选择性注意对听觉内隐学习的影响

内隐学习被认为是人类无意识、无目的获得复杂规则的自动化过程。已有研究表明, 在人工语法学习范式下, 视觉内隐学习的发生需要选择性注意。为了考察选择性注意对内隐学习的影响是否具有通道特异性, 本研究以90名大学生为被试, 以人工语法为学习任务, 采用双耳分听技术, 在听觉通道同时呈现具有不同规则的字母序列和数字序列, 考查被试在听觉刺激下对注意序列和未注意序列构成规则的习得情况。结果发现：只有选择注意的序列规则被习得, 未选择注意的序列规则未能被习得。研究表明：在人工语法学习范式下, 只有选择注意的刺激维度能够发生内隐学习。选择性注意对内隐学习的影响具有跨通道的适用性, 不仅适用于视觉刺激, 也同样适用于听觉刺激。     

The impact of adjacent-dependencies and staged-input on the learnability of center-embedded hierarchical structures

A theoretical debate in artificial grammar learning (AGL) regards the learnability of hierarchical structures. Recent studies using an AⁿBⁿ grammar draw conflicting conclusions (Bahlmann and Friederici, 2006 and [De Vries et al., 2008] ). We argue that 2 conditions crucially affect learning AⁿBⁿ structures: sufficient exposure to zero-level-of-embedding (0-LoE) exemplars and a staged-input. In 2 AGL experiments, learning was observed only when the training set was staged and contained 0-LoE exemplars. Our results might help understanding how natural complex structures are learned from exemplars.     

Development of Different Forms of Skill Learning Throughout the Lifespan

The acquisition of complex motor, cognitive, and social skills, like playing a musical instrument or mastering sports or a language, is generally associated with implicit skill learning (SL). Although it is a general view that SL is most effective in childhood, and such skills are best acquired if learning starts early, this idea has rarely been tested by systematic empirical studies on the developmental pathways of SL from childhood to old age. In this paper, we challenge the view that childhood and early school years are the prime time for skill learning by tracking age‐related changes in performance in three different paradigms of SL. We collected data from participants between 7 and 87 years for (1) a Serial Reaction Time Task (SRT) testing the learning of motor sequences, (2) an Artificial Grammar Learning (AGL) task testing the extraction of regularities from auditory sequences, and (3) Probabilistic Category Learning in the Weather Prediction task (WP), a non‐sequential categorization task. Results on all three tasks show that adolescence and adulthood are the most efficient periods for skill learning, since instead of becoming less and less effective with age, SL improves from childhood into adulthood and then later declines with aging.     

Social intuition as a form of implicit learning: Sequences of body movements are learned less explicitly than letter sequences

In the current paper, we first evaluate the suitability of traditional serial reaction time (SRT) and artificial grammar learning (AGL) experiments for measuring implicit learning of social signals. We then report the results of a novel sequence learning task which combines aspects of the SRT and AGL paradigms to meet our suggested criteria for how implicit learning experiments can be adapted to increase their relevance to situations of social intuition. The sequences followed standard finite-state grammars. Sequence learning and consciousness of acquired knowledge were compared between 2 groups of 24 participants viewing either sequences of individually presented letters or sequences of body-posture pictures, which were described as series of yoga movements. Participants in both conditions showed above-chance classification accuracy, indicating that sequence learning had occurred in both stimulus conditions. This shows that sequence learning can still be found when learning procedures reflect the characteristics of social intuition. Rule awareness was measured using trial-by-trial evaluation of decision strategy (Dienes & Scott, 2005; Scott & Dienes, 2008). For letters, sequence classification was best on trials where participants reported responding on the basis of explicit rules or memory, indicating some explicit learning in this condition. For body-posture, classification was not above chance on these types of trial, but instead showed a trend to be best on those trials where participants reported that their responses were based on intuition, familiarity, or random choice, suggesting that learning was more implicit. Results therefore indicate that the use of traditional stimuli in research on sequence learning might underestimate the extent to which learning is implicit in domains such as social learning, contributing to ongoing debate about levels of conscious awareness in implicit learning.     

Explicit feedback maintains implicit knowledge

The role of feedback was investigated with respect to conscious and unconscious knowledge acquired during artificial grammar learning (AGL). After incidental learning of training sequences, participants classified further sequences in terms of grammaticality and reported their decision strategy with or without explicit veridical feedback. Sequences that disobeyed the learning structure conformed to an alternative structure. Feedback led to an increase in the amount of reported conscious knowledge of structure (derived rules and recollections) but did not increase its accuracy. Conversely, feedback maintained the accuracy of unconscious knowledge of structure (intuition or familiarity-based responses) which otherwise degraded. Results support a dual-process account of AGL. They suggest that implicit learning of the to-be-rejected structure at test contaminates familiarity-based classifications whereas feedback allows competing familiarity signals to be contextualised, which is incompatible with theories that consider familiarity context-insensitive.     

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.     

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.     

Semantics boosts syntax in artificial grammar learning tasks with recursion

Center-embedded recursion (CER) in natural language is exemplified by sentences such as "The malt that the rat ate lay in the house." Parsing center-embedded structures is in the focus of attention because this could be one of the cognitive capacities that make humans distinct from all other animals. The ability to parse CER is usually tested by means of artificial grammar learning (AGL) tasks, during which participants have to infer the rule from a set of artificial sentences. One of the surprising results of previous AGL experiments is that learning CER is not as easy as had been thought. We hypothesized that because artificial sentences lack semantic content, semantics could help humans learn the syntax of center-embedded sentences. To test this, we composed sentences from 4 vocabularies of different degrees of semantic content due to 3 factors (familiarity, meaning of words, and semantic relationship between words). According to our results, these factors have no effect one by one but they make learning significantly faster when combined. This leads to the assumption that there were different mechanisms at work when CER was parsed in natural and in artificial languages. This finding questions the suitability of AGL tasks with artificial vocabularies for studying the learning and processing of linguistic CER.     

Learnability of embedded syntactic structures depends on prosodic cues

The ability to process center-embedded structures has been claimed to represent a core function of the language faculty. Recently, several studies have investigated the learning of center-embedded dependencies in artificial grammar settings. Yet some of the results seem to question the learnability of these structures in artificial grammar tasks. Here, we tested under which exposure conditions learning of center-embedded structures in an artificial grammar is possible. We used naturally spoken syllable sequences and varied the presence of prosodic cues. The results suggest that mere distributional information does not suffice for successful learning. Prosodic cues marking the boundaries of the major relevant units, however, can lead to learning success. Thus, our data are consistent with the hypothesis that center-embedded syntactic structures can be learned in artificial grammar tasks if language-like acoustic cues are provided.     

Want to block earworms from conscious awareness?B(u)y gum!

In a recent article, Jamieson and Mewhort (2009) proposed a novel account of artificial grammar learning (AGL), which is based on a multitrace model of episodic memory, the Minerva 2 model. According to this account, test performance in AGL is based on an assessment of global similarity of the test strings to the memory traces of the training strings. In this article, simulation studies are presented, showing for three different AGL experiments that the predictions of the Minerva 2 model strikingly deviate from participants' performance. It is argued that participants' test performance is not generally based on general similarity.     

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.     

Sequential learning in non-human primates

Sequential learning plays a role in a variety of common tasks, such as human language processing, animal communication, and the learning of action sequences. In this article, we investigate sequential learning in non-human primates from a comparative perspective, focusing on three areas: the learning of arbitrary, fixed sequences; statistical learning; and the learning of hierarchical structure. Although primates exhibit many similarities to humans in their performance on sequence learning tasks, there are also important differences. Crucially, non-human primates appear to be limited in their ability to learn and represent the hierarchical structure of sequences. We consider the evolutionary implications of these differences and suggest that limitations in sequential learning may help explain why non-human primates lack human-like language.     

人工语法学习中意识加工的渐进发展

内隐学习研究致力于探讨学习活动的意识加工程度, 当前相关的研究逻辑从传统的意识二分观转向渐进意识假设。本研究通过对人工语法范式进行改造, 并借鉴加工分离程序的有关思想, 在学习阶段引入双重测量任务, 并根据双重任务成绩计算分离出相应学习时段的意识与无意识成分的贡献分数, 从而考察在内隐学习过程中两种加工成分的动态变化特征。研究结果支持渐进意识假设, 在学习进程中, 无意识成分和意识成分都呈现出渐进发展的趋势; 而随学习的深入, 两种成分呈现出不同的变化模式, 中后期意识加工快速增长, 无意识加工则保持平缓发展, 渐进意识系统整体呈现向外显学习推进的发展态势。     

What is learned about fragments in artificial grammar learning? A transitional probabilities approach

Learning local regularities in sequentially structured materials is typically assumed to be based on encoding of the frequencies of these regularities. We explore the view that transitional probabilities between elements of chunks, rather than frequencies of chunks, may be the primary factor in artificial grammar learning (AGL). The transitional probability model (TPM) that we propose is argued to provide an adaptive and parsimonious strategy for encoding local regularities in order to induce sequential structure from an input set of exemplars of the grammar. In a variant of the AGL procedure, in which participants estimated the frequencies of bigrams occurring in a set of exemplars they had been exposed to previously, participants were shown to be more sensitive to local transitional probability information than to mere pattern frequencies.     

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.     

Enhanced Verbal Statistical Learning in Glossolalia

Glossolalia (“speaking in tongues”) is a rhythmic utterance of word-like strings of sounds, regularly occurring in religious mass gatherings or various forms of private religious practices (e.g., prayer and meditation). Although specific verbal learning capacities may characterize glossolalists, empirical evidence is lacking. We administered three statistical learning tasks (artificial grammar, phoneme sequence, and visual-response sequence) to 30 glossolalists and 30 matched control volunteers. In artificial grammar, participants decide whether pseudowords and sentences follow previously acquired implicit rules or not. In sequence learning, they gradually draw out rules from repeating regularities in sequences of speech sounds or motor responses. Results revealed enhanced artificial grammar and phoneme sequence learning performances in glossolalists compared to control volunteers. There were significant positive correlations between daily glossolalia activity and artificial grammar learning. These results indicate that glossolalists exhibit enhanced abilities to extract the statistical regularities of verbal information, which may be related to their unusual language abilities.     

The effects of positive versus negative feedback on information-integration category learning

A number of studies have shown that in category learning, providing feedback about errors allows faster learning than providing feedback about correct responses. However, these previous studies used explicit, rule-based tasks in which the category structures could be separated by a simple rule that was easily verbalized. Here, the results of the first experiment known to compare the efficacy of positive versus negative feedback during information-integration category learning are reported. Information-integration tasks require participants to integrate perceptual information from incommensurable dimensions, and evidence suggests that optimal responding recruits procedural learning. The results show that although nearly all of the full-feedback control participants demonstrated information-integration learning, participants receiving either positive-only or negative-only feedback generally used explicit, rule-based strategies. It thus appears that, unlike rule-based learning, consistent information-integration learning requires full feedback. The theoretical implications of these findings for current models of information-integration learning are discussed.     

Mirror neurons, the representation of word meaning, and the foot of the third left frontal convolution

Previous neuroimaging research has attempted to demonstrate a preferential involvement of the human mirror neuron system (MNS) in the comprehension of effector-related action word (verb) meanings. These studies have assumed that Broca's area (or Brodmann's area 44) is the homologue of a monkey premotor area (F5) containing mouth and hand mirror neurons, and that action word meanings are shared with the mirror system due to a proposed link between speech and gestural communication. In an fMRI experiment, we investigated whether Broca's area shows mirror activity solely for effectors implicated in the MNS. Next, we examined the responses of empirically determined mirror areas during a language perception task comprising effector-specific action words, unrelated words and nonwords. We found overlapping activity for observation and execution of actions with all effectors studied, i.e., including the foot, despite there being no evidence of foot mirror neurons in the monkey or human brain. These "mirror" areas showed equivalent responses for action words, unrelated words and nonwords, with all of these stimuli showing increased responses relative to visual character strings. Our results support alternative explanations attributing mirror activity in Broca's area to covert verbalisation or hierarchical linearisation, and provide no evidence that the MNS makes a preferential contribution to comprehending action word meanings.     

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.     

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.