内隐学习中的知识习得及其无意识性测量*

本研究采用经典的人工语法范式,并引入迁移任务探讨在记忆和规则探索两种指导语条件下,被试所习得的究竟是规则还是组块？同时采用结构知识主观测量的方法,对内隐学习习得知识的无意识性进行测量。结果发现记忆指导语条件下的被试能成功区分合法串和非法串,即出现语法学习效应,但规则探索指导语条件下的被试并没有表现出语法或组块学习效应,结构知识的主观测量发现这种内隐学习的优势效应主要来自无意识结构知识的贡献;在迁移研究中发现附带学习组和有意学习组均在迁移任务上表现出语法学习效应,这表明在人工语法学习中所获得的语法规则是抽象的和可迁移的。     

人工语法范式下内隐学习的脑机制及其教育启示

人工语法范式是研究内隐学习实验的重要范式。利用脑成像技术的研究发现：人工语法范式下内隐学习发生时,额叶、枕叶、顶叶以及基底神经节与内隐学习有密切关系。该结果对当前教育教学产生一定启示,即教师要多给学生创造知识迁移的机会;培养学生的动手实践活动;重视学习过程中内隐学习与外显学习的先后顺序和有序结合。     

内隐运动技能学习中的注意需求研究

内隐学习是无意识地掌握某些事物变化的内在规律,同时其本人又无法将所获得的知识用语言表述出来。继Reber(1976)等人在“人工语法学习”任务中发现了内隐学习效应之后,在运动技能学习领域,研究者也发现运动技能可以内隐地获得。一般认为,个体只有专心致志才会学有所成,对对象的注意集中是人类认知加工的必要条件。而内隐学习是一种无意识的学习过程,这一过程是否需要有意识的注意呢?此类问题大多采用双重任务的方法进行研究。     

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。     

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。     

人工语法中的内隐学习实验研究

用Reber等人发明的人工语法和人工语法学习程序,研究在复杂规则学习中的内隐学习与外显学习过程.实验结果发现内隐学习中启动效应存在,内隐学习效应明显,支持Reber等人提出的内隐学习理论;对内隐记忆与外显记忆关系的研究,支持杨治良等(1998)提出的内隐和外显记忆的"钢筋水泥"结构性模型的假设.     

内隐学习研究的核心方法——人工语法范式

本文是对近三十年来内隐学习的核心研究方法--人工语法范式--的一个总结.文章分三个部分,分别介绍了人工语法范式的三个要素.第一部分介绍了学习材料要素,即限定状态语法和双条件语法.第二部分介绍了指导语要素,着重讲解了它的实验性分离功能.第三部分介绍了程序要素,伴随内隐学习研究问题的深入和复杂化趋势,循序演绎了研究者对人工语法范式程序所做的调整.     

人工语法学习中习得知识的分离:基于信号检测论和结构知识的视角

习得知识的意识觉知问题一直以来是内隐学习领域的研究热点和难点。以加工分离程序来分解击中率和虚报率、以结构知识中意识和无意识测量为视角,采用2（测验方式：SDTT,SKT）×2（学习程度：30试次,60试次）混合设计,试图去分离人工语法学习中习得的意识知识和无意识知识。结果发现：（1）学习程度影响到习得的无意识知识,但还未影响到习得的意识知识;（2）在分离人工语法学习的习得知识上,SDTT的敏感性高于SKT;（3）SKT夸大了元认知中的意识成分。     

应当关注时间心理无意识的研究

随着心理无意识研究在记忆、学习和思维等领域的兴起,内隐时间认知也逐步成为时间心理学中的一个研究热点。本文分析了探究内隐时间认知的四种方法:分离范式、序列学习范式、表征动量范式和计时分布相关范式,指出内隐时间认知实验范式在无意识时间人格与社会心理学中的研究也是有相当前景的。     

内隐序列学习中的渐进意识：来自习得知识的发展与遗忘的证据

内隐学习过程中习得知识的发展变化是内隐学习的一个重要特征。本研究采用序列反应时范式通过操纵学习进程（实验一）和学习与测验间的时间间隔（实验二）来考察内隐序列学习进程中习得知识的发展与遗忘特征,结果发现,（1）内隐序列学习中习得的无意识知识多于意识知识;（2）随着学习的推进,意识知识逐渐增加,而无意识知识则呈减弱趋势,证实了内隐学习的长时功效;（3）习得的无意识知识具有抗遗忘性,而意识知识在间隔8小时之后才开始出现遗忘。     

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.     

Domain-specific learning of grammatical structure in musical and phonological sequences

Artificial grammar learning depends on acquisition of abstract structural representations rather than domain-specific representational constraints, or so many studies tell us. Using an artificial grammar task, we compared learning performance in two stimulus domains in which respondents have differing tacit prior knowledge. We found that despite grammatically identical sequence structures, learning was better for harmonically related chord sequences than for letter name sequences or harmonically unrelated chord sequences. We also found transfer effects within the musical and letter name tasks, but not across the domains. We conclude that knowledge acquired in implicit learning depends not only on abstract features of structured stimuli, but that the learning of regularities is in some respects domain-specific and strongly linked to particular features of the stimulus domain.     

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.     

样例数量对内隐序列学习的影响

本研究采用序列学习范式,探讨了样例数量对内隐序列学习的影响,以揭示意识在学习中的作用以及内隐学习获得的知识表征。研究发现,在内隐序列学习中：（1）被试既可以记忆具体的样例知识也可以抽取抽象的规则知识;（2）样例数量影响被试对样例和规则知识的获得,在样例较少时被试可以获得更多的样例和规则知识;（3）样例数量影响被试对意识知识的获得,在本研究中仅当样例数为4时被试获得了有关样例和规则的意识知识。     

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.     

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.     

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.     

Implicit learning of complex information in amnesia

Implicit learning abilities of nine amnesic patients were explored by using an artificial grammar learning task in which the test strings were constructed in such a way that grammaticality judgments could not be based on a simple knowledge of bigrams and trigrams (chunks). Results show that amnesic patients and controls performed at the same level during the classification task, whereas amnesic patients performed worse than controls in an explicit generation task. Moreover, there was no correlation between the implicit and explicit measures. These results are compatible with the existence of two kinds of representation intervening in artificial grammar learning. The first one based on processes leading to fragment-specific knowledge (the chunks, which can be accessed explicitly), and the second based on the learning of simple associations and more complex conditional relations between elements.     

The subliminal mere exposure effect does not generalize to structurally related stimuli.

R.F. Bornstein (1994) questioned whether subliminal mere exposure effects might generalize to structurally related stimuli, thereby providing evidence for the existence of implicit learning. Two experiments examined this claim using letter string stimuli constructed according to the rules of an artificial grammar. Experiment 1 demonstrated that brief, masked exposure to grammatical strings impaired recognition but failed to produce a mere exposure effect on novel structurally related strings seen at test. Experiment 2 replicated this result but also demonstrated that a reliable mere exposure effect could be obtained, provided the same grammatical strings were presented at test. The results suggest that the structural relationship between training and test items prevents the mere exposure effect when participants are unaware of the exposure status of stimuli, and therefore provide no evidence for the existence of implicit learning.     

Kompositionsbildung bei Symbolfolgen und Bediensequenzen: Empirische Befunde und die Theorie des „Competitive Chunking”

In the paradigm of artificial grammar learning subjects have to reproduce regularly structured sequences of symbols. In a subsequent and unexpected test, they have to discriminate sequences that correspond to those of thememorization task from sequences that do not. Discriminability of grammatical and nongrammatical strings is judged as evidence for unconscious learning. This effect has been explained by the creation of chunks: Memorization of strings is facilitated by composing elements to chunks. The hierarchical organization of these units allows the representation of grammatical strings by a few high level chunks. The fewer chunks are necessary to represent a given string the higher is its familiarity and the probability to be judged grammatical. For the incidental acquisition of knowledge about operating a device the creation of chunks could also be shown. In contrast to letter series, two peculiarities were found: (a) discriminability was rather low, (b) irregularities at the end of input sequences were most effective. These peculiarities dissappeared, if — similar to letter strings — the information about a single operation of a sequence was permanently available. In the present simulations it was investigated whether these effects are consistent with the theory of competitive chunking. The temporal grain within sequences was varied to investigate the impact of the availability of sequential information on discriminability. Three different modes of chunk creation were simulated in the test phase to investigate whether the creation of chunks is obligatory for perceptual integration or requires a memorization task. The variation of temporal grain had only a marginal impact on discriminability. The simulation of the empirical data required that grammatical chunks are created within the test phase. Implications of these findings are discussed.