Working memory limits are best defined in terms of the complexity of the relations that can be processed in parallel. Complexity is defined as the number of related dimensions or sources of variation. A unary relation has one argument and one source of variation; its argument can be instantiated in only one way at a time. A binary relation has two arguments, two sources of variation, and two instantiations, and so on. Dimensionality is related to the number of chunks, because (...) both attributes on dimensions and chunks are independent units of information of arbitrary size. Studies of working memory limits suggest that there is a soft limit corresponding to the parallel processing of one quaternary relation. More complex concepts are processed by or In segmentation, tasks are broken into components that do not exceed processing capacity and can be processed serially. In conceptual chunking, representations are to reduce their dimensionality and hence their processing load, but at the cost of making some relational information inaccessible. Neural net models of relational representations show that relations with more arguments have a higher computational cost that coincides with experimental findings on higher processing loads in humans. Relational complexity is related to processing load in reasoning and sentence comprehension and can distinguish between the capacities of higher species. The complexity of relations processed by children increases with age. Implications for neural net models and theories of cognition and cognitive development are discussed. (shrink)

This paper explores the interaction between implicit and explicit processes during skill learning, in terms of top-down learning (that is, learning that goes from explicit to implicit knowledge) versus bottom-up learning (that is, learning that goes from implicit to explicit knowledge). Instead of studying each type of knowledge (implicit or explicit) in isolation, we stress the interaction between the two types, especially in terms of one type giving rise to the other, and its effects on learning. The work presents an (...) integrated model of skill learning that takes into account both implicit and explicit processes and both top-down and bottom-up learning. We examine and simulate human data in the Tower of Hanoi task. The paper shows how the quantitative data in this task may be captured using either top-down or bottom-up approaches, although top-down learning is a more apt explanation of the human data currently available. These results illustrate the two different directions of learning (top-down versus bottom-up), and thereby provide a new perspective on skill learning. Ó 2003 Elsevier B.V. All rights reserved. (shrink)

We focus on Karmiloff-Smith's Representational redescription model, arguing that it poses some problems concerning the architecture of a redescribing system. To discuss the topic, we consider the implicit/explicit dichotomy and the relations between natur al language and the language of thought. We argue that the model regards how knowledge is employed rather than how it is represented in the system.

The implicit-explicit distinction is applied to knowledge representations. Knowledge is taken to be an attitude towards a proposition which is true. The proposition itself predicates a property to some entity. A number of ways in which knowledge can be implicit or explicit emerge. If a higher aspect is known explicitly then each lower one must also be known explicitly. This partial hierarchy reduces the number of ways in which knowledge can be explicit. In the most important type of implicit knowledge, (...) representations merely reflect the property of objects or events without predicating them of any particular entity The dearest cases of explicit knowledge of a fact are representations of one's own attitude of knowing that fact. These distinctions are discussed in their relationship to similar distinctions such as procedural-declarative, conscious unconscious, verbalizable-nonverbalizable, direct-indirect tests, and automatic voluntary control. This is followed by an outline of how these distinctions can be used to integrate and relate the often divergent uses of the implicit-explicit distinction in different research areas. We illustrate this for visual perception, memory, cognitive development, and artificial grammar learning. (shrink)

Online collection of papers by Devitt, Dretske, Guarino, Hochberg, Jackson, Petitot, Searle, Tye, Varzi and other leading thinkers on philosophy and the foundations of cognitive Science. Topics dealt with include: Wittgenstein and Cognitive Science, Content and Object, Logic and Foundations, Language and Linguistics, and Ontology and Mereology.

While the study of implicit learning is nothing new, the field as a whole has come to embody — over the last decade or so — ongoing questioning about three of the most fundamental debates in the cognitive sciences: The nature of consciousness, the nature of mental representation (in particular the difficult issue of abstraction), and the role of experience in shaping the cognitive system. Our main goal in this chapter is to offer a framework that attempts to integrate current (...) thinking about these three issues in a way that specifically links consciousness with adaptation and learning. Our assumptions about this relationship are rooted in further assumptions about the nature of processing and of representation in cognitive systems. When considered together, we believe that these assumptions offer a new perspective on the relationships between conscious and unconscious processing and on the function of consciousness in cognitive systems. (shrink)

When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many of them have been doing recently, there are two fundamentally distinct approaches available. Either consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or it is to be explained in terms of the computational processes defined over these vehicles. We call versions of these two approaches _vehicle_ and _process_ theories of consciousness, respectively. However, while there may be space (...) for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because of the influence exerted, on the one hand, by a large body of research which purports to show that the explicit representation of information in the brain and conscious experience are _dissociable_, and on the other, by the _classical_ computational theory of mind – the theory that takes human cognition to be a species of symbol manipulation. But two recent developments in cognitive science combine to suggest that a reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the experimental methodologies employed in the dissociation studies – so critical, in fact, it’s no longer reasonable to assume that the dissociability of conscious experience and explicit representation has been adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in cognitive science as it once was. It now has a lively competitor in the form of _connectionism; _and connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory of consciousness. In this paper we develop and defend this connectionist vehicle theory of consciousness. It takes the form of the following simple empirical hypothesis: _phenomenal experience consists in the explicit_ _representation of information in neurally realized PDP networks_.. (shrink)

This paper discusses some requirements on a folk-psychological, computational account of concepts. Although most psychological views take the folk-psychological stance that concept-possession requires capacities of both representation and classification, such views lack a philosophical context. In contrast, philosophically motivated views stress one of these capacities at the expense of the other. This paper seeks to provide some philosophical motivation for the (folk-) psychological stance. Philosophical and psychological constraints on a computational level account provide the context for evaluating two theses. The (...) first, the Classificatory View, is that concept-possession is constituted by the ability to classify states of the world. I argue, against this view, that to be able to classify, a thinker must also be able to represent the world. The second thesis, the Representational View, is that to possess a concept is constituted by the ability to represent the world. I argue that ascribing this ability is incoherent without ascribing an ability to classify. Hence, a detailed computational specification of concept-possession suggests that the folk-psychological stance is accurate. Philosophical views of concepts, (e.g. Fodor, 1987), adhering to one of the strong theses, whilst adverting to folk-psychological motivations, are thus both insufficiently complex and incoherent. (shrink)

The initial cause of Cotard delusion is pervasive dyshomeostasis (dysregulation of basic bodily function).This explanation draws on interoceptive active inference account of self‐representation. In this framework, the self is an hierarchical predictive model made by the brain to facilitate homeostatic regulation. The account I provide is an alternative to two factor accounts of the Cotard delusion that treat depersonalisation experience as the first factor in genesis of the Cotard delusion. I argue that depersonalisation experience and the Cotard delusion are produced (...) by different breakdowns in the process of self‐modelling. (shrink)

Interdisciplinary understanding requires integration of insights from different perspectives, yet it appears questionable whether disciplinary experts are well prepared for this. Indeed, psychological and cognitive scientific studies suggest that expertise can be disadvantageous because experts are often more biased than non-experts, for example, or fixed on certain approaches, and less flexible in novel situations or situations outside their domain of expertise. An explanation is that experts’ conscious and unconscious cognition and behavior depend upon their learning and acquisition of a set (...) of mental representations or knowledge structures. Compared to beginners in a field, experts have assembled a much larger set of representations that are also more complex, facilitating fast and adequate perception in responding to relevant situations. This article argues how metacognition should be employed in order to mitigate such disadvantages of expertise: By metacognitively monitoring and regulating their own cognitive processes and representations, experts can prepare themselves for interdisciplinary understanding. Interdisciplinary collaboration is further facilitated by team metacognition about the team, tasks, process, goals, and representations developed in the team. Drawing attention to the need for metacognition, the article explains how philosophical reflection on the assumptions involved in different disciplinary perspectives must also be considered in a process complementary to metacognition and not completely overlapping with it. (Disciplinary assumptions are here understood as determining and constraining how the complex mental representations of experts are chunked and structured.) The article concludes with a brief reflection on how the process of Reflective Equilibrium should be added to the processes of metacognition and philosophical reflection in order for experts involved in interdisciplinary collaboration to reach a justifiable and coherent form of interdisciplinary integration. An Appendix of “Prompts or Questions for Metacognition” that can elicit metacognitive knowledge, monitoring, or regulation in individuals or teams is included at the end of the article. (shrink)

In Section 1, I introduce three views that explain human cognitive development from different standpoints: Marcus’s neo-nativism, standard neuroconstructivism, and neo-neuroconstructivism. In Section 2, I assess Marcus’s attempt to reconcile nativism with developmental flexibility. In Section 3, I argue that in structurally reconfiguring nativism, Marcus ends up transforming it into an unrecognizable form, and I claim that his view could be accommodated within the more general framework provided by standard neuroconstructivism. In Section 4, I focus on recent empirical findings in (...) neuropsychology and cultural/social neuroscience, and propose a friendly revision to standard neuroconstructivism, thus developing the neo-neuroconstructivist view. I conclude the article in Section 5 by analysing the implications of the results discussed in Section 4 for both neo-nativism and standard neuroconstructivism. 1 Introduction2 Marcus’s Neo-nativism3 Is Marcus’s Neo-nativism Really a Form of Nativism?4 Neo-neuroconstructivism and Dynamic Enskillment5 Conclusion. (shrink)

In this article, we provide a broad overview of the interplay among cognition, belief systems, and institutions, and how they affect economic performance. We argue that a deeper understanding of institutions’ emergence, their working properties, and their effect on economic and political outcomes should begin from an analysis of cognitive processes. We explore the nature of individual and collective learning, stressing that the issue is not whether agents are perfectly or boundedly rational, but rather how human beings actually reason and (...) choose, individually and in collective settings. We then tie the processes of learning to institutional analysis, providing arguments in favor of what can be characterized as “cognitive institutionalism.” Besides, we show that a full treatment of the phenomenon of path dependence should start at the cognitive level, proceed at the institutional level, and culminate at the economic level. (shrink)

How can we explain the intentional nature of an expert’s actions, performed without immediate and conscious control, relying instead on automatic cognitive processes? How can we account for the differences and similarities with a novice’s performance of the same actions? Can a naturalist explanation of intentional expert action be in line with a philosophical concept of intentional action? Answering these and related questions in a positive sense, this dissertation develops a three-step argument. Part I considers different methods of explanations in (...) cognitive neuroscience (Bennett & Hacker’s philosophical, conceptual analysis; Marr’s three levels of explanation; Neural Correlates of Consciousness research; mechanistic explanation), defending ‘mechanistic explanation’ as a method that provides the necessary tools for integrating interdisciplinary insights into human action. Furthermore, a dynamic, explanatory mechanism allows the assessment of the impact of learning and development on expert action in a valuable way that other methods don’t. Part II continues by scrutinizing several cognitive neuroscientific theories of learning and development (neuroconstructivism; dual-processing theories; simulation theory; extended mind/cognition hypothesis), arguing for the complex interactions between different types of processing and different action representations involved in expert action performances. Moreover, according to our discussion of a particular ‘simulation theory’ these interactions can be influenced in several ways with the use of language, allowing an agent to configure a specific action representation for performance at a later stage. The results of Parts I and II are then applied in Part III to a parallel discussion of philosophical analyses of intentional action (discussing i.a. Frankfurt, Bratman, Pacherie and Ricoeur) and cognitive neuroscientific insights in it. Both approaches are found to converge in emphasizing the importance for an expert to develop stable patterns of actions that comply maximally not only with his intentions, but also with his motor expertise and with situational conditions. Consequently, his actions – automatic, or not – rely on this ‘sculpted space of actions’. (shrink)

What is creativity? One new idea may be creative, whereas another is merely new: What's the difference? And how is creativity possible? These questions about human creativity can be answered, at least in outline, using computational concepts. There are two broad types of creativity, improbabilist and impossibilist. Improbabilist creativity involves novel combinations of familiar ideas. A deeper type involves METCS: the mapping, exploration, and transformation of conceptual spaces. It is impossibilist, in that ideas may be generated which – with respect (...) to the particular conceptual space concerned – could not have been generated before. The more clearly conceptual spaces can be defined, the better we can identify creative ideas. Defining conceptual spaces is done by musicologists, literary critics, and historians of art and science. Humanist studies, rich in intuitive subtleties, can be complemented by the comparative rigour of a computational approach. Computational modelling can help to define a space, and to show how it may be mapped, explored, and transformed. Impossibilist creativity can be thought of in “classical” Al terms, whereas connectionism illuminates improbabilist creativity. Most Al models of creativity can only explore spaces, not transform them, because they have no self-reflexive maps enabling them to change their own rules. A few, however, can do so. A scientific understanding of creativity does not destroy our wonder at it, nor does it make creative ideas predictable. Demystification does not imply dehumanization. (shrink)

Beyond modularityattempts a synthesis of Fodor's anticonstructivist nativism and Piaget's antinativist constructivism. Contra Fodor, I argue that: (1) the study of cognitive development is essential to cognitive science, (2) the module/central processing dichotomy is too rigid, and (3) the mind does not begin with prespecified modules; rather, development involves a gradual process of “modularization.” Contra Piaget, I argue that: (1) development rarely involves stagelike domain-general change and (2) domainspecific predispositions give development a small but significant kickstart by focusing the infant's (...) attention on proprietary inputs. Development does not stop at efficient learning. A fundamental aspect of human development (“representational redescription”) is the hypothesized process by which information that isina cognitive system becomes progressively explicit knowledgetothat system. Development thus involves two complementary processes of progressive modularization and progressive “explicitation.” Empirical findings on the child as linguist, physicist, mathematician, psychologist, and notator are discussed in support of the theoretical framework. Each chapter concentrates first on the initial state of the infant mind/brain and on subsequent domain-specific learning in infancy and early childhood. It then goes on to explore data on older children's problem solving and theory building, with particular focus on evolving cognitive flexibility. Emphasis is placed throughout on the status of representations underlying different capacities and on the multiple levels at which knowledge is stored and accessible. Finally, consideration is given to the need for more formal developmental models, and a comparison is made between representational redescription and connectionist simulations of development. In conclusion, I consider what is special about human cognition by speculating on the status of representations underlying the structure of behavior in other species. (shrink)

Work on analogy has been done from a number of disciplinary perspectives throughout the history of Western thought. This work is a multidisciplinary guide to theorizing about analogy. It contains 1,406 references, primarily to journal articles and monographs, and primarily to English language material. classical through to contemporary sources are included. The work is classified into eight different sections (with a number of subsections). A brief introduction to each section is provided. Keywords and key expressions of importance to research on (...) analogy are discussed in the introductory material. Electronic resources for conducting research on analogy are listed as well. (shrink)

This bold and brilliant book asks the ultimate question of the life sciences: How did the human mind acquire its incomparable power? In seeking the answer, Merlin Donald traces the evolution of human culture and cognition from primitive apes to the era of artificial intelligence, and presents an original theory of how the human mind evolved from its presymbolic form. In the emergence of modern human culture, Donald proposes, there were three radical transitions. During the first, our bipedal but still (...) apelike ancestors acquired "mimetic" skill—the ability to represent knowledge through voluntary motor acts—which made Homo erectus successful for over a million years. The second transition—to "mythic" culture —coincided with the development of spoken language. Speech allowed the large-brained Homo sapiens to evolve a complex preliterate culture that survives in many parts of the world today. In the third transition, when humans constructed elaborate symbolic systems ranging from cuneiforms, hieroglyphics, and ideograms to alphabetic languages and mathematics, human biological memory became an inadequate vehicle for storing and processing our collective knowledge. The modern mind is thus a hybrid structure built from vestiges of earlier biological stages as well as new external symbolic memory devices that have radically altered its organization. According to Donald, we are symbol-using creatures, more complex than any that went before us, and we may have not yet witnessed the final modular arrangement of the human mind. (shrink)

A crucial socio-political challenge for our age is how to rede!ne or extend group membership in such a way that it adequately responds to phenomena related to globalization like the prevalence of migration, the transformation of family and social networks, and changes in the position of the nation state. Two centuries ago Immanuel Kant assumed that international connectedness between humans would inevitably lead to the realization of world citizen rights. Nonetheless, globalization does not just foster cosmopolitanism but simultaneously yields the (...) development of new group boundaries. Group membership is indeed a fundamental issue in political processes, for: “the primary good that we distribute to one another is membership in some human community” – it is within the political community that power is being shared and, if possible, held back from non-members. In sum, it is appropriate to consider group membership a fundamental ingredient of politics and political theory. How group boundaries are drawn is then of only secondary importance. Indeed, Schmitt famously declared that “[e]very religious, moral, economic, ethical, or other antithesis transforms into a political one if it is suffciently strong to group human beings e#ectively according to friend and enemy”. Even though Schmitt’s idea of politics as being constituted by such antithetical groupings is debatable, it is plausible to consider politics among other things as a way of handling intergroup di#erences. Obviously, some of the group-constituting factors are more easily discernable from one’s appearance than others, like race, ethnicity, or gender. As a result, factors like skin color or sexual orientation sometimes carry much political weight even though individuals would rather con!ne these to their private lives and individual identity. Given the potential tension between the political reality of particular groupmembership defnitions and the – individual and political – struggles against those definitions and corresponding attitudes, citizenship and civic behavior becomes a complex issue. As Kymlicka points out, it implies for citizens an additional obligation to non-discrimination regarding those groups: “[t]his extension of non-discrimination from government to civil society is not just a shift in the scale of liberal norms, it also involves a radical extension in the obligations of liberal citizenship”. Unfortunately, empirical research suggests that political intolerance towards other groups “may be the more natural and ‘easy’ position to hold”. Indeed, since development of a virtue of civility or decency regarding other groups is not easy, as it often runs against deeply engrained stereotypes and prejudices, political care for matters like education is justified. Separate schools, for example, may erode children’s motivation to act as citizens, erode their capacity for it and!nally diminish their opportunities to experience transcending their particular group membership and behave as decent citizens. This chapter outlines a possible explanation for such consequences. That explanation will be found to be interdisciplinary in nature, combining insights from political theory and cognitive neuroscience. In doing so, it does not focus on collective action, even though that is a usual focus for political studies. For example, results pertaining to collective political action have demonstrated that the relation between attitudes and overt voting behavior or political participation is not as direct and strong as was hoped for. Several conditions, including the individual’s experiences, self-interest, and relevant social norms, turned out to interfere in the link between his or her attitude and behavior. Important as collective action is, this chapter is concerned with direct interaction between agents and the in$uence of group membership on such interaction – in particular joint action. Although politics does include many forms of action that require no such physical interaction, such physical interaction between individuals remains fundamental to politics – this is the reason why separate schooling may eventually undermine the citizenship of its isolated pupils. This chapter will focus on joint action, de!ned as: “any form of social interaction whereby two or more individuals coordinate their actions in space and time to bring about a change in the environment”. Cognitive neuroscienti!c evidence demonstrates that for such joint action to succeed, the agents have to integrate the actions and expected actions of the other person in their own action plans at several levels of speci!city. Although neuroscienti!c research is necessarily limited to simple forms of action, this concurs with a philosophical analysis of joint action, which I will discuss below. (shrink)

Dienes and Seth (2010) conclude that confidence ratings and post-decision wagering are two comparable and recommendable measures of conscious experience. In a recently submitted paper, we have however found that both methods are problematic and seem less suited to measure consciousness than a direct introspective measure. Here, we discuss the methodology and conclusions put forward by Dienes and Seth, and why we think the two experiments end up with so different recommendations.

According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation (...) and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individual's existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction. Key Words: concept learning; conceptual development; features; perceptual learning; stimulus encoding. (shrink)

Research in computational cognitive modeling investigates the nature of cognition through developing process-based understanding by specifying computational models of mechanisms (including representations) and processes. In this enterprise, a cognitive architecture is a domaingeneric computational cognitive model that may be used for a broad, multiple-level, multipledomain analysis of behavior. It embodies generic descriptions of cognition in computer algorithms and programs. Developing cognitive architectures is a difficult but important task. In this article, discussions of issues and challenges in developing cognitive architectures will (...) be undertaken, and an example cognitive architecture (CLARION) will be described. (shrink)

Sciences able to identify appropriate analytical units for their domain, their natural kinds, have tended to be more progressive. In the biological sciences, evolutionary natural kinds are adaptations that can be identified by their common history of selection for some function. Human brains are the product of an evolutionary history of selection for component systems which produced behaviours that gave adaptive advantage to their hosts. These structures, behaviour production systems, are the natural kinds that psychology seeks. We argue these can (...) be identified deductively by classing behaviour first according to its level of behavioural control. Early animals in our lineage used only reactive production, Vertebrates evolved motivation, and later Primates developed executive control. Behaviour can also be classified by the type of evolutionary benefit it bestows: it can deliver either immediate benefits (food, gametes), improvements in the individual’s position with respect to the world (resource access, social status), or improvements in the ability to secure future benefits (knowledge, skill). Combining history and function implies the existence of seven types of behaviour production systems in human brains responsible for reflexive, instinctual, exploratory, driven, emotional, playful and planned behaviour. Discovering scientifically valid categories of behaviour can provide a fundamental taxonomy and common language for understanding, predicting and changing behaviour, and a way of discovering the organs in the brain––its natural kinds––that are responsible for behaviour. (shrink)

Advocates of dynamical systems theory (DST) sometimes employ revolutionary rhetoric. In an attempt to clarify how DST models differ from others in cognitive science, I focus on two issues raised by DST: the role for representations in mental models and the conception of explanation invoked. Two features of representations are their role in standing-in for features external to the system and their format. DST advocates sometimes claim to have repudiated the need for stand-ins in DST models, but I argue that (...) they are mistaken. Nonetheless, DST does offer new ideas as to the format of representations employed in cognitive systems. With respect to explanation, I argue that some DST models are better seen as conforming to the covering-law conception of explanation than to the mechanistic conception of explanation implicit in most cognitive science research. But even here, I argue, DST models are a valuable complement to more mechanistic cognitive explanations. (shrink)

The constructivist notion that features are purely functional is incompatible with the classical computational metaphor of mind. I suggest that the discontent expressed by Schyns, Goldstone and Thibaut about fixed-features theories of categorization reflects the growing impact of connectionism, and show how their perspective is similar to recent research on implicit learning, consciousness, and development. A hard problem remains, however: How to bridge the gap between subsymbolic and symbolic cognition.

Figure 1: A pr ototyp ical exa mple of a three-layer feed forward network, used by Plunkett and M archm an (1 991 ) to simulate learning the past-tense of En glish verbs. The inpu t units encode representations of the three phonemes of the present tense of the artificial words used in this simulation. Th e netwo rk is trained to produce a representation of the phonemes employed in the past tense form and the suffix (/d/, /ed/, or /t/) (...) used on regular verbs. To run the network, each input unit is assigned an activation value o f 0 or 1 , dep ending on whethe r the featu re is present or not. Eac h input unit is conne cted to each of the 30 hidden units by a we ighted conn ection and p rovid es an inp ut to each hidden unit equal to the product of the input unit's activation and the weight. Each hidd en unit's activation is then determined by summing ov er the va lues co ming fro m each inp ut unit to deter mine a netinput, and then applying a non-linear function (e.g., the logistic function 1/(1+e^netinput). Th is whole proced ure is. (shrink)

In this article it is defended that the notion known as “The mark of the cognitive” is better characterized as a process that performs the function of generating intelligent behavior, in a flexible and adaptive way, capable of adapting to circumstances, given it is a context sensitive process. For that, some relevant definitions of cognition are examined. In the end, it is pointed out that the definition of the mark of cognition as a context-sensitive process takes into account several factors (...) that were added as constitutive parts of cognitive phenomena over the years, especially the 4E Cognition, which cannot be satisfactorily accommodated alongside the preceding notions of cognition. Accordingly, this essay shouldn’t be mistaken merely as an exercise in intellectual history, but as a brief and accessible attempt to advance the debate. (shrink)

Language has long been a problem‐case for subsymbolic theories of mind. The reason for this is obvious: Language seems essentially symbolic. However, recent work has developed a potential solution to this problem, arguing that linguistic symbols are public objects which augment a fundamentally subsymbolic mind, rather than components of cognitive symbol‐processing. I shall argue that this strategy cannot work, on the grounds that human language acquisition consists in projecting linguistic structure onto environmental entities, rather than extracting this structure from them.

This book proposes a theory of human cognitive evolution, drawing from paleontology, linguistics, anthropology, cognitive science, and especially neuropsychology. The properties of humankind's brain, culture, and cognition have coevolved in a tight iterative loop; the main event in human evolution has occurred at the cognitive level, however, mediating change at the anatomical and cultural levels. During the past two million years humans have passed through three major cognitive transitions, each of which has left the human mind with a new way (...) of representing reality and a new form of culture. Modern humans consequently have three systems of memory representation that were not available to our closest primate relatives: mimetic skill, language, and external symbols. These three systems are supported by new types of “hard” storage devices, two of which (mimetic and linguistic) are biological, one technological. Full symbolic literacy consists of a complex of skills for interacting with the external memory system. The independence of these three uniquely human ways of representing knowledge is suggested in the way the mind breaks down after brain injury and confirmed by various other lines of evidence. Each of the three systems is based on aninventivecapacity, and the products of those capacities – such as languages, symbols, gestures, social rituals, and images – continue to be invented and vetted in the social arena. Cognitive evolution is not yet complete: the externalization of memory has altered the actual memory architecture within which humans think. This is changing the role of biological memory and the way in which the human brain deploys its resources; it is also changing the form of modern culture. (shrink)

This article provides a broad overview of the interplay among cognition, belief systems and institutions, fleshing out a position best characterized as 'cognitive institutionalism'. We argue that a deeper understanding of institutions, emergence, their working properties and their effect on economic performance should start with the analysis of cognitive processes. Exploring the nature of individual and collective learning the article suggests that the issue is not whether agents are perfectly or boundedly rational, but rather how human beings actually reason and (...) choose. We also show how a full treatment of the phenomenon of path dependence should look like; there is a path dependence at the cognitive level, at the institutional level and at the economic level and there are links among them. (shrink)