Shin and DuBrow propose that a key principle driving event segmentation relates to causal analyses: specifically, that experiences that are attributed as having the same underlying cause are grouped together into an event. This offers an alternative to accounts of segmentation based on prediction error.

Pursuing a precise, focused train of thought requires cognitive effort. Even more effort is necessary when more alternatives need to be considered or when the imagined situation becomes more complex. Cognitive resources available to us limit the cognitive effort we can spend. In line with previous work, an information-theoretic, Bayesian brain approach to cognitive effort is pursued: to solve tasks in our environment, our brain needs to invest information, that is, negative entropy, to impose structure, or focus, away from a (...) uniform structure or other task-incompatible, latent structures. To get a more complete formalization of cognitive effort, a resourceful event-predictive inference model is introduced, which offers computational and algorithmic explanations about the latent structure of our generative models, the active inference dynamics that unfold within, and the cognitive effort required to steer the dynamics—to, for example, purposefully process sensory signals, decide on responses, and invoke their execution. REPI suggests that we invest cognitive resources to infer preparatory priors, activate responses, and anticipate action consequences. Due to our limited resources, though, the inference dynamics are prone to task-irrelevant distractions. For example, the task-irrelevant side of the imperative stimulus causes the Simon effect and, due to similar reasons, we fail to optimally switch between tasks. An actual model implementation simulates such task interactions and offers first estimates of the involved cognitive effort. The approach may be further studied and promises to offer deeper explanations about why we get quickly exhausted from multitasking, how we are influenced by irrelevant stimulus modalities, why we exhibit magnitude interference, and, during social interactions, why we often fail to take the perspective of others into account. (shrink)

In contrast to Pietraszewski's account, latent structure learning neither requires conflict nor relies on observation of explicit coalitional behavior to support group inference. This alternative addresses how even non-conflict-based groups may be defined and is supported by experimental evidence in human behavior.

Whenever the use of non-experimental data for discovering causal relations or predicting the outcomes of experiments or interventions is contemplated, two difficulties are routinely faced. One is the problem of latent variables, or confounders: factors influencing two or more measured variables may not themselves have been measured or recorded. The other is the problem of sample selection bias: values of the variables or features under study may themselves influence whether a unit is included in the data sample.

An early conference paper version of "The Inferences of Common Causes Reduced to Common Origins," Studies in History and Philosophy of Science Part A, volume 81, June 2020, 105-115.

If one investigates a process that has several causes but assumes that it has only one cause, one risks ruling out important causal factors. Three mechanisms account for this mistake: either the significance of the single cause under test is masked by noise contributed by the unsuspected and uncontrolled factors, or the process appears only when two or more causes interact, or the process appears when there are present any of a number of sufficient causes which are not (...) mutally exclusive. In ecology and evolutionary biology, experiments usually test single factor hypotheses, and many scientists apparently believe that hypotheses incorporating several factors are so much more difficult to test that to do so would not be practical. We discuss several areas in ecology and evolutionary biology in which the presupposition of simple causation has apparently impeded progress. We also examine a more mature field, the study of atherosclerosis, in which single factor studies did significantly delay progress towards understanding what now appears to be a multifactor process. The problem has three solutions: either factorial experiments, dynamic models that make quantitative predictions, response-surface methods, or all three. In choosing a definition for ‘cause’, we make a presupposition that profoundly influences subsequent observations and experimental designs. Alternative definitions of causation should be considered as contributing to potential cures for research problems. (shrink)

When a cause interacts with unobserved factors to produce an effect, the contingency between the observed cause and effect cannot be taken at face value to infer causality. Yet, it would be computationally intractable to consider all possible unobserved, interacting factors. Nonetheless, two experiments found that when an unobserved cause is assumed to be fairly stable over time, people can learn about such interactions and adjust their inferences about the causal efficacy of the observed cause. When (...) they observed a period in which a cause and effect were associated followed by a period of the opposite association, rather than concluding a complete lack of causality, subjects inferred an unobserved, interacting cause. The interaction explains why the overall contingency between the cause and effect is low and allows people to still conclude that the cause is efficacious. (shrink)

This paper advocates the reduction of the inference of common cause to that of common origins. It distinguishes and subjects to critical analysis thirteen interpretations of “the inference of common cause” whose conclusions do not follow from their assumptions. Instead, I introduce six types of inferences of common origins of information signals from their receivers to reduce, in the sense of supersede and replace, the thirteen inferences of common causes. I show how the paradigmatic examples of (...) inferences of common cause, as well as a broader scope of inferences in the historical sciences, are better explained by inferences of origins. Inferences of origins from information rich coherences between receivers of information signals both fit more closely and explain better the range of examples that have traditionally been associated with inferences of common causes, as well as a broader scope of examples from the historical sciences. Shannon’s concept of information as reduction in uncertainty, rather than physicalist concepts of information that relate it to entropy or waves, simplifies the inferences, preempts objections, and avoids the underdetermination of conclusions that challenge models of inferences of causes from information transmissions. In the second part of the paper I model inferences of information about common origins from information preserved in their receivers. I distinguish information poor inferences that there were some common origins of receivers from the information richer inferences of ranges of possible common origins and the information transmission channels by which they transmitted signals to receivers. Lastly and most information rich, I distinguish the inference of the defining properties of common origins. The information transmission model from origins to receivers allows the reconceptualization of the concepts of independence as absence of intersections between information channels and reliability as the preservation of information from origins in receivers. Finally, I show how inferences of origins form the epistemic basis of the historical sciences. (shrink)

On a purely epistemic understanding of experimental realism, manipulation affords a particularly robust kind of causal warrant, which is – like any other warrant – defeasible. I defend a version of Nancy Cartwright’s inference to the most likely cause, and I conclude that this minimally epistemic version of experimental realism is a coherent, adequate and plausible epistemology for science.

On a purely epistemic understanding of experimental realism, manipulation affords a particularly robust kind of causal warrant, which is – like any other warrant – defeasible. I defend a version of Nancy Cartwright’s inference to the most likely cause, and I conclude that this minimally epistemic version of experimental realism is a coherent, adequate and plausible epistemology for science.

Plausibly, an inference is an act of coming to believe something on the basis of something else you already believe. But what is it to come to believe some- thing on the basis of something else? I propose a disjunctive answer: it is either for some beliefs to rationally cause another—where rational causation is understood as causation that is either actually or potentially productive of knowledge—or for some beliefs to “deviantly” cause another, but for the believer mistakenly (...) to come thereby to believe that the former have rationally caused the latter. The result, I argue, is both a theoretically satisfying account of the act of inferring and a demonstration of the power of a knowledge-first approach to the philosophy of mind. (shrink)

The overwhelming majority of those who theorize about implicit biases posit that these biases are caused by some sort of association. However, what exactly this claim amounts to is rarely specified. In this paper, I distinguish between different understandings of association, and I argue that the crucial senses of association for elucidating implicit bias are the cognitive structure and mental process senses. A hypothesis is subsequently derived: if associations really underpin implicit biases, then implicit biases should be modulated by counterconditioning (...) or extinction but should not be modulated by rational argumentation or logical interventions. This hypothesis is false; implicit biases are not predicated on any associative structures or associative processes but instead arise because of unconscious propositionally structured beliefs. I conclude by discussing how the case study of implicit bias illuminates problems with popular dual-process models of cognitive architecture. (shrink)

In his seminal Inference to the Best Explanation, Peter Lipton adopted a causal view of explanation and a broadly Millian view of how causal knowledge is obtained. This made his account vulnerable to critics who charged that Inference to the Best Explanation is merely a dressed-up version of Mill’s methods, which in the critics’ view do the real inductive work. Lipton advanced two arguments to protect Inference to the Best Explanation against this line of criticism: the problem (...) of multiple differences and the problem of inferred differences. Lipton claimed that these two problems show Mill’s method of difference to be largely unworkable unless it is embedded in an explanationist framework. Here I consider both arguments as well as the best Millian defense against them. Since the existing Millian defense is only partially successful, I will develop a new and improved account. As an integral part of the argument, I show that my solutions to the problems of multiple and inferred differences offer new insight into Lipton’s main case study: Ignaz Semmelweis’s discovery of the cause of childbed fever. I conclude that the method of difference can overcome Lipton’s challenges outside an explanationist framework. (shrink)

Researchers routinely face the problem of inferring causal relationships from large amounts of data, sometimes involving hundreds of variables. Often, it is the causal relationships between "latent" (unmeasured) variables that are of primary interest. The problem is how causal relationships between unmeasured variables can be inferred from measured data. For example, naval manpower researchers have been asked to infer the causal relations among psychological traits such as job satisfaction and job challenge from a data base in which neither trait (...) is measured directly, but in which answers to interview questions are plausibly associated with each trait. By combining background knowledge with an algorithm that searches for causal structure among the unobserved variables, we have created a tool that can reliably extract useful causal information about latent variables from large data bases. In what follows we describe the class of causal models to which our.. (shrink)

The statistical evidence for the detrimental effect of exposure to low levels of lead on the cognitive capacities of children has been debated for several decades. In this paper I describe how two techniques from artificial intelligence and statistics help make the statistical evidence for the accepted epidemiological conclusion seem decisive. The first is a variable-selection routine in TETRAD III for finding causes, and the second a Bayesian estimation of the parameter reflecting the causal influence of Actual Lead Exposure, a (...) latent variable, on the measured IQ score of middle class suburban children. (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 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 thesis brings together two concerns. The first is the nature of inference—what it is to infer—where inference is understood as a distinctive kind of conscious and self-conscious occurrence. The second concern is the possibility of doxastic agency. To be capable of doxastic agency is to be such that one is capable of directly exercising agency over one’s beliefs. It is to be capable of exercising agency over one’s beliefs in a way which does not amount to mere (...) self-manipulation. Subjects who can exercise doxastic agency can settle questions for themselves. A challenge to the possibility of doxastic agency stems from the fact that we cannot believe or come to believe “at will”, where this in turn seems to be so because belief “aims at truth”. It must be explained how we are capable of doxastic agency despite that we cannot believe or come to believe at will. On the orthodox ‘causalist’ conception of inference for an inference to occur is for one act of acceptance to cause another in some specifiable “right way”. This conception of inference prevents its advocates from adequately seeing how reasoning could be a means to exercise doxastic agency, as it is natural to think it is. Suppose, for instance, that one reasons and concludes by inferring where one’s inference yields belief in what one infers. Such an inference cannot be performed at will. We cannot infer at will when inference yields belief any more than we can believe or come to believe at will. When it comes to understanding the extent to which one could be exercising agency in such a case the causalist conception of inference suggests that we must look to the causal history of one’s concluding act of acceptance, the nature of the act’s being determined by the way in which it is caused. What results is a picture on which such reasoning as a whole cannot be action. We are at best capable of actions of a kind which lead causally to belief fixation through “mental ballistics”. The causalist account of inference, I argue, is in fact either inadequate or unmotivated. It either fails to accommodate the self-consciousness of inference or is not best placed to play the very explanatory role which it is put forward to play. On the alternative I develop when one infers one’s inference is the conscious event which is one’s act of accepting that which one is inferring. The act’s being an inference is determined, not by the way it is caused, but by the self-knowledge which it constitutively involves. This corrected understanding of inference renders the move from the challenge to the possibility of doxastic agency to the above ballistics picture no longer tempting. It also yields an account of how we are capable of exercising doxastic agency by reasoning despite being unable to believe or come to believe at will. In order to see how such reasoning could amount to the exercise of doxastic agency it needs to be conceived of appropriately. I suggest that paradigm reasoning which potentially amounts the exercise of doxastic agency ought to be conceived of as primarily epistemic agency—agency the aim of which is knowledge. With inference conceived as suggested, I argue, it can be seen how to engage in such reasoning can just be to successfully exercise such agency. (shrink)

De Ray argues that relying on inference to the best explanation (IBE) requires the metaphysical belief that most phenomena have explanations. I object that instead the metaphysical belief requires the use of IBE. De Ray uses IBE himself to establish theism that God is the cause of the metaphysical belief, and thus he has the burden of establishing the metaphysical belief independently of using IBE. Naturalism that the world is the cause of the metaphysical belief is preferable (...) to theism, contrary to what de Ray thinks. (shrink)

Peter Lipton has attempted to flesh out a model of Inference to the Best Explanation (IBE) by clarifying explanation in terms of a causal model. But Lipton's account of explanation makes an adequate explanation depend on a principle which is virtually identical to Mill's Method of Difference. This has the result of collapsing IBE on Lipton's account of it into causal inference as conceived by the Causal-Inference model of induction. According to this model, many of our inductions (...) are inferences from effects to their probable causes, and Mill's Methods are canons to guide such inferences. Thus, Lipton's account of IBE fails to represent an advance over the already familiar Causal-Inference Model of induction. (shrink)

An accessible and contemporary introduction to the methods for determining cause and effect in the social sciences Causal inference encompasses the tools that allow social scientists to determine what causes what. Economists--who generally can't run controlled experiments to test and validate their hypotheses--apply these tools to observational data to make connections. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied, whether the impact (or lack thereof) of increases (...) in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the introduction of malaria nets in developing regions on economic growth. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and Stata programming languages. - -. (shrink)

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations and interventions (...) they saw. We replicated these findings with less‐informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge. (shrink)

The literature contains a disconnect between accounts of how humans learn lexical semantic representations for words. Theories generally propose that lexical semantics are learned either through perceptual experience or through exposure to regularities in language. We propose here a model to integrate these two information sources. Specifically, the model uses the global structure of memory to exploit the redundancy between language and perception in order to generate inferred perceptual representations for words with which the model has no perceptual experience. We (...) test the model on a variety of different datasets from grounded cognition experiments and demonstrate that this diverse set of results can be explained as perceptual simulation (cf. Barsalou, Simmons, Barbey, & Wilson, 2003) within a global memory model. (shrink)

I will argue that a person is causally responsible for believing what she does. Through inference, she can sustain and change her perspective on the world. When she draws an inference, she causes herself to keep or to change her take on things. In a literal sense, she makes up her own mind as to how things are. And, I will suggest, she can do this voluntarily. It is in part because she is causally responsible for believing what (...) she does that there are things that she ought to believe, and that what she believes can be to her credit or discredit. I won’t pursue these ethical matters here, but will focus instead on the metaphysics that underpin them. (shrink)

The sense of agency refers to an experience in which one’s own action causes a change in environment. It is strongly modulated by both the contingency between action and its outcome and the consistency between predicted and actual action outcomes. Recent studies have suggested that the action outcome can retrospectively modulate action awareness. We suspect that the sense of agency can also be retrospectively modulated. This study examined whether the quantity of action outcome could influence the sense of agency. The (...) participants’ task was to trigger dot motion in a display and rate the extent to which they could control the initiation of dot motion. Independently of both the temporal contiguity between action and its outcome and the consistency between predicted and actual action outcomes, the speed of dot motion as an action’s outcome strongly influenced the sense of agency rating. The present study suggests that the sense of agency stems partly from the inference of action efficiency based on the quantitative aspect of action outcome. (shrink)

What is an inference and when is an inference deductive rather than inductive, abductive, etc. The goal of this paper is precisely to determine what is that we, humans, do when we engage in deduction, i.e., whether there is something that satisfies both our pre-theoretical intuitions and theoretical presuppositions about deduction, as a cognitive process. The paper is structured in two parts: the first one deals with the issue of what is an inference. There, I will defend (...) the hypothesis that an inference is a causal process where an initial cognitive state causes another in such a way that the causal translation depends essentially on the content of the involved states. In other words, in an inference, a mental state A causes another B, partially because they have the content they have. Based on the conclusion from the first part, I will develop an account of deductive inference in the second part of the article. In order to achieve this, I will start by setting the stage and developing four constraints that an adequate account of deductive inference ought to satisfy. Then, I will present six different hypotheses that one might propose to characterize inferences of this kind. I have divided the proposals in two groups, the first one – which I will call the “logical proposals” – assume that whether an inference is a deduction or not depends exclusively on the content of the mental states involved in the inference, while the second – which I will call the “cognitive proposals” – reject the aforementioned assumption and instead recognize that extra cognitive information is required to determine whether an inference is a deduction or not. I will evaluate each proposal in turn, according to whether they satisfy the constraints introduced at the beginning of this second part. As a result of such evaluation, I will conclude that only one of the cognitive proposals actually satisfies the four constraints and therefore should be considered to give an adequate characterization of deductive inference. (shrink)

This paper is a discussion of the nature of inferring and focusses on the relation between reasons for belief and causes of belief. Two standard approaches to the analysis of inference, the epistemological and the psychological, are identified and discussed. While both approaches incorporate insights concerning, inference, counterexamples show that neither provides by itself an adequate account. A third account is developed and recommended on the grounds that it encompasses the essential insights of the rejected analyses while being (...) immune to their counterexamples. On this account coming to believe for a reason is taken to be central to our concept of inferring, but a causal relation holds between taking something to be a reason and belief. (shrink)

The design inference uncovers intelligent causes by isolating their key trademark: specified events of small probability. Just about anything that happens is highly improbable, but when a highly improbable event is also specified undirected natural causes lose their explanatory power. Design inferences can be found in a range of scientific pursuits from forensic science to research into the origins of life to the search for extraterrestrial intelligence. This challenging and provocative 1998 book shows how incomplete undirected causes are for (...) science and breathes new life into classical design arguments. It will be read with particular interest by philosophers of science and religion, other philosophers concerned with epistemology and logic, probability and complexity theorists, and statisticians. (shrink)

This chapter presents a typology of the different kinds of inductive inferences we can draw from our evidence, based on the explanatory relationship between evidence and conclusion. Drawing on the literature on graphical models of explanation, I divide inductive inferences into (a) downwards inferences, which proceed from cause to effect, (b) upwards inferences, which proceed from effect to cause, and (c) sideways inferences, which proceed first from effect to cause and then from that cause to an (...) additional effect. I further distinguish between direct and indirect forms of downwards and upwards inferences. I then show how we can subsume canonical forms of inductive inference mentioned in the literature, such as inference to the best explanation, enumerative induction, and analogical inference, under this typology. Along the way, I explore connections with probability and confirmation, epistemic defeat, the relation between abduction and enumerative induction, the compatibility of IBE and Bayesianism, and theories of epistemic justification. (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)

Semmelweis’s discovery of the cause of puerperal fever around the middle of the 19th century counts among the paradigm cases of scientific discovery. For several decades, philosophers of science have used the episode to illustrate, appraise and compare views of proper scientific methodology.Here I argue that the episode can be profitably reexamined in light of two cognate notions: causal reasoning and mechanisms. Semmelweis used several causal reasoning strategies both to support his own and to reject competing hypotheses. However, these (...) strategies have gone unappreciated in the existing literature. I show that a causal reasoning approach makes sense of the multitude of tables in Semmelweis’s main text, which in later editions were often abridged because they appeared redundant.Moreover, the existing literature tends to focus on Semmelweis’s clinical intervention and on the extent to which it alone confirms his theoretical conclusions. This neglects Semmelweis’s efforts to show by animal experiments that his clinical results are in agreement with a demonstrable mechanism of puerperal fever pathogenesis. I argue that the full evidential force of Semmelweis’s argument can only be appreciated if both his clinical and his laboratory investigations are taken into account. (shrink)

New research on affective computing aiming to develop computer systems that recognize and respond to affective states can also contribute to the issues raised by Coan. Research on how humans interact with computers, and computer models that automatically recognize affective states from features in our physiology, behaviour, and language, may provide insights on how emotions that are experienced and expressed come to be. For example, there is empirical evidence that affect recognition techniques using several modalities are more accurate than those (...) using a single modality, but it is not clear if these improvements are caused by superadditivity (i.e., emergence) or redundancy. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Journal of the History of Philosophy 39.1 (2001) 49-75 [Access article in PDF] Sensible Ends:Latent Teleology in Descartes' Account of Sensation Alison Simmons One of Descartes' hallmark contributions to natural philosophy is his denunciation of teleology. It is puzzling, then, to find him arguing in Meditation VI that human beings have sensations in order to preserve the union of mind and body (AT VII 83). 1 This appears (...) to be just the sort of teleological explanation that he proscribes. Descartes' Anglo-American commentators have had little to say about the teleological overtones of his claims about sensation. His French commentators acknowledge these overtones, but seem largely untroubled by them. 2 It is worth pausing to be troubled, for a closer analysis reveals that Descartes' famous proscription against teleology is not as simple as it is generally thought to be. In the first half of this essay, I argue that Descartes advocates a genuinely teleological conception of the senses. My aim is not to charge Descartes with employing illicit teleology, but to argue that there is a place for teleology even in his revisionist natural philosophy. Accordingly, in the second half of this essay, I argue that Descartes' assault on teleological explanation is [End Page 49] not a sweeping assault on finality, but a more directed attack on particular uses of ends in natural philosophy. Descartes' assault leaves standing a form of teleological explanation that proves crucial to his own treatment of sensation. 1. Descartes' Rejection of Teleological Explanation at a Glance A teleological explanation is one that purports to account for something in terms of its ends or its function relative to the ends of the system of which it is a part. Aristotle's physics provides many classic examples: plants produce leaves for the protection of their fruit; spiders spin webs in order to catch food; animals grow sharp front teeth and dull back teeth in order to facilitate biting and chewing respectively; eyes are for seeing. According to Aristotle, all natural phenomena are directed toward ends and no explanation is complete without a teleological component that specifies the end for the sake of which the phenomenon occurs. Indeed ends take explanatory priorityover other causes: "Both causes [the end and the matter] must be stated by the student of nature, but especially the end; for that is the cause of the matter, not vice versa." 3 And: [T]he causes concerned in natural generation are, as we see, more than one. There is the cause for the sake of which [the end], and the cause whence the beginning of motion comes [the efficient cause]. Now we must decide which of these two causes comes first, which second. Plainly, however, that cause is the first which we call that for the sake of which. For this is the account of the thing, and the account forms the starting-point.4In other words, Aristotle argues that natural substances have the matter they do, have the parts or organs they do, are organized as they are and act as they do for the sake of their ends. To explain why things are as they are, the natural philosopher must therefore explain the ways in which they contribute to ends.Among the late scholastic Aristotelians familiar to Descartes there is considerable debate concerning the causal efficacy of ends. Genuine final causation, in which something is said to be acted on by an end, is limited to the intentional behavior of rational agents who consciously recognize their ends: only something that recognizes an end can be "moved" by one. Even here the sort of causation involved is peculiar: a rational agent is "moved" by an end insofar as the end induces a "metaphorical motion" toward (or desire for) the end in the agent's will. 5 While these scholastic Aristotelians resist attributing final causation [End Page 50] to non-rational creatures, they nevertheless persist in attributing ends to them. When Toletus, Rubio, the Coimbrans and Suarez take up the question whether nature acts propter finem, each answers it affirmatively: natural... (shrink)

This chapter explores some issues having to do with the structure of the evidential reasoning we use to infer causal and lawful claims. It is argued that such reasoning always makes use of prior, causally, or nomologically committed information, thus undercutting various views that attempt to reduce causal and lawful claims to claims about regularities. A non-reductive account of laws and causes built around the notion of invariance is advanced as an alternative.

Cognitive niche construction is the process whereby organisms create and maintain cause–effect models of their niche as guides for fitness influencing behavior. Extended mind theory claims that cognitive processes extend beyond the brain to include predictable states of the world. Active inference and predictive processing in cognitive science assume that organisms embody predictive (i.e., generative) models of the world optimized by standard cognitive functions (e.g., perception, action, learning). This paper presents an active inference formulation that views cognitive (...) niche construction as a cognitive function aimed at optimizing organisms' generative models. We call that process of optimization extended active inference. (shrink)

Throughout the Covid-19 pandemic, people have been eager to learn what factors, and especially what public health policies, cause infection rates to wax and wane. But figuring out conclusively what causes what is difficult in complex systems with nonlinear dynamics, such as pandemics. We review some of the challenges that scientists have faced in answering quantitative causal questions during the Covid-19 pandemic, and suggest that these challenges are a reason to augment the moral dimension of conversations about causal (...) class='Hi'>inference. We take a lesson from Martha Nussbaum—who cautions us not to think we have just one question on our hands when we have at least two—and apply it to modeling for causal inference in the context of cost-benefit analysis. (shrink)

In this book, Reiss argues in favor of a tight fit between evidence, concept and purpose in our causal investigations in the sciences. There is no doubt that the sciences employ a vast array of techniques to address causal questions such as controlled experiments, randomized trials, statistical and econometric tools, causal modeling and thought experiments. But how do these different methods relate to each other and to the causal inquiry at hand? Reiss argues that there is no "gold standard" in (...) settling causal issues against which other methods can be measured. Rather, the various methods of inference tend to be good only relative to certain interpretations of the word "cause", and each interpretation, in turn, helps to address some salient purpose but not others. The main objective of this book is to explore the metaphysical and methodological consequences of this view in the context of numerous cases studies from the natural and social sciences. (shrink)

We investigated people's ability to infer others’ mental states from their emotional reactions, manipulating whether agents wanted, expected, and caused an outcome. Participants recovered agents’ desires throughout. When the agent observed, but did not cause the outcome, participants’ ability to recover the agent's beliefs depended on the evidence they got. When the agent caused the event, participants’ judgments also depended on the probability of the action ; when actions were improbable given the mental states, people failed to recover the (...) agent's beliefs even when they saw her react to both the anticipated and actual outcomes. A Bayesian model captured human performance throughout, consistent with the proposal that people rationally integrate information about others’ actions and emotional reactions to infer their unobservable mental states. (shrink)