The indeterminacy of computation

Do the dynamics of a physical system determine what function the system computes? Except in special cases, the answer is no: it is often indeterminate what function a given physical system computes. Accordingly, care should be taken when the question ‘What does a particular neuronal system do?’ is answered by hypothesising that the system computes a particular function. The phenomenon of the indeterminacy of computation has important implications for the development of computational explanations of biological systems. Additionally, the phenomenon lends some support to the idea that a single neuronal structure may perform multiple cognitive functions, each subserved by a different computation. We provide an overarching conceptual framework in order to further the philosophical debate on the nature of computational indeterminacy and computational explanation.

The neural correlates of consciousness under the free energy principle: From computational correlates to computational explanation

How can the free energy principle contribute to research on neural correlates of consciousness, and to the scientific study of consciousness more generally? Under the free energy principle, neural correlates should be defined in terms of neural dynamics, not neural states, and should be complemented by research on computational correlates of consciousness – defined in terms of probabilities encoded by neural states. We argue that these restrictions brighten the prospects of a computational explanation of consciousness, by addressing two central problems. The first is to account for consciousness in the absence of sensory stimulation and behaviour. The second is to allow for the possibility of systems that implement computations associated with consciousness, without being conscious, which requires differentiating between computational systems that merely simulate conscious beings and computational systems that are conscious in and of themselves. Given the notion of computation entailed by the free energy principle, we derive constraints on the ascription of consciousness in controversial cases (e.g., in the absence of sensory stimulation and behaviour). We show that this also has implications for what it means to be, as opposed to merely simulate a conscious system.

When Mechanical Computations Explain Better

In this paper I defend the epistemic value of the representational-computational view of cognition by arguing that it has explanatory merits that cannot be ignored. To this end, I focus on the virtue of a computational explanation of optic ataxia, a disorder characterized by difficulties in executing visually-guided reaching tasks, although ataxic patients do not exhibit any specific disease of the muscular apparatus. I argue that addressing cases of patients who are suffering from optic ataxia by invoking a causal role for internal representations is more effective than merely relying on correlations between bodily and environmental variables. This argument has consequences for the epistemic assessment of radical enactivism, whichRE invokes the Dynamical System Theory as the best tool for explaining cognitive phenomena.

The neural correlates of consciousness under the free energy principle: From computational correlates to computational explanation

How can the free energy principle contribute to research on neural correlates of consciousness, and to the scientific study of consciousness more generally? Under the free energy principle, neural correlates should be defined in terms of neural *dynamics*, not neural states, and should be complemented by the research on *computational* correlates of consciousness -- defined in terms of probabilities encoded by neural states.We argue that these restrictions brighten the prospects of a computational explanation of consciousness, by addressing two central problems. The first is to account for consciousness in the absence of sensory stimulation and behaviour. The second is to allow for the possibility of systems that implement computations associated with consciousness, without being conscious, which requires differentiating between computational systems that merely simulate conscious beings and computational systems that are conscious in and of themselves.Given the notion of computation entailed by the free energy principle, we will derive constraints on the ascription of consciousness in controversial cases (e.g., in the absence of sensory stimulation and behaviour). We show that this also has implications for what it means to *be*, as opposed to merely *simulate* a conscious system.

Computation and the Function of Consciousness

This chapter discusses the connection between computation and consciousness. Three theses are sometimes conflated. Functionalism is the view that the mind is the functional organization of the brain. The Computational Theory of Mind (CTM) is the view that the whole mind—not only cognition but consciousness as well—has a computational explanation. When combined with the empirical discovery that the brain is the organ of the mind, CTM entails that the functional organization of the brain is computational. Computational functionalism is the conjunction of the two: the mind is the computational organization of the brain. Contrary to a common assumption, functionalism entails neither CTM nor computational functionalism. This finding makes room for an underexplored possibility: that consciousness be (at least partly) due to the functional organization of the brain without being computational in nature. This is a noncomputational version of functionalism about consciousness.

Familiar faces as islands of expertise

Most people recognise and match pictures of familiar faces effortlessly, while struggling to match unfamiliar face images. This has led to the suggestion that true human expertise for faces applies only to familiar faces. This paper extends that idea to the notion that we have isolated ‘islands’ of expertise surrounding each familiar face that allow us to perform better with faces that resemble those we already know. The idea is tested in three experiments. The first shows that familiarity with a person facilitates identification of their relatives. The second shows that people are better able to remember faces that resemble someone they already know. The third shows that while prompting participants to think about resemblance at study produces a large effect on subsequent recognition, there is still a significant effect if there is no such prompt. Face-Space-R is used to illustrate a possible computational explanation of the processes involved.

Neural Computation, Multiple Realizability, and the Prospects for Mechanistic Explanation

There is an ongoing philosophical and scientific debate concerning the nature of computational explanation in the neurosciences. Recently, some have cited modeling work involving so-called canonical neural computations—standard computational modules that apply the same fundamental operations across multiple brain areas—as evidence that computational neuroscientists sometimes employ a distinctive explanatory scheme from that of mechanistic explanation. Because these neural computations can rely on diverse circuits and mechanisms, modeling the underlying mechanisms is supposed to be of limited explanatory value. I argue that these conclusions about computational explanations in neuroscience are mistaken, and rest upon a number of confusions about the proper scope of mechanistic explanation and the relevance of multiple realizability considerations. Once these confusions are resolved, the mechanistic character of computational explanations can once again be appreciated.