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)

Consciousness remains a mystery—“a phenomenon that people do not know how to think about—yet” (Dennett, , p. 21). Here, I consider how the connectionist perspective on information processing may help us progress toward the goal of understanding the computational principles through which conscious and unconscious processing differ. I begin by delineating the conceptual challenges associated with classical approaches to cognition insofar as understanding unconscious information processing is concerned, and to highlight several contrasting computational principles that are constitutive of the connectionist (...) approach. This leads me to suggest that conscious and unconscious processing are fundamentally connected, that is, rooted in the very same computational principles. I further develop a perspective according to which the brain continuously and unconsciously learns to redescribe its own activity itself based on constant interaction with itself, with the world, and with other minds. The outcome of such interactions is the emergence of internal models that are metacognitive in nature and that function so as to make it possible for an agent to develop a (limited, implicit, practical) understanding of itself. In this light, plasticity and learning are constitutive of what makes us conscious, for it is in virtue of our own experiences with ourselves and with other people that our mental life acquires its subjective character. The connectionist framework continues to be uniquely positioned in the Cognitive Sciences to address the challenge of identifying what one could call the “computational correlates of consciousness” (Mathis & Mozer, ) because it makes it possible to focus on the mechanisms through which information processing takes place. (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)

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)

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)

Are morphological patterns learned in the form of rules? Some models deny this, attributing all morphology to analogical mechanisms. The dual mechanism model (Pinker, S., & Prince, A. (1998). On language and connectionism: analysis of a parallel distributed processing model of language acquisition. Cognition, 28, 73-193) posits that speakers do internalize rules, but that these rules are few and cover only regular processes; the remaining patterns are attributed to analogy. This article advocates a third approach, which uses multiple stochastic rules (...) and no analogy. We propose a model that employs inductive learning to discover multiple rules, and assigns them confidence scores based on their performance in the lexicon. Our model is supported over the two alternatives by new "wug test" data on English past tenses, which show that participant ratings of novel pasts depend on the phonological shape of the stem, both for irregulars and, surprisingly, also for regulars. The latter observation cannot be explained under the dual mechanism approach, which derives all regulars with a single rule. To evaluate the alternative hypothesis that all morphology is analogical, we implemented a purely analogical model, which evaluates novel pasts based solely on their similarity to existing verbs. Tested against experimental data, this analogical model also failed in key respects: it could not locate patterns that require abstract structural characterizations, and it favored implausible responses based on single, highly similar exemplars. We conclude that speakers extend morphological patterns based on abstract structural properties, of a kind appropriately described with rules. (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)

The contextual mind is found attested in various usages of the term complement, in the background of Kant. The difficulties of Kant's intuitionism are taken up through Quine, but referential opacity is resolved as semantic presence in lived context. A further critique of rationalist linguistics is developed from Jakobson, showing generic functions in thought supporting abstraction, binding and thereby semantic categories. Thus Bolzano's influential philosophy of mathematics and science gives way to a critical view of the ancient heritage acknowledged by (...) Plato. (shrink)

This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data (...) can be accounted for by the approach. A computational model, CLARION, is then used to simulate a range of quantitative data. The results demonstrate the plausibility of the model, which provides a new perspective on skill learning. (PsycINFO Database Record (c) 2012 APA, all rights reserved). (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.

This paper describes how meta-cognitive processes (i.e., the self monitoring and regulating of cognitive processes) may be captured within a cognitive architecture Clarion. Some currently popular cognitive architectures lack sufficiently complex built-in meta-cognitive mechanisms. However, a sufficiently complex meta-cognitive mechanism is important, in that it is an essential part of cognition and without it, human cognition may not function properly. We contend that such a meta-cognitive mechanism should be an integral part of a cognitive architecture. Thus such a mechanism has (...) been developed within the Clarion cognitive architecture. The paper demonstrates how human data of two meta-cognitive experiments are simulated using Clarion. The simulations show that the meta-cognitive processes represented by the experimental data (and beyond) can be adequately captured within the Clarion framework. (shrink)

This paper explicates the interaction between the implicit and explicit learning processes in skill acquisition, contrary to the common tendency in the literature of studying each type of learning in isolation. It highlights the interaction between the two types of processes and its various effects on learning, including the synergy effect. This work advocates an integrated model of skill learning that takes into account both implicit and explicit processes; moreover, it embodies a bottom-up approach (first learning implicit knowledge and then (...) explicit knowledge on its basis) towards skill learning. The paper shows that this approach accounts for various effects in the process control task data, in addition to accounting for other data reported elsewhere. (shrink)

This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist (...) and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects. (shrink)

We argue that existing learning algorithms are often poorly equipped to solve problems involving a certain type of important and widespread regularity that we call “type-2 regularity.” The solution in these cases is to trade achieved representation against computational search. We investigate several ways in which such a trade-off may be pursued including simple incremental learning, modular connectionism, and the developmental hypothesis of “representational redescription.”.

Two standard epistemological accounts are conflated in Dienes & Perner's account of knowledge, and this conflation requires the rejection of their four conditions of knowledge. Because their four metarepresentations applied to the explicit-implicit distinction are paired with these conditions, it follows by modus tollens that if the latter are inadequate, then so are the former. Quite simply, their account misses the link between true reasoning and knowledge.

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)

O'Brien & Opie's (O&O's) theory demands a view of unconscious processing that is incompatible with virtually all current PDP models of neural activity. Relative to the alternatives, the theory is closer to an AI than a parallel distributed processing (PDP) perspective, and its treatment of phenomenology is ad hoc. It raises at least one important question: Could features of network relaxation be the “switch” that turns an unconscious into a conscious network?

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)

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)

_role, especially in learning, and through devising hybrid neural network models that (in a qualitative manner) approxi-_ _mate characteristics of human consciousness. In doing so, the paper examines explicit and implicit learning in a variety_ _of psychological experiments and delineates the conscious/unconscious distinction in terms of the two types of learning_ _and their respective products. The distinctions are captured in a two-level action-based model C_larion_. Some funda-_ _mental theoretical issues are also clari?ed with the help of the model. Comparisons with (...) existing models of conscious-_. (shrink)

Electronic Mail: [email protected] Abstract Stability of activation, while it may be necessary for information to become available to consciousness, is not sufficient to produce phenomenal experience. We suggest that consciousness involves access to information and that access makes information symbolic. From this perspective, implicit representations exist, and are best thought of as sub-symbolic. Crucially, such representations can be causally efficacious in the absence of consciousness.

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)

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 paper considers the problems involved in getting neural networks to learn about highly structured task domains. A central problem concerns the tendency of networks to learn only a set of shallow (non-generalizable) representations for the task, i.e., to miss the deep organizing features of the domain. Various solutions are examined, including task specific network configuration and incremental learning. The latter strategy is the more attractive, since it holds out the promise of a task-independent solution to the problem. Once we (...) see exactly how the solution works, however, it becomes clear that it is limited to a special class of cases in which (1) statistically driven undersampling is (luckily) equivalent to task decomposition, and (2) the dangers of unlearning are somehow being minimized. The technique is suggestive nonetheless, for a variety of developmental factors may yield the functional equivalent of both statistical AND informed undersampling in early learning. (shrink)

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)

The dispute between individualism and anti-individualism is about the individuation of psychological states, and individualism, on some accounts, is committed to the claim that psychological subjects together with their environments do not constitute integrated computational systems. Hence on this view the computational states that explain psychological states in computational accounts of mind will not involve the subject''s natural and social environment. Moreover, the explanation of a system''s interaction with the environment is, on this view, not the primary goal of computational (...) theorizing. Recent work in computational developmental psychology (by A. Karmiloff-Smith and J. Rutkowska) as well as artificial agents or embedded artificial systems (by L.P. Kaelbling, among others) casts doubt on these claims. In these computational models, the environment does not just trigger and sustain input for computational operations, but some computational operations actually involve environmental structures. (shrink)

O'Brien & Opie do not address the question of the psychotherapeutic role of unconscious representational states such as beliefs. A dilemma is proposed: if they accept the legitimacy of such states then they should modify what they say about dissociation, and if they do not, they owe us an account of why.