汉语儿童英语语音特征内隐与外显学习的比较研究

实验选取64个英文单词为实验材料,以不同的分类方式为自变量．探讨在给予高强度练习和准确反馈的前提下．汉语儿童完成同时具有显著性特征(字母组合)维度和非显著性特征(发音)维度的英语语音分类任务时,内隐学习和外显学习各自的特点,发现(1)在及时分类条件下,有显著的内隐学习效应;(2)外显学习的抗干扰性弱,内隐学习的抗干扰性强．而且内隐学习所获得的知识具有稳固性;(3)及时分类有利于内隐学习,延时分类有利于外显学习。     

注意负荷对内隐序列学习的影响研究

运用次级任务研究范式,采用符号计数的次级任务和歧义序列中的次级条件序列,将内隐序列学习分为单任务组和双任务组,统一在单任务条件下测试。结果发现:(1)两组被试内隐学习成绩差异不显著;(2)双任务组被试在学习组块和测试组块的内隐学习差异也不显著。结果进一步支持了在次级任务占用注意资源的条件下内隐学习仍然可以正常地获得、内隐序列学习不受注意负荷影响的观点。     

知识类别和特点对内隐序列学习的影响

用序列学习中的反应时和错误个数间接测量被试的内隐知识,用再认成绩和预测成绩直接测量被试的外显知识,通过倒转不同的规则探讨了知识类别和特点对内隐序列学习的影响。结果表明：(1)内隐学习和外显学习可能分别依赖于两个独立的学习系统,且互不干扰;(2)当内隐序列学习既包含运动知识又包含概念知识时,知识类别和特点影响被试对序列知识的获得,被试较易获得运动知识;(3)在内隐序列学习中练习的数量也影响被试对序列知识的获得,序列学习是一个动态的过程。     

内隐序列学习不受注意负荷的影响:来自眼动的证据

利用眼动记录方法,采用内隐序列学习的观察范式,通过操纵注意负荷高和低,考察内隐序列学习是否需要注意参与。结果发现:(1)在高、低注意负荷条件下都出现了内隐序列学习效应;(2)高注意负荷条件下眼跳反应时长于低注意负荷条件下的;(3)高、低注意负荷序列内隐序列学习量差异不显著,表明内隐序列学习不受注意负荷影响。     

内隐人格观对他人知觉的影响

以大学生为被试,采用投射测验法分离出两类不同性质的内隐人格观,然后通过效价匹配实验和错觉关联实验,初步探讨了内隐人格观对他人知觉的信息加工模式的影响。研究结果表明:(1)跨性格、能力等不同人格属性有共通一致的内隐观;(2)固存观的反面就是渐变观,它们是同一维度的两极;(3)与渐变观者相比,固存观者更易以一种评价性方式知觉他人信息;同时,渐变观者不像固存观者那样对他人特质作出静态判断,而是集中注意力于理解他人行为发生的动态过程。     

内隐序列学习与注意负荷关系的实验研究

采用2(注意负荷:高、低)单因素被试内实验设计,以18名在校大学生为被试,运用行为实验及事件相关电位技术,对内隐序列学习是否需要注意负荷的参与进行实验研究。结果发现:(1)在高、低注意负荷条件下都出现了内隐序列学习效应;(2)高注意负荷序列探测刺激诱发的P1、N1波幅显著大于低注意负荷序列探测刺激诱发的,表明高注意负荷条件下需要更多的注意;(3)高、低注意负荷序列内隐序列学习量差异不显著,表明内隐序列学习不受注意负荷影响。     

内隐学习本质特征的实验研究

本实验以人工语法范式的常规模式和Reber语法的一个变式对内隐与外显学习进行了分离,采用的手段为记忆指导语与规则发现指导语。此外,我们还增添了字母集变量以了解内隐学习的抽象性,及时间变量(单元)以推测内隐学习的理解性。通过本实验,我们再次证明了内隐学习的自动性,初步验证了内隐学习的抽象性、间接测得了内隐学习的理解性。     

专家与新手教师内隐与外显学习的比较

教师作为典型的知识密集、技术密集的群体,其隐性知识也自然比较集中和典型,而专长研究可以有效地揭示教师的知识获取特点和学习规律。然而,以往的专长研究往往通过测量专家教师和新手教师在某方面的外显行为或外显知识的差异了解教师的知识结构和行为特点,这种做法由于忽略了教师的内隐学习和隐性知识,因此难以有效地揭示专家教师与新手教师在知识和能力结构上的真实差异。本研究借鉴认知心理学对专长的研究方式（即专家--新手比较）,运用经典的序列反应时任务（SRT）调整后的范式,采用带颜色边框的同一个学生的四种真实课堂表情图片作为刺激材料,以表情作为内隐学习的维度,以颜色作为外显学习的维度,对61名专家教师与88名新手教师在内隐/外显序列学习上的差异进行了比较研究。结果发现：（1）专家和新手教师被试群体均能够习得内隐/外显序列规则;（2）专家教师与新手教师在内隐学习上存在显著差异,专家教师内隐学习效果要优于新手教师;（2）专家教师与新手教师在外显学习维度上差异并不显著。结论：专家教师与新手教师有着近乎相当的外显学习能力和外显知识水平,但专家教师的内隐学习能力和隐性知识水平要显著高于新手教师。     

内隐序列学习的表征机制及年龄效应研究

该研究从年龄效应这一独特视角探讨了内隐序列学习的表征机制。结果表明:(1)内隐序列学习存在具体联结和抽象层级编码双重表征机制,且在该研究中以抽象编码为主;(2)老、青、幼三组被试的内隐序列学习无显著差异,间接支持了具体联结机制不起主要作用的结论。该研究的推论是:(1)内隐序列学习的表征是一个遵循“最优级编码”的动态过程;(2)该过程不涉及注意机制和短时记忆。     

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。     

Implicit learning of fifth- and sixth-order sequential probabilities

Serial reaction time (SRT) task studies have established that people can implicitly learn sequential contingencies as complex as fourth-order probabilities. The present study examined people’s ability to learn fifth-order (Experiment 1) and sixth-order (Experiment 2) probabilities. Remarkably, people learned fifth- and sixth-order probabilities. This suggests that the implicit sequence learning mechanism can operate over a range of at least seven sequence elements.     

Specific sequence effects in the serial reaction time task

The Serial Reaction Time (SRT) task was used in 2 experiments to determine if the specific sequence used by the task influences implicit learning. In Experiment 1, participants performed the SRT task with the locations of the stimuli determined by a commonly used fixed sequence (taken from P. J. Reber & L. R. Squire, 1998), by a fixed sequence that was uniquely generated for each participant, or at random. Experiment 2 used the same basic design, but the random condition was changed so that each possible transition between responses was seen the same number of times, making it more comparable to the fixed-sequence conditions. Results from both experiments indicate that the specific sequence used in the SRT task influences the strength of implicit learning effects. Follow-up analyses show that the strength of implicit learning can be linked to the presence of particular types of triads within the sequence. The authors suggest that future researchers should not use on a single fixed sequence across all participants when using the SRT task but should instead generate different fixed sequences for each participant to enhance the generalizability of their results.     

Attentional load and implicit sequence learning

A widely employed conceptualization of implicit learning hypothesizes that it makes minimal demands on attentional resources. This conjecture was investigated by comparing learning under single-task and dual-task conditions in the sequential reaction time (SRT) task. Participants learned probabilistic sequences, with dual-task participants additionally having to perform a counting task using stimuli that were targets in the SRT display. Both groups were then tested for sequence knowledge under single-task (Experiments 1 and 2) or dual-task (Experiment 3) conditions. Participants also completed a free generation task (Experiments 2 and 3) under inclusion or exclusion conditions to determine if sequence knowledge was conscious or unconscious in terms of its access to intentional control. The experiments revealed that the secondary task impaired sequence learning and that sequence knowledge was consciously accessible. These findings disconfirm both the notion that implicit learning is able to proceed normally under conditions of divided attention, and that the acquired knowledge is inaccessible to consciousness. A unitary framework for conceptualizing implicit and explicit learning is proposed.     

Operating characteristics of the implicit learning system supporting serial interception sequence learning

The memory system that supports implicit perceptual-motor sequence learning relies on brain regions that operate separately from the explicit, medial temporal lobe memory system. The implicit learning system therefore likely has distinct operating characteristics and information processing constraints. To attempt to identify the limits of the implicit sequence learning mechanism, participants performed the serial interception sequence learning (SISL) task with covertly embedded repeating sequences that were much longer than most previous studies: ranging from 30 to 60 (Experiment 1) and 60 to 90 (Experiment 2) items in length. Robust sequence-specific learning was observed for sequences up to 80 items in length, extending the known capacity of implicit sequence learning. In Experiment 3, 12-item repeating sequences were embedded among increasing amounts of irrelevant nonrepeating sequences (from 20 to 80% of training trials). Despite high levels of irrelevant trials, learning occurred across conditions. A comparison of learning rates across all three experiments found a surprising degree of constancy in the rate of learning regardless of sequence length or embedded noise. Sequence learning appears to be constant with the logarithm of the number of sequence repetitions practiced during training. The consistency in learning rate across experiments and conditions implies that the mechanisms supporting implicit sequence learning are not capacity-constrained by very long sequences nor adversely affected by high rates of irrelevant sequences during training.     

Implicit motor sequence learning is not purely perceptual

Many reports have indicated that implicit learning of sequences in a choice response time task is primarily perceptual; subjects learn the sequence of stimuli rather than the sequence of motor responses. Three experiments tested whether implicit motor sequence learning could be purely perceptual: no support was found for that hypothesis. Subjects who merely watched stimuli did not learn the sequence implicitly (Experiment 1), and sequence learning transferred robustly to a different set of stimulus cues (Experiment 2). In the final experiment, the stimulus-response mapping was changed at transfer so that one group of subjects pushed the same sequence of keys but saw new stimuli, whereas another group pushed a different sequence of keys but saw the same stimuli. Transfer to the new mapping was shown only if the motor sequence was kept constant, not the perceptual sequence. It is proposed that subjects learn a sequence of response locations in this and similar tasks.     

Implicit chaining in cotton-top tamarins (Saguinus oedipus) with elements equated for probability of reinforcement

Three experiments examined the implicit learning of sequences under conditions in which the elements comprising a sequence were equated in terms of reinforcement probability. In Experiment 1 cotton-top tamarins (Saguinus oedipus) experienced a five-element sequence displayed serially on a touch screen in which reinforcement probability was equated across elements at .16 per element. Tamarins demonstrated learning of this sequence with higher latencies during a random test as compared to baseline sequence training. In Experiments 2 and 3, manipulations of the procedure used in the first experiment were undertaken to rule out a confound owing to the fact that the elements in Experiment 1 bore different temporal relations to the intertrial interval (ITI), an inhibitory period. The results of Experiments 2 and 3 indicated that the implicit learning observed in Experiment 1 was not due to temporal proximity between some elements and the inhibitory ITI. The results taken together support two conclusion: First that tamarins engaged in sequence learning whether or not there was contingent reinforcement for learning the sequence, and second that this learning was not due to subtle differences in associative strength between the elements of the sequence.     

Serial reaction time learning in preschool- and school-age children

Visuomotor sequence learning was assessed in 4- to 10-year-old children using a serial reaction time (SRT) task with both random and sequenced trials. One-half of the children received exposure to the sequence prior to performing the reaction time (RT) task. In Experiment 1, 7- and 10-year-old children demonstrated sequence-specific decreases in RT. As in the adult SRT literature, participants with explicit awareness of the sequence at the end of the session showed larger sequence-specific reaction time decrements than those without explicit awareness. Contrary to expectation, preexposure to the sequence did not reliably predict the level of awareness attained. Results from Experiment 2 indicate that 4-year-olds also demonstrate significant sequence learning on a variant of the SRT task. This article provides preliminary data regarding developmental changes in sequential learning and the development and use of implicit and explicit knowledge. Age-related differences emerged primarily in explicit rather than implicit knowledge.     

Visual artificial grammar learning by rhesus macaques (<Emphasis Type="Italic">Macaca mulatta</Emphasis>): exploring the role of grammar complexity and sequence length

Humans and nonhuman primates can learn about the organization of stimuli in the environment using implicit sequential pattern learning capabilities. However, most previous artificial grammar learning studies with nonhuman primates have involved relatively simple grammars and short input sequences. The goal in the current experiments was to assess the learning capabilities of monkeys on an artificial grammar-learning task that was more complex than most others previously used with nonhumans. Three experiments were conducted using a joystick-based, symmetrical-response serial reaction time task in which two monkeys were exposed to grammar-generated sequences at sequence lengths of four in Experiment 1, six in Experiment 2, and eight in Experiment 3. Over time, the monkeys came to respond faster to the sequences generated from the artificial grammar compared to random versions. In a subsequent generalization phase, subjects generalized their knowledge to novel sequences, responding significantly faster to novel instances of sequences produced using the familiar grammar compared to those constructed using an unfamiliar grammar. These results reveal that rhesus monkeys can learn and generalize the statistical structure inherent in an artificial grammar that is as complex as some used with humans, for sequences up to eight items long. These findings are discussed in relation to whether or not rhesus macaques and other primate species possess implicit sequence learning abilities that are similar to those that humans draw upon to learn natural language grammar.     

The influence of the response–stimulus interval on implicit and explicit learning of stimulus sequence

Three experiments investigated the influence of the response-stimulus interval (RSI) on implicit and explicit learning of stimulus sequences. Participants responded to numerals presented in predetermined positions with alternating long and short RSIs. Half of the participants were instructed explicitly to learn the position sequence. In the transfer phase of Experiments 1 and 2, changing RSI patterns reduced the expression of incidental and intentional learning of position sequence. In Experiment 3 the position sequence was transformed, except that sub-sequences demarcated by long RSIs remained unchanged; this greatly reduced the expression of intentional learning, and slightly reduced that of incidental learning. These results indicate that in implicit learning, stimulus sequences are learned under the constraints of RSIs, whereas in explicit learning, learning independent of RSIs, as well as learning constrained by RSIs, occurs.     

Sequence learning by action,observation and action observation

The serial reaction time (SRT) task was used to compare learning of a complex sequence by action (participants responded to sequential stimuli), by observation (participants watched but did not respond to sequential stimuli), and by action‐observation (participants watched an expert model responding to sequential stimuli). Each of these groups was compared with an untrained control group. Experiment 1 indicated that both observation and action‐observation were sufficient to support learning of a 12‐item second‐order conditional (SOC) sequence. Experiment 2 confirmed these findings, and showed that, as indexed by reaction time (RT), the extent of learning by observation and by action‐observation was comparable to that of action‐based learning. Using a recognition test, Experiment 2 and 3 also provided evidence that, whereas learning by stimulus observation was explicit, learning by action‐observation was implicit. These findings are consistent with a connection between motor systems and implicit learning, but do not support the hypothesis that overt action is necessary for implicit learning.