The Nature-Nurture controversy has been with us since it was first outlined by Plato and Aristotle. Nobody likes it anymore. All reasonable scholars today agree that genes and environment interact to determine complex cognitive outcomes. So why does the controversy persist? First, it persists because it has practical implications that cannot be postponed (i.e., what can we do to avoid bad outcomes and insure better ones?), a state of emergency that sometimes tempts scholars to stake out claims they cannot defend. Second, the controversy persists because we lack a precise, testable theory of the process by which genes and environment interact. In the absence of a better theory, innateness is often confused with (1) domain specificity (Outcome X is so peculiar that it must be innate), (2) species specificity (we are the only species who do X, so X must lie in the human genome), (3) localization (Outcome X is mediated by a particular part of the brain, so X must be innate), and (4) learnability (we cannot figure out how X could be learned, so X must be innate). We believe that an explicit and plausible theory of interaction is now around the corner, and that many of the classic maneuvers to defend or attack innateness will soon disappear. In the interim, some serious errors can be avoided if we keep these confounded issues apart. That is the major goal of this paper, i.e., not to attack innateness but to clarify what

To investigate the relations between structure and function in both artificial and natural neural networks, we present a series of simulations and analyses with modular neural networks. We suggest a number of design principles in the form of explicit ways in which neural modules can cooperate in recognition tasks. These results may supplement recent accounts of the relation between structure and function in the brain. The networks used consist out of several modules, standard subnetworks that serve as higher-order units with a distinct structure and function. The simulations rely on a particular network module called CALM (Murre, Phaf, and Wolters, 1989, 1992). This module, developed mainly for unsupervised categorization and learning, is able to adjust its local learning dynamics. The way in which modules are interconnected is an important determinant of the learning and categorization behaviour of the network as a whole. Based on arguments derived from neuroscience, psychology, compu...

“Origins of communicative disorders ” to Elizabeth Bates, and by a grant from the John D. and Catherine T. MacArthur Foundation. We are grateful to Larry Juarez and Meiti Opie The effects of focal brain injury are investigated in the first stages of language development, during the passage from first words to grammar. Parent report and/or free speech data are reported for 53 infants and preschool children between 10- 44 months of age. All children had suffered a single, unilateral brain injury to the left or right hemisphere, incurred before six months of age (usually in the pre- or perinatal period). This is the period in which we should expect to see maximal plasticity, but it is also the period in which the initial specializations of particular cortical regions ought to be most evident. In direct contradiction of hypotheses based on the adult aphasia literature, results from 10- 17 months suggest that children with righthemisphere injuries are at greater risk for delays in word comprehension, and in the gestures that normally precede and accompany language onset. Although there were no differences between left- vs. right-hemisphere injury per se on expressive language, children whose lesions include the left temporal lobe did show significantly greater delays in expressive vocabulary and

The term “aphasia ” refers to acute or chronic impairment of language, an acquired condition that is most often associated with damage to the left side of the brain, usually due to trauma or stroke. We have known about the link between left-hemisphere damage and language loss for more than a century (Goodglass, 1993). For almost as long, we have also known that the lesion/symptom correlations observed in adults do not appear to hold for very young children (Basser, 1962; Lenneberg, 1967). In fact, in the absence of other complications, infants with congenital damage to one side of the brain (left or right) usually go on to acquire language abilities that are well within the normal range (Eisele & Aram, 1995; Feldman, Holland, & Janosky, 1992; Vargha-Khadem, Isaacs, & Muter,

The author reviews reemerging coconstructive conceptions of development and recent empirical findings of developmental plasticity at different levels spanning several fields of developmental and life sciences. A cross-level dynamic biocultural coconstructive framework is endorsed to understand cognitive and behavioral development across the life span. This framework integrates main conceptions of earlier views into a unifying frame, viewing the dynamics of life span development as occurring simultaneously within different time scales (i.e., moment-to-moment microgenesis, life span ontogeny, and human phylogeny) and encompassing multiple levels (i.e., neurobiological, cognitive, behavioral, and sociocultural). Viewed through this metatheoretical framework, new insights of potential interfaces for reciprocal cultural and experiential influences to be integrated with behavioral genetics and cognitive neuroscience research can be more easily prescribed. Metaphorically speaking, two related pendulums, one swinging back and forth from nature to nurture and the other from brain to mind, have been running the clocks of developmental and cognitive inquiries for centuries. Instead of polarizing toward either end, this review of recent advances in life and developmental sciences

The Nature-Nurture controversy has been with us since it was first outlined by Plato and Aristotle. Nobody likes it anymore. All reasonable scholars today agree that genes and environment interact to determine complex cognitive outcomes. So why does the controversy persist? First, it persists because it has practical implications that cannot be postponed (i.e., what can we do to avoid bad outcomes and insure better ones?), a state of emergency that sometimes tempts scholars to stake out claims they cannot defend. Second, the controversy persists because we lack a precise, testable theory of the process by which genes and environment interact. In the absence of a better theory, innateness is often confused with (1) domain specificity (Outcome X is so peculiar that it must be

This paper presents a method for designing artificial neural network architectures. The method implies a reverse engineering of the processes resulting in the mammalian brain. The method extends the brain metaphor in neural network design with genetic algorithms and L-systems, modelling natural evolution and growth. It will be argued that a principle of modularity, which is inherent to the design method as well as the resulting network architectures, improves network performance. 1. Introduction Several neural network simulation studies show that many problems can not be solved by a learning algorithm in conventional fully connected layered neural networks [e.g. 2, 5, 7, 10, 14, 18, 24]. Two problems that occur frequently are: a lack of generalization and a problem called interference. A trained network is said to generalize if it gives correct responses to input patterns not seen during training. Many networks fail to generalize because they have too many degrees of freedom. A large...

The neural basis of cognitive development: A constructivist manifesto