Social discounting and delay discounting

Social discounting was measured as the amount of money a participant was willing to forgo to give a fixed amount (usually $75) to another person. In the first experiment, amount forgone was a hyperbolic function of the social distance between the giver and receiver. In the second experiment, degree of social discounting was an increasing function of reward magnitude whereas degree of delay discounting was a decreasing function of reward magnitude. In the third experiment, the shape of the function relating delayed rewards to equally valued immediate rewards for another person was predicted from individual delay and social discount functions. All in all, the studies show that the social discount function, like delay and probability discount functions, is hyperbolic in form. Copyright © 2007 John Wiley & Sons, Ltd.     

Person–Situation Interaction in Adaptive Emotional Functioning

Two studies applied a person–situation model to examine the effect of emotional affordances of situations. Participants rated their emotional functioning as more extensive in situations classified as being high in emotional affordance than those classified as low in emotional affordance. Participants who scored higher on the individual difference characteristic of emotional intelligence were more interested in entering high emotional affordance situations than were individuals lower in emotional intelligence, and participants who scored higher on emotional intelligence were rated by others as being more successful in high emotional affordance situations than individuals lower in emotional intelligence. These results provide preliminary evidence that the interaction between emotional intelligence and situations may influence emotional functioning.     

小学儿童算术估算能力的发展

本研究深入考察了我国现阶段小学儿童算术估算能力的发展状况。通过运用自编材料对1027名儿童的团体与个别测试,发现其估算能力的发展具有如下特点：（1）明显受到题目类型的影响,但在不同年龄阶段影响又不尽相同。（2）三年级可能是整数和小数估算能力发展的一个关键期,五年级是发展分数估算能力的较好时期。（3）不同估算策略的发展进程差别相当大,不同时期都有发展的侧重点。（4）小学儿童在估算时易发生多种错误,而且不同年级均有一些典型错误。另外研究还对儿童估算能力、策略与错误类型的发展进行了深入讨论。     

Nonparametric Estimation of Standard Errors in Covariance Analysis Using the Infinitesimal Jackknife

The infinitesimal jackknife provides a simple general method for estimating standard errors in covariance structure analysis. Beyond its simplicity and generality what makes the infinitesimal jackknife method attractive is that essentially no assumptions are required to produce consistent standard error estimates, not even the requirement that the population sampled has the covariance structure assumed. Commonly used covariance structure analysis software uses parametric methods for estimating parameters and standard errors. When the population sampled has the covariance structure assumed, but fails to have the distributional form assumed, the parameter estimates usually remain consistent, but the standard error estimates do not. This has motivated the introduction of a variety of nonparametric standard error estimates that are consistent when the population sampled fails to have the distributional form assumed. The only distributional assumption these require is that the covariance structure be correctly specified. As noted, even this assumption is not required for the infinitesimal jackknife. The relation between the infinitesimal jackknife and other nonparametric standard error estimators is discussed. An advantage of the infinitesimal jackknife over the jackknife and the bootstrap is that it requires only one analysis to produce standard error estimates rather than one for every jackknife or bootstrap sample.     

Regularized Partial and/or Constrained Redundancy Analysis

Methods of incorporating a ridge type of regularization into partial redundancy analysis (PRA), constrained redundancy analysis (CRA), and partial and constrained redundancy analysis (PCRA) were discussed. The usefulness of ridge estimation in reducing mean square error (MSE) has been recognized in multiple regression analysis for some time, especially when predictor variables are nearly collinear, and the ordinary least squares estimator is poorly determined. The ridge estimation method was extended to PRA, CRA, and PCRA, where the reduced rank ridge estimates of regression coefficients were obtained by minimizing the ridge least squares criterion. It was shown that in all cases they could be obtained in closed form for a fixed value of ridge parameter. An optimal value of the ridge parameter is found by G-fold cross validation. Illustrative examples were given to demonstrate the usefulness of the method in practical data analysis situations. We thank Jim Ramsay for his insightful comments on an earlier draft of this paper. The work reported in this paper is supported by Grants 10630 from the Natural Sciences and Engineering Research Council of Canada to the first author.     

不同亚型ADHD儿童的视通道时距估计能力

本研究选取ADHD混合型儿童11名,ADHD注意缺损型儿童13名以及正常儿童15名,通过"平均估计长度"、"平均估计误差百分比"、"估计变异值"三个指标,考察不同亚型ADHD儿童在时距复制和言语估计上的稳定性和精确性。研究发现,在视觉刺激条件下,ADHD儿童的时距估计缺损并不受时距长度的影响;ADHD混合型儿童在多项任务中所表现的稳定性和精确性都差,而ADHD缺损型儿童只是稳定性差,说明ADHD混合型是ADHD中更严重的亚类型;ADHD儿童在时距估计任务中稳定性差,反映了其状态调节能力的落后。     

数学焦虑、问题形式对乘法估算的影响

采用由研究者所修订的数学焦虑量表和自行设计的估算问题,通过对大学生被试进行团体测验,考察了在纯数字运算和应用题两种情境中数学焦虑对估算加工的影响以及估算中的问题大小效应。结果显示：（1）数学焦虑情绪在纯数字与应用题两种情境下都对估算有显著影响;（2）情境对估算准确率影响显著,纯数字情境下的估算准确率显著高于应用题情境;（3）在应用题情境估算中发现明显的问题大小效应,在纯数字情境估算中却未发现该效应。     

New multinomial models for the Chechile-Meyer task

This paper provides a critique of the Chechile and Meyer (J. Math. Psychol. 13 (1976) 269) multinomial processing tree (MPT) models that were generated for the measurement of storage and retrieval components of the correct recall rate. These models were developed for a specific test procedure that involved the random mixing of recall and recognition trials. A key problem with the Chechile and Meyer (J. Math. Psychol. 13 (1976) 269) models is the validity of an assumption made for foil recognition test trials. Three new MPT models for obtaining separate storage and retrieval measures are provided. These new models circumvent the difficulties of the Chechile and Meyer (J. Math. Psychol. 13 (1976) 269) models. Both maximum likelihood estimates (MLE) and population-parameter mapping (PPM) estimates (discussed in Chechile (J. Math. Psychol. 42 (1998) 432)) are provided for the model parameters. Monte Carlo studies were conducted to compare the relative accuracy of the MLE and PPM storage estimates. Both methods have the same average error rate for samples that are very large in size; however, for all the more practical sample sizes, the PPM estimates were more accurate. Statistical methods for model selection were also developed and tested. Finally, the new models were used to reanalyze some existing data. The new analyses provide strong validation evidence for the new models.     

Do Visually Salient Stimuli Reduce Children’s Risky Decisions?

Children tend to overestimate their physical abilities, and that tendency is related to risk for unintentional injury. This study tested whether or not children estimate their physical ability differently when exposed to stimuli that were highly visually salient due to fluorescent coloring. Sixty-nine 6-year-olds judged physical ability to complete laboratory-based physical tasks. Half judged ability using tasks that were painted black; the other half judged the same tasks, but the stimuli were striped black and fluorescent lime-green. Results suggest the two groups judged similarly, but children took longer to judge perceptually ambiguous tasks when those tasks were visually salient. In other words, visual salience increased decision-making time but not accuracy of judgment. These findings held true after controlling for demographic and temperament characteristics.     

View-invariant object category learning, recognition, and search: how spatial and object attention are coordinated using surface-based attentional shrouds

How does the brain learn to recognize an object from multiple viewpoints while scanning a scene with eye movements? How does the brain avoid the problem of erroneously classifying parts of different objects together? How are attention and eye movements intelligently coordinated to facilitate object learning? A neural model provides a unified mechanistic explanation of how spatial and object attention work together to search a scene and learn what is in it. The ARTSCAN model predicts how an object's surface representation generates a form-fitting distribution of spatial attention, or "attentional shroud". All surface representations dynamically compete for spatial attention to form a shroud. The winning shroud persists during active scanning of the object. The shroud maintains sustained activity of an emerging view-invariant category representation while multiple view-specific category representations are learned and are linked through associative learning to the view-invariant object category. The shroud also helps to restrict scanning eye movements to salient features on the attended object. Object attention plays a role in controlling and stabilizing the learning of view-specific object categories. Spatial attention hereby coordinates the deployment of object attention during object category learning. Shroud collapse releases a reset signal that inhibits the active view-invariant category in the What cortical processing stream. Then a new shroud, corresponding to a different object, forms in the Where cortical processing stream, and search using attention shifts and eye movements continues to learn new objects throughout a scene. The model mechanistically clarifies basic properties of attention shifts (engage, move, disengage) and inhibition of return. It simulates human reaction time data about object-based spatial attention shifts, and learns with 98.1% accuracy and a compression of 430 on a letter database whose letters vary in size, position, and orientation. The model provides a powerful framework for unifying many data about spatial and object attention, and their interactions during perception, cognition, and action.