类别学习多重系统理论的研究与展望

介绍了国外学者为验证类别学习的多重系统理论所做的行为实验研究及主要结果,包括反馈训练、延迟反馈、反应位置、间断的类别刺激分布等因素对信息整合的类别学习的影响,和数字Stroop任务、序列记忆扫描任务、类别数量等因素对基于规则的类别学习的影响;同时,作者指出了实验中没有很好地解决两种类别结构之间的难度差异等问题,并提出了今后需要进一步研究的课题     

认知神经和神经心理角度的多重类别学习系统

归类是一项非常重要的认知技能,近十多年来归类的研究尤其关注类别学习的多重系统问题.本文回顾了近年来国外对类别学习的认知神经和神经心理研究,结果表明不同的类别学习任务下的学习机制和所依赖的神经生理基础是不同的,支持类别学习的多系统理论.     

基于理论观的类别研究进展

介绍了西方类别研究中基于理论的观点及其研究进展。该理论强调知识背景对所形成的类别表征的影响,主张类别知识的组织是多范畴的,不同的学习任务可以导致基于原型或样例的类别表征,类别知识的运用反过来也会影响原有的类别表征。按时间顺序对理论观的产生、主要观点和经典研究进行了总结。     

相关维度数量对两种结构类别学习的影响

在多重系统理论研究基础上,运用即时反馈训练范式,以大学生为被试,考查相关维度数量对基于规则和信息整合两种结构的类别学习的影响.结果表明:(1)相关维度数量是影响类别学习的重要因素,基于规则单维度结构的学习显著优于信息整合结构和基于规则双维度结构的学习,基于规则双维度结构的学习与信息整合结构的学习没有显著差异.(2)反应策略受到规则复杂性的影响,当类别结构中隐含的规则比较简单时,学习者使用规则策略;当类别结构中隐含的规则比较复杂时,学习者难以掌握规则,就会使用相似性策略.研究结果支持"难度观",而不支持多重系统理论.     

类别学习的阻碍效应与双机制理论

通过对类别学习中的阻碍效应进行系统研究,尝试性的提出了类别学习的双机制理论。三个实验分别考察：当样例特征随机呈现时;定义特征固定呈现在样例首位时;以及刺激材料为知觉图形材料时,类别学习中的阻碍效应。三个实验的研究结果都发现：在类别学习中存在一定程度的阻碍效应,支持类别学习同时存在联结学习机制和认知学习机制的双机制观点。     

儿童类别学习研究综述

有关儿童类别学习的理论有基于规则理论、原型理论、样例理论、解释理论、多重系统理论。目前研究主要集中在儿童色形抽象能力、儿童类概念、类标准及分类能力、儿童概括能力发展、儿童类别学习影响因素等方面,揭示了儿童思维发展的特点,但已有研究也存在着不一致性,基于多重系统理论对儿童类别学习的研究可以作为下一步研究的方向。     

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。     

基于规则的类别学习和信息整合的类别学习

该文介绍了类别学习的双重系统理论:基于规则的类别学习指的是学习一种可以用言语表述的规则,是一种外显学习系统;信息整合的类别学习指的是学习一种难以用言语表述的规则,是一种内隐学习系统。同时介绍了类别学习的主要理论模型,以及支持双系统理论的实证研究,包括行为、临床和认知神经实验的研究结果。     

教师自主支持与小学生的科学学习无畏：学习兴趣和创造力效能感的中介作用

学习无畏(intellectual risk-taking)是指学生在学习中勇于尝试、不怕失败,这种品质对学生的科学学习和创造力发展非常重要。本研究基于自我决定理论和自我效能理论,采用自我报告式调查问卷,对南方某省方便取样的四所学校的757名四、五、六年级小学生进行调查,探究教师自主支持与学生科学学习无畏的关系,以及科学学习兴趣和创造力自我效能感的中介作用。结果表明：(1)教师自主支持行为对学生的学习无畏具有显著的正向预测作用。学生感受到的教师自主支持越多,越可能在科学学习中勇于尝试、不怕失败;(2)在教师自主支持与学生科学学习无畏之间的关系中,学生的科学学习兴趣和创造力效能感起完全中介作用。即教师自主支持行为可能通过提高学生对科学学习的兴趣和创造力自我效能感,间接提高学生的学习无畏水平。     

类别数量对基于规则和信息整合类别学习的影响

研究以大学生为被试,运用即时反馈训练范式,探讨类别数量对基于规则和信息整合两种结构的类别学习的影响,并探讨被试在类别学习过程中的反应策略。实验结果显示:(1)类别数量不仅影响基于规则结构的类别学习,也影响信息整合结构的类别学习,类别学习成绩随类别数量的增多而下降;(2)在基于规则类别学习中,大多数被试使用理想的分类规则进行类别判断;在信息整合类别学习中,大多数被试使用极端值策略。研究结果支持难度观,不支持多重系统理论。     

Paralinguistic correlates of conceptual structure

How is conceptual knowledge transmitted during conversation? When a speaker refers to an object, the name that the speaker chooses conveys information about categoryidentity. In addition, I propose that a speaker’s confidence in a classification can convey information about categorystructure. Because atypical instances of a category are more difficult to classify than typical instances, when speakers refer to these instances their lack of confidence will manifest itself “paralinguistically”—that is, in the form of hesitations, filled pauses, or rising prosody. These features can help listeners learn by enabling them to differentiate good from bad examples of a category. So that this hypothesis could be evaluated, in a category learning experiment participants learned a set of novel colors from a speaker. When the speaker’s paralinguistically expressed confidence was consistent with the underlying category structure, learners acquired the categories more rapidly and showed better category differentiation from the earliest moments of learning. These findings have important implications for theories of conversational coordination and language learning.     

Leaping to Conclusions: Why Premise Relevance Affects Argument Strength

Everyday reasoning requires more evidence than raw data alone can provide. We explore the idea that people can go beyond this data by reasoning about how the data was sampled. This idea is investigated through an examination of premise non‐monotonicity, in which adding premises to a category‐based argument weakens rather than strengthens it. Relevance theories explain this phenomenon in terms of people's sensitivity to the relationships among premise items. We show that a Bayesian model of category‐based induction taking premise sampling assumptions and category similarity into account complements such theories and yields two important predictions: First, that sensitivity to premise relationships can be violated by inducing a weak sampling assumption; and second, that premise monotonicity should be restored as a result. We test these predictions with an experiment that manipulates people's assumptions in this regard, showing that people draw qualitatively different conclusions in each case.     

儿童朴素物理学的错误概念及影响概念转换的因素

建构主义的学习观认为,新知识的学习总是建立在旧知识的基础上,新旧知识经验进行双向交流。儿童在日常生活中形成的自发概念是科学学习的重要基础,而其中与基本科学理论不一致的错误概念阻碍了学习过程。该文概述了儿童朴素物理学的错误概念研究,探讨自发概念的结构及其争论,并总结了影响概念转换的重要因素,包括认知冲突感,元认知和动机因素,认识论信念因素等     

Just do it? When to do what you judge you ought to do

While it is generally believed that justification is a fallible guide to the truth, there might be interesting exceptions to this general rule. In recent work on bridge-principles, an increasing number of authors have argued that truths about what a subject ought to do are truths we stand in some privileged epistemic relation to and that our justified normative beliefs are beliefs that will not lead us astray. If these bridge-principles hold, it suggests that justification might play an interesting role in our normative theories. In turn, this might help us understand the value of justification, a value that’s notoriously difficult to understand if we think of justification as but a fallible means to a desired end. We will argue that these bridge-principles will be incredibly difficult to defend. While we do not think that normative facts necessarily stand in any interesting relationship to our justified beliefs about them, there might well be a way of defending the idea that our justified beliefs about what to do won’t lead us astray. In turn, this might help us understand the value of justification, but this way of thinking about justification and its value comes with costs few would be willing to pay.     

What is learned in knowledge-related categories? Evidence from typicality and feature frequency judgments

When a category’s features are tied together by integrative knowledge, subjects learn the category faster than when the features are not directly related. What do subjects learn about the category in such circumstances? Some research has suggested that the subjects can use the knowledge itself in performing the category learning task and, thus, do not learn the details of the category’s features. Two experiments investigated this hypothesis by collecting feature frequency estimates after category learning. The results showed that integrative knowledge about a category did not decrease subjects’ sensitivity to feature frequency—if anything, knowledge improved it. A third experiment found that integrative knowledge did reduce sensitivity to feature frequency in typicality ratings. The results suggest that knowledge does not inhibit the learning of detailed category information, though it may replace its use in some tasks.     

非监控类别学习的分类策略与表征

类别学习是人类对不同类别加以归类的过程。类别信息的表征、分类策略运用的特点一直是类别学习研究的重点。非监控类别学习可分为直接的非监控类别学习和间接的非监控类别学习。直接的非监控类别学习(非限制任务, 限制任务)中被试的分类策略具有分类“单维度倾向”策略特点,类别变异程度会影响类别表征; 间接的非监控类别学习更倾向形成相似性表征, 直接的非监控类别学习则为基于规则表征。现有的非监控类别学习的理论对分类策略和表征的解释仍显薄弱, 不同学习任务下类别迁移和知识效应的研究还存在不足, 未来研究还需要进一步验证知识效应对非监控类别学习的认知加工过程的影响、探索影响类别表征形成的因素等问题。     

Incommensurability,Comparability, and Non-reductive Ontological Relations

We begin by highlighting some points related to Kuhn’s later thoughts on the incommensurability thesis and then show to what extent the standard version of the thesis given by the structuralist metatheory allows us to capture Kuhn’s ideas. Our main aim is to establish what constitutes the basis of comparability between incommensurable theories, even in cases of incommensurability with respect to theoretical and non-theoretical terms. We propose that comparability between (radical) incommensurable theories requires some connection between their respective ontologies that can be captured by means of an ontological non-reductive relation. At this point, the structuralist notion of “echeloned partial substructure” may be of help. When the incommensurability is very drastic, the existence of ontological connections between the theories involved may only be evident by common terms of their non-characteristic vocabularies. So, this notion of non-characteristic vocabulary becomes relevant in our proposal.     

On the Interaction of Theory and Data in Concept Learning

Standard models of concept learning generally focus on deriving statistical properties of a category based on data (i.e., category members and the features that describe them) but fail to give appropriate weight to the contact between people's intuitive theories and these data. Two experiments explored the role of people's prior knowledge or intuitive theories on category learning by manipulating the labels associated with the category. Learning differed dramatically when categories of children's drawings were meaningfully labeled (e.g., “done by creative children”) compared to when they were labeled in a neutral manner. When categories are meaningfully labeled, people bring intuitive theories to the learning context. Learning then involves a process in which people search for evidence in the data that supports abstract features or hypotheses that have been activated by the intuitive theories. In contrast, when categories are labeled in a neutral manner, people search for simple features that distinguish one category from another. Importantly, the final study suggests that learning involves an interaction of people's intuitive theories with data, in which theories and data mutually influence each other. The results strongly suggest that straight-forward, relatively modular ways of incorporating prior knowledge into models of category learning are inadequate. More telling, the results suggest that standard models may have fundamental limitations. We outline a speculative model of learning in which the interaction of theory and data is tightly coupled. The article concludes by comparing the results to recent artificial intelligence systems that use prior knowledge during learning.     

空间知识记忆和提取的理论模型

对物理空间知识记忆和提取规律的探讨一直是空间认知领域研究中的一个重点和热点。对其进行深入研究,不仅有助于了解人类的空间行为,而且为相关的诸如界面设计、虚拟环境等空间认知应用领域（研究）提供支持。该文从空间记忆的内在参照系理论、坐标系统模型、空间情境模型和位置记忆的空间类属模型4个方面对当前有关物理环境中空间知识记忆和提取的理论或模型进行了回顾并作了初步评价。