Optogenetic techniques are described as “revolutionary” for the unprecedented causal control they allow neuroscientists to exert over neural activity in awake-behaving animals. In this article, I demonstrate by means of a case study that optogenetic techniques will only illuminate causal links between the brain and behavior to the extent that their error characteristics are known and, further, that determining these error characteristics requires (1) comparison of optogenetic techniques with techniques having well-known error characteristics (methodological pluralism) and (2) consideration of the broader neural and behavioral context in which the targets of optogenetic interventions are situated (perspectival pluralism).
Distinguishing between perception and thought is a vacuous task. At least this is what most adopters of predictive coding accounts express. Here I want to argue for the opposite. Although I concur that perception can no longer be equated with strictly bottom-up processing, I argue that thought, in virtue of being at the top of the hierarchy, can be equated with a distinctive kind of process: It predicts but is not predicted by any other level. Using this argument and some recent collaborative experimental work on the much discussed example of racial biases in vision, I show why it makes a difference to the way we frame the issue of whether thought influences perception : What we have is a much more tractable and interesting problem of how much cognitive and metacognitive control we have over our perceptual biases.
Similarity-based cognition is apparently commonplace. It occurs whenever an agent or system exploits the similarities that holding between two or more items — e.g. events, processes, objects, and so on— in order to perform cognitive tasks. This kind of cognition is of special interest to cognitive neuroscience. This presentation explicates how similarity-based cognition can be understood through the lens of radical enactivism and why doing so have advantages over its representationalist rival which posit the existence of structural representations or S-representations. Specifically, it is argued that there are problems accounting for the content of S-representations and in understanding how that putative content of such representations makes a casual difference in guiding intelligent behavior. Finally, it is clarified in which respect adopting a radically enactive account of similarity-based cognition commits to an eliminativist take on neurodynamics and which respect it does not.
I will present a case study from neurophysiology in this paper. The case study concerns a causal question about what drives the electrical potential (a.k.a membrane or action potential) of a specific neuron. This causal question is commonly asked in molecular neuroscience and has been discussed in detail by some philosophers of neuroscience (Craver, 2007). But my case study addresses the causal question from neurophysiology, which mainly focuses on investigating the electrical properties of neurons, not their chemical or genetic properties. In this paper, I aim to use this case study to point out various puzzles in their causal investigative strategies. Having done that, I will adopt some existing philosophical tools and demonstrate how to evaluate the success of the relevant causal investigative strategies in the case study. To this end, the paper will proceed as follows. In section 2, I will present the detail of the case study by organizing them into five components. In the course of analyzing these five components, I will point out the puzzles regarding their causal investigative strategies. In section 3, I will review some existing philosophical tools for evaluating the success of investigative strategies in biological sciences. I will specifically focus on Craver and Darden (2013) and Potochnik (2017). I aim to integrate tools from these philosophers in order to propose a more powerful toolbox for evaluating the success of causal investigative strategies in neurophysiology. In section 4, I will use the proposed toolbox to demonstrate how to evaluate the success of causal investigative strategies in the case study