Not evidence for separable controlled and automatic influences in artificial grammar learning: comment on Higham, Vokey, and Pritchard (2000)

P. A. Higham, J. R. Vokey, and J. L. Pritchard (2000) claimed to provide evidence for separable controlled and automatic processes in artificial grammar learning. It is argued that their results are compatible with a single controlled influence: Participants might mistakenly identify more grammatical items than nongrammatical items as belonging to the other grammar, because the grammars are very similar to each other, and the nongrammatical items are relatively highly dissimilar. Participants' knowledge may be ambiguous, rather than automatic. It is further argued that even if Higham et al.'s data do support automatic effects, opposition logic, in this case, cannot be said to have succeeded where dissociation logic has failed, because it is used to address the issue of whether participants have conscious control over the knowledge they acquire, rather than whether they possess conscious awareness of that knowledge.     

Opposition logic and neural network models in artificial grammar learning

Following neural network simulations of the two experiments of, argued that the opposition logic advocated by was incapable of distinguishing between single and multiple influences on performance of artificial grammar learning and more generally. We show that their simulations do not support their conclusions. We also provide different neural network simulations that do simulate the essential results of Higham et al. (2000).     

反向对抗逻辑范式的创立与证实——人工语法PDP对抗逻辑的改进

人工语法对抗逻辑范式不能排除相似性和辨别力,导致自动反应不纯粹甚至虚假或无法检测到。本研究创立反向对抗逻辑范式,采用单因素(相容/对抗条件)被试间设计:内隐学习阶段将肯定标签与语法A绑定,否定标签与语法B绑定;测量阶段相容条件组对语法A和B做与学习阶段一致的判断,对抗条件组对语法B做与学习阶段对抗的肯定判断。实验证明:(1)外显否定标签可与语法B绑定学习而获得自动化特征,产生内隐联结自动化。(2)内隐否定知识比肯定知识更自动,知识从肯定转为否定易,从否定转为肯定难。(3)反向对抗逻辑范式能有效检测到自动反应,不受语法间形式相似性和辨别力影响。(4)首次析出高概率判断偏向效应,得到纯粹受控反应。     

Sequential learning in individuals with agrammatic aphasia: evidence from artificial grammar learning

We examined sequential learning in individuals with agrammatic aphasia (n = 12) and healthy age-matched participants (n = 12) using an artificial grammar. Artificial grammar acquisition, 24-hour retention, and the potential benefits of additional training were examined by administering an artificial grammar judgment test (1) immediately following auditory exposure-based training, (2) one day after training, and (3) after a second training session on the second day. An untrained control group (n = 12 healthy age-matched participants) completed the tests on the same time schedule. The trained healthy and aphasic groups showed greater sensitivity to the detection of grammatical items than the control group. No significant correlations between sequential learning and language abilities were observed among the aphasic participants. The results suggest that individuals with agrammatic aphasia show sequential learning, but the underlying processes involved in this learning may be different than for healthy adults.     

Rapid communication: Sex differences in artificial grammar learning: evidence for different strategies in men and women

In this article we report an experiment that investigated differences in classification performance of women and men in artificial grammar learning (AGL). Women's and men's responses correspond to a large extent. Consistent differences, however, were found in a variable that codes overlaps between training and test items in terms of string fragments. The results are best explained by the assumption that women and men apply different cognitive strategies at test.     

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.     

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.     

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.     

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.     

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.     

ADHD children outperform normal children in an artificial grammar implicit learning task: ERP and RT evidence

This study focuses on Implicit learning (IL) in children. One of the main debates in this field concerns the occurrence of IL indicators in experimental settings and its manifestation in different populations. In this research, we are looking for evidence of the occurrence of IL in normal children and in children with Attention Deficit Hyperactive Disorder (ADHD), based on the relationship between accuracy, reaction time and event-related potentials (ERPs). Our results show differences between the analyzed groups with respect to markers for electrophysiological activity and reaction time, but not for accuracy. In consequence, we suggest that research in IL should explore different indicators and their relationship with the cognitive processing levels involved. In addition, IL might involve different forms of information processing in normal children and children with ADHD. We discuss the possible impact of these findings for future research.     

Stimulus set size and statistical coverage of the grammar in artificial grammar learning

Adults and children acquire knowledge of the structure of their environment on the basis of repeated exposure to samples of structured stimuli. In the study of inductive learning, a straightforward issue is how much sample information is needed to learn the structure. The present study distinguishes between two measures for the amount of information in the sample: set size and the extent to which the set of exemplars statistically covers the underlying structure. In an artificial grammar learning experiment, learning was affected by the sample’s statistical coverage of the grammar, but not by its mere size. Our result suggests an alternative explanation of the set size effects on learning found in previous studies (McAndrews & Moscovitch, 1985; Meulemans & Van der Linden, 1997), because, as we argue, set size was confounded with statistical coverage in these studies. nt]mis|This research was supported by a grant from the Netherlands Organization for Scientific Research. We thank Jarry Porsius for his help with the data analyses.     

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.     

The role of similarity in artificial grammar learning

The authors examine the role of similarity in artificial grammar learning (AGL; A. S. Reber, 1989). A standard finite-state language was used to create stimuli that were arrangements of embedded geometric shapes (Experiment 1), connected lines (Experiment 2), and sequences of shapes (Experiment 3). Main effects for well-known predictors from the literature (grammaticality, associative global and anchor chunk strength, novel global and anchor chunk strength, length of items, and edit distance) were observed, thus replicating previous work. However, the authors extend previous research by using a widely known similarity-based exemplar model of categorization (the generalized context model; R. M. Nosofsky, 1989) to fit grammaticality judgments, by nested regression analyses. The results suggest that any explanation of AGL that is based on the existing theories is incomplete without a similarity process as well. Also, the results provide a foundation for further interpreting AGL in the wider context of categorization research.     

Role of selective attention in artificial grammar learning

To investigate the role of selective attention in artificial grammar (AG) learning, participants were presented with “GLOCAL” strings—that is, chains of compound global and local letters. The global and local levels instantiated different grammars. The results of this experiment revealed that participants learned only the grammar for the level to which they attended. The participants were not even able to choose presented but unattended strings themselves. These results show that selective attention plays a critical role in AG learning.     

The basis of transfer in artificial grammar learning

In two experiments, we examined the extent to which knowledge of sequential dependencies and/or patterns of repeating elements is used during transfer in artificial grammar learning. According to one view of transfer, learners abstract the grammar's sequential dependencies and then learn a mapping to new vocabulary at test (Dienes, Altmann, & Gao, 1999). Elements that are repeated have no special status on this view, and so a logical prediction is that learners should transfer as well after exposure to a grammar without repetitions as after exposure to a grammar with them. On another view, repetition structure is the very basis of transfer (Brooks & Vokey, 1991; Mathews & Roussel, 1997). Learners were trained on grammars with or without repeating elements to test these competing views. Learners demonstrated considerable knowledge of sequential dependencies in their training vocabulary but did not use such knowledge to transfer to a new vocabulary. Transfer only occurred in the presence of repetition structure, demonstrating this to be the basis of transfer.