Disruptive effects of stimulus intensity on two variations of a temporal discrimination procedure

Previous reports using stimulus intensity changes to disrupt temporal discrimination have shown shifts in the psychophysical curve for time, while studies using other disruptors have shown a flattening of the curve. The current study investigated the impact of increases and decreases in stimulus intensity on temporal discrimination in pigeons, to determine if a flattening of the curve could be extended to this disruptor. The brightness of the sample to be timed was manipulated under two procedural variations, in which the response alternatives were differentiated by color or location. Results showed that all subjects in the color procedure, and one in the location procedure, showed a flattening of the psychophysical curve when they experienced increased stimulus intensity in descending order. No subjects exposed to an ascending order of stimulus intensities, and none of the other subjects in the location procedure, showed any impact of changed stimulus intensity. Minimal disruption was found when test sessions presented decreased stimulus intensity levels in a second series. These results, together with those using other types of disruptors, add to the evidence of a flattening of the psychophysical curve when temporal discrimination is disrupted.     

一种对动物学习记忆能力突变筛查的新途径

本文详细描述了一种全自动化的行为检测方案.在自然饲养环境/检测环境(24/7)中,我们测量了小鼠针对两个给食器中获得食物的比例与它们在相应给食器停留时长的比例进行匹配的精确性与准确性.该方案是对传统条件性习得(trials-to-acquisition)行为测验设备的改进,可以检测动物时间间隔能力的精确性与准确性,对定时目标选择的相关概率的效果,以及记忆一天中从不同给食器中获得食物次数的精确性与准确性.该压缩系统避免了在整个实验过程中对小鼠的持握操作,可忽略实验者/技术员的实验操作时间,而且可以递送小鼠置入实验环境后,7～9个实验日中全部3组实验流程产生的大量结果.其中,第一个实验流程为单个24小时周期内完成的时间匹配能力的筛查,它对动物的时间、空间估计能力的记忆机制进行精确检测.因此,该系统允许在有限的实验空间、较短的实验周期内,对大量的实验小鼠进行有可能存在的学习记忆能力缺陷进行大规模筛查.此外,该系统运行所依赖的软件可以在公共开放平台获得.     

过去时间距离对自我行为表征的影响

建构水平理论认为,人们对心理上远距离(如远期过去)的客体或事件的表征倾向于采用高水平建构,而对心理上近距离(如近期过去)的客体或事件的表征倾向于采用低水平建构。本研究以特质性和行动性行为描述句子为实验材料,通过两个实验考察了过去时间距离对自我正性行为和负性行为的表征特征的影响。实验结果发现:(1)相对于近期过去,被试对远期过去自我的正性行为更倾向于特质性表征,但在负性行为上未获得明确的类似效应;(2)相对于远期过去的自我行为,个体对近期过去的自我行为倾向于负性表征,提示被试对近期过去自我的满意度较低和对未来自我提升的心理期待。     

The strategies in past temporal discounting

对过去事件的加工涉及到对现在信息的加工,过去获得的价值能够为现在的生活带来的收益可能对过去事件的价值估计具有影响。本研究探讨了过去事件对现在生活的影响在过去时间贴现中的作用。采用延迟时间贴现范式任务,选取2周到50年共10个过去时间长度,要求被试在现在获得的奖金和过去获得的奖金之间进行偏好选择,并且完成对过去时间贴现决策策略的问题回答。采用曲线下的单位面积UAUr(t1,t1) 为过去时间贴现率,发现过去时间贴现率在过去2周和过去1个月之间[Z = 2.662,p = .008]、过去1年和过去3年之间[Z = 2.587, p = .010] 差异显著。对决策策略的回答进行内容分析(频次统计信度为0.875),发现过去事件能够为现在生活带来的收益影响了被试对过去事件的价值估计和偏好选择。从现在起到过去2周内,被试看重近的过去获得的奖金对现在的生活的帮助,偏好选择过去获得的奖金;在较远的过去,从过去2周起到过去1年内,奖金数量和获得奖金的时间之间的比例影响了被试的偏好选择;从过去1年起到过去50年内,远的过去获得的奖金对现在生活的影响微乎其微,被试偏好现在获得的奖金。本研究的结果支持了过去时间贴现和未来时间贴现具有不同的心理加工机制,并且有助于进一步了解过去时间的差别感受性的变化规律。     

Algorithmic correspondence and completeness in modal logic. V. Recursive extensions of SQEMA

The previously introduced algorithm SQEMA computes first-order frame equivalents for modal formulae and also proves their canonicity. Here we extend SQEMA with an additional rule based on a recursive version of Ackermann's lemma, which enables the algorithm to compute local frame equivalents of modal formulae in the extension of first-order logic with monadic least fixed-points FOμ. This computation operates by transforming input formulae into locally frame equivalent ones in the pure fragment of the hybrid μ-calculus. In particular, we prove that the recursive extension of SQEMA succeeds on the class of ‘recursive formulae’. We also show that a certain version of this algorithm guarantees the canonicity of the formulae on which it succeeds.     

演绎推理是基于规则还是模型?——来自空间推理的证据

对空间推理的研究有助于探明其加工机制以及验证心理逻辑理论和心理模型理论。行为研究通过考察多种影响空间推理的因素(如心理模型数量、前提的对象顺序等)证实了心理模型理论的预测。脑成像的研究表明,空间推理活动主要激活枕-顶-额脑区网络,顶区可能是推理的特异性脑区。研究的结果同样支持心理模型理论。未来的研究应该整合演绎推理的不同理论,加强对不同演绎推理任务的研究,并将脑成像技术与EEG或ERP结合起来,进一步探明人类空间推理的机制。     

Multilevel Model Prediction

Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling—model disturbances, random coefficients, and future response outcomes—and provides a unified procedure for predicting them. These predictors are best linear unbiased and are commonly known via the acronym BLUP; they are optimal in the sense of minimizing mean square error and are Bayesian under a diffuse prior. For parameter estimation purposes, a multilevel model can be written as a linear mixed-effects model. In this way, parameters of the many equations can be estimated simultaneously and hence efficiently. For prediction purposes, we show that it is more convenient to retain the multiple equation feature of multilevel models. In this way, the efficient BLUPs are easy to compute and retain their intuitively appealing recursive form. We also derive explicit equations for standard errors of these different types of predictors. Prediction in multilevel modeling is important in a wide range of applications. To demonstrate the applicability of our results, this paper discusses prediction in the context of a study of school effectiveness. This research was supported by a grant from the Graduate School at the University of Wisconsin at Madision and the National Science Foundation, Grant number SES-0436274. We are grateful to Norman Webb at Wisconsin Center for Education Research for making available the data used in the reported application.