Estimating latent distributions in recurrent choice data

This paper introduces a flexible class of stochastic mixture models for the analysis and interpretation of individual differences in recurrent choice and other types of count data. These choice models are derived by specifying elements of the choice process at the individual level. Probability distributions are introduced to describe variations in the choice process among individuals and to obtain a representation of the aggregate choice behavior. Due to the explicit consideration of random effect sources, the choice models are parsimonious and readily interpretable. An easy to implement EM algorithm is presented for parameter estimation. Two applications illustrate the proposed approach.     

Exploratory longitudinal factor analysis in multiple populations

For multiple populatios, a longtidinal factor analytic model which is entirely exploratory, that is, no explicit identification constraints, is proposed. Factorial collapse and period/practice effects are allowed. An invariant and/or stationary factor pattern is permitted. This model is formulated stochastically. To implement this model a stagewise EM algorithm is developed. Finally a numerical illustration utilizing Nesselroade and Baltes' data is presented.The authors wish to thank Barbara Mellers and Henry Kaiser for their helpful comments and John Nesselroade for providing us the data for our illustration. This research wwa supported in part by a grant (No. AG03164) from the National Institute on Aging to William Meredith. Details of the derivations and a copy of the PROC MATRIX program are available upon request from the first author.     

IRT中最小化χ2/EM参数估计方法

对IRT的双参数Logistic模型（2PLM）中未知参数估计问题,给出了一个新的估计方法――最小化χ2/EM估计。新方法在充分考虑项目反应理论(IRT)与经典测量理论(CTT)之间的差异的前提下,从统计计算的角度改进了Berkson的最小化χ2估计,取消了Berkson实施最小化χ2估计时需要已知能力参数的不合实际的前提,扩大了应用范围。实验结果表明新方法能力参数的估计结果与BILOG相比,精确度要高,且当样本容量超过2000时,项目参数的估计结果也优于BILOG。实验还表明新方法稳健性好     

Generation of referring expressions: assessing the Incremental Algorithm

A substantial amount of recent work in natural language generation has focused on the generation of 'one-shot' referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms. It turns out that the success of the IA depends substantially on the 'preference order' (PO) employed by the IA, particularly in complex domains. While some POs cause the IA to produce referring expressions that are very similar to expressions produced by human subjects, others cause the IA to perform worse than its main competitors; moreover, it turns out to be difficult to predict the success of a PO on the basis of existing psycholinguistic findings or frequencies in corpora. We also examine the computational complexity of the algorithms in question and argue that there are no compelling reasons for preferring the IA over some of its main competitors on these grounds. We conclude that future research on the generation of referring expressions should explore alternatives to the IA, focusing on algorithms, inspired by the Greedy Algorithm, which do not work with a fixed PO.     

How to administer the Initial Preference Task

Individuals like their name letters more than non‐name letters. This effect has been termed the Name Letter Effect (NLE) and is widely exploited to measure implicit (i.e. automatic, unconscious) self‐esteem, predominantly by means of the Initial Preference Task (IPT). Methodological research on how to best administer the IPT is, however, scarce. In order to bridge this gap, the present paper assessed the advantages and disadvantages of different types of IPT administrations with two meta‐analyses (k = 49; N = 11,514) and a follow‐up experiment (N = 449). As a result, a new type of administration is recommended which (1) treats the effects of the first and the last name initials separately, (2) uses a duplicate administration for reliability reasons, (3) uses the likability as well as the attractiveness item wording and (4) exploits not only letters but also numbers (i.e. birthday number effect) to measure implicit self‐esteem. Copyright © 2011 John Wiley & Sons, Ltd.     

Latent variable selection in multidimensional item response theory models using the expectation model selection algorithm

The aim of latent variable selection in multidimensional item response theory (MIRT) models is to identify latent traits probed by test items of a multidimensional test. In this paper the expectation model selection (EMS) algorithm proposed by Jiang et al. (2015) is applied to minimize the Bayesian information criterion (BIC) for latent variable selection in MIRT models with a known number of latent traits. Under mild assumptions, we prove the numerical convergence of the EMS algorithm for model selection by minimizing the BIC of observed data in the presence of missing data. For the identification of MIRT models, we assume that the variances of all latent traits are unity and each latent trait has an item that is only related to it. Under this identifiability assumption, the convergence of the EMS algorithm for latent variable selection in the multidimensional two-parameter logistic (M2PL) models can be verified. We give an efficient implementation of the EMS for the M2PL models. Simulation studies show that the EMS outperforms the EM-based L₁ regularization in terms of correctly selected latent variables and computation time. The EMS algorithm is applied to a real data set related to the Eysenck Personality Questionnaire.     

算法拒绝的三维动机理论

算法拒绝意指尽管算法通常能比人类做出更准确的决策, 但人们依然更偏好人类决策的现象。算法拒绝的三维动机理论归纳了算法主体怀疑、道德地位缺失和人类特性湮没这三个主要原因, 分别对应信任、责任和掌控三种心理动机, 并对应改变算法拒绝的三种可行方案: 提高人类对算法的信任度, 强化算法的主体责任, 探索个性化算法设计以突显人对算法决策的控制力。未来研究可进一步以更社会性的视角探究算法拒绝的发生边界和其他可能动机。     

MIRT模型中多维能力及其相关矩阵估计的影响因素

多维项目反应理论因其模型本身的天然优势及其兼具因素分析与项目反应理论于一身的优点,而被广大研究者及应用者所重视.本研究在前人研究基础上,重点讨论MIRT多维能力及能力间相关矩阵的参数估计问题.研究采用Monte Carlo模拟方法进行,在三因素完全随机设计（4 ×3×3）下,使用MCMC算法,探讨测验维度数、维度间的相关大小和测验项目数三个因素对MIRT能力及其相关矩阵估计的影响.     

寂寞身后事,蓄势待来年——让·皮亚杰(J.Piaget)逝世30周年祭

皮亚杰逝世已30年,但皮亚杰所创立的发生认识论研究不应走向历史。皮亚杰的理论遭到许多人的误解。遗传论和生物学化是两项最为不恰当的帽子。皮亚杰不是传统意义上的心理学家,心理学只是其研究康德意义上的"先验范畴"之个体发生发展的"方法论插曲"。发生认识论具有鲜明的跨学科性质,应该进行"发生认识论"与当代"认知科学"的比较研究。应正确认识理论生物学、结构主义方法论、代数工具和逻辑学在发生认识论研究中的作用和地位,尤其应注重晚年皮亚杰运用新的意义逻辑(logic of meaning)和态射(morphisms)、范畴(categories)等新的代数概念作为形式化认知结构之工具的价值和意义。要深入研究发生认识论对以"具身化"和"回归大脑"为特色的第二代认知科学的启示,探讨它们之间的联系。至少在两个方面可以明显看到这种联系:一是发生认识论中的"动作协调"及对由协调而产生的逻辑—数学经验的"反省抽象"这两个概念,表明了皮亚杰对心智具身性的卓越洞见;二是在皮亚杰所主张的"表型复制"(phynocopy)观点中体现的衍生论(epigenesis)和建构论(constructionism)思想,它们与第二代认知科学的动力系统理论之基本思想——所涉因素或变量之间的关系是一种互为因果、相互塑造的对偶(coupling)关系而不是线性的单向因果关系——两者是完全一致的。皮亚杰及其发生认识论定会随着认知科学的未来发展而被"再发现"。     

Intact emotion–cognition interaction in schizophrenia patients and first-degree relatives: Evidence from an emotional antisaccade task

Schizophrenia patients have deficits in cognitive control as well as in a number of emotional domains. The antisaccade task is a measure of cognitive control that requires the inhibition of a reflex-like eye movement to a peripheral stimulus. Antisaccade performance has been shown to be modulated by the emotional content of the peripheral stimuli, with emotional stimuli leading to higher error rates than neutral stimuli, reflecting an implicit emotion processing effect. The aim of the present study was to investigate the impact on antisaccade performance of threat-related emotional facial stimuli in schizophrenia patients, first-degree relatives of schizophrenia patients and healthy controls. Fifteen patients, 22 relatives and 26 controls, matched for gender, age and verbal intelligence, carried out an antisaccade task with pictures of faces displaying disgusted, fearful and neutral expressions as peripheral stimuli. We observed higher antisaccade error rates in schizophrenia patients compared to first-degree relatives and controls. Relatives and controls did not differ significantly from each other. Antisaccade error rate was influenced by the emotional nature of the stimuli: participants had higher antisaccade error rates in response to fearful faces compared to neutral and disgusted faces. As this emotional influence on cognitive control did not differ between groups we conclude that implicit processing of emotional faces is intact in patients with schizophrenia and those at risk for the illness.