Nawet jeżeli integrację poznania rozproszonego z mechanistycznymi koncepcjami wyjaśniania można uznać za ruch interesujący, a w przypadku powodzenia prowadzący do niebanalnego rozszerzenia kognitywistycznych badań nad poznaniem, to z perspektywy teoretyka poznania rozproszonego należy uznać ten ruch za ryzykowny. W poniższej pracy, w dyskusji z propozycją Witolda Wachowskiego (2022), postaram się przedstawić ryzyko, z jakim wiąże się wspomniana integracja i zaproponuję rozwiązanie alternatywne, polegające na połączeniu rozproszenia poznania z teorią sieci. Teoria ta, w mojej opinii, pozwala na bardziej owocne badanie wzorców (...) rozproszenia poznania. ----------------------------------------- Zgłoszono: 26/09/2023. Zrecenzowano: 26/03/2024. Zaakceptowano do publikacji: 10/06/2024. (shrink)

This article evaluates the idea of the modularity of mind and domain specificity. This concept has penetrated the behavioral disciplines, and in the case of some of these—for example, the cognitive study of religion—has even formed their foundation. Although the theoretical debate relating to the idea of modularity is ongoing, this debate has not been reflected in the use of modularity in behavioral research. The idea of domain specificity or modularity of mind is not without its controversies, and there is (...) no consensus regarding its acceptance. Many neuroscientists, as well as several evolutionary psychologists and philosophers, have raised a number of objections that cannot remain overlooked. I will show the areas in which the idea is problematic, what attempts have been made to preserve it, and how social scientific research can move beyond post-Fodorian modularity without losing any valuable insights. (shrink)

As a discipline of its own, the philosophy of science can be traced back to the founding of its academic journals, some of which go back to the first half of the twentieth century. While the discipline has been the object of many historical studies, notably focusing on specific schools or major figures of the field, little work has focused on the journals themselves. Here, we investigate contemporary philosophy of science by means of computational text-mining approaches: we apply topic-modeling algorithms (...) to eight major philosophy of science journals, from the 1930s up until 2017. Based on the full-text content of some 15,897 articles, we identified 25 research themes and 8 thematic clusters that show how the research agenda of the philosophy of science has changed in its content over the course of the last eight decades, up to the philosophy of science we now know. We also show how each one of the journals contributed in its own way to this thematic evolution. (shrink)

Network neuroscience represents the brain as a collection of regions and inter-regional connections. Given its ability to formalize systems-level models, network neuroscience has generated unique explanations of neural function and behavior. The mechanistic status of these explanations and how they can contribute to and fit within the field of neuroscience as a whole has received careful treatment from philosophers. However, these philosophical contributions have not yet reached many neuroscientists. Here we complement formal philosophical efforts by providing an applied perspective from (...) and for neuroscientists. We discuss the mechanistic status of the explanations offered by network neuroscience and how they contribute to, enhance, and interdigitate with other types of explanations in neuroscience. In doing so, we rely on philosophical work concerning the role of causality, scale, and mechanisms in scientific explanations. In particular, we make the distinction between an explanation and the evidence supporting that explanation, and we argue for a scale-free nature of mechanistic explanations. In the course of these discussions, we hope to provide a useful applied framework in which network neuroscience explanations can be exercised across scales and combined with other fields of neuroscience to gain deeper insights into the brain and behavior. (shrink)

Smaldino suggests that patterns that give rise to group-level cultural traits can also increase individual-level cultural diversity. I distinguish social roles and related social network structures and discuss ways in which each might maintain diversity. I suggest that cognitive analogs of “cohesion,” a property of networks that helps maintenance of diversity, might mediate the effects of social roles on diversity.

Arithmetical cognition is the result of enculturation. On a personal level of analysis, enculturation is a process of structured cultural learning that leads to the acquisition of evolutionarily recent, socio-culturally shaped arithmetical practices. On a sub-personal level, enculturation is realized by learning driven plasticity and learning driven bodily adaptability, which leads to the emergence of new neural circuitry and bodily action patterns. While learning driven plasticity in the case of arithmetical practices is not consistent with modularist theories of mental architecture, (...) it can be enriched by the theory of neural reuse. According to neural reuse, cerebral regions are reused to contribute to multiple neural circuits in functionally constrained ways throughout ontogeny. By hypothesis, learning driven plasticity is complemented by learning driven bodily adaptability, which suggests that there is an interesting functional relationship between finger gnosis, finger counting, and arithmetical practices. The emerging perspective on enculturated arithmetical cognition will be complemented by considerations on associated developmental and acquired disorders, namely developmental dyscalculia and acquired acalculia. The upshot is that we need to take the cerebral, extra-cerebral bodily, and socio-cultural dimensions of enculturation into account in order to arrive at a better understanding of the phylogenetic and ontogenetic conditions of arithmetical cognition. (shrink)

Mechanist philosophers have examined several strategies scientists use for discovering causal mechanisms in neuroscience. Findings about the anatomical organization of the brain play a central role in several such strategies. Little attention has been paid, however, to the use of network analysis and causal modeling techniques for mechanism discovery. In particular, mechanist philosophers have not explored whether and how these strategies incorporate information about the anatomical organization of the brain. This paper clarifies these issues in the light of the distinction (...) between structural, functional and effective connectivity. Specifically, we examine two quantitative strategies currently used for causal discovery from functional neuroimaging data: dynamic causal modeling and probabilistic graphical modeling. We show that dynamic causal modeling uses findings about the brain’s anatomical organization to improve the statistical estimation of parameters in an already specified causal model of the target brain mechanism. Probabilistic graphical modeling, in contrast, makes no appeal to the brain’s anatomical organization, but lays bare the conditions under which correlational data suffice to license reliable inferences about the causal organization of a target brain mechanism. The question of whether findings about the anatomical organization of the brain can and should constrain the inference of causal networks remains open, but we show how the tools supplied by graphical modeling methods help to address it. (shrink)

How can we best understand human cognitive architectural variability? We believe that the relationships between theories in neurobiology, cognitive science and evolutionary biology posited by evolutionary psychology’s Integrated Causal Model has unduly supported various essentialist conceptions of the human cognitive architecture, monomorphic minds, that mask HCA variability, and we propose a different set of relationships between theories in the same domains to support a different, non-essentialist, understanding of HCA variability. To set our case against essentialist theories of HCA variability, we (...) detail the general notion of an ICM and the specific ICM at the heart of evolutionary psychology. We briefly illustrate the type of essentialism fostered by evolutionary psychology’s ICM by showing how it grounds essentialist theories of cognitive gender. We shall not criticize these theories here since the literature is replete with compelling objections to them, but shall instead focus on motivating a replacement ICM to destabilize evolutionary psychology’s ICM wholesale. ICMs usually span larger than the models they support, hence larger than arguments against these models, and one reason the essentialist theories addressed here have the kind of staying power they do is that they are partly supported by the ICM in which they are grounded. In short, we offer “A New Hope” against the essentialist empire. True to the Hollywood trope, this new hope rests on an alliance between a young theory, cognitive network neuroscience, and two older, but still quite young, epistemic rebels: enactive cognitive science and developmental systems theory. Accordingly, we detail and discuss the proposed emerging ICM and test-drive it by sketching the multimorphic view of gender it grounds. (shrink)

Two long-standing arguments in cognitive science invoke the assumption that holistic inference is computationally infeasible. The first is Fodor’s skeptical argument toward computational modeling of ordinary inductive reasoning. The second advocates modular computational mechanisms of the kind posited by Cosmides, Tooby and Sperber. Based on advances in machine learning related to Bayes nets, as well as investigations into the structure of scientific and ordinary information, I maintain neither argument establishes its architectural conclusion. Similar considerations also undermine Fodor’s decades-long diagnosis of (...) artificial intelligence research as confounded by an inability to circumscribe the amount of information relevant to inferential processes. This diagnosis is particularly inapposite with respect to Bayes nets, since one of their strengths as machine learning systems has been their capacity to reason probabilistically about large data sets whose size overwhelms the capacities of individual human reasoners. A general moral follows from these criticisms: Insights into artificial and human cognitive systems are likely to be cultivated by focusing greater attention on the structure and density of connections among items of information that are available to them. (shrink)

We examine recent work in cognitive neuroscience that investigates brain networks. Brain networks are characterized by the ways in which brain regions are functionally and anatomically connected to one another. Cognitive neuroscientists use various noninvasive techniques (e.g., fMRI) to investigate these networks. They represent them formally as graphs. And they use various graph theoretic techniques to analyze them further. We distinguish between knowledge of the graph theoretic structure of such networks (structural knowledge) and knowledge of what instantiates that structure (nonstructural (...) knowledge). And we argue that this work provides structural knowledge of brain networks. We explore the significance of this conclusion for the scientific realism debate. We argue that our conclusion should not be understood as an instance of a global structural realist claim regarding the structure of the unobservable part of the world, but instead, as a local structural realist attitude towards brain networks in particular. And we argue that various local approaches to the realism debate, i.e., approaches that restrict realist commitments to particular theories and/or entities, are problematic insofar as they don't allow for the possibility of such a local structural realist attitude. (shrink)