Response error and processing biases in confidence judgment

Previous studies showed that random error can explain overconfidence effects typically observed in the literature. One of these studies concluded that, after accounting for random error effects in the data, there is little support for cognitive‐processing biases in confidence elicitation. In this paper, we investigate more closely the random error explanation for overconfidence. We generated data from four models of confidence and then estimated the magnitude of random error in the data. Our results show that, in addition to the true magnitude of random error specified in the simulations, the error estimates are influenced by important cognitive‐processing biases in the confidence elicitation process. We found that random error in the response process can account for the degree of overconfidence found in calibration studies, even when that overconfidence is actually caused by other factors. Thus, the error models say little about whether cognitive biases are present in the confidence elicitation process. Copyright © 2008 John Wiley & Sons, Ltd.     

Learning to Make Selection and Detection Decisions: The Roles of Base Rate and Feedback

The effects of type of feedback and base rate on threshold learning in a multiple‐cue decision task were examined. In most such decision experiments, participants receive feedback after every trial (full feedback), and a single base rate (usually 0.5) is used. Our experiment explored conditional feedback (feedback only after positive decisions) representing common selection and detection tasks (such as hiring), where the decision maker receives no feedback unless the decision is positive (e.g., hire the applicant). We used three base rates (0.2, 0.5, and 0.8). As expected, performance was best in full feedback, but after 300 learning trials, the difference was small. Conditional feedback generally resulted in fewer positive decisions than full feedback, but this difference was not found in the low (0.2) base rate condition. There were interactions between base rates and types of feedback. Results provide partial support for the constructivist encoding hypothesis of Elwin and colleagues. Simulation results suggest that our results may reflect overconfidence when feedback is not given. With respect to rate of learning, when the base rate was 0.2, conditional feedback participants reached approximately the same selection rate but did so more slowly than the full feedback participants. Partial feedback participants learned slower and appeared to be still learning after 500 trials. When the base rate was 0.5 or 0.8, partial feedback was nearly as good as full feedback, but conditional feedback resulted in a systematically lower rate of positive decisions. Copyright © 2011 John Wiley & Sons, Ltd.     

A Test of the Overconfidence Phenomenon Using Audio Signals

The existence of the overconfidence phenomenon was examined using a signal detection paradigm. Fifty-five participants were asked to decide whether they heard a signal or noise only, and to rate how certain they were of their decisions. The results confirmed the existence of overconfidence as well as the “hard-easy” effect.     

Do Maximizers Predict Better than Satisficers?

We examine the relationship between maximizing (i.e., seeking the best) versus satisficing (i.e., seeking the good enough) tendencies and forecasting ability in a real‐world prediction task: forecasting the outcomes of the 2010 FIFA World Cup. In Studies 1 and 2, participants gave probabilistic forecasts for the outcomes of the tournament and completed a measure of maximizing tendencies. We found that although maximizers expected themselves to outperform others much more than satisficers, they actually forecasted more poorly. Hence, on net, they were more overconfident about their relative performance. Decompositional analyses of overall accuracy revealed that differences in forecasting abilities were primarily driven by maximizers' tendency to give more noisy estimates. In Study 3, participants played a betting task where they could choose between safe and uncertain gambles linked to World Cup outcomes. Again, maximizers did more poorly and earned less, because of greater noise in their choice‐based responses. Copyright © 2011 John Wiley & Sons, Ltd.     

True Overconfidence in Interval Estimates: Evidence Based on a New Measure of Miscalibration

Overconfidence is often regarded as one of the most prevalent judgment biases. Several studies show that overconfidence can lead to suboptimal decisions of investors, managers, or politicians. Recent research, however, questions whether overconfidence should be regarded as a bias and shows that standard “overconfidence” findings can easily be explained by different degrees of knowledge of agents plus a random error in predictions. We contribute to the current literature and ongoing research by extensively analyzing interval estimates for knowledge questions, for real financial time series, and for artificially generated charts. We thereby suggest a new method to measure overconfidence in interval estimates, which is based on the implied probability mass behind a stated prediction interval. We document overconfidence patterns, which are difficult to reconcile with rationality of agents and which cannot be explained by differences in knowledge as differences in knowledge do not exist in our task. Furthermore, we show that overconfidence measures are reliable in the sense that there exist stable individual differences in the degree of overconfidence in interval estimates, thereby testing an important assumption of behavioral economics and behavioral finance models: stable individual differences in the degree of overconfidence across people. We do this in a “field experiment,” for different levels of expertise of subjects (students on the one hand and professional traders and investment bankers on the other hand), over time, by using different miscalibration metrics, and for tasks that avoid common weaknesses such as a non‐representative selection of trick questions. Copyright © 2012 John Wiley & Sons, Ltd.     

Do positive illusions predict overconfidence in judgment? A test using interval production and probability evaluation measures of miscalibration

We address the question as to whether judgmental overconfidence, as assessed by probability miscalibration, is related to positive illusions about the self. We first demonstrate that judgmental overconfidence measured with interval production procedures can be considered a trait, due to correlations observed in miscalibration scores in two sets of general‐knowledge questions of varying difficulty administered at different times. In addition, the hard‐easy effect operated in different ways on overprecision and self‐placement of one's performance relative to others: The more difficult the calibration task, the greater the over precision but the greater the under placement of one's performance. Finally, there was no evidence that miscalibration was related to dispositional optimism and self‐efficacy. A second study extended these results by including further measures of disposition to experience positive illusions such as unrealistic optimism, a general tendency to consider oneself “better‐than‐average,” and two indexes of dispositional perception of control. The positive illusion measures showed considerable inter‐correlations, but did not correlate with miscalibration on the interval production task, and correlated negatively with optimism concerning societal risks. A final study replicated this pattern of findings, but showed that disposition to positive illusions did predict miscalibration on the same questions measured with a probability evaluation technique. Our research demonstrates that “overconfidence” is not a unitary construct, but a series of overlapping ones. Copyright © 2010 John Wiley & Sons, Ltd.     

过分自信的进化模型

过分自信有狭义和广义之分, 狭义的过分自信是指人们对自身绝对水平的高估, 使得对自己的评价优于实际水平, 广义的过分自信是一种非理性的过于优化的估计, 还包括对自身相对水平的高估。基于进化心理学理论和生态理性假设提出的进化模型认为, 过分自信是一种快速启发式认知策略, 在适应和生存中具有优势, 是进化选择的结果。根据对个体知觉偏差的不同假设, 进化模型分为二项式模型和正态模型, 都得出了一致的研究结论。与过分自信的自我提升理论、权重差异理论和信息差异理论相比较, 进化模型具有许多优点, 但也存在一些不足, 需要进一步对其进行探讨和检验。     

Inventor perseverance after being told to quit: the role of cognitive biases

We find that approximately one third (29%) of independent inventors continue to spend money and 51% continue to spend time on projects after receiving highly diagnostic advice to cease effort. Using survey data from actual inventors, this paper studies the role of overconfidence, optimism, and the sunk‐cost bias in these decisions. We find that inventors are more overconfident and optimistic than the general population. We also find that optimism and past expenditures increased perseverance after being told to quit, while overconfidence in judgment ability had no effect. After being told to quit, optimists spend 166% more than pessimists and those having already spent, for example, $10 000 spend another $10 000. Copyright © 2007 John Wiley & Sons, Ltd.     

任务类型与信息清晰度对社会比较中认知偏差的影响

社会比较中的认知偏差是指个体在社会比较中表现出的过度自信与不自信现象.本研究在前人研究的基础之上,以在校大学生为被试,采用两个系列实验,探讨了社会比较中认知偏差现象的原因以及对判断和决策的影响.研究结果发现:在社会比较中,面对不同类型的问题,会出现不同的认知偏差;在社会比较中,不同类型的问题和信息清晰度共同影响认知偏差,且二者之间存在交互作用;社会比较中的认知偏差会影响个体的决策意图.     

Explanation and the Hard Problem

This paper argues that the form of explanation at issue in the hard problem of consciousness is scientifically irrelevant, despite appearances to the contrary. In particular, it is argued that the ‘sense of understanding’ that plays a critical role in the form of explanation implicated in the hard problem provides neither a necessary nor a sufficient condition on satisfactory scientific explanation. Considerations of the actual tools and methods available to scientists are used to make the case against it being a necessary condition, and work by J.D. Trout that exploits psychological research on the hindsight and overconfidence biases is used to show that it is not a sufficient condition. It is argued, however, that certain intellectual and moral concerns give us good reason to still try to meet the hard problem’s explanatory challenge, despite its extrascientific nature.