Mettant en perspective historique le parallèle entre sciences cognitives et phénoménologie, nous revenons sur « l’architecture ouverte », qui pour Bernstein expliquait la richesse du comportement à la lumière de la physiologie cérébrale. Sa conception de la « dextérité » sera interprétée en rapport à la « dégénérescence » du système nerveux au sens d’Edelman, de façon à mettre au jour les « contraintes dynamiques » entre l’environnement, le corps humain sensori-moteur et le cerveau. Les deux concepts renvoient, en effet, (...) à « l’ouverture » des processus mentaux et comportementaux, dont la plasticité conditionne l’adaptation de l’humain à son environnement. (shrink)

As with biological systems, spoken languages are strikingly robust against perturbations. This paper shows that languages achieve robustness in a way that is highly similar to many biological systems. For example, speech sounds are encoded via multiple acoustically diverse, temporally distributed and functionally redundant cues, characteristics that bear similarities to what biologists call “degeneracy”. Speech is furthermore adequately characterized by neutrality, with many different tongue configurations leading to similar acoustic outputs, and different acoustic variants understood as the same by recipients. (...) This highlights the presence of a large neutral network of acoustic neighbors for every speech sound. Such neutrality ensures that a steady backdrop of variation can be maintained without impeding communication, assuring that there is “fodder” for subsequent evolution. Thus, studying linguistic robustness is not only important for understanding how linguistic systems maintain their functioning upon the background of noise, but also for understanding the preconditions for language evolution. (shrink)

To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness (...) in cognitive systems. We identify three mechanisms that enhance robustness in biological and engineered systems: system control, redundancy, and adaptability. After surveying the psychological literature for evidence of these mechanisms, we provide simulations illustrating how each contributes to robust cognition in a different psychological domain: psychomotor vigilance, semantic memory, and strategy selection. These simulations highlight features of a mathematical approach for quantifying robustness, and they provide concrete examples of mechanisms for robust cognition. (shrink)

Since cognitive neuroscience aims at giving an integrated account of mind and brain, its ontology should include both neural and cognitive entities and specify their relations. According to what we call the standard ontological framework of cognitive neuroscience, the aim of cognitive neuroscience should be to establish one-to-one mappings between neural and cognitive entities. Where such entities do not yet closely align, this can be achieved by reforming the cognitive ontology, the neural ontology, or both. In order to assess the (...) limits and the possibilities of the SOFCN, we will examine a paradigmatic case study: the concept of Broca’s area, which indicates an alleged mapping between the left inferofrontal gyrus and the production of language. We review evidence showing that such a mapping does not hold, thus calling into question either the status of Broca’s area or the validity of the SOFCN. We then propose some strategies for addressing the issue and suggest that it may be solved within the SOFCN by adopting both of the following strategies: first, more accurately defining the relevant neural structures and second, switching the focus of neural ontology from structures to events, individuated by a where conjoint with a how. (shrink)

Neural Darwinism (ND) is a large scale selectionist theory of brain development and function that has been hypothesized to relate to consciousness. According to ND, consciousness is entailed by reentrant interactions among neuronal populations in the thalamocortical system (the ‘dynamic core’). These interactions, which permit high-order discriminations among possible core states, confer selective advantages on organisms possessing them by linking current perceptual events to a past history of value-dependent learning. Here, we assess the consistency of ND with 16 widely recognized (...) properties of consciousness, both physiological (for example, consciousness is associated with widespread, relatively fast, low amplitude interactions in the thalamocortical system), and phenomenal (for example, consciousness involves the existence of a private flow of events available only to the experiencing subject). While no theory accounts fully for all of these properties at present, we find that ND and its recent extensions fare well. (shrink)

The dynamics of neural populations capture the laws of the mindThis paper focuses on the level of neural networks. Examining the case of recurrent neural networks, the paper argues that the dynamics of neural populations form a privileged level of explanation in cognitive science. According to Schöner, this level is privileged, because it enables cognitive scientists to discover the laws governing organisms’ cognition and behaviour.

Classical (Bayesian) probability (CP) theory has led to an influential research tradition for modeling cognitive processes. Cognitive scientists have been trained to work with CP principles for so long that it is hard even to imagine alternative ways to formalize probabilities. However, in physics, quantum probability (QP) theory has been the dominant probabilistic approach for nearly 100 years. Could QP theory provide us with any advantages in cognitive modeling as well? Note first that both CP and QP theory share the (...) fundamental assumption that it is possible to model cognition on the basis of formal, probabilistic principles. But why consider a QP approach? The answers are that (1) there are many well-established empirical findings (e.g., from the influential Tversky, Kahneman research tradition) that are hard to reconcile with CP principles; and (2) these same findings have natural and straightforward explanations with quantum principles. In QP theory, probabilistic assessment is often strongly context- and order-dependent, individual states can be superposition states (that are impossible to associate with specific values), and composite systems can be entangled (they cannot be decomposed into their subsystems). All these characteristics appear perplexing from a classical perspective. However, our thesis is that they provide a more accurate and powerful account of certain cognitive processes. We first introduce QP theory and illustrate its application with psychological examples. We then review empirical findings that motivate the use of quantum theory in cognitive theory, but also discuss ways in which QP and CP theories converge. Finally, we consider the implications of a QP theory approach to cognition for human rationality. (shrink)

We sketch a framework for building a unified science of cognition. This unification is achieved by showing how functional analyses of cognitive capacities can be integrated with the multilevel mechanistic explanations of neural systems. The core idea is that functional analyses are sketches of mechanisms , in which some structural aspects of a mechanistic explanation are omitted. Once the missing aspects are filled in, a functional analysis turns into a full-blown mechanistic explanation. By this process, functional analyses are seamlessly integrated (...) with multilevel mechanistic explanations. (shrink)

Functional localization is a central aim of cognitive neuroscience. But the nature and extent of functional localization in the human brain have been subjects of fierce theoretical debate since the 19th Century. In this essay, I first examine how concepts of functional localization have changed over time. I then analyze contemporary challenges to functional localization drawing from research on neural reuse, neural degeneracy, and the context-dependence of neural functions. I explore the consequences of these challenges for topics in philosophy of (...) science and philosophy of mind including localizationist versus anti-localizationist approaches to cognitive neuroscience, multiple realizability, reverse inference in functional neuroimaging, and the modularity of mind. (shrink)

Degeneracy is a poorly understood process, essential to natural selection. In the 18th and 19th centuries, the concept of degeneracy was commandeered by the colonial imagination. A rigid understanding of species, race, and culture grew to dominate the normative thinking that persisted well into the burgeoning new industrial age. A 20th-century reconfiguration of the concept by George Gamow highlighted a form of intraorganismic variation that is still underexplored. Degeneracy exists in a population of variants where structurally different components perform a (...) similar, but not necessarily identical, function with respect to context. The presence of degeneracy increases a system’s complexity and robustness against perturbations. The loss of a genetic component in biological systems, for example, can be compensated by redundant elements , or by degenerate elements . A historical survey of the use of the term “degeneracy” reveals how and why the processes it once designated, and the mechanisms it now represents, have largely escaped the purview of contemporary science. Despite confusion and general oversight, degeneracy has been characterized by select researchers at the molecular, genetic, and neuronal levels. The concept is a potent analytical tool to understand selection, variation, and transmission. (shrink)

Reverse Inference ( RI ) is an imaging-based type of inference from brain states to mental states, which has become highly widespread in neuroscience, most especially in neuroeconomics. Recent critical studies of RI may be taken to show that, if cautiously used, RI can help achieve research goals that may be difficult to achieve by way of behavior-based procedures alone. But can RI exceed the limits of these procedures and achieve research goals that are impossible for them to achieve alone? (...) By way of answering this question we show that a conception of the mind—type identity—under which the answer is in the positive, is untenable for reasons that strongly support another conception of the mind—functionalism—under which the answer must be in the negative. On this basis we then conclude that RI cannot exceed the limits of behavior-based procedures in cognitive psychology. (shrink)

Degeneracy is ubiquitous across biological systems where structurally different elements can yield a similar outcome. Degeneracy is of particular interest in neuroscience too. On the one hand, degeneracy confers robustness to the nervous system and facilitates evolvability: Different elements provide a backup plan for the system in response to any perturbation or disturbance. On the other, a difficulty in the treatment of some neurological disorders such as chronic pain is explained in light of different elements all of which contribute to (...) the pathological behavior of the system. Under these circumstances, targeting a specific element is ineffective because other elements can compensate for this modulation. Understanding degeneracy in the physiological context explains its beneficial role in the robustness of neural circuits. Likewise, understanding degeneracy in the pathological context opens new avenues of discovery to find more effective therapies. (shrink)

The web-model: A new model of explanation for neuroimaging studies What can neuroimaging tell us about the relation between our brain and our mind? A lot, or so I argue. But neuroscientists should update their model of explanation. Currently, many explanations are (implicitly) based on what I call the ‘mapping model’: a model of explanation which centers on mapping relations between cognition and the brain. I argue that these mappings give us very little information, and that instead, we should focus (...) on finding causal relations. If we take a difference-making approach to causation, we can find manipulation patterns between neural and cognitive phenomena and start constructing satisfying explanations in neuroimaging studies: explanations based on what I call the web-model of explanation. This model of explanation not only contrasts with the mapping model, but is also different from Craver’s constitutive mechanistic model of explanation (2007), which takes the constitutive relation to be the main explanatory relation. Taking the difference-making idea of the importance of manipulation and control seriously, means that sometimes, causal relations are preferred over constitutive relations. If we follow the web-model of explanation, we can do justice to the central role that causation should play in neuroscientific explanations. (shrink)

As neuroscience has gained an increased ability to enchant the general public, it has become more and more common to appeal to it as an authority on a wide variety of questions about how humans do and should act. This is especially apparent with the question of gender roles. The term ‘neurosexism’ has been coined to describe the phenomenon of using neuroscientific practices and results to promote sexist conclusions; its feminist response is called ‘neurofeminism’. Here, our aim is to survey (...) the phenomena of neurosexism and neurofeminism using a largely philosophical approach, incorporating concepts from the philosophy of mind, the philosophy of science, ethics, and feminist philosophy. First, we delineate how neuroscientific studies purporting to show sex brain differences may be prone to bias at a number of methodological levels – including the choice of categories to be studied, and the choice of tools for data gathering, analysis, and presentation. Then, we show how interpretations of such studies may wrongly assume the notion of ‘hard-wiring’. Furthermore, lack of attention to distinctions within philosophy of mind may result in a mistaken supposition that brain differences lead to mental and/or psychological and/or behavioral ones. It is not difficult to see how these forms of neurosexism, leading to claims of ‘hard-wired’ gender differences that map onto traditional and harmful gender stereotypes, raise ethical questions. We conclude by briefly considering one: are the harms caused by neurosexist studies and their interpretations outweighed by their potential benefits? (shrink)

Many empirically minded philosophers have used neuroscientific data to argue against the multiple realization of cognitive functions in existing biological organisms. I argue that neuroscientists themselves have proposed a biologically based concept of multiple realization as an alternative to interpreting empirical findings in terms of one‐to‐one structure‐function mappings. I introduce this concept and its associated research framework and also how some of the main neuroscience‐based arguments against multiple realization go wrong. *Received October 2009; revised December 2009. †To contact the author, (...) please write to: Department of Philosophy, 260 English‐Philosophy Building, University of Iowa, Iowa City, IA 52242; e‐mail: carrie‐[email protected]. (shrink)

Modules are widely held to play a central role in explaining mental development and in accounts of the mind generally. But there is much disagreement about what modules are, which shows that we do not adequately understand modularity. This paper outlines a Fodoresque approach to understanding one type of modularity. It suggests that we can distinguish modular from nonmodular cognition by reference to the kinds of process involved, and that modular cognition differs from nonmodular forms of cognition in being a (...) special kind of computational process. The paper concludes by considering implications for the role of modules in explaining mental development. (shrink)

In everyday situations, and particularly in some sport and working contexts, humans face an inherently unpredictable and uncertain environment. All sorts of unpredictable and unexpected things happen but typically people are able to skillfully adapt. In this paper, we address two key questions in cognitive science. First, how is an agent able to bring its previously learned skill to bear on a novel situation? Second, how can an agent be both sensitive to the particularity of a given situation, while remaining (...) flexibly poised for many other possibilities for action? We will argue that both the sensitivity to novel situations and the sensitivity to a multiplicity of action possibilities are enabled by the property of skilled agency that we will call metastable attunement. We characterize a skilled agent’s flexible interactions with a dynamically changing environment in terms of metastable dynamics in agent-environment systems. What we find in metastability is the realization of two competing tendencies: the tendency of the agent to express their intrinsic dynamics and the tendency to search for new possibilities. Metastably attuned agents are ready to engage with a multiplicity of affordances, allowing for a balance between stability and flexibility. On the one hand, agents are able to exploit affordances they are attuned to, while at the same time being ready to flexibly explore for other affordances. Metastable attunement allows agents to smoothly transition between these possible configurations so as to adapt their behaviour to what the particular situation requires. We go on to describe the role metastability plays in learning of new skills, and in skilful behaviour more generally. Finally, drawing upon work in art, architecture and sports science, we develop a number of perspectives on how to investigate metastable attunement in real life situations. (shrink)

Pothos & Busemeyer (P&B) argue how key concepts of quantum probability, for example, order/context, interference, superposition, and entanglement, can be used in cognitive modeling. Here, we suggest that these concepts can be extended to analyze neurophysiological measurements of cognitive tasks in humans, especially in functional neuroimaging investigations of large-scale brain networks.

This thesis investigates the representation of phonological language aspects in the healthy human brain, especially the contribution of the right hemisphere. Using functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS), it is demonstrated that the left and right supramarginal gyri are essential for phonological processing. It is also shown that the left as well as the right posterior inferior frontal gyri contribute to efficient phonological decisions. Finally, an fMRI study reveals a frontal network for phonological aspects of language (...) production. These results demonstrate a contribution of both hemispheres to efficient phonological aspects of language comprehension as well as production. Together, these results question the notion of a left-dominant network of brain regions for language processing. (shrink)

Decision-making has traditionally been modelled as a serial process, consisting of a number of distinct stages. The traditional account assumes that an agent first acquires the necessary perceptual evidence, by constructing a detailed inner repre- sentation of the environment, in order to deliberate over a set of possible options. Next, the agent considers her goals and beliefs, and subsequently commits to the best possible course of action. This process then repeats once the agent has learned from the consequences of her (...) actions and subsequently updated her beliefs. Under this interpretation, the agent’s body is considered merely as a means to report the decision, or to acquire the relevant goods. However, embodied cognition argues that an agent’s body should be understood as a proper part of the decision-making pro- cess. Accepting this principle challenges a number of commonly held beliefs in the cognitive sciences, but may lead to a more unified account of decision-making. This thesis explores an embodied account of decision-making using a recent frame- work known as predictive processing. This framework has been proposed by some as a functional description of neural activity. However, if it is approached from an embodied perspective, it can also offer a novel account of decision-making that ex- tends the scope of our explanatory considerations out beyond the brain and the body. We explore work in the cognitive sciences that supports this view, and argue that decision theory can benefit from adopting an embodied and predictive perspective. (shrink)