决策信心的认知机制与神经基础

决策信心是指在进行判断或决策时, 个体对其决策最优性或正确性的信心程度。决策信心的单过程理论认为决策与信心评估是并行的同一加工过程; 而决策后理论认为决策和信心评估是串行的两个加工过程。决策信心存在两种典型的偏差效应：过分自信和信心不足。决策信心的神经机制研究指出, 信心评估可能是决策过程中最基本并且普遍存在的一个成分; 对决策信心评估敏感的脑区包括扣带回、背外侧前额叶和顶上小叶等。未来研究应该在决策信心产生的机制、信心与决策如何交互的心理及神经机制等领域展开大量研究。     

心理韧性及其神经机制：来自非人类动物模型的证据

心理韧性是指个体对不幸、逆境或挫折等压力情形的有效适应。以非人类动物为模型的心理韧性研究一般改称心理韧性为“抗逆力”, 本文也将沿用“抗逆力”来区分不同物种的韧性研究。近年来采用非人类动物模型对抗逆力神经机制的探索取得了重要进展, 内侧前额叶、海马、奖赏环路等脑区及其投射均参与调控抗逆力的表现, 操控其神经环路的影响与所面对的压力形式有关, 并且高抗动物拥有特异性适应机制。鉴于人类研究的伦理及实验手段等原因, 抗逆力的研究将为人类的韧性研究提供重要参考。     

假设形成与检验的神经机制

假设形成与检验是解决任何复杂问题的重要思维工具, 是人们发现规则, 形成概念, 建立假说, 获得知识的基础。为进一步考察这一思维能力的神经机制, 本项目在已有行为及脑电研究的基础上以“类别概括－规则发现”为研究范式, 给被试提供包含多个知觉维度的刺激, 要求被试通过知觉观察与反馈学习发现基于类别的规则(假设)。本项目重点考察被试在此任务中选择与收敛假设,以及加工否定(冲突)信息的脑机制, 并尝试揭示假设形成与检验的个体发展特征。     

卵巢激素影响女性恐惧情绪加工的神经机制

大脑结构和功能的性别差异导致恐惧情绪加工的两性差别。女性对恐惧情绪加工受到体内雌激素和孕激素浓度的影响。近年来, 人类脑成像技术、遗传学和分子生物学的综合研究发现了卵巢激素影响女性恐惧情绪加工的神经机制。其中, 雌激素水平升高导致杏仁核活动增强, 使女性对恐惧面孔识别的准确性增加, 而孕激素水平则相反。这与女性在月经周期不同阶段对社会交往的需求和妊娠期间的自我保护机制有关。     

The use of artificial neural networks and multiple linear regression in modelling work–health relationships: Translating theory into analytical practice

Although psychological theory acknowledges the existence of complex systems and the importance of nonlinear effects, linear statistical models have been traditionally used to examine relationships between environmental stimuli and outcomes. The way we analyse these relationships does not seem to reflect the way we conceptualize them. The present study investigated the application of connectionism (artificial neural networks) to modelling the relationships between work characteristics and employee health by comparing it with a more conventional statistical linear approach (multiple linear regression) on a sample of 1003 individuals in employment. Comparisons of performance metrics indicated differences in model fit, with neural networks to some extent outperforming the linear regression models, such that R ² for worn-out and job satisfaction were significantly higher in the neural networks. Most importantly, comparisons revealed that the predictors in the two approaches differed in their relative importance for predicting outcomes. The improvement is attributed to the ability of the neural networks to model complex nonlinear relationships. Being unconstrained by assumptions of linearity, they can provide a better approximation of such psychosocial phenomena. Nonlinear approaches are often better fitted for purpose, as they conform to the need for correspondence between theory, method, and data.     

Representations of Complexity: How Nature Appears in Our Theories

In science we study processes in the material world. The way these processes operate can be discovered by conducting experiments that activate them, and findings from such experiments can lead to functional complexity theories of how the material processes work. The results of a good functional theory will agree with experimental measurements, but the theory may not incorporate in its algorithmic workings a representation of the material processes themselves. Nevertheless, the algorithmic operation of a good functional theory may be said to make contact with material reality by incorporating the emergent computations the material processes carry out. These points are illustrated in the experimental analysis of behavior by considering an evolutionary theory of behavior dynamics, the algorithmic operation of which does not correspond to material features of the physical world, but the functional output of which agrees quantitatively and qualitatively with findings from a large body of research with live organisms.