Constructing the exact significance level for a person fit statistic

This paper uses an extension of the network algorithm originally introduced by Mehta and Patel to construct exact tail probabilities for testing the general hypothesis that item responses are distributed according to the Rasch model. By assuming that item difficulties are known, the algorithm is applicable to the statistical tests either given the maximum likelihood ability estimate or conditioned on the total score. A simulation study indicates that the network algorithm is an efficient tool for computing the significance level of a person fit statistic based on test lengths of 30 items or less.     

An automated test development of parallel tests from a seed test

Binary programming models are presented to generate parallel tests from an itembank. The parallel tests are created to match item for item an existing seed test and match user supplied taxonomic specifications. The taxonomic specifications may be either obtained from the seed test or from some other user requirement. An algorithm is presented along with computational results to indicate the overall efficiency of the process. Empirical findings based on an itembank for the Arithmetic Reasoning section of the Armed Services Vocational Aptitude Battery are given.The Office of Naval Research, Program in Cognitive Science, N00014-87-C-0696 partially supported the work of Douglas H. Jones. The Rutgers Research Resource Committee of the Graduate School of Management partially supported the work of Douglas H. Jones and Ing-Long Wu. A Thomas and Betts research fellowship partially supported the work of Ing-Long Wu. The Human Resources Laboratory, United States Air Force, partially supported the work of Ronald Armstrong. The authors benefited from conversations with Dr. Wayne Shore, Operational Technologies, San Antonio, Texas. The order of authors' names is alphabetical and denotes equal authorship.     

Parallel distributed processing and neuropsychology: A neural network model of Wisconsin card sorting and verbal fluency

Neural networks can be used as a tool in the explanation of neuropsychological data. Using the Hebbian Learning Rule and other such principles as competition and modifiable interlevel feedback, researchers have successfully modeled a widely used neuropsychological test, the Wisconsin Card Sorting Test. One of these models is reviewed here and extended to a qualitative analysis of how verbal fluency might be modeled, which demonstrates the importance of accounting for the attentional components of both tests. Difficulties remain in programming sequential cognitive processes within a parallel distributed processing (PDP) framework and integrating exceedingly complex neuropsychological tests such as Proverbs. PDP neural network methodology offers neuropsychologists co-validation procedures within narrowly defined areas of reliability and validity.     

Connectionist models of conditioning: A tutorial

Models containing networks of neuron-like units have become increasingly prominent in the study of both cognitive psychology and artificial intelligence. This article describes the basic features of connectionist models and provides an illustrative application to compound-stimulus effects in respondent conditioning. Connectionist models designed specifically for operant conditioning are not yet widely available, but some current learning algorithms for machine learning indicate that such models are feasible. Conversely, designers for machine learning appear to have recognized the value of behavioral principles in producing adaptive behavior in their creations.     

内侧前额皮层-伏隔核环路在决策冲动中的作用：基于动物模型的研究

注意缺陷多动障碍(attentiondeficit/hyperactivitydisorder,ADHD)行为控制不足与决策冲动密切相关,后者受内侧前额皮层(medial prefrontal cortex, mPFC)与伏隔核(nucleus accumbens, NAc)调节。为调查ADHD决策冲动与m PFC-NAc间功能耦合的关系,研究采用ADHD模型SHR (spontaneously hypertensive rat, SHR)大鼠,结合延迟折扣任务和在体电生理,研究发现,与对照Wistar (WIS)大鼠相比, SHR大鼠对延迟大奖赏的选择百分比降低; WIS大鼠m PFC-NAc的Theta频段相干值表现为延迟选择时显著大于立即选择时、首次选择时大于连续选择时、转换试次时大于连续试次时,而SHR大鼠在上述条件均低于WIS大鼠。回归分析发现m PFC-NAc的相干差值与延迟大奖赏选择率显著正相关。结果表明m PFC-NAc间功能联系减弱是ADHD决策冲动缺陷的重要环路基础,该缺陷与其深度信息加工以及策略转换能力受损有关,扩展了ADHD决策冲动的认知和神经机制的认识。     

人类颜色视觉的计算理论

该文有机地结合了计算视觉理论和生态学视觉理论,指出颜色信息处理的根本任务是检测环境中的光不变量。在此基础上,作者提出了颜色视觉的计算理论以及计算理论本身的生物学标准。初级视觉计算是典型的不适定问题,动物的视觉系统则利用视环境中存在的条件将该不适定问题转化为定解问题。本文引入颜色视觉计算的免要条件,客观性约束,以及颜色认知的神经表象,证明了上述约束下颜色算法的存在性。本文给出了构造颜色知觉的基本假设。同时,该文还讨论了与上述问题密切相关的几个基本问题:神经表象的完备性,主观色觉的客观性,明度知觉和颜色知觉的统一,人类主观色觉的实现方式。     

Fuzzy Logic and Arithmetical Hierarchy,II

A very simple many-valued predicate calculus is presented; a completeness theorem is proved and the arithmetical complexity of some notions concerning provability is determined.     

飞行错觉水平评定方法的初步研究

本文从飞行错觉发生的类型和频率出发,尝试对飞行员在实际飞行中所产生的错觉水平或空间定向能力做出基本的评定。本研究采用区间模糊统计的方法,对飞行错觉的类型（倾斜错觉、俯仰错觉、方位错觉、反旋转错觉和倒飞错觉）和频率（从无、偶尔、时有、经常、总是）做了经验的赋值工作,分别计算出了这些模糊概念（除从无外）的心理量表值和模糊度。同时我们认为,飞行错觉水平是频率与类型的函数,其数学表达式为：,并将这一表达式作为评定飞行员飞行错觉水平的基本模型。