The Chinese Lexicon Project: A megastudy of lexical decision performance for 25,000+ traditional Chinese two-character compound words.

Using a megastudy approach, we developed a database of lexical variables and lexical decision reaction times and accuracy rates for more than 25,000 traditional Chinese two-character compound words. Each word was responded to by about 33 native Cantonese speakers in Hong Kong. This resource provides a valuable adjunct to influential mega-databases, such as the Chinese single-character, English, French, and Dutch Lexicon Projects. Three analyses were conducted to illustrate the potential uses of the database. First, we compared the proportion of variance in lexical decision performance accounted for by six word frequency measures and established that the best predictor was Cai and Brysbaert's (PLoS One, 5, e10729, 2010) contextual diversity subtitle frequency. Second, we ran virtual replications of three previously published lexical decision experiments and found convergence between the original experiments and the present megastudy. Finally, we conducted item-level regression analyses to examine the effects of theoretically important lexical variables in our normative data. This is the first publicly available large-scale repository of behavioral responses pertaining to Chinese two-character compound word processing, which should be of substantial interest to psychologists, linguists, and other researchers.

The myth of cognitive decline: non-linear dynamics of lifelong learning.

As adults age, their performance on many psychometric tests changes systematically, a finding that is widely taken to reveal that cognitive information-processing capacities decline across adulthood. Contrary to this, we suggest that older adults'; changing performance reflects memory search demands, which escalate as experience grows. A series of simulations show how the performance patterns observed across adulthood emerge naturally in learning models as they acquire knowledge. The simulations correctly identify greater variation in the cognitive performance of older adults, and successfully predict that older adults will show greater sensitivity to fine-grained differences in the properties of test stimuli than younger adults. Our results indicate that older adults'; performance on cognitive tests reflects the predictable consequences of learning on information-processing, and not cognitive decline. We consider the implications of this for our scientific and cultural understanding of aging.

Now you see it, now you don't: on emotion, context, and the algorithmic prediction of human imageability judgments.

Many studies have shown that behavioral measures are affected by manipulating the imageability of words. Though imageability is usually measured by human judgment, little is known about what factors underlie those judgments. We demonstrate that imageability judgments can be largely or entirely accounted for by two computable measures that have previously been associated with imageability, the size and density of a word's context and the emotional associations of the word. We outline an algorithmic method for predicting imageability judgments using co-occurrence distances in a large corpus. Our computed judgments account for 58% of the variance in a set of nearly two thousand imageability judgments, for words that span the entire range of imageability. The two factors account for 43% of the variance in lexical decision reaction times (LDRTs) that is attributable to imageability in a large database of 3697 LDRTs spanning the range of imageability. We document variances in the distribution of our measures across the range of imageability that suggest that they will account for more variance at the extremes, from which most imageability-manipulating stimulus sets are drawn. The two predictors account for 100% of the variance that is attributable to imageability in newly-collected LDRTs using a previously-published stimulus set of 100 items. We argue that our model of imageability is neurobiologically plausible by showing it is consistent with brain imaging data. The evidence we present suggests that behavioral effects in the lexical decision task that are usually attributed to the abstract/concrete distinction between words can be wholly explained by objective characteristics of the word that are not directly related to the semantic distinction. We provide computed imageability estimates for over 29,000 words.

Exploring lexical co-occurrence space using HiDEx.

Hyperspace analog to language (HAL) is a high-dimensional model of semantic space that uses the global co-occurrence frequency of words in a large corpus of text as the basis for a representation of semantic memory. In the original HAL model, many parameters were set without any a priori rationale. We have created and publicly released a computer application, the High Dimensional Explorer (HiDEx), that makes it possible to systematically alter the values of these parameters to examine their effect on the co-occurrence matrix that instantiates the model. We took an empirical approach to understanding the influence of the parameters on the measures produced by the models, looking at how well matrices derived with different parameters could predict human reaction times in lexical decision and semantic decision tasks. New parameter sets give us measures of semantic density that improve the model's ability to predict behavioral measures. Implications for such models are discussed.

Word frequency effects in high-dimensional co-occurrence models: A new approach.

The HAL (hyperspace analog to language) model of lexical semantics uses'global word co-occurrence from a large corpus of text to calculate the distance between words in co-occurrence space. We have implemented a system called HiDEx (High Dimensional Explorer) that extends HAL in two ways: It removes unwanted influence of orthographic frequency from the measures of distance, and it finds the number of words within a certain distance of the word of interest (NCount, the number of neighbors). These two changes to the HAL model produce measures of word neighborhood density that are reliably predictive of human lexical decision reaction times.