Nonlinear Indicator-Level Moderation in Latent Variable Models

Linear, nonlinear, and nonparametric moderated latent variable models have been developed to investigate possible interaction effects between a latent variable and an external continuous moderator on the observed indicators in the latent variable model. Most moderation models have focused on moderators that vary across persons but not across the indicators (e.g., moderators like age and socioeconomic status). However, in many applications, the values of the moderator may vary both across persons and across indicators (e.g., moderators like response times and confidence ratings). Indicator-level moderation models are available for categorical moderators and linear interaction effects. However, these approaches require respectively categorization of the continuous moderator and the assumption of linearity of the interaction effect. In this article, parametric nonlinear and nonparametric indicator-level moderation methods are developed. In a simulation study, we demonstrate the viability of these methods. In addition, the methods are applied to a real data set pertaining to arithmetic ability.     

Nonlinear Regime-Switching State-Space (RSSS) Models

Nonlinear dynamic factor analysis models extend standard linear dynamic factor analysis models by allowing time series processes to be nonlinear at the latent level (e.g., involving interaction between two latent processes). In practice, it is often of interest to identify the phases—namely, latent “regimes” or classes—during which a system is characterized by distinctly different dynamics. We propose a new class of models, termed nonlinear regime-switching state-space (RSSS) models, which subsumes regime-switching nonlinear dynamic factor analysis models as a special case. In nonlinear RSSS models, the change processes within regimes, represented using a state-space model, are allowed to be nonlinear. An estimation procedure obtained by combining the extended Kalman filter and the Kim filter is proposed as a way to estimate nonlinear RSSS models. We illustrate the utility of nonlinear RSSS models by fitting a nonlinear dynamic factor analysis model with regime-specific cross-regression parameters to a set of experience sampling affect data. The parallels between nonlinear RSSS models and other well-known discrete change models in the literature are discussed briefly.     

Exploring Nonlinear Models in Personality Assessment: Development and Preliminary Validation of a Negative Emotionality Scale

ABSTRACT In this article we discuss the applicability of several new measurement models to the construction of personality scales, and we contrast these models with more traditional approaches in common usage, such as the principal factor analysis model. Our goal is to illustrate how nonlinear item-response models can be profitably used in personality research. We describe the development of a 30-item Negative Emotionality scale that was constructed using nonlinear factor analysis and item-response theory. We also show how traditional (linear) factor analysis can produce misleading results when it is applied to personality items with dichotomous response formats (e.g., true/false, agree/disagree). No formal training in modern measurement theory is assumed of the reader as we describe the nonlinear models that are used in this study in nontechnical language with a minimum of mathematics.     

Probing Theories of Individual Well-being: A Comparison of Quality-of-life Models Assessing Neighborhood Satisfaction

The extensively documented approach to quality of life proposed by Campbell, Converse, and Rodgers (1976) is translated into a testable theoretical model. Herein labeled the "first-order structural model," this approach springs from a popular interpretation of Lewinian concepts. Critical examination of this interpretation leads to several modifications of the measurement and causal structures hypothesized by Campbell et al., each of which is submitted to test. Likewise, an alternative theoretical approach based on a second, less well known interpretation of the same Lewinian concepts, herein labeled the "second-order structural model," is tested. These models are evaluated by structural equation techniques on a sample of 419 respondents dealing with neighborhood satisfaction. Analyses indicate that both the first-order and second-order structural models yield statistically acceptable accounts of neighborhood satisfaction, but that the second-order model performs better on several modeling criteria. The implications for using structural equation techniques to evaluate competing theory-motivated models are discussed.     

口语产生中的认知年老化及其神经机制

综述了口语产生的认知年老化现象及其认知神经机制, 总结了年龄因素对口语产生各阶段的影响及认知机制, 主要从从舌尖效应、同音词的图片命名及其启动效应等方面进行了阐述。在此基础上阐述了关于口语产生认知年老化的理论：激活不充分假说、传递不足假说和抑制不足假说, 从不同角度对认知年老化进行了分析。总结了采用fMRI和ERP技术所进行的口语产生认知年老化过程的认知神经科学研究, 概括了其认知神经机制及其发生的时间进程, 重点阐述了舌尖效应发生的神经机制。最后, 从“认知年老化”和“口语产生”这两个研究领域, 对口语产生认知年老化的研究提出了展望。     

Timing of multiple overlapping intervals: how many clocks do we have?

Humans perceive and reproduce short intervals of time (e.g. 1-60 s) relatively accurately, and are capable of timing multiple overlapping intervals if these intervals are presented in different modalities [e.g., Rousseau, L., & Rousseau, R. (1996). Stop-reaction time and the internal clock. Perception and Psychophysics, 58(3), 434-448]. Tracking multiple intervals can be explained either by assuming multiple internal clocks or by strategic arithmetic using a single clock. The underlying timescale (linear or nonlinear) qualitatively influences the predictions derived from these accounts, as assuming a nonlinear timescale introduces systematic errors in added or subtracted intervals. Here, we present two experiments that provide support for a single clock combined with a nonlinear underlying timescale. When two equal but partly overlapping time intervals had to be estimated, the second estimate was positively correlated with the stimulus onset asynchrony. This effect was also found in a second experiment with unequal intervals that showed evidence of subtraction of intervals. The findings were supported by computational models implemented in a previously validated account of interval timing [Taatgen, N. A., Van Rijn, H., & Anderson, J. R. (2007). An integrated theory of prospective time interval estimation: The role of cognition, attention and learning. Psychological Review, 114(3), 577-598].     

Asymmetries in cue competition in forward and backward blocking designs: Further evidence for causal model theory

A hallmark feature of elemental associative learning theories is that multiple cues compete for associative strength when presented with an outcome. Cue competition effects have been observed in humans, both in forward and in backward blocking procedures (e.g., Shanks, 1985) and are often interpreted as evidence for an associative account of human causal learning (e.g., Shanks & Dickinson, 1987). Waldmann and Holyoak (1992), however, demonstrated that cue competition only occurs in predictive, and not diagnostic, learning paradigms. While unexplainable from an associative perspective, this asymmetry readily follows from structural considerations of causal model theory. In this paper, we show that causal models determine the extent of cue competition not only in forward but also in backward blocking designs. Implications for associative and inferential accounts of causal learning are discussed.     

How scenes containing visual errors affect speech fluency in young and older adults

We compared young and older adults’ speech during an error detection task, with some pictures containing visual errors and anomalies and other pictures error-free. We analyzed three disfluency types: mid-phrase speech fillers (e.g., It’s a little, um, girl), repetitions (e.g., He’s trying to catch the- the birds), and repairs (e.g., She- you can see her legs). Older adults produced more mid-phrase fillers than young adults only when describing pictures containing errors. These often reflect word retrieval problems and represent clear disruptions to fluency, so this interaction indicates that the need to form and maintain representations of novel information can specifically compromise older adults’ speech fluency. Overall, older adults produced more repetitions and repairs than young adults, regardless of picture type, indicating general age-related increases in these disfluencies. The obtained patterns are discussed in the context of the Transmission Deficit Hypothesis and other approaches to age-related changes in speech fluency.     

The role of memory consolidation in generalisation of new linguistic information

Accounts of memory that postulate complementary learning systems (CLS) have become increasingly influential in the field of language learning. These accounts predict that generalisation of newly learnt linguistic information to untrained contexts requires offline memory consolidation. Such generalisation should not be observed immediately after training, as these accounts claim unconsolidated representations are context and hippocampus-dependent and gain contextual and hippocampal independence only after consolidation. We trained participants on new affixes (e.g., -nule) attached to familiar word stems (e.g., buildnule), testing them immediately or 2days later. Participants showed an immediate advantage for trained affixes in a speeded shadowing task as long as these affixes occurred in the stem contexts in which they were learnt (e.g., buildnule). This learning effect generalised to words with untrained stems (e.g., sailnule) only in the delayed test condition. By contrast, a non-speeded definition selection task showed immediate generalisation. We propose that generalisation can be supported by initial context-dependent memories given sufficient processing time, but that context-independent lexical representations emerge only following consolidation, as predicted by CLS accounts.     

On Insensitivity of the Chi-Square Model Test to Nonlinear Misspecification in Structural Equation Models

In this paper, we show that for some structural equation models (SEM), the classical chi-square goodness-of-fit test is unable to detect the presence of nonlinear terms in the model. As an example, we consider a regression model with latent variables and interactions terms. Not only the model test has zero power against that type of misspecifications, but even the theoretical (chi-square) distribution of the test is not distorted when severe interaction term misspecification is present in the postulated model. We explain this phenomenon by exploiting results on asymptotic robustness in structural equation models. The importance of this paper is to warn against the conclusion that if a proposed linear model fits the data well according to the chi-quare goodness-of-fit test, then the underlying model is linear indeed; it will be shown that the underlying model may, in fact, be severely nonlinear. In addition, the present paper shows that such insensitivity to nonlinear terms is only a particular instance of a more general problem, namely, the incapacity of the classical chi-square goodness-of-fit test to detect deviations from zero correlation among exogenous regressors (either being them observable, or latent) when the structural part of the model is just saturated.     

Long-term persistence of sort strategy in free classification

Two free classification experiments that investigate the persistence of sort strategy are reported. Participants tend to persist with their initial categorization type (family resemblance or unidimensional) for the remaining sorts, overriding the effects of otherwise influential stimulus properties. Sort type was found to persist even after a one-week delay. Stimulus-driven models of free classification (e.g., the SUSTAIN model, [Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: A network model of category learning. Psychological Review, 111, 309-332]) cannot predict the sort type persistence effects we observe, but they are naturally accounted for by theories that posit strategic selection of a problem-solving strategy (e.g., Hypothesis theory, [Levine, M. (1971). Hypothesis theory and nonlearning despite ideal S-R-reinforcement contingencies. Psychological Review, 78, 130-140]).     

Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models

Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the homoscedasticity assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diagnostics and nine homoscedasticity tests that can be used to make decisions concerning the direction of effects in linear models. Results of a Monte Carlo simulation that demonstrate the adequacy of the approach are presented. An empirical example is provided, and applicability of the methodology in cases of violated assumptions is discussed.     

Not Propositions

Current computational accounts of meaning in the cognitive sciences are based on abstract, amodal symbols (e.g., nodes, links, propositions) that are arbitrarily related to their referents. We argue that such accounts lack convincing empirical support and that they do not provide a satisfactory account for linguistic meaning. One historic set of results supporting the abstract symbol view has come from investigation into comprehension of negated sentences, such as “The buttons are not black.” These sentences are presumed to be understood as two propositions composed of abstract symbols. One proposition corresponds to “the buttons are black,” and it is embedded in another proposition corresponding to “it is not true.” Thus, the propositional account predicts (a) that comprehension of negated sentences should take longer than comprehension of the corresponding positive sentence (because of the time needed to construct the embedding), but (b) that the resulting embedded propositions are informationally equivalent (but of opposite valence) to the simple proposition underlying the positive sentence. Contrary to these predictions, Experiment 1 demonstrates that negated sentences out of context are interpreted as situationally ambiguous, that is, as conveying less specific information than positive sentences. Furthermore, Experiment 2 demonstrates that when negated sentences are used in an appropriate context, readers do not take longer to understand them. Thus, difficulty with negation is demonstrated to be an artifact of presentation out of context. After discussing other serious problems with the use of abstract symbols, we describe the Indexical Hypothesis. This embodied account of meaning does not depend on abstract symbols, and hence it provides a more satisfactory account of meaning.     

无均值结构的潜变量交互效应模型的标准化估计

潜变量交互效应建模研究近年来有两项重要进展, 一是提出了潜变量交互效应模型的标准化估计及其计算公式; 二是发现无均值结构模型可以取代传统的有均值结构模型, 建模大为简化。但标准化估计是在传统的有均值结构模型中建立的, 在简化的模型中同样适用吗？本文在无均值结构模型的框架内, 给出了潜变量交互效应模型的标准化形式、计算公式和建模步骤, 并通过模拟研究比较了极大似然和广义最小二乘两种估计方法、配对乘积指标和全部乘积指标两种指标类型, 结果表明, 在计算交互效应的标准化估计时, 应当使用配对乘积指标建模, 并且首选极大似然估计。     

When vaccinating against information reduction works and when it does not work

Practice not only affects how information is processed, but also which information is processed. The Information-Reduction Hypothesis (i.e. Haider & Frensch, 1996) holds that — with practice — irrelevant task information (i.e. information that is not logically needed to correctly perform the task) is discarded from processing. Recently, Gaschler and Frensch (2007) have demonstrated that Information Reduction is not affected by the frequency with which individual task configurations are presented: well-practiced and little-practiced irrelevant task configurations are discarded at the same point in time during practice; Information Reduction is thus an item-general phenomenon. These findings suggest that Information Reduction is at least in part a consequence of top-down, voluntary control. In the present research, we ask how tasks can be constructed such that Information Reduction is avoided. Our results show that item-general Information Reduction is observed even when it leads to severe processing costs (i.e. errors). On the whole, the present results are in line with models of skill acquisition incorporating top-down modulation (e.g. Haider & Frensch, 2002) and are incompatible with purely data-driven accounts of skill acquisition (e.g. Logan, 1988; Nosofski & Palmeri, 1997; Palmeri, 1997; Rickard, 2004).     

Word association in early Alzheimer's disease

The hypothesis that Alzheimer's disease (AD) degrades semantic representations predicts that AD qualitatively alters spontaneous thoughts. In two experiments contrasting free associations to words with strong (e.g., bride-groom) versus weak (e.g., body-leg) associates participants with AD produced less common responses (e.g., bride-pretty) than normal controls but only for words with strong associations, and only on the first (but not on second or third) association response. Furthermore, all participants produced fewer semantically related responses to words with weak associates. Because strong associations should be retrieved more easily than weak associations these results are problematic for retrieval-based accounts of AD. Instead we propose that AD entails a semantic deficit, and that strong associations involve more semantic processing than weak associations (in all speakers).     

Estimation of the hypothesis hierarchy in probabilistic inference tasks*

Is there a hierarchical order among the hypotheses about functional rules in probabilistic inference tasks, i.e. what is the construction and the procedure of the “hypothesis sampling mechanism” employed by the subjects in this kind of task? According to the hypothesis sampling model initially proposed by Brehmer (1974) there should be a hierarchical order among the hypotheses in the subject's hypothesis pools. The procedures of hypothesis sampling and testing ought to follow this strict data independent order (see e.g. Sniezek, 1986; Brehmer, 1987). Knez (1991a, b) showed, however, that this assumption may be incorrect. As a follow up to these results the question regarding the construction of the subject's hypothesis pools was reapproached in the present study. The results indicated a consistency with the hierarchical assumption (Brehmer, 1974) only regarding the relation between the linear and nonlinear rules but not within these types of rules.     

Comparing decision bound and exemplar models of categorization

The performance of a decision bound model of categorization (Ashby, J992a; Ashby & Maddox, in press) is compared with the performance of two exemplar models. The first is the generalized context model (e.g., Nosofsky, 1986, 1992) and the second is a recently proposed deterministic exemplar model (Ashby & Maddox, in press), which contains the generalized context model as a special case. When the exemplars from each category were normally distributed and the optimal decision bound was linear, the deterministic exemplar model and the decision bound model provided roughly equivalent accounts of the data. When the optimal decision bound was nonlinear, the decision bound model provided a more accurate account of the data than did either exemplar model. When applied to categorization data collected by Nosofsky (1986, 1989), in which the category exemplars are not normally distributed, the decision bound model provided excellent accounts of the data, in many cases significantly outperforming the exemplar models. The decision bound model was found to be especially successful when(1) single subject analyses were performed, (2) each subject was given relatively extensive training, and (3) the subject's performance was characterized by complex suboptimalities. These results support the hypothesis that the decision bound is of fundamental importance in predicting asymptotic categorization performance and that the decision bound models provide a viable alternative to the currently popular exemplar models of categorization.