THE EFFECT OF THE KOREAN NUMBER SYSTEM ON YOUNG CHILDREN'S COUNTING: A NATURAL EXPERIMENT IN NUMERICAL BILINGUALISM

In Korea, two number systems are in simultaneous use. One is somewhat irregular (like the English language number system), and the other regular (similar to the Chinese number system). This natural experiment allows a direct test of the hypothesis that structural regularity of the number system facilitates the acquisition of counting. Korean children initially suffer from their numerical ‘bilingualism’: they find it difficult to learn two counting systems at the same time. Yet, after this initial difficulty, they soon master the generative rules which are transparent in the regular system. The cognitive advantage provided by the regular number system then combines with other factors, like intensive teaching and parental pressure, to produce school achievement in mathematics superior to US children's.     

Assessing the distinguishability of models and the informativeness of data

A difficulty in the development and testing of psychological models is that they are typically evaluated solely on their ability to fit experimental data, with little consideration given to their ability to fit other possible data patterns. By examining how well model A fits data generated by model B, and vice versa (a technique that we call landscaping), much safer inferences can be made about the meaning of a model's fit to data. We demonstrate the landscaping technique using four models of retention and 77 historical data sets, and show how the method can be used to: (1) evaluate the distinguishability of models, (2) evaluate the informativeness of data in distinguishing between models, and (3) suggest new ways to distinguish between models. The generality of the method is demonstrated in two other research areas (information integration and categorization), and its relationship to the important notion of model complexity is discussed.     

Flexibility versus generalizability in model selection

Which quantitative method should be used to choose among competing mathematical models of cognition? Massaro, Cohen, Campbell, and Rodriguez (2001) favor root mean squared deviation (RMSD), choosing the model that provides the best fit to the data. Their simulation results appear to legitimize its use for comparing two models of information integration because it performed just as well as Bayesian model selection (BMS), which had previously been shown by Myung and Pitt (1997) to be a superior alternative selection method because it considers a model's complexity in addition to its fit. In the present study, after contrasting the theoretical approaches to model selection espoused by Massaro et al. and Myung and Pitt, we discuss the cause of the inconsistencies by expanding on the simulations of Massaro et al. Findings demonstrate that the results from model recovery simulations can be misleading if they are not interpreted relative to the data on which they were evaluated, and that BMS is a more robust selection method.     

Measuring model flexibility with parameter space partitioning: an introduction and application example

A primary criterion on which models of cognition are evaluated is their ability to fit empirical data. To understand the reason why a model yields a good or poor fit, it is necessary to determine the data-fitting potential (i.e., flexibility) of the model. In the first part of this article, methods for comparing models and studying their flexibility are reviewed, with a focus on parameter space partitioning (PSP), a general-purpose method for analyzing and comparing all classes of cognitive models. PSP is then demonstrated in the second part of the article in which two connectionist models of speech perception (TRACE and ARTphone) are compared to learn how design differences affect model flexibility.     

Modeling the word recognition data of Vitevitch and Luce (1998): Is it ARTful?

Vitevitch and Luce (1998) showed that the probability with which phonemes co-occur in the language (phonotactic probability) affects the speed with which words and nonwords are named. Words with high phonotactic probabilities between phonemes were named more slowly than words with low probabilities, whereas with nonwords, just the opposite was found. To reproduce this reversal in performance, a model would seem to require not merely sublexical representations, but sublexical representations that are relatively independent of lexical representations. ARTphone (Grossberg, Boardman, & Cohen, 1997) is designed to meet these requirements. In this study, we used a technique called parameter space partitioning to analyze ARTphone’s behavior and to learn if it can mimic human behavior and, if so, to understand how. To perform best, differences in sublexical node probabilities must be amplified relative to lexical node probabilities to offset the additional source of inhibition (from top-down masking) that is found at the sublexical level.     

Gami-Chunghyuldan ameliorates memory impairment and neurodegeneration induced by intrahippocampal Aβ 1-42 oligomer injection

Soluble oligomeric forms of amyloid beta (AβO) are regarded as a main cause of synaptic and cognitive dysfunction in Alzheimer’s disease (AD) and have been a primary target in the development of drug treatments for AD. The present study utilized a mouse model of AD induced by intrahippocampal injection of AβO (10 μM) to investigate the effects of Gami-Chunghyuldan (GCD), a standardized multi-herbal medicinal formula, on the presentation of memory deficits and neurohistological pathogenesis. GCD (10 and 50 mg/kg/day, 5 days, p.o.) improved AβO-induced memory impairment as well as reduced neuronal cell death, astrogliosis, and microgliosis in the hippocampus. In addition, GCD prevented AβO-triggered synaptic disruption and cholinergic fiber loss. These results suggest that GCD may be useful in the prevention and treatment of AD.     

Reaction Times and Intelligence in Korean Children

Nine-year-old Korean children (N = 299) were tested for reaction time (RT) and intelligence measured by Raven's Standard Progressive Matrices. Reaction times were broken into decision times and movement times and into three degrees of complexity. The results showed low but generally statistically significant correlations between decision times and intelligence. Generally, no significant differences existed between movement times and intelligence. Boys showed significantly faster movement times than girls did.