The view that special science properties are multiply realizable has been attacked in recent years by Shapiro, Bechtel and Mundale, Polger, and others. Focusing on psychological and neuroscientific properties, I argue that these attacks are unsuccessful. By drawing on interspecies physiological comparisons I show that diverse physical mechanisms can converge on common functional properties at multiple levels. This is illustrated with examples from the psychophysics and neuroscience of early vision. This convergence is compatible with the existence of general constraints on (...) the evolution of cognitive systems, and does not involve any ad hoc typing of coarse-grained higher level properties. The mechanisms that realize these common higher level properties are really distinct by the criteria laid down by critics of multiple readability. Finally, I present an account of how such functional properties might constitute special science kinds by playing a central explanatory role in a range of cognitive models. Behavioral science kinds in particular are the functionally defined constituents picked out by our most successful models of the multilevel systems and mechanisms that explain cognitive capacities. (shrink)

Many cognitive scientists, having discovered that some computational-level characterization f of a cognitive capacity φ is intractable, invoke heuristics as algorithmic-level explanations of how cognizers compute f. We argue that such explanations are actually dysfunctional, and rebut five possible objections. We then propose computational-level theory revision as a principled and workable alternative.

A critical survey of some attempts to define ‘computer’, beginning with some informal ones, then critically evaluating those of three philosophers, and concluding with an examination of whether the brain and the universe are computers.

Computationalism has been the mainstream view of cognition for decades. There are periodic reports of its demise, but they are greatly exaggerated. This essay surveys some recent literature on computationalism. It concludes that computationalism is a family of theories about the mechanisms of cognition. The main relevant evidence for testing it comes from neuroscience, though psychology and AI are relevant too. Computationalism comes in many versions, which continue to guide competing research programs in philosophy of mind as well as psychology (...) and neuroscience. Although our understanding of computationalism has deepened in recent years, much work in this area remains to be done. (shrink)

Qualia have proved difficult to integrate into a broadly physicalistic worldview. In this paper, I argue that despite popular wisdom in the philosophy of mind, qualia’s intrinsicality is not sufficient for their non-reducibility. Second, I diagnose why philosophers mistakenly focused on intrinsicality. I then proceed to argue that qualia are categorical and end with some reflections on how the conceptual territory looks when we keep our focus on categoricity.

Deep learning is a kind of machine learning which happens in a certain type of artificial neural networks called deep networks. Artificial deep networks, which exhibit many similarities with biological ones, have consistently shown human-like performance in many intelligent tasks. This poses the question whether this performance is caused by such similarities. After reviewing the structure and learning processes of artificial and biological neural networks, we outline two important reasons for the success of deep learning, namely the extraction of successively (...) higher level features and the multiple layer structure, which are closely related to each other. Then some indications about the framing of this heated debate are given. After that, an assessment of the value of artificial deep networks as models of the human brain is given from the similarity perspective of model representation. Finally, a new version of computational functionalism is proposed which addresses the specificity of deep neural computation better than classic, program based computational functionalism. (shrink)

The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It (...) also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists. (shrink)

This paper motivates taking seriously the possibility that brains are basically protean: that they make use of neural structures in inventive, on-the-fly improvisations to suit circumstance and context. Accordingly, we should not always expect cognition to divide into functionally stable neural parts and pieces. We begin by reviewing recent work in cognitive ontology that highlights the inadequacy of traditional neuroscientific approaches when it comes to divining the function and structure of cognition. Cathy J. Price and Karl J. Friston, and Colin (...) Klein identify the limitations of relying on forward and reverse inferences to cast light on the relation between cognitive functions and neural structures. There is reason to prefer Klein’s approach to that of Price and Friston’s. But Klein’s approach is neurocentric - it assumes that we ought to look solely at neural contexts to fix cognitive ontology. Using recent work on mindreading as a case study, we motivate adopting a radically different approach to cognitive ontology. Promoting the Protean Brain Hypothesis, we posit the possibility that we may need to look beyond the brain when deciding which functions are being performed in acts of cognition and in understanding how the brain contributes to such acts by adapting to circumstance. (shrink)

In 1956 U. T. Place proposed that consciousness is a brain process. More attention should be paid to his word ‘process’. There is near-universal agreement that experiences are processive—as witnessed in the platitude that experiences are occurrent states. The abandonment of talk of brain processes has benefited functionalism, because a functional state, as it is usually conceived, cannot be a process. This point is dimly recognized in a well-known but little-discussed argument that conscious experiences cannot be functional states because the (...) former are occurrent, while the latter are dispositional. That argument fails, but it can be made sound if we reformulate it with the premise that occurrent states are processive. The only way for functionalists to meet the resulting challenge is to abandon the standard individuation of functional states in terms of purely abstract causal roles. (shrink)