James Woodward

This paper argues that when brain networks figure in explanations of cognition and behavior they often do so in conjunction with independent dynamical assumptions about signal transmission in the brain. In such cases explanation does not depend on network structure alone but on network structure operating in conjunction with dynamical assumptions. Moreover, dynamical assumptions embody causal information, so that the resulting explanations are not entirely non-causal. In addition, it is argued that the directional features of explanations that appeal to networks can be understood in terms of the independence of network structure and dynamics.

This chapter employs an interventionist framework to elucidate some issues having to do with explanation in neurobiology. I argue that this framework can be used to distinguish theories and models that are explanatory from those that are merely descriptive. This framework can also be used to characterize a notion of a mechanistic explanation, according to which mechanistic explanations are those that meet interventionist criteria for successful explanation and certain additional constraints as well. However, from an interventionist perspective, although mechanistic theories have a number of virtues, it is a mistake to think that such models are the only legitimate kind of explanation in neuroscience and psychology. In particular, some (but not all) dynamical systems models in neuroscience are explanatory as are many models, such as the Hodgkin-Huxley model, that abstract away from mechanistically relevant low-level detail.

The discussions on topological explanations are gaining a significant momentum in the philosophy and in science, as the number of both scientific and philosophical papers on this topic are growing every year. In their recent paper Kostić and Halffman (Kostić and Halffman 2023) have shown that in 2,2 million neuroscience papers between the years 1990-2021, the literature on topological explanations not only grew at unprecedented rates, but also have a much higher rate of growth than the general increase of neuroscience literature since 2006. Earlier work on topological explanations has focused on their unique features and relationship to other dominant explanatory strategies (Kostić 2018a; 2018b; 2019a; 2019b; 2020; 2022; 2023; Kostić and Khalifa 2021; 2023; Kostić, Hilgetag, and Tittgemeyer 2020; Darrason 2018; Rathkopf 2018; 2017; Serban 2020; Bechtel 2020; Levy and Bechtel 2013; Green et al. 2018; Craver 2016; Ross 2021). So even though some of the core concepts and analyses of topological explanations have been explored in the literature, many issues remain relatively underdiscussed. For example, it would be desirable to have a clearer understanding of how topological explanations relate to the more general theories of explanation in the philosophical literature and how accounts of the directionality of topological explanations relate to accounts of directionality in other sorts of explanations. It would also be desirable to have a better understanding of the circumstances in which topological explanations and analyses are most likely to the fruitful and how what they tell us differs from the information provided by other sorts of explanations. Yet another issue concerns the fact that network analyses can be used for a variety of purposes that seem prima-facie different from explanation-- for example, to predict whether a patient has a mental disorder from a study of functional connectivity. Studies of such uses-- their structure and when they are warranted --would also be desirable. Another potential use of networks which again may not have to do directly with explanation concerns their role in scientific discovery. Finally, there are a number of interesting issues about how claims about networks and more particularly claims about the role of networks in explaining other phenomena can be tested, given that straightforward interventions on networks are not always possible. Here researchers have increasingly resorted to simulation experiments and these raise interesting methodological questions. Our hope is that this topical collection will result in papers that address such questions. We invite submissions for a Topical Collection of papers on all of these issues as well as related ones.

This paper explores the interpretation and use of polygenic risk scores (PRSs). We argue that PRSs generally do not directly embody causal information. Nonetheless, they can assist us in tracking other causal relationships concerning genetic effects. Although their purely predictive/correlational use is important, it is this tracking feature that contributes to their potential usefulness in other applications, such as genetic dissection, and their use as controls, which allow us, indirectly, to "see" more clearly the role of environmental variables.

What is the relationship between, on the one hand, the sorts of causal claims found in the special sciences (and in common sense) and, on the other hand, the world as described by physics? A standard picture goes like this: the fundamental laws of physics are causal laws in the sense that they can be interpreted as telling us that realizations of one set of physical factors or properties “causes” realizations of other properties. Causal claims in the special sciences are then true (to the extent that they are) in virtue of “instantiating” these underlying causal laws; as it is often put, the latter serve as “truth-makers” for the former. The picture is thus one according to which the notion of cause, as it occurs in the special sciences, is reflected or “grounded” in a fairly straightforward and transparent way in a similar notion that occurs in fundamental physics. This paper explores some alternatives to this picture.

This paper makes use of recent empirical results, mainly from experimental economics, to expore the conditions under which people will cooperate and to assess competing explantions of this cooperation. It is argued that the evidence supports the claim that people differ in type, with some being conditional cooperators and others being motivated by more or less sophisticated forms of self-interest. Stable cooperation requires, among other things, rules and institutions that protect conditional cooperators from myopically self-interested types. Additional empirical features of the behavior of conditional cooperators also imply that rules and institutions are required to produce stable cooperation.

The Faradayan hypothesis of inductive coupling of the electromagnetic and gravitational fields is briefly discussed. An experiment designed to test the hypothesis wherein samples are spun to see if any electrogravitational charge is induced is described. Results of the experiment are reported. They imply the induction of a charge density ρ* for spinning samples that behaves as ρ*=βρma, where ρm is the mass density of an element of matter experiencing an acceleration a, and β is the coupling coefficient for the hypothetical electrogravitational induction effect. In this experiment, β is found to have the value(9.6±3.3)×10 −13 statcoulombs/dyne. Tests that seem to rule out explanations of the observed charges in terms of conventional charging mechanisms are considered

This is a wonderful book; indeed, it is easily one of the most important and creative books I have ever read on the subject of causation and causal inference. Causality is impressive on many levels and should be of great interest to many different audiences. Philosophers will find of particular interest Pearl's defense of what might be described as a broadly manipulationist or interventionist treatment of causation: Causal claims have to do with what would happen under ideal, suitably surgical experimental manipulations . But Pearl moves far beyond philosophical generalities on this theme: He makes the case for the approach that he favors by developing a formal apparatus for talking about interventions and how they relate to causation and information about probabilities that is at once both intuitively compelling and genuinely useful for purposes of calculation and estimation. He also actively engages the philosophical literature on causation at many points – for example, there are illuminating discussions of evidential decision theory, of probabilistic theories of causation of the sort favored by Eells and Cartwright, of Lewisian counterfactual theories of causation, and of the relationship between so-called type and token causal claims, as well as a quite novel treatment of the latter

Hierarchical Bayesian models (HBMs) provide an account of Bayesian inference in a hierarchically structured hypothesis space. Scientific theories are plausibly regarded as organized into hierarchies in many cases, with higher levels sometimes called ‘paradigms’ and lower levels encoding more specific or concrete hypotheses. Therefore, HBMs provide a useful model for scientific theory change, showing how higher‐level theory change may be driven by the impact of evidence on lower levels. HBMs capture features described in the Kuhnian tradition, particularly the idea that higher‐level theories guide learning at lower levels. In addition, they help resolve certain issues for Bayesians, such as scientific preference for simplicity and the problem of new theories. *Received July 2009; revised October 2009. †To contact the authors, please write to: Leah Henderson, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 32D‐808, Cambridge, MA 02139; e‐mail: [email protected].

How do people judge the degree of causal responsibility that an agent has for the outcomes of her actions? We show that a relatively unexplored factor -- the robustness of the causal chain linking the agent’s action and the outcome -- influences judgments of causal responsibility of the agent. In three experiments, we vary robustness by manipulating the number of background circumstances under which the action causes the effect, and find that causal responsibility judgments increase with robustness. In the first experiment, the robustness manipulation also raises the probability of the effect given the action. Experiments 2 and 3 control for probability-raising, and show that robustness still affects judgments of causal responsibility. In particular, Experiment 3 introduces an Ellsberg type of scenario to manipulate robustness, while keeping the conditional probability and the skill deployed in the action fixed. Experiment 4, replicates the results of Experiment 3, while contrasting between judgments of causal strength and of causal responsibility. The results show that in all cases, the perceived degree of responsibility increases with the robustness of the action-outcome causal chain.

This paper explores some issues concerning how we should think about interventions (in the sense of unconfounded manipulations) of "upper-level" variables in contexts in which these supervene on but are not identical with lower-level realizers. It is argued that we should reject the demand that interventions on upper-level variables must leave their lower-level realizers unchanged– a requirement that within an interventionist framework would imply that upper-level variables are causally inert. Instead an intervention on an upper-level variable at the same time changes its lower-level realizer in a way that is consistent with the change in the upper- level variable. The lower-level realizer should not be regarded as a potential "confounder" of upper-level causal relations, as in "exclusionist" arguments. Several proposals for making this precise are considered and several pro-exclusionist arguments are criticized.