Representation is typically taken to be importantly separate from its physical implementation. This is exemplified in Marr’s three-level framework, widely cited and often adopted in neuroscience. However, the separation between representation and physical implementation is not a necessary feature of information-processing systems. In particular, when it comes to analog computational systems, Marr’s representational/algorithmic level and implementational level collapse into a single level. Insofar as analog computation is a better way of understanding neural computation than other notions, Marr’s three-level framework must (...) then be amended into a two-level framework. However, far from being a problem or limitation, this sheds lights on how to understand physical media as being representational, but without a separate, medium-independent representational level. (shrink)

Various theorists contend that we may live in a computer simulation. David Chalmers in turn argues that the simulation hypothesis is a metaphysical hypothesis about the nature of our reality, rather than a sceptical scenario. We use recent work on consciousness to motivate new doubts about both sets of arguments. First, we argue that if either panpsychism or panqualityism is true, then the only way to live in a simulation may be as brains-in-vats, in which case it is unlikely that (...) we live in a simulation. We then argue that if panpsychism or panqualityism is true, then viable simulation hypotheses are substantially sceptical scenarios. We conclude that the nature of consciousness has wide-ranging implications for simulation arguments. (shrink)

Relative to digital computation, analogue computation has been neglected in the philosophical literature. To the extent that attention has been paid to analogue computation, it has been misunderstood. The received view—that analogue computation has to do essentially with continuity—is simply wrong, as shown by careful attention to historical examples of discontinuous, discrete analogue computers. Instead of the received view, I develop an account of analogue computation in terms of a particular type of analogue representation that allows for discontinuity. This account (...) thus characterizes all types of analogue computation, whether continuous or discrete. Furthermore, the structure of this account can be generalized to other types of computation: analogue computation essentially involves analogue representation, whereas digital computation essentially involves digital representation. Besides being a necessary component of a complete philosophical understanding of computation in general, understanding analogue computation is important for computational explanation in contemporary neuroscience and cognitive science. (shrink)

A weak version of the life-mind continuity thesis entails that every living system also has a basic mind (with a non-representational form of intentionality). The strong version entails that the same concepts that are sufficient to explain basic minds (with non-representational states) are also central to understanding non-basic minds (with representational states). We argue that recent work on the free energy principle supports the following claims with respect to the life-mind continuity thesis: (i) there is a strong continuity between life (...) and mind; (ii) all living systems can be described as if they had representational states; (iii) the ’as-if representationality’ entailed by the free energy principle is central to understanding both basic forms of intentionality and intentionality in non-basic minds. In addition to this, we argue that the free energy principle also renders realism about computation and representation compatible with a strong life-mind continuity thesis (although the free energy principle does not entail computational and representational realism). In particular, we show how representationality proper can be grounded in ’as-if representationality’. (shrink)

The notion of representation in neuroscience has largely been predicated on localizing the components of computational processes that explain cognitive function. On this view, which I call “algorithmic homuncularism,” individual, spatially and temporally distinct parts of the brain serve as vehicles for distinct contents, and the causal relationships between them implement the transformations specified by an algorithm. This view has a widespread influence in philosophy and cognitive neuroscience, and has recently been ably articulated and defended by Shea. Still, I am (...) skeptical about algorithmic homuncularism, and I argue against it by focusing on recent methods for complex data analysis in systems neuroscience. I claim that analyses such as principle components analysis and linear discriminant analysis prevent individuating vehicles as algorithmic homuncularism recommends. Rather, each individual part contributes to a global state space, trajectories of which vary with important task parameters. I argue that, while homuncularism is false, this view still supports a kind of “vehicle realism,” and I apply this view to debates about the explanatory role of representation. (shrink)

In the 70 years since Alan Turing’s ‘Computing Machinery and Intelligence’ appeared in Mind, there have been two widely-accepted interpretations of the Turing test: the canonical behaviourist interpretation and the rival inductive or epistemic interpretation. These readings are based on Turing’s Mind paper; few seem aware that Turing described two other versions of the imitation game. I have argued that both readings are inconsistent with Turing’s 1948 and 1952 statements about intelligence, and fail to explain the design of his game. (...) I argue instead for a response-dependence interpretation. This interpretation has implications for Turing’s view of free will: I argue that Turing’s writings suggest a new form of free will compatibilism, which I call response-dependence compatibilism. The philosophical implications of rethinking Turing’s test go yet further. It is assumed by numerous theorists that Turing anticipated the computational theory of mind. On the contrary, I argue, his remarks on intelligence and free will lead to a new objection to computationalism. (shrink)

Can information theory be used to understand neural signaling? Yes, but assumptions have to be made about the nature of that signaling. The traditional view is that the individual neural spike is an all-or-none phenomenon, which allows neural spikes to be viewed as discrete, binary pulses, similar in kind to the signals in digital computers. Under this assumption, the tools of information theory can be used to derive results about the properties of neural signals. However, new results from neuroscience demonstrate (...) that the precise shape of the individual spike can be functionally significant, thus violating the assumption that spikes can always be treated as a binary pulse. Instead, spikes must sometimes be viewed as a continuous signal. Fortunately, information-theoretic tools exist for the study of continuous signals; unfortunately, their use in the continuous domain is very different from their use in the discrete domain, and not always well understood. Researchers interested in making precise claims about the nature of the information used, stored, and processed in neural systems must pay careful attention to these differences. (shrink)

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory (...) to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year. (shrink)

There is growing evidence that plants possess abilities associated with cognition, such as decision-making, anticipation and learning. And yet, the cognitive status of plants continues to be contested. Among the threats to plant cognitive status is the ‘Representation Demarcation Challenge’ which points to the absence of a seemingly defining aspect of cognition, namely, computation over representation with non-derived content. Defenders of plant cognition may appeal to post-cognitivist perspectives, such as enactivism, which challenge the assumptions of the Representation Demarcation Challenge. This (...) points to an impasse in the debate over plant cognition as it collapses into perennial disagreements over the best way to conceptualise the very nature of cognition. I propose a path that allows us to bypass this quagmire by reconceiving the question of what is cognitive about ‘plant cognition’ in terms of a quest to map the many possible adaptive capacities and behaviours more-or-less associated with cognition, alongside their underlying processes and mechanisms. In turn, we can examine the degrees of similarity between plants and more paradigmatically cognitive creatures. The ‘piecemeal approach’ thus shifts attention away from the abstract and dichotomous question of whether plants are cognitive and towards a series of more precise questions about the many ways and extent to which plants possess features associated with cognition. Ultimately, I suggest, the value of viewing plants through a cognitive lens may lie less in determining whether they are bona fide cognitive creatures and more in guiding research into concrete abilities and their underlying causes. (shrink)

The historical debate on representation in cognitive science and neuroscience construes representations as theoretical posits and discusses the degree to which we have reason to posit them. We reject the premise of that debate. We argue that experimental neuroscientists routinely observe and manipulate neural representations in their laboratory. Therefore, neural representations are as real as neurons, action potentials, or any other well-established entities in our ontology.

The aim of this paper is to begin developing a version of Gualtiero Piccinini’s mechanistic account of computation that does not need to appeal to any notion of proper (or teleological) functions. The motivation for doing so is a general concern about the role played by proper functions in Piccinini’s account, which will be evaluated in the first part of the paper. I will then propose a potential alternative approach, where computing mechanisms are understood in terms of Carl Craver’s perspectival (...) account of mechanistic functions. According to this approach, the mechanistic function of ‘performing a computation’ can only be attributed relative to an explanatory perspective, but such attributions are nonetheless constrained by the underlying physical structure of the system in question, thus avoiding unlimited pancomputationalism. If successful, this approach would carry with it fewer controversial assumptions than Piccinini’s original account, which requires a robust understanding of proper functions. Insofar as there are outstanding concerns about the status of proper functions, this approach would therefore be more generally acceptable. (shrink)