Annette Karmiloff-Smith

Bio: Annette Karmiloff-Smith is an academic researcher from Birkbeck, University of London. The author has contributed to research in topics: Williams syndrome & Cognitive development. The author has an hindex of 74, co-authored 307 publications receiving 23249 citations. Previous affiliations of Annette Karmiloff-Smith include UCL Institute of Child Health & Max Planck Society.

Beyond Modularity: A Developmental Perspective on Cognitive Science

Abstract: This is an original, important and stimulating book, which attempts a Hegelian synthesis of Piagetian constructivism and Fodorian modularity in terms of the author's own model of ‘representational ...

Rethinking Innateness: A Connectionist Perspective on Development

Abstract: Rethinking Innateness asks the question, "What does it really mean to say that a behavior is innate?" The authors describe a new framework in which interactions, occurring at all levels, give rise to emergent forms and behaviors. These outcomes often may be highly constrained and universal, yet are not themselves directly contained in the genes in any domain-specific way. One of the key contributions of Rethinking Innateness is a taxonomy of ways in which a behavior can be innate. These include constraints at the level of representation, architecture, and timing; typically, behaviors arise through the interaction of constraints at several of these levels.The ideas are explored through dynamic models inspired by a new kind of "developmental connectionism," a marriage of connectionist models and developmental neurobiology, forming a new theoretical framework for the study of behavioral development. While relying heavily on the conceptual and computational tools provided by connectionism, Rethinking Innateness also identifies ways in which these tools need to be enriched by closer attention to biology.

Development itself is the key to understanding developmental

Abstract: It is a truism that development involves contributions from both genes and environment, but theories differ with respect to the roles they attribute to each, which deeply affects the ways in which developmental disorders are researched. The strict nativist approach to abnormal phenotypes, inspired by adult neuropsychology and evolutionary psychology, seeks to identify impairments to domain-specific cognitive modules and studies the purported juxtaposition of impaired and intact abilities. The neuroconstructivist approach differs in several respects: (i) it seeks more indirect, lower-level causes of abnormality than impaired cognitive modules; (ii) modules are thought to emerge from a developmental process of modularization; (iii) unlike empiricism, neuroconstructivism accepts some form of innately specified starting points, but unlike nativism, these are considered to be initially ‘domain-relevant’, only becoming domain-specific with the process of development and specific environmental interactions; and (iv) different cognitive disorders are considered to lie on a continuum rather than to be truly specific. These alternative theoretical positions are briefly considered as they apply to Specific Language Impairment, and followed by a more detailed case study of a well-defined neurodevelopmental disorder, Williams syndrome. It is argued that development itself plays a crucial role in phenotypical outcomes.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Learning by expanding: An activity-theoretical approach to developmental research

Abstract: 1. Introduction 2. The emergence of learning activity as a historical form of human learning 3. The zone of proximal development as the basic category of expansive research 4. The instruments of expansion 5. Toward an expansive methodology 6. Epilogue.