The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: Blue Brain, used for particular simulations of the cortical column in hybrid models, and Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational explanation, I (...) argue that large-scale simulations require that the explanandum phenomenon is identified; otherwise, the explanatory value of such explanations is difficult to establish, and testing the model empirically by comparing its behavior with the explanandum remains practically impossible. The completeness of the explanation, and hence of the explanatory value of the explanatory model, is to be assessed vis-à-vis the explanandum phenomenon, which is not to be conflated with raw observational data and may be idealized. I argue that idealizations, which include building models of a single phenomenon displayed by multi-functional mechanisms, lumping together multiple factors in a single causal variable, simplifying the causal structure of the mechanisms, and multi-model integration, are indispensable for complex systems such as brains; otherwise, the model may be as complex as the explanandum phenomenon, which would make it prone to so-called Bonini paradox. I conclude by enumerating dimensions of empirical validation of explanatory models according to new mechanism, which are given in a form of a “checklist” for a modeler. (shrink)

Artykuł ten ma celu zaakcentowanie i uzupełnienie wybranych wątków książki Witolda Wachowskiego Poznanie rozproszone. Od heurystyk do mechanizmów (2022). W tekście uwzględniono aktualną sytuację, w jakiej znajdują się nauki poznawcze, z perspektywy koncepcji poznania rozproszonego i związanych z nią badań. Podczas gdy książka koncentruje się bardziej na strukturze tej koncepcji i jej osadzeniu w tradycjach badawczych, w artykule nacisk kładzie się na przedstawienie wciąż niedocenianego potencjału poznania rozproszonego jako uniwersalnego rozumienia poznania, faktycznych konsekwencji badawczych tej perspektywy, przy jednoczesnym doprecyzowaniu relacji (...) z mechanicyzmem, jak i potencjału integracyjnego – wszystko to w świetle aktualnej kondycji kognitywistyki. ----------------------------------------- Zgłoszono: 31/12/2023. Zrecenzowano: 10/04/2024. Zaakceptowano do publikacji: 13/05/2024. (shrink)

Multiple realizability (MR) is traditionally conceived of as the feature of computational systems, and has been used to argue for irreducibility of higher-level theories. I will show that there are several ways a computational system may be seen to display MR. These ways correspond to (at least) five ways one can conceive of the function of the physical computational system. However, they do not match common intuitions about MR. I show that MR is deeply interest-related, and for this reason, difficult (...) to pin down exactly. I claim that MR is of little importance for defending computationalism, and argue that it should rather appeal to organizational invariance or substrate neutrality of computation, which are much more intuitive but cannot support strong antireductionist arguments. (shrink)