Expertise and the explanation effect

The effect of professional experience on the “explanation effect”, i.e., generation of an explanation for an event occurrence increasing the judged likelihood of the event, is investigated in a risk assessment (financial auditing) context. An explanation effect was predicted for inexperienced auditors (auditing students); however, audit judgment experience was predicted to mediate, or eliminate, any explanation effect. Two competing hypotheses for the origin of the effect, the causal construction and recall-availability hypotheses, are tested given the presence of antecedent conditions for, and against, the explanation events. Audit risk judgments were provided by 58 novice and 42 experienced auditors. Written explanation for occurrence of the target event resulted in the explanation effect for novice subjects, both for specifie event and aggregate risk assessments. The pattern of results supported the recallavailability over the causal construction hypothesis. The judgments of the experienced auditors, however, did not indicate any explanation effect.     

Order effects and memory for evidence in individual versus group decision making in auditing

Sequential processing of evidence may lead to recency effect, a potential bias in judgment. The present research seeks to extend the literature on recency effects by assessing the potential moderating influence of team work: whether group decision making moderates the severity of recency effects predicted by Hogarth and Einhorn (1992), and whether group processing influences the accuracy of, and confidence in memory for evidence. Experienced auditors from a Big‐6 accounting firm made audit judgments, either individually or as groups. They were randomly assigned to one of two levels of evidence presentation order. After performing the judgment task, participants completed two evidence recognition tests. Consistent with prior findings, recency effects on judgments were observed, but only for individuals. Group judgments or audit reports were not affected by recency. Order effects, however, did not translate into different choices of audit reports, and did not persist in memories of either individuals or groups. As expected, group memory was more accurate than individual memory and groups were more confident than individuals. Overall, confidence in accurate memories was greater than in inaccurate ones. Copyright © 1999 John Wiley & Sons, Ltd.     

A pattern recognition account of decision making

In the domain of pattern recognition, experiments have shown that perceivers integrate multiple sources of information in an optimal manner. In contrast, other research has been interpreted to mean that decision making is nonoptimal. As an example, Tversky and Kahneman (1983) have shown that subjects commit a conjunction fallacy because they judge it more likely that a fictitious person named Linda is a bank teller and a feminist than just a bank teller. This judgment supposedly violates probability theory, because the probability of two events can never be greater than the probability of either event alone. The present research tests the hypothesis that subjects interpret this judgment task as a pattern recognition task. If this hypothesis is correct, subjects’ judgments should be described accurately by the fuzzy logical model of perception (FLMP)—a successful model of pattern recognition. In the first experiment, the Linda task was extended to an expanded factorial design with five vocations and five avocations. The probability ratings were described well by the FLMP and described poorly by a simple probability model. The second experiment included (1) two fictitious people, Linda and Joan, as response alternatives and (2) both ratings and categorization judgments. Although the ratings were accurately described by both the FLMP and an averaging of the sources of information, the categorization judgments were described better by the FLMP. These results reveal important similarities in recognizing patterns and in decision making. Given that the FLMP is an optimal method for combining multiple sources of information, the probability judgments appear to be optimal in the same manner as pattern-recognition judgments.     

Eliminating recency with self‐review: the case of auditors' ‘going concern’ judgments

This paper examines the use of self‐review to debias recency. Recency is found in the ‘going concern’ judgments of staff auditors, but is successfully eliminated by the auditor's use of a simple self‐review technique that would be extremely easy to implement in audit practice. Auditors who self‐review are also less inclined to make audit report choices that are inconsistent with their going concern judgments. These results are important because the judgments of staff auditors often determine the type and extent of documentation in audit workpapers and serve as preliminary inputs for senior auditors' judgments and choices. If staff auditors' judgments are affected by recency, the impact of this bias may be impounded in the ultimate judgments and choices of senior auditors. Since biased judgments can expose auditors to significant costs involving extended audit procedures, legal liability and diminished reputation, simple debiasing techniques that reduce this exposure are valuable. The paper also explores some future research needs and other important issues concerning judgment debiasing in applied professional settings. Copyright © 2002 John Wiley & Sons, Ltd.     

A quantum theoretical explanation for probability judgment errors

A quantum probability model is introduced and used to explain human probability judgment errors including the conjunction and disjunction fallacies, averaging effects, unpacking effects, and order effects on inference. On the one hand, quantum theory is similar to other categorization and memory models of cognition in that it relies on vector spaces defined by features and similarities between vectors to determine probability judgments. On the other hand, quantum probability theory is a generalization of Bayesian probability theory because it is based on a set of (von Neumann) axioms that relax some of the classic (Kolmogorov) axioms. The quantum model is compared and contrasted with other competing explanations for these judgment errors, including the anchoring and adjustment model for probability judgments. In the quantum model, a new fundamental concept in cognition is advanced--the compatibility versus incompatibility of questions and the effect this can have on the sequential order of judgments. We conclude that quantum information-processing principles provide a viable and promising new way to understand human judgment and reasoning.     

The impact of affective information on the professional judgments of more experienced and less experienced auditors

This study examines whether the presence of realistic, yet irrelevant, affective information differentially influences the professional judgments of more experienced and less experienced auditors. In this study, auditors with different experience levels were either provided or not provided with information designed to elicit a negative interpersonal emotional reaction towards a manufacturing client when making an inventory obsolescence risk judgment. The results indicate that the inventory obsolescence risk assessments of less experienced auditors were significantly higher when they were provided with negative affective information on a client than when no such information was provided. No such differences were found for the more experienced auditors. This study suggests that professional experience is one factor that influences individuals' assessments of the informational value of affective reactions. This has implications for developing effective training programs to increase professionals' awareness of the influence that emotional reactions can have on their judgment. Copyright © 2002 John Wiley & Sons, Ltd.     

经验决策：概念、研究和展望

传统风险决策研究范式中, 决策信息是事先限定的, 即在决策之前呈现各个决策选项的概率和收益, 被试基于这些信息进行决策。已有研究表明, 在传统风险决策任务中人们会高估小概率事件(rare event)。然而最近出现的一种基于不完整信息的决策形式, 即经验决策却对这一发现提出了挑战。研究发现, 人们在进行经验决策时会表现出对小概率事件的低估, 经验决策和传统决策形式之间存在差异。本文主要介绍经验决策的概念及其研究, 并从学习和不确定程度等角度对两种决策形式进行再认识, 以进一步理解经验决策并提出展望。     

任务情境与概率知识经验对中学生合取判断的影响研究

本研究采用了一套由不同任务情境(数字情境和日常情境)组成的合取判断问卷与一套概率基础知识问卷,对106名中学生的合取判断情况进行调查,结果显示:(1)无论在哪种任务情境下,概率知识经验均未对合取判断成绩产生显著影响。(2)不同概率知识经验的被试在数字任务情境下采用的策略有所不同。     

Diagnostic causal reasoning with verbal information

In diagnostic causal reasoning, the goal is to infer the probability of causes from one or multiple observed effects. Typically, studies investigating such tasks provide subjects with precise quantitative information regarding the strength of the relations between causes and effects or sample data from which the relevant quantities can be learned. By contrast, we sought to examine people’s inferences when causal information is communicated through qualitative, rather vague verbal expressions (e.g., “X occasionally causes A”). We conducted three experiments using a sequential diagnostic inference task, where multiple pieces of evidence were obtained one after the other. Quantitative predictions of different probabilistic models were derived using the numerical equivalents of the verbal terms, taken from an unrelated study with different subjects. We present a novel Bayesian model that allows for incorporating the temporal weighting of information in sequential diagnostic reasoning, which can be used to model both primacy and recency effects. On the basis of 19,848 judgments from 292 subjects, we found a remarkably close correspondence between the diagnostic inferences made by subjects who received only verbal information and those of a matched control group to whom information was presented numerically. Whether information was conveyed through verbal terms or numerical estimates, diagnostic judgments closely resembled the posterior probabilities entailed by the causes’ prior probabilities and the effects’ likelihoods. We observed interindividual differences regarding the temporal weighting of evidence in sequential diagnostic reasoning. Our work provides pathways for investigating judgment and decision making with verbal information within a computational modeling framework.     

Probability judgment in hierarchical learning: a conflict between predictiveness and coherence

Why are people's judgments incoherent under probability formats? Research in an associative learning paradigm suggests that after structured learning participants give judgments based on predictiveness rather than normative probability. This is because people's learning mechanisms attune to statistical contingencies in the environment, and they use these learned associations as a basis for subsequent probability judgments. We introduced a hierarchical structure into a simulated medical diagnosis task, setting up a conflict between predictiveness and coherence. Thus, a target symptom was more predictive of a subordinate disease than of its superordinate category, even though the latter included the former. Under a probability format participants tended to violate coherence and make ratings in line with predictiveness; under a frequency format they were more normative. These results are difficult to explain within a unitary model of inference, whether associative or frequency-based. In the light of this, and other findings in the judgment and learning literature, a dual-component model is proposed.     

A dual-process account of the development of scientific reasoning: The nature and development of metacognitive intercession skills

Metacognitive knowledge of the dual-processing basis of judgment is critical to resolving conflict between analytic and experiential processing responses [Klaczynski, P. A. (2004). A dual-process model of adolescent development: Implications for decision making, reasoning, and identity. In R. V. Kail (Ed.), Advances in child development and behavior, Vol. 31 (pp. 73–123). San Diego, CA: Academic Press]. Such conflict is ubiquitous when reasoning scientifically. Three studies explored the nature, development, and stability of this metacognitive knowledge. In each study, participants completed the ratio-bias judgment task, which assessed their tendency to make analytically based responses, and the ratio-bias evaluation task, which assessed their metacognitive knowledge of the processing basis of judgments on the task (Metacognitive Status). In Study 1, college students’ judgment performance was related to metacognitive status but not to general cognitive ability. In Study 2, metacognitive status was related to age and mathematics-related changes. Metacognitive status again predicted participants’ tendency to make analytically based judgments. In Study 3, college students’ judgments, but not metacognitive status, were affected by task conditions. The evidence suggests that assessing metacognitive knowledge is important for understanding how conflict between analytically and experientially based judgments is resolved.     

Normative and Substantive Expertise in Multiple Hypotheses Evaluation

We distinguish two criteria for evaluating the judgments of trained professionals. One criterion is conformance with a theoretical model and the other is conformance with known external criteria. Further, we label judgments that depart from a theoretical model as errors and those that depart from known external criteria as mistakes. Following this distinction, we hypothesize that auditors′ multiple hypotheses judgments will be characterized by errors but not mistakes. This hypothesis was tested by asking professional auditors to evaluate multiple hypotheses. The results confirm our expectations. Auditors′ judgments reflected ecological base rate information, and they appropriately ignored nondiagnostic evidence. Moreover, auditors did not exhibit a perseverance bias and 84% of them identified the correct hypothesis. The absence of mistakes reflect substantive expertise. Conversely, auditors′ probabilities were not additive, and, when a hypothesis was eliminated, they did not adjust beliefs for the remaining hypotheses. These errors reflect a lack of normative expertise. A second study employed inexperienced subjects to establish whether the strong substantive performance of the professional auditors was attributed to their expertise or was an artifact of the task. The results from the second study were strongly supportive of the substantive expertise explanation. Taken together, these results suggest that substantive expertise can help contain mistakes but it is not sufficient to mitigate errors. Further, lack of normative expertise can lead to errors but these errors do not translate into mistakes. The paper concludes that the distinction between normative and substantive expertise on one hand and errors and mistakes on the other is crucial to understanding when basic findings will generalize to professional settings.     

Threshold adjustment in response to asymmetric loss functions: The case of auditors’ “substantial doubt” thresholds

Thresholds play a critical role in linking judgments and choices. One way they do so is by dividing a continuous judgment variable, such as probability, into two regions that imply different choices and actions, such as operate/do not operate, admit/do not admit, or invest/do not invest. We examine this link by focusing on the tendency of professional decision makers to adjust their thresholds when the risks associated with negative decision outcomes are elevated. We report two studies involving experienced auditors that investigate thresholds in a “going-concern” setting. In this setting, the auditor assesses the probability that a business firm will be unable to continue in existence for the coming year; if that probability exceeds a “substantial doubt” threshold (also assessed by the auditor), the auditor is required to disclose the relevant information in the firm’s annual report to the public. Our studies use two experimental cases based on actual business firms that differ in their objective likelihoods of not continuing as going concerns. We derive the auditors’ substantial doubt thresholds from the relationship between their probability assessments and their disclosure choices, unlike all prior research which has simply asked auditors to state the probability they believe represents substantial doubt. We find that auditors’ derived thresholds are adjusted downward for the more problematic firm, a result that we attribute to the asymmetric loss functions inherent in going-concern settings. The second study has some of the auditors directly provide their substantial doubt thresholds (SDTs). The downward adjustment is again found for derived thresholds but not for elicited thresholds, suggesting that the method of capturing thresholds may be an important issue in understanding judgment and choice.     

Ambiguity and Human Versus Technological Sources of Information in Judgments Involving Base Rate and Individuating Information1

This experiment examined the role that ambiguity and uncertainty play in the use of base rate and individuating information in probability judgments. Subjects responded to a number of inference problems that varied in terms of base rates and the accuracy of the source of the individuating information. The ambiguity in the decision situation was also manipulated by varying the human or technological nature of the source of the individuating information and the causal relevance of the base rate information. The results provide substantial support for the predictions derived from the ambiguity conceptualization. Several interactions were uncovered involving attributes of the base rate and individuating information suggesting complex judgmental processes similar to anchoring and adjustment. Discussion focused on the role that human versus technological sources of information may play in judgment and decision making, and the utility of the ambiguity notion for understanding the use of base rate and individuating information in probabilistic inference problems.     

When do children overestimate their knowledge of unfamiliar targets?

We report four investigations in which 5 to 7 year olds judged whether or not they knew familiar- and unfamiliar-named targets. Under some conditions judgments were made in relation to performance on a relevant task, e.g., children judged whether they knew the picture they had picked from a set was the target. Under other conditions, children judged their state of knowledge not in relation to performance, e.g., they answered "Do you know who (target) is?" Under both types of condition, children nearly always made correct "know" judgments when targets were familiar. Correct "don't know" judgments about unfamiliar targets were relatively common when judgments were not made in relation to performance on a task (average scores 80-90% correct), but were less frequent when knowledge was judged in relation to performance on a task (average scores 40-70% correct). Knowledge judged in relation to performance was overestimated even when children did not choose the picture they thought might be the target before making their judgment, when the pictures were face-down, and when children predicted performance on a task not yet present. It seems that although children can make relatively accurate judgments of their own knowledge states, they tend to overestimate their competence when assessing their knowledge in relation to performance on a task. We discuss why this might be.     

决策与判断中的分解效应

分解效应是人类主观判断中的一种较稳固的行为偏差,并且判断结果会对随后的决策产生重要影响,因此,对该领域研究成果的全面梳理具有重要的理论意义和实际价值。本文主要介绍了支持理论中的分解效应,总结梳理了概率判断与时间判断中分解效应的研究,综述了其他社会判断中的分解效应研究成果,展望了决策与判断中的分解效应的未来研究方向。通过上述内容的阐述,希望能为该领域研究者提供新的思路,推动国内相关领域研究的发展。     

Understanding conjunction effects in probability judgments: the role of implicit mental models

Subjective probability judgments often violate a normative principle in that the conjunction of two events is judged to be more likely than the probability of either of the two events occurring separately. Most previous explanations of these conjunction effects have assumed that probability judgments depend on some psychological relation (e.g. representativeness) between the constituents mentioned explicitly in the stimulus information. In contrast, the present approach highlights the fundamental role of implicitly inferred information. Participants are assumed to transform the explicit stimulus information into implicit mental models in their attempt to make sense of the experimental task. Probability judgments should then reflect the degree of activation of such a mental model in memory given a set of propositions, rather than the quantitative fit or likelihood of the propositions themselves. Two studies are reported which provide converging evidence for the proposed mental model approach. In the first study, using graded conjunctions of one to five propositions, probability judgments are shown to vary as a function of the activation of a mental model rather than the likelihood of the component events. In a second study, a priming procedure is employed to activate mental models that either fit an event conjunction or do not, leading to an increase or decrease of conjunction effects in probability judgment. Copyright © 1999 John Wiley & Sons, Ltd.     

Constructivist coding: learning from selective feedback

Although much learning in real-life environments relies on highly selective feedback about outcomes, virtually all cognitive models of learning, judgment, and categorization assume complete and representative feedback. We investigated empirically the effect of selective feedback on decision making and how people code experience with selective feedback. The results showed that, in contrast to a commonly raised concern, performance was not impaired following learning with selective and biased feedback. Furthermore, even in a simple decision task, the experience that people acquired was not a mere recording of the observed outcomes, but rather a reconstruction from general task knowledge.