Inflexibility of experts--reality or myth? Quantifying the Einstellung effect in chess masters

How does the knowledge of experts affect their behaviour in situations that require unusual methods of dealing? One possibility, loosely originating in research on creativity and skill acquisition, is that an increase in expertise can lead to inflexibility of thought due to automation of procedures. Yet another possibility, based on expertise research, is that experts' knowledge leads to flexibility of thought. We tested these two possibilities in a series of experiments using the Einstellung (set) effect paradigm. Chess players tried to solve problems that had both a familiar but non-optimal solution and a better but less familiar one. The more familiar solution induced the Einstellung (set) effect even in experts, preventing them from finding the optimal solution. The presence of the non-optimal solution reduced experts' problem solving ability was reduced to about that of players three standard deviations lower in skill level by the presence of the non-optimal solution. Inflexibility of thought induced by prior knowledge (i.e., the blocking effect of the familiar solution) was shown by experts but the more expert they were, the less prone they were to the effect. Inflexibility of experts is both reality and myth. But the greater the level of expertise, the more of a myth it becomes.     

Specialization effect and its influence on memory and problem solving in expert chess players

Expert chess players, specialized in different openings, recalled positions and solved problems within and outside their area of specialization. While their general expertise was at a similar level, players performed better with stimuli from their area of specialization. The effect of specialization on both recall and problem solving was strong enough to override general expertise—players remembering positions and solving problems from their area of specialization performed at around the level of players 1 standard deviation (SD) above them in general skill. Their problem-solving strategy also changed depending on whether the problem was within their area of specialization. When it was, they searched more in depth and less in breadth; with problems outside their area of specialization, the reverse. The knowledge that comes from familiarity with a problem area is more important than general purpose strategies in determining how an expert will tackle it. These results demonstrate the link in experts between problem solving and memory of specific experiences and indicate that the search for context-independent general purpose problem-solving strategies to teach to future experts is unlikely to be successful.     

Why good thoughts block better ones: the mechanism of the pernicious Einstellung (set) effect

The Einstellung (set) effect occurs when the first idea that comes to mind, triggered by familiar features of a problem, prevents a better solution being found. It has been shown to affect both people facing novel problems and experts within their field of expertise. We show that it works by influencing mechanisms that determine what information is attended to. Having found one solution, expert chess players reported that they were looking for a better one. But their eye movements showed that they continued to look at features of the problem related to the solution they had already thought of. The mechanism which allows the first schema activated by familiar aspects of a problem to control the subsequent direction of attention may contribute to a wide range of biases both in everyday and expert thought - from confirmation bias in hypothesis testing to the tendency of scientists to ignore results that do not fit their favoured theories.     

Choosing between hearts and minds: Children's understanding of moral advisors

Moral development research has often focused on the development of moral reasoning without considering children's understanding of moral advisors. We investigated how children construe sources of moral advice by examining the characteristics that children deem necessary for reasoning about moral or scientific problems. In two experiments, children in grades K, 2, and 4 were presented with dilemmas of a moral nature or scientific nature and chose between two advisors. Second and fourth graders chose advisors differentially based on their expertise, while kindergartners did not discriminate between advisors. In a third experiment, older children indicated that only certain characteristics are needed to solve moral or scientific problems, and they endorsed these characteristics differentially based on the problem to be solved. Thus, by middle childhood, children construe moral knowledge as distinct from scientific knowledge and select advisors in each area accordingly.     

The Curse of Expertise: When More Knowledge Leads to Miscalibrated Explanatory Insight

Does expertise within a domain of knowledge predict accurate self‐assessment of the ability to explain topics in that domain? We find that expertise increases confidence in the ability to explain a wide variety of phenomena. However, this confidence is unwarranted; after actually offering full explanations, people are surprised by the limitations in their understanding. For passive expertise (familiar topics), miscalibration is moderated by education; those with more education are accurate in their self‐assessments (Experiment 1). But when those with more education consider topics related to their area of concentrated study (college major), they also display an illusion of understanding (Experiment 2). This “curse of expertise” is explained by a failure to recognize the amount of detailed information that had been forgotten (Experiment 3). While expertise can sometimes lead to accurate self‐knowledge, it can also create illusions of competence.     

心理定势现象的认知神经机制

问题解决活动中的心理定势,是指人们固执地沿用熟悉方案、忽略其他方案的反应倾向。它可能源于人们已经掌握的陈述性知识,也可能源于程序性知识,两类不同来源的心理定势的发生条件和影响效果都不尽相同。然而,过往的研究都没有做出严格区分。本文通过梳理相关研究,描述心理定势现象,解释心理定势是怎么发生的,分析心理定势是在什么条件下发生的、会对心理与行为活动造成什么样的影响,从而评述两种来源的心理定势的认知神经机制,指出尚未解决的科学问题。     

Deliberation Versus Intuition: Decomposing the Role of Expertise in Judgment and Decision Making

What produces better judgments: deliberating or relying on intuition? Past research is inconclusive. We focus on the role of expertise to increase understanding of the effects of judgment mode. We propose a framework in which expertise depends on a person's experience with and knowledge about a domain. Individuals who are relatively experienced but have modest knowledge about the subject matter (“intermediates”) are expected to suffer from deliberation and to benefit from a more intuitive approach, because they lack the formal knowledge to understand the reasons underlying their preferences. Individuals who are high (“experts”) or low (“novices”) on both experience and knowledge are expected to do well or poorly, respectively, regardless of decision mode. We tested these predictions in the domain of art. Experiments 1 and 2 showed that intermediates performed better when relying on intuition than after deliberation. Judgments of experts and novices were unaffected. In line with previous research relating processing style to judgment mode, Experiment 3 showed that the effect of processing style (global versus local) on judgment quality is similarly moderated by expertise. Copyright © 2012 John Wiley & Sons, Ltd.     

Medical expertise asa function of task difficulty

This paper is concerned with factors that disrupt the pattern of forward reasoning characteristic of experts with accurate performance. Two experiments are described. In the first, the performances of cardiologists, psychiatrists, and surgeons in diagnostic explanation of a clinical problem in cardiology were examined. In the second, the performances of cardiologists and endocrinologists in diagnostic explanation of clinical problems within and outside their domains of expertise were examined. The performances of researchers and practicing physicians are also compared. The results of Experiment 1 replicated earlier results regarding the relationship between forward reasoning and accurate diagnosis. There were no differences in recall as a function of expertise. Experts did not show any bias toward using specific knowledge from their own areas of expertise. The results of Experiment 2 showed that the breakdown of forward reasoning was related to the structure of the task. In particular, nonsalient cues induced some backward reasoning even in subjects with accurate diagnoses. Some differences were also found between the types of explanation used by researchers and practitioners. The practitioners referred more to clinical components in their explanations, whereas the researchers focused more on the biomedical components.     

Expert Trespassing Testimony and the Ethics of Science Communication

Scientific expert testimony is crucial to public deliberation, but it is associated with many pitfalls. This article identifies one—namely, expert trespassing testimony—which may be characterized, crudely, as the phenomenon of experts testifying outside their domain of expertise. My agenda is to provide a more precise characterization of this phenomenon and consider its ramifications for the role of science in society. I argue that expert trespassing testimony is both epistemically problematic and morally problematic. Specifically, I will argue that scientific experts are subject to a particular obligation. Roughly, this is the obligation to qualify their assertions when speaking outside their domain of scientific expertise in certain contexts. Thus, I argue that scientists who possess expert knowledge are confronted with hard questions about when and how to testify and, therefore, that being a scientific expert comes with great responsibility. Consequently, I provide a concrete “expert guideline” according to which scientific experts, in certain contexts, face an obligation to qualify their assertions when speaking outside their domain of expertise. Furthermore, I consider a number of the conditions in which the guideline is waived or overridden. On this basis, I consider the broader aspects of the roles of scientific experts in a society with a high division of cognitive labor that calls for trust in scientific expert testimony.     

When Experts Argue: Explaining the Best and the Worst of Reasoning

Expert reasoning is responsible for some of the most stunning human achievements, but also for some of the most disastrous decisions ever made. The argumentative theory of reasoning has proven very effective at explaining the pattern of reasoning’s successes and failures. In the present article, it is expanded to account for expert reasoning. The argumentative theory predicts that reasoning should display a strong confirmation bias. If argument quality is not sufficiently high in a domain, the confirmation bias will make experts tap into their vast knowledge to defend whatever opinion they hold, with polarization and overconfidence as expected results. By contrast, experts should benefit even more from the power of group discussion to make the best of the confirmation bias—when they genuinely disagree that is, otherwise polarization is again likely to ensue. When experts interact with laymen other mechanisms can take the lead, in particular trust calibration and consistency checking. They can yield poor outcomes if experts do not have a sustained interaction with laymen, or if the laymen have strong opinions when they witness a debate between experts. Seeing reasoning as a mechanism of epistemic vigilance aimed at finding and evaluating arguments helps make better sense of expert reasoning performance, be it in individual ratiocination, in debates with other experts, or in interactions with laymen.     

Abstract Planning and Perceptual Chunks: Elements of Expertise in Geometry

We present a new model of skilled performance in geometry proof problem solving called the Diagram Configuration model (DC). While previous models plan proofs in a step-by-step fashion, we observed that experts plan at a more abstract level: They focus on the key steps and skip the less important ones. DC models this abstract planning behavior by parsing geometry problem diagrams into perceptual chunks, called diagram configurations, which cue relevant schematic knowledge. We provide verbal protocol evidence that DC's schemas correspond with the step-skipping inferences experts make in their initial planning. We compare DC with other models of geometry expertise and then, in the final section, we discuss more general implications of our research. DC's reasoning has important similarities with Larkin's (1988) display-based reasoning approach and Johnson-Laird's (1983) mental model approach. DC's perceptually based schemas are a step towards a unified explanation of (1) experts' superior problem-solving effectiveness, (2) experts' superior problem-state memory, and (3) experts' ability, in certain domains, to solve relatively simple problems by pure forward inferencing. We also argue that the particular and efficient knowledge organization of DC challenges current theories of skill acquisition as it presents an end-state of learning that is difficult to explain within such theories. Finally, we discuss the implications of DC for geometry instruction.     

Knowing Who Knows: Laypersons' Capabilities to Judge Experts' Pertinence for Science Topics

Because modern societies are built on elaborate divisions of cognitive labor, individuals remain laypersons in most knowledge domains. Hence, they have to rely on others' expertise when deciding on many science‐related issues in private and public life. Even children already locate and discern expertise in the minds of others (e.g., Danovitch & Keil, 2004). This study examines how far university students accurately judge experts' pertinence for science topics even when they lack proficient knowledge of the domain. Participants judged the pertinence of experts from diverse disciplines based on the experts' assumed contributions to texts adapted from original articles from Science and Nature. Subjective pertinence judgments were calibrated by comparing them with bibliometrics of the original articles. Furthermore, participants' general science knowledge was controlled. Results showed that participants made well‐calibrated pertinence judgments regardless of their level of general science knowledge.     

Is case-based reasoning a source of knowledge generalisation?

This study examines the link between case-based reasoning and knowledge generalisation. Experiment 1 showed that case-based reasoning is not in itself a source of generalisation. In this experiment, subjects were able to adapt knowledge acquired during analysis of an example in order to successfully solve a very similar problem. But immediately afterwards, they failed on a problem symmetrical to the one they had just solved. Experiments 2A and 2B showed, in this same situation, that it is possible to lead subjects to generalise simply by stating that the practice problems were similar to the examples. These findings suggest that knowledge generalisation accompanying case-based reasoning is the result of a process that must be actively implemented by the subject, and not an automatic outcome of the source-to-target transfer.     

From models to rules: mechanization of reasoning as a way to cope with cognitive overloading in combinatorial problems

It is hypothesized that the transition from unfamiliar problems to familiar, well-learned problems coincides with the transition from a model-based style of reasoning to a rule-based style of reasoning; model-based style of reasoning helps understanding the problem structure, but can overload working memory when the number of models required increases; rule-based style of reasoning avoids cognitive overloading, at the cost of making individuals liable to mechanization errors. In Experiment 1, the number of models required to respond to a verification task affected response latencies with unfamiliar problems, but not with familiar problems, supporting the initial hypothesis. In Experiment 2, participants were prone to mechanization errors when confronted with slightly modified problems in the late stages of the experiment, supporting the hypothesis that they had developed a reasoning rule in the early stages and were blindly applying it. The findings suggest that model-based reasoning and rule-based reasoning serve different purposes and have different costs and benefits, are both available to human reasoners, and familiarization with a problem may induce the transition from the former to the latter. The findings also suggest that mechanization of reasoning may be the first step along a gradient of decreasing cognitive load, whose end-point is automatization, as discussed in theories of automatization of information processing.     

Learning of nondomain facts in high- and low-knowledge domains

Research on expertise has repeatedly documented that experts learn new information better than do novices, but only when the information is relevant to the expert's domain. It was found in Experiment 1 that participants showed superior learning and recall of a large quantity of new, non-domain-relevant facts about concepts within their domain of high knowledge than about concepts for which they had low domain knowledge. Experiment 2 investigated whether the participants' superior recall of new facts related to concepts within their domain of high knowledge was due to the number of prior facts associated with the concept or to the prior frequency of repetition of those concepts. It was found that participants' recall of new facts was better for concepts with 5 prior associated facts than for concepts with a single prior association but that the number of previous repetitions of each concept did not affect the level of recall for the new facts.     

Adaptive expertise: Effects of type of experience and the level of theoretical understanding it generates

This research investigates the development of transferable—“adaptive”—expertise. The study contrasts problem-solving performance of two kinds of experts (business consultants and restaurant managers) on novel problems at the intersection of their two domains, as well as a group of novices (non-business undergraduates). Despite a lack of restaurant experience, consultants performed better than restaurant managers and undergraduates, even though the problems concerned a restaurant. Process measures suggest this was due to the use of more theoretical reasoning. Analyses show this resulted from differences in work experience and not other factors (e.g., education). We discuss aspects of experience that might be responsible for development of theoretical understanding and, thus, expertise that transfers to novel problems. One possible explanation, consistent with existing research from multiple approaches, is that to transfer to novel problems, experience must include substantive variability. The social context of learning may also play a role.     

The role of relational reasoning in children's addition concepts

The study addresses the relational reasoning of different‐aged children and how addition reasoning is related to problem‐solving skills within addition and to reasoning skills outside addition. Ninety‐two 5‐ to 8‐year‐olds were asked to solve a series of conceptually related and unrelated addition problems, and the speed and accuracy of all self‐reported strategies were used to monitor their addition performance. Children were also given a series of general relational reasoning tasks to assess their ability to solve problems based on thematic, causal and visual relations. The results revealed that, while children were able to reason about commutativity relations, recognition of relations based on additive composition was rare. Furthermore, children's ability to reason about addition concepts increased with age and problem‐solving proficiency. Reasoning about addition concepts was related to performance on the thematic, causal and visual reasoning tasks for older children but not for younger children. Overall, the findings suggest that while children's early knowledge of addition relations is domain specific, as children develop in their broader reasoning abilities these developments enhance their addition reasoning.     

The Role of Domain-Specific Knowledge in Generative Reasoning About Complicated Multileveled Phenomena

Promoting the ability to reason generatively about novel phenomena and problems students may encounter in their everyday lives is a major goal of science education. This goal proves to be a formidable challenge in domains, such as molecular genetics, for which the accumulated scientific understandings are daunting in both amount and complexity. To develop effective instruction that fosters generative reasoning we need to have a sound understanding of the types of knowledge in the domain that are critical for such reasoning. In this study I examined the ensemble of knowledge, both general and domain-specific, undergraduate students employed in reasoning about problems in genetics. I found that students initially formulate a solution in terms that are not domain specific and that serve as a frame–solution frame–that outlines and constrains a more specific and domain-appropriate explanation. This solution frame is then filled in with two powerful forms of domain-specific knowledge I term: domain-specific heuristics and domain-specific explanatory schemas. These knowledge forms embody understandings of central mechanisms and entities in molecular genetics. By invoking these domain-specific knowledge forms, students were able to reason about a variety of both familiar and novel genetics problems. I present a cognitive model that highlights the role of these powerful conceptual understandings in promoting generative reasoning in genetics.