Abstract not found

Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models help explain how the rest can be acquired. To illustrate this claim, we develop models that acquire two kinds of overhypotheses — overhypotheses about feature variability (e.g. the shape bias in word learning) and overhypotheses about the grouping of categories into ontological kinds like objects and substances.

Experiments investigated whether infants infer that a hidden, freely moving object will move continuously and smoothly. Infants aged 6 and 10 months, like the $-month-old infants in previous experiments, inferred that the object’s path would be connected and unobstructed, in accord with the principle of continuity. In contrast, 4- and 6-month-old infants did not appear to infer that the object’s path would be smooth, in accord with the principle of inertia. At 8 and 10 months, knowledge of inertia appeared to be emerging but remained weaker than knowledge of continuity. These findings are consistent with the view that common sense knowledge of physical objects develops by enrichment around constant core principles. The core knowledge thesis Human adults generally can predict how the things around them will behave. When a ball rolls from view on a table, for example, adults infer that it will continue to exist and to move on a connected path, that it will move smoothly in the absence of obstacles or surface irregularities, that it will rebound from or Supported by grants from NSF (BNS-8613390) and NIH (HD-23103) and by a fellowship to E.S.S. from the John Simon Guggenheim Memorial Foundation. We thank Frank Keil for comments and

Piaget defines intelligence as adaptation, or the ability to maintain a balance between stability and change, or, in his own words, between assimilation and accommodation. When people assimilate the world to their current knowledge, they impose their order upon things. This momentary closure is useful to build "invariants " that lend existence to the world, independent of immediate interaction. In accommodation, people become one with the object of attention. This may lead to momentary loss of control, since fusion loosens boundaries, but allows for change. I choose the domain of perspective-taking to illustrate how this alternation between assimilation and accommodation punctuate individuals ' interactions with the world. I show that the ability to move away from one's own standpoint, and to take on another person's view, requires the construction of cognitive invariants: a recasting of the world's stabilities that transcends any given viewpoint. I conclude that separation is a necessary step toward the construction of a deeper understanding, and that adopting a "god's eyes view " is by no means contrary to situating one's one stance in the world.

The article first addresses the importance of cognitive modeling, in terms of its value to cognitive science (as well as other social and behavioral sciences). In particular, it emphasizes the use of cognitive architectures in this undertaking. Based on this approach, the article addresses, in detail, the idea of a multi-level approach that ranges from social to neural levels. In physical sciences, a rigorous set of theories is a hierarchy of descriptions/explanations, in which causal relationships among entities at a high level can be reduced to causal relationships among simpler entities at a more detailed level. We argue that a similar hierarchy makes possible an equally productive approach toward cognitive modeling. The levels of models that we conceive in relation to cognition include, at the highest level, sociological/anthropological models of collective human behavior, behavioral models of individual performance, cognitive models involving detailed mechanisms, representations, and processes, as well as biological/physiological models of neural circuits, brain regions, and other detailed biological processes.

Three experiments tested preschoolers ’ use of abstract principles to classify and label objects by shape or function. Three- and 4-year-olds were instructed to match objects by shape or function. Four-year-olds readily adopted either rule, but 3-year-olds followed only the shape rule. Without a rule, 4-year-olds tended to match by shape unless object function was shown during matching (Experiment 2). Threeyear-olds’ ability to use a function rule was tested in several conditions (re-presenting functions; reminders to “use the rule”; repeating rule on every trial). None induced consistent function matching (Experiment 3). Supplemental memory and verbal tasks showed that 3-year-olds have trouble using function as an abstract basis of comparison. Naming data, however, show that preschoolers are learning that object labels are based on function. The results show preschoolers ’ growing flexibility in adopting abstract generalization rules and growing knowledge of conventions for extending words. For over three decades, researchers have debated whether children classify objects by common shape or common function. Infants ’ first object concepts might be based on dynamic properties related to function (Madole, Oakes, & Cohen, 1993; Mandler, 2000), and toddlers ’ first object words might be based on functions

The aim of this research project was to study the first stages of cognitive, communicative and linguistic development in a group of Italian-speaking infants who had suffered focal brain lesions to the left or right hemisphere before the time when language acquisition would normally begin (i. e. prenatally or in the first months of life). This study is part of a continuing collaboration between laboratories in the USA and Italy, using many of the same inclusion and exclusion criteria to define the sample (Marchman et al. 1991, Thal et al. 1991a, Wulfeck et al. 1991), together with a parallel set of parental report instruments for the evaluation of early language and communication. The present study had a somewhat more limited focus than its

In the physical sciences a rigorous theory is a hierarchy of descriptions in which causal relationships between many general types of entity at a phenomenological level can be derived from causal relationships between smaller numbers of simpler entities at more detailed levels. The hierarchy of descriptions resembles the modular hierarchy created in electronic systems in order to be able to modify a complex functionality without excessive side effects. Such a hierarchy would make it possible to establish a rigorous scientific theory of consciousness. The causal relationships implicit in definitions of access consciousness and phenomenal consciousness are made explicit, and the corresponding causal relationships at the more detailed levels of perception, memory, and skill learning described. Extension of these causal relationships to physiological and neural levels is discussed. The general capability of a range of current consciousness models to support a modular hierarchy which could generate these causal relationships is reviewed, and the specific capabilities of two models with good general capabilities are compared in some detail.

Research on implicit learning has shown that the knowledge generated from memorizing patterned symbol sequences can be used to make familiarity judgements of novel sequences with similar structure. However, the degree to which these knowledge representations can be used for subsequent cognitive processing is not known. In this study, participants memorized either patterned number strings (patterned training) or random number strings (random training) and then solved either a number or letter sequence extrapolation problem. Patterned training participants performed significantly better on number problems than on letter problems, thus implying that patterned training influences performance, but only on near transfer problems. Function of Implicit Knowledge To support successful performance on complex, unfamiliar tasks, knowledge must be both abstract and generative. The origin of such knowledge is a central question for cognitive psychologists, developmental psychologists, educators, machine learning researchers and philosophers of science. Many theoretical proposals conceptualize the acquisition of deep knowledge as a deliberate, effortful and constructive process. For example, one frequently stated hypothesis with roots in both philosophy (Popper, 1972/1959) and psychology (Thorndike, 1898) claims that learners replace or revise their knowledge when the latter is falsified by contradictory information; on this view, deep learning is driven by the evaluation of evidence (Gopnick & Meltzoff, 1997; Posner, Strike, Hewson & Gertzog, 1982). The hypothesis of analogical learning (e.g., Holyoak & Thagard, 1995) claims that the learner retrieves a possible analog to his or her current problem from memory and discovers their shared structure by constructing a mapping between them. According to the idea of representational redescription (Karmiloff-Smith, 1992), the learner reflects on his or her knowledge and, as a consequence, generates a higher-order representation of it. Many other proposed learning mechanisms share this active character (Ram & Leake,

All students of cognitive development agree that the central questions in development are 1) specifying the initial state of a human infant, 2) specifying the final state of development for a human adult, and 3) specifying how to get from the initial state to the final state. Then academic disputes ensue. Cognitive developmental psychologists are roughly divided into two camps: those who are more or less nativists and those who are more or less empiricists. Some psychologists do not like these terms, and some alternatives are “those who believe in innate knowledge ” and “those who believe in learning, ” or “those who believed in initial conceptual knowledge ” and “those who believe in initial perceptual capabilities. ” This division is also correlated with whether a researcher believes in domain specificity or not: nativists tend to argue for domain-specific knowledge (even at the beginning of development) and domain-specific learning mechanisms; empiricists tend to argue for domain-general learning mechanisms that may result in domain-specific knowledge some years into development (for some representative explications of these views, see Carey &