The medium‐independence of computational descriptions has shaped common conceptions of computational explanation. So long as our goal is to explain how a system successfully carries out its computations, then we only need to describe the abstract series of operations that achieve the desired input–output mapping, however they may be implemented. It is argued that this abstract conception of computational explanation cannot be applied to so‐called real‐time computing systems, in which meeting temporal deadlines imposed by the systems with which a device (...) interfaces are constitutive of the computing tasks that a device performs. Instead, real‐time computing reveals the need for alternative conceptions of computational explanation, as well as computational implementation, that eschew medium‐independence. (shrink)

Mechanistic explanations are often said to explain because they reveal the causal structure of the world. Conversely, dynamical models supposedly lack explanatory power because they do not describe causal structure. The only way for dynamical models to produce causal explanations is via the 3M criterion: the model must be mapped onto a mechanism. This framing of the situation has become the received view around the viability of dynamical explanation. In this paper, I argue against this position and show that dynamical (...) models can themselves reveal causal structure and consequently produce nonmechanistic, dynamical explanations. Taking the example of cell fates from systems biology, I show how dynamical models, and specifically the attractor landscapes they describe, identify the causes of cell differentiation and explain why cells select particular fates. These dynamical features of the system better fit Woodward’s (2010, 2018) criteria of specificity and proportionality and make them the best candidate causes of cell fates than mechanisms. I also show how these causes are irreducible and inaccessible to mechanistic models, making 3M unworkable and counterproductive in this case. Dynamical models can reveal dynamical causes and thereby provide causal explanations. (shrink)

Mechanistic explanations, according to one prominent account, are derived from objective explanations (Craver 2007, 2014 ). Mechanistic standards of explanation are in turn pulled from nature, and are thereby insulated from the values of investigators, since explanation is an objectively defined achievement grounded in the causal structure of the world (Craver 2014 ). This results in the closure of mechanism’s explanatory standards—it is insulated from the values, norms and goals of investigators. I raise two problems with this position. First, it (...) relies on several ontological claims which, while plausible, fail to guarantee the objectivity of mechanistic explanatory standards to the degree of certainty required. Second, Craver’s position itself introduces a value–laden explanatory standard—the 3M requirement (Kaplan & Craver 2011 )—which undermines the closure of explanatory standards. I show how in practice mechanistic explanation is in part guided by explanatory taste, shorthand for background contextual values that influence our standards of explanation. Mechanism often has a particular pragmatically-oriented taste for control, and gerrymanders explanatory standards in order to obtain it. I conclude by arguing that objectivity, rather than being obtained through the right set of explanatory standards, is better thought of as the result of processes of intersubjective criticism, which renders visible the contextual values of communities of investigators and allows them to be controlled for (Longino 1990 ). (shrink)

Virtually all philosophers of science have construed fundamental theories as descriptions of entities, properties, and/or structures. Call this the “descriptive-ontological” view. I argue that this view is incorrect, at least insofar as physical theories are concerned. I propose a novel construal of theories that I call the “prescriptive-dynamical” view. The central tenet of this view, roughly put, is that the _essential_ content of fundamental physical theories is a _prescription for interfacing with natural systems and translating local data into compact theoretical (...) language_. The descriptive-ontological aspects of theories, if any, are taken as _inessential_ content on this view: they do not contribute to the predictive success of the theory. Rather than describing _what is there_, the essence of a physical theory is to tell us _what to do_ when interfacing with a physical system. (shrink)

This paper advances the view that some explanations in cognitive science are extra-mathematical explanations. Demonstrating the plausibility of this interpretation centers around certain efficient coding cases which ineliminably enlist information theoretic laws, facts and theorems to identify in-principle, mathematical constraints on neuronal information processing capacities. The explanatory structure in these cases is shown to parallel other putative instances of mathematical explanation. The upshot for cognitive mathematical explanations is thus two-fold: first, the view capably rebuts standard mechanistic objections to non-mechanistic explanation; (...) second, this view serves to importantly widen the possibility space for non-mechanistic forms of explanation in cognitive science. (shrink)

COVID-19 epidemic models raise important questions for science and philosophy of science. Here I provide a brief preliminary exploration of three: what kinds of predictions do epidemic models make, are they causal models, and how do different kinds of epidemic models differ in terms of what they represent?

Many epidemics consist in individuals spreading infection to others. From the population perspective, they also have population characteristics important in modeling, explaining, and intervening in epidemics. I analyze epidemiology’s contemporary population perspective through the example of epidemics by examining two central principles attributed to Geoffrey Rose: a distinction between the causes of cases and the causes of incidence, and between “high-risk” and “population” strategies of prevention. Both principles require revision or clarification to capture the sense in which they describe distinct (...) perspectives on the same phenomenon, each perspective capturing a different level of contrastive analysis. (shrink)

The study of active matter systems demonstrates how interactions might co-constitute agential dynamics. Active matter systems are comprised of self-propelled independent entities which, en masse, take part in complex and interesting collective group behaviors at a far-from-equilibrium state (Menon, 2010 ; Takatori & Brady, 2015 ). These systems are modelled using very simple rules (Vicsek at al. 1995), which reveal the interactive nature of the collective behaviors seen from humble to highly complex entities. Here I show how the study of (...) active matter systems supports two related proposals regarding interaction and agency. First, I argue that the study of interactive dynamics in these systems evidences the utility of treating interaction as an ontological category (Longino, 2021 ) and challenges methodological individualism as the received explanatory primitive in the study of agency. Second, the methods used to research active matter systems demonstrate how a minimal approach to agency can scale up in studying interactive agential dynamics in more complex systems. The examples of coordination dynamics (Kelso, 2001 ) and participatory sense-making (De Jaegher & Di Paolo, 2007 ) are provided to show how understanding agency requires us to look beyond the individuals to the interactive agential dynamics that can guide, scaffold, or constrain their activity. (shrink)

We provide two programmatic frameworks for integrating philosophical research on understanding with complementary work in computer science, psychology, and neuroscience. First, philosophical theories of understanding have consequences about how agents should reason if they are to understand that can then be evaluated empirically by their concordance with findings in scientific studies of reasoning. Second, these studies use a multitude of explanations, and a philosophical theory of understanding is well suited to integrating these explanations in illuminating ways.

The long standing and still highly influential mindreading framework claims that social cog- nition is best understood as an ability to predict and explain others’ behavior in terms of their mental states. This ability is explained by appealing to mental representations and inferential reasoning via rule-based knowledge. However, recent enactive work on social cognition questions most, if not all, of the main assumptions on which mindreading is founded. Enac- tivism’s emphasis on the structural coupling of the brain-body-world constitutes the foundation (...) of the framework, which rejects the representational and inferential reasoning claims of the mindreading framework. For enactivists, social cognition is best explained in terms of the direct embodied and embedded interactions an agent has with her socio-material world. In continuing this work, the thesis provides new developments of the enactive project for investigating and explaining social cognition. The thesis utilizes two approaches to achieve this goal. First, it dialectally defends enactivism vis-a-vis the mindreading framework. It does this by both securing established enactive claims from criticism, and by developing new objections against the mindreading framework. Secondly, the thesis offers new enactive interpretations of empirical developmental data, and presents new ways of investigating three central areas of debate within the field of social cognition: the metaphysical basis of social cognitive processes, the false-belief test literature, and the concept of empathy in relation to therapeutic practices and autism. The thesis is composed of five different, but thematically intertwined papers. The first paper targets the constitutive basis of social cognition by attempting to dissolve the causal-constitutive fallacy through appealing to a diachronic conception of constitution. This move both secures established enactive claims and develops a more thorough account of what is meant by enactive constitutive claims. The second paper then examines infant social cognitive capacities, arguing cognitivist explanations of these capacities rely on fallacious as- sumptions regarding the nature of perception. The paper then offers an alternative enactive and ecological account of these capacities. The third paper argues both innate and construc- tivist mindreading accounts of the folk psychological know-how required to reliably succeed on false-belief tests fall prey to an infinite regress problem. The paper ends by briefly pre- senting an alternative enactive, narrative explanation of the empirical data. The fourth paper then shifts focus to examine the concept empathy and practice of empathizing. It argues there are advantages to conceiving of empathy as enactive and exploratory, in the sense that when we empathize with others we understand them in a deeper and richer way, through exploring their self-authored narratives. Finally, the fifth paper re-examines our understand- ing of autism by integrating the enactive framework with a neurodiversity approach to cognition. It argues this integration can help us better understand how neurotypical social practices and institutions effect the development, and well-being, of autistic individuals. Through these substantial expansions of established enactivist accounts, and by offering new objections to the mindreading framework, the thesis provides reasons to prefer an enactivist framework in exploring human abilities for social cognition. (shrink)