视知觉学习的认知与神经机制研究

知觉学习是指由于训练或经验而引起的长期稳定的知觉变化,是一种内隐性的学习。近20年来,视知觉学习的大量研究结果提示大脑皮层的各个区域,甚至包括初级感觉皮层,在成熟之后仍然具有一定的可塑性。该文根据近年来的研究进展,对视知觉学习在大脑的什么地方,什么时候,以何种方式发生等热点问题进行了探讨。研究提示,视知觉学习涉及了包括初级视皮层在内的多个大脑皮层,并且存在一种自上而下的调控机制;视知觉学习可以在不同的时间尺度上发生,快速学习之后将伴随着慢速学习;通过视知觉学习,人们对于复杂物体的表征将从高级皮层区域移向低级皮层区域,任务执行也将趋于自动化     

Theorising Interventions as Events in Systems

Conventional thinking about preventive interventions focuses over simplistically on the “package” of activities and/or their educational messages. An alternative is to focus on the dynamic properties of the context into which the intervention is introduced. Schools, communities and worksites can be thought of as complex ecological systems. They can be theorised on three dimensions: (1) their constituent activity settings (e.g., clubs, festivals, assemblies, classrooms); (2) the social networks that connect the people and the settings; and (3) time. An intervention may then be seen as a critical event in the history of a system, leading to the evolution of new structures of interaction and new shared meanings. Interventions impact on evolving networks of person-time-place interaction, changing relationships, displacing existing activities and redistributing and transforming resources. This alternative view has significant implications for how interventions should be evaluated and how they could be made more effective. We explore this idea, drawing on social network analysis and complex systems theory.     

Similarity and rules United: similarity- and rule-based processing in a single neural network

A central controversy in cognitive science concerns the roles of rules versus similarity. To gain some leverage on this problem, we propose that rule- versus similarity-based processes can be characterized as extremes in a multidimensional space that is composed of at least two dimensions: the number of features (Pothos, 2005) and the physical presence of features. The transition of similarity- to rule-based processing is conceptualized as a transition in this space. To illustrate this, we show how a neural network model uses input features (and in this sense produces similarity-based responses) when it has a low learning rate or in the early phases of training, but it switches to using self-generated, more abstract features (and in this sense produces rule-based responses) when it has a higher learning rate or is in the later phases of training. Relations with categorization and the psychology of learning are pointed out.     

Social network composition and attitude strength: Exploring the dynamics within newly formed social networks

Recent evidence suggests that people who are embedded within attitudinally congruent social networks have stronger attitudes than those embedded in attitudinally diverse networks. The current research examines the causal processes responsible for this relation. Two studies capitalized on naturally occurring experiments whereby college students are quasi-randomly assigned to social contexts containing varying levels of attitudinal diversity. Replicating past research, individuals in attitudinally diverse social networks exhibited less resistance to attitude change and less attitude stability than those in more attitudinally congruent networks. Although there was evidence of attitude projection and selective network construction, neither of these processes could account for the relation between social network composition and attitude strength. These findings corroborate and extend previous results, reinforcing the notion that the social context in which people are embedded has important implications for the durability of their attitudes.     

人工神经网络在临床应用中的伦理思考

现代临床医学中的海量信息具有非线性特质,人工神经网络的自我学习、记忆和归纳功能,刚好适应了医学的新发展,在医学领域有良好的应用前景。事实上,人工神经网络临床应用的理论研究成果丰富,但商业应用并不多见。本文探讨人工神经网络在临床应用中可能引发的社会伦理争议,对其临床适用范围进行初步的论述。     

It’s not just what you know, it’s who you know: Testing a model of the relative importance of social networks to academic performance

Applying three mathematical modeling techniques, this study proposes and tests the fit of an academic performance model, and then estimates the relative importance of four performance predictors: academic ability, performance goal orientation, educational technology use, and social network density. Drawing on social network theory, findings from this study show that social network density is a statistically important and unique predictor of academic performance, suggesting that “who you know” matters in large classroom settings. However, this study also indicates that academic ability is the most dominant factor in the explanation of academic performance. Practical and theoretical implications for enhancing academic performance through social networks are discussed.

错误加工的神经机制

错误加工在认知控制和行为监控中起着关键的作用。监控自己的认知和行为结果,觉察到应该的反应和实际的反应之间的差别（即错误）,纠正这种错误并防止再犯是错误加工的主要内容。错误反应或由代表反应错误等负性反馈刺激诱发的ERP相对于正确反应或由代表反应正确等正性反馈刺激诱发的ERP,表现出一个相对负走向的波形变化,称为错误相关负波（error-related negativity, ERN）和反馈相关负波（feedback-related negativity, FRN）,两者都定位于前扣回附近。错误加工神经机制的研究主要集中于前扣回与ERN、错误意识与错误加工、ERN与FRN的关系。对于前扣带回和ERN的功能意义的解释主要有错误检测理论、冲突监控理论、强化学习理论和预期违反假说。目前错误加工神经机制的研究在研究内容、研究方法和理论观点等方面还存在些问题,这些可能是未来进一步的研究方向     

情绪影响行为抑制的脑机制

行为抑制是一种重要的执行功能。考察情绪影响行为抑制的脑机制, 将有助于理解个体如何适应环境, 特别是如何在情绪情境中控制自己的行为。近年来, 研究者开始使用脑成像技术考察情绪影响行为抑制的脑机制。研究发现, 在情绪影响行为抑制的过程中不仅涉及前额叶抑制控制系统和边缘结构情绪加工系统, 而且涉及这两个系统的相互作用, 即情绪加工与行为抑制加工的整合。腹外侧前额皮层、背外侧前额皮层、前扣带回、杏仁核、脑岛等脑区在情绪加工与行为抑制加工的整合中起着重要的作用, 但情绪整合到行为抑制加工中的时间进程还不清楚。今后的研究应着眼于情绪影响行为抑制的时间进程、神经网络、个体差异的影响以及改进研究范式等方向。     

厌恶加工的神经基础

厌恶是由令人不愉悦、反感的事物诱发的情绪。根据刺激类型的不同, 厌恶可以分为不同的类型。脑岛和基底节是厌恶加工的主要脑区, 前扣带回、杏仁核、丘脑、内侧前额叶也参与厌恶加工。对已有研究的总结发现, 不同类型的厌恶、不同感觉通道的厌恶加工可能具有不同的神经基础。在未来的研究中, 应当注重研究厌恶加工的认知机制、神经基础以及与厌恶相关的神经递质等问题。     

刻板印象的认知神经机制

刻板印象是大脑对社会信息的一种自动的类别化加工过程。近期脑成像的研究表明, 刻板印象的注意编码、语义表征以及抑制控制等认知成分可以分别通过相应的ERP成分表现出来; 而基于ERP源定位以及fMRI的研究表明, 杏仁核、前扣带回、大脑前额皮层以及外侧顶叶皮层附近的颞顶联结区可能参与到了它们的认知加工。最后结合已有的研究, 提出了刻板印象可能的认知通道, 并简要分析了当前研究的不足与今后的发展方向。