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Department of Science Education, Kyungnam University, Kyungnam 631-260, KoreaAbstract: This study investigated students’ alternative conceptions of plate boundaries and their conception revisionaccording to the pattern of students’ reasoning. Participants were twenty-two 10th grade high school students. Allparticipants were asked to draw the three types of plate boundaries and to explain their drawings. Nine studentsparticipated in the reasoning activity. To this end, a semi-structured interview was conducted during which key questionswere asked for the students to individually answer. The key questions used in the reasoning activity were created, byutilizing questions used in the previous studies. The findings revealed that the alternative conceptions of plate boundarieswere classified into three levels based on established criteria. Students who attempted a variety of reasoning strategiessuch as causal reasoning, using an analogy, abductive reasoning, data reconstruction and concept combination, revised theiralternative conception to a scientific conception after the reasoning activity. On the other hand, some students could notrevise their alternative conceptions because they only conducted an incomplete reasoning strategy. The study also foundthat they were unable to use other reasoning strategies, either.Keywords: alternative conceptions, plate tectonics, causal reasoning, using an analogy, abductive reasoning

Preferences play a crucial role in the theory of rationality, and therefore also to computational social choice and artificial intelligence. In formal models it is usually assumed that preferences are primitive objects, and little concern is devoted to the question of how they are formed or where they come from. Recent work in rational choice theory challenges this assumption, however, and aims to give more internal structure to the notion of a preference. The focus so far has been on modeling faculties of individual agents, such as their mood, mindset, and motivating reasons. In this paper we contribute to this development by developing a modal logic for reasoning about preferences that depend on a set of motivationally salient properties. The main result is a translation showing how reasoning in this logic can be captured by reasoning in a standard modal logic (KT with universal modality). It follows that reasoning systems and algorithms developed for modal logic (with universal modality) can be employed for reasoning about reason-based preferences. We then discuss how the approach can be generalised to the multi-agent case, and allows us to reason about agents who disagree because they are motivated by different factors, and who might be able to reach consensus simply by changing their perspective.

We combine Peirce’s rule, case, and result with Toulmin’s data, claim, and warrant to differentiate between deductive, inductive, abductive, and analogical reasoning within collective argumentation. In this theoretical article, we illustrate these kinds of reasoning in episodes of collective argumentation using examples from one teacher’s practice. Examining different kinds of reasoning in collective argumentation can inform how students engage in generating and examining hypotheses using inductive and abductive reasoning and move toward the deductive reasoning required for proof. Mathematics educators can build on their understanding of these kinds of reasoning to support students in reasoning in productive ways.

A wide range of human reasoning patterns can be explained as conditioning in probabilistic models; however, conditioning has traditionally been viewed as an operation applied to such models, not represented in such models. We describe how probabilistic programs can explicitly represent conditioning as part of a model. This enables us to describe reasoning about others' reasoning using nested conditioning. Much of human reasoning is about the beliefs, desires, and intentions of other people; we use probabilistic programs to formalize these inferences in a way that captures the flexibility and inherent uncertainty of reasoning about other agents. We express examples from game theory, artificial intelligence, and linguistics as recursive probabilistic programs and illustrate how this representation language makes it easy to explore new directions in each of these fields. We discuss the algorithmic challenges posed by these kinds of models and describe how dynamic programming techniques can help address these challenges.

This position paper advocates combining formal epistemology and the new paradigm psychology of reasoning in the studies of conditionals and reasoning with uncertainty. The new paradigm psychology of reasoning is characterized by the use of probability theory as a rationality framework instead of classical logic, used by more traditional approaches to the psychology of reasoning. This paper presents a new interdisciplinary research program which involves both formal and experimental work. To illustrate the program, the paper discusses recent work on the paradoxes of the material conditional, nonmonotonic reasoning, and Adams’ Thesis. It also identifies the issue of updating on conditionals as an area which seems to call for a combined formal and empirical approach.

Although diagrams have been widely used as methods for introducing students to elementary logical reasoning, it is still open to debate in cognitive psychology whether logic diagrams can aid untrained people to successfully conduct deductive reasoning. In our previous work, some empirical evidence was provided for the effectiveness of Euler diagrams in the process of solving categorical syllogisms. In this paper, we discuss the question of why Euler diagrams have such inferential efficacy in the light of a logical and proof-theoretical analysis of categorical syllogisms and diagrammatic reasoning. As a step towards an explanatory theory of reasoning with Euler diagrams, we argue that the effectiveness of Euler diagrams in supporting syllogistic reasoning derives from the fact that they are effective ways of representing and reasoning about relational structures that are implicit in categorical sentences. A special attention is paid to how Euler diagrams can facilitate the task of checking the invalidity of an inference, a task that is known to be particularly difficult for untrained reasoners. The distinctive features of our conception of diagrammatic reasoning are made clear by comparing it with the model-theoretic conception of ordinary reasoning developed in the mental model theory.

We argue that the epistemic modal ‘might’ is a generator of alternatives in the sense of Hamblin semantics (Kratzer & Shimoyama 2002) or inquisitive semantics (Ciardelli, Groenendijk & Roelofsen 2009). Building on methodologies from the psychology of reasoning, we show that ‘might’ patterns with disjunctions and with indefinites in giving rise to a particular kind of illusory inference. The best extant accounts of these illusory inferences crucially involve alternatives, paired with matching strategies (Walsh & Johnson-Laird 2004) or with question-answer dynamics (Koralus & Mascarenhas 2013). Our results constitute further steps toward convergence between theories and methodologies in natural language semantics and the psychology of reasoning.

In this paper, a critical discussion is made of the role of entailments in the so-called New Paradigm of psychology of reasoning based on Bayesian models of rationality (Elqayam & Over, 2013). It is argued that assessments of probabilistic coherence cannot stand on their own, but that they need to be integrated with empirical studies of intuitive entailment judgments. This need is motivated not just by the requirements of probability theory itself, but also by a need to enhance the interdisciplinary integration of the psychology of reasoning with formal semantics in linguistics. The constructive goal of the paper is to introduce a new experimental paradigm, called the Dialogical Entailment task, to supplement current trends in the psychology of reasoning towards investigating knowledge-rich, social reasoning under uncertainty (Oaksford & Chater, 2019). As a case study, this experimental paradigm is applied to reasoning with conditionals and negation operators (e.g. CEM and wide and narrow-scope negation). As part of the investigation, participants' entailment judgments are evaluated against their probability evaluations to assess participants' cross-task consistency over two experimental sessions.

This short article provides an introduction to a special section, consisting of six papers on human evolution and the imago Dei. These papers are the result of dialogue between theologians and philosophers of religion at the University of Oxford and the Catholic University of Leuven. All contributors focus on the imago Dei, and consider how this theological notion can be understood from an evolutionary perspective, looking at a variety of disciplines, including the psychology of reasoning, cognitive science of religion, paleoanthropology, evolutionary psychology, and evolutionary ethics.

The primary goal of this study was the broad assessment and modeling of scientific reasoning in elementary school age. One hundred fifty-five fourth graders were tested on 20 recently developed paper-and-pencil items tapping four different components of scientific reasoning (understanding the nature of science, understanding theories, designing experiments, and interpreting data). As confirmed by Rasch analyses, the scientific reasoning items formed a reliable scale. Model comparisons differentiated scientific reasoning as a separate construct from measures of intelligence and reading skills and revealed discriminant validity. Furthermore, we explored the relationship between scientific reasoning and the postulated prerequisites inhibitory control, spatial abilities and problem-solving skills. As shown by correlation and regression analyses, beside general cognitive abilities (intelligence, reading skills) problem-solving skills and spatial abilities predicted performance in scientific reasoning items and thus contributed to explaining individual differences in elementary school children's scientific reasoning competencies.

The new Bayesian paradigm in the psychology of reasoning aims to integrate the study of human reasoning, decision making, and rationality. It is supported by two findings. One, most people judge the probability of the indicative conditional, P(if A then B), to be the conditional probability, P(B|A), as implied by the Ramsey test. Two, they judge if A then B to be void when A is false. Their three-valued response table used to be called 'defective', but should be termed the de Finetti table. We show how to study general de Finetti truth tables for negations, conjunctions, disjunctions, and conditionals.

In this paper, we suggest that individual experience and reasoning, as applied to new endeavors in internationalization, are concepts with high potential to advance conceptual and empirical research in international entrepreneurship (IE). Experience is known to be important in internationalization, but the logic or reasoning with which it is applied is insufficiently understood. Cognitive, comparison–based reasoning theories explain how individuals draw on experience to make sense of uncertain, novel, and complex situations. Drawing on two such theories, heuristics and analogical reasoning, we delineate the logic of experience and advance speculative propositions on its utility in the context of internationalization research.

We present an implementation of the hypothetical-thinking capacities of our cognitive architecture, based on Stanovich’s tripartite framework ( Stanovich, 2009 ). To illustrate and study processing by this new feature, we simulated a well-known task in the psychology of reasoning (the Wason card selection task) with four different cognitive styles (strongly reactive, purely executive, weakly reflective, purely reflective) and were able to reproduce the results and types of errors found in studies of human reasoning abilities. The first three profiles account for the results of 90% of human subjects (all those who provide answers that are not acceptable by logical standards). The strongly reactive profile gave a plausible account of the way humans provide the logically incorrect answer to the task. The purely executive and weakly reflective gave an account of how subjects can provide part of the correct answer. The last profile (purely reflective), a much a much slower process, produces a complete and correct answer by logical standards. While the purely reflective process was the only one able to do this, it is to be noted that the purely executive system was able to provide a correct (but incomplete) answer using less computational resources (time).

Diagnostic reasoning and medical decision making have been focal areas of research in the fields of medical education, cognition, and artificial intelligence in medicine. Drawing on several decades worth of research, we propose an integrated summary of prior research on diagnostic reasoning and decision making—in terms of both historical development and theoretical shifts. We also characterize the changes in research and theory resulting from the incorporation and adoption of health information technology in the clinical work place. In this paper, we differentiate between the various forms of diagnostic reasoning and trace the evolution of the various models of reasoning, including knowledge-based, exemplar-based, and visual strategies. We also discuss the effect of clinical expertise on reasoning processes. Within the medical decision-making research, we delineate the various approaches highlighting decision-making errors that arise due to the nature of heuristics and biases and other factors. Although there has been significant progress in our understanding, there is still a need for greater theoretical integration of disparate empirical phenomena. Specifically, there is a need to reconcile the various characterizations of reasoning and to evaluate the similarity and differences in the context of current health care practice. Finally, we discuss the role of human factors research in the study of clinical environments and also in relation to devising approaches and methodologies for understanding, evaluating, and supporting the diagnostic reasoning and decision processes.

Beginning with studying the characteristics of the war from a complex system, the limitations of rule-based reasoning approach was discussed, case-based reasoning to the integrated reasoning mechanism was supplemented. In accordance with epistemological, researched on case-based reasoning mechanism of integration and components of decision model, studied feature vector based case representation and matrix representation of non-isomorphic case set, presented a case retrieval method based on feature.

What are the developmental origins of our concept of animal? There has long been controversy concerning this question. At issue is whether biological reasoning develops from earlier forms of reasoning, such as physical and psychological reasoning, or whether from a young age children endow animals with biological properties. Here we demonstrate that 8-mo-old infants already expect novel objects they identify as animals to have insides. Infants detected a violation when an object that was self-propelled and agentive (but not an object that lacked one or both of these properties) was revealed to be hollow. Infants also detected a violation when an object that was self-propelled and furry (but not an object that lacked one or both of these properties) either was shown to be hollow or rattled (when shaken) as although mostly hollow. Young infants’ expectations about animals’ insides may serve as a foundation for the development of more advanced biological knowledge.

To what extent can the psychology of reasoning “serve humanity”? Three case studies suggest that the psychology of reasoning has failed to make itself as relevant to the welfare of humanity as its ...

Confusion about the nature of human reasoning and its appropriate application to patients has hampered veterinary students' development of these skills. Expertise is associated with greater ability to deploy pattern recognition (type 1 reasoning), which is aided by progressive development of data-driven, forward reasoning (in contrast to scientific, backward reasoning), analytical approaches that lead to schema acquisition. The associative nature of type 1 reasoning makes it prone to bias, particularly in the face of “cognitive miserliness,” when clues that indicate the need for triangulation with an analytical approach are ignored. However, combined reasoning approaches, from the earliest stages, are more successful than one approach alone, so it is important that those involved in curricular design and delivery promote student understanding of reasoning generally, and the situations in which reasoning goes awry, and develop students' ability to reason safely and accurately whether presented with a familiar case or with a case that they have never seen before.

Mental probability logic (MPL) has been proposed as a competence theory of human inference. MPL interprets indicative conditionals as conditional events. While recent probabilistic approaches assum...

Reasoning about inference problems that allow for multiple interpretations requires maintaining intermediate representations that, if necessary, may be reconsidered at a later stage of the solution process. In that respect we describe the process of annotating premises in spatial relational reasoning that allows for the derivation of alternative representations. Furthermore, we show how ACT-R's subsymbolic processing principles substantially contribute to the underlying theoretical framework of the Preferred Mental Model Theory as they add a powerful component making precise accuracy predictions possible, a feature that in previous symbolic approaches has been neglected. In addition, we implemented and compared two strategies to investigate the persistence of the outcomes of the reasoning process. Furthermore, we examined how well data and predictions meet the central assumption that reasoning difficulty increases with the number of mental operations necessary to validate a putative conclusion.

This article describes a framework for practical social reasoning designed to be used for analysis, specification, and implementation of the social layer of agent reasoning in multiagent systems. Our framework, called the expectation strategy behavior ESB framework, is based on i using sets of update rules for social beliefs tied to observations so-called expectations, ii bounding the amount of reasoning to be performed over these rules by defining a reasoning strategy, and iii influencing the agent's decision-making logic by means of behaviors conditioned on the truth status of current and future social beliefs. We introduce the foundations of ESB conceptually and present a formal framework and an actual implementation of a reasoning engine, which is specifically combined with a general belief-desire-intention-based practical reasoning programming system. We illustrate the generality of ESB through select case studies, which show that it is able to represent and implement different typical styles of social reasoning. The broad coverage of existing social reasoning methods, the modularity that derives from its declarative nature, and its focus on practical implementation make ESB a useful tool for building advanced socially reasoning agents.

The human species enjoys uniquely developed capacities for analytical reasoning and rational decision making, but these capacities come with a price: They make us aware of our inevitable physical death. Drawing on terror management theory and dual-process theories of cognition, we investigate the impact of mortality awareness on analytical reasoning. Two experiments show that experimentally induced thoughts of death impair analytical reasoning performance, just as cognitive load would. When made aware of their own mortality, reasoners allocate their executive resources to the suppression of this disturbing thought, therefore impairing their performance on syllogisms that require analytic thought. This finding has consequences for all aspects of rational thinking that draw on executive resources, and calls for an integrated approach to existential psychology and the psychology of rational thought.

This paper argues that practical reasoning is a mental process which leads a person from a set of existent mental states to an intention. In Section 1, I defend this view against two other proposals according to which practical reasoning either concludes in an action itself or in a normative belief. Section 2 discusses the correctness of practical reasoning and explains how the correctness of instrumental reasoning can be explained by the logical relations that hold between the contents of the mental states. In Section 3, I explore the correctness of normative practical reasoning. I conclude with the sceptical view that correct practical reasoning cannot require us to intend to do what we believe we ought to do.

New paradigms in the psychology of reasoning have included a consideration for general contextual factors that may impact on the reasoning process, including individuals’ goals and motivations. We ...

In this paper, there will be a particular focus on mental models and their application to inductive reasoning within the realm of instruction. A basic assumption of this study is the observation that the construction of mental models and related reasoning is a slowly developing capability of cognitive systems that emerges effectively with proper contextual and social support. More specifically, we first will identify some key elements of the structure and function of mental models in contrast to schemas. Next, these key elements of modeling will be used to generate some conjectures about the foundations of model-based reasoning. In the next section, we will describe the learning-dependent progression of mental models as a suitable approach for understanding the basics of deductive and inductive reasoning based on models as ''tools for thought.'' The rationale of mental models as tools for reasoning will be supported by empirical research to be described in a particular section of this paper. Finally, we will turn to the instructional implications of model-based reasoning by discussing appropriate instructional methods to affect the construction of mental models for performing deductive and inductive reasoning.

Recent experiments have suggested that infants' expectations about the actions of agents are guided by a principle of rationality: In particular, infants expect agents to pursue their goals efficiently, expending as little effort as possible. However, these experiments have all presented infants with infrequent or odd actions, which leaves the results open to alternative interpretations and makes it difficult to determine whether infants possess a general expectation of efficiency. We devised a critical test of the rationality principle that did not involve infrequent or odd actions. In two experiments, 16-month-olds watched events in which an agent faced two identical goal objects; although both objects could be reached by typical, everyday actions, one object was physically (Experiment 1) or mentally (Experiment 2) more accessible than the other. In both experiments, infants expected the agent to select the more-accessible object. These results provide new evidence that infants possess a general and robust expectation of efficiency. © The Author(s) 2013.

There are individual differences in reasoning which go beyond dimensions of ability. Valid models of cognition must take these differences into account, otherwise they characterise group mean phenomena which explain nobody. The gap is closing between formal cognitive models, which are designed from the ground up to explain cognitive phenomena, and statistical models, which traditionally concern the more modest task of modelling relationships in data. This paper critically reviews three illustrative statistical models of individual differences in reasoning which embed some notion of cognitive process. Although the models are each developed in different frameworks, it is shown that they are more similar than would first appear. The cognitive meaning of elements in the example models is explored and some sketches are developed for future directions of research.

Understanding that individuals can be mistaken, or hold false beliefs, about the world is an important human ability that plays a vital role in social interactions. When and how does this ability develop? Traditional investigations using elicited-response tasks suggested that false-belief understanding did not emerge until at least age 4. However, more recent studies have shown that children demonstrate false-belief understanding much earlier when tested via other means. In the present article, I summarize recent evidence that a robust, flexible understanding of false belief emerges in infancy and discuss why older children fail elicited-response tasks despite their ability to represent beliefs.

Recent studies suggest that by the second year of life, infants can attribute false beliefs to agents. However, prior studies have largely focused on infants' ability to predict a mistaken agent's physical actions on objects. The present research investigated whether 20-month-old infants could also reason about belief-based emotional displays. In Experiments 1 and 2, infants viewed an agent who shook two objects: one rattled and the other was silent. Infants expected the agent to express surprise at the silent object if she had a false belief that both objects rattled, but not if she was merely ignorant about the objects' properties. Experiment 3 replicated and extended these findings: if an agent falsely believed that two containers held toy bears (when only one did so), infants expected the agent to express surprise at the empty, but not the full, container. Together, these results provide the first evidence that infants in the second year of life understand the causal relationship between beliefs and emotional displays. These findings thus provide new evidence for false-belief understanding in infancy and suggest that infants, like older children, possess a robust understanding of belief that applies to a broad range of belief-based responses.

The ability to reach logical conclusions on the basis of prior information is central to human cognition. Yet, it is generally agreed that the state of our knowledge regarding the mechanisms underlying logical reasoning remains incomplete and highly fragmented (e.g., Khemlani and Johnson-Laird, 2012). The emergence of functional neuroimaging over the past 20 years—and its ability to examine reasoning at the level of recruitment of cortical systems—provides an additional source of data to, not only better understand reasoning as a phenomenon, but to test different theoretical approaches. This has the potential to both prune the number of theoretical explanations of reasoning, but also to expand the space of possibilities in directions unanticipated by behavioral data. This Research Topic explores the extent to which neuroimaging and brain-lesion studies have informed cognitive theories of reasoning. It includes a selection of 20 empirical and theoretical papers from 69 authors. Below we briefly review these papers by breaking them down into two types of contribution, (i) original research articles, and (ii) review and methodological articles.

KEOD 2017 - 9th International Conference on Knowledge Engineering and Ontology Development Accurate, relevant, and timely combat identification (CID) enables warfighters to locate and identify critical airborne targets with high precision. The current CID processes included a wide combination of platforms, sensors, networks, and decision makers. There are diversified doctrines, rules of engagements, knowledge databases, and expert systems used in the current process to make the decision making very complex. Furthermore, the CID decision process is still very manual. Decision makers are constantly overwhelmed with the cognitive reasoning required. Soar is a cognitive architecture that can be used to model complex reasoning, cognitive functions, and decision making for warfighting processes like the ones in a kill chain. In this paper, we present a feasibility study of Soar, and in particular the reinforcement learning (RL) module, for optimal decision making using existing expert systems and smart data. The system has the potential to scale up and automate CID decision-making to reduce the cognitive load of human operators. Naval Postgraduate School

Inspired by insights from theories of human reasoning and language, we propose additions to the Manchester OWL Syntax to improve comprehensibility. These additions cover: functional and inverse functional properties, negated conjunction, the definition of exceptions, and existential and universal restrictions. By means of an empirical study, we demonstrate the effectiveness of a number of these additions, in particular: the use of solely to clarify the uniqueness of the object in a functional property; the replacement of and with intersection in conjunction, which was particularly beneficial in negated conjunction; the use of except as a substitute for and not; and the replacement of some with including and only with noneOrOnly, which helped in certain situations to clarify the nature of these restrictions.

We humans are spectacularly good at figuring things out, but this is not necessarily what makes our thinking special. Other primates are already quite good at abstract, logical, causal inferences. Here I argue that our reasoning abilities skyrocketed because of the requirements of our hypercooperative way of life, which relies on coordination, division of labor, and cumulative culture. The psychology of reasoning should certainly concern itself with the highly derived abilities that allow us to handle formal logic or probability calculus—but it should also give full attention to what reasoning mostly did and still mostly does for us, which is to unravel the horrendous complexity of cooperating with our conspecifics.

Thought experimentation is at least as old as Western philosophy. Scholars have made much use of it in many disciplines. For instance, philosophical discussions on ethics, morality, knowledge, and language abound with thought experiments. Likewise, great scientific developments, such as in physics and mathematics, have been achieved via thought experimentation. This is true even long before the introduction of the term, between the late seventeenth and nineteenth centuries. But what is a thought experiment? Although giving a clear answer to this question is a very complicated task, it is quite common to consider thought experiments as pieces of reasoning about imaginary cases mainly performed with the aim of increasing our knowledge or understanding of the world. In this chapter, I review the lively debate on thought experiments. First, I introduce some famous examples and detail six of them (Sect. 21.1). Second, I give some historical background (Sect. 21.2). Then, I focus on three of the main questions asked in the literature, namely: What is a thought experiment? (Sect. 21.3), What is the function of thought experiments? (Sect. 21.4), How do thought experiments achieve their function? (Sect. 21.5). These issues will lead to tackle other important points, such as the relationship between real and thought experimentation, the differences between philosophical and scientific thought experimentation, the role played by intuitions and imagination in thought experimentation.

Model-based reasoning consists of cycles of proposing, instantiating, checking, revising to find an apt model for a given purpose in a given situation, and reasoning about the situation through the model. Results from cognitive research can help us understand and assess both the experiential and reflective aspects of model-based reasoning. This chapter reviews research on model-based reasoning and the inquiry cycle to define aspects of model-based reasoning that can be used to guide assessment design.

According to standard logic, only one of the five indemonstrables proposed by Chrysippus of Soli is actually indemonstrable. The other four schemata are demonstra-ble in that logic. The question hence is, if four of them are not really indemonstrable, why Chrysippus considered them to be so. López-Astorga showed that, if we ignore standard propositional calculus and assume that a current cognitive theory, the mental logic theory, truly describes human reasoning, it can be explained why Chrysippus thought that all of his indemonstrables were so basic. However, in this paper, I try to argue that the mental logic theory is not the only framework that can account for that. In particular, I hold that another important reasoning theory at present, the mental models theory, can offer an explanation in that regard as well. Resumen Atendiendo a la lógica estándar, solo uno de los cinco indemostrables propuestos por Crisipo de Solos es realmente indemostrable. Sus otros cuatro esquemas son demostrables en tal lógica. La pregunta, por tanto, es: si cuatro de ellos no son verdaderamente indemostrables, por qué Crisipo consideró que sí lo eran. López-Astorga mostró que si ignoramos el cálculo proposicional estándar y asumimos que una teoría cognitiva contemporánea, la teoría de la lógica mental, describe correctamente el razonamiento humano, se puede entender por qué Crisipo pensó que todos sus indemostrables eran tan básicos. No obstante, en este trabajo trato de argumentar que la teoría de la lógica mental no es el único marco que puede explicar esto. En concreto, sostengo que otra importante teoría sobre el razonamiento en el presente, la teoría de los modelos mentales, también puede ofrecer una explicación al respecto.

This notebook presents an introductory overview to the cognitive perspective on the psychology of human behaviour for social science students. Starting with an introduction to cognitive developmental theories of how babies reason, the overview then moves to discuss how children develop into better thinkers. Adult theories of cognition are subsequently outlined and critically evaluated. A chronology of topics include: the rise of 'this thing we call cognition', Piaget's theory of cognitive development and its evaluation, problem space theory, and theories of mental representation in adult thought examining, amongst other types of thinking and reasoning, deduction and induction and an evaluation of mental representation theories.

Although decisions based on uncertain events are critical in everyday life, people perform remarkably badly when reasoning with probabilistic information. A well-documented example is performance on Bayesian reasoning problems, where people fail to take into account the base-rate. However, framing these problems as frequencies improves performance spectacularly. Popular evolutionary theories have explained this facilitation by positing a specialised module that automatically operates on natural frequencies. Here we test the key prediction from these accounts, namely that the performance of the module functions independently from general-purpose reasoning mechanisms. In three experiments we examined the relationship between cognitive capacity and performance on Bayesian reasoning tasks in various question formats, and experimentally manipulated cognitive resources in a dual task paradigm. Results consistently indicated that performance on classical Bayesian reasoning tasks depends on participants’ availabl...

Inferences about spatial arrangements and relations like "The Porsche is parked to the left of the Dodge and the Ferrari is parked to the right of the Dodge, thus, the Porsche is parked to the left of the Ferrari," are ubiquitous. However, spatial descriptions are often interpretable in many different ways and compatible with several alternative mental models. This article suggests that individuals tackle such indeterminate multiple-model problems by constructing a single, simple, and typical mental model but neglect other possible models. The model that first comes to reasoners' minds is the preferred mental model. It helps save cognitive resources but also leads to reasoning errors and illusory inferences. The article presents a preferred model theory and an instantiation of this theory in the form of a computational model, preferred inferences in reasoning with spatial mental models (PRISM). PRISM can be used to simulate and explain how preferred models are constructed, inspected, and varied in a spatial array that functions as if it were a spatial working memory. A spatial focus inserts tokens into the array, inspects the array to find new spatial relations, and relocates tokens in the array to generate alternative models of the problem description, if necessary. The article also introduces a general measure of difficulty based on the number of necessary focus operations (rather than the number of models). A comparison with results from psychological experiments shows that the theory can explain preferences, errors, and the difficulty of spatial reasoning problems.

When tested with traditional false-belief tasks, which require answering a standard question about the likely behavior of an agent with a false belief, children perform below chance until age 4 y or later. When tested without such questions, however, children give evidence of false-belief understanding much earlier. Are traditional tasks difficult because they tap a more advanced form of false-belief understanding (fundamental-change view) or because they impose greater processing demands (processing-demands view)? Evidence that young children succeed at traditional false-belief tasks when processing demands are reduced would support the latter view. In prior research, reductions in inhibitory-control demands led to improvements in young children’s performance, but often only to chance (instead of below-chance) levels. Here we examined whether further reductions in processing demands might lead to success. We speculated that: (i) young children could respond randomly in a traditional low-inhibition task because their limited information-processing resources are overwhelmed by the total concurrent processing demands in the task; and (ii) these demands include those from the response-generation process activated by the standard question. This analysis suggested that 2.5-y-old toddlers might succeed at a traditional low-inhibition task if response-generation demands were also reduced via practice trials. As predicted, toddlers performed above chance following two response-generation practice trials; toddlers failed when these trials either were rendered less effective or were used in a high-inhibition task. These results support the processing-demands view: Even toddlers succeed at a traditional false-belief task when overall processing demands are reduced. Accepted Version

It was long assumed that the capacity to represent false beliefs did not emerge until at least age four, as evidenced by children’s performance on elicited-response tasks. However, recent evidence that infants appear to demonstrate false-belief understanding when tested with alternative, non-elicited-response measures has led some researchers to conclude that the capacity to represent beliefs emerges in the first year of life. This mentalistic view has been criticized for failing to offer an explanation for the well-established positive associations between social factors and preschoolers’ performance on elicited-response false-belief tasks. In this paper, we address this criticism by offering an account that reconciles these associations with the mentalistic claim that false-belief understanding emerges in infancy. We propose that rather than facilitating the emergence of the capacity to represent beliefs, social factors facilitate the use of this ability via effects on attention, inference, retrieval, and response production. Our account predicts that the relationship between social factors and false-belief understanding should not be specific to preschoolers’ performance in elicited-response tasks: this relationship should be apparent across the lifespan in a variety of paradigms. We review an accumulating body of evidence that supports this prediction.

The central aim of the EQUALPRIME project was to examine the ways in which teachers of science in the different countries support student reasoning. A significant methodological challenge in comparing classrooms from different cultures and contexts is the need to adopt a perspective that makes sense of what is happening in ways that allow informed comparison, yet does not privilege particular culturally framed views of the video data. A further challenge is presented by the fact that reasoning is not a unitary construct. There are many different perspectives on reasoning represented in the literature including teacher-student interactions, representation coordination and inquiry perspectives. This Chapter reviews a number of such perspectives that have been taken on reasoning during the analysis of the EQUALPRIME video data, each of which allows a different view of the variation in practice within and across countries. Each is valid, but each is partial. It is argued that practices in support of reasoning cannot be captured through a single lens, but in any classroom need to be viewed from multiple theoretical stances in order to adequately capture the particular forms of reasoning, the systemic context and individual teacher beliefs that underpin these different practices. In order to legitimately compare and contrast reasoning in these different classrooms in the three countries, a narrative must be constructed of each case that draws on these multiple perspectives. The Chapter will present analyses using different theoretical perspectives and the way they might be combined to open a richer window into the different beliefs and traditions that frame teacher support of student reasoning in the three countries.

International audience; We summarize the argumentative theory of reasoning, which claims that the main function of reasoning is to argue. In this theory, argumentation is seen as being essentially cooperative (people have to listen to others' arguments and be ready to change their mind) but with an adversarial dimension (their goal as argument producers is to convince). Consistent with this theory, the experimental literature shows that solitary reasoning is biased and lazy, whereas reasoning in group discussion produces good results, provided some conditions are met. We formulate recommendations for improving reasoning performance, mainly, to make people argue more and better by creating felicitous conditions for group discussion. We also make some suggestions for improving solitary reasoning, in particular to maximize students' exposure to arguments challenging their positions. Teaching people about the value of argumentation is likely to improve not only immediate reasoning performance but also long-term solitary reasoning skills.

Scientific reasoning represents a set of skills students need to acquire in order to successfully participate in scientific practices. Hence, educational research has focused on developing and validating assessments of student learning that capture the two different components of the construct, namely formal and informal reasoning. In this opinion paper, we explain why we believe that it is time for a new era of scientific reasoning assessments that bring these components together, and how computer-based assessments (CBAs) might accomplish this. Reasoning is a mental process that enables people to construct new representations from existing knowledge (Rips, 2004). It includes cognitive processing that is directed at finding solutions to problems by drawing conclusions based on logical rules or rational procedures (Mayer and Wittrock, 2006). When people reason, they attempt to go “beyond the information given” to create a new representation that is assumed to be true (Bruner, 1957). The process of scientific reasoning comprises formal and informal reasoning (Galotti, 1989; Kuhn, 1993). Formal reasoning is characterized by rules of logic and mathematics, with fixed and unchanging premises (Perkins et al., 1991; Sadler, 2004). It encompasses the ability to formulate a problem, design scientific investigations, evaluate experimental outcomes, and make causal inferences in order to form and modify theories related to the phenomenon under investigation (Zimmerman, 2007). Formal scientific reasoning can be applied not only within the context of science, but in almost every other domain of society (Han, 2013). It can be used to make informed decisions regarding everyday life problems (Amsterlaw, 2006); for example, individuals use proportional reasoning to decide the fastest way to travel from one place to another. In informal reasoning, students draw inferences from uncertain premises as they ponder ill-structured, open-ended, and debatable problems without definitive solutions (Kuhn, 1991). When students reason formally, they work with the given premises in belief mode, which concerns arriving at true and warranted conclusions whereas informal reasoning is carried out in design mode, which focuses on identifying relevant premises that can be used to establish a strong argument (Bereiter and Scardamalia, 2006). Since a premise of informal reasoning is uncertain and can be questioned, its conclusion can be withdrawn in the light of new evidence (Evans, 2005). This process involves weighing the pros and cons of a particular decision (Voss et al., 1991). Learners engage in informal reasoning when they deal with socio-scientific issues—controversial issues that are influenced by social norms and conceptually related to science, such as whether or not to consume genetically modified food or support government's plan for a car-free city (Sadler and Zeidler, 2005). Both types of reasoning are used to manipulate existing information and share the same goal of generating new knowledge. While formal reasoning is judged by whether or not conclusions are valid, informal reasoning is assessed based on the quality of premises and their potential for strengthening conclusions. The manipulation of existing information in formal and informal reasoning processes can be described with dual-process theories of reasoning (Evans, 2007; Glockner and Witteman, 2010). According to these theories, there are two distinct processing modes: Type 1 processes are autonomous and intuitive processes that do not heavily rely on individuals' working memory, whereas Type 2 processes involve using mental simulation or thought experiments to support hypothetical thinking and reflective processes that require working memory (Evans and Stanovich, 2013). An individual's first response to a problem tends to be processed automatically and refers to their past experiences and personal beliefs (i.e., Type 1 process: Evans, 2008). For example, when using formal reasoning to decide the fastest way to travel from A to B, an individual's first thought might be to take a plane since it is commonly considered the fastest means of transport. However, the individual might change his or her mind after processing all necessary information, such as the travel time to and from the airport. Not every individual is able to progress after the first stage and produce a rational decision. Those who are confined to Type 1 processes make intuitive decisions, whereas more experienced individuals utilize Type 2 processes to construct a well-informed choice (Wu and Tsai, 2011). In the example of using informal reasoning to decide whether or not to support a government's plan for a car-free city, intuitive thought might lead individuals to support the plan based on their experiences with pollution. However, with the purpose of generating new representations, only those who can (a) elaborate on their intuitive decision with acceptable justifications; (b) address opposite arguments; and (c) think about how the plan can be further improved are utilizing Type 2 processes. In this regard, there is a strong connection between formal and informal reasoning, in which both types of reasoning share the common goal of generating new knowledge by processing available information through the dual stages. Activity in belief mode covers a broad range of scientific practices in school science (Bereiter and Scardamalia, 2006). Outside the classroom, however, students need to make decisions regarding problems with uncertain premises by working in design mode. Teachers should have ways to assess how students improve on their existing ideas by searching beyond what they already know rather than simply making sure their ideas align with accepted theories. It is therefore important to build a scientific reasoning assessment that incorporates both formal and informal reasoning skills in order to better measure the constructs underlying scientific reasoning. In the following, we argue that these complex skills can be best assessed using computer-based testing.

Context reasoning is an important issue for a context-aware system. Generally, context reasoning is adopted to deduce new context based on the available contexts. The rule-based reasoning is one of the most well-known methods for context reasoning. However, it is difficult for the rule-based algorithm to reason personalized context, because it requires a large number of rules to apply the user's preferences. To address this weakness, in this paper we suggest the Profile-Applied Reasoning Engine (PARE). PARE is an enhanced rule-based reasoning method which uses profiles while reasoning contexts. By using profiles, PARE can become aware of the context that is preferred by a specific individual. To validate the effectiveness of the proposed reasoning engine, we compared the reasoning result of PARE with traditional rule-based reasoning in smart home domain. PARE shows better outcome for reasoning the personalized contexts than the traditional rule-based reasoning. In addition, by using profiles, a significant number of rules have been omitted and consequently the running time is also decreased. Moreover, PARE occupies less memory space which is restricted with number of variables of a rule. Therefore, PARE optimizes both runtime and memory space, which is valuable when making embedded context-aware system.

One of the major debates concerning the nature of inferential reasoning is between counterexample-based strategies such as mental model theory and statistical strategies underlying probabilistic models. The dual-strategy model, proposed by Verschueren, Schaeken, & d'Ydewalle (2005a, 2005b), which suggests that people might have access to both kinds of strategy has been supported by several recent studies. These have shown that statistical reasoners make inferences based on using information about premises in order to generate a likelihood estimate of conclusion probability. However, while results concerning counterexample reasoners are consistent with a counterexample detection model, these results could equally be interpreted as indicating a greater sensitivity to logical form. In order to distinguish these 2 interpretations, in Studies 1 and 2, we presented reasoners with Modus ponens (MP) inferences with statistical information about premise strength and in Studies 3 and 4, naturalistic MP inferences with premises having many disabling conditions. Statistical reasoners accepted the MP inference more often than counterexample reasoners in Studies 1 and 2, while the opposite pattern was observed in Studies 3 and 4. Results show that these strategies must be defined in terms of information processing, with no clear relations to "logical" reasoning. These results have additional implications for the underlying debate about the nature of human reasoning. (PsycINFO Database Record

International audience; The new paradigm in the psychology of reasoning redirects the investigation of deduction conceptually and methodologically because the premises and the conclusion of the inferences are assumed to be uncertain. A probabilistic counterpart of the concept of logical validity and a method to assess whether individuals comply with it must be defined. Conceptually, we used de Finetti's coherence as a normative framework to assess individuals' performance. Methodologically, we presented inference schemas whose premises had various levels of probability that contained non-numerical expressions (e.g., "the chances are high") and, as a control, sure levels. Depending on the inference schemas, from 60% to 80% of the participants produced coherent conclusions when the premises were uncertain. The data also show that (i) except for schemas involving conjunction, performance was consistently lower with certain than uncertain premises, (ii) the rate of conjunction fallacy was consistently low (not exceeding 20%, even with sure premises), and (iii) participants' interpretation of the conditional agreed with de Finetti's "conditional event" but not with the material conditional.

This article presents a model of ethical reasoning. The article reviews lapses in ethical reasoning and the great costs they have had for society. It presents an eight-step model of ethical reasoning that can be applied to ethical challenges and illustrates its application. It proposes that ethical reasoning can be taught across the curriculum. It further points to a source of frustration in the teaching and application of ethics: ethical drift. Finally it draws conclusions.