Confidence in Dyadic Decision Making: The Role of Individual Differences

Groups typically express more confidence than individuals, yet how individual‐level confidence combines during collaborative decision tasks is not well understood. We prescreened 686 community members using a novel confidence measure (a true/false trivia test) intentionally designed to be difficult (accuracy rates were not significantly better than chance) and randomly assigned 72 individuals to collaborate on a matched version of the same test in dyads composed of two low‐confidence individuals, two high‐confidence individuals, or one of each (“mixed”). Consistent with past research, we found that the confidence expressed by dyads was higher than the confidence expressed by individuals; importantly, however, this pattern varied markedly by dyad type, with low‐confidence dyads showing the largest increase, mixed dyads showing a moderate increase, and high‐confidence dyads showing no increase—despite the fact that all dyads showed similarly low accuracy (about 55%). These results highlight the conditions under which groups express greater confidence than individuals and offer insights for the composition of collaborative decision‐making teams. Copyright © 2015 John Wiley & Sons, Ltd.     

Confidently Biased: Comparisons with Anchors Bias Estimates and Increase Confidence

Across a wide variety of situations, exposure to anchors has been shown to bias people's estimates. What is not known, however, is whether externally provided anchors influence the confidence that people have in their estimates. Our studies had two goals. First, we tested whether exposure to anchors influenced people's subjective confidence levels (Studies 1 and 2). These studies revealed that people who made estimates after making comparisons with externally provided anchors tended to be more confident in their estimates than people who did not see anchors. The second goal was to test two explanations as to why anchors increase people's confidence. In Study 3, we tested the explanation that anchors increase confidence because participants thought the anchors provided useful information. In Study 4, we tested the explanation that exposure to anchors causes people to consider a narrower range of plausible values as compared to when not exposed to anchors. Support was found only for the explanation that comparisons with anchors increase confidence because people who are exposed to anchors consider a narrower range of plausible values. Taken together, these studies reveal the powerful influence anchors can have—they not only bias estimates, but also increase people's confidence in their biased estimates. Copyright © 2016 John Wiley & Sons, Ltd.     

Probabilities Associated with Precise and Vague Forecasts

Forecasts of future outcomes, such as the consequences of climate change, are given with different degrees of precision. Logically, more precise forecasts (e.g., a temperature increase of 3–4°) have a smaller probability of capturing the actual outcome than less precise forecasts (e.g., a temperature increase of 2–6°). Nevertheless, people often trust precise forecasts more than vague forecasts, perhaps because precision is associated with knowledge and expertise. In five experiments, we ask whether people expect highly confident forecasts to be associated with wider or narrower outcome ranges than less confident forecasts (Experiments 1, 2, and 5), and, conversely, whether they expect precise forecasts to be issued with higher or lower confidence than vague forecasts (Experiments 3 and 4). The results revealed two distinct ways of thinking about confidence intervals, labeled distributional (wide intervals seen as more probable than narrow intervals) and associative (wide intervals seen as more uncertain than narrow intervals). Distributional responses occurred somewhat more often in within‐subjects designs, where wide and narrow prediction intervals and high and low probability estimates can be directly compared, whereas separate evaluations (in between‐subjects design) suggested associative responses to be slightly more frequent. These findings are relevant for experts communicating forecasts through confidence intervals. Copyright © 2017 John Wiley & Sons, Ltd.     

决策信心在信息化从众中的作用

研究采用JAS范式,通过操纵群体信息的性质（支持和反对）以及一致性程度,考察了决策信心在信息化从众过程中的作用。结果表明：（1）个体仅在接收到反对信息时会发生决策的偏转,表现出从众;（2）群体参照信息能够显著影响个体的信心：支持性的群体信息使个体的信心显著增高,而反对性的群体信息使信心显著降低,且表现出一种“负性偏向”（即个体对来自群体的负性信息更加敏感）;（3）在反对条件下,个体信心降低的程度能够很好地预测其决策偏转的概率,即信息化从众行为。这说明,决策信心可能在信息化从众中起着核心的中介作用——反对性的群体信息使得决策信心下降,而决策信心的下降导致了决策的偏转,从而表现出从众行为。     

An analysis of instructor- and tablet-presented conditional discriminations: Fidelity and rapidity

Matching-to-sample arrangements are commonly used to teach conditional discriminations. In these arrangements, instructors must systematically arrange instruction to ensure that a learner's response comes under the intended sources of stimulus control. Given the multitude of instructional considerations, the instructors' procedural fidelity has been a significant concern. Recently, LeBlanc et al. found that brief training and access to enhanced data sheets produced high levels of fidelity with experienced service providers. The current study extended LeBlanc et al. by examining the effects of a similar training on the fidelity and instructional pacing by participants with and without previous experience. The participants' performance was also compared when using a flashcard or binder (i.e., printed) arrays and relative to a tablet-delivered instructional program. High levels of fidelity were observed following training, although pacing was slow. Slight differences in performance were observed across comparison arrays; nevertheless, the tablet-based program outperformed instructors.     

时间因素对空间维度非线索更新的影响

采用实时窗口阅读技术,探讨文本阅读中时间因素在情境模型空间维度非线索更新中的作用。被试阅读角色在空间转换中插入时间间隔的短文,并对其间的物体进行再认探测。结果表明,在文本叙述中,如果在角色空间位置转换之后,增添一定的时间间隔,则可以促进相应空间设置内物体的非线索更新,并且,不同的时间间隔所起的作用是不同的。     

中介效应的三类区间估计方法

由于中介效应ab的估计量通常不是正态分布, 因此需用不对称置信区间进行中介效应分析。详述了三类获得不对称置信区间的方法, 包括乘积分布法(M法和经验M法)、Bootstrap方法(偏差校正和未校正的非参数百分位Bootstrap方法、偏差校正和未校正的参数百分位残差Bootstrap方法)和马尔科夫链蒙特卡罗(MCMC)方法。比较了三类方法在单层(简单和多重)和多层中介效应分析中的表现, 发现三类方法的表现相近, 与乘积分布法相比, 偏差校正的百分位Bootstrap方法表现较好, 但有先验信息的MCMC方法能更有效降低均方误。最后对中介效应不对称置信区间研究的拓展方向做了展望。     

Are you sure about that? Eliciting confidence ratings may influence performance on Raven's progressive matrices

Confidence ratings (CR) have often been integrated into reasoning and intelligence tasks as a means for assessing meta-reasoning processes. Although it is often assumed that eliciting these judgements throughout reasoning tasks has no effect on the underlying performance outcomes, this is yet to be established empirically. The current study examines whether eliciting CR from participants during a fluid-reasoning task influences their performance and how this effect is moderated by their initial self-confidence in their own reasoning abilities. In a first experiment, we found that participants performing CR during Raven's Progressive Matrices significantly outperformed a control group who did not provide ratings. Additionally, a second experiment demonstrated that CR only facilitated performance in participants who have a high level of initial self-confidence in their reasoning ability, whereas they were detrimental to participants low in self-confidence.     

The elimination‐plus lineup: Testing a modified lineup procedure with confidence

The present study examined whether a modified form of a preidentification confidence rating would provide evidence of a suspect's guilt in addition to the identification decision confidence. Participants (N  = 241) viewed a videotaped mock crime and were presented with a target‐present or target‐absent simultaneous, sequential, elimination, or elimination‐plus lineup procedure; both elimination procedures required 2 separate judgments from the witness (i.e., relative and absolute). The elimination‐plus procedure was identical to that of the elimination procedure with the addition of the confidence rating in between judgment 1 and judgment 2. Confidence after judgment 1, confidence after judgment 2, and the average of the 2 confidence ratings with the elimination‐plus procedure significantly predicted accuracy for choosers. Given that confidence has been recognised by the Supreme Court of the United States, these results shed light on a novel way of utilising confidence in the investigative process.     

Profile-likelihood Confidence Intervals in Item Response Theory Models

Confidence intervals (CIs) are fundamental inferential devices which quantify the sampling variability of parameter estimates. In item response theory, CIs have been primarily obtained from large-sample Wald-type approaches based on standard error estimates, derived from the observed or expected information matrix, after parameters have been estimated via maximum likelihood. An alternative approach to constructing CIs is to quantify sampling variability directly from the likelihood function with a technique known as profile-likelihood confidence intervals (PL CIs). In this article, we introduce PL CIs for item response theory models, compare PL CIs to classical large-sample Wald-type CIs, and demonstrate important distinctions among these CIs. CIs are then constructed for parameters directly estimated in the specified model and for transformed parameters which are often obtained post-estimation. Monte Carlo simulation results suggest that PL CIs perform consistently better than Wald-type CIs for both non-transformed and transformed parameters.