In this paper, I defend Non-Inferentialism about mental states, the view that we can perceive some mental states in a direct, non-inferential way. First, I discuss how the question of mental state perception is to be understood in light of recent debates in the philosophy of perception, and reconstruct Non-Inferentialism in a way that makes the question at hand—whether we can perceive mental states or not—scientifically tractable. Next, I motivate Non-Inferentialism by showing that under the assumption of the widely-accepted Principle (...) of Cognitive Economy, any account that treats mental state perception as an inferential process commits itself to an unrealistically inefficient picture of our cognitive architecture. Drawing on research in cognitive science, I will then show that my Non-Inferentialist view receives direct support by the available empirical evidence. I conclude that there is no psychologically relevant sense in which perception of mental states differs from paradigmatic cases of perception, such as the perception of ordinary objects. (shrink)

Functional localization is a central aim of cognitive neuroscience. But the nature and extent of functional localization in the human brain have been subjects of fierce theoretical debate since the 19th Century. In this essay, I first examine how concepts of functional localization have changed over time. I then analyze contemporary challenges to functional localization drawing from research on neural reuse, neural degeneracy, and the context-dependence of neural functions. I explore the consequences of these challenges for topics in philosophy of (...) science and philosophy of mind including localizationist versus anti-localizationist approaches to cognitive neuroscience, multiple realizability, reverse inference in functional neuroimaging, and the modularity of mind. (shrink)

The target article criticises reductionist programs in cognitive science for failing to take into account important explanatory features of the organism's physical embodiment and task environment. My aim in this commentary is to show how such features are increasingly being taken seriously by (some) researchers in cognitive neuroscience, who describe the functional activity of neural structures in terms that are context-sensitive rather than intrinsic. This approach can allow us to take seriously the concerns presented in Gallagher’s [2019] target article without (...) having to completely give up on neuroscientific explanations of human behaviour. (shrink)

Reverse inference is a crucial inferential strategy used in cognitive neuroscience to derive conclusions about the engagement of cognitive processes from patterns of brain activation. While widely employed in experimental studies, it is now viewed with increasing scepticism within the neuroscience community. One problem with reverse inference is that it is logically invalid, being an instance of abduction in Peirce’s sense. In this paper, we offer the first systematic analysis of reverse inference as a form of abductive reasoning and highlight (...) some relevant implications for the current debate. We start by formalising an important distinction that has been entirely neglected in the literature, namely the distinction between weak and strong reverse inference. Then, we rely on case studies from recent neuroscientific research to systematically discuss the role and limits of both strong and weak reverse inference; in particular, we offer the first exploration of weak reverse inference as a discovery strategy within cognitive neuroscience. (shrink)

Several developments for diverse scientific goals, mostly in physics and physiology, had to take place, which eventually gave us fMRI as one of the central research paradigms of contemporary cognitive neuroscience. This technique stands on solid foundations established by the physics of magnetic resonance and the physiology of hemodynamics and is complimented by computational and statistical techniques. I argue, and support using concrete examples, that these foundations give rise to a productive theory-ladenness in fMRI, which enables researchers to identify and (...) control for the types of methodological and inferential errors. Consequently, this makes it possible for researchers to represent and investigate cognitive phenomena in terms of hemodynamic data and for experimental knowledge to grow independently of large scale theories of cognition. (shrink)

In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I argue that (...) MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience. (shrink)

Several developments for diverse scientific goals, mostly in physics and physiology, had to take place, which eventually gave us fMRI as one of the central research paradigms of contemporary cognitive neuroscience. This technique stands on solid foundations established by the physics of magnetic resonance and the physiology of hemodynamics and is complimented by computational and statistical techniques. I argue, and support using concrete examples, that these foundations give rise to a productive theory-ladenness in fMRI, which enables researchers to identify and (...) control for the types of methodological and inferential errors. Consequently, this makes it possible for researchers to represent and investigate cognitive phenomena in terms of hemodynamic data and for experimental knowledge to grow independently of large scale theories of cognition. (shrink)