This is one of those cases to which Dr. 8 oodhouse's remark applies with all its force, that a method which leads to true results must have its logic — H.S Smith (" On Some of the Methods at Present in Use in Pure Geometry," p. 6) A goodly amount of modern metaphysics has concerned itself, in one form or another, with the question: what attitude should we take in regard to a language whose semantic underpinnings seem less than certain? (...) Since much of standard English is surely of this type, many philosophers have felt driven to extreme and unpalatable resolutions — wholesale rejection of attractive logical rules, radical "antirealism", "minimalism" about truth, and so forth, Scientists, however, have long dealt with analogous foundational insecurities and, under the duress of practicality, have framed a set of shifting attitudes towards language and reasoning that seem far more commonsensical than the extreme positions to which pure philosophy has often been led. Following this lead, this essay will recommend a more nuanced approach to linguistic insecurity that is neither naively optimistic nor mired in the gloomy sloughs of antirealism. (shrink)

The failure to recognize a correlation as spurious can lead people to adopt strategies to bring about a specific outcome that manipulate something other than a cause of the outcome. However, in a 2008 paper appearing in the journal Analysis, Bert Leuridan, Erik Weber and Maarten Van Dyck suggest that knowledge of spurious correlations can, at least sometimes, justify adopting a strategy aiming at bringing about some change. This claim is surprising and, if true, throws into question the claim of (...) Nancy Cartwright and others that knowledge of laws of association is insufficient for distinguishing effective and ineffective strategies. This paper examines the nature of spurious correlations and their value in crafting strategies for change. The conclusion of the paper is that while knowledge of a spurious correlation may have practical value, the value depends on either having knowledge of the causal structure underlying the correlation or it depends on the use of ‘causal criteria’. (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)

This monograph proposes a new way of studying the different forms of correlational inference, known in the Islamic jurisprudence as qiyās. According to the authors’ view, qiyās represents an innovative and sophisticated form of dialectical reasoning that not only provides new epistemological insights into legal argumentation in general but also furnishes a fine-grained pattern for parallel reasoning which can be deployed in a wide range of problem-solving contexts and does not seem to reduce to the standard forms of (...) analogical reasoning studied in contemporary philosophy of science and argumentation theory. After an overview of the emergence of qiyās and of the work of al-Shīrāzī penned by Soufi Youcef, the authors discuss al-Shīrāzī’s classification of correlational inferences of the occasioning factor. The second part of the volume deliberates on the system of correlational inferences by indication and resemblance. The third part develops the main theoretical background of the authors’ work, namely, the dialogical approach to Martin-Löf's Constructive Type Theory. The authors present this in a general form and independently of adaptations deployed in parts I and II. Part III also includes an appendix on the relevant notions of Constructive Type Theory, which has been extracted from an overview written by Ansten Klev. The book concludes with some brief remarks on contemporary approaches to analogy in Common and Civil Law and also to parallel reasoning in general. (shrink)

"Correlation is not causation" is one of the mantras of the sciences—a cautionary warning especially to fields like epidemiology and pharmacology where the seduction of compelling correlations naturally leads to causal hypotheses. The standard view from the epistemology of causation is that to tell whether one correlated variable is causing the other, one needs to intervene on the system—the best sort of intervention being a trial that is both randomized and controlled. In this paper, we argue that some purely (...) class='Hi'>correlational data contains information that allows us to draw causal inferences: statistical noise. Methods for extracting causal knowledge from noise provide us with an alternative to randomized controlled trials that allows us to reach causal conclusions from purely correlational data. (shrink)

This paper establishes a connection between structure sensitive categorial inference and classical modal logic. The embedding theorems for non-associative Lambek Calculus and the whole class of its weak Sahlqvist extensions demonstrate that various resource sensitive regimes can be modelled within the framework of unimodal temporal logic. On the semantic side, this requires decomposition of the ternary accessibility relation to provide its correlation with standard binary Kripke frames and models.

A statistical method for inference of formal causes was introduced. The procedure, referred to as the method of corresponding regressions, was explained and illustrated using a variety of simulated causal models. The method reflects IV/DV relations among variables traditionally limited to correlational or structural equation analysis. The method was applied to additive, subtractive, multiplicative, recursive and reflected models, as well as models of unrelated and correlated dependent variables. Initial applications to data from physical science, biology, economics, marketing and (...) psychology were developed, with generally supportive results. (shrink)

Participants were asked to draw inferences about correlation from single x,y observations. In Experiment 1 statistically sophisticated participants were given the univariate characteristics of distributions of x and y and asked to infer whether a single x, y observation came from a correlated or an uncorrelated population. In Experiment 2, students with a variety of statistical backgrounds assigned posterior probabilities to five possible populations based on single x, y observations, again given knowledge of the univariate statistics. In Experiment 3, statistically (...) naïve participants were given a problem analogous to that given in Experiment 1, framed verbally. Experiment 4 replicated Experiment 3 but added an “impossible to determine” response option. Models that rely on computing sample correlations make no predictions about these investigations. From a Bayesian perspective, participants’ inferences in all four experiments tended to make probabilistically valid inferences as long as the single datum was directional. The results are discussed in light of the Brunswikian notion of vicarious functioning. (shrink)

There has been an intense discussion, albeit largely an implicit one, concerning the inference of causal hypotheses from statistical correlations in quantum mechanics ever since John Bell’s first statement of his notorious theorem in 1966. As is well known, its focus has mainly been the so-called Einstein-Podolsky-Rosen (“EPR”) thought experiment, and the ensuing observed correlations in real EPR like experiments. But although implicitly the discussion goes as far back as Bell’s work, it is only in the last two decades (...) that it has become recognizably and explicitly a debate about causal inference in the quantum realm. The bulk of this paper is devoted to a review of three influential arguments in the philosophical literature that aim to show that causal models for the EPR correlations are impossible, due to Bas Van Fraassen, Daniel Hausman and Huw Price. I contend that all these arguments are inconclusive since they contain premises or presuppositions that are false, unwarranted, or at least controversial. Five different common cause models are outlined that seem perfectly viable for the EPR correlations. These models are then employed to illustrate various difficulties with the premises and presuppositions underlying Van Fraassen’s, Hausman’s and Price’s arguments. In all these cases it is argued that the difficulties cut deep against these authors’ own theories of causation and causal inference. My conclusions are that causal models for the EPR correlations remain viable, that philosophical work is still required to assess their relative virtues, and that in any case the mere theoretical conceivability and empirical possibility of these models sheds doubts over Van Fraassen’s, Hausman’s and (important elements in) Price’s theories of causation and causal inference. (shrink)

This article discusses neurophysiological correlates of those processes that mediate face recognition. Neurophysiological research in the past few years has been strongly focused on the N170, a putative correlate of the detection of a face-like visual configuration or structural encoding of faces. Neural correlates of recognition may be inferred from a difference between a repeated and an unrepeated presentation of a particular face. It focuses on effects related to face repetition and familiarity and correlates of face identification that involve retrieval (...) of semantic information or names. A debate around the face-selectivity of event-related potentials dominates underlying neural processes mediating face recognition. The main challenge is to investigate the functional significance of various neural responses in the context of cognitive models. (shrink)

Philosophers and scientists have maintained that causation, correlation, and partial correlation are essentially related. These views give rise to various rules of causal inference. This essay considers the claims of several philosophers and social scientists for causal systems with dichotomous variables. In section 2 important commonalities and differences are explicated among four major conceptions of correlation. In section 3 it is argued that whether correlation can serve as a measure of A's causal influence on B depends upon the conception (...) of causation being used and upon certain background assumptions. In section 4 five major kinds of partial correlation are explicated, and some of the important relations are established among two conceptions of partial correlation, the conception of screening off, the conception of partitioning, and the measures of causal influence which have been suggested by advocates of path analysis or structural equation methods. In section 5 it is argued that whether any of these five conceptions of partial correlation can serve as a measure of causal influence depends upon the conception of causation being used and upon certain background assumptions.The important conclusion is that each of the approaches (considered here) to causal inference for causal systems with dichotomous variables stands in need of important qualifications and revisions if they are to be justified. (shrink)

Drawing on neuroscientific research and metacognitive theory, this ground-breaking volume examines the theoretical implications that are elicited when neural correlates of consciousness are identified. The relationship between consciousness and the brain has concerned philosophers for centuries, yet a tacit assumption in much empirically-minded consciousness research seems to be that if we can only develop a map of correlations, no further questions remain to be asked. Beyond Neural Correlates of Consciousness starts where others stop, by asking what these correlations may tell (...) us about the nature of consciousness. The book contains chapters considering the upshots of finding the neural correlates of consciousness in light of the most prominent contemporary theories in the field. This illuminates the theoretical consequences of succeeding in the quest for the neural correlates of consciousness from the perspective of global workspace theory, higher-order thought theory, local recurrency theory, and REFCON models, in addition to considering how this quest is shaped by different conscious phenomena, such as dreaming, altered states of consciousness and different levels of consciousness. This insightful text features sophisticated theories that goes beyond correlational inferences and neural mapping, and will be of interest to students and researchers of consciousness, particularly those interested in interpreting neural correlates. (shrink)

There is a steadily growing literature on the role of the immune system in psychiatric disorders. So far, these advances have largely taken the form of correlations between specific aspects of inflammation (e.g. blood plasma levels of inflammatory markers, genetic mutations in immune pathways, viral or bacterial infection) with the development of neuropsychiatric conditions such as autism, bipolar disorder, schizophrenia and depression. A fundamental question remains open: why are psychiatric disorders and immune responses intertwined? To address this would require a (...) step back from a historical mind–body dualism that has created such a dichotomy. We propose three contributions of active inference when addressing this question: translation, unification, and simulation. To illustrate these contributions, we consider the following questions. Is there an immunological analogue of sensory attenuation? Is there a common generative model that the brain and immune system jointly optimise? Can the immune response and psychiatric illness both be explained in terms of self-organising systems responding to threatening stimuli in their external environment, whether those stimuli happen to be pathogens, predators, or people? Does false inference at an immunological level alter the message passing at a psychological level (or vice versa) through a principled exchange between the two systems? (shrink)

This paper concerns how motor actions are neurally represented and coded. Action planning and motor preparation can be studied using a specific type of representational activity, motor imagery. A close functional equivalence between motor imagery and motor preparation is suggested by the positive effects of imagining movements on motor learning, the similarity between the neural structures involved, and the similar physiological correlates observed in both imaging and preparing. The content of motor representations can be inferred from motor images at a (...) macroscopic level, based on global aspects of the action and the motor rules and constraints which predict the spatial path and kinematics of movements. A more microscopic neural account calls for a representation of object-oriented action. Object attributes are processed in different neural pathways depending on the kind of task the subject is performing. During object-oriented action, a pragmatic representation is activated in which object affordances are transformed into specific motor schemas. Animal as well as human clinical data implicate the posterior parietal and premotor cortical areas in schema instantiation. A mechanism is proposed that is able to encode the desired goal of the action and is applicable to different levels of representational organization. (shrink)

Based on the growing problem of heart diseases, their efficient diagnosis is of great importance to the modern world. Statistical inference is the tool that most physicians use for diagnosis, though in many cases it does not appear powerful enough. Clustering of patient instances allows finding out groups for which statistical models can be built more efficiently. However, the performance of such an approach depends on the features used as clustering attributes. In this paper, the methodology that consists of (...) combining unsupervised feature selection and grouping to improve the performance of statistical analysis is considered. We assume that the set of attributes used in clustering and statistical analysis phases should be different and not correlated. Thus, the method consisting of selecting reversed correlated features as attributes of cluster analysis is considered. The proposed methodology has been verified by experiments done on three real datasets of cardiovascular cases. The obtained effects have been evaluated regarding the number of detected dependencies between parameters. Experiment results showed the advantage of the presented approach compared to other feature selection methods and without using clustering to support statistical inference. (shrink)

We analyze the quantum mechanical measuring process from the standpoint of information theory. Statistical inference is used in order to define the most likely state of the measured system that is compatible with the readings of the measuring instrument and the a priori information about the correlations between the system and the instrument. This approach has the advantage that no reference to the time evolution of the combined system need be made. It must, however, be emphasized that the result (...) is to be interpreted as the statistically inferred state of the original system rather than the state of the system after measurement. The phenomenon of “reduction of states” appears in this light as a consequence of incomplete information rather than the physical interaction between measured system and measuring instrument. (shrink)

Changes in the methodology of the historical sciences make them more vulnerable to unjustifiable speculations being passed off as scientific results. The integrity of historical science is in peril due the way speculative and often unexamined causal assumptions are being used to generate data and underpin the identification of correlations in such data. A step toward a solution is to distinguish between plausible and speculative assumptions that facilitate the inference from measured and observed data to causal claims. One way (...) to do that is by comparing these assumptions against a well-attested set of aspects of causation, such as the so-called “Bradford Hill Criteria”. The BHC do not provide a test for causation or necessary and sufficient conditions for causation but do indicate grounds for further investigation. By revising the BHC to reflect the needs and focus of historical sciences, it will be possible to assess the cogency of methods of investigation. These will be the Historical Sciences Bradford Hill Criteria. An application to one area in historical science is used to demonstrate the effectiveness of the HSBHC, namely biogeography. Four methods are assessed in order to show how the HSBHC can be used to examine the assumptions between our data and the causal biogeographical processes we infer. (shrink)

Current neuroimaging techniques with high spatial resolution constrain participant motion so that many natural tasks cannot be carried out. The aim of this paper is to show how a time-locked correlation-analysis of cerebral blood flow velocity (CBFV) lateralization data, obtained with functional TransCranial Doppler (fTCD) ultrasound, can be used to infer cerebral activation patterns across tasks. In a first experiment we demonstrate that the proposed analysis method results in data that are comparable with the standard Lateralization Index (LI) for within-task (...) comparisons of CBFV patterns, recorded during cued word generation (CWG) at two difficulty levels. In the main experiment we demonstrate that the proposed analysis method shows correlated blood-flow patterns for two different cognitive tasks that are known to draw on common brain areas, CWG, and Music Synthesis. We show that CBFV patterns for Music and CWG are correlated only for participants with prior musical training. CBFV patterns for tasks that draw on distinct brain areas, the Tower of London and CWG, are not correlated. The proposed methodology extends conventional fTCD analysis by including temporal information in the analysis of cerebral blood-flow patterns to provide a robust, non-invasive method to infer whether common brain areas are used in different cognitive tasks. It complements conventional high resolution imaging techniques. (shrink)

It is still a matter of controversy whether the Principle of the Common Cause (PCC) can be used as a basis for sound causal inference. It is thus to be expected that its application to quantum mechanics should be a correspondingly controversial issue. Indeed the early 90’s saw a flurry of papers addressing just this issue in connection with the EPR correlations. Yet, that debate does not seem to have caught up with the most recent literature on causal (...) class='Hi'>inference generally, which has moved on to consider the virtues of a generalised PCC-inspired condition, the so-called Causal Markov Condition (CMC). In this paper we argue that the CMC is an appropriate benchmark for debating possible causal explanations of the EPR correlations. But we go on to take issue with some pronouncements on EPR by defenders of the CMC. (shrink)

Corresponding correlations is a method that allows us to infer formal causation from correlational data. In this paper, causal terms are traced to their philosophical and etymological roots. It is argued that causes are parts of their mutual whole . Nominalism, normal distributions and disjunctive causes are linked. Causal manifolds and sampling by potential are used to model conjunctive causes. Corresponding correlations are then demonstrated through simulations, in which causal relations are differentiated from spurious correlations. An algebraic method for (...) unraveling confounded variables is presented. Distinctions between laws and causes are made and related to corresponding correlations. The conclusion is that corresponding correlations should be a significant advance in causal inference. (shrink)

Correlational measures are probably the most spread statistical tools in psychological research. They are used by researchers to investigate, for example, relations between self-report measures usually collected using paper-pencil or online questionnaires. Like many other statistical analysis, also correlational measures can be seriously affected by specific sources of bias which constitute serious threats to the final observed results. In this contribution, we will focus on the impact of the fake data threat on the interpretation of statistical results for (...) two well-know correlational measures (the Pearson product-moment correlation and the Spearman rank-order correlation). By using the Sample Generation by Replacement (SGR) approach, we analyze uncertainty in inferences based on possible fake data and evaluate the implications of fake data for correlational results. A population-level analysis and a Monte Carlo simulation are performed to study different modulations of faking on bivariate discrete variables with finite supports and varying sample sizes. We show that by using our paradigm it is always possible, under specific faking conditions, to increase (resp. decrease) the original correlation between two discrete variables in a predictable and systematic manner. (shrink)

Twenty-four aphasic and fifteen right brain-damaged subjects were compared on their ability to identify the objects whose use was depicted in a series of twenty videotaped pantomimes. Aphasics were inferior to right brain-damaged patients in inferring object use. Success was correlated with Performance IQ, but not with language measures. Analysis of movement features contributing to subjects' choices reveal speed of movement and object weight to be the most robust and hand shape and size to be the most fragile.

The goal of this paper is to generalize a notion of quasi-characteristic inference rule in the following way: with every finite partial algebra we associate a rule, and study the properties of these rules. We prove that any equivalential logic can be axiomatized by such rules. We further discuss the correlations between characteristic rules of the finite partial algebras and canonical rules. Then, with every algebra we associate a set of characteristic rules that correspond to each finite partial subalgebra (...) of this algebra. Finally, we demonstrate that in many respects these sets enjoy the same properties as regular quasi-characteristic rules. (shrink)

We apply a cognitive modeling approach to the problem of measuring expertise on rank ordering problems. In these problems, people must order a set of items in terms of a given criterion (e.g., ordering American holidays through the calendar year). Using a cognitive model of behavior on this problem that allows for individual differences in knowledge, we are able to infer people's expertise directly from the rankings they provide. We show that our model-based measure of expertise outperforms self-report measures, taken (...) both before and after completing the ordering of items, in terms of correlation with the actual accuracy of the answers. These results apply to six general knowledge tasks, like ordering American holidays, and two prediction tasks, involving sporting and television competitions. Based on these results, we discuss the potential and limitations of using cognitive models in assessing expertise. (shrink)

According to C. S. Peirce, abduction is a rational attempt to locate an explanation for a puzzling phenomenon, where this is a process that includes both generating explanatory hypotheses and selecting certain hypotheses for further scrutiny. Since inference is a controlled process that can be subjected to normative standards, essential to his view of abductive rasoning is that it is correlated to a unique species of correctness that cannot be reduced to deductive validity or inductive strength. This irreducibility claim (...) is of considerable importance for the logical and epistemological scrutiny of scientific methods, but it is not clear that Peirce produced a convicing argument for it. To the contrary, when the full structure of abductive argumentation is clarified, especially as presented in Peirce’s later writings on the topic, a good case can be made for viewing every inferential step in the abductive process as dissolving into familiar forms of deductive and inductive reasoning. Specifically, hypothesis-selection is a special type of practical inference that, if correct, is deductively valid, while the creative phase, hypothesis-generation, is not inferential at all. Once this is understood, it emerges that the familiar descriptions of abduction as “inference to the best explanation” or as a process of “belief revision” are, at best, misleading. (shrink)

Of the many proposals for inferring genetic regulatory structure from microarray measurements of mRNA transcript hybridization, several aim to estimate regulatory structure from the associations of gene expression levels measured in repeated samples. The repeated samples may be from a single experimental condition, or from several distinct experimental conditions; they may be “equilibrium” measurements or time series; the associations may be estimated by correlation coefficients or by conditional frequencies (for discretized measurements) or by some other statistic. This paper describes two (...) elementary statistical difficulties for all such procedures, no matter whether based on Bayesian updating, conditional independence testing, or other machine learning procedures such as simulated annealing or neural net pruning. One difficulty obtains if large numbers of cells are aggregated in a measurement of expression levels from a common population of cells; the other obtains if small numbers of cells are aggregated or if samples are separately aggregated over different populations of cells. (shrink)

Filipe Drapeau Vieira Contim,Pascal Ludwig | : La plupart des théoriciens de l’identité des types souscrivent à un physicalisme a posteriori à l’égard des propriétés phénoménales. Selon cette conception, les énoncés d’identité esprit/cerveau peuvent être justifiés par une inférence à la meilleure explication (IME) partant du fait empirique des corrélations esprit/cerveau. Nous soutenons que la théorie de l’identité ne peut pas s’appuyer sur cette méthodologie abductive. Nous montrons tout d’abord que l’on ne peut pas justifier les énoncés d’identité esprit/cerveau au (...) moyen d’une IME partant des corrélations esprit/cerveau car cette stratégie conduit à un dilemme : ou bien les corrélations ne sont pas des explananda, ou bien les identités esprit/cerveau doivent être admises comme des faits bruts au même titre que les lois dualistes. Nous montrons ensuite que ces énoncés d’identité peuvent être néanmoins justifiés par une IME partant du pouvoir causal des états phénoménaux. Cette seconde stratégie n’est toutefois d’aucune aide pour la forme a posteriori de théorie de l’identité ici discutée dans la mesure où elle débouche sur un physicalisme de type a priori. | : Most type identity theorists endorse a posteriori physicalism about phenomenal properties. On such a view, mind/brain identity statements can be justified by an inference to the best explanation (IBE) starting from the empirical premise of mind/brain correlations. We contend that Identity Theory cannot be based on such an abductive methodology. First, we argue that mind/brain identity statements cannot be justified by an IBE from correlations as it leads to the following dilemma : either correlations are trivial facts that cannot pretend to the status of explananda, or mind/brain identities must be admitted as brute facts, just like dualist laws. Second, we show that these identity statements can still be justified by an IBE starting from the premise of mental causation. However, this strategy is of no help for the a posteriori brand of Identity Theory here challenged since the same premise forces one to embrace a priori physicalism. (shrink)

MaxEnt inference algorithm and information theory are relevant for the time evolution of macroscopic systems considered as problem of incomplete information. Two different MaxEnt approaches are introduced in this work, both applied to prediction of time evolution for closed Hamiltonian systems. The first one is based on Liouville equation for the conditional probability distribution, introduced as a strict microscopic constraint on time evolution in phase space. The conditional probability distribution is defined for the set of microstates associated with the (...) set of phase space paths determined by solutions of Hamilton’s equations. The MaxEnt inference algorithm with Shannon’s concept of the conditional information entropy is then applied to prediction, consistently with this strict microscopic constraint on time evolution in phase space. The second approach is based on the same concepts, with a difference that Liouville equation for the conditional probability distribution is introduced as a macroscopic constraint given by a phase space average. We consider the incomplete nature of our information about microscopic dynamics in a rational way that is consistent with Jaynes’ formulation of predictive statistical mechanics, and the concept of macroscopic reproducibility for time dependent processes. Maximization of the conditional information entropy subject to this macroscopic constraint leads to a loss of correlation between the initial phase space paths and final microstates. Information entropy is the theoretic upper bound on the conditional information entropy, with the upper bound attained only in case of the complete loss of correlation. In this alternative approach to prediction of macroscopic time evolution, maximization of the conditional information entropy is equivalent to the loss of statistical correlation, and leads to corresponding loss of information. In accordance with the original idea of Jaynes, irreversibility appears as a consequence of gradual loss of information about possible microstates of the system. (shrink)

We incorporate social reasoning about groups of informants into a model of word learning, and show that the model accounts for infant looking behavior in tasks of both word learning and recognition. Simulation 1 models an experiment where 16-month-old infants saw familiar objects labeled either correctly or incorrectly, by either adults or audio talkers. Simulation 2 reinterprets puzzling data from the Switch task, an audiovisual habituation procedure wherein infants are tested on familiarized associations between novel objects and labels. Eight-month-olds outperform (...) 14-month-olds on the Switch task when required to distinguish labels that are minimal pairs, but 14-month-olds' performance is improved by habituation stimuli featuring multiple talkers. Our modeling results support the hypothesis that beliefs about knowledgeability and group membership guide infant looking behavior in both tasks. These results show that social and linguistic development interact in non-trivial ways, and that social categorization findings in developmental psychology could have substantial implications for understanding linguistic development in realistic settings where talkers vary according to observable features correlated with social groupings, including linguistic, ethnic, and gendered groups. (shrink)

Predicate nominals (e.g., “is a female”) seem to label or categorize their subjects, while their adjectival correlates (e.g., “is female”) merely attribute a property. Predicate nominals also elicit essentializing inferential judgments about inductive potential and stable explanatory membership. Data from psychology and semantics support that this distinction is robust and productive. I argue that while the difference between predicate nominals and predicate adjectives is elided by standard semantic theories, it ought not be. I then develop and defend a psychologically motivated (...) semantic account on which predicate nominals attribute kind membership and trigger a presupposition that underpins our essentialist judgments. (shrink)

Research into the neural correlates of consciousness (NCCs) has blossomed, due to the advent of new and increasingly sophisticated brain research tools. Neuroimaging has uncovered a variety of brain processes that relate to conscious perception, obtained in a range of experimental paradigms. But methods such as functional magnetic resonance imaging or electroencephalography do not always afford inference on the functional role these brain processes play in conscious vision. Such empirical NCCs could reflect neural prerequisites, neural consequences, or neural substrates (...) of a conscious experience. Here, we take a closer look at the use of non-invasive brain stimulation (NIBS) techniques in this context. We discuss and review how NIBS methodology can enlighten our understanding of brain mechanisms underlying conscious vision by disentangling the empirical NCCs. (shrink)

Philosophers and scientists have maintained that causation, correlation, and "partial correlation" are essentially related. These views give rise to various rules of causal inference. This essay considers the "claims of several philosophers and social scientists for causal systems with dichotomous variables. In section 2 important commonalities and differences are explicated among four major conceptions of correlation. In section 3 it is argued that whether correlation can serve as a measure of A's causal influence on B depends upon the conception (...) of causation being used and upon certain background assumptions. In section 4 five major kinds of "partial correlation" are explicated, and some of the important relations are established among two conceptions of "partial correlation", the conception of "screening off", the conception of "partitioning", and the measures of causal influence which have been suggested by advocates of path analysis or structural equation methods. In section 5 it is argued that whether any of these five conceptions of "partial correlation" can serve as a measure of causal influence depends upon the conception of causation being used and upon certain background assumptions. The important conclusion is that each of the approaches (considered here) to causal inference for causal systems with dichotomous variables stands in need of important qualifications and revisions if they are to be justified. (shrink)

This investigation examined whether speakers produce reliable prosodic correlates to meaning across semantic domains and whether listeners use these cues to derive word meaning from novel words. Speakers were asked to produce phrases in infant‐directed speech in which novel words were used to convey one of two meanings from a set of antonym pairs (e.g., big/small). Acoustic analyses revealed that some acoustic features were correlated with overall valence of the meaning. However, each word meaning also displayed a unique acoustic signature, (...) and semantically related meanings elicited similar acoustic profiles. In two perceptual tests, listeners either attempted to identify the novel words with a matching meaning dimension (picture pair) or with mismatched meaning dimensions. Listeners inferred the meaning of the novel words significantly more often when prosody matched the word meaning choices than when prosody mismatched. These findings suggest that speech contains reliable prosodic markers to word meaning and that listeners use these prosodic cues to differentiate meanings. That prosody is semantic suggests a reconceptualization of traditional distinctions between linguistic and nonlinguistic properties of spoken language. (shrink)

Sentences such as "The author started the book" are indeterminate because they do not make explicit what the subject (the author) started doing with the object (the book). In principle, indeterminate sentences allow for an infinite number of interpretations. One theory, however, assumes that these sentences are resolved by semantic coercion, a linguistic process that forces the noun "book" to be interpreted as an activity (e.g., writing the book) or by a process that interpolates this activity information in the resulting (...) enriched semantic composition. An alternative theory, pragmatic, assumes classical semantic composition, whereby meaning arises from the denotation of words and how they are combined syntactically, with enrichment obtained via pragmatic inferences beyond linguistic-semantic processes. Cognitive neuroscience studies investigating the neuroanatomical and functional correlates of indeterminate sentences have shown activations either at the ventromedial pre-frontal cortex or at the left inferior frontal gyrus. These studies have supported the semantic coercion theory assuming that one of these regions is where enriched semantic composition takes place. Employing fMRI, we found that indeterminate sentences activate bilaterally the superior temporal gyrus, the right inferior frontal gyrus, and the anterior cingulate cortex, more so than control sentences ("The author wrote the book"). Activation of indeterminate sentences exceeded that of anomalous sentences ("…drank the book") and engaged more left- and right-hemisphere areas than other sentence types. We suggest that the widespread activations for indeterminate sentences represent the deployment of pragmatic-inferential processes, which seek to enrich sentence content without necessarily resorting to semantic coercion. (shrink)

Instrumental variables allow for quantification of cause and effect relationships even in the absence of interventions. To achieve this, a number of causal assumptions must be met, the most important of which is the independence assumption, which states that the instrument and any confounding factor must be independent. However, if this independence condition is not met, can we still work with imperfect instrumental variables? Imperfect instruments can manifest themselves by violations of the instrumental inequalities that constrain the set of correlations (...) in the scenario. In this article, we establish a quantitative relationship between such violations of instrumental inequalities and the minimal amount of measurement dependence required to explain them for the case of discrete observed variables. As a result, we provide adapted inequalities that are valid in the presence of a relaxed measurement dependence assumption in the instrumental scenario. This allows for the adaptation of existing and new lower bounds on the average causal effect for instrumental scenarios with binary outcomes. Finally, we discuss our findings in the context of quantum mechanics. (shrink)

The article discusses the problem of the naturalistic methodology of social ontology. Following Katherine Hawley's (2018) analysis, the author considers three approaches: conceptual analysis, the ameliorative (or normative) approach, and inference to the best explanation (from best social science to social ontology). Hawley concludes that only the first two can provide a viable naturalistic social metaphysics, and the latter cannot. The author, drawing on the notion of naturalistic limitations of social ontology, shows that only a conclusion to the best (...) explanation can lead to a consistent realistic social ontology; this methodology avoids the problem of being rooted in concepts that do not have an empirical basis, which contradicts realism, and also does not imply normative prescription, which also contradicts the realistic description of the world. The problem critics attribute to inference to the best explanation - the lack of a predictively strong theoretical core of social theory from which to infer social ontology - can be resolved by satisfying three criteria (Turner, 2007): physical realizability, computability, and cognitive realism. The author proposes to implement them through the introduction of naturalistic restrictions into game theory. Formal models of coordination described by a correlated equilibrium - coupled with evolutionary explanations of the cognitive mechanisms responsible for the causal processes that make formally described coordination possible - may provide the desired theoretical core from which ontology can be derived. (shrink)

Deformation of ground during tunnelling projects is one of the complex issues that is required to be monitored carefully to avoid the unexpected damages and human losses. Accurate prediction of ground settlement is a crucial concern for tunnelling problems, and the adequate predictive model can be a vital tool for tunnel designers to simulate the ground settlement accurately. This study proposes relatively new hybrid artificial intelligence models to predict the ground settlement of earth pressure balance shield tunnelling in the Bangkok (...) MRTA project. The predictive models were various nature-inspired frameworks, such as differential evolution, particle swarm optimization, genetic algorithm, and ant colony optimizer to tune the adaptive neuro-fuzzy inference system. To obtain the accurate and reliable results, the modeling procedure is established based on four different dataset scenarios including preprocessed and normalized, preprocessed and nonnormalized, non-preprocessed and normalized, and non-preprocessed and nonnormalized datasets. Results indicated that PPN dataset scenario significantly affected the prediction models in terms of their perdition accuracy. Among all the developed hybrid models, ANOFS-PSO model achieved the best predictability performance. In quantitative terms, PPN-ANFIS-PSO model attained the least root mean square error value of 7.98 and a correlation coefficient value of 0.83. Overall, the attained results confirmed the superiority of the explored hybrid AI models as robust predictive model for ground settlement of earth pressure balance shield tunnelling. (shrink)

Matching in observational studies faces complications when units enroll in treatment on a rolling basis. While each treated unit has a specific time of entry into the study, control units each have many possible comparison, or “pseudo-treatment,” times. Valid inference must account for correlations between repeated measures for a single unit, and researchers must decide how flexibly to match across time and units. We provide three important innovations. First, we introduce a new matched design, GroupMatch with instance replacement, allowing (...) maximum flexibility in control selection. This new design searches over all possible comparison times for each treated-control pairing and is more amenable to analysis than past methods. Second, we propose a block bootstrap approach for inference in matched designs with rolling enrollment and demonstrate that it accounts properly for complex correlations across matched sets in our new design and several other contexts. Third, we develop a falsification test to detect violations of the timepoint agnosticism assumption, which is needed to permit flexible matching across time. We demonstrate the practical value of these tools via simulations and a case study of the impact of short-term injuries on batting performance in major league baseball. (shrink)

One of the positive arguments for the type-identity theory of mental states is an inference-to-the-best-explanation (IBE) argument, which purports to show that type-identity theory is likely true since it is the best explanation for the correlations between mental states and brain states that we find in the neurosciences. But given the methods of neuroscience, there are other relations besides identity that can explain such correlations. I illustrate some of these relations by examining the literature on the function of the (...) hypothalamus and its correlation with sensations of thirst. Given that there are relations besides identity that can explain such correlations, the type-identity theorist is left with a dilemma: either the correlations we consider are weak, in which case we do not have an IBE to an identity claim, or else the correlations we look at are maximally strong, in which case there are too few cases for the inductive part of the strategy to work. (shrink)

Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations among multiple factors, and instantiation consists of observational or hypothetical assignment of factors to the patient whose disease is being explained. This paper first discusses inference from correlation to causation, integrating recent psychological discussions of causal reasoning with epidemiological approaches to understanding disease causation, particularly concerning ulcers and lung cancer. It then shows how (...) causal mechanisms represented by causal networks can contribute to reasoning involving correlation and causation. The understanding of causation and causal mechanisms provides the basis for a presentation of the causal network instantiation model of medical explanation. (shrink)

Bell's theorem is expounded as an analysis in Bayesian inference. Assuming the result of a spin measurement on a particle is governed by a causal variable internal (hidden, “local”) to the particle, one learns about it by making a spin measurement; thence about the internal variable of a second particle correlated with the first; and from there predicts the probabilistic result of spin measurements on the second particle. Such predictions are violated by experiment: locality/causality fails. The statistical nature of (...) the observations rules out signalling; acausal, superluminal, or otherwise. Quantum mechanics is irrelevant to this reasoning, although its correct predictions of experiment imply that it has a nonlocal/acausal interpretation. Cramer's newtransactional interpretation, which incorporates this feature by adapting the Wheeler-Feynman idea of advanced and retarded processes to the quantum laws, is advocated. It leads to an invaluable way of envisaging quantum processes. The usual paradoxes melt before this, and one, the “delayed choice” experiment, is chosen for detailed inspection. Nonlocality implies practical difficulties in influencing hidden variables, which provides a very plausible explanation for why they have not yet been found; from this standpoint, Bell's theoremreinforces arguments in favor of hidden variables. (shrink)

Qiyās, or “correlational inference”, comprises a primary set of methodological tools recognized by a majority of premodern Sunnī jurists. Its elements, valid modes, and proper applications were the focus of continual argument and refinement. A particular area of debate was the methodology of determining or justifying the ʿilla: the legal cause giving rise to a ruling in God’s Law. This was most often discussed under the rubric of “the modes of causal justification”. Among these modes was the much (...) debated test of dawarān. In brief, proponents of dawarān employed it to justify claims that a property occasioned the ruling in an authoritative source-case. In concert with other considerations, the demonstrated co-presence and co-absence of property and ruling—that is, their concomitance “in existence” and “in nonexistence” — was taken as an indication that the property was the ruling’s ʿilla. Delving further into dawarān and causation, the current study interprets “in existence” and “in nonexistence” not as a kind of metaphor for true and false, but as an accurate terminology vis-à-vis the meaning of causality statements, fully compatible with dominant Islamicate views on causal agency. In brief, a deeper logical and linguistic analysis of the different existential modes of dawarān strongly suggests that we should distinguish property and ruling as types as opposed to tokens. Our reading of dawarān as shaped by a finer-grained structure not only allows us to identify the efficient occasioning process as a function which takes some particular token of the ʿilla and renders a token of the general ruling type, but it allows us to elucidate the role of taʿlīl in shaping an epistemological theory of argument to the best explanation: a sophisticated, premodern manifestation of abductive reasoning. (shrink)

Several authors have claimed that mechanisms play a vital role in distinguishing between causation and mere correlation in the social sciences. Such claims are sometimes interpreted to mean that without mechanisms, causal inference in social science is impossible. The author agrees with critics of this proposition but explains how the account of how mechanisms aid causal inference can be interpreted in a way that does not depend on it. Nevertheless, he shows that this more charitable version of the (...) account is still unsuccessful as it stands. Consequently, he advances a proposal for shoring up the account, which is founded on the possibility of acquiring knowledge of social mechanisms by linking together norms or practices found in a society. Key Words: causality • social mechanisms • interpretation • anthropology. (shrink)

Transcranial magnetic stimulation (TMS) is a method capable of transiently modulating neural excitability. Depending on the stimulation parameters information processing in the brain can be either enhanced or disrupted. This way the contribution of different brain areas involved in mental processes can be studied, allowing a functional decomposition of cognitive behavior both in the temporal and spatial domain, hence providing a functional resolution of brain/mind processes. The aim of the present paper is to argue that TMS with its ability to (...) draw causal inferences on function and its neural representations is a valuable neurophysiological tool for investigating the causal basis of neuronal functions and can provide substantive insight into the modern interdisciplinary and (anti)reductionist neurophilosophical debates concerning the relationships between brain functions and mental abilities. Thus, TMS can serve as a heuristic method for resolving causal issues in an arena where only correlative tools have traditionally been available. (shrink)

Graphical models are a powerful tool for causal model specification. Besides allowing for a hierarchical representation of variable interactions, they do not require any a priori specification of the functional dependence between variables. The construction of such graphs hence often relies on the mere testing of whether or not model variables are marginally or conditionally independent. The identification of causal relationships then solely requires some general assumptions on the relation between stochastic and causal independence, such as the Causal Markov Condition (...) and the Faithfulness Condition (Spirtes et al. 2000; Pearl 2000). However, a procedure would require further assumptions to hold. Namely those the independence tests themselves are based on. In continuous settings, Spirtes et al. (2000) suggest causal inference based on a very restrictive formulation of independence, that is, vanishing partial correlations. Such a measure does, however, limit the applicability of causal inference to linear systems. This constitutes a serious drawback especially for the social sciences where an a priori specification of the functional form proves difficult or at odds with linearity. In short: graphical models theoretically reduce specification uncertainty regarding functional dependence, but their implementation in practice deprives them of this virtue. In this paper we investigate how causal structures in continuous settings can be identified when both functional forms and probability distributions of the variables remain unspecified. We focus on tests exploiting the fact that if X and Y are conditionally independent given a set of variables Z, the two conditional densities f (X|Y, Z) and f (X|Z) must coincide. We start by estimating the conditional densities f(X|Y, Z) and f(X|Z) via nonparametric techniques (kernel methods). We proceed by testing if some metric expressing the distance between these very conditional densities is sufficiently close to zero. Out of several metrics available in the literature to express such distance we choose two, the Euclidean, and the Hellinger distance. We investigate in a Monte Carlo study how different tests involving either measure are able to detect statistical independence, conditioned on a small set of variables. One limitation may result from nonparametric density estimation being subject to the curse of dimensionality. As the number of variables increases, the estimated empirical density converges at a slower rate to its population value. To compensate this drawback we use a local bootstrap procedure which consists of resampling the data for each test. While local bootstrap strongly increases the computational time of the test, it succeeds in counterbalancing the curse of dimensionality. (shrink)

For nearly as long as the word “correlation” has been part of statistical parlance, students have been warned that correlation does not prove causation, and that only experimental studies, e.g., randomized clinical trials, can establish the existence of a causal relationship. Over the last few decades, somewhat of a consensus has emerged between statisticians, computer scientists, and philosophers on how to represent causal claims and connect them to probabilistic relations. One strand of this work studies the conditions under which evidence (...) accumulated from non-experimental (observational) studies can be used to infer a causal relationship. In this paper, I compare the typical conditions required to infer that one variable is a direct cause of another in observational and experimental studies. I argue that they are essentially the same. (shrink)

We advance a theory of inductive reasoning based on similarity, and test it on arguments involving mammal categories. To measure similarity, we quantified the overlap of neural activation in left Brodmann area 37 (lBA37) in response to pictures of different categories; the choice of lBA37 is motivated by previous literature. The theory was tested against estimated probability judgments for 160 arguments generated from 16 categories and a common predicate. The theory’s predictions (based on neural similarity) correlate strongly with these estimates. (...) Other brain regions in the ventral visual pathway yield similarities that also allow the model to predict inductive judgments accurately whereas use of rated similarity in place of neural similarity is less successful. We conclude by extending the theory to aspects of causal reasoning and considering the relation between similarity and feature ratings of categories. (shrink)

Emotional experiences typically labelled “being moved” or “feeling touched” may belong to one universal emotion. This emotion, which has been labelled “kama muta”, is hypothesised to have a positive valence, be elicited by sudden intensifications of social closeness, and be accompanied by warmth, goosebumps and tears. Initial evidence on correlations among the kama muta components has been collected with self-reports after or during the emotion. Continuous measures during the emotion seem particularly informative, but previous work allows only restricted inferences on (...) intra-individual processes because time series were cross-correlated across samples. In the current studies, we instead use a within-subject design to replicate and extend prior work. We compute intra-individual cross-correlations between continuous self-reports on feeling moved and (1) positive and negative affect; (2) goosebumps and subjective warmth and (3) appraisals of closeness and morality. Results confirm the predictions of kama muta theory that feeling moved by intensified communal sharing cross-correlates with appraised closeness, positive affect, warmth and (less so) goosebumps, but not with negative affect. Contrary to predictions, appraised morality cross-correlated with feeling moved as much as appraised closeness did. We conclude that strong inferences on emotional processes are possible using continuous measures, replace earlier findings, and are largely in line with theorising. (shrink)

In this paper, I show that the availability of what some authors have called the weak reading and the strong reading of donkey sentences with relative clauses is systematically related to monotonicity properties of the determiner. The correlation is different from what has been observed in the literature in that it concerns not only right monotonicity, but also left monotonicity (persistence/antipersistence). I claim that the reading selected by a donkey sentence with a double monotone determiner is in fact the one (...) that validates inference based on the left monotonicity of the determiner. This accounts for the lack of strong reading in donkey sentences with MON determiners, which have been neglected in the literature. I consider the relevance of other natural forms of inference as well, but also suggest how monotonicity inference might play a central role in the actual process of interpretation. The formal theory is couched in dynamic predicate logic with generalized quantifiers. (shrink)