A nonparametric approach for assessing latent trait unidimensionality

Assuming a nonparametric family of item response theory models, a theory-based procedure for testing the hypothesis of unidimensionality of the latent space is proposed. The asymptotic distribution of the test statistic is derived assuming unidimensionality, thereby establishing an asymptotically valid statistical test of the unidimensionality of the latent trait. Based upon a new notion of dimensionality, the test is shown to have asymptotic power 1. A 6300 trial Monte Carlo study using published item parameter estimates of widely used standardized tests indicates conservative adherence to the nominal level of significance and statistical power averaging 81 out of 100 rejections for examinee sample sizes and psychological test lengths often incurred in practice.The referees' comments were remarkably detailed and greatly enhanced the writeup and sensitized the author to certain pertinent issues. Discussions with Fritz Drasgow, Lloyd Humphreys, Dennis Jennings, Brian Junker, Robert Linn, Ratna Nandakumar, and Robin Shealy were also very useful.This research was supported by the Office of Naval Research under grant N00014-84-K-0186; NR 150-533, and by the National Science Foundation under grant DMS 85-03321.     

A more powerful method for testing for agreement between a judge and a known standard

We assume that a judge's task is to categorize each ofN subjects into one ofr known classes. The design of primary interest is employed if the judge is presented withs groups, each containingr subjects, such that each group of sizer consists of exactly one subject of each of ther types. The probability distribution for the total number of correct choices is developed and used to test the null hypothesis that the judge is guessing in favor of the alternative that he or she is operating at a better than chance level. The power of the procedure is shown to be superior to two other procedures which appear in the literature.The authors are grateful for the suggestions of the referees and for computer funding provided by the Northeast Regional Data Center at the University of Florida.     

Choosing what to choose from: Preference for inclusion over exclusion when constructing consideration sets from large choice sets

Decision making is a two‐stage process, consisting of, first, consideration set construction and then final choice. Decision makers can form a consideration set from a choice set using one of two strategies: including the options they wish to further consider or excluding those they do not wish to further consider. The authors propose that decision makers have a relative preference for an inclusion (vs. exclusion) strategy when choosing from large choice sets and that this preference is driven primarily by a lay belief that inclusion requires less effort than exclusion, particularly in large choice sets. Study 1 demonstrates that decision makers prefer using an inclusion (vs. exclusion) strategy when faced with large choice sets. Study 2 replicates the effect of choice set size on preference for consideration set construction strategy and demonstrates that the belief that exclusion is more effortful mediates the relative preference for inclusion in large choice sets. Studies 3 and 4 further support the importance of perceived effort, demonstrating a greater preference for inclusion in large choice sets when decision makers are primed to think about effort (vs. accuracy; Study 3) and when the choice set is perceived as requiring more effort because of more information being presented about each alternative (vs. more alternatives in the choice set; Study 4). Finally, Study 5 manipulates consideration set construction strategy, showing that using inclusion (vs. exclusion) in large choice sets leads to smaller consideration sets, greater confidence in the decision process, and a higher quality consideration set.     

自然数码奇象记忆法记忆复杂数字的神经机制

本研究采用学习-再认范式和复杂数字记忆材料,考察自然数码奇象记忆法相对于机械记忆法在记忆提取上的优势及神经机制。行为结果表明,自然数码奇象记忆法比机械记忆法的再认准确率更高。事件相关电位分析结果显示,再认提取阶段奇象记忆条件下诱发的N400和N700波幅显著更低,这说明奇象记忆提取更容易。在自然数码奇象记忆条件下,正确再认旧数字诱发的前额区、左顶枕叶区及中顶枕叶区N700成分与使用自然数码奇象记忆有关。本研究表明,采用自然数码奇象记忆可以减少或跨越语义加工,从而提高个体对材料的记忆效率。     

略论《周易》对中国古代历法的影响--兼与李申先生商榷

在古代历法的发展过程中,<周易>起了积极的作用,而且这种作用在不同时期有着不同的特征.早期主要表现为用易数解释历数,这种作用在唐以后逐渐消失;"卦气说"曾在一段时期内被作为历法的内容;而<周易>的"治历明时"思想对于历法研究一直起着积极的作用,成为重要的治历原则之一.无论如何,<周易>对于古代历法发展的作用是不可低估的.     

语义信息在大小数量表征中的作用

本研究采用注意瞬脱实验范式探讨了语义信息在数量表征中的作用。结果发现：（1）在双任务方式中,字母辨认任务对大数（5及以上）的辨认产生干扰,但不影响小数（4及以下）的辨认绩效;（2）数量表征在视野上出现差异性,表现为在小数辨认时,左侧视野呈现绩效高于右侧;大数辨认正好相反,即右侧视野呈现高于左侧。结果表明,语义信息在表征小数和大数中的作用不同：小数的表征无需语言参与;而大数则更多地涉及语义信息,可能需语音回路中无声复述机制的参与。     

Two-Stage maximum likelihood estimation in the misspecified restricted latent class model

The maximum likelihood classification rule is a standard method to classify examinee attribute profiles in cognitive diagnosis models (CDMs). Its asymptotic behaviour is well understood when the model is assumed to be correct, but has not been explored in the case of misspecified latent class models. This paper investigates the asymptotic behaviour of a two-stage maximum likelihood classifier under a misspecified CDM. The analysis is conducted in a general restricted latent class model framework addressing all types of CDMs. Sufficient conditions are proposed under which a consistent classification can be obtained by using a misspecified model. Discussions are also provided on the inconsistency of classification under certain model misspecification scenarios. Simulation studies and a real data application are conducted to illustrate these results. Our findings can provide some guidelines as to when a misspecified simple model or a general model can be used to provide a good classification result.     

A bijection between a set of lexicographic semiorders and pairs of non-crossing Dyck paths

It is a known result that the set of distinct semiorders on n elements, up to permutation, is in bijective correspondence with the set of all Dyck paths of length 2n. I generalize this result by defining a bijection between a set of lexicographic semiorders, termed simple lexicographic semiorders, and the set of all pairs of non-crossing Dyck paths of length 2n. Simple lexicographic semiorders have been used by behavioral scientists to model intransitivity of preference (e.g., Tversky, 1969). In addition to the enumeration of this set of lexicographic semiorders, I discuss applications of this bijection to decision theory and probabilistic choice.     

How do we convert a number into a finger trajectory?

How do we understand two-digit numbers such as 42? Models of multi-digit number comprehension differ widely. Some postulate that the decades and units digits are processed separately and possibly serially. Others hypothesize a holistic process which maps the entire 2-digit string onto a magnitude, represented as a position on a number line. In educated adults, the number line is thought to be linear, but the “number sense” hypothesis proposes that a logarithmic scale underlies our intuitions of number size, and that this compressive representation may still be dormant in the adult brain. We investigated these issues by asking adults to point to the location of two-digit numbers on a number line while their finger location was continuously monitored. Finger trajectories revealed a linear scale, yet with a transient logarithmic effect suggesting the activation of a compressive and holistic quantity representation. Units and decades digits were processed in parallel, without any difference in left-to-right vs. right-to-left readers. The late part of the trajectory was influenced by spatial reference points placed at the left end, middle, and right end of the line. Altogether, finger trajectory analysis provides a precise cognitive decomposition of the sequence of stages used in converting a number to a quantity and then a position.     

The law of large numbers in children's diversity-based reasoning

Adults increase the certainty of their inductive inferences by observing more diverse instances. However, most young children fail to do so. The present study tested the hypothesis that children's sensitivity to instance diversity is determined by three variables: ability to discriminate among instances (Discrimination); an intuition that large numbers of instances increase the strength of conclusion (Monotonicity); ability to detect subcategories and evaluate numerical differences between the subcategories, or Extraction. A total of 219 Chinese children aged 6 to 11 were tested for sensitivity to diversity by means of Discrimination, Monotonicity, and Extraction. The results indicated that children at all ages were able to discriminate instances and attend to set size. However, only 9- and 11-year-olds demonstrated Extraction and sensitivity to diversity. Furthermore, among all children diversity scores increased linearly with the level of Extraction. These results suggest that the law of large numbers plays a role in children's diversity-based reasoning.