Theories of skill acquisition have made radically different predictions about the role of general problem‐solving methods in acquiring rules that promote effective transfer to new problems. Under one view, methods that focus on reaching specific goals, such as means‐ends analysis, are assumed to provide the basis for efficient knowledge compilation (Anderson, 1987), whereas under an alternative view such methods are believed to disrupt rule induction (Sweller, 1988). We suggest that the role of general methods in learning varies with both the (...) specificity of the problem solver's goal and the systematicity of the strategies used for testing hypotheses about rules. In the absence of a specific goal people are more likely to use a rule‐induction learning strategy, whereas provision of a specific goal fosters use of difference reduction, which tends to be a non‐rule‐induction strategy. We performed two experiments to investigate the impact of goal specificity and systematicity of rule‐induction strategies in learning and transfer within a complex dynamic system. The results of Experiment 1 indicated that during free exploration of a problem space, greater learning occurred if participants adopted more systematic strategies for rule induction, and that participants come to favor such strategies. Experiment 2 revealed that participants who were provided with a specific goal performed well on the initial problem but were impaired on a transfer test using a similar problem with a different goal. Instruction on a systematic rule‐induction strategy facilitated solution for both the initial and transfer problems, but participants' use of this strategy declined if they had a specific goal. Our results support Sweller's (1988) proposal that general problemsolving methods applied to a specific goal foster acquisition of knowledge about an isolated solution path but do not provide an effective way of learning the overall structure of a problem space. We interpret these results in terms of dualspace theories of search through problem space. (shrink)

The term conceptual simulation refers to a type of everyday reasoning strategy commonly called “what if” reasoning. It has been suggested in a number of contexts that this type of reasoning plays an important role in scientific discovery; however, little direct evidence exists to support this claim. This article proposes that conceptual simulation is likely to be used in situations of informational uncertainty, and may be used to help scientists resolve that uncertainty. We conducted two studies to investigate the relationship (...) between conceptual simulation and informational uncertainty. Study 1 was an in vivo study of expert scientists; the results suggest that scientists do use conceptual simulation in situations of informational uncertainty, and that they use conceptual simulation to make inferences from their data using the analogical reasoning process of alignment by similarity detection. Study 2 experimentally manipulated experts' level of uncertainty and provides further support for the hypothesis that conceptual simulation is more likely to be used in situations of informational uncertainty. Finally, we discuss the relationship between conceptual simulation and other types of reasoning using qualitative mental models. (shrink)

In 1983, Dr. J. Robin Warren and Dr. Barry Marshall reported finding a new kind of bacteria in the stomachs of people with gastritis. Warren and Marshall were soon led to the hypothesis that peptic ulcers are generally caused, not by excess acidity or stress, but by a bacterial infection. Initially, this hypothesis was viewed as preposterous, and it is still somewhat controversial. In 1994, however, a U. S. National Institutes of Health Consensus Development Panel concluded that infection appears to (...) play an important contributory role in the pathogenesis of peptic ulcers, and recommended that antibiotics be used in their treatment. Peptic ulcers are common, affecting up to 10% of the population, and evidence has mounted that many ulcers can be cured by eradicating the bacteria responsible for them. (shrink)

What role can technology play in cultivating a disciplinary stance — raising questions, planning investigations, interpreting data and constructing explanations in a way that reflects disciplinary values and principles? How can overt and tacit expert scientific knowledge be captured, represented and used to design software that enables novices to assume a disciplinary stance in their investigations? We present The Galapagos Finches software designed to foster a biological and evolutionary stance. Our approach, Discipline-Specific Strategic Support , translates the main variable types, (...) comparison types and relationships in a discipline into manipulable objects in the interface. Pre/post-tests show how DSSS helps achieve a balance between content and process goals. A contrastive-case microanalysis of high, medium and low-achieving students' inquiry shows progress toward a disciplinary stance. Our study shows how software representations carry multiple levels of meaning, and that the efficacy of learning technologies hinges on reflection at both the navigation and disciplinary-signification levels. (shrink)

Humans appear to follow normative rules of inductive reasoning in "premise diversity tasks" that is, they know that dissimilar rather than similar evidence is better for generalising hypotheses. In three experiments, we use a "hypothesis limitation task" to compare a related inductive reasoning skill knowing how to limit hypotheses by using a negative test strategy. Participants are told that one category member has some property (e.g. Dogs have a merocrine gland) and are asked what evidence they would test to ensure (...) that either all (generalisation) or only (limitation) category members have that property (e.g. All/Only mammals have merocrine glands; tests: wolf, bull, crocodile). Despite participants' reluctance to use negative tests in the Wason 2-4-6 task and other reasoning tasks, participants do use normatively correct negative tests in the hypothesis limitation task as often as they use diverse positive tests in the premise diversity task. Moreover, when given a hypothesis limitation task before a rule evaluation task (similar to the 2-4-6 task), the use of negative tests increases. Thus, when testing hypotheses, people can and do use the right kind of test strategy for the task. (shrink)

A productive way to think about imagistic mental models of physical systems is as though they were sources of quasi‐empirical evidence. People depict or imagine events at those points in time when they would experiment with the world if possible. Moreover, just as they would do when observing the world, people induce patterns of behavior from the results depicted in their imaginations. These resulting patterns of behavior can then be cast into symbolic rules to simplify thinking about future problems and (...) to reveal higher order relationships. Using simple gear problems, three experiments explored the occasions of use for, and the inductive transitions between, depictive models and number‐based rules. The first two experiments used the convergent evidence of problem‐solving latencies, hand motions, referential language and error data to document the initial use of a model, the induction of rules from the modeling results, and the fallback to a model when a rule fails. The third experiment explored the intermediate representations that facilitate the induction of rules from depictive models. The strengths and weaknesses of depictive modeling and more analytic systems of reasoning are delineated to motivate the reasons for these transitions. (shrink)

The present study discusses findings that replicate and extend the original work of Burns and Vollmeyer (2002), which showed that performance in problem solving tasks was more accurate when people were engaged in a non-specific goal than in a specific goal. The main innovation here was to examine the goal specificity effect under both observation-based and conventional action-based learning conditions. The findings show that goal specificity affects the accuracy of problem solving in the same way, both when the learning stage (...) of the task is observationbased and when it is action-based. Additionally, the findings show that, when instructions do not promote goal specificity, observation-based problem solving is as effective as action-based problem solving. (shrink)

Why do some groups succeed where others fail? We hypothesise that collaborative success is achieved when the relationship between the dyad's prior expertise and the complexity of the task creates a situation that affords constructive and interactive processes between group members. We call this state the zone of proximal facilitation in which the dyad's prior knowledge and experience enables them to benefit from both knowledge-based problem-solving processes (e.g., elaboration, explanation, and error correction) andcollaborative skills (e.g., creating common ground, maintaining joint (...) attention to the task). To test this hypothesis we conducted an experiment in which participants with different levels of aviation expertise, experts (flight instructors), novices (student pilots), and non-pilots, read flight problem scenarios of varying complexity and had to identify the problem and generate a solution with either another participant of the same level of expertise or alone. The non-pilots showed collaborative inhibition on problem identification in which dyads performed worse than their predicted potential for both simple and complex scenarios, whereas the novices and experts did not. On solution generation the non-pilot and novice dyads performed at their predicted potential with no collaborative inhibition on either simple or complex scenarios. In contrast, expert dyads showed collaborative gains, withdyads performing above their predicted potential, but only for the complex scenarios. On simple scenarios the expert dyads showed collaborative inhibition and performed worse than their predicted potential. We discuss the implications of these results for theories of collaborative problem solving. (shrink)

Collecting information that one expects to be useful is a powerful way to facilitate learning. However, relatively little is known about how people decide which information is worth sampling over the course of learning. We describe several alternative models of how people might decide to collect a piece of information inspired by “active learning” research in machine learning. We additionally provide a theoretical analysis demonstrating the situations under which these models are empirically distinguishable, and we report a novel empirical study (...) that exploits these insights. Our model-based analysis of participants' information gathering decisions reveals that people prefer to select items which resolve uncertainty between two possibilities at a time rather than items that have high uncertainty across all relevant possibilities simultaneously. Rather than adhering to strictly normative or confirmatory conceptions of information search, people appear to prefer a “local” sampling strategy, which may reflect cognitive constraints on the process of information gathering. (shrink)

Locked and unlocked strategies are at the center of this article, as ways of shedding new light on the cognitive aspects of deep learning machines. The character and the role of these cognitive strategies, which are occurring both in humans and in computational machines, is indeed strictly related to the generation of cognitive outputs, which range from weak to strong level of knowledge creativity. I maintain that these differences lead to important consequences when we analyze computational AI programs, such as (...) AlphaGo, which aim at performing various kinds of abductive hypothetical reasoning. In these cases, the programs are characterized by _locked_ abductive strategies: they deal with weak (even if sometimes amazing) kinds of hypothetical creative reasoning, because they are limited in what I call eco-cognitive openness, which instead qualifies human cognizers who are performing higher kinds of abductive creative reasoning, where cognitive strategies are instead _unlocked_. (shrink)

Despite the accumulation of substantial cognitive science research relevant to education, there remains confusion and controversy in the application of research to educational practice. In support of a more systematic approach, we describe the Knowledge-Learning-Instruction (KLI) framework. KLI promotes the emergence of instructional principles of high potential for generality, while explicitly identifying constraints of and opportunities for detailed analysis of the knowledge students may acquire in courses. Drawing on research across domains of science, math, and language learning, we illustrate the (...) analyses of knowledge, learning, and instructional events that the KLI framework affords. We present a set of three coordinated taxonomies of knowledge, learning, and instruction. For example, we identify three broad classes of learning events (LEs): (a) memory and fluency processes, (b) induction and refinement processes, and (c) understanding and sense-making processes, and we show how these can lead to different knowledge changes and constraints on optimal instructional choices. (shrink)

This paper describes an abductive process model of anomalous data integration. The model makes use of the entrenchment of the current explanation and the probability of alternative explanations. It is hypothesised that increasing confirmation of the anom-aly itself increases the probability of alternative explanations. In an experimental study we found that both the entrenchment of an existing explanation and confirmation of the anomaly clearly influence how people resolve anomalous data. These results are in agreement with the predic-tions of the model.