机械特征相似程度不同情况下类别联想对类别学习的影响

探讨机械特征相似程度不同情况下类别联想对类别学习的影响,共包括2个实验.被试是广州某大学70名学生.探讨类别内部机械特征相似程度不同情况下,由主题特征激活的类别联想对类别学习的影响.结果表明:(1)在类别内部项目机械特征相似程度高的情况下,一致主题特征激活的类别联想对类别学习起促进作用;不一致主题特征激活的类别联想没有对类别学习起阻碍作用.(2)在类别内部项目机械特征相似程度低的情况下,不一致主题特征激活的类别联想对类别学习起阻碍作用.     

先前知识对初中学生学习家族相似性类别的影响研究

采用特征—主题的类别学习范式,考察了先前知识对初中学生学习具有家族相似性结构人工类别的影响。结果表明:基于先前知识的主题关系对初中学生的类别学习有显著促进;初中学生在基于已有知识的类别学习中更多地获得了知识关联特征的信息,同时也能对机械特征保持敏感。     

阻碍效应范式中主题知识对类别学习的影响

三个实验采用阻碍效应研究范式探讨主题知识对类别学习的影响。实验1探讨定义特征维度为机械特征时,主题知识对类别学习的影响;实验2和实验3,通过考察定义特征维度为知识特征时,类别学习中阻碍效应的大小,继续探讨主题知识对类别学习的影响。实验结果发现：（1）当定义特征维度为机械特征时,主题知识的存在没有促进类别学习。这可能因为定义特征维度为机械特征,主题知识难以发现所致;（2）当定义特征维度为知识特征时,类别学习中的阻碍效应消失,证明主题知识的存在促进了类别学习。研究结果再次支持了类别学习不仅仅受到减少归类错误驱动的观点。     

因果关系及典型性程度对类别特征推理的影响

采用学习―测试二阶段实验范式,探讨了因果关系及典型性程度对类别特征推理的影响。研究结果表明:(1)因果关系影响类别特征推理任务;(2)典型性程度影响类别特征推理任务;(3)类别特征间存在因果关系的前提下,原因特征维度值与典型性程度间存在交互作用。典型性程度对不含原因特征的项目的特征推理影响是有限的。     

类别标签与典型性程度对类别特征推理的影响

本研究采用学习-测试二阶段实验范式,探讨了类别标签及典型性程度对类别特征推理任务的共同影响,结果表明,类别标签及典型性程度都会影响类别特征推理任务,而且,类别标签及典型性程度之间存在交互作用,典型性程度为高条件下类别标签对类别特征推理任务的影响要高于典型性程度为低条件,类别标签匹配条件下,典型性程度对类别特征推理任务的影响要高于类别标签不匹配条件。     

类别标签在类别特征推理中的极化效应

采用学习—测试二阶段实验范式,对比了类别标签与类别特征在类别特征推理中的极化效应.研究结果表明,在类别标签组,标签匹配项目上的特征推理分数显著高于标签不匹配项目上的特征推理分数;在特征标签组,标签匹配项目上的特征推理分数与标签不匹配项目上的特征推理分数之间差异不显著.类别标签组的失匹配分数显著高于特征标签组.即在类别特征推理任务中,类别标签的极化效应显著高于类别特征的极化效应,因此,类别标签与类别特征存在本质差异,类别标签在类别特征推理中起主导作用.而且,本研究还发现,高前提概率条件下的特征推理分数都显著高于低前提概率条件下的特征推理分数,所以,前提概率也影响类别特征推理任务.     

类别学习中的认知学习机制

采用归类任务,比较不同知识条件、不同特征呈现方式下,对非定义特征维度的掌握,验证类别学习的认知学习机制。结果发现对非定义特征维度的掌握,在样例特征固定位置呈现条件下较多,而在样例特征随机呈现条件下较少;在知识定义特征维度条件下较多,而在机械定义特征维度条件下较少。研究结果支持了类别学习的认知学习机制。     

类别特征推理的认知机制与神经基础

分类及类别特征推理是类别知识的两大重要功能。研究表明, 两种任务之间存在本质差异。因此, 探讨类别特征推理的认知及神经机制对于拓展类别相关理论非常重要。已有研究发现, 类别标签、典型性程度以及类别特征间的因果关系都会影响类别特征推理任务, 而且, 类别特征推理任务与分类任务的神经基础不同。未来研究方向应当关注类别标签以及类别特征间的因果关系分别与典型性程度之间的交互作用, 在未来神经机制方面的进一步研究应当设计更符合自然情境下的特征推理任务范式。     

特征关联的类别间面孔适应

研究通过系列实验探讨了面孔适应不仅仅发生在形状选择性上, 也能发生在任务相关的特征上有内在关联的两个不同类别的物体间。实验1以带有明显性别倾向的物品图片作为适应刺激, 让被试对男女之间morphing程度不同的图片面孔进行性别辨别, 考察了不同适应刺激呈现时间的类别间面孔适应。结果表明适应刺激呈现时间大于50 ms时均存在类别间面孔适应效应。实验2评估了“性别”这一特征以及适应刺激形式在类别间面孔适应中所起的作用, 结果发现带有性别倾向的物品图片、相应的物品名称和性别文字(“男性”、“女性”) 3种适应刺激类型均能产生类别间适应。实验3通过操纵适应刺激上的注意负荷(高负荷、低负荷和无负荷), 探究了注意对类别间面孔适应的影响。结果表明随着注意负荷的增加, 类别间面孔适应效应减小。3个实验报告了一个新异的类别间适应后效, 证明了适应也能发生于在任务相关特征上有内在关联的两个不同类别的物体间。     

线索呈现位置对概率类别学习的影响

当前概率类别学习中主要存在多系统和单系统两种观点之争, 而持不同观点的研究者在其实验中分别采用了不同的线索位置呈现方式, 因此本研究采用经典的天气预测任务通过操纵线索位置的呈现方式来进一步探讨其对概率类别学习的影响。本研究包括2个实验：实验1考察所有线索位置固定和随机对概率类别学习系统的影响; 实验2通过单线索模式下线索位置固定考察概率类别学习的策略。结果发现, 当所有线索的呈现位置固定时, 概率类别学习是外显学习; 而当所有线索的呈现位置随机时, 概率类别学习是内隐学习; 而当只在线索单独出现时固定其位置, 概率类别学习仍是内隐学习。结果表明, 线索位置的不同呈现方式会影响概率类别学习中外显和内隐学习系统的竞争, 研究支持了多系统观点, 且概率类别学习的主要策略可能是多线索策略而不是单模式策略。     

Prior knowledge and correlational structure in unsupervised learning.

Prior knowledge has been shown to facilitate both supervised and unsupervised category learning, but questions remain about how this facilitation occurs. This article describes two experiments that investigate the effects of prior knowledge on unsupervised learning, using the exemplar-memory task of Clapper and Bower (2002). Experiment 1 demonstrates that prior knowledge facilitates learning in this task, as expected, and that this facilitation extends to both knowledge-relevant and knowledge-irrelevant features of the new categories. Experiment 2 shows that knowledge facilitates learning not only by increasing the probability that people will discover separate categories, but also by making the features of different categories seem less interchangeable, thereby reducing interference and confusion among them. Taken together, these experiments demonstrate that prior knowledge has multiple effects on unsupervised learning and suggests that the exemplar-memory task may provide a useful procedure for disentangling and investigating these effects.     

Children's category‐based inferences affect classification

Children learn many new categories and make inferences about these categories. Much work has examined how children make inferences on the basis of category knowledge. However, inferences may also affect what is learned about a category. Four experiments examine whether category‐based inferences during category learning influence category knowledge and thereby affect later classifications for 5‐ to 7‐year‐olds. The children learned to classify pictures of new types of creatures on the basis of a salient feature (colour) and then answered a question that required them to make an inference on the basis of other features. At test, children classified pictures that included only some features (without colour). Experiment 1 showed that the features relevant to the inference during learning led to better classification than did features irrelevant to the inference. Experiment 2 replicated this finding even when the relevant features were physically close to the irrelevant features. Experiments 3 and 4 found this effect even when the classification was learned prior to the inference task and even when no mention was made of the categories during inference learning. Taken together, these results show that making inferences during category learning can influence category knowledge and suggest a need to integrate the work on category learning and category‐based inferences.     

Feature distribution and background knowledge in category learning

Three experiments examined the interactions of category structure and prior knowledge in category learning.Experiment 1 examined the distribution of atypical or 'crossover' features in category learning. In real categories, crossover features may be unevenly distributed found primarily in very unusual examples of a category (like whales or ostriches). In contrast, in many psychology experiments, each item has exactly one crossover feature. Even versus uneven distribution of crossover features did not affect category learning when the features were neutral. However, when the features were connected by prior knowledge, it was much harder for subjects to learn the structure with the uneven distribution of crossover features. Experiments 2 and 3 found similar results with a slightly less uneven condition. We conclude that learning is a function of the interaction of category structure and prior knowledge rather than either one alone. Furthermore, knowledge benefits learning even when the category contains contradictions of the knowledge, so long as the contradictions are not very salient.     

Modeling the effects of prior knowledge on learning incongruent features of category members

The authors conducted 3 experiments addressing the issue of how observations and multiple sources of prior knowledge are put together in category learning. In Experiments 1 and 2, learning was faster for critical features, which were predictable on the basis of prior knowledge, than for filler features, and this advantage increased as more observations were made. In addition, learning was fastest for incongruent features that could only be predicted using knowledge from other domains. In Experiment 3, presenting contradictory features that violated prior knowledge led to rote learning rather than use of prior knowledge. The results were simulated with the Baywatch model, which addresses how observations of category members lead to recruitment and selection of sources of prior knowledge.     

Category learning with minimal prior knowledge

In 6 experiments, the authors examined the use of prior knowledge in category learning. Previous studies of the effects of knowledge on category learning have used categories in which knowledge was related to all of the category's features. However, people's knowledge of real-world categories often consists of many "rote" features that are not related to their prior knowledge. Five experiments found that even minimal prior knowledge (1 knowledge-relevant feature and 5 rote features per exemplar) can facilitate category learning. Posttests revealed that although the knowledge aided learning, subjects also acquired the rote features that were not related to knowledge, contradicting predictions of an attentional explanation of the knowledge effect. The results of Experiment 6 suggested that subjects attempt to link even rote features to their knowledge.     

类别成员跨维度特征关系对类别学习的影响

探讨类别成员具有多维度相同特征与多维度匹配特征条件下对人们类别学习的影响。被试来自华南师范大学一年级本科生,要求他们学习不同特征结构的类别材料,然后进行测试,以学习阶段的单元数与错误率、测试阶段的反应时与错误率为指标。共包括3个实验,实验1探讨了类别成员具有双维度相同特征、双维度匹配特征与非匹配特征条件下进行类别学习的成绩。实验2探讨类别成员在空间位置分离的两个维度上具有相同特征对类别学习的影响。实验3进一步探讨类别间成员具有双维度相同特征对类别学习的影响。结果表明：（1）类别成员具有双维度相同特征时,人们在归类学习中会将这两个维度看成是一个维度,从而促进类别学习;（2）类别成员具有双维度匹配特征,或者在两个不相邻维度具有相同特征时,对于类别学习没有起到这种特定的促进作用;（3）如果类别间成员具有双维度相同特征,则对类别学习产生消极影响。研究结果表明,在类别学习过程中,特征相似性状况对维度的确立也会产生影响。     

Explanation and prior knowledge interact to guide learning

How do explaining and prior knowledge contribute to learning? Four experiments explored the relationship between explanation and prior knowledge in category learning. The experiments independently manipulated whether participants were prompted to explain the category membership of study observations and whether category labels were informative in allowing participants to relate prior knowledge to patterns underlying category membership. The experiments revealed a superadditive interaction between explanation and informative labels, with explainers who received informative labels most likely to discover (Experiments 1 and 2) and generalize (Experiments 3 and 4) a pattern consistent with prior knowledge. However, explainers were no more likely than controls to discover multiple patterns (Experiments 1 and 2), indicating that effects of explanation are relatively targeted. We suggest that explanation recruits prior knowledge to assess whether candidate patterns are likely to have broad scope (i.e., to generalize within and beyond study observations). This interpretation is supported by the finding that effects of explanation on prior knowledge were attenuated when learners believed prior knowledge was irrelevant to generalizing category membership (Experiment 4). This research provides evidence that explanation can serve as a mechanism for deploying prior knowledge to assess the scope of observed patterns.     

Learning about the internal structure of categories through classification and feature inference

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a “partial inference” condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.     

注意和工作记忆对交错呈现优势的影响

以家族相似性图案为材料,让被试在单任务或双任务条件下以集中呈现或交错呈现的方式进行观察（实验1）或反馈（实验2）学习,记录眼动,探究注意对交错呈现优势的影响,以及工作记忆在其中的作用。发现当进行观察学习时,注意影响交错呈现优势,结果支持区别对比理论和注意衰减理论;当进行反馈学习时,注意的影响还有待进一步探究。同时,工作记忆影响交错呈现优势,但工作记忆并非完全通过影响注意从而影响交错呈现优势。     

Dual-task interference in perceptual category learning

The effect of a working-memory-demanding dual task on perceptual category learning was investigated. In Experiment 1, participants learned unidimensional rule-based or information integration category structures. In Experiment 2, participants learned a conjunctive rule-based category structure. In Experiment 1, unidimensional rule-based category learning was disrupted more by the dual working memory task than was information integration category learning. In addition, rule-based category learning differed qualitatively from information integration category learning in yielding a bimodal, rather than a normal, distribution of scores. Experiment 2 showed that rule-based learning can be disrupted by a dual working memory task even when both dimensions are relevant for optimal categorization. The results support the notion of at least two systems of category learning a hypothesis-testing system that seeks verbalizable rules and relies on working memory and selective attention, and an implicit system that is procedural-learning based and is essentially automatic.