David Danks

In this essay, we examine the use of resting state fMRI data for psychological inferences. We argue that resting state studies hold the paired promises of discovering novel functional brain networks, and of avoiding some of the limitations of task-based fMRI. However, we argue that the very features of experimental design that enable resting state fMRI to support exploratory science also generate a novel confound. We argue that seemingly key features of resting state functional connectivity networks may be artefacts resulting from sampling a ‘mixture distribution’ of diverse brain networks active at different times during the scan. We explore the consequences of this ‘mixture view’ for attempts to theorize about the cognitive or psychological functions of resting state networks, as well as the value of exploratory experiments.

Many investigations into the world, including philosophical ones, aim to discover causal knowledge, and many experimental methods have been developed to assist in causal discovery. More recently, algorithms have emerged that can also learn causal structure from purely or mostly observational data, as well as experimental data. These methods have started to be applied in various philosophical contexts, such as debates about our concepts of free will and determinism. This paper provides a “user's guide” to these methods, though not in the sense of specifying exact button presses in a software package. Instead, we explain the larger “pipeline” within which these methods are used and discuss key steps in moving from initial research idea to validated causal structure.

Many approaches to evidence amalgamation focus on relatively static information or evidence: the data to be amalgamated involve different variables, contexts, or experiments, but not measurements over extended periods of time. However, much of scientific inquiry focuses on dynamical systems; the system’s behavior over time is critical. Moreover, novel problems of evidence amalgamation arise in these contexts. First, data can be collected at different measurement timescales, where potentially none of them correspond to the underlying system’s causal timescale. Second, missing variables have a significantly different impact on time series measurements than they do in the traditional static setting; in particular, they make causal and structural inference much more difficult. In this paper, we argue that amalgamation should proceed by integrating causal knowledge, rather than at the level of “raw” evidence. We defend this claim by first outlining both of these problems, and then showing that they can be solved only if we operate on causal structures. We therefore must use causal discovery methods that are reliable given these problems. Such methods do exist, but their successful application requires careful consideration of the problems that we highlight.

We make constant use of causal beliefs in our everyday lives without giving much thought to the source of those beliefs, even for situations about which we have no specific prior causal knowledge. We can ask two distinct types of questions about these causal judgments: descriptive questions and normative questions. The primary goal of this dissertation is to apply normative research on causal judgment to our descriptive theories. ;I begin this dissertation by describing the primary results of research on the normative question: the framework of Bayesian networks, and algorithms that provably extract maximal causal information from observations and interventions in the environment. I then show that we can use the framework of Bayesian networks to represent the three major descriptive theories of causal judgment. The three theories are not directly comparable, however, since one of them is algorithmic, while the other two are computational. Therefore, I provide a fully general characterization of the Rescorla-Wagner model. ;With this computational description of the Rescorla-Wagner model in hand, we can compare all three of the theories against the known experimental data. Unfortunately, the experimental data has all been obtained using one particular experimental methodology, which contains serious problems. In addition to critiquing the currently dominant methodology, I provide a "proof of concept" of a different methodology that does not face these problems. ;More importantly, the psychological theories all focus on parameter estimation in a fixed causal structure, rather than learning the actual structure. Therefore, I consider the experimental question: can people learn complex causal structures? The data from those experiments reveal that people appear to use a wide array of learning strategies. The epistemology of our causal beliefs is substantially more complex than has previously been thought. ;Finally, at the end of this dissertation, I step back and examine the implications of these experimental results for descriptive theories of causal judgment. I also point towards several open research questions, both descriptive and normative, raised by the work in this dissertation.

Standard time series structure learning algorithms assume that the measurement timescale is approximately the same as the timescale of the underlying system. In many scientific contexts, however, this assumption is violated: the measurement timescale can be substantially slower than the system timescale. This assumption violation can lead to significant learning errors. In this paper, we provide a novel learning algorithm to extract systemtimescale structure from measurement data that undersample the underlying system. We employ multiple algorithmic optimizations that exploit the problem structure in order to achieve computational tractability. The resulting algorithm is highly reliable at extracting system-timescale structure from undersampled data

Debates about the moral permissibility of mass electronic surveillance often turn on whether consequentialist considerations legitimately trump relevant deontological rights and principles. In order to establish such overriding consequences, many proponents of mass surveillance employ a modern analogue of Pascal’s wager: they contend that the consequences of no surveillance are so severe that any probability of such outcomes legitimates the abrogation of the relevant rights. In this paper, I briefly review Pascal’s original wager about whether to live a pious life, including two classes of objections that were almost immediately leveled against his argument. I then show that analogues of those objections apply straightforwardly to the modern versions of Pascal’s wager. Mass electronic surveillance might be ethically permissible or even obligatory in some circumstances, but the details matter in ways that are systematically ignored or assumed away in a Pascal’s wager-type argument. Careful consideration of the complexities of our modern situation is required to decide whether consequentialist considerations override deontological principles in this domain.

In everyday matters, as well as in law, we allow that someone’s reasons can be causes of her actions, and often are. That correct reasoning accords with Bayesian principles is now so widely held in philosophy, psychology, computer science and elsewhere that the contrary is beginning to seem obtuse, or at best quaint. And that rational agents should learn about the world from energies striking sensory inputs nerves in people—seems beyond question. Even rats seem to recognize the difference between correlation and causation,1 and accordingly make different inferences from passive observation than from interventions. A few statisticians aside,” so do most of us. To square these views with the demands of computability, increasing numbers of psychologists and others have embraced a particular formalization, causal Bayes nets, as an account of human reasoning about and to causal connections.111 Such structures can be used by rational agents, including humans in so far as they are rational, to have degrees of belief in various conceptual contents, which they use to reason to expectations, which are realized or defeated by sensory inputs, which cause them to change their degrees of belief in other contents in accord with Bayes Rule, or some generalization of it. How is all of this supposed to be carried out? l. Representing Causal Structures The causal Bayes net framework adopted by a growing number of psychologists goes like this: Our representations of causal relations are captured in a graphical causal.

Models based on causal capacities, or independent causal influences/mechanisms, are widespread in the sciences. This paper develops a natural mathematical framework for representing such capacities by extending and generalizing previous results in cognitive psychology and machine learning, based on observations and arguments from prior philosophical debates. In addition to its substantial generality, the resulting framework provides a theoretical unification of the widely-used noisy-OR/AND and linear models, thereby showing how they are complementary rather than competing. This unification helps to explain many of the shared cognitive and mathematical properties of those models.

Research on human causal learning has largely focused on strength learning, or on computational-level theories; there are few formal algorithmic models of how people learn causal structure from covariations. We introduce a model that learns causal structure in a local manner via prediction-error learning. This local learning is then integrated dynamically into a unified representation of causal structure. The model uses computationally plausible approximations of rational learning, and so represents a hybrid between the associationist and rational paradigms in causal learning research. We conclude by showing that the model provides a good fit to data from a previous experiment

Structure learning algorithms for graphical models have focused almost exclusively on stable environments in which the underlying generative process does not change; that is, they assume that the generating model is globally stationary. In real-world environments, however, such changes often occur without warning or signal. Real-world data often come from generating models that are only locally stationary. In this paper, we present LoSST, a novel, heuristic structure learning algorithm that tracks changes in graphical model structure or parameters in a dynamic, real-time manner. We show by simulation that the algorithm performs comparably to batch-mode learning when the generating graphical structure is globally stationary, and significantly better when it is only locally stationary

Many different, seemingly mutually exclusive, theories of categorization have been proposed in recent years. The most notable theories have been those based on prototypes, exemplars, and causal models. This chapter provides “representation theorems” for each of these theories in the framework of probabilistic graphical models. More specifically, it shows for each of these psychological theories that the categorization judgments predicted and explained by the theory can be wholly captured using probabilistic graphical models. In other words, probabilistic graphical models provide a lingua franca for these disparate categorization theories, and so we can quite directly compare the different types of theories. These formal results are used to explain a variety of surprising empirical results, and to propose several novel theories of categorization.

It is “well known” that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of “instrumental variables” sometimes permits an estimation of the influence of one variable on another even when the association between the variables may be confounded by unobserved common causes; (3) the association (or conditional probability distribution of one variable given another) of two variables connected by a path or pair of paths with a single common vertex (a trek) can be computed directly from the parameter values associated with each edge in the trek; (4) the association of two variables produced by multiple treks can be computed from the parameters associated with each trek; and (5) the independence of two variables conditional on a third implies the corresponding independence of the sums of the variables over all units conditional on the sums over all units of each of the original conditioning variables.

The Rescorla–Wagner model has been a leading theory of animal causal induction for nearly 30 years, and human causal induction for the past 15 years. Recent theories 367) have provided alternative explanations of how people draw causal conclusions from covariational data. However, theoretical attempts to compare the Rescorla–Wagner model with more recent models have been hampered by the fact that the Rescorla–Wagner model is an algorithmic theory, while the more recent theories are all computational. This paper provides a detailed derivation of the long-run behavior of the Rescorla– Wagner model under a wide range of parameters and experimental setups, so that the model can be compared with computational theories. It also shows that the model agrees with competing theories on a wider range of cases than had previously been thought. The paper concludes by showing how recently suggested modifications of the Rescorla–Wagner model impact the long-run behavior of the model.

Causal structure learning from time series data is a major scientific challenge. Existing algorithms assume that measurements occur sufficiently quickly; more precisely, they assume that the system and measurement timescales are approximately equal. In many scientific domains, however, measurements occur at a significantly slower rate than the underlying system changes. Moreover, the size of the mismatch between timescales is often unknown. This paper provides three distinct causal structure learning algorithms, all of which discover all dynamic graphs that could explain the observed measurement data as arising from undersampling at some rate. That is, these algorithms all learn causal structure without assuming any particular relation between the measurement and system timescales; they are thus rate-agnostic. We apply these algorithms to data from simulations. The results provide insight into the challenge of undersampling

It is the height of banality to observe that people, not bullets, fight kinetic wars. The machinery of kinetic warfare is obviously relevant to the conduct of each particular act of warfare, but the reasons for, and meanings of, those acts depend critically on the fact that they are done by humans. Any attempt to understand warfare—its causes, strategies, legitimacy, dynamics, and resolutions—must incorporate humans as an intrinsic part, both descriptively and normatively. Humans from general staff to “boots on the ground” play key roles in all aspects of kinetic warfare, and the literature about it reflects this focus. 2 In contrast, many discussions of cyberwarfare and cyber-conflict focus principally on the technical aspects of machines, systems, and data, 3 and human agents are included only as collateral effects, or as loci of moral responsibility.

Various causal details of the genetic process of translation have been singled out to account for its privileged status as a ‘code'. We explicate the biological uses of coding talk by characterizing a class of special causal processes in which topological properties are the causally relevant ones. This class contains both the process of translation and communication theoretic coding processes as special cases. We propose a formalism in terms of graphs for expressing our theory of biological codes and discuss its utility in understanding biological systems. *Received May 2007; revised May 2008. †To contact the authors, please write to: Department of Philosophy, Baker Hall 135, Carnegie Mellon University, Pittsburgh, PA 15213; e-mail: [email protected] or [email protected].

Causal structure learning algorithms have focused on learning in ”batch-mode”: i.e., when a full dataset is presented. In many domains, however, it is important to learn in an online fashion from sequential or ordered data, whether because of memory storage constraints or because of potential changes in the underlying causal structure over the course of learning. In this paper, we present TDSL, a novel causal structure learning algorithm that processes data sequentially. This algorithm can track changes in the generating causal structure or parameters, and requires significantly less memory in realistic settings. We show by simulation that the algorithm performs comparably to batch-mode learning when the causal structure is stationary, and significantly better in non-stationary environments.

Learning by artificial intelligence systems-what I will typically call machine learning-has a distinguished history, and the field has experienced something of a renaissance in the past twenty years. Machine learning consists principally of a diverse set of algorithms and techniques that have been applied to problems in a wide range of domains. Any overview of the methods and applications will inevitably be incomplete, at least at the level of specific algorithms and techniques. There are many excellent introductions to the formal and statistical details of machine learning algorithms and techniques available elsewhere. The present chapter focuses on machine learning as a general way of “thinking about the world,” and provides a high-level characterization of the major goals of machine learning. There are a number of philosophical concerns that have been raised about machine learning, but upon closer examination, it is not always clear whether the objections really speak against machine learning specifically. Many seem rather to be directed towards machine learning as a particular instantiation of some more general phenomenon or process. One of the general morals of this chapter is that machine learning is, in many ways, less unusual or peculiar than is sometimes thought.

Most learning models assume, either implicitly or explicitly, that the goal of learning is to acquire a complete and veridical representation of the world, but this view assumes away the possibility that pragmatic goals can play a central role in learning. We propose instead that people are relatively frugal learners, acquiring goal-relevant information while ignoring goal-irrelevant features of the environment. Experiment 1 provides evidence that learning is goal-dependent, and that people are relatively frugal when given a specific, practical goal. Experiment 2 investigates possible mechanisms underlying this effect, and finds evidence that people exhibit goal-driven attention allocation, but not goaldriven reasoning. We conclude by examining how frugality can be integrated into Bayesian models of learning

In the latter half of the twentieth century, philosophers of science have argued (implicitly and explicitly) that epistemically rational individuals might compose epistemically irrational groups and that, conversely, epistemically rational groups might be composed of epistemically irrational individuals. We call the conjunction of these two claims the Independence Thesis, as they together imply that methodological prescriptions for scientific communities and those for individual scientists might be logically independent of one another. We develop a formal model of scientific inquiry, define four criteria for individual and group epistemic rationality, and then prove that the four definitions diverge, in the sense that individuals will be judged rational when groups are not and vice versa. We conclude by explaining implications of the inconsistency thesis for (i) descriptive history and sociology of science and (ii) normative prescriptions for scientific communities.