Showing papers on "Abductive reasoning published in 2017"

4 Quantitative Reasoning and the Development of Algebraic Reasoning

Abstract: Recent discussions of algebra reform have focused on having more students take algebra courses and on improving their mathematical content. We believe that neither approach can be successful without substantial changes in the K-8 mathematics curriculum's current focus on numbers and arithmetic operations. This curriculum does not prepare students for the use of explicit, rulegoverned notational systems to express, manipulate, and formalize ways of thinking about quantitative and numerical relationships. So students’ difficulties with algebra result not only from algebra curricula that lack meaning and coherence, but also from elementary curricula that fail to develop students’ abilities to reason about complex additive and multiplicative relationships. Without mathematical concepts and relationships to express and manipulate, many students find algebra a meaningless symbolic exercise. We argue that a focus on quantitative reasoning can develop students' abilities to conceptualize, reason about, and operate on quantities and relationships in sensible problem situations. We describe a broad view of quantitative reasoning as it relates to algebraic and arithmetical reasoning and show how it actually provides content for algebra. Algebra from Quantitative Reasoning 3 No doubt, it is difficult for a teacher to teach something which does not satisfy him entirely, but the satisfaction of the teacher is not the unique goal of teaching; one has at first to take care of what is the mind of the student and what one wants it to become. (Poincare, 1904, p. 255)

Digital technologies and artificial intelligence's present and foreseeable impact on lawyering, judging, policing and law enforcement

Abstract: `AI & Law' research has been around since the 1970s, even though with shifting emphasis. This is an overview of the contributions of digital technologies, both artificial intelligence and non-AI smart tools, to both the legal professions and the police. For example, we briefly consider text mining and case-automated summarization, tools supporting argumentation, tools concerning sentencing based on the technique of case-based reasoning, the role of abductive reasoning, research into applying AI to legal evidence, tools for fighting crime and tools for identification.

Reorienting the Logic of Abduction

Abstract: Abduction, still a comparatively neglected kind of premiss-conclusion reasoning, gives rise to the questions I want to consider here One is whether abduction’s epistemic peculiarities can be accommodated happily in the mainline philosophical theories of knowledge The other is whether abduction provides any reason to question the assumption that the goodness of drawing a conclusion from premisses depends on an underlying relation of logical consequence My answer each time is no I will spend most of my time on the first Much of what I’ll say about the second is a promissory note

Enabling Data Sharing in Contextual Environments: Policy Representation and Analysis

Abstract: Internet of Things environments enable us to capture more and more data about the physical environment we live in and about ourselves The data enable us to optimise resources, personalise services and offer unprecedented insights into our lives However, to achieve these insights data need to be shared (and sometimes sold) between organisations imposing rights and obligations upon the sharing parties and in accordance with multiple layers of sometimes conflicting legislation at international, national and organisational levels In this work, we show how such rules can be captured in a formal representation called "Data Sharing Agreements" We introduce the use of abductive reasoning and argumentation based techniques to work with context dependent rules, detect inconsistencies between them, and resolve the inconsistencies by assigning priorities to the rules We show how through the use of argumentation based techniques use-cases taken from real life application are handled flexibly addressing trade-offs between confidentiality, privacy, availability and safety

Fear of knowledge: Clinical hypotheses in diagnostic and prognostic reasoning.

Abstract: Patients are interested in receiving accurate diagnostic and prognostic information. Models and reasoning about diagnoses have been extensively investigated from a foundational perspective; however, for all its importance, prognosis has yet to receive a comparable degree of philosophical and methodological attention, and this may be due to the difficulties inherent in accurate prognostics. In the light of these considerations, we discuss a considerable body of critical thinking on the topic of prognostication and its strict relations with diagnostic reasoning, pointing out the distinction between nosographic and pathophysiological types of diagnosis and prognosis, underlying the importance of the explication and explanation processes. We then distinguish between various forms of hypothetical reasoning applied to reach diagnostic and prognostic judgments, comparing them with specific forms of abductive reasoning. The main thesis is that creative abduction regarding clinical hypotheses in diagnostic process is very unlikely to occur, whereas this seems to be often the case for prognostic judgments. The reasons behind this distinction are due to the different types of uncertainty involved in diagnostic and prognostic judgments.

Logical reasoning versus information processing in the dual-strategy model of reasoning.

Abstract: 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

The Logic of Abduction: An Introduction

Abstract: In this chapter, the focus will be on formal models of hypothetical reasoning , in particular on those concerned with abductive reasoning

Types of abduction in tool behavior

Abstract: Tool-use behavior is currently one of the most intriguing and widely debated topics in cognitive neuroscience. Different accounts of our ability to use tools have been proposed. In the first part of the paper we review the most prominent interpretations and suggest that none of these accounts, considered in itself, is sufficient to explain tool use. In the second part of the paper we disentangle three different types of reasoning on tools, characterized by a different distribution of motor and cognitive ingredients. At the conceptual level, these types of reasoning reflect the distinction between three types of abductive inference as they are described in semiotic studies. At the functional level, we suggest that these types of reasoning on tools may correspond to different mental processes, possibly implemented in different regions of the left inferior parietal lobe. This proposal can account for the different interpretations commonly associated with the role of the left parietal cortex in tool use.

Determinants of judgments of explanatory power: Credibility, Generality, and Statistical Relevance

Abstract: Explanation is a central concept in human psychology. Drawing upon philosophical theories of explanation, psychologists have recently begun to examine the relationship between explanation, probability and causality. Our study advances this growing literature in the intersection of psychology and philosophy of science by systematically investigating how judgments of explanatory power are affected by (i) the prior credibility of a potential explanation, (ii) the causal framing used to describe the explanation, (iii) the generalizability of the explanation, and (iv) its statistical relevance for the evidence. Collectively, the results of our five experiments support the hypothesis that the prior credibility of a causal explanation plays a central role in explanatory reasoning: first, because of the presence of strong main effects on judgments of explanatory power, and second, because of the gate-keeping role it has for other factors. Highly credible explanations are not susceptible to causal framing effects, but they are sensitive to the effects of normatively relevant factors: the generalizability of an explanation, and its statistical relevance for the evidence. These results advance current literature in the philosophy and psychology of explanation in three ways. First, they yield a more nuanced understanding of the determinants of judgments of explanatory power, and the interaction between these factors. Second, they show the close relationship between prior beliefs and explanatory power. Third, they elucidate the nature of abductive reasoning.

Novel aspects in diagnostic approach to respiratory patients: is it the time for a new semiotics?

Abstract: Medical approach to patients is a fundamental step to get the correct diagnosis. The aim of this paper is to analyze some aspects of the reasoning process inherent in medical diagnosis in our era. Pathologic signs (anamnestic data, symptoms, semiotics, laboratory and strumental findings) represent informative phenomena to be integrated for inferring a diagnosis. Thus, diagnosis begins with “signs” and finishes in a probability of disease. The abductive reasoning process is the generation of a hypothesis to explain one or more observations (signs) in order to decide between alternative explanations searching the best one. This process is iterative during the diagnostic activity while collecting further observations and it could be creative generating new knowledge about what has not been experienced before. In the clinical setting the abductive process is not only theoretical, conversely the physical exploitation of the patient (palpation, percussion, auscultation) is always crucial. Through this manipulative abduction, new and still unexpressed information is discovered and evaluated and physicians are able “to think through doing” to get the correct diagnosis. Abductive inferential path originates with an emotional reaction (discovery of the signs), step by step explanations are formed and it ends with another emotional reaction (diagnosis). Few bedside instruments are allowed to physicians to amplify their ability to search for signs. Stethoscope is an example. Similarities between ultrasound exploration and percussion can be found. Bedside ultrasonography can be considered an external amplifier of signs, a particular kind of percussion and represents a valid example of abductive manipulation. In this searching for signs doctors act like detectives and sometimes the discovering of a strategic, unsuspected sign during abductive manipulation could represent the key point for the correct diagnosis. This condition is called serendipity. Ultrasound is a powerful tool for detecting soft, hidden, unexpected and strategic signs.

Forms of Abduction and an Inferential Taxonomy

Abstract: In recent years, the Peircean concept of abduction has been differentiated into different forms and made fruitful in a variety of contexts. However, the very notion of abduction still seems to be in need of clarification. The present contribution takes very seriously Peirce’s claim (1) that there are only three kinds of reasoning, that is, abduction, deduction, and induction, and (2) that these are mutually distinct. Therefore, the fundamental features of the three inferences canvassed, in particular as regards inferential subprocesses and the validity of each kind of reasoning. It is also argued that forms of abduction have to be distinguished along two dimensions: one concerns levels of abstraction (from elementary embodied and perceptual levels to high-level scientific theorizing). The other concerns domains of reasoning such as explanatory, instrumental, and moral reasoning. Moreover, Peirce’s notion of theorematic deduction is taken up and reconstructed as inverse deduction. Based on this, inverse abduction and inverse induction are introduced as complements of the ordinary forms. All in all, the contribution suggests a taxonomy of inferential reasoning, in which different forms of abduction (as well as deduction and induction) can be systematically accommodated. The chapter ends with a discussion on forms of abduction found in the current literature.

An Introduction to Deep Visual Explanation

Abstract: The practical impact of deep learning on complex supervised learning problems has been significant, so much so that almost every Artificial Intelligence problem, or at least a portion thereof, has been somehow recast as a deep learning problem. The applications appeal is significant, but this appeal is increasingly challenged by what some call the challenge of explainability, or more generally the more traditional challenge of debuggability: if the outcomes of a deep learning process produce unexpected results (e.g., less than expected performance of a classifier), then there is little available in the way of theories or tools to help investigate the potential causes of such unexpected behavior, especially when this behavior could impact people's lives. We describe a preliminary framework to help address this issue, which we call "deep visual explanation" (DVE). "Deep," because it is the development and performance of deep neural network models that we want to understand. "Visual," because we believe that the most rapid insight into a complex multi-dimensional model is provided by appropriate visualization techniques, and "Explanation," because in the spectrum from instrumentation by inserting print statements to the abductive inference of explanatory hypotheses, we believe that the key to understanding deep learning relies on the identification and exposure of hypotheses about the performance behavior of a learned deep model. In the exposition of our preliminary framework, we use relatively straightforward image classification examples and a variety of choices on initial configuration of a deep model building scenario. By careful but not complicated instrumentation, we expose classification outcomes of deep models using visualization, and also show initial results for one potential application of interpretability.

The capacity to generate alternative ideas is more important than inhibition for logical reasoning in preschool-age children.

Abstract: There is little consensus about the nature of logical reasoning and, equally important, about how it develops. To address this, we looked at the early origins of deductive reasoning in preschool children. We examined the contribution of two factors to the reasoning ability of very young children: inhibitory capacity and the capacity to generate alternative ideas. In a first study, a total of 32 preschool children were all given generation, inhibition, and logical reasoning measures. Logical reasoning was measured using knowledge-based premises such as “All dogs have legs,” and two different inferences: modus ponens and affirmation of the consequent. Results revealed that correctly reasoning with both inferences is not related to the measure of inhibition, but is rather related to the capacity to generate alternative ideas. In a second study, 32 preschool children were given either the generation or the inhibition task before the logical reasoning measure. Results showed that receiving the generation task beforehand significantly improved logical reasoning compared to the inhibition task given beforehand. Overall, these results provide evidence for the greater importance of idea generation in the early development of logical reasoning.

A Model-Based Diagnosis System for a Traction Power Supply System

Abstract: The high-speed, heavy-load, high-traffic density of railway demands the high reliability of a traction power supply system (TPSS). To achieve this, a diagnosis system is essential. This paper presents a reliable, general, and easy-to-maintain diagnosis system, based on the system model with the purpose of online fault detection, location, and recognition of the TPSS. Two kinds of model-based diagnosis (MBD) are combined to achieve high diagnosis efficiency and recognition ability of fault types. The model library and diagnosis engine are the two main parts of the diagnosis system, both of which have the two-level structure that contains a consistency-based level and an abductive level. In the consistency-based level, the model and diagnosis engine of consistency-based MBD are established, which contribute to the fault detection and diagnosis candidate generation. The minimal support environment offline searching algorithm and binary particle swarm optimization with a genetic algorithm are proposed to enhance the consistency-based reasoning. In the abductive level, the model and diagnosis engine of abductive MBD are utilized to locate the faults and recognize the fault types. With the diagnosis candidates, the abductive reasoning efficiency can be dramatically improved. In addition, to improve the fault location and recognition performance, the Bayes theorem is utilized in the abductive reasoning. As the system relies on the sensor information, a fault-tolerant strategy for fault reasoning is proposed to enhance the diagnosis system reliability. Finally, three cases are presented to illustrate the effectiveness and efficiency of this system.

Visual Explanation by High-Level Abduction: On Answer-Set Programming Driven Reasoning about Moving Objects

Abstract: We propose a hybrid architecture for systematically computing robust visual explanation(s) encompassing hypothesis formation, belief revision, and default reasoning with video data. The architecture consists of two tightly integrated synergistic components: (1) (functional) answer set programming based abductive reasoning with space-time tracklets as native entities; and (2) a visual processing pipeline for detection based object tracking and motion analysis. We present the formal framework, its general implementation as a (declarative) method in answer set programming, and an example application and evaluation based on two diverse video datasets: the MOTChallenge benchmark developed by the vision community, and a recently developed Movie Dataset.

Peirce's Conception of Metaphysics

Abstract: This thesis develops and defends a Peircean conception of the task of metaphysics and critically compares it with recent anti-metaphysical forms of pragmatism. Peirce characterises metaphysics in terms of its place within his hierarchical classification of the sciences. According to the classification, metaphysics depends on logic for principles and provides principles to the natural and social sciences. This arrangement of the sciences is defended by appeal to Peirce's account of philosophy as 'cenoscopy'. The dependence of the natural and social sciences on cenoscopy is then argued for on the basis of Peirce's rejection of psychologism and in terms of the necessity of abductive inference. Peirce's position is then compared with recent forms of pragmatism. While it is less naturalistic, Peirce's position is defended on pragmatist grounds. An account of Peirce on truth is then developed. Peirce's account of truth in terms of an ideal limit of inquiry is defended as consistent with recent, more deflationary, approaches. The truth of 'abstract propositions' is a matter of local indefeasibility. These abstract propositions are related to the 'absolute truth', understood as a single non-abstract proposition. The truth of this proposition is then understood in terms of an identity theory. Two conceptions of Peircean metaphysics are presented. Both are 'abductive'. Their task is to explain the possibility of success in inquiry. However, only one proposal accepts the notion of an absolute truth. The 'absolutist' proposal is defended as an interpretation of Peirce and as a contemporary option for pragmatist philosophers. The thesis concludes by comparing recent anti-metaphysical arguments due to Huw Price with the Peircean position. Room for the absolutist proposal is defended by means of an account of recent exchanges between Price and Robert Brandom on dispositional modality.

Creativity in Doubt: Toward Understanding What Drives Creativity in Learning

Abstract: What propels creativity in learning? In this chapter, we discuss a long-standing—yet often overlooked—form of reasoning that helps address this question. That form of reasoning is called abductive reasoning (introduced by the early American Pragmatist, Charles Sanders Peirce). Abductive reasoning represents a special form of creative reasoning that is triggered by states of genuine doubt. Genuine doubt occurs whenever our everyday habits and beliefs fall short in making sense of a situation. In the context of learning, genuine doubt occurs anytime a learner is unable to inductively or deductively reason through an academic task or situation. As we will discuss, these states of doubt represent opportunities for creative learning. Specifically, our aim in this chapter is to demonstrate, by way of example, how abduction and creativity work together in every day learning. We will also discuss how understanding this link will help clarify efforts aimed at supporting creativity in the classroom, expand current conceptions of creativity, and provide directions for research on creativity in educational settings.

Reasoning Biases, Non-Monotonic Logics and Belief Revision

Abstract: A range of formal models of human reasoning have been proposed in a number of fields such as philosophy, logic, artificial intelligence, computer science, psychology, cognitive science, etc.: various logics (epistemic logics; non-monotonic logics), probabilistic systems (most notably, but not exclusively, Bayesian probability theory), belief revision systems, neural networks, among others. Now, it seems reasonable to require that formal models of human reasoning be (minimally) empirically adequate if they are to be viewed as models of the phenomena in question. How are formal models of human reasoning typically put to empirical test? One way to do so is to isolate a number of key principles of the system, and design experiments to gauge the extent to which participants do or do not follow them in reasoning tasks. Another way is to take relevant existing results and check whether a particular formal model predicts these results. The present investigation provides an illustration of the second kind of empirical testing by comparing two formal models for reasoning – namely the non-monotonic logic known as preferential logic; and a particular version of belief revision theories, screened belief revision – against the reasoning phenomenon known as belief bias in the psychology of reasoning literature: human reasoners typically seek to maintain the beliefs they already hold, and conversely to reject contradicting incoming information. The conclusion of our analysis will be that screened belief revision is more empirically adequate with respect to belief bias than preferential logic and non-monotonic logics in general, as what participants seem to be doing is above all a form of belief management on the basis of background knowledge. The upshot is thus that, while it may offer valuable insights into the nature of human reasoning, preferential logic (and non-monotonic logics in general) is ultimately inadequate as a formal model of the phenomena in question.

¬Peirce’s Creative Hallucinations in the Ontogeny of Abductive Reasoning

Abstract: This inquiry proposes that Peirce’s ultimate concept of dreams, which can be sub-divided in seven different functions, supply the raw material for habit-change inherent in every plausible inference. With references to developmental literature, I propose that dreams represent an outlet whereby children rely upon vivid directional icons of events (virtual habits) in reconciling logical anomalies provoked by unexpected happenings. Accordingly, dreams supply insights into how potential happenings materialize – identifying which factors can enhance/enrich the effectiveness of potential event outcomes. Dreams of this creative kind are not obsessional or socially driven but rather form the bedrock for conceiving of many meritorious insights, as shown in phenomena like children’s prelinguistic habits, word substitutions, overextensions, role-play and perspective taking.

Logical Abilities in Children: Volume 3: Reasoning in Adolescence: Deductive Inference

Abstract: A minimal expectation of a book claiming to study logical abilities is that it be logical and well reasoned. Osherson's third volume on logical abilities in children meets this expectation; indeed, exceeds it, in that a good deal of the book deals with the logical process from each minute step in the theory-building process to the next. This highly detailed treatment of logic in the adolescent consequently makes significant demands on even the serious student of the topic, and extraordinary demands on the more casual observer. Several comments on the style of presentation will follow a consideration of the substance of the theory and the data it generates. Osherson's overall question for this series is the central one in cognitive development. How can one decide whether the thought or, more specifically, the mental structures of the child, the adolescent, and the adult are similar or dissimilar? In a lucid introduction, the author reviews all the problems inherent in obtaining an answer to this central question. He examines the Piagetian position, which argues for major dissimilarities; justly criticizes it for failing to provide reasonable empirical verification of the hypothesized dissimilarities; and concludes that "the issue of qualitative discontinuities between children and adolescents in logical reasoning is still open, despite the obviously important work of Piaget" (p. 8). What then is the basis for decision? Employing a useful analogy to different dialects of the same language, the author argues that "we should judge the underlying thought processes governing these (logical) abilities to be similar if and only if the theories accounting for the abilities at the two ages are similar" (p. 9). The core of the book is an attempt to construct a theory of the logic of adolescents that will provide the basis for comparison to a theory of the logic of children or of adults. In fairness to the prospective reader, it should be noted that the author offers something less than strong encouragement to those who might travel with him that the voyage will be ultimately worthwhile. In the preface, Osherson concludes with the candid statement that the "model developed in this book is innocent of concerns that I now consider central to the study of deductive reasoning. Discussion of these matters begins in Sections 21.2 and 21.3 and resumes in Volume 4." This follows a pattern in the series: the outcome of volume 1 was eschewed in volume 2. While perhaps setting a record for flexibility in psychological theorizing in response to additional evidence and problem finding, this may also lessen one's desire to part with $15.00 for volume 3 when the central issues are to be found in volume 4. A wiser course might have been to follow more traditional routes of publication (i.e., journals and monographs) for such theoretical development and empirical testing. 345

Applied Systems Theory [2nd Edition]

Abstract: Offering an up-to-date account of systems theories and its applications, this book provides a different way of resolving problems and addressing challenges in a swift and practical way, without losing overview and grip on the details. From this perspective, it offers a different way of thinking in order to incorporate different perspectives and to consider multiple aspects of any given problem. Drawing examples from a wide range of disciplines, it also presents worked cases to illustrate the principles. The multidisciplinary perspective and the formal approach to modelling of systems and processes of ‘Applied Systems Theory’ makes it suitable for managers, engineers, students, researchers, academics and professionals from a wide range of disciplines; they can use this ‘toolbox’ for describing, analysing and designing biological, engineering and organisational systems as well as getting a better understanding of societal problems. This revised, updated and expanded second edition includes coverage of abductive reasoning, the relevance of systems theories for research methods and a new chapter about problem analysis and solving based on systems theories.

Patterns of Abductive Inference

Abstract: This article understands abductive inference as encompassing several special patterns of inference to the best explanation whose structure determines a promising explanatory conjecture (an abductive conclusion) for phenomena that are in need of explanation (Sect. 7.1 ). A classification of different patterns of abduction is given in Sect. 7.2 , which is intended to be as complete as possible. A central distinction is that between selective abductions, which choose an optimal candidate from a given multitude of possible explanations (Sects. 7.3 and 7.4 ), and creative abductions, which introduce new theoretical models or concepts (Sects. 7.5–7.7). While the discussion of selective abduction has dominated the literature, creative abductions are rarely discussed, although they are essential in science. This paper introduces several kinds of creative abduction, such as theoretical model abduction, common-cause abduction, and statistical factor analysis. A demarcation between scientifically fruitful abductions and speculative abductions is proposed, by appeal to two interrelated criteria: independent testability and explanatory unification. Section 7.8 presents applications of abductive inference in the domains of belief revision and instrumental/technological reasoning.

Relation between Manual Rotation and Abductive Reasoning in Q-Methodology

Abstract: Subjectivity is usually evaluated using qualitative research methods. However, Q-methodology offers a different set of techniques for measuring and evaluating subjective viewpoints. Q-methodology is a combination of qualitative and quantitative research techniques that is used to identify unique as well as common viewpoints. The quantitative component of Q-methodology is based on factor analysis and factor rotation. A common approach of analysis in Q-methodology is the use of a centroid factor extraction followed by a manual rotation. Some advocates of manual rotation technique claim that manual rotation is based on the abductive reasoning principle. This article shows that manual rotation and abductive reasoning are two different approaches serving different purposes. Abductive reasoning is a method of hypothesis generation while manual rotation is a method of hypothesis testing. Manual rotation does not conform to abductive reasoning principle if there is no pre-specified theory or hypothesis and consecutive manual rotation of factors toward a satisfactory solution is not the same as rotating factors based on adductive reasoning principle.

Studying design abduction in the context of novelty

Abstract: Design abduction has been studied over the last several decades in order to increase our understanding in design reasoning. Yet, there is still considerable confusion and ambiguity regarding this topic. Some scholars contend that all regressive inferences in design — and design is mostly done by such backwards or regressive reasoning — are in fact abductions. Others focus on formal syllogistic forms in their attempt to clarify abduction. In contrast, we argue here that a defining characteristic of abduction is the production of, or the potential to produce, novel outcomes. Novelty is shown to be relative and depend mostly on what is known to the “reasoner” at the time of making the inference. Novelty is also shown to not necessarily be part of the direct outcome of an abductive inference; but rather, an attribute of an abductive design strategy that is intended to produce a new idea.

Abduction: Can Non-human Animals Make Discoveries?

Abstract: The aim of this paper is to investigate the relationship between information and abductive reasoning in the context of problem-solving, focusing on non-human animals. Two questions guide our investigation: (1) What is the relation between information and abductive reasoning in the context of human and non-human animals? (2) Do non-human animals perform discovery based on inferential processes such as abductive reasoning? In order to answer these questions, we discuss the semiotic concept of information in relation to the concept of abductive reasoning and, more specifically, to the notion of manipulative abduction proposed by Magnani (2009). Finally, we investigate a case study of corvids’ intelligence, namely, their capacity of causal cognition.

A video-based analysis of science students’ computer-mediated abductive reasoning / by Joseph Paul Ferguson

Abstract: This research developed a framework of abductive reasoning based on detailed video-based analysis of the multimodal and distributed nature of science students’ interactions with agent-based digital simulations of the genetics of natural selection in relation to malaria and sickle cell anaemia.