Visual aids can improve comprehension of risks associated with medical treatments, screenings, and lifestyles. Do visual aids also help decision makers accurately assess their risk comprehension? That is, do visual aids help them become well calibrated? To address these questions, we investigated the benefits of visual aids displaying numerical information and measured accuracy of self-assessment of diagnostic inferences (i.e., metacognitive judgment calibration) controlling for individual differences in numeracy. Participants included 108 patients who made diagnostic inferences about (...) three medical tests on the basis of information about the sensitivity and false-positive rate of the tests and disease prevalence. Half of the patients received the information in numbers without a visual aid, while the other half received numbers along with a grid representing the numerical information. In the numerical condition, many patients --especially those with low numeracy-- misinterpreted the predictive value of the tests and profoundly overestimated the accuracy of their inferences. Metacognitive judgment calibration mediated the relationship between numeracy and accuracy of diagnostic inferences. In contrast, in the visual aid condition, patients at all levels of numeracy showed high-levels of inferential accuracy and metacognitive judgment calibration. Results indicate that accurate metacognitive assessment may explain the beneficial effects of visual aids and numeracy --a result that accords with theory suggesting that metacognition is an essential part of risk literacy. We conclude that well-designed risk communications can inform patients about health-relevant numerical information while helping them assess the quality of their own risk comprehension. (shrink)

I reconstruct the discovery of the Higgs boson by the ATLAS collaboration at CERN as the application of a series of inferences from effects to causes. I show to what extent such diagnostic causal inferences can be based on well established knowledge gained in previous experiments. To this extent, causal reasoning can be used to infer the existence of entities, rather than just causal relationships between them. The resulting account relies on the principle of causality, attributes only (...) a heuristic role to the theory’s predictions, and shows how, and to what extent, data selection can be used to exclude alternative causes, even “unconceived” ones. (shrink)

The complexity of cognitive emulation of human diagnostic reasoning is the major challenge in the implementation of computer-based programs for diagnostic advice in medicine. We here present an epistemological model of diagnosis with the ultimate goal of defining a high-level language for cognitive and computational primitives. The diagnostic task proceeds through three different phases: hypotheses generation, hypotheses testing and hypotheses closure. Hypotheses generation has the inferential form of abduction (from findings to hypotheses) constrained under the criterion of (...) plausibility. Hypotheses testing is achieved by a deductive inference (from generated hypotheses to expected findings), followed by an eliminative induction, constrained under the criterion of covering, which matches expected findings against patient''s findings to select the best explanation. Hypotheses closure is a deductive-inductive type of inference very similar to the inferences operating in hypotheses testing. In this case induction matches the consequences of the generated hypotheses against the patient''s characteristics or preferences under the criterion of utility. By using the language exploited in this epistemological model, it is possible to describe the cognitive tasks underlying the most influential knowledge-based diagnostic systems. (shrink)

BackgroundThe aim of this study was to explore the construct validity and diagnostic properties of the Reading the Mind in the Eyes Test in non-demented patients with amyotrophic lateral sclerosis.MaterialsA total of 61 consecutive patients and 50 healthy controls were administered the 36-item RMET. Additionally, patients underwent a comprehensive assessment of social cognition via the Story-Based Empathy Task, which encompasses three subtests targeting Causal Inference, Emotion Attribution, and Intention Attribution, as well as global cognitive [the Edinburgh Cognitive and Behavioral (...) ALS Screen ] and behavioral screening [the Frontal Behavioral Inventory ; the Dimensional Apathy Scale ; the Beck Depression Inventory ; and the State and Trait Anxiety Inventory-Y]. The construct validity of the RMET was tested by regressing it within a stepwise model that encompassed as predictors the abovementioned cognitive and behavioral measures, covarying for demographic and motor confounders. Receiver-operating characteristics analyses allowed exploring intrinsic and post-test properties of the RMET both in discriminating patients from HCs and in identifying patients with a defective SET-EA performance.ResultsThe RMET was solely predicted by the SET-EA and SET-IA. RMET scores showed high accuracy both in discriminating patients from HCs and in identifying patients with a defective SET-EA score, with adequate-to-optimal both intrinsic and post-test properties.DiscussionThe RMET is a convergently and divergently valid measure of affective social cognition in non-demented ALS patients, also featuring optimal intrinsic and post-test diagnostic properties in both case-control and case-finding scenarios. (shrink)

This paper trials new experimental methods for the analysis of natural language reasoning and the development of critical ordinary language philosophy in the wake of J.L. Austin. Philosophical arguments and thought experiments are strongly shaped by default pragmatic inferences, including stereotypical inferences. Austin suggested that contextually inappropriate stereotypical inferences are at the root of some philosophical paradoxes and problems, and that these can be resolved by exposing those verbal fallacies. This paper builds on recent efforts to empirically (...) document inappropriate stereotypical inferences that may drive philosophical arguments. We demonstrate that previously employed questionnaire-based output measures do not suffice to exclude relevant confounds. We then report an experiment that combines reading time measurements with plausibility ratings. The study seeks to provide evidence of inappropriate stereotypical inferences from appearance verbs that have been suggested to lie at the root of the influential ‘argument from illusion’. Our findings support a diagnostic reconstruction of this argument. They provide the missing component for proof of concept for an experimental implementation of critical ordinary language philosophy that is in line with the ambitions of current ‘evidential’ experimental philosophy. (shrink)

This study investigates the performance of 21 monolingual and 56 bilingual children aged 5;6-9;0 on German-LITMUS-sentence-repetition (SRT; Hamann et al., 2013) and nonword-repetition-tasks (NWRT; Grimm et al., 2014), which were constructed according to the LITMUS-principles (Language Impairment Testing in Multilingual Settings; Armon-Lotem et al., 2015). Both tasks incorporate complex structures shown to be cross-linguistically challenging for children with Specific Language Impairment (SLI) and aim at minimizing bias against bilingual children while still being indicative of the presence of language impairment across (...) language combinations. Given the great variability in bilingual language exposure and the potential effect of language experience on language performance in bilingual children, we examined whether background variables related to bilingualism, particularly, the degree language dominance as measured by relative amount of use and exposure, could compromise the diagnostic accuracy of the German-LITMUS-SRT and NWRT. We further investigated whether a combination of the two tasks provides better diagnostic accurracy. To address this, we used an unsupervised machine learning algorithm, the Partitioning-Around-Medoids (PAM), for deriving a clinical category for the children as ±language-impaired based on performance scores on SRT and/or NWRT while withholding information about their clinical status based on standardized assessment in L1/L2. Subsequently, we calculated diagnostic accuracy and investigated which background variables (age of onset, length of exposure, degree of language-dominance, socio-economic-status, and risk factors for SLI) best explained clinical-group-membership yielded from the PAM-analysis based on NWRT and/or SRT scores. Results show that although language-dominance influences the performance of bilingual typically-developing children, especially in the SRT, the diagnostic accuracy of the tools is not compromised by language dominance: while risk factors for SLI were significant predictors for clinical group membership in all models, language dominance did not contribute at all to explaining clinical cluster membership as typically-developing or SLI based on any of the combinations of the SRT and NWRT variables. Additionally, results confirm that a combination of SRT scored by correct target structure and the structurally more complex language-dependent part of the NWRT yields better diagnostic accuracy than single measures and is only sensitive for risk factors for SLI and not to dominance levels or SES. (shrink)

Children and adults with autism do worse on tests of inferences than controls. This fact has been attributed to poor language skills, a tendency to focus on detail, and poor social understanding. This study examines whether children with autism with age-appropriate language and cognitive skills have difficulties drawing inferences from academic, expository texts. Sixteen children with autism and a control group of twenty-four children were matched on language skills, nonverbal cognitive ability, and auditory and nonverbal working memory and (...) compared on their responses to questions that require inferences. The children with autism scored significantly lower on inference questions than the controls. Although language skills explain much of the variance in inference scores, diagnostic background also made a contribution. The results are discussed in the light of theories of suppression of irrelevant information and recognition of text writers’ communicative intention. (shrink)

From the standpoint of the theory of medicine, a formulation is given of three types of reasoning used by physicians. The first is deduction from probability models (as in prognosis or genetic counseling for Mendelian disorders). It is a branch of mathematics that leads to predictive statements about outcomes of individual events in terms of known formal assumptions and parameters. The second type is inference (as in interpreting clinical trials). In it the arguments from replications of the same process (data) (...) lead to conclusions about the parameters of a system, without calling into question either the probabilistic model or the criteria of evidence. The third is illation (as in the elucidation of symptoms in a patient). It is a process whereby, in the light of the total evidence and the conclusions from the other types of reasoning, one may modify, expand, simplify or demolish a conceptual framework proposed for deductions, and modify the nature of the evidence sought, the criteriology, the axioms, and the surmised complexity of the scientific theory. (The process of diagnosis as applied to a patient may in extreme cases lead to the discovery of an entirely new disease with its own, quite new, set of diagnostic criteria. This course cannot be accommodated inside either of the other two types of reasoning.) Illation has something of the character of Kuhn's scientific revolution in physics; but it differs in that it is the nature, not the degree or frequency of change that distinguishes it from Kuhn's normal science. (shrink)

A diagnostic judgment of a teacher can be seen as an inference from manifest observable evidence on a student’s behavior to his or her latent traits. This can be described by a Bayesian model of in-ference: The teacher starts from a set of assumptions on the student (hypotheses), with subjective probabilities for each hypothesis (priors). Subsequently, he or she uses observed evidence (stu-dents’ responses to tasks) and knowledge on conditional probabilities of this evidence (likelihoods) to revise these assumptions. Many (...) systematic deviations from this model (biases, e.g. base-rate neglect, inverse fallacy) are reported in literature on Bayesian reasoning. In a teacher’s situation the information (hypotheses, priors, likelihoods) is usually not explicitly represented numerically (as in most research on Bayesian reasoning), but only by qualitative esti-mations in the mind of the teacher. In our study, we ask to which extent individuals (approximately) apply a rational Bayesian strategy or resort on other biased strategies of processing information for their diagnostic judgments. We explicitly pose this question with respect to non-numerical settings. To investigate this question, we developed a scenario that visually displays all relevant information (hypotheses, priors, likelihoods) in a graphically displayed hypothesis space (called “hypothegon”) – without recurring to numerical representations or mathematical procedures. 42 pre-service teach-ers were asked to judge the plausibility of different misconceptions of six students based on their responses to decimal comparison tasks (e.g. 3.39 > 3.4). Applying a Bayesian classification proce-dure, we identified three updating strategies: A Bayesian update strategy (BUS, processing all probabilities), a combined evidence strategy (CES, ignoring the prior probabilities but including all likelihoods), and a single evidence strategy (SES, only using the likelihood of the most probable hypothesis). In study 1, an instruction on the relevance of using all probabilities (priors and likelihoods) only weakly increased the processing of more information. In study 2, we found strong evidence that a visual explication of the prior-likelihood interaction led to an increase of processing the interaction of all relevant information. These results show that the phenomena found in general research on Bayesian reasoning in numerical settings extend to diagnostic judgments in non-numerical settings. (shrink)

The following topics are considered: - 1) Computer-based medical consultation: good doctors have the capacity to make right guesses. - 2) Elementary diagnostic logic: illness as a boolean combination of signs or symptoms, diagnosis as a deductive process. - 3) Clinical decision under uncertainty: partial and/or elusive evidence, overlapping types. - 4) Local heuristic strategies: (1) statistical methods, (2) probabilistic (bayesian) methods, (3) fuzzy methods and Mycin-type expert systems. - 5) General heuristic strategies: representing medical knowledge (rules, nets, frames), (...) simulating expert behavior. - 6) Diagnostic inference and pattern recognition: natural species vs. expert judgement. (shrink)

The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more probable when it was linked to evidence by a causal chain than when both variables shared a common cause; (b) predictive chains in which evidence is a cause of (...) the hypothesis gave rise to higher judgments than diagnostic chains in which evidence is an effect of the hypothesis; and (c) direct chains gave rise to higher judgments than indirect chains. A Bayesian learning model was applied to our data but failed to explain them. An explanation-based hypothesis stating that statistical information will affect judgments only to the extent that it changes beliefs about causal structure is consistent with the results. (shrink)

A generalized Bayesian inference nets model is proposed to aid researchers to construct Bayesian inference nets for various applications. The benefit of such a model is well demonstrated by applying GBINM in constructing a hierarchical Bayesian fuzzy inference nets to diagnose five important types of cardiovascular diseases. The patients' medical records with doctors' confirmed diagnostic results obtained from two hospitals in China are used to design and verify HBFIN. Bayesian theorem is used to calculate the propagation of probability and (...) address the uncertainties involved in each sequential stage of inference nets to deduce the disease. The validity and effectiveness of proposed approach is witnessed clearly from testing results obtained. (shrink)

Exploring the early development of an area of medical literature can inform contemporary medical debates. Different methods of inference include deduction, induction, abduction, and inference to the best explanation. I argue that early shaken baby research is best understood as using abduction to tentatively suggest that infants with unexplained intracranial and ocular bleeding have been assaulted. However, this tentative conclusion was quickly interpreted, by some at least, as a general rule that infants with these pathological signs were certainly cases of (...) abuse. Rather than focusing on inductive arguments, researchers today may be better off focusing on making a compelling inference to the best explanation. (shrink)

The study of causal inference has seen recent momentum in machine learning and artificial intelligence, particularly in the domains of transfer learning, reinforcement learning, automated diagnostics, and explainability. Yet, despite its increasing application to address many of the boundaries in modern AI, causal topics remain absent in most AI curricula. This work seeks to bridge this gap by providing classroom-ready introductions that integrate into traditional topics in AI, suggests intuitive graphical tools for the application to both new and traditional lessons (...) in probabilistic and causal reasoning, and presents avenues for instructors to impress the merit of climbing the “causal hierarchy” to address problems at the levels of associational, interventional, and counterfactual inference. Finally, this study shares anecdotal instructor experiences, successes, and challenges integrating these lessons at multiple levels of education. (shrink)

In medical diagnostic examination three main stages may be distinguished: (a) initial exploration, (b) hypothesis-directed investigation, and (c) final diagnosis making. The purpose of this work is to study some methodological problems concerning the second of the above stages of the diagnosis and to prepare a background for a mathematical model [30] of this process.In diagnostic problem solving, the reasoning proceeds along the main lines traced by some initial suggestions and passes through various intermediate elements which are connected (...) with one another forming ramifying chains and nets of inferences and hypotheses. Such a complex mental construction is based on laws which form medical knowledge and reflect various regularities and relations, causal, structural, functional, and others. (shrink)

Conflict resolution is a core component of healthcare ethics consultation (HEC) and proficiency in this skill set is recognized by the national bioethics organization and its HEC certification process. Difficult interpersonal interactions between the clinical team and patients or their families are often inexorably connected to the normative disputes that are the catalyst for the consult. Ethics consultants are often required to navigate challenging dynamics that have become entrenched and work with patient-provider or family-provider relationships that have already broken down. (...) The first step in conflict resolution is diagnosing the source of the conflict. Because so many interpersonal and normative conflicts rest on misunderstanding and mischaracterization, the diagnosis of the problem requires untangling the actual positions and perspectives of the conflicting parties from the fallacious assumptions made about the parties’ respective positions and views. Developed in management science, the Ladder of Inference (LOI) is a diagnostic tool for assisting stakeholders in re-examining the process they used to form beliefs about others involved in the conflict. The LOI is a device that detects errors in reasoning, including implicit racial bias, that lead to false judgments and counterproductive responses to those judgments. The LOI is an instrument that can be used by ethics consultants to help resolve contentious bedside conflicts, but the LOI can also be employed as a teaching tool used by healthcare ethics consultants in training the clinical staff in how to avoid such conflicts in the first place. (shrink)

Order of information plays a crucial role in the process of updating beliefs across time. In fact, the presence of order effects makes a classical or Bayesian approach to inference difficult. As a result, the existing models of inference, such as the belief-adjustment model, merely provide an ad hoc explanation for these effects. We postulate a quantum inference model for order effects based on the axiomatic principles of quantum probability theory. The quantum inference model explains order effects by transforming a (...) state vector with different sequences of operators for different orderings of information. We demonstrate this process by fitting the quantum model to data collected in a medical diagnostic task and a jury decision-making task. To further test the quantum inference model, a new jury decision-making experiment is developed. Using the results of this experiment, we compare the quantum inference model with two versions of the belief-adjustment model, the adding model and the averaging model. We show that both the quantum model and the adding model provide good fits to the data. To distinguish the quantum model from the adding model, we develop a new experiment involving extreme evidence. The results from this new experiment suggest that the adding model faces limitations when accounting for tasks involving extreme evidence, whereas the quantum inference model does not. Ultimately, we argue that the quantum model provides a more coherent account for order effects that was not possible before. (shrink)

Many recent developments in artificial intelligence research are relevant for traditional issues in the philosophy of science. One of the developments in AI research we want to focus on in this article is diagnostic reasoning, which we consider to be of interest for the theory of explanation in general and for an understanding of explanatory arguments in economic science in particular. Usually, explanation is primarily discussed in terms of deductive inferences in classical logic. However, in recent AI research (...) it is observed that a diagnostic explanation is actually quite different from deductive reasoning. In diagnostic reasoning the emphasis is on restoring consistency rather than on deduction. Intuitively speaking, the problem diagnostic reasoning is concerned with is the following. Consider a description of a system in which the normal behavior of the system is characterized and an observation that conflicts with this normal behavior. The diagnostic problem is to determine which of the components of the system can, when assumed to be functioning abnormally, account for the conflicting observation. A diagnosis is a set of allegedly malfunctioning components that can be used to restore the consistency of the system description and the observation. In this article, this kind of reasoning is formalized and we show its importance for the theory of explanation. We will show how the diagnosis nondeductively explains the discrepancy between the observed and the correct system behavior. The article also shows the relevance of the subject for real scientific arguments by showing that examples of diagnostic reasoning can be found in Friedman's Theory of the Consumption Function. Moreover, it places the philosophical implications of diagnostic reasoning in the context of Mill's aprioristic methodology. (shrink)

The article is based on research conducted at the actual radiology department. It presents a range of descriptions and analyses of concrete operations performed by radiologists during their daily professional routine. After careful ethnographic observations, phenomenological analysis is employed with a view to examining the enactive cognition in the radiologist’s “life-world”. The paper uses both ethnography and phenomenology in order to reveal the essential regularities and sedimentations of everyday radiological processes, and the “everyday background” of certain scientific-cognitive operations. The method (...) of research includes “enactive proofs”— observations and analysis of the externalization of a radiologist’s professional memory through the enaction of medical imaging and imageconsciousness. The findings of this research have much to offer to both philosophy and radiological praxis. While the observations strongly support the development of enactive phenomenology, critique of representationalism, primacy of inference in cognition, and shared intentions, they also provide insight into concrete operations in coping with radiology’s paraphernalia, habituality, the origin of mistakes, multilayered communication, and improving professional praxis. (shrink)

The minimally conscious sta te (MCS) is usually ascribed when a patientwith brain damage exhibits obser vable volitional behaviors that predict recovery ofcognitive funct ions. Nevertheless, a patient with brain damage who lacks motorcapacit y might nonetheless be in MCS. For this reason, some clinicians use neuralsignals as a communicative means for MCS ascription. For instance, a vegetativestate patient is diagnosed with MCS if activity in the motor area is observed whenthe instruction to imagine wiggling toes is given. The validi (...) ty of using neuralsignals in ascribing MCS requires a special sort of inference. That is, no-reportparadigmatic assessments must have inductively strong ways of inferring a pur-ported informationa l content from the observed neural signal that grounds the factthat the patient has top-down cognitive control (or residual volition). Shannon’smathemat ical theory of communication and Bayes’ theorem reveals the formalstructure of neural communication. On the basis of relevant data from the neuro-science literature, I conclude that the formal structure combined with the data showsthat neural signals can be used as a communicative means for operational diagnosticcriteria for MCS ascription. (shrink)

ABSTRACTMedical diagnosis is sometimes used by critical realists and others as an exemplar of a form of inference across time in which a current empirical observation points backwards to the conditions of its emergence and forwards to a possible future outcome or progression. Accordingly, its practice warrants critical exploration to confirm its legitimacy as a philosophical reference point. The strengths and weakness of the exemplar are appraised using case brief case studies. The limitations of medical diagnosis are discussed in the (...) wider context of the sociology of medical knowledge, the social determinants of ill-health and the role of commercial interests in shaping diagnostic practice. The conclusion is cautious, not nihilistic. Diachronic inference in medicine, as in other disciplines working in and with open systems, can be respected for its advantages to humanity. However, this brings with it the perennial caution of epistemic humility and a needed tolerance of uncertainty.. (shrink)

In the digital age individuals face key choices about whether and how to share intimate details of their lives, “images of the body, biological data in general and diagnostic information” (Pyrrho,...

This paper describes in some detail a pattern of justification which seems to be part of common sense logic and also part of the logic of scientific investigations. Calling this pattern “abduction,” the paper lays out an “abduction-prediction” model of scientific inference as an update to the traditional hypothetico-deductive model. According to this newer model, scientific theories receive their claims for acceptance and belief from the abductive arguments that support them, and the processes of scientific discovery aim to develop theories (...) with strong abductive support. It is suggested that the study of diagnosis presents a good opportunity for studying abduction under somewhat simpler and more reproducible conditions than occur in scientific discovery. A computer-based diagnostic system is described which provides a small-scale validation of the abduc-tion-prediction model by showing that a version of it can be made precise enough to be implemented and to perform correctly for diagnosis. (shrink)

Tyreman and Loughlin make many varied and interesting comments, but neither, it is encouraging to find, is prepared to defend the psychogenic inference. Indeed, Loughlin finds the paper “somewhat shocking.” “The psychogenic inference,” Loughlin writes, “seems such an obvious fallacy, yet he [Sykes] shows, with detailed reference to both diagnostic practice and the literature on mental disorders, the extraordinary pervasiveness of its influence, extending even to the systematic ambiguities built into key diagnostic terms” (2010, 305). The main points (...) of disagreement are Tyreman’s defense of the term functional in certain contexts and Loughlin’s criticism of the claim that there has been a paradigm shift .. (shrink)

The Paper Begins by introducing the Siren “psychogenic inference”. It then deals with the impact of this inference on the navigation of medical and psychiatric seafarers. The next two parts are more theoretical; the first deals with the entrenchment of the psychogenic inference in some central terms used in discussing medically unexplained symptoms (MUS). The second uncovers the damaging influence of the psychogenic inference on the navigational charts—on the somatoform disorder sections of the two major classifications used internationally, namely the (...) Diagnostic and Statistical Manual, 4th edition Text revision (DSM-IV-TR; American Psychiatric Association [APA] 1994), produced by the APA and of the International .. (shrink)

Explanation and prediction play an important role in medical decision making, particularly for diagnostic and treatment decisions. For the most part, explanations as well as predictions are derived from causal knowledge and have to be made under uncertainty. In cognitive psychology, these phenomena have been approached from two directions. On the one hand, there is research on knowledge representation and inferential reasoning (Holland et al. 1986; Anderson 1990). On the other hand, there is research on heuristics and biases in (...) judgments under uncertainty (Kahneman et al. 1982; Rehm & Gadenne 1990). However, no exact models have yet been developed to describe the process that leads from a given knowledge base to judgments of probabilities, i.e., from causal knowledge about the world to subjective expressions of the uncertainty associated with explanations or predictions. (shrink)

Mind and environment evolve in tandem—almost a platitude. Much of judgment and decision making research, however, has compared cognition to standard statistical models, rather than to how well it is adapted to its environment. The author argues two points. First, cognitive algorithms are tuned to certain information formats, most likely to those that humans have encountered during their evolutionary history. In par ticular, Bayesian computations are simpler when the information is in a frequency format than when it is in a (...) probability format. The author investigates whether fre quency formats can make physicians reason more often the Bayesian way. Second, cognitive algorithms need to operate under constraints of limited time, knowledge, and computational power, and they need to exploit the structures of their environments. The author describes a fast and frugal algorithm, Take The Best, that violates standard principles of rational inference but can be as accurate as sophisticated "optimal" mod els for diagnostic inference. Key words: Bayes' theorem; bounded rationality; infor mation format; probabilistic reasoning; satisficing; training; medical education. (shrink)

The paper investigates the epistemological and communicative competences the experts need to use and communicate evidence in the reasoning process leading to diagnosis. The diagnosis and diagnosis communication are presented as intertwined processes that should be jointly addressed in medical consultations, to empower patients’ compliance in illness management. The paper presents defeasible reasoning as specific to the diagnostic praxis, showing how this type of reasoning threatens effective diagnosis communication and entails that we should understand diagnostic evidence as defeasible (...) as well. It argues that metaphors might be effective communicative devices to let the patients understand the relevant defeasors in the diagnostic reasoning process, helping to improve effective diagnosis communication, and also encouraging a change in patients’ beliefs and attitudes on their own experience of illness and illness’ management. (shrink)

This thesis investigates the nature of psychiatric disorders, and to what extent they can form a basis for classification, explanation, and treatment interventions. These questions are important in the light of the “crisis of validity” in psychiatry, according to which current diagnostic categories do not pick out real disorders. I address the questions by defending an account of psychiatric disorders that can better accommodate social aspects and non-epistemic values than the symptom-based model of the Diagnostic and Statistical Manual (...) of Mental Disorders (DSM) and the brain disease model of the biomedical approach, including the Research Domain Criteria (RDoC). I concentrate on three ways that psychiatric disorders can differ from prototypical natural kinds, such as biological species and chemical elements. First, the concept of psychiatric disorder or mental disorder is partly value-laden and cannot be defined based only on scientific facts. Second, the objects of psychiatric classifications are interactive human kinds because people with disorders respond to their classifications through looping effects. Third, sociocultural factors can shape disorders in complex ways, which is indicated by their cross-cultural variation. I argue that these challenges can be overcome with a pluralistic explanatory account that grants an explicit role to value-sensitive considerations and social scientific research. Based on this, I argue that particular disorders can in principle support inductive inferences. ------------------------------------------------------------------------------------------ My key argument is that although whether a condition is considered pathological is partly a value-laden question, this does not rule out a realist account of particular psychiatric disorders as homeostatic property cluster kinds (HPC). I assert that causal mechanisms responsible for psychiatric kinds can be understood non-metaphysically based on the contrastive-counterfactual and interventionist theories of explanation. An advantage of this account is that it can address the concern that psychiatric explanations require one to relate heterogeneous causal factors on different levels, such as genetic, neurological, psychological, and social. My novel argument is that mechanistic explanations of scientific kinds have applicability domains over which they account for specific aspects of kinds and warrant inductive inferences. That is, identifying the applicability domain of an explanation spells out the conditions under which the explanation is expected to be reliable and when it can break down. This helps to understand how different discipline approaches, including social and cultural ones, can be complementary and make an explanation of a psychiatric kind more reliable. On the other hand, the account also shows how the complex nature of psychiatric kinds can license re-classifications for different epistemic and non-epistemic purposes. Finally, in the light of the social implications of psychiatric classifications, I suggest a value-sensitive or ameliorative approach to engineering the concept of psychiatric disorder. In conclusion, my account shows that research on the social and cultural factors that shape psychiatric disorders, and weighing the non-epistemic values that influence classificatory practices, can and should inform classificatory choices as well as policy and treatment interventions. (shrink)

We propose a critique of normativism, defined as the idea that human thinking reflects a normative system against which it should be measured and judged. We analyze the methodological problems associated with normativism, proposing that it invites the controversial “is-ought” inference, much contested in the philosophical literature. This problem is triggered when there are competing normative accounts (the arbitration problem), as empirical evidence can help arbitrate between descriptive theories, but not between normative systems. Drawing on linguistics as a model, we (...) propose that a clear distinction between normative systems and competence theories is essential, arguing that equating them invites an “is-ought” inference: to wit, supporting normative “ought” theories with empirical “is” evidence. We analyze in detail two research programmes with normativist features – Oaksford and Chater’s rational analysis and Stanovich and West’s individual differences approach – demonstrating how, in each case, equating norm and competence leads to an is-ought inference. Normativism triggers a host of research biases in the psychology of reasoning and decision making: focusing on untrained participants and novel problems, analyzing psychological processes in terms of their normative correlates, and neglecting philosophically significant paradigms when they do not supply clear standards for normative judgement. For example, in a dual-process framework, normativism can lead to a fallacious “ought-is” inference, in which normative responses are taken as diagnostic of analytic reasoning. We propose that little can be gained from normativism that cannot be achieved by descriptivist computational-level analysis, illustrating our position with Hypothetical Thinking Theory and the theory of the suppositional conditional. We conclude that descriptivism is a viable option, and that theories of higher mental processing would be better off freed from normative considerations. (shrink)

The psychology of reasoning is increasingly considering agents' values and preferences, achieving greater integration with judgment and decision making, social cognition, and moral reasoning. Some of this research investigates utility conditionals, ‘‘if p then q’’ statements where the realization of p or q or both is valued by some agents. Various approaches to utility conditionals share the assumption that reasoners make inferences from utility conditionals based on the comparison between the utility of p and the expected utility of q. (...) This article introduces a new parameter in this analysis, the underlying causal structure of the conditional. Four experiments showed that causal structure moderated utility-informed conditional reasoning. These inferences were strongly invited when the underlying structure of the conditional was causal, and significantly less so when the underlying structure of the conditional was diagnostic. This asymmetry was only observed for conditionals in which the utility of q was clear, and disappeared when the utility of q was unclear. Thus, an adequate account of utility-informed inferences conditional reasoning requires three components: utility, probability, and causal structure. (shrink)

Medical diagnosis has been traditionally recognized as a privileged field of application for so called probabilistic induction. Consequently, the Bayesian theorem, which mathematically formalizes this form of inference, has been seen as the most adequate tool for quantifying the uncertainty surrounding the diagnosis by providing probabilities of different diagnostic hypotheses, given symptomatic or laboratory data. On the other side, it has also been remarked that differential diagnosis rather works by exclusion, e.g. by modus tollens, i.e. deductively. By drawing on (...) a case history, this paper aims at clarifying some points on the issue. Namely: 1) Medical diagnosis does not represent, strictly speaking, a form of induction, but a type, of what in Peircean terms should be called ‘abduction’ (identifying a case as the token of a specific type); 2) in performing the single diagnostic steps, however, different inferential methods are used for both inductive and deductive nature: modus tollens, hypothetical-deductive method, abduction; 3) Bayes’ theorem is a probabilized form of abduction which uses mathematics in order to justify the degree of confidence which can be entertained on a hypothesis given the available evidence; 4) although theoretically irreconcilable, in practice, both the hypothetical- deductive method and the Bayesian one, are used in the same diagnosis with no serious compromise for its correctness; 5) Medical diagnosis, especially differential diagnosis, also uses a kind of “probabilistic modus tollens”, in that, signs (symptoms or laboratory data) are taken as strong evidence for a given hypothesis not to be true: the focus is not on hypothesis confirmation, but instead on its refutation [Pr (¬ H/E1, E2, …, En)]. Especially at the beginning of a complicated case, odds are between the hypothesis that is potentially being excluded and a vague “other”. This procedure has the advantage of providing a clue of what evidence to look for and to eventually reduce the set of candidate hypotheses if conclusive negative evidence is found. 6) Bayes’ theorem in the hypothesis-confirmation form can more faithfully, although idealistically, represent the medical diagnosis when the diagnostic itinerary has come to a reduced set of plausible hypotheses after a process of progressive elimination of candidate hypotheses; 7) Bayes’ theorem is however indispensable in the case of litigation in order to assess doctor’s responsibility for medical error by taking into account the weight of the evidence at his disposal. (shrink)

Cost-based abduction, which is a technique for identifying the best explanation for a given observation based on the assumption of a set of hypothesis, is a useful knowledge processing framework for practical problems such as diagnosis, design and planning. However, the speed of reasoning of this approach is often slow. To overcome this problem, Kato et al. previously presented a more efficient cost-based abduction system, that utilized the A * search technique, however, the time and space complexities in this technique (...) are exponential, so the identification of the optimal solution is difficult in practical applications. In this paper, we present three new systems in which the user can define the computational complexity, for identification of a near-optimal solution. First, we introduce two search control techniques; a real-time A * search approach in which the user can define the look-ahead depth or space, and the multi-agent real-time A * approach in which the user can define the number of real-time A * agents used in the search. We describe the implementation of three cost-based abduction reasoning systems for predicate logic knowledge bases and test the proposed systems using a diagnostic logic circuit problem. The results show that proposed systems can identify a near-optimal solution according to the predefined polynomial order of complexity, including the selection of either linear or exponential computational complexity. It is also shown that inference time and success rate are dependent on the user-defined parameters, that the three proposed systems exhibit similar performance characteristics, and that they all offer significant speed advantages over the previously described technique. (shrink)

The study of creative, diagnostic, visual, spatial, analogical, and temporal reasoning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of traditional notions of reasoning such as classical logic. Understanding the contribution of modeling practices to discovery and conceptual change in science requires expanding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these (...) heuristic ways of reasoning is situated at the crossroads of philosophy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science. There are several key ingredients common to the various forms of model-based reasoning. The term "model" comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. Moreover, in the modeling process, various forms of abstraction are used. Evaluation and adaptation take place in light of structural, causal, and/or functional constraints. Model simulation can be used to produce new states and enable evaluation of behaviors and other factors. Several of the papers in this volume aim at increasing epistemological knowledge about the role of model-based reasoning in various scientific tasks, other papers address fundamental cognitive issues related to model-based reasoning and illustrate novel analyses of cognitive "logical" models of model-based reasoning and of the interplay abduction/model-based reasoning/creative inferences. The volume is based on the papers that were presented at the International Conference Model-Based Reasoning in Science and Engineering: Abduction, Visualization, Simulation, held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in December 2004. (shrink)

Progress in the medical diagnostic is relentlessly pushing the measurement technology as well with its intertwined mathematical models and solutions. Mathematics has applications to many problems that are vital to human health but not for all. In this article we describe how the mathematics of acoustocerebrography has become one of the most important applications of mathematics to the problems of brain monitoring as well we will show some algebraic problems which still have to be solved. Acoustocerebrography is a set (...) of techniques of visualizing the state of brain tissue and its changes with use of ultrasounds, which mainly rely on a relation between the tissue density and speed of propagation for ultrasound waves in this medium. Propagation speed or, equivalently, times of arriving for an ultrasound pulse, can be inferred from phase relations for various frequencies. Since, due to Kramers-Kronig relations, the propagation speeds depend significantly on the frequency of investigated waves, we consider multispectral wave packages of the form W = ∑Hh=1 Ah · sin, n = 0,..., N – 1 with appropriately chosen frequencies fh, h = 1,...,H, amplifications Ah, h = 1,...,H, start phases ψh, h = 1,..., H and sampling frequency F. In this paper we show some problems of algebraic and, to some extend, algorithmic nature which raise up in this topic. Like, for instance, the influence of relations between the signal length and frequency values on the error on estimated phases or on neutralizing alien frequencies. Another problem is finding appropriate initial phases for avoiding improper distributions of peaks in the resulting signal or finding a stable algorithm of phase unwinding which is resistant to sudden random disruptions. (shrink)

One purpose of cognitive diagnostic model is designed to make inferences about unobserved latent classes based on observed item responses. A heuristic for test construction based on the CDM information index proposed by Henson and Douglas has a far-reaching impact, but there are still many shortcomings. He and other researchers had also proposed new methods to improve or overcome the inherent shortcomings of the CDI test assembly method. In this study, one test assembly method of maximizing the minimum (...) inter-class distance is proposed by using mixed-integer linear programming, which aims to overcome the shortcomings that the CDI method is limited to summarize the discriminating power of each item into a single CDI index while neglecting the discriminating power for each pair of latent classes. The simulation results show that compared with the CDI test assembly and random test assembly, the new test assembly method performs well and has the highest accuracy rate in terms of pattern and attributes correct classification rates. Although the accuracy rate of the new method is not very high under item constraints, it is still higher than the CDI test assembly with the same constraints. (shrink)

This article concerns the structure of defeasible arguments like: 'If Bob has red spots, Bob has the measles; Bob has red spots; therefore Bob has the measles.' The issue is whether such arguments have the form of modus ponens or not. Either way there is a problem. If they don't have the form of modus ponens, the common opinion to the contrary taught in leading logic textbooks is wrong. But if they do have the form of modus ponens, doubts are (...) raised about the conventional dogma that all arguments having the form of modus ponens are deductively valid. By carefully examining arguments on both sides of the issue, reasonable doubts are raised about the view that all arguments having a modus ponens form are valid. (shrink)

We propose a critique ofnormativism, defined as the idea that human thinking reflects a normative system against which it should be measured and judged. We analyze the methodological problems associated with normativism, proposing that it invites the controversial “is-ought” inference, much contested in the philosophical literature. This problem is triggered when there are competing normative accounts (the arbitration problem), as empirical evidence can help arbitrate between descriptive theories, but not between normative systems. Drawing on linguistics as a model, we propose (...) that a clear distinction between normative systems and competence theories is essential, arguing that equating them invites an “is-ought” inference: to wit, supporting normative “ought” theories with empirical “is” evidence. We analyze in detail two research programmes with normativist features – Oaksford and Chater's rational analysis and Stanovich and West's individual differences approach – demonstrating how, in each case, equating norm and competence leads to an is-ought inference. Normativism triggers a host of research biases in the psychology of reasoning and decision making: focusing on untrained participants and novel problems, analyzing psychological processes in terms of their normative correlates, and neglecting philosophically significant paradigms when they do not supply clear standards for normative judgement. For example, in a dual-process framework, normativism can lead to a fallacious “ought-is” inference, in which normative responses are taken as diagnostic of analytic reasoning. We propose that little can be gained from normativism that cannot be achieved by descriptivist computational-level analysis, illustrating our position with Hypothetical Thinking Theory and the theory of the suppositional conditional. We conclude that descriptivism is a viable option, and that theories of higher mental processing would be better off freed from normative considerations. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Religion and the Body in Medical ResearchCourtney S. Campbell (bio)AbstractReligious discussion of human organs and tissues has concentrated largely on donation for therapeutic purposes. The retrieval and use of human tissue samples in diagnostic, research, and education contexts have, by contrast, received very little direct theological attention. Initially undertaken at the behest of the National Bioethics Advisory Commission, this essay seeks to explore the theological and religious questions (...) embedded in nontherapeutic use of human tissue. It finds that the “donation paradigm” typically invoked in religious discourse to justify uses of the body for therapeutic reasons is inadequate in the context of nontherapeutic research, while the “resource paradigm” implicit in scientific discourse presumes a reductionist account of the body that runs contrary to important religious values about embodiment. The essay proposes a “contribution paradigm” that provides a religious perspective within which research on human tissue can be both justified and limited.The retrieval and use of human tissue samples for diagnostic, therapeutic, research, and educational purposes represent further developments in scientific study of the human body as a source of medical information. Following the completion of its examination of the ethical and policy issues pertaining to human cloning in the spring of 1997, the National Bioethics Advisory Commission (NBAC) returned to the set of questions for which it had been originally constituted, including ethical issues that arise in human subjects research. One issue that proved controversial for the human genetics subcommittee of NBAC was the scientific, philosophical, and legal status of tissue and other body parts (usually surgical specimens) that had been retrieved from patients (not research subjects) who had given general consent to the use of their body materials for research and educational purposes. The subcommittee addressed questions about the adequacy of general versus specific [End Page 275] consent, the scientific merits of identifiable versus anonymous samples and claims about patient confidentiality, and the justification for patient refusals and requests that the materials be returned to the patient or discarded rather than made available for scientific research. In an effort to better understand the ramifications of research on human tissue, the subcommittee commissioned several background papers that examined these questions from ethical, legal, research, and religious perspectives. What follows is a modified version of the report on religious considerations originally presented to the genetics subcommittee of NBAC.The central question examined in this article is whether tissue banking for purposes of research presents distinctive theological issues or prompts objections from religious communities or scholars. Religious discussion of human organs and tissues has focused largely on donation for therapeutic purposes (what I describe as the “donation paradigm”); hence, there is very little direct consideration of nontherapeutic uses of such tissue. It is possible, however, to describe implications and suggest inferences by considering (1) religious attitudes to the human body and to organs, tissues, and cells removed from the body, and (2) religious discussions of modes of transfer of body parts, such as donations, offerings, sales, and abandonment. By and large, this discussion will reflect themes that emerge from the central Western religious traditions, with some attention given to Eastern and Native perspectives.The Body: Religious Holism, Scientific Reductionism, Private PropertyThe human body as an organic totality has long been the subject of theological reflection and a symbol for religious communities. However, much less attention has been devoted to the religious status of organs, tissues, cells, and DNA. The reflection of religious scholars and communities on the status of body parts has been prompted by the necessity to confront practical questions in personal and public health and in communal life, such as justifications for surgery, autopsies, organ donation, or burial. Scientific and research interest in parts of the body can sometimes conflict with religious values about bodily integrity. Indeed, E. Richard Gold cites the “disparate claims of scientific investigation and religious belief on the body” as the exemplary case of incommensurate values regarding the body. According to Gold (1996, p. 149), “The body, from a scientific viewpoint, is a source of knowledge of physical development, aging, and disease. From a religious perspective, the body is understood [End Page 276] as a sacred object, being created in the image... (shrink)

Projective contents, which include presuppositional inferences and Potts's conventional implicatures, are contents that may project when a construction is embedded, as standardly identified by the FAMILY-OF-SENTENCES diagnostic. This article establishes distinctions among projective contents on the basis of a series of diagnostics, including a variant of the family-of-sentences diagnostic, that can be applied with linguistically untrained consultants in the field and the laboratory. These diagnostics are intended to serve as part of a toolkit for exploring projective contents (...) across languages, thus allowing generalizations to be examined and validated crosslinguistically. We apply the diagnostics in two languages, focusing on Paraguayan Guaraní, and comparing the results to those for English. Our study of Paraguayan Guaraní is the first systematic exploration of projective content in a language other than English. Based on the application of our diagnostics to a wide range of constructions, four subclasses of projective contents emerge. The resulting taxonomy of projective content has strong implications for contemporary theories of projection, which were developed for the projective properties of particular subclasses and fail to generalize to the full set of projective contents. (shrink)

The very expertise with which psychologists wield their tools for achieving laboratory control may have had the unwelcome effect of blinding psychologists to the possibilities of discovering principles of behavior without conducting experiments. When creatively interrogated, a diverse range of large, real-world data sets provides powerful diagnostic tools for revealing principles of human judgment, perception, categorization, decision-making, language use, inference, problem solving, and representation. Examples of these data sets include patterns of website links, dictionaries, logs of group interactions, collections (...) of images and image tags, text corpora, history of financial transactions, trends in twitter tag usage and propagation, patents, consumer product sales, performance in high-stakes sporting events, dialect maps, and scientific citations. The goal of this issue is to present some exemplary case studies of mining naturally existing data sets to reveal important principles and phenomena in cognitive science, and to discuss some of the underlying issues involved with conducting traditional experiments, analyses of naturally occurring data, computational modeling, and the synthesis of all three methods. (shrink)

The concept of the digital phenotype has been used to refer to digital data prognostic or diagnostic of disease conditions. Medical conditions may be inferred from the time pattern in an insomniac’s tweets, the Facebook posts of a depressed individual, or the web searches of a hypochondriac. This paper conceptualizes digital data as an extended phenotype of humans, that is as digital information produced by humans and affecting human behavior and culture. It argues that there are ethical obligations to (...) persons affected by generalizable knowledge of a digital phenotype, not only those who are personally identifiable or involved in data generation. This claim is illustrated by considering the health-related digital phenotypes of precision medicine and digital epidemiology. (shrink)

Just as sadness is not always a symptom of mood disorder, irrational beliefs are not always symptoms of illness. Pathological irrational beliefs are distinguished from non-pathological ones by considering whether their existence is best explained by assuming some underlying dysfunctions. The features from which to infer the pathological nature of irrational beliefs are: un-understandability of their progression; uniqueness; coexistence with other psycho-physiological disturbances and/or concurrent decreased levels of functioning; bizarreness of content; preceding organic diseases known to be associated with irrational (...) beliefs; treatment response to medical intervention, etc. Severe irrationality is sometimes caused by normal human motivation rather than by mental or physical dysfunction. Pure forms of self-deception may satisfy the diagnostic criteria of delusional disorder, but there may be no evidence that suggests that they are caused by illness. Although those with pathological delusions do not recognize their delusions as symptomatic of illness, differentiating pathological beliefs from normal irrational beliefs is vitally important: If a belief is pathological, psychiatrists must seriously consider treating the patient against her will. If it is not pathological, conversely, involuntary treatments are prohibited because they offend her basic autonomy. (shrink)

Where is logic heading today? There is a general feeling that the discipline is broadening its scope and agenda beyond classical foundational issues, and maybe even a concern that, like Stephen Leacock’s famous horseman, it is ‘riding off madly in all directions’. So, what is the resultant vector? There seem to be two broad answers in circulation today. One is logical pluralism, locating the new scope of logic in charting a wide variety of reasoning styles, often marked by non-classical structural (...) rules of inference. This is the new program that I subscribed to in my work on sub-structural logics around 1990, and it is a powerful movement today. But gradually, I have changed my mind about the crux of what logic should become. I would now say that the main issue is not variety of reasoning styles and notions of consequence, but the variety of informational tasks performed by intelligent interacting agents, of which inference is only one among many, involving observation, memory, questions and answers, dialogue, or general communication. And logical systems should deal with a wide variety of these, making information-carrying events first-class citizens in their set-up. The purpose of this brief paper is to contrast and compare the two approaches, drawing freely on some insights from earlier published papers. In particular, I will argue that logical dynamics sets itself the more ambitious diagnostic goal of explaining why substructural phenomena occur, by ‘deconstructing’ them into classical logic plus an explicit account of the relevant informational events. (shrink)

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the (...) development of the community-based Coronavirus Infectious Disease Ontology in early 2020. -/- As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. -/- CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications. (shrink)

_Locked_ and _unlocked_ strategies are illustrated in this article as concepts that deal with important cognitive aspects of deep learning systems. They indicate different inference routines that refer to poor (locked) to rich (unlocked) cases of creative production of creative cognition. I maintain that these differences lead to important consequences when we analyze computational deep learning programs, such as AlphaGo/AlphaZero, which are able to realize various types of abductive hypothetical reasoning. These programs embed what I call locked abductive strategies, so, (...) even if they present spectacular performances for example in games, they are characterized by poor types of hypothetical creative cognition insofar as they are constrained in what I call _eco-cognitive openness_. This openness instead characterizes unlocked human cognition that pertains to higher kinds of abductive reasoning, in both the creative and diagnostic cases, in which cognitive strategies are instead unlocked. This special kind of “openness” is physically rooted in the fundamental character of the human brain as an open system constantly coupled with the environment (that is, an “open” or “dissipative” system): its activity is the uninterrupted attempt to achieve the equilibrium with the environment in which it is embedded, and this interplay can never be switched off without producing severe damage to the brain. The brain cannot be conceived as deprived of its physical quintessence that is its openness. In the brain, contrary to the computational case, ordering is not derived from the outside thanks to what I have called in a recent book “computational domestication of ignorant entities”, but it is the direct product of an “internal” open dynamical process of the system. (shrink)

Critical Thinking: An Introduction to Analytical Reading and Reasoning, Second Edition, provides a nontechnical vocabulary and analytic apparatus that guide students in identifying and articulating the central patterns found in reasoning and in expository writing more generally. Understanding these patterns of reasoning helps students to better analyze, evaluate, and construct arguments and to more easily comprehend the full range of everyday arguments found in ordinary journalism. Critical Thinking, Second Edition, distinguishes itself from other texts in the field by emphasizing analytical (...) reading as an essential skill. It also provides detailed coverage of argument analysis, diagnostic arguments, diagnostic patterns, and fallacies. Opening with two chapters on analytical reading that help students recognize what makes reasoning explicitly different from other expository activities, the text then presents an interrogative model of argument to guide them in the analysis and evaluation of reasoning. This model allows a detailed articulation of "inference to the best explanation" and gives students a view of the pervasiveness of this form of reasoning. The author demonstrates how many common argument types--from correlations to sampling--can be analyzed using this articulated form. He then extends the model to deal with several predictive and normative arguments and to display the value of the fallacy vocabulary. Ideal for introductory courses in critical thinking, critical reasoning, informal logic, and inductive reasoning, Critical Thinking, Second Edition, features hundreds of exercises throughout and includes worked-out solutions and additional exercises at the end of each chapter. An Instructor's Manual--offering solutions to the text's unanswered exercises and featuring other pedagogical aids--is available on the book's Companion Website at www.oup.com/us/wright. (shrink)

This dissertation challenges two prevalent views on the topic of scientific explanation: that science explains by revealing causal mechanisms, and that science explains by unifying our knowledge of the world. ;My methodological strategy is to compare our best current philosophical accounts of scientific explanation with evidence from contemporary scientific research. In particular, I focus on evidence from dynamical explanations, that is, explanations which appeal to nonlinear dynamical modeling for their force. Nonlinear dynamical modeling is a type of mathematical modeling which (...) is used by scientists in many different disciplines, including population biology, ecology, physics, and psychology. ;Chapter 1 argues that dynamical explanations are a philosophically important class of explanations, based on their prevalence and their close relationship with the semantic view of theories. ;Chapters 2 and 3 conclude that extant causal and unification accounts encounter serious difficulties when applied to dynamical explanations. Chapter 2 argues that causal relevance is neither a necessary nor a sufficient condition for explanatory relevance. Chapter 3 clarifies and evaluates different claims about the unifying power of science. Here, I argue that dynamical explanations provide some support for the thesis that unification is constitutive of explanation. However, dynamical explanations also indicate that some of the intuitions embodied in extant unification accounts are untenable. ;Chapter 4 concludes by advancing diagnostic criticisms of existing causal and unification accounts. I explore the relationships between dynamical explanations and three conceptions of scientific explanation, as well as the relationships between different types of inference and scientific understanding. I suggest that philosophers look to cognitive science and the semantic view of theories for aid in constructing a viable positive account of explanation. ;The technical appendix introduces several key definitions and concepts of dynamical systems theory. (shrink)