决策中个体差异研究现状述评

决策中个体差异的研究有助于进一步检验和深化传统决策理论。文章回顾了近十年决策个体差异研究的主要变量（人格、认知能力、认知风格和年龄）和研究结果,并简要总结了该类研究的背景、研究范式和理论意义。这些研究一致发现个体差异变量对决策过程、决策表现等具有影响作用,说明个体差异与决策加工中复杂的人-情境间联系有关。文章提出,未来研究应该综合考虑决策情境、情绪和决策主体的理论模型,以期对个体差异在决策中的作用做出更好解释     

Do Health Care Workplaces Affect Treatment Environments?

Previous research has demonstrated that characteristics of the health care workplace influence staff outcomes such as morale and burnout, but the potential effect of health care workplaces on the treatment environment has been little studied. Building on a model proposed by Schaefer and Moos (1993), we propose that the workplace factors of supervisory work environment (e.g. support from supervisors, managerial control) and programme philosophical orientation (e.g. disease model of addiction, psychosocial learning model of addiction) predict four treatment environment elements: patient autonomy, staff control, staff sensitivity, and patient alienation. Multiple regression analysis of data drawn from a survey (response rate = 86%) of 327 staff members at 15 Veterans Affairs (VA) inpatient substance abuse treatment programmes revealed that greater managerial control over staff predicted greater patient alienation, lower staff sensitivity towards patients, and greater staff control over patients. Stronger disease model programme orientations predicted less patient alienation and greater staff sensitivity, whereas stronger psychosocial model programme orientations predicted less staff control. These results suggest that health care workplaces may influence treatment environments. Implications for further research and practice are discussed. © 1997 John Wiley & Sons, Ltd.     

大语言模型在临床医学的可应用性探讨

科学技术的发展推动以大语言模型为代表的人工智能领域取得了重大突破,大语言模型优势与风险并存,其展现了在临床医学领域巨大的可能性。新的医疗技术的引入可以极大地提升工作效率,同时也带来一定的道德伦理及法律风险。为应对大语言模型产生的不利影响,需要加快立法进程,制定明确的使用规范和监管机制,在技术迭代的同时完善立法及规范。期望大语言模型未来在临床医学的应用方面能够助力我国卫生健康事业的发展。

     

使用验证性补偿多维IRT模型进行认知诊断评估

随着人们对测验反馈结果精细化的需求逐渐提高, 具有认知诊断功能的测量方法逐渐受到人们的关注。在认知诊断模型(CDMs)闪耀着光芒的同时, 另一类能够在连续量尺上提供精细反馈的多维IRT模型(MIRTMs)似乎受到些许冷落。为探究MIRTMs潜在的认知诊断功能, 本文以补偿模型为视角, 聚焦于分别属于MIRTMs的多维两参数logistic模型(M2PLM)和属于CDMs的线性logistic模型(LLM); 之后为使两者具有可比性, 可对补偿M2PLM引入验证性矩阵(Q矩阵)来界定题目与维度之间的关系, 进而得到验证性的补偿M2PLM (CC-M2PLM), 并通过把潜在特质按切点划分为跨界属性, 以期使CC-M2PLM展现出其本应具有的认知诊断功能; 预研究表明logistic量尺上的0点可作为相对合理的切点; 然后, 通过模拟研究对比探究CC-M2PLM和LLM的认知诊断功能, 结果表明CC-M2PLM可用于分析诊断测验数据, 且认知诊断功能与直接使用LLM的效果相当; 最后, 以两则实证数据为例来说明CC-M2PLM在实际诊断测验分析中的可行性。     

空间场景表征中的参照系选取

被试在矩形房间中从两个不同的观察点学习物体场景并在多个朝向上对物体形成的空间关系进行判断,通过控制场景中物体主要内在轴相对于环境结构（房间和地毯）的方向和被试的学习顺序,探讨被试在场景空间表征中采用何种参照系和参照系选取时的影响因素。两个实验结果发现：（1）内在参照系（intrinsic reference systems）和环境参照系均可以用于物体场景的表征,两类参照系之间的关系却是影响被试物体场景表征时参照系选取的重要因素,即当内在参照系与环境参照系方向一致时,被试无论从哪个朝向学习,都选择从垂直于内在参照系和环境参照系的朝向进行表征。反之,当二者方向不一致时,表征时参照系的选择取决于被试的学习经历;（2）无论内在参照系与环境参照系方向是否一致,物体场景本身内在结构的规则性都能够促进空间记忆,即内在结构的规则性既有助于准确编码物体的相对位置,也有助于提高空间关系判断的准确性。     

Consciousness as a concrete physical phenomenon

The typical empirical approach to studying consciousness holds that we can only observe the neural correlates of experiences, not the experiences themselves. In this paper we argue, in contrast, that experiences are concrete physical phenomena that can causally interact with other phenomena, including observers. Hence, experiences can be observed and scientifically modelled. We propose that the epistemic gap between an experience and a scientific model of its neural mechanisms stems from the fact that the model is merely a theoretical construct based on observations, and distinct from the concrete phenomenon it models, namely the experience itself. In this sense, there is a gap between any natural phenomenon and its scientific model. On this approach, a neuroscientific theory of the constitutive mechanisms of an experience is literally a model of the subjective experience itself. We argue that this metatheoretical framework provides a solid basis for the empirical study of consciousness.     

多水平研究中的概念转换模式--以气氛研究为例

在组织环境中开展多水平研究会涉及到对处于组织中不同水平七的概念、变量或过程进行界定的问题。在已有概念基础上提出新的概念必须保证其有效性。本文以气氛研究为例介绍了与此问题相应的五种模式。     

Learning to divide the labor: an account of deficits in light and heavy verb production

Theories of sentence production that involve a convergence of activation from conceptual‐semantic and syntactic‐sequential units inspired a connectionist model that was trained to produce simple sentences. The model used a learning algorithm that resulted in a sharing of responsibility (or “division of labor”) between syntactic and semantic inputs for lexical activation according to their predictive power. Semantically rich, or “heavy”, verbs in the model came to rely on semantic cues more than on syntactic cues, whereas semantically impoverished, or “light”, verbs relied more on syntactic cues. When the syntactic and semantic inputs were lesioned, the model exhibited patterns of production characteristic of agrammatic and anomic aphasic patients, respectively. Anomic models tended to lose the ability to retrieve heavy verbs, whereas agrammatic models were more impaired in retrieving light verbs. These results obtained in both sentence production and single‐word naming simulations. Moreover, simulated agrammatic lexical retrieval was more impaired overall in sentences than in single‐word tasks, in agreement with the literature. The results provide a demonstration of the division‐of‐labor principle, as well as general support for the claim that connectionist learning principles can contribute to the understanding of non‐transparent neuropsychological dissociations.     

From human to artificial cognition and back: New perspectives on cognitively inspired AI systems

We overview the main historical and technological elements characterising the rise, the fall and the recent renaissance of the cognitive approaches to Artificial Intelligence and provide some insights and suggestions about the future directions and challenges that, in our opinion, this discipline needs to face in the next years.