Identifying expectations about the strength of causal relationships

When we try to identify causal relationships, how strong do we expect that relationship to be? Bayesian models of causal induction rely on assumptions regarding people’s a priori beliefs about causal systems, with recent research focusing on people’s expectations about the strength of causes. These expectations are expressed in terms of prior probability distributions. While proposals about the form of such prior distributions have been made previously, many different distributions are possible, making it difficult to test such proposals exhaustively. In Experiment 1 we used iterated learning—a method in which participants make inferences about data generated based on their own responses in previous trials—to estimate participants’ prior beliefs about the strengths of causes. This method produced estimated prior distributions that were quite different from those previously proposed in the literature. Experiment 2 collected a large set of human judgments on the strength of causal relationships to be used as a benchmark for evaluating different models, using stimuli that cover a wider and more systematic set of contingencies than previous research. Using these judgments, we evaluated the predictions of various Bayesian models. The Bayesian model with priors estimated via iterated learning compared favorably against the others. Experiment 3 estimated participants’ prior beliefs concerning different causal systems, revealing key similarities in their expectations across diverse scenarios.     

Development and Initial Validation of a Scale Measuring the Beliefs of Educators Regarding Response to Intervention

This article presents information on the development and initial validation of the 16-item Response to Intervention (RTI) Beliefs Scale. The scale is designed to measure the extent to which educators working in schools hold beliefs consistent with the tenets of RTI. The authors administered the instrument to 2,430 educators in 62 elementary schools in the fall of 2007 and 2,443 educators in 68 elementary schools in the spring of 2008. Exploratory, single-level confirmatory, and multilevel confirmatory factor analysis procedures were used to examine construct validity. Results supported a correlated 3-factor model (Academic Abilities and Performance of Students with Disabilities, Data-Based Decision Making, and Functions of Core and Supplemental Instruction) at both the school and educator levels of analysis. Furthermore, the factor scores derived from the model demonstrated significant, positive relations to RTI implementation. Reliability estimates for two of the three factor scores exceeded.70. Implications for research on educator beliefs and implementation of RTI as well as implications for school psychologists supporting RTI implementation are discussed.     

医务人员对医患共同决策辅助工具的态度研究

循证医学实践要求在临床决策中整合最佳临床研究证据、医者经验、患者自身价值取向与所处具体临床情境。使用决策辅助工具进行医患共同决策可作为一种实践模式,有必要了解来自临床一线医务人员的态度。采用多阶段分层抽样方法,于国内11省及3个直辖市共32个城市的51所二级和三级医院发放问卷,探究使用决策辅助工具促进共同决策实施的可能性。对1 212份问卷的分析显示,73.2%的医务人员认可患者是高质量临床决策的参与主体;支持患者获知诊疗决策依据、认同患者决策辅助工具的积极效果与高学历等正相关。     

医患共享决策的价值意蕴、影响因素及辅助工具

当今社会,受消费行为和人权意识的影响,患者愈发渴望参与到医疗决策的过程之中。医患共享决策体现了对患者自主权的尊重,并有助于提高医疗服务品质。然而我国的医患共享决策起步较晚,面对的困难复杂且多样,需要克服来自患方、医方,以及医疗决策过程本身等方面的因素。患者与医生的有效沟通,是提高患者的就医体验,真正实现以患者为中心的使然。在其他国家的实践经验中,决策辅助工具为实现共享决策提供了有益的参考,但现有的决策辅助工具尚不完善,尤其缺乏适合我国国情的决策工具。     

Why Higher Working Memory Capacity May Help You Learn: Sampling,Search, and Degrees of Approximation

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies (“knowledge restructuring”). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles—increasing the number of particles leads to both faster category learning and improved strategy‐switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best‐fit number of particles for strategy switching. However, no association between WMC and best‐fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability.     

Make‐or‐Break: Chasing Risky Goals or Settling for Safe Rewards?

Humans regularly pursue activities characterized by dramatic success or failure outcomes where, critically, the chances of success depend on the time invested working toward it. How should people allocate time between such make‐or‐break challenges and safe alternatives, where rewards are more predictable (e.g., linear) functions of performance? We present a formal framework for studying time allocation between these two types of activities, and we explore optimal behavior in both one‐shot and dynamic versions of the problem. In the one‐shot version, we illustrate striking discontinuities in the optimal time allocation policy as we gradually change the parameters of the decision‐making problem. In the dynamic version, we formulate the optimal strategy—defined by a giving‐up threshold—which adaptively dictates when people should stop pursuing the make‐or‐break goal. We then show that this strategy is computationally inaccessible for humans, and we explore boundedly rational alternatives. We compare the performance of the optimal model against (a) a myopic giving‐up threshold that is easier to compute, and even simpler heuristic strategies that either (b) only decide whether or not to start pursuing the goal and never give up or (c) consider giving up at a fixed number of control points. Comparing strategies across environments, we investigate the cost and behavioral implications of sidestepping the computational burden of full rationality.     

Measuring information demand of a dynamic in-vehicle display while driving – A study evaluating the MARS (Masking Action Relevant Stimuli) method

The MARS (Masking Action Relevant Stimuli) method assesses information demand for dynamic stimuli while driving. An action relevant stimulus is masked and the driver presses a button to unmask the stimulus for a limited period. We interpreted button presses as information demand. Following our previous research (Rittger, Kiesel, Schmidt, & Maag, 2014), the current study further evaluates the method. We applied the MARS method to a dynamic in-vehicle display containing recommendations from a traffic light assistant. In a driving simulator, drivers approached intersections with different traffic light phasing. The display either presented simple or complex information. In half of the drives, the participants used the MARS method. The study had a full within subjects design and fixations were recorded in all conditions. The results showed that the information demand varied according to the information in the display and the traffic light phase. A comparison of button presses with fixations showed that one unmasking interval came along with one fixation on the display. As a conclusion, the MARS method can distinguish between conditions with high and low information demand for the display. Button presses relate to fixations on the display. Hence, the MARS method is a promising tool to assess the information demand in dynamic environments and can be applied as an extension or alternative for eye tracking.     

团队决策及其影响因素

随着组织结构的变化,团队决策为越来越多的组织所采用。该文将团队决策的研究范式概括为以下四类：社会决策图式（social decision scheme, SDS）、信息取样模型（information sampling model）、项目排序任务（ranking item task）团队以及组织中以安全优先的团队（safety priority team）;并从影响团队决策的因素,提高团队决策质量的策略等角度分别对四类团队决策的研究进行探讨,在此基础上本文对未来研究进行了展望     

Financial prospect relativity: context effects in financial decision‐making under risk

We report three studies in which methodologies from psychophysics are adapted to investigate context effects on individual financial decision‐making under risk. The aim was to determine how the range and the rank of the options offered as saving amounts and levels of investment risk influence people's decisions about these variables. In the range manipulation, participants were presented with either a full range of choice options or a limited subset, while in the rank manipulation they were presented with a skewed set of feasible options. The results showed that choices are affected by the position of each option in the range and the rank of presented options, which suggests that judgments and choices are relative. Copyright © 2006 John Wiley & Sons, Ltd.