SAwSu: An Integrated Model of Associative and Reinforcement Learning

Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid‐navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The second and third simulations contrast the performances of the three models in behavioral experiments, demonstrating advantages for the integrated model in accounting for behavioral data.     

Need for Autonomy and Resistance to Standardized Employee Selection Practices

Two experiments used a hypothetical hiring scenario to examine (a) how standardizing employee selection practices affects decision makers’ perceptions of autonomy potential, and (b) if increasing the level of autonomy inherent in standardized practices reduces decision makers’ reluctance to use them. The results of Experiment 1 suggest that decision makers perceived the structured interview and the mechanical approach to data combination as having less autonomy potential than their unstructured and intuition-based counterparts. The results of Experiment 2 suggest that the amount of autonomy inherent in a structured interview and a mechanical data combination procedure significantly affected decision makers’ intentions to use them. Together, these findings provide empirical support for the notion that autonomy is an important factor contributing to people’s reluctance to use standardized selection practices and that standardized practices may be modified to enhance people’s perceptions of autonomy and subsequent use intentions. Individual differences relating to perceptions of autonomy potential are also identified.     

多级评分模型下的题库结构对CAT的影响分析

在多级评分模型下,项目的难度参数或步骤参数有多个,在对多级评分模型进行选题时,通常对项目的多个难度参数用一个综合的指标来概括.当对每个项目的难度参数进行有效的综合后,综合后的难度参数分布发生了改变,这时如果增加适量的平均难度较难或较易的项目进入题库,测验的精度和项目的曝光率都有一定的改善.     

Ebola,epidemics, and ethics - what we have learned

The current Ebola epidemic has presented challenges both medical and ethical. Although we have known epidemics of untreatable diseases in the past, this particular one may be unique in the intensity and rapidity of its spread, as well as ethical challenges that it has created, exacerbated by its geographic location. We will look at the infectious agent and the epidemic it is causing, in order to understand the ethical problems that have arisen.     

对恐怖情绪的调节:认知重评和表达抑制的差异——一项ERP研究

本研究采用事件相关电位(ERP)技术,通过测量由视觉诱发的脑电成分晚正波(LPP)来比较情绪调节策略在生理电特征上的差异,探讨认知重评和表达抑制的优劣。实验结果表明:(1)从波幅的角度来比较:在400—600ms时间窗口内,认知重评条件下恐怖图片诱发的LPP波幅显著小于在自由观看条件下的波幅,而表达抑制和自由观看组所诱发的LPP波幅差异较小。(2)从时间进程的角度来比较:认知重评与自由观看组的差异发生在400ms并持续到600ms;而表达抑制和自由观看之间的差异发生在450ms但是只持续到550ms。这表明认知重评相较于表达抑制,对负性情绪起调节作用开始较早,并且持续时间较长。这为认知重评优于表达抑制策略提供了神经生理学上的证据。