F-test bias for experimental designs of the Latin square type

In an earlier paper, a method of analysis, due to Neyman and now known generally as variance component analysis, was used to examineF-test bias for experimental designs in education of the randomized block type. The same method is now applied to studyF-test bias for designs of the Latin square type. The results, in general, disprove the view that, for a valid application of Latin square techniques, it is necessary that all interactions are zero.     

F-test bias for experimental designs in educational research

Reference is made to Neyman's study ofF-test bias for the randomized blocks and Latin square designs employed in agriculture, and some account is given of later statistical developments which sprang from his work—in particular, the classification of model-types and the technique of variance component analysis. It is claimed that there is a need to carry out an examination ofF-test bias for experimental designs in education and psychology which will utilize the method and, where appropriate, the known' results of this new branch of variance analysis. In the present paper, such an investigation is carried out for designs which may be regarded as derivatives of the agricultural randomized blocks design. In a paper to follow, a similar investigation will be carried out for experimental designs of the Latin square type.     

Clinical significance of serum phenytoin level]

Phenytoin serum concentrations in 115 epileptics treated on an inpatient basis showed, in two groups, only relatively weak correlation with the dosage used. The range of variation was assigned a factor of 16. For individual patients there was observed a correlation between the dosage in mg/kg and the corresponding phenytoin serum concentration. The level of phenytoin serum concentration was not found to be related to the frequency of attacks. The effective phenytoin serum level showed interindividual differences and can be low as well as relatively high. Resulting from this is the need for determining, from the determination of serum concentrations, the effective serum concentration for each individual patient.     

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.     

表象计算心理模型研究的新进展

对大脑工作机理的研究和解释一直是认知心理学研究的基本问题。知识表征图式的研究源于对基于模型推理的认知和计算。在人工智能中,基于模型的推理主要包括在语义领域用计算提供真命题,基于规则的推理系统则是一个规则建构过程。现代认知心理学的研究不断的表明．许多人类的空间推理是基于模型的,而不是基于规则的;心理模型是通过知觉输入或自然语言的描述而建构的,它们可以看作是从一表征形式向另一表征形式的转换。     

Parallel Computation and the Mind-Body Problem

The position in the philosophy of mind called functionalism claims that mental states are to be understood in terms of their functional relationships to other mental states, not in terms of their material instantiation in any particular kind of hardware. But the argument that material instantiation is irrelevant to functional relationships is computationally naive. This paper uses recent work on parallel computation to argue that software and hardware are much more intertwined than the functionalists allow. Parallelism offers qualitative as well as quantitative advantages, leading to different styles of programming as well as increased speed. Hence hardware may well matter to the mental: only by further empirical investigations of the relation between the mind and brain and between artificial intelligence software and underlying hardware will we be able to achieve a defensible solution to the mind-body problem. The major disadvantage of parallel systems is the need to coordinate their subprocesses, but recent proposals that consciousness provides a serial control for parallel computation are implausible.     

Computation of psychophysical thresholds using the probit technique

Probit analysis is a statistical technique that can be used for the computation of psychophysical thresholds, although it was originally developed for the analysis of pharmacological and toxicological data. Probit analysis may be applied to psychophysical data if the method of constant stimuli has been used for data collection. A computer program written in APPLESOFT BASIC that performs probit analyses on such psychophysical data is presented and explained.     

Anaphoric Pronouns and the Computation of Prominence Profiles

Journal of Psycholinguistic Research - Previous research has investigated anaphoric resolution at the anaphor. Using a self-paced reading study we show that prominence profiles, i.e. the ranking of...     

Neural Computation and the Computational Theory of Cognition

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non‐neural notions of computation ought to be replaced or reinterpreted in terms of neural computation.     

The Value of Symbolic Computation

Standard generative linguistic theory, which uses discrete symbolic models of cognition, has some strengths and weaknesses. It is strong on providing a network of outposts that make scientific travel in the jungles of natural language feasible. It is weak in that it currently depends on the elaborate and unformalized use of intuition to develop critical supporting assumptions about each data point. In this regard, it is not in a position to characterize natural language systems in the lawful terms that ecological psychologists strive for. Connectionist learning models offer some help: They define lawful relations between linguistic environments and language systems. But our understanding of them is currently weak, especially when it comes to natural language syntax. Fortunately, symbolic linguistic analysis can help connectionism if the two meet via dynamical systems theory. I discuss a case in point: Insights from linguistic explorations of natural language syntax appear to have identified information structures that are particularly relevant to understanding ecologically appealing but analytically mysterious connectionist learning models.     

Limits of Neural Computation in Humans and Machines

Science and Engineering Ethics - Aicardi et al. (Ethical and social aspects of neurorobotics, Science and Engineering Ethics, 2020) look to neuroscience to mitigate the limitations of current...     

Computation of Aristotle's and Gergonne's syllogisms

A connection between Aristotle's syllogistic and the calculus of relations is investigated. Aristotle's and Gergonne's syllogistics are considered as some algebraic structures. It is proved that Gergonne's syllogistic is isomorphic to closed elements algebra of a proper approximation relation algebra. This isomorphism permits to evaluate Gergonne's syllogisms and also Aristotle's syllogisms, laws of conversion and relations in the square of oppositions by means of regular computations with Boolean matrices.     

MPROB: Computation of multinomial probabilities

An algorithm for computing exact multinomial probabilities is presented that uses the fewest number of operations that are possible without symbolic simplification of the multinomial coefficient and performs them in a sequence that minimizes the potential for overflow or underflow errors. Multinomial probabilities computed with this algorithm are accurate to machine precision, unlike those based on logarithmic transformations and/or the evaluation of factorials, using Stirling’s approximation.     

Multiple Realization,Computation and the Taxonomy of Psychological States

The paper criticizes standard functionalist arguments for multiple realization. It focuses on arguments in which psychological states are conceived as computational, which is precisely where the multiple realization doctrine has seemed the strongest. It is argued that a type-type identity thesis between computational states and physical states is no less plausible than a multiple realization thesis. The paper also presents, more tentatively, positive arguments for a picture of local reduction.     

A Model of Students' Decimal Computation Procedures

A model that describes the construction and execution of decimal computation procedures is presented. Our hypothesis is that students compute by relying solely on syntax-based rules; semantic knowledge has no effect on performance. To test the claim, a model is developed in which computation procedures are viewed as chains of component symbol manipulation rules. The model assumes that students acquire through instruction the individual rules that achieve subgoals in the computation process. The task for the procedural system is to select rules that satisfy each subgoal in sequence. The model specifies the rules of the system and identifies the syntactic features of the task that affect the selection of individual rules at each decision point. It then predicts the relative difficulty of decimal computation items and predicts the procedural flaw that will occur most frequently on each item. Written test and interview data are presented to test the predictions. Concluding comments discuss the nature of students' computation procedures, compare the model with other models of computation performance, and outline how the model might inform instruction.