Intuitive physics and cognitive algebra: A review

IntroductionIntuitive physics explores how people without a formal instruction in physics intuitively understand physical phenomena. After a general overview of the topics of current research in intuitive physics and a discussion of current debates, this paper provides an introduction to Information Integration Theory (IIT).ObjectiveBy means of examples, it is shown how IIT can be used to directly compare the algebraic structure of physical laws and the algebraic structure of cognitive representations of these laws.MethodThe review considers about 40 years of research on the application of IIT in the field of intuitive physics. Occasionally, reference is also made to intuitive physics studies outside this theoretical framework.ResultsThe reviewed studies highlight four main factors that affect the degree of consistency between physical laws and cognitive algebraic laws: the participants’ age, their familiarity with the event under study, the type of task, and possible learning processes.ConclusionThe last part of the article discusses the implications of the results of the reviewed studies for the two main current hypotheses on the nature of intuitive physics, namely, that intuitive physics may be based on sub-optimal heuristics or may be based on the internalization of physical laws.     

离散还是连续?——语义信息加工性质的实验研究

通过两个行为实验,采用句子核证作业,将SAD技术和普通反应时实验有机地结合起来,对语义信息加工的性质进行了详细的考察。实验一采用普通反应时实验,探讨了语义信息加工机制的性质,结果表明,在熟悉度、语义距离和快慢反应时之间存在复杂的交互作用,说明在语义信息加工中存在两种不同性质的加工机制,熟悉性是影响这两种不同性质加工机制的因素。实验二采用SAD技术,研究熟悉性对语义信息加工性质的影响。结果表明,在高熟悉句中的部分信息积累具有离散的趋势;而低熟悉句中的部分信息积累则是连续的,进一步证明了两种性质加工机制的存在。结论认为：语义信息加工中存在着两种性质的加工：离散性加工机制和连续性加工机制,熟悉性是影响二者关系的一个重要因素。     

Quantifying false positives in simulated events using partial interval recording and momentary time sampling with dual‐criteria methods

The dual‐criteria (DC) and conservative dual‐criteria (CDC) methods allow clinicians and researchers to quantify the occurrence of false‐positive outcomes within single‐case experimental designs. The purpose of the current study was to use these DC and CDC methods to measure the incidence of false positives with simulated data collected via discontinuous interval methods (i.e., momentary time sampling, partial‐interval recording) as a function of data series length. We generated event data to create 316,800 unique simulated data series for analysis. In Experiment 1, we evaluated how changes in relevant parameters (i.e., interval sizes, event durations, IRT‐to‐event‐run ratios) produced false positives with momentary time sampling procedures. We also assessed the degree that the CDC method produced fewer false positives than the DC method with simulated interval data. In Experiment 2, we used similar procedures to quantify the occurrence of false positives with partial‐interval recording data. We successfully replicated outcomes from previous research in the current study, though such results only highlight the generality of the procedures relating to false positive (and not false negative) outcomes. That is, these results indicate MTS and PIR may adequately control for false positives, but our conclusions are limited by a lack of data on power.     

A comparative evaluation of factor- and component-based structural equation modelling approaches under (in)correct construct representations

Structural equation modelling (SEM) has evolved into two domains, factor-based and component-based, dependent on whether constructs are statistically represented as common factors or components. The two SEM domains are conceptually distinct, each assuming their own population models with either of the statistical construct proxies, and statistical SEM approaches should be used for estimating models whose construct representations correspond to what they assume. However, SEM approaches have often been evaluated and compared only under population factor models, providing misleading conclusions about their relative performance. This is partly because population component models and their relationships have not been clearly formulated. Also, it is of fundamental importance to examine how robust SEM approaches can be to potential misrepresentation of constructs because researchers may often lack clear theories to determine whether a factor or component is more representative of a given construct. Addressing these issues, this study begins by clarifying several population component models and their relationships and then provides a comprehensive evaluation of four SEM approaches – the maximum likelihood approach and factor score regression for factor-based SEM as well as generalized structured component analysis (GSCA) and partial least squares path modelling (PLSPM) for component-based SEM – under various experimental conditions. We confirm that the factor-based SEM approaches should be preferred for estimating factor models, whereas the component-based SEM approaches should be chosen for component models. Importantly, the component-based approaches are generally more robust to construct misrepresentation than the factor-based ones. Of the component-based approaches, GSCA should be chosen over PLSPM, regardless of whether or not constructs are misrepresented.     

PLS model building: A multivariate approach to personality test data

The aim of this study was to demonstrate how personality test data can be plotted with a multivariate method known as Partial Least Squares of Latent Structures (PLS). The basic methodology behind PLS modeling is presented and the example demonstrates how a PLS model of personality test data can be used for diagnostic prediction. Principles for validating the models are also presented. The conclusion is that PLS modeling appears to be a powerful method for extracting clinically relevant information from complex personality test data matrixes. It could be used as a complement to more hard modeling methods in the process of examining a new area of interest.     

Phonological coding in reading comprehension: The importance of individual differences

Abstract

In two experiments, texts were presented word by word at a fixed pace to subjects high or low in test anxiety, under various concurrent interference conditions: no interference, articulatory suppression, finger tapping, unattended meaningful speech, reversed (meaningless) speech and nonword (meaningless) speech. High-anxiety subjects produced overt articulation more frequently than low-anxiety subjects, especially in the speech conditions. There was an interaction between anxiety and interference on comprehension performance: Conditions thought to interfere with phonological coding (i.e. articulatory suppression and nonword speech) were detrimental to comprehension for high but not for low-anxiety subjects; in contrast, there were no differences in comprehension performance as a function of anxiety under conditions causing no (proper) phonological interference (i.e. no interference, finger tapping and reversed speech). There were mixed results in the meaningful speech condition, partly attributable to its semantic interference component. These findings reveal that the use and importance of phonological coding in reading comprehension vary with differences in test anxiety-high-anxiety subjects are vulnerable to disruptive effects on phonological coding. This suggests that high-anxiety individuals are especially likely to resort to speech-based coding during reading, and that phonological coding has a compensatory role for them.     

代数应用题项目生成中的认知过程与任务特征分析

认知项目设计起源于理论驱动的测量模式,要求在详尽阐述测量建构理论的基础上,鉴别测验项目类型的任务特征,建立和标定与测量建构认知变量之间的对应关系,从而通过系统变换任务特征生成项目.本文以Greeno (1994)提出的代理者-情境交互作用(agent-situation interaction)的问题解决观为基础,对代数应用题解决的认知过程和任务蕴含性(affordance)进行了系统分析.具体而言,代数应用题被视为以文本形式呈现的,嵌套在现实情境中的某种量化结构.在代数应用题解决的转译、整合、计划、执行的认知过程中需要形成三种不同的表征,即文本基、情境模型和问题模型.其形成及解决过程受到个体问题图式知识的深刻影响.对应于不同表征方式,对代数应用题语法/语义结构、量化元素及结构、问题情境特征进行了系统分析和综合.该分析表明,为了保证代数应用题项目生成的科学性,当前研究的关键在于建立任务特征与问题解决认知变量之间的经验关系,并在问题领域整体水平上检验这种关系的可推广性.     

The effects of cumulative practice on mathematics problem solving

This study compared three different methods of teaching five basic algebra rules to college students. All methods used the same procedures to teach the rules and included four 50-question review sessions interspersed among the training of the individual rules. The differences among methods involved the kinds of practice provided during the four review sessions. Participants who received cumulative practice answered 50 questions covering a mix of the rules learned prior to each review session. Participants who received a simple review answered 50 questions on one previously trained rule. Participants who received extra practice answered 50 extra questions on the rule they had just learned. Tests administered after each review included new questions for applying each rule (application items) and problems that required novel combinations of the rules (problem-solving items). On the final test, the cumulative group outscored the other groups on application and problem-solving items. In addition, the cumulative group solved the problem-solving items significantly faster than the other groups. These results suggest that cumulative practice of component skills is an effective method of training problem solving.