification strategy other than analogy to remembered exemplars. The results offer qualified support for the hypothesis (Reber & Allen, 1978) that rule-based information can be implicitly abstracted given limited experience with richly structured stimulus domains, and these results are inconsistent with a strong version of the instance-based model of categorization.     

Visual feature learning in artificial grammar classification

The Artificial Grammar Learning task has been used extensively to assess individuals' implicit learning capabilities. Previous work suggests that participants implicitly acquire rule-based knowledge as well as exemplar-specific knowledge in this task. This study investigated whether exemplar-specific knowledge acquired in this task is based on the visual features of the exemplars. When a change in the font and case occurred between study and test, there was no effect on sensitivity to grammatical rules in classification judgments. However, such a change did virtually eliminate sensitivity to training frequencies of letter bigrams and trigrams (chunk strength) in classification judgments. Performance of a secondary task during study eliminated this font sensitivity and generally reduced the contribution of chunk strength knowledge. The results are consistent with the idea that perceptual fluency makes a contribution to artificial grammar judgments.     

Implicit and explicit processes in a second-language learning task

Abstract

Two experiments examined the learning of a set of Greek pronunciation rules through explicit and implicit modes of rule presentation. Experiment 1 compared the effectiveness of implicit and explicit modes of presentation in two modalities, visual and auditory. Subjects in the explicit or rule group were presented with the rule set, and those in the implicit or natural group were shown a set of Greek words, composed of letters from the rule set, linked to their pronunciations. Subjects learned the Greek words to criterion and were then given a series of tests which aimed to tap different types of knowledge. The results showed an advantage of explicit study of the rules. In addition, an interaction was found between mode of presentation and modality. Explicit instruction was more effective in the visual than in the auditory modality, whereas there was no modality effect for implicit instruction. Experiment 2 examined a possible reason for the advantage of the rule groups by comparing different combinations of explicit and implicit presentation in the study and learning phases. The results suggested that explicit presentation of the rules is only beneficial when it is followed by practice at applying them.