We use evidence from cognitive psychology and the history of science to examine the issue of the theory-ladenness of perceptual observation. This evidence shows that perception is theory-laden, but that it is only strongly theory-laden when the perceptual evidence is ambiguous or degraded, or when it requires a difficult perceptual judgment. We argue that debates about the theory-ladenness issue have focused too narrowly on the issue of perceptual experience, and that a full account of the scientific process requires an examination (...) of theory-ladenness in attention, perception, data interpretation, data production, memory, and scientific communication. We conclude that the evidence for theory-ladenness does not lead to a relativist account of scientific knowledge. (shrink)

There is a demand to nurture scientific creativity in science education. This paper proposes that the relevant conceptual infrastructure with which to teach scientific creativity is often already included in philosophy of science courses, even those that do not cover scientific creativity explicitly. More precisely, it is shown how paradigm theory can serve as a framework with which to introduce the differences between combinational, exploratory, and transformational creativity in science. Moreover, the types of components given in Kuhn’s disciplinary matrix are (...) argued to indicate a further subdivision within transformational creativity that makes explicit that this most radical type of creativity that aims to go beyond and thus to transform the current paradigm can take many different directions. More generally, it is argued that there are several synergies between the topic of scientific creativity and paradigm theory that can be utilized in most philosophy of science courses at relative ease. Doing so should promote the understanding of scientific creativity among students, provide another way to signify the relevance of paradigm theory, and more strategically be a way of reinforcing the place of philosophy of science in science education. (shrink)

Future professionals should be prepared for scientific reasoning, i.e., to construct and apply scientific knowledge, in order to analyze and solve problems in their professional practice. Yet, future practitioners’ scientific reasoning skills often seem to be deficient when solving practical problems. This dissertation explores to what extent collaboration may foster the engagement of future practitioners in scientific reasoning: i.e., in epistemic processes and in referring to scientific content knowledge. Therefore, two studies were conducted to compare collaborative and individual problem solving (...) of pre-service teachers regarding their scientific reasoning. Study 1 investigates the effect of group heterogeneity with respect to problem solving scripts on scientific reasoning. Study 2 explores to what extent Epistemic Network Analysis can serve as a methodological approach for measuring scientific reasoning. As part of Study 1, pre-service teachers solved an educational problem either as individuals or as pairs. Collaboration showed a mixed effect on scientific reasoning processes: pairs engaged more in explaining and reasoning about the problem and drew more conclusions, while individuals engaged more in generating solutions. Additional analyses showed that the more heterogeneous pairs were regarding their members’ problem solving scripts the more they engaged in hypothesizing and evaluating evidence and the less they engaged in generating solutions. Finally, pairs less often referred to scientific content than individuals did during problem solving. Study 2 further analyzed the data by applying Epistemic Network Analysis. This method has the advantage of analyzing patterns of connections between epistemic processes, i.e., epistemic networks of scientific reasoning. The central epistemic process for pairs was evidence evaluation, which they frequently used in combination with hypothesizing and communicating and scrutinizing. On the other hand, the most characteristic process in individuals’ scientific reasoning was solution generation, which very often co-occurred with hypothesizing and evidence evaluation. The overall results indicate that if the aim is to develop a more reflective understanding of the problem, future practitioners should collaborate with each other, especially in heterogeneous settings. However, they should be supported to share knowledge regarding scientific theories and evidence as well as to reach a mutual understanding on the problem after a certain time so as to be able to have the capacity of generating solutions. Moreover, the different effects of collaboration on the process and content aspects of scientific reasoning imply that scientific reasoning might not be a unidimensional construct, and its process and content levels should be differentiated in future research. A further important methodological implication is that the process aspect of scientific reasoning can be analyzed as a network of interconnected skills and such analysis might bring more explanatory value than the mere reliance on frequencies of occurrences of isolated activities. (shrink)

The purpose of this paper is to provide a detailed technical protocol analysis of chess masters' evaluative expertise, paying particular attention to the analysis of the structure of their memory process in evaluating foreseen possibilities in games of dynamic equilibrium. The paper has two purposes. First, to publish a results chapter from my DPhil thesis (in revised journal article form) attending to the measurement of foresight in chess masters' evaluation process, testing alternative theories of cognitive expertise in the domain of (...) chess; and second to provide a subset of the technical graphical analysis that corresponds to that measurement to preserve this protocol analysis for access in the academic domain for future studies of expert memory and foresight (e.g., Ericsson & Simon, 1993). The step-by-step protocol analysis consists of: (i) an introduction to foresight cognition as hypothesis testing, (ii) a theoretical review in the domain of chess masters' expertise according to the theoretical frameworks in that field purporting hypotheses relevant to chess masters' evaluative skill processes, and (iii) summary tables and non-parametric statistical analysis corroborating chunking theory frameworks of expert cognition (e.g., DeGroot, 1965; Newell & Simon, 1972; Gobet, 1998; Gobet et al., 2004), and refuting the alternative search-evaluation models (e.g., Holding & Reynolds, 1982). Moreover, the journal article espouses the preservation of the traditional protocol analysis method core to the field of expert cognition (DeGroot, 1969; Kotov, 1971). The full protocol analysis can be found in monograph form here on my SSRN profile in ‘The role of falsification in hypothesis testing’. It takes the form of a specialist population study (e.g., detailed case study work; Luria, 1987). Thus the outline consists of a short introduction, a theoretical methodological review discussing protocol analysis methods for specialist population studies in cognition (with particular attention to the preservation of protocol analysis methods for chess studies in cognition and expert memory/ with a fresh angle on the foresight process), and the full set of protocol analyses with corresponding problem behaviour graphs. A subset of the main results has been published elsewhere (e.g., Cowley & Byrne, 2004; Cowley 2006), receiving scientific and scientific journalistic acclaim (e.g., Nature Online News 2004). (shrink)

In three empirical studies we examined how people reason about prior convictions in child abuse cases. We tested whether the disclosure of similar prior convictions prompts a mental representation or an additive probative value (Criminal Justice Act, 2003). Asymmetrical use of similar priors were observed in three studies. A pilot study showed that disclosure of a second prior did not contribute a weight equivalent to that of the first disclosure. Study 1 showed jurors did not see left-handed evidence (i.e. matching (...) victim bruising) as more indicative of guilt than right-handedness unless a prior conviction was present, and the presence of priors suppressed the generation of alternative possibilities indicative of innocence. Study 2 showed that disclosure did not decrease community ratings of reoffending propensity and dangerousness as much as a similar prior conviction increased them. We consider the results in the context of a new psychological theory of prior conviction bias and the consequences for the implementation of Section 100 of the Criminal Justice Act (2003). (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)

What is creativity? One new idea may be creative, whereas another is merely new: What's the difference? And how is creativity possible? These questions about human creativity can be answered, at least in outline, using computational concepts. There are two broad types of creativity, improbabilist and impossibilist. Improbabilist creativity involves novel combinations of familiar ideas. A deeper type involves METCS: the mapping, exploration, and transformation of conceptual spaces. It is impossibilist, in that ideas may be generated which – with respect (...) to the particular conceptual space concerned – could not have been generated before. The more clearly conceptual spaces can be defined, the better we can identify creative ideas. Defining conceptual spaces is done by musicologists, literary critics, and historians of art and science. Humanist studies, rich in intuitive subtleties, can be complemented by the comparative rigour of a computational approach. Computational modelling can help to define a space, and to show how it may be mapped, explored, and transformed. Impossibilist creativity can be thought of in “classical” Al terms, whereas connectionism illuminates improbabilist creativity. Most Al models of creativity can only explore spaces, not transform them, because they have no self-reflexive maps enabling them to change their own rules. A few, however, can do so. A scientific understanding of creativity does not destroy our wonder at it, nor does it make creative ideas predictable. Demystification does not imply dehumanization. (shrink)

Beyond modularityattempts a synthesis of Fodor's anticonstructivist nativism and Piaget's antinativist constructivism. Contra Fodor, I argue that: (1) the study of cognitive development is essential to cognitive science, (2) the module/central processing dichotomy is too rigid, and (3) the mind does not begin with prespecified modules; rather, development involves a gradual process of “modularization.” Contra Piaget, I argue that: (1) development rarely involves stagelike domain-general change and (2) domainspecific predispositions give development a small but significant kickstart by focusing the infant's (...) attention on proprietary inputs. Development does not stop at efficient learning. A fundamental aspect of human development (“representational redescription”) is the hypothesized process by which information that isina cognitive system becomes progressively explicit knowledgetothat system. Development thus involves two complementary processes of progressive modularization and progressive “explicitation.” Empirical findings on the child as linguist, physicist, mathematician, psychologist, and notator are discussed in support of the theoretical framework. Each chapter concentrates first on the initial state of the infant mind/brain and on subsequent domain-specific learning in infancy and early childhood. It then goes on to explore data on older children's problem solving and theory building, with particular focus on evolving cognitive flexibility. Emphasis is placed throughout on the status of representations underlying different capacities and on the multiple levels at which knowledge is stored and accessible. Finally, consideration is given to the need for more formal developmental models, and a comparison is made between representational redescription and connectionist simulations of development. In conclusion, I consider what is special about human cognition by speculating on the status of representations underlying the structure of behavior in other species. (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)

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)

With the advent of computers in the experimental labs, dynamic systems have become a new tool for research on problem solving and decision making. A short review of this research is given and the main features of these systems (connectivity and dynamics) are illustrated. To allow systematic approaches to the influential variables in this area, two formal frameworks (linear structural equations and finite state automata) are presented. Besides the formal background, the article sets out how the task demands of system (...) identification and system control can be realised in these environments, and how psychometrically acceptable dependent variables can be derived. (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)

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)

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)

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)

How do expert performers practice as they develop creatively? This study investigated the processes involved in the practice of new skills by expert breakdancers. A great deal of evidence supports the theory of “deliberate practice” (Ericsson, Krampe, & Tesch‐Römer, 1993,Psychological Review, 100,363) in skill acquisition; however, expert creative performers may emphasize other forms of practice for skill development. Four case studies collected through fieldwork and laboratory observation were analyzed to evaluate expert dancers’ practice processes as they developed proficiency in new, (...) specific skills. We focused on three aspects of learning: the degree of skill acquisition, the content of skills included in practice, and dancers’ stated purposes for practicing. The results showed that dancers’ practice improved skills (as suggested by deliberate practice) and engaged the exploration of new, original skills, along with coordinating skills within performance. In their practice, these dance experts went beyond deliberative practice to highly exploratory processes for skill development. (shrink)

In this paper, we present a competency model for complex problem solving by building on the categories of Knowledge, Skills, Abilities, and Other components. We highlight domain-general and domain-specific components in each of these categories, review established conceptualizations of CPS, and present a new model of CPS competency that is meant to provide a starting point for systematic research on training and assessment. The model highlights the idea that complex problems differ with regard to the KSAO components they demand from (...) a problem solver and that performance in one problem does not necessarily predict performance in a different problem. Implications for research on the training and assessment of CPS competency are discussed, and a selection of well-established tests for various components of the KSAO model is provided. (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)