Unlike behaviorism, cognitive psychology relies on mental concepts to explain behavior. Yet mental processes are not directly observable and multiple explanations are possible, which poses a challenge for finding a useful framework. In this article, I distinguish three new frameworks for explanations that emerged after the cognitive revolution. The first is called tools‐to‐theories: Psychologists' new tools for data analysis, such as computers and statistics, are turned into theories of mind. The second proposes as‐if theories: Expected utility theory and Bayesian statistics (...) are turned into theories of mind, describing an optimal solution of a problem but not its psychological process. The third studies the adaptive toolbox (formal models of heuristics) that describes mental processes in situations of uncertainty where an optimal solution is unknown. Depending on which framework researchers choose, they will model behavior in either situations of risk or of uncertainty, and construct models of cognitive processes or not. The frameworks also determine what questions are asked and what kind of data are generated. What all three frameworks have in common, however, is a clear preference for formal models rather than explanations by general dichotomies or mere verbal concepts. The frameworks have considerable potential to inform each other and to generate points of integration. (shrink)

There is common agreement that preschool-level science education affects children’s curiosity, their positive approach towards science, and their desire to engage with the subject. Children’s natural curiosity drives them to engage enthusiastically in all forms of exploration. Engaging in scientific exploration necessitates self-regulation capabilities and a wide repertoire of cognitive and metacognitive strategies. The purpose of this study was to examine to what extent preschoolers (aged 5‒6 years) implement nascent inquiry skills, metacognitive awareness, and self-regulation capabilities during play-based scientific exploration (...) tasks. An additional purpose was to investigate the relationships between these capabilities, a relationship not yet investigated in the context of play-based, scientific exploration among young children. The study consisted of 215 preschoolers, from 10 preschools. For this study, we developed two scientific exploration tasks—structured and open-ended. Our motivation was to examine whether preschoolers’ capabilities will differ in the context of structured task which is aligned with the view that young children need guidance and explicit instructions compared to the context of open-ended, play-based task–allowing the children to apply and test their intuitive theories and skills. During performance participants were videotaped. Their verbal and non-verbal responses were analyzed by means of a scoring scheme. The results of a micro-analysis of about 100 hours of video showed that given the opportunity, even without setting explicit goals and instructions, children exhibit inquiry capabilities: they ask questions, plan, hypothesize, use tools, draw conclusions. Asking questions and planning were better manifested during the structured task. Children also manifested higher levels of attention, persistence, and autonomy during the structured task. However, significant higher scores of self-regulation indications were revealed in the context of the open-ended, play-based, exploration task. Moreover, results indicate significant correlations between the five measures of preschoolers’ inquiry capabilities and measures of metacognitive strategic awareness and self-regulation. The results of the present study suggest the importance of combining various learning environments and experiences in early science education that encourage children to engage in structured exploration alongside play-based, open-ended, exploration. (shrink)

This paper is about guessing: how people respond to a question when they aren’t certain of the answer. Guesses show surprising and systematic patterns that the most obvious theories don’t explain. We argue that these patterns reveal that people aim to optimize a tradeoff between accuracy and informativity when forming their guess. After spelling out our theory, we use it to argue that guessing plays a central role in our cognitive lives. In particular, our account of guessing yields new theories (...) of belief, assertion, and the conjunction fallacy—the psychological finding that people sometimes rank a conjunction as more probable than one of its conjuncts. More generally, we suggest that guessing helps explain how boundedly rational agents like us navigate a complex, uncertain world. (shrink)

The rise of conspiracy culture and the growing influence of conspiracy theories have attracted the attention of scholars from a range of fields. In recent years, Daniel Jolley, Asbjørn Dyrendal, and others have noted the prevalence of conspiracy theories amongst adolescent schoolchildren in Scandinavia and the UK. This article draws on Paulo Freire’s concept of the ‘banking model’ of education to make the case against a ‘debunking’ approach to anticonspiracist education. It argues that conspiracism should be understood as a feature (...) of our culture and as such it should be interrogated rather than dismissed by educators who are engaged in the process of educating for democracy. (shrink)

A substantial body of evidence shows that people tend to rely too heavily on explanations when trying to justify an opinion. Some research suggests these errors may arise from an inability to distinguish between explanations and the evidence that bears upon them. We examine an alternative account, that many people do distinguish between explanations and evidence, but rely more heavily on unsubstantiated explanations when evidence is scarce or absent. We examine the philosophical and psychological distinctions between explanation and evidence, and (...) show that participants use explanations as a substitute for missing evidence. Experiment 1 replicates the results of other researchers, but further shows that participants generate more evidence when they are not constrained by their lack of data. Merely mentioning a source of data can alter both their evaluation (Experiment 2) and their production (Experiment 3) of explanations and evidence. In Experiment 4, we show that participants can explicitly consider the availability of evidence and other pragmatic factors when evaluating arguments. Finally, we consider the implications of using explanations to replace missing evidence as a strategy in argument. (shrink)

A substantial body of evidence shows that people tend to rely too heavily on explanations when trying to justify an opinion. Some research suggests these errors may arise from an inability to distinguish between explanations and the evidence that bears upon them. We examine an alternative account, that many people do distinguish between explanations and evidence, but rely more heavily on unsubstantiated explanations when evidence is scarce or absent. We examine the philosophical and psychological distinctions between explanation and evidence, and (...) show that participants use explanations as a substitute for missing evidence. Experiment 1 replicates the results of other researchers, but further shows that participants generate more evidence when they are not constrained by their lack of data. Merely mentioning a source of data can alter both their evaluation (Experiment 2) and their production (Experiment 3) of explanations and evidence. In Experiment 4, we show that participants can explicitly consider the availability of evidence and other pragmatic factors when evaluating arguments. Finally, we consider the implications of using explanations to replace missing evidence as a strategy in argument. (shrink)

Constructing an intuitive theory from data confronts learners with a “chicken‐and‐egg” problem: The laws can only be expressed in terms of the theory's core concepts, but these concepts are only meaningful in terms of the role they play in the theory's laws; how can a learner discover appropriate concepts and laws simultaneously, knowing neither to begin with? We explore how children can solve this chicken‐and‐egg problem in the domain of magnetism, drawing on perspectives from computational modeling and behavioral experiments. We (...) present 4‐ and 5‐year‐olds with two different simplified magnet‐learning tasks. Children appropriately constrain their beliefs to two hypotheses following ambiguous but informative evidence. Following a critical intervention, they learn the correct theory. In the second study, children infer the correct number of categories given no information about the possible causal laws. Children's hypotheses in these tasks are explained as rational inferences within a Bayesian computational framework. (shrink)

The ability to recognize and correct errors in one's explanatory understanding is critically important for learning. However, little is known about the mechanisms that determine when and under what circumstances errors are detected and how they are corrected. The present study investigated thought experiments as a potential tool that can reveal errors and trigger belief revision in the service of error correction. Across two experiments, 1149 participants engaged in reasoning about force and motion (a domain with well‐documented misconceptions) in a (...) pre‐training—training—post‐training design. The two experiments manipulated the type of mental model manipulated in the thought experiments (i.e., whether participants reasoned about forces acting on their own bodies vs. on external objects), as well as the level of relational and argumentative reasoning about the outcomes of the thought experiments. The results showed that: (i) thought experiments can serve as a tool to elicit inconsistencies in one's representations; (ii) the level of relational and argumentative reasoning determines the level of belief revision in the service of error correction; and (iii) the type of mental model manipulated in a thought experiment determines its outcome and its potential to initiate belief revision. Thought experiments can serve as a valuable teaching and learning tool, and they can help us better understand the nature of error detection and correction systems. (shrink)

Can the output of human cognition be predicted from the assumption that it is an optimal response to the information-processing demands of the environment? A methodology called rational analysis is described for deriving predictions about cognitive phenomena using optimization assumptions. The predictions flow from the statistical structure of the environment and not the assumed structure of the mind. Bayesian inference is used, assuming that people start with a weak prior model of the world which they integrate with experience to develop (...) stronger models of specific aspects of the world. Cognitive performance maximizes the difference between the expected gain and cost of mental effort. Memory performance can be predicted on the assumption that retrieval seeks a maximal trade-off between the probability of finding the relevant memories and the effort required to do so; in categorization performance there is a similar trade-off between accuracy in predicting object features and the cost of hypothesis formation; in casual inference the trade-off is between accuracy in predicting future events and the cost of hypothesis formation; and in problem solving it is between the probability of achieving goals and the cost of both external and mental problem-solving search. The implemention of these rational prescriptions in neurally plausible architecture is also discussed. (shrink)

The topic of my thesis is individual and collective responsibility for collectively caused systemic harms, with climate change as the case study. Can an individual be responsible for these harms, and if so, how? Furthermore, what does it mean to say that a collective is responsible? A related question, and the second main theme, is how ignorance and knowledge affect our responsibility. -/- My aim is to show that despite the various complexities involved, an individual can have responsibility to address (...) climate change. I argue that climate change is not a problem just for states and international bodies, but also for individuals. There are three possible sources of moral responsibility for individuals in relation to climate change harms: direct responsibility (individuals qua individuals), shared responsibility as members (individuals qua members of collective agents), and shared responsibility as constituents (individuals qua constituents of unorganised collectives). -/- Accounts that deny individual responsibility fail to either take our interdependent reality seriously or fail to understand marginal participation (or in the case direct responsibility, fail to appreciate the nature of the climate change phenomenon). Individuals can be complicit in climate change harms, either as members of collective agents (e.g. as citizens of states or employees of a corporation) or as constituents of unorganised collectives (e.g. as consumers or polluters). -/- Although I focus on individual complicity, I do not deny the obligations of collective agents. However, nation-states, governments, and international bodies are not the only relevant collective agents in climate ethics: other collective agents, such as corporations, matter also and can have obligations concerning making sure that their activities are as carbon-neutral as possible. In addition, those corporations that have engaged in lobbying against climate regulation through creating and disseminating misleading information have acquired themselves additional obligations to mitigate climate change and compensate for the harm they have caused. Even so, the ethical claims can only be understood by individual members of these collective agents because only they can feel the pull of moral claims. I suggest that we could distinguish between what one must possess in order to be capable of making moral claims (i.e. moral agency conditions), and what it means to have the ability to exhibit such claims through one’s conduct. -/- Individual direct responsibility is to not to increase the probable risk of serious harm to other people, at least as long as we can do so at a less than significant cost to ourselves. It is limited to relatively wealthy individuals. Offsetting is not a reliable way to meet this duty; we need to look at the emissions from our lifestyle choices (within the available infrastructure). Shared responsibility qua members of collective agents is the key individual responsibility, and it presses especially on those occupying key positions within key collective agents. Saying that, our shared responsibility qua constituents of unorganised collectives has the potential to be decisive in whether some action is taken or not, either through a set of actions that can signal certain acceptance or support, or as a form of political support from the grass roots. (shrink)

I examined the proposition that there are psychological limits on what scientific problems can be solved, and that these limits may be based on a failure to be able to produce imagable, observation-based models for any possible solution, a position suggested by philosopher Colin McGinn in an argument attempting to prove that the mind-body problem is unsolvable. I examined another likely candidate for an unsolvable problem -- the ultimate origin of the universe (i.e., what might have preceded the Big Bang (...) or any other starting point; why there should be something rather than nothing) -- by exploring the reasoning of physicists about this problem and measuring visual imagery frequency and vividness, with the expectation that those who most believed the problem unsolvable would be more frequent/vivid imagers and therefore more affected by the apparent impossibility of producing an imagable solution. Eight physicists were interviewed and imagery frequency and vividness measurements performed using Cohen & Saslona's IDQ-IHS and Marks's VVIQ, respectively. All subjects considered the problem unsolvable within today's physics and all but one thought the problem still meaningful, though none were optimistic about a solution. The one subject who dismissed the problem had the lowest imagery frequency score, and there was also a significant rank order correlation (r = 0.83, p < .02) between degree of belief in problem unsolvability (extended to include viewing the problem as meaningful and not already solved) and a composite imagery frequency/vividness score, though the sample was too small to control for some possible confounding factors. (shrink)

Within the last ten to fifteen years, models emerged for describing and developing undergraduate research and inquiry. This article discusses four examples of modelling didactical issues around undergraduate research and inquiry. The aim of the first part of this article is to scrutinize the epistemological and the didactical purpose of these models. As essential dimensions of undergraduate research and inquiry are neglected, two new models are developed. The first puts the coordination of theory and evidence in the centre and determines (...) three different horizons of significance in the field of science. In relation to these horizons, the second model highlights the broader societal context of scientific practices. The role of research and science in society is recognized here on the one hand as an affirmative process of institutionalization of approved knowledge, and on the other, as practices of criticizing and breaking away from established forms of knowing, testing, evaluating, and approving. Concomitantly, the education of students through and within scientific practices of research and inquiry is interpreted against the backdrop of two opposite trajectories influencing students’ ambitions to engage with problems of science. Didactics of higher education therefore need to distinguish between long-term and short-term goals. It is argued that the latter concretizes the former. This understanding of teaching is corroborated against the backdrop of the concept of learning as ‘enculturation’ in science. The argumentation refers back to insights by Lev S. Vygotsky, Ludwik Fleck and Michael Polanyi. (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)