How do minds emerge from developing brains? According to the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity. Contrary to popular selectionist models that emphasize regressive mechanisms, the neurobiological evidence suggests that this growth is a progressive increase in the representational properties of cortex. The interaction between the environment and neural growth results in a flexible type of learning: minimizes the need for prespecification in accordance with (...) recent neurobiological evidence that the developing cerebral cortex is largely free of domain-specific structure. Instead, the representational properties of cortex are built by the nature of the problem domain confronting it. This uniquely powerful and general learning strategy undermines the central assumption of classical learnability theory, that the learning properties of a system can be deduced from a fixed computational architecture. Neural constructivism suggests that the evolutionary emergence of neocortex in mammals is a progression toward more flexible representational structures, in contrast to the popular view of cortical evolution as an increase in innate, specialized circuits. Human cortical postnatal development is also more extensive and protracted than generally supposed, suggesting that cortex has evolved so as to maximize the capacity of environmental structure to shape its structure and function through constructive learning. (shrink)

An attractive feature of Neuroconstructivism, Vol. I: How the Brain Constructs Cognition is its emphasis on the active role of the individual in neural and behavioural development and the importance of the interplay with the environment. Certain aspects of development are omitted, however, such as specializations for the distinctive ecologies of infancy and childhood and the scaffolding-like features of behaviour seen during development. It was also a pity that so little credit was given to many scientists (...) who have contributed to just those aspects of development on which the authors focus. (shrink)

This article provides a retrospective, current and prospective overview on developments in brain research and neuroscience. Both theoretical and empirical studies are considered, with emphasis in the concept of multivariability and metastability in the brain. In this new view on the human brain, the potential multivariability of the neuronal networks appears to be far from continuous in time, but confined by the dynamics of short-term local and global metastable brain states. The article closes by suggesting some of the implications of (...) this view in future multidisciplinary brain research. (shrink)

Missing from Quartz & Sejnowski's (Q&S's) unique and valuable effort to relate cognitive development to neural constructivism is an examination of the global emergent properties of adding new neural circuits. Such emergent properties can be studied with computational models. Modeling with generative connectionist networks shows that synaptogenic mechanisms can account for progressive increases in children's representational power.

In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. (...) Given that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description—four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity, to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene–environment interactions. We argue that gene–behavior associations can inform cognitive theory with respect to effect size, specificity, and timing. The model demonstrates a means by which researchers can undertake multiscale modeling with respect to cognition and develop highly specific and complex hypotheses across multiple levels of description. (shrink)

Adaptive training adjusts a training task with the goal of improving learning outcomes. Adaptive training has been shown to improve human performance in attention, working memory capacity, and motor control tasks. Additionally, correlations have been observed between neural EEG spectral features and the performance of some cognitive tasks. This relationship suggests some EEG features may be useful in adaptive training regimens. Here, we anticipated that adding a neural measure into a behavioral-based adaptive training system would improve human (...) performance on a subsequent transfer task. We designed, developed, and conducted a between-subjects study of 44 participants comparing three training regimens: Single Item Fixed Difficulty, Behaviorally Adaptive Training, and Combined Adaptive Training using both behavioral and EEG measures. Results showed a statistically significant transfer task performance advantage of the CAT-based system relative to SIFD and BAT systems of 6 and 9 percentage points, respectively. Our research shows a promising pathway for designing closed-loop BCI systems based on both users' behavioral performance and neural signals for augmenting human performance. (shrink)

With the rapid development of information and communication technology, social media-based donation platforms emerged.1 These platforms innovatively demonstrate peer information on the donation page, which inevitably brings the peer influence into donors’ donation decision process. However, how the peer influence will affect the psychological process of donation decisions are remained unknown. This study used the number of donated peers to examine the effects of peer influence on donors’ donation decisions and extracted event-related potential from electroencephalographic data to explore the (...) underlying psychological process. The behavioral results indicated that the number of donated peers positively influenced donors’ willingness to donate. The ERP results suggested that a larger number of donated peers might indicate a higher level of conformity and greater perceived emotional rewards, as a larger P2 amplitude was observed. Following the early processing of emotional stimuli, cognitive detection of decisional risk took place, and the donors reckoned a smaller number of donated peers as a high potential risk, which was reflected by a larger N2 amplitude. In the later stage, the larger number of donated peers, which represented a higher magnitude of prospective emotional rewards, led to a higher incentive to donate, and reflected in a larger amplitude of P3. Additionally, implications and future directions were discussed. (shrink)

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)

The role of the prefrontal cortex is one of the most topical and important areas of research in contemporary neuropsychology. This cortical region appears to be linked with executive processes affecting many diverse areas of cognitive function. Working memory, information processing, behavioural organization, attention, judgement, and the ability to cope with novel experiences are just some of the diverse processes it affects. This book brings together contributions from some of the world's leading researchers on the prefrontal cortex. They discuss (...) the many recent theoretical and technical advances in the field - for example in our understanding of the neural architecture of the prefrontal cortex, in the development of comparable texts of cognition in humans and other primates, in our understanding of the relationships between neuronal activity and behaviour, and in the increasing use of functional neuroimaging to identify different levels of organization within the prefrontal cortex. These important developments make this an ideal time to address the many questions and debates that have arisen about the role and functional organization of this area of the brain. One of the first books to be written on the subject, The Prefrontal Cortex is a state-of-the-art account of our knowledge of this exciting subject. It will be welcomed by all researchers and students in neuro- and cognitive psychology, and neuroscience. (shrink)

Despite the ubiquity of knowledge attribution in human social cognition, its associated neural and cognitive mechanisms are poorly documented. A wealth of converging evidence in cognitive neuroscience has identified independent perspective-taking and inhibitory processes for belief attribution, but the extent to which these processes are shared by knowledge attribution isn't presently understood. Here, we present the findings of an EEG study designed to directly address this shortcoming. These findings suggest that belief attribution is not a component process (...) in knowledge attribution, contra a standard attitude taken by philosophers. Instead, observed differences in P3b amplitude indicate that knowledge attribution doesn't recruit the strong self-perspective inhibition characteristic of belief attribution. However, both belief and knowledge attribution were observed to display a late slow wave widely associated with mental state attribution, indicating that knowledge attribution also shares in more general processing of others' mental states. These results provide a new perspective both on how we think about knowledge attribution, as well as Theory of Mind processes generally. (shrink)

Cognitive control has long been one of the most active areas of computational modeling work in cognitive science. The focus on computational models as a medium for specifying and developing theory predates the PDP books, and cognitive control was not one of the areas on which they focused. However, the framework they provided has injected work on cognitive control with new energy and new ideas. On the occasion of the books' anniversary, we review computational modeling in (...) the study of cognitive control, with a focus on the influence that the PDP approach has brought to bear in this area. Rather than providing a comprehensive review, we offer a framework for thinking about past and future modeling efforts in this domain. We define control in terms of the optimal parameterization of task processing. From this vantage point, the development of control systems in the brain can be seen as responding to the structure of naturalistic tasks, through the filter of the brain systems with which control directly interfaces. This perspective lays open a set of fascinating but difficult research questions, which together define an important frontier for future computational research. (shrink)

What is attention? How does it go wrong? Do attention deficits arise from genes or from the environment? Can we cure it with drugs or training? Are there disorders of attention other than deficit disorders? The past decade has seen a burgeoning of research on the subject of attention. This research has been facilitated by advances on several fronts: New methods are now available for viewing brain activity in real time, there is expanding information on the complexities of the biochemistry (...) of neural activity, individual genes can be isolated and their functions identified, analysis of the component processes included under the broad umbrella of "attention" has become increasingly sophisticated, and ingenious methods have been devised for measuring typical and atypical development of these processes, from infancy into childhood, and then into adulthood. In this book, Kim Cornish and John Wilding are concerned with attention and its development, both typical and atypical, particularly in disorders with a known genetic etiology or assumed genetic linkage. Tremendous advances across seemingly diverse disciplines - molecular genetics, pediatric neurology, child psychiatry, developmental cognitive neuroscience, and education - have culminated in a wealth of new methods for elucidating disorders at multiple levels, possibly paving the way for new treatment options. Cornish and Wilding use three specific-yet-interlinking levels of analysis: genetic blueprint, the developing brain, and the behavioral-cognitive outcomes, as the basis for charting the attention profiles of six well-documented neurodevelopmental disorders: ADHD, autism, fragile X syndrome, Down syndrome, Williams syndrome, and 22q11 deletion syndrome. Their overarching aim in this book is to provide the most authoritative and extensive account to date of disorder-specific attention profiles and their development from infancy through adolescence. (shrink)

This paper lays out some of the empirical evidence for the importance of neural reuse—the reuse of existing (inherited and/or early-developing) neural circuitry for multiple behavioral purposes—in defining the overall functional structure of the brain. We then discuss in some detail one particular instance of such reuse: the involvement of a local neural circuit in finger awareness, number representation, and other diverse functions. Finally, we consider whether and how the notion of a developmental homology can help us (...) understand the relationships between the cognitive functions that develop out of shared neural supports. (shrink)

In recent years, the family of algorithms collected under the term ``deep learning'' has revolutionized artificial intelligence, enabling machines to reach human-like performances in many complex cognitive tasks. Although deep learning models are grounded in the connectionist paradigm, their recent advances were basically developed with engineering goals in mind. Despite of their applied focus, deep learning models eventually seem fruitful for cognitive purposes. This can be thought as a kind of biological exaptation, where a physiological structure becomes applicable (...) for a function different from that for which it was selected. In this paper, it will be argued that it is time for cognitive science to seriously come to terms with deep learning, and we try to spell out the reasons why this is the case. First, the path of the evolution of deep learning from the connectionist project is traced, demonstrating the remarkable continuity, and the differences as well. Then, it will be considered how deep learning models can be useful for many cognitive topics, especially those where it has achieved performance comparable to humans, from perception to language. It will be maintained that deep learning poses questions that cognitive sciences should try to answer. One of such questions is the reasons why deep convolutional models that are disembodied, inactive, unaware of context, and static, are by far the closest to the patterns of activation in the brain visual system. (shrink)

The paper is dedicated to particular cases of interaction and mutual impact of philosophy and cognitive science. Thus, philosophical preconditions in the middle of the 20th century shaped the newly born cognitive science as mainly based on conceptual and propositional representations and syntactical inference. Further developments towards neural networks and statistical representations did not change the prejudice much: many still believe that network models must be complemented with some extra tools that would account for proper human (...) class='Hi'>cognitive traits. I address some real implemented connectionist models that show how ‘new associationism ’ of the neural network approach may not only surpass Humean limitations, but, as well, realistically explain abstraction, inference and prediction. Then I stay on Predictive Processing theories in a little more detail to demonstrate that sophisticated statistical tools applied to a biologically realist ontology may not only provide solutions to scientific problems or integrate different cognitive paradigms but propose some philosophical insights either. To conclude, I touch on a certain parallelism of Predictive Processing and philosophical inferentialism as presented by Robert Brandom. (shrink)

The ability to group perceptual objects into functionally relevant categories is vital to our comprehension of the world. Such categorisation aids in how we search for objects in familiar scenes and how we identify an object and its likely uses despite never having seen that specific object before. The systems that mediate this process are only now coming to be understood through considerable research efforts combining neurological, psychological and behavioural studies. What is much less well understood are the differences between (...) the categories, how they are formed and how they are used by experts and non-experts in a complex task that can take decades to master. In a quite different direction to previous studies, this work infers the different categorical structures that might be used by amateurs and professionals in the oriental game of Go. This is achieved by using a newly developed combination of artificial neural networks (Self-organising Maps) and perceptual inference to show that categories of strategic scenes can be learned while playing games using a model of ‘conditional perceptual learning’. Applying this technique to two databases of games, one of amateurs and one of professionals, shows that a structural hierarchy of scene information develops that can be readily incorporated into traditional psychological models of decisions and readily implemented in computational systems. The results are discussed in terms of the heuristics and biases literature, emphasising where the significant similarities and differences lie between this work and previous work. (shrink)

Dan lloyd (2011) issues a salutary warning against the assumption of what I shall call neural modularity—the view that there is a one-to-one mapping between cognitive functions and distinct brain regions. He shows how the assumption can distort the interpretation of neuroimaging studies and blind researchers to global structures and activity patterns that may be crucial to many aspects of cognitive function and dysfunction.In this note, I want to add a further dimension to the discussion by making (...) connections with the notion of mental modularity developed by evolutionary psychologists. What is the relation between mental and neural modularity? Do the arguments for massive mental modularity also support neural .. (shrink)

Spatial neglect is a disorder of space-related behaviour. It is characterized by failure to explore the side of space contralateral to a brain lesion, or to react or respond to stimuli or subjects located on this side. Research on spatial neglect and related disorders has developed rapidly inrecent years. These advances have been made as a result of neuropsychological studies of patients with brain damage, behavioural studies of animal models, as well as through functional neurophysiological experiments and functional neuroimaging.The (...) class='Hi'>Cognitive and Neural Bases of Spatial Neglect provides an overview of this wide-ranging field of scientific endeavour, providing a cohesive synthesis of the most recent observations and results. As well as being a fascinating clinical phenomenon, the study of spatial neglect helps us tounderstand normal mechanisms of directing and maintaining spatial attention and is relevant to the contemporary search for the cerebral correlates of conscious experience, voluntary action and the nature of personal identity itself.The book is divided into seven sections covering the anatomical and neurophysiological bases of the disorder, frameworks of neglect, perceptual and motor factors, the relation to attention, the cognitive processes involved, and strategies for rehabilitation.Chapters have been written by a team of the leading international experts in this field.This will be essential reading for neuropsychologists, neurologists, neurophysiologists, cognitive neuroscientists and psychologists. (shrink)

Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff described four fundamental problems for a neural instantiation of combinatorial (...) structures: the massiveness of the binding problem, the problem of 2, the problem of variables, and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural “blackboard” architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception. Perspectives and potential developments of the architectures are discussed. Key Words: binding; blackboard architectures; combinatorial structure; compositionality; language; dynamic system; neurocognition; sentence complexity; sentence structure; working memory; variables; vision. (shrink)

Quartz & Sejnowski's (Q&S's) model for constructive learning agrees with the basic assumptions of mainstream cognitive developmental theories. However, it does not detail the neural equivalents of (1) the process of cognitive change per se, (2) the construction and functioning of thought modules, and (3) the involvement of “mindreading” and “mindsteering” in constructive learning. Specifying these equivalents is necessary if cognitive developmental neuroscience and mainstream cognitive development are to be directly connected.

Music can be described as sequences of events that are structured in pitch and time. Studying music processing provides insight into how complex event sequences are learned, perceived, and represented by the brain. Given the temporal nature of sound, expectations, structural integration, and cognitive sequencing are central in music perception (i.e., which sounds are most likely to come next and at what moment should they occur?). This paper focuses on similarities in music and language cognition research, showing that music (...) cognition research provides insight into the understanding of not only music processing but also language processing and the processing of other structured stimuli. The hypothesis of shared resources between music and language processing and of domain-general dynamic attention has motivated the development of research to test music as a means to stimulate sensory, cognitive, and motor processes. (shrink)

Differences in socioeconomic status (SES) correlate both with differences in cognitive development and in brain structure. Associations between SES and brain measures such as cortical surface area and cortical thickness mediate differences in cognitive skills such as executive function and language. However, causal accounts that link SES, brain, and behavior are challenging because SES is a multidimensional construct: correlated environmental factors, such as family income and parental education, are only distal markers for proximal causal pathways. Moreover, the (...) causal accounts themselves must span multiple levels of description, employ a developmental perspective, and integrate genetic effects on individual differences. Nevertheless, causal accounts have the potential to inform policy and guide interventions to reduce gaps in developmental outcomes. In this article, we review the range of empirical data to be integrated in causal accounts of developmental effects on the brain and cognition associated with variation in SES. We take the specific example of language development and evaluate the potential of a multiscale computational model of development, based on an artificial neural network, to support the construction of causal accounts. We show how, with bridging assumptions that link properties of network structure to magnetic resonance imaging (MRI) measures of brain structure, different sets of empirical data on SES effects can be connected. We use the model to contrast two possible causal pathways for environmental influences that are associated with SES: differences in prenatal brain development and differences in postnatal cognitive stimulation. We then use the model to explore the implications of each pathway for the potential to intervene to reduce gaps in developmental outcomes. The model points to the cumulative effects of social disadvantage on multiple pathways as the source of the poorest response to interventions. Overall, we highlight the importance of implemented models to test competing accounts of environmental influences on individual differences. (shrink)

This article first analyzes the research background of the design elements of cognitive psychology and neural networks at home and abroad, roughly understands the research status and research background of these two courses at home and abroad, and discusses the application of cognitive psychology to neural networks. The design method has not yet formed a systematic theoretical system. Then, a systematic theoretical analysis of the research in this article is carried out to analyze the relationship between (...) the various characteristics of cognitive psychology and the design elements of the neural network, and it uses these relationships to guide the design practice. Second, it analyzes the relationship between the influence and interaction of cognitive psychology on neural network design and connects cognitive psychology with neural network design. Finally, according to the theoretical analysis and research of the system, the application of cognitive psychology in neural network design, design practice, and the relationship between the two are systematically reviewed. Through the exploratory research on cognitive psychology in neural network design, we can see that the combination of neural network design and psychology, art aesthetics, and other cross-disciplinary and multidisciplinary research is necessary, which can promote the scientific and technological progress of neural network design in the context of the information age and the improvement of public mental health. Under the background of the era in which the neural network design becomes the link between people's emotions and culture, we must fully understand the essential role of each element in neural network design and build a design concept based on cognitive psychology and emotional experience. It is hoped that the content of this topic can provide a certain reference value for the future development of neural network design and cognitive psychology and clarify the new development direction. (shrink)

This article reviews some recent research on the development of temporal cognition, with reference to Weist's (1989) account of the development of temporal understanding. Weist's distinction between two levels of temporal decentering is discussed, and empirical studies that may be interpreted as measuring temporal decentering are described. We argue that if temporal decentering is defined simply in terms of the coordination of the temporal locations of three events, it may fail to fully capture the properties of mature temporal (...) understanding. Characterizing the development of mature temporal cognition may require, in addition, distinguishing between event-dependent and event-independent thought about time. Experimental evidence relevant to such a distinction is described; these findings suggest that there may be important changes between 3 and 5 years in children's ability to think about points in time independently of the events that occur at those times. (shrink)

In agreement with Blair, I favor the idea of dissociative patterns in cognitive performance, even more when it comes to development. However, such dissociations are present not only between fluid cognition and general intelligence, but also within fluid cognition itself. Heterogeneity of executive attention, even when indexed with a single paradigm, is further discussed in relation to anterior cingulate cortex. (Published Online April 5 2006).

Siyaves Azeri (2020) quite well shows that arithmetical thinking emerges on the basis of specific social practices and material engagement (clay tokens for economic exchange practices beget number concepts, e.g.). But his discussion here is relegated mostly to Neolithic and Bronze Age practices. While surely such practices produced revolutions in the cognitive abilities of many humans, much of the cognitive architecture that allows normative conceptual thought was already in place long before this time. This response, then, is an (...) attempt to sketch the deep prehistory of human cognition in order to show the inter-social bases of normative thought in general. To do this, I will look first to the work of Vygotsky and Leontiev, two often neglected psychologists whose combined efforts culminate in a developmental account of human cognitive origins. Then, I will review some key insights from the contemporary comparative psychologist Michael Tomasello—whose project is admittedly a Vygotskian one—in order to further shed light on the social-practical basis of abstract thought, of which mathematical cognition is surely a part. (shrink)

Identifying biomarkers of developing mental disorder is crucial to improving early identification and treatment—a key strategy for reducing the burden of mental disorders. Cross-frequency coupling between two different frequencies of neural oscillations is one such promising measure, believed to reflect synchronization between local and global networks in the brain. Specifically, in adults phase–amplitude coupling (PAC) has been shown to be involved in a range of cognitive processes, including working and long-term memory, attention, language, and fluid intelligence. Evidence suggests (...) that increased PAC mediates both temporary and lasting improvements in working memory elicited by transcranial direct-current stimulation and reductions in depressive symptoms after transcranial magnetic stimulation. Moreover, research has shown that abnormal patterns of PAC are associated with depression and schizophrenia in adults. PAC is believed to be closely related to cortico-cortico white matter (WM) microstructure, which is well established in the literature as a structural mechanism underlying mental health. Some cognitive findings have been replicated in adolescents and abnormal patterns of PAC have also been linked to ADHD in young people. However, currently most research has focused on cross-sectional adult samples. Whereas initial hypotheses suggested that PAC was a state-based measure due to an early focus on cognitive, task-based research, current evidence suggests that PAC has both state-based and stable components. Future longitudinal research focusing on PAC throughout adolescent development could further our understanding of the relationship between mental health and cognition and facilitate the development of new methods for the identification and treatment of youth mental health. (shrink)

The ability to improve in speed and accuracy as a result of repeating some task is an important hallmark of intelligent biological systems. Although gradual behavioral improvements from practice have been modeled in spiking neural networks, few such models have attempted to explain cognitive development of a task as complex as addition. In this work, we model the progression from a counting-based strategy for addition to a recall-based strategy. The model consists of two networks working in parallel: (...) a slower basal ganglia loop and a faster cortical network. The slow network methodically computes the count from one digit given another, corresponding to the addition of two digits, whereas the fast network gradually “memorizes” the output from the slow network. The faster network eventually learns how to add the same digits that initially drove the behavior of the slower network. Performance of this model is demonstrated by simulating a fully spiking neural network that includes basal ganglia, thalamus, and various cortical areas. Consequently, the model incorporates various neuroanatomical data, in terms of brain areas used for calculation and makes psychologically testable predictions related to frequency of rehearsal. Furthermore, the model replicates developmental progression through addition strategies in terms of reaction times and accuracy, and naturally explains observed symptoms of dyscalculia. (shrink)

Menary OpenMIND, MIND Group, Frankfurt am Main, 2015) has argued that the development of our capacities for mathematical cognition can be explained in terms of enculturation. Our ancient systems for perceptually estimating numerical quantities are augmented and transformed by interacting with a culturally-enriched environment that provides scaffolds for the acquisition of cognitive practices, leading to the development of a discrete number system for representing number precisely. Numerals and the practices associated with numeral systems play a significant role (...) in this process. However, the details of the relationship between the ancient number system and the discrete number system remain unclear. This lack of clarity is exacerbated by the problem of symbolic estrangement and the fact that unique features of how numeral systems represent require our ancient number system to play a dual role. These issues highlight that Dehaene’s From monkey brain to human brain, MIT Press, Cambridge, pp 133–157, 2005) neuronal recycling hypothesis may be insufficient to explain the neural mechanisms underlying the process of enculturation. In order to explain mathematical enculturation, and enculturation more generally, it may be necessary to adopt Anderson’s :245–266, 2010; After phrenology: neural reuse and the interactive brain, MIT Press, Cambridge, 2014) theory of neural reuse. (shrink)

In Concepts, Kinds, and Cognitive Development, Frank C. Keil provides a coherent account of how concepts and word meanings develop in children, adding to our understanding of the representational nature of concepts and word meanings at all ages. Keil argues that it is impossible to adequately understand the nature of conceptual representation without also considering the issue of learning. Weaving together issues in cognitive development, philosophy, and cognitive psychology, he reconciles numerous theories, backed by empirical (...) evidence from nominal kinds studies, natural-kinds studies, and studies of fundamental categorical distinctions. He shows that all this evidence, when put together, leads to a better understanding of semantic and conceptual development. The book opens with an analysis of the problems of modeling qualitative changes in conceptual development, investigating how concepts of natural kinds, nominal kinds, and artifacts evolve. The studies on nominal kinds document a powerful and unambiguous developmental pattern indicating a shift from a reliance on global tabulations of characteristic features to what appears to be a small set of defining ones. The studies on natural kinds document an analogous shift toward a core theory instead of simple definition. Both sets of studies are strongly supported by cross cultural data. While these patterns seem to suggest that the young child organizes concepts according to characteristic features, Keil argues that there is a framework of conceptual categories and causal beliefs that enables even very young children to understand kinds at a deeper, theoretically guided, level. This account suggests a new way of understanding qualitative change and carries strong implications for how concepts are represented at any point in development. A Bradford Book. (shrink)

A network of gene regulation organized in a hierarchical and combinatorial manner is crucially involved in the development of the neural network, and has to be considered one of the main substrates of genetic change in its evolution. Though qualitative features may emerge by way of the accumulation of rather unspecific quantitative changes, it is reasonable to assume that at least in some cases specific combinations of regulatory parts of the genome initiated new directions of evolution, leading to (...) novel capabilities of the brain. These notions are applied, in this paper, to the evolution of the capability of cognition-based human empa­thy. It is suggested that it has evolved as a secondary effect of the evolution of strategic thought. Development of strategies depends on abstract representations of one’s own pos­sible future states in one’s own brain to allow assessment of their emotional desirability, but also on the representation and emotional evaluation of possible states of others, allowing anticipation of their behaviour. This is best achieved if representations of others are con­nected to one’s own emotional centres in a manner similar to self-representations. For this reason, the evolution of the human brain is assumed to have established representations with such linkages. No group selection is involved, because the quality of strategic thought affects the fitness of the individual. A secondary effect of this linkage is that both the actual states and the future perspectives of others elicit vicarious emotions, which may contribute to the motivations of altruistic behaviour. (shrink)

Neural reuse is a form of neuroplasticity whereby neural elements originally developed for one purpose are put to multiple uses. A diverse behavioral repertoire is achieved by means of the creation of multiple, nested, and overlapping neural coalitions, in which each neural element is a member of multiple different coalitions and cooperates with a different set of partners at different times. Neural reuse has profound implications for how we think about our continuity with other species, (...) for how we understand the similarities and differences between psychological processes, and for how best to pursue a unified science of the mind.After Phrenology: Neural Reuse and the Interactive Brain surveys the terrain and advocates for a series of reforms in psychology and cognitive neuroscience. The book argues that, among other things, we should capture brain function in a multidimensional manner, develop a new, action-oriented vocabulary for psychology, and recognize that higher-order cognitive processes are built from complex configurations of already evolved circuitry. (shrink)

Evolutionary psychology and the selectionist theories of neural development are usually regarded as two unrelated theories addressing two logically distinct questions. The focus of evolutionary psychology is the phylogeny of the human mind, whereas the selectionist theories of neural development analyse the ontogeny of the mind. This paper will endeavour to combine these two approaches in the explanation of the human mind. Doing so might help in overcoming some of the criticisms of both theories. The first (...) part of the paper mentions three standard objections to evolutionary psychology and then outlines three philosophical problems evolutionary psychology has to offer a solution to. The second part will try to show that an approach combining evolutionary psychology and the selectionist theory of neural development might overcome some of these objections. (shrink)

Does motor behavior early in development have the same signatures of habituation, dishabituation, and Spencer-Thompson dishabituation known from infant perception and cognition? And do these signatures explain the choice preferences in A not B motor decision tasks? We provide new empirical evidence that gives an affirmative answer to the first question together with a unified neural dynamic model that gives an affirmative answer to the second question.In the perceptual and cognitive domains, habituation is the weakening of an (...) orientation response to a stimulus over perceptual experience. Switching to a novel stimulus leads to dishabituation, the re-establishment of the orientation response. In Spencer-Thompson dishabituation, the renewed orientation response transfers to the original stimulus. The change in orientation responses over perceptual experience explains infants' behavior in preferential looking tasks: Familiarity preference early during exposure and novelty preference late during exposure. In the motor domain, perseveration in the A not B task could be interpreted as a form of familiarity preference. There are hints that this preference reverses after enough experience with the familiar movement. We provide a unified account for habituation and patterns of preferential selection in which neural dynamic fields generate perceptual or motor representations. The build-up of activation in excitatory fields leads to familiarity preference, the build-up of activation in inhibitory fields leads to novelty preference. We show that the model accounts for the new experimental evidence for motor habituation, but is also compatible with earlier accounts for perceptual habituation and motor perseveration. We discuss how excitatory and inhibitory memory traces may regulate exploration and exploitation for both orientation to objects and motor behaviors. (shrink)

Since entering the new century, People’s living standards are constantly improving, with the continuous improvement of living conditions, people are becoming more and more important in education, which is the embodiment of the enhancement of national strength. The education level is getting higher and higher, and a good education level needs a good teacher–student relationship. To solve these problems, we use the emotional cognition of God’s network to study the teacher–student relationship, and collect and analyze the data of the teacher–student (...) relationship. In this chapter, we use GABP neural network algorithm DHNN algorithm and discrete Hopfield neural network to make the collected data more convenient to be analyzed. The research shows that there is a close relationship between the educational level and the relationship between teachers and students in China, and a good relationship between teachers and students will promote the improvement of educational level. According to the research data, “face-to-face” is the most important way of interaction between tutors and postgraduates in various types of teacher–student relationship. QQ WeChat is also one of the main ways of interaction between students and teachers, which shows that the interaction between students and teachers is talking about the interaction between online and Internet. The education industry is becoming more and more important, and the teacher–student relationship is the most important part of the education industry. Good teacher–student relationship is helpful to cultivate students’ healthy personality. In view of the cold relationship between teachers and students at present, we need to make some measures the relationship between teachers and students and effectively use the relationship between teachers and students to promote the better development of the education industry. (shrink)

For well over two decades, Andy Clark has been gleaning theoretical lessons from the leading edge of cognitive science, applying a combination of empirical savvy and philosophical instinct that few can match. Clark’s most recent book, Supersizing the Mind: Embodiment, Action, and Cognitive Extension, brilliantly expands his oeuvre. It offers a well-informed and focused survey of research in the burgeoning field of situated cognition, a field that emphasizes the contribution of environmental and non-neural bodily structures to the (...) production of intelligent behavior. The situated research program, fledgling though it may be in some respects, has reached an age at which its philosophical stock can reasonably be taken; and Clark is just the person to take it. Supersizing the Mind consists of three main divisions. The first develops the case for the distinctively extended view of cognition, according to which the human mind or cognitive system literally comprises elements beyond the boundary of the human organism. The second responds to critics of the extended outlook: Frederick Adams, Kenneth Aizawa, Keith Butler, Brie Gertler, Rick Grush, and me, among others. The third major division evaluates nonextended strands in the situated program, in particular, those that emphasize the role of the non-neural body in cognition. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Philosophic Foundations of Mimetic Theory and Cognitive Science(Including Artificial Intelligence)Jean-Pierre Dupuy (bio)In the mid 1970s I discovered at the same time cognitive science and mimetic theory. Being a philosopher with a scientific background, I immediately brought them together and tried to reconceptualize the latter in terms of the former. In a sense, I haven't stopped doing that in the last 45 years. That is why I (...) feel fully at home with the goals pursued by the organizers of this conference.I do not believe it is feasible or even desirable to operate the rapprochement between the two disciplines at a global level. The history of cognitive science is exceedingly involved, and the part played by artificial intelligence in it compared to the part played by the neurosciences has varied greatly over time.1 Only a piecemeal approach makes sense. I present here four short case studies that cover the whole period, from the beginnings in cybernetics to today's artificial intelligence (AI).In order to better situate these studies within a broader frame, I propose the diagram in Figure 1. To comment on it would require more time than available for my whole presentation. Let me just say the following. [End Page 1]It is essential to distinguish between two kinds of AI. The first to bear this name was launched by economist Herbert Simon and computer scientist Allan Newell at a Dartmouth conference in 1956. It was based on the premise that thinking is computing, with this computation bearing on sentences of a "language of thought" implementable in a digital computer. It turns out that this AI(1) was progressively substituted from the 1980s onward by an AI(2), which, to some extent, was a return to a fundamental result obtained by cybernetics in 1943. Neurophysiologist Warren McCulloch and mathematician Walter Pitts had constructed a mathematical model of a network of elementary calculators called "neurons" and explored its properties. The capacity to "think" was one of these. It was not situated at the elementary level, as was the case in AI(1), but it emerged at the collective level from the interactions between the neurons.This model was heavily criticized by the supporters of AI(1),2 and its first implementations, in the notorious "Perceptron" in particular (1957), were pitiful failures. However, the emergence of "second-order cybernetics" under the guidance of cybernetician Heinz von Foerster and the theory of complex, self-organizing systems that it helped develop, in cooperation and rivalry with similar efforts in the physical sciences, enabled the neural network model to come to a second life under different names: formal neurons, deep learning, big data, and the like. Together, they constitute AI(2).The following network of influences wouldn't have been possible without its stem, that is, the boxes containing the names of Kurt Gödel and Alan Turing. The former revolutionized logic by showing that reasoning about arithmetic and arithmetic are one and the same, insofar as it is possible to code in a 1-to-1 fashion propositions about integers (for instance, "7 is a prime number") by means of integers. The latter demonstrated that some mechanisms can self-transcend inasmuch as they generate an ensemble of outputs that cannot be defined mechanistically. Later, John von Neumann, who is credited along with Alan Turing with having invented the digital computer, would use this logical possibility to define complexity: A mechanism is complex if it is capable of generating one more complex than itself. The gate was open for the theories of complex, self-organizing systems to thrive and, by way of consequence, for AI(2) to emerge.fascination with modelsHerbert Simon (1916–2001), winner of the Nobel Prize in economics for his work on what he called "bounded rationality," is generally considered to be one [End Page 2] Click for larger view View full resolutionFigure 1.of the founders of artificial intelligence. Some cognitive scientists still rely on the computer program that he devised with Alan Newell to simulate the creative processes of human thought—a program aptly named "General Problem Solver." The title of one of his principal works, The Sciences of the... (shrink)

The essays gathered here demonstrate and justify the excitement and promise of cognitive historicism, providing a lively introduction to this new and quickly growing area of literary studies. Written by eight leading critics whose work has done much to establish the new field, they display the significant results of a largely unprecedented combination of cultural and cognitive analysis. The authors explore both narrative and dramatic genres, uncovering the tensions among presumably universal cognitive processes, and the local contexts (...) within which complex literary texts are produced. Alan Richardson's opening essay evaluates current approaches to the study of literature and cognition, locating them on the map of recent literary studies, indicating their most compelling developments to date, and suggesting the most promising future directions. The seven essays that follow provide innovative readings of topics ranging from Shakespeare (Othello, Macbeth, Cymbeline, The Rape of Lucrece) through Samuel Richardson's Clarissa, to contemporary authors Ian McEwan and Gilbert Sorrentino. They underscore some of the limitations of new historicist and post-structuralist approaches to literary cultural studies while affirming the value of supplementing rather than supplanting them with insights and methods drawn from cognitive and evolutionary theory. Together, they demonstrate the analytical power of considering these texts in the context of recent studies of cultural universals, 'theory of mind, ' cognitive categorization and genre, and neural-materialist theories of language and consciousness. This groundbreaking collection holds appeal for a broad audience, including students and teachers of literary theory, literary history, cultural studies, and literature and science studies. (shrink)

According to the hypotheses of distributed and extended cognition, remembering does not always occur entirely inside the brain but is often distributed across heterogeneous systems combining neural, bodily, social, and technological resources. These ideas have been intensely debated in philosophy, but the philosophical debate has often remained at some distance from relevant empirical research, while empirical memory research, in particular, has been somewhat slow to incorporate distributed/extended ideas. This situation, however, appears to be changing, as we witness an increasing (...) level of interaction between the philosophy and the empirical research. In this editorial, we provide a high-level historical overview of the development of the debates around the hypotheses of distributed and extended cognition, as well as relevant theory and empirical research on memory, considering both the role of memory in theoretical debates around distributed/extended ideas and strands of memory research that resonate with those ideas; we emphasize recent trends towards increased interaction, including new empirical paradigms for investigating distributed memory systems. We then provide an overview of the special issue itself, drawing out a number of general implications from the contributions, and conclude by sketching promising directions for future research on distributed memory. (shrink)

On standard readings of Grice, Gricean communication requires (a) possession of a concept of belief, (b) the ability to make complex inferences about others’ goal-directed behaviour, and (c) the ability to entertain fourth order meta-representations. To the extent that these abilities are pre-requisites of Gricean communication they are inconsistent with the view that Gricean communication could play a role in their development. In this paper, I argue that a class of ‘minimally Gricean acts’ satisfy the intentional structure described by (...) Grice, but require none of abilities (a)-(c). As a result, Gricean communicative abilities may indeed contribute to the development of (a)-(c) – in particular, by enabling language development. This conclusion has important implications for our theorising about cognitive development. (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)

"Cognitive psychology," "cognitive neuroscience," and "philosophy of mind" are names for three very different scientific fields, but they label aspects of the same scientific goal: to understand the nature of mental phenomena. Today, the three disciplines strongly overlap under the roof of the cognitive sciences. The book's purpose is to present views from the different disciplines on one of the central theories in cognitive science: the theory of mental models. Cognitive psychologists report their research on (...) the representation and processing of mental models in human memory. Cognitive neuroscientists demonstrate how the brain processes visual and spatial mental models and which neural processes underlie visual and spatial thinking. Philosophers report their ideas about the role of mental models in relation to perception, emotion, representation, and intentionality. The single articles have different and mutually complementing goals: to introduce new empirical methods and approaches, to report new experimental results, and to locate competing approaches for their interpretation in the cross-disciplinary debate. The book is strongly interdisciplinary in character. It is especially addressed to researchers in any field related to mental models theory as both a reference book and an overview of present research on the topic in other disciplines. However, it is also an ideal reader for a specialized graduate course. (shrink)

In Reading in the Brain, Stanislas Dehaene presents a compelling account of how the brain learns to read. Central to this account is his neuronal recycling hypothesis: neural circuitry is capable of being ‘recycled’ or converted to a different function that is cultural in nature. The original function of the circuitry is not entirely lost and constrains what the brain can learn. It is argued that the neural niche co-evolves with the environmental niche in a way that does (...) not undermine the core ideas of neuronal recycling, but which is quite different from the models of cognitive and cultural evolution provided by evolutionary psychology and epidemiology. Dehaene contrasts neuronal recycling with a naïve model of the brain as a general learning device that is unconstrained in what it can learn. Consequently a tension develops in Dehaene's account of the role of plasticity in the acquisition of language. It is argued that the functional and structural changes in the brain that Dehaene documents in great detail are driven by learning and that this learning-driven plasticity does not commit us to a naïve model of the brain. (shrink)

For well over two decades, Andy Clark has been gleaning theoretical lessons from the leading edge of cognitive science, applying a combination of empirical savvy and philosophical instinct that few can match. Clark’s most recent book, Supersizing the Mind: Embodiment, Action, and Cognitive Extension, brilliantly expands his oeuvre. It offers a well-informed and focused survey of research in the burgeoning field of situated cognition, a field that emphasizes the contribution of environmental and non-neural bodily structures to the (...) production of intelligent behavior. The situated research program, fledgling though it may be in some respects, has reached an age at which its philosophical stock can reasonably be taken; and Clark is just the person to take it. Supersizing the Mind consists of three main divisions. The first develops the case for the distinctively extended view of cognition, according to which the human mind or cognitive system literally comprises elements beyond the boundary of the human organism. The second responds to critics of the extended outlook: Frederick Adams, Kenneth Aizawa, Keith Butler, Brie Gertler, Rick Grush, and me, among others. The third major division evaluates nonextended strands in the situated program, in particular, those that emphasize the role of the non-neural body in cognition. (shrink)