Andrew N. Meltzoff

Bio: Andrew N. Meltzoff is an academic researcher from University of Washington. The author has contributed to research in topics: Imitation & Cognitive development. The author has an hindex of 101, co-authored 318 publications receiving 41549 citations. Previous affiliations of Andrew N. Meltzoff include University of Oxford & Chiba University.

Imitation of facial and manual gestures by human neonates.

Abstract: Infants between 12 and 21 days of age can imitate both facial and manual gestures; this behavior cannot be explained in terms of either conditioning or innate releasing mechanisms. Such imitation implies that human neonates can equate their own unseen behaviors with gestures they see others perform.

Understanding the Intentions of Others: Re-Enactment of Intended Acts by 18-Month-Old Children.

Abstract: Investigated was whether children would re-enact what an adult actually did or what the adult intended to do. In Experiment 1 children were shown an adult who tried, but failed, to perform certain target acts. Completed target acts were thus not observed. Children in comparison groups either saw the full target act or appropriate controls. Results showed that children could infer the adult's intended act by watching the failed attempts. Experiment 2 tested children's understanding of an inanimate object that traced the same movements as the person had followed. Children showed a completely different reaction to the mechanical device than to the person: They did not produce the target acts in this case. Eighteen-month-olds situate people within a psychological framework that differentiates between the surface behavior of people and a deeper level involving goals and intentions. They have already adopted a fundamental aspect of folk psychology-persons (but not inanimate objects) are understood within a framework involving goals and intentions.

Words, thoughts, and theories

Abstract: The other Socratic method: Socrates's problem Augustine's problem a road map. Part 1 The theory theory: the scientist as child - but surely it can't really be a theory? a cognitive view of science, naturalistic epistemology and development - an evolutionary speculation, science as horticulture, objections - sociology, objections - timing and convergence, objections - magic, empirical advances, what is theory? structural features of theories, functional features of theories, dynamic features of theories, theories in childhood, theories as representations theories, modules, and empirical generalizations - modules, theories and development, modules and development, modularity in peripheral and central processing, empirical generalization - scripts, narratives, and nets, interactions among theories, modules, and empirical generalizations, nonconceptual development - information processing and social construction. Part 2 Evidence for the theory theory: the child's theory of appearances - the adult theory, the initial theory, the paradox of invisible objects, an alternative - a theory-change account, the nine-month-old's theory, the A-not-B error as an auxiliary hypothesis, the 18-month-old's theory, other evidence for the theory theory, semantic developments - from objects permanence to perspective taking, later semantic developments - "gone" and "see", conclusion the child's theory of action - the adult theory, the initial theory, the nine-month-old's theory, the 18-month-old's theory, other evidence for the theory theory, semantic development - "no", "uh-oh" and "there", later developments - from actions to desires, later semantic developments - "want", conclusion the child's theory of kinds - the adult theory, categories and kinds, the initial theory, the nine-month-old's theory, the 18-month-old's theory , other evidence for the theory theory, semantic development - the naming Spurt, later developments, conclusion. Part 3 Theories and language: language and thought - prerequisites, interactions, a theory-theory view, methodological issues - specificity and correlation, developmental relations between language and cognition, theories and constraints, crosslinguistic studies, individual-difference studies, conclusion the Darwinian conclusion - who's afraid of semantic holism? a developmental cognitive science, computational and neurological mechanisms, after Piaget, sailing in Neurath's boat.

Children with autism fail to orient to naturally occurring social stimuli.

Abstract: Children with autism were compared to developmentally matched children with Down syndrome or typical development in terms of their ability to visually orient to two social stimuli (name called, hands clapping) and two nonsocial stimuli (rattle, musical jack-in-the-box), and in terms of their ability to share attention (following another's gaze or point). It was found that, compared to children with Down syndrome or typical development, children with autism more frequently failed to orient to all stimuli, and that this failure was much more extreme for social stimuli. Children with autism who oriented to social stimuli took longer to do so compared to the other two groups of children. Children with autism also exhibited impairments in shared attention. Moreover, for both children with autism and Down syndrome, correlational analyses revealed a relation between shared attention performance and the ability to orient to social stimuli, but no relation between shared attention performance and the ability to orient to nonsocial stimuli. Results suggest that social orienting impairments may contribute to difficulties in shared attention found in autism.

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.).

The mirror-neuron system.

Abstract: A category of stimuli of great importance for primates, humans in particular, is that formed by actions done by other individuals. If we want to survive, we must understand the actions of others. Furthermore, without action understanding, social organization is impossible. In the case of humans, there is another faculty that depends on the observation of others' actions: imitation learning. Unlike most species, we are able to learn by imitation, and this faculty is at the basis of human culture. In this review we present data on a neurophysiological mechanism--the mirror-neuron mechanism--that appears to play a fundamental role in both action understanding and imitation. We describe first the functional properties of mirror neurons in monkeys. We review next the characteristics of the mirror-neuron system in humans. We stress, in particular, those properties specific to the human mirror-neuron system that might explain the human capacity to learn by imitation. We conclude by discussing the relationship between the mirror-neuron system and language.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

Abstract: Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.