We provide a taxonomy of the two most important debates in the philosophy of the cognitive and neural sciences. The first debate is over methodological individualism: is the object of the cognitive and neural sciences the brain, the whole animal, or the animal--environment system? The second is over explanatory style: should explanation in cognitive and neural science be reductionist-mechanistic, inter-level mechanistic, or dynamical? After setting out the debates, we discuss the ways in which they are interconnected. Finally, we make some (...) recommendations that we hope will help philosophers interested in the cognitive and neural sciences to avoid dead ends. (shrink)

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience must literally exist the system being modelled. Computational models are important tools to test a theory about how the collected data has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in (...) the model. While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling that combines dynamical systems theory mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science. (shrink)

I propose a systematic survey of the various attitudes proponents of enaction (or enactivism) entertained or are entertaining towards representationalism and towards the use of the concept “mental representation” in cognitive science. For the sake of clarity, a set of distinctions between different varieties of representationalism and anti-representationalism are presented. I also recapitulate and discuss some anti-representationalist trends and strategies one can find the enactive literature, before focusing on some possible limitations of eliminativist versions of enactive anti-representationalism. These limitations are (...) here taken as opportunities for reflecting on the fate of enactivism in its relations with representationalism and anti-representationalism. (shrink)

How should we understand the claim that people comply with social norms because they possess the right kinds of beliefs and preferences? I answer this question by considering two approaches to what it is to believe (and prefer), namely: representationalism and dispositionalism. I argue for a variety of representationalism, viz. neural representationalism. Neural representationalism is the conjunction of two claims. First, what it is essential to have beliefs and preferences is to have certain neural representations. Second, neural representations are often (...) necessary to adequately explain behaviour. After having canvassed one promising way to understand what neural representations could be, I argue that the appeal to beliefs and preferences in explanations of paradigmatic cases of norm compliance should be understood as an appeal to neural representations. (shrink)

Conscious events interact with memory systems in learning, rehearsal and retrieval (Ebbinghaus 1885/1964; Tulving 1985). Here we present hypotheses that arise from the IDA computional model (Franklin,Kelemen and McCauley 1998; Franklin 2001b) of global workspace theory (Baars 1988, 2002). Our primary tool for this exploration is a flexible cognitive cycle employed by the IDA computational model and hypothesized to be a basic element of human cognitive processing. Since cognitive cycles are hypothesized to occur five to tentimes a second and include (...) interaction between conscious contents and several of the memory systems, they provide the means for an exceptionally fine-grained analysis of various cognitive tasks. We apply this tool to the small effect size of subliminal learning compared to supraliminal learning, to process dissociation, to implicit learning, to recognition vs. recall, and to the availability heuristic in recall. The IDA model elucidates the role of consciousness in the updating of perceptual memory, transient episodic memory, and procedural memory. In most cases, memory is hypothesized to interact with conscious events for its normal functioning. The methodology of the paper is unusual in that the hypotheses and explanations presented are derived from an empirically based, but broad and qualitative computational model of human cognition. (shrink)

The discovery of so-called ‘mirror neurons’ - found to respond both to own actions and the observation of similar actions performed by others - has been enormously influential in the cognitive sciences and beyond. Given the self-other symmetry these neurons have been hypothesized as underlying a ‘mirror mechanism’ that lets us share representations and thereby ground core social cognitive functions from intention understanding to linguistic abilities and empathy. I argue that mirror neurons are important for very different reasons. Rather than (...) a symmetric ubiquitous or context- independent mechanism, I propose that these neurons are part of broader sensorimotor circuits, which help us navigate and predict the social affordance space that we meet others in. To develop both the critical and positive project I analyze the interpretive choices and the debate surrounding the mirror neuron research and show how the field is marred by highly questionable assumptions about respectively motor and social cognition. The discovery of mirror neurons - and the sensorimotor circuits of which these neurons are a part – actually empirically challenge many of these tacit assumptions. Findings of sensorimotor goal representations at levels of abstraction well beyond actual sensory information and kinetic movements challenge the idea of motor cognition as primarily output production. Additionally, the focus on 3rd person mindreading of hidden mental states is misguiding the field of social cognition. Much ‘mind-reading’ seems rooted in sensorimotor representations and a developmentally primary 2nd person understanding of actions and the mental lives of others, which precisely breaks the assumed dichotomy between mind and behavior. I propose a Social Affordance model where parallel fronto-parietal sensorimotor circuits support representations not just of other people’s actions but of the overall social affordance space. It is a process that monitors concrete goals and teleological possibilities that the environment affords respectively oneself and other present agents. With this model I hypothesize that the complex spectrum of sensorimotor integrations are indeed essential not only to normal action choice calibration but also to social cognitive abilities, as the sensorimotor teleological representations let us relate to others and understand their action choices in a shared pragmatic and intentional context. (shrink)

Enactivist (Embodied, Embedded, etc.) approaches in cognitive science and philosophy of mind are sometimes, though not always, conjoined with an anti-representational commitment. A weaker anti-representational claim is that ascribing representational content to internal/sub-personal processes is not compulsory when giving psychological explanations. A stronger anti-representational claim is that the very idea of ascribing representational content to internal/sub-personal processes is a theoretical confusion. This paper criticises some of the arguments made by Hutto & Myin (2013, 2017) for the stronger anti-representational claim and (...) suggests that the default Enactivist view should be the weaker non-representational position. (shrink)

Standard semantic information processing models—information in; information processed; information out —lend themselves to standard models of the functioning of the brain in terms, e.g., of threshold-switch neurons connected via classical synapses. That is, in terms of sophisticated descendants of McCulloch and Pitts models. I argue that both the cognition and the brain sides of this framework are incorrect: cognition and thought are not constituted as forms of semantic information processing, and the brain does not function in terms of passive input (...) processing units organized as neural nets. An alternative framework is developed that models cognition and thought not in terms of semantic information processing, and, correspondingly, models brain functional processes also not in terms of semantic information processing. As alternative to such models: I outline a pragmatist oriented, interaction based, model of representation; derive from this model a fundamental framework of constraints on how the brain must function; show that such a framework is in fact found in the brain, and develop the outlines of a broader model of how mental processes can be realized within this alternative framework. Part I of this discussion focuses on some criticisms of standard modeling frameworks for representation and cognition, and outlines an alternative interactivist, pragmatist oriented, model. In part II, the focus is on the fact that the brain does not, in fact, function in accordance with standard passive input processing models—e.g., information processing models. Instead, there are multiple endogenously active processes at multiple spatial and temporal scales across multiple kinds of cells. A micro-functional model that accounts for, and even predicts, these multi-scale phenomena in generating emergent representation and cognition is outlined. That is, I argue that the interactivist model of representation outlined offers constraints on how the brain should function that are in fact empirically found, and, in reverse, that the multifarious details of brain functioning entail the pragmatist representational model—a very strong interrelationship. In the sequel paper, starting with part III, this model is extended to address macro-functioning in the CNS. In part IV, I offer a discussion of an approach to brain functioning that has some similarities with, as well as differences from, the model presented here: sometimes called the predictive brain approach. (shrink)

Robert Cummins [(1996) Representations, targets and attitudes, Cambridge, MA: Bradford/MIT, p. 1] has characterized the vexed problem of mental representation as "the topic in the philosophy of mind for some time now." This remark is something of an understatement. The same topic was central to the famous controversy between Nicolas Malebranche and Antoine Arnauld in the 17th century and remained central to the entire philosophical tradition of "ideas" in the writings of Locke, Berkeley, Hume, Reid and Kant. However, the scholarly, (...) exegetical literature has almost no overlap with that of contemporary cognitive science. I show that the recurrence of certain deep perplexities about the mind is a systematic and pervasive pattern arising not only throughout history, but also in a number of independent domains today such as debates over visual imagery, symbolic systems and others. Such historical and contemporary convergences suggest that the fundamental issues cannot arise essentially from the theoretical guise they take in any particular case. (shrink)

I examine two explanatory models of choking: the representationalist model and the anti-representationalist model. The representationalist model is based largely on Anderson's ACT model of procedural knowledge and is developed by Masters, Beilock and Carr. The antirepresentationalist model is based on dynamical models of cognition and embodied action and is developed by Dreyfus who employs an antirepresentational view of know-how. I identify the models' similarities and differences. I then suggest that Dreyfus is wrong to believe representational activity requires reflection and (...) attention. I also argue that the representationalist model of choking is preferable, since some embodied actions require appeals to representations, something not available to Dreyfus's anti-representational model. (shrink)

The paper develops a positive account of the representational capacity of cognitive systems: simple, associationist learning mechanisms and an architecture that supports bootstrapping are sufficient conditions for symbol tool use. In terms of the debates within the philosophy of mind, this paper offers a plausibility account of representation externalism, an alternative to the reductive, computational/representational models of intentionality that still play a leading role in the field. Although the central theme here is representation, methodologically this view complements embodied, enactivist approaches (...) to studying cognition. (shrink)

Selection theory requires multiple, distinct, simultaneously-actualized states. In cognition, each thought or cognitive state changes the 'selection pressure' against which the next is evaluated; they are not simultaneously selected amongst. Creative thought is more a matter of honing in a vague idea through redescribing successive iterations of it from different real or imagined perspectives; in other words, actualizing potential through exposure to different contexts. It has been proven that the mathematical description of contextual change of state introduces a non-Kolmogorovian probability (...) distribution, and a classical formalism such as selection theory cannot be used. This paper argues that creative thought evolves not through a Darwinian process, but a process of context-driven actualization of potential. (shrink)

The computational metaphor for mind is still the central guiding idea in cognitive science despite many insightful and well-founded rejections of it. There is good reason for its staying power: when we are at our cognitive best, we reason about our world with our concepts. But the challengers are right, I argue, in insisting that no reductive account of that capacity is forthcoming. Here I describe an externalist account that grounds representations in organism-level engagement with its environment, not in its (...) neural activity. (shrink)

This dissertation focuses on generative models in the Predictive Processing framework. It is commonly accepted that generative models are structural representations; i.e. physical particulars representing via structural similarity. Here, I argue this widespread account is wrong: when closely scrutinized, generative models appear to be non-representational control structures realizing an agent’s sensorimotor skills. The dissertation opens (Ch.1) introducing the Predictive Processing account of perception and action, and presenting some of its connectionist implementations, thereby clarifying the role generative models play in Predictive (...) Processing. Subsequently, I introduce the conceptual framework guiding the research (ch.2). I briefly elucidate the metaphysics of representations, emphasizing the specific functional role played by representational vehicles within the systems of which they are part. I close the first half of the dissertation (Ch.3) introducing the claim that generative models are structural representations, and defending it from intuitive but inconclusive objections. I then move to the second half of the dissertation, switching from exposition to criticism. First (Ch.4), I claim that the argument allegedly establishing that generative models are structural representations is flawed beyond repair, for it fails to establish generative models are structurally similar to their targets. I then consider alternative ways to establish that structural similarity, showing they all either fail or violate some other condition individuating structural representations. I further argue (Ch.5) that the claim that generative models are structural representations would not be warranted even if the desired structural similarity were established. For, even if generative models were to satisfy the relevant definition of structural representation, it would still be wrong to consider them as representations. This is because, as currently defined,structural representations fail to play the relevant functional role of representations, and thus cannot be rightfully identified as representations in the first place. This conclusion prompts a direct examination of generative models, to determine their nature (Ch.6). I thus analyze the simplest generative model I know of: a neural network functioning as a robotic “brain” and allowing different robotic creatures to swiftly and intelligently interact with their environments. I clarify how these networks allow the robots to acquire and exert the relevant sensorimotor abilities needed to solve the various cognitive tasks the robots are faced with, and then argue that neither the entire architecture nor any of its parts can possibly qualify as representational vehicles. In this way, the structures implementing generative models are revealed to be non-representational structures that instantiate an agent’s relevant sensorimotor skills. I show that my conclusion generalizes beyond the simple example I considered, arguing that adding computational ingredients to the architecture, or considering altogether different implementations of generative models, will in no way force a revision of my verdict. I further consider and allay a number of theoretical worries that it might generate, and then briefly conclude the dissertation. (shrink)

Digital computers play a special role in cognitive science—they may actually be instances of the phenomenon they are being used to model. This paper surveys some of the main issues involved in understanding the relationship between digital computers and cognition. It sketches the role of digital computers within orthodox computational cognitive science, in the light of a recently emerging alternative approach based around dynamical systems.

This article deals with the links between the enaction paradigm and artificial intelligence. Enaction is considered a metaphor for artificial intelligence, as a number of the notions which it deals with are deemed incompatible with the phenomenal field of the virtual. After explaining this stance, we shall review previous works regarding this issue in terms of artificial life and robotics. We shall focus on the lack of recognition of co-evolution at the heart of these approaches. We propose to explicitly integrate (...) the evolution of the environment into our approach in order to refine the ontogenesis of the artificial system, and to compare it with the enaction paradigm. The growing complexity of the ontogenetic mechanisms to be activated can therefore be compensated by an interactive guidance system emanating from the environment. This proposition does not however, resolve that of the relevance of the meaning created by the machine (sense-making). Such reflections lead us to integrate human interaction into this environment in order to construct relevant meaning in terms of participative artificial intelligence. This raises a number of questions with regards to setting up an enactive interaction. The article concludes by exploring a number of issues, thereby enabling us to associate current approaches with the principles of morphogenesis, guidance, the phenomenology of interactions and the use of minimal enactive interfaces in setting up experiments which will deal with the problem of artificial intelligence in a variety of enaction-based ways. (shrink)