Conditions for factor (in)determinacy in factor analysis

The subject of factor indeterminacy has a vast history in factor analysis (Guttman, 1955; Lederman, 1938; Wilson, 1928). It has lead to strong differences in opinion (Steiger, 1979). The current paper gives necessary and sufficient conditions for observability of factors in terms of the parameter matrices and a finite number of variables. Five conditions are given which rigorously define indeterminacy. It is shown that (un)observable factors are (in)determinate. Specifically, the indeterminacy proof by Guttman is extended to Heywood cases. The results are illustrated by two examples and implications for indeterminacy are discussed.     

Examination of efficacious,efficient, and socially valid error‐correction procedures to teach sight words and prepositions to children with autism spectrum disorder

Prior research shows that learners have idiosyncratic responses to error‐correction procedures during instruction. Thus, assessments that identify error‐correction strategies to include in instruction can aid practitioners in selecting individualized, efficacious, and efficient interventions. The current investigation conducted an assessment to compare 5 error‐correction procedures that have been evaluated in the extant literature and are common in instructional practice for children with autism spectrum disorder (ASD). Results showed that the assessment identified efficacious and efficient error‐correction procedures for all participants, and 1 procedure was efficient for 4 of the 5 participants. To examine the social validity of error‐correction procedures, participants selected among efficacious and efficient interventions in a concurrent‐chains assessment. We discuss the results in relation to prior research on error‐correction procedures and current instructional practices for learners with ASD.     

Why we can still believe the error theory

The error theory is a metaethical theory that maintains that normative judgments are beliefs that ascribe normative properties, and that these properties do not exist. In a recent paper, Bart Streumer argues that it is impossible to fully believe the error theory. Surprisingly, he claims that this is not a problem for the error theorist: even if we can’t fully believe the error theory, the good news is that we can still come close to believing the error theory. In this paper I show that Streumer’s arguments fail. First, I lay out Streumer’s argument for why we can’t believe the error theory. Then, I argue against the unbelievability of the error theory. Finally, I show that Streumer’s positive proposal that we can come close to believing the error theory is actually undermined by his own argument for why we can’t believe the error theory.     

Empowering and legitimizing the fundamental attribution error: Power and legitimization exacerbate the translation of role‐constrained behaviors into ability differences

Our daily interactions are influenced by the social roles we endorse. People however underestimate these role constraints in their everyday explanation relying on individual dispositions to make sense of behaviors. Two studies investigated whether this bias is exacerbated when role structure is legitimated and when power matches the advantages conferred by the social roles of a quiz game. Legitimacy as well as power increased the tendency for both advantaged (questioner) and disadvantaged (answerer) actors (Study 1) as well as naïve observers of the quiz game (Study 2) to attribute to ability the behaviors elicited by social roles. These results extend previous findings. People are more prone to explain constrained behaviors by differences in ability when role structure is legitimated and when power asymmetry matches role structure. Legitimacy and power may then play an important role in the translation of role constraints into inferences about ability.     

Beyond inference by eye: Statistical and graphing practices in JEAB, 1992‐2017

Debates about the utility of p values and correct ways to analyze data have inspired new guidelines on statistical inference by the American Psychological Association (APA) and changes in the way results are reported in other scientific journals, but their impact on the Journal of the Experimental Analysis of Behavior (JEAB) has not previously been evaluated. A content analysis of empirical articles published in JEAB between 1992 and 2017 investigated whether statistical and graphing practices changed during that time period. The likelihood that a JEAB article reported a null hypothesis significance test, included a confidence interval, or depicted at least one figure with error bars has increased over time. Features of graphs in JEAB, including the proportion depicting single‐subject data, have not changed systematically during the same period. Statistics and graphing trends in JEAB largely paralleled those in mainstream psychology journals, but there was no evidence that changes to APA style had any direct impact on JEAB. In the future, the onus will continue to be on authors, reviewers and editors to ensure that statistical and graphing practices in JEAB continue to evolve without interfering with characteristics that set the journal apart from other scientific journals.     

Load Perturbation Facilitates Interpersonal Error Compensation in a Dyadic Force Production Task

The present study tested the hypothesis that a load perturbation facilitates interpersonal compensation for force error. Ten groups performed both control and perturbation conditions. In the control condition, a target discrete peak force was the sum of 10% of the maximum voluntary contraction produced by two participants. In the perturbation condition, two cooperative participants a and b produced the same target force as the control condition, and the force produced by a non-cooperative participant c increased or decreased the total forces produced by participants a and b. The load perturbation facilitated interpersonal compensation for force error but negatively affected performance during joint action. The participants further learned to improve the error compensation over experimental blocks in the perturbation condition.     

Four Covariance Structure Models for Canonical Correlation Analysis: A COSAN Modeling Approach

The mathematical connection between canonical correlation analysis (CCA) and covariance structure analysis was first discussed through the Multiple Indicators and Multiple Causes (MIMIC) approach. However, the MIMIC approach has several technical and practical challenges. To address these challenges, a comprehensive COSAN modeling approach is proposed. Specifically, we define four COSAN-CCA models to correspond with four possible combinations of the data to be analyzed and the unique parameters to be estimated. In terms of the data, one can analyze either the unstandardized or standardized variables. In terms of the unique parameters, one can estimate either the weights or loadings. Besides the unique parameters of each COSAN-CCA model, all four COSAN-CCA models also estimate the canonical correlations as their common parameters. Taken together, the four COSAN-CCA models provide the correct point estimates and standard error estimates for all commonly used CCA parameters. Two numeric examples are used to compare the standard error estimates obtained from the MIMIC approach and the COSAN modeling approach. Moreover, the standard error estimates from the COSAN modeling approach are validated by a simulation study and the asymptotic theory. Finally, software implementation and future extensions are discussed.     

Effects of auditory rhythm on movement accuracy in dance performance

The present study addresses the impact of the rhythmic complexity of music on the accuracy of dance performance. This study examined the effects of different levels of auditory syncopation on the execution of a dance sequence by trained dancers and exercisers (i.e., nondancers). It was hypothesized that nondancers would make more errors in synchronizing movements with moderately and highly syncopated rhythms while no performance degradation would manifest among trained dancers. Participants performed a dance sequence synchronized with three different rhythm tracks that were regular, moderately syncopated, and highly syncopated. We found significant performance degradation when comparing conditions of no syncopation vs. high syncopation for both trained dancers (p = .002) and nondancers (p = .001). Dancers and nondancers did not differ in how they managed to execute the task with increasing levels of syncopation (p = .384). The pattern of difference between trained dancers and nondancers was similar across the No Syncop and Highly Syncop conditions. The present findings may have marked implications for practitioners given that the tasks employed were analogous to those frequently observed in real-life dance settings.     

听觉预测编码：对声音重复和变化的神经反应

预测编码被认为是脑与复杂环境交互的重要机制之一, 有效感知外界环境并对未来事件做出预测, 对个体生存有着至关重要的意义。人类大脑会基于感觉输入以迭代的方式持续优化表征外部环境的内部模型, 并不断预测接下来的感觉输入。以听觉模态为例, 人类及动物对声音重复和听觉变化的神经反应(如失匹配负波和刺激特异性适应)是大脑预测编码的重要体现, 表现为重复抑制和预测误差。结合人类和动物模型在此理论框架下开展跨物种研究将有助于加深我们对听觉加工, 甚至是大脑工作机制的认识。