Contrasting Acquisition-Focused and Performance-Focused Models of Acquired Behavior

What is encoded during a learning experience? In a learning situation, a training (acquisition) phase is often followed by a test phase. Acquisition-focused models (most associative models) emphasize information processing that occurs during training and assume that only summary statistics (associative values) are retained to influence behavior during testing. Performance-focused models (a k a "computational" models) emphasize information processing that occurs at test and often assume fairly comprehensive memory of prior experiences. In this article, we contrast these two families of models. We note that neither family can claim unique support from the various learning phenomena commonly cited as favoring one or the other position. Within each family, there are highly diverse models that preclude blanket generalizations. However, the acquisition-focused view that subjects retain only summary statistics suggests unrealistically impoverished memories relative to the fairly veridical memories that clearly are formed. But this oversimplifying assumption of acquisition-focused associative theories is exactly what has made them superior to performance-focused models to date in stimulating research.     

信息保持、短时存贮与执行控制的脑模型

工作记忆的脑机制是认知神经科学研究的重要问题。研究者在分析大量脑成像研究数据的基础上,创建了许多解释工作记忆神经基础的模型。Smith和Jonides发展了工作记忆成分结构模型和贮存与执行加工分离模型。Postle和D’Esposito建构了工作记忆相对表征混合模型,D’Esposito, Postle和Rypma阐述了工作记忆加工阶段动态模型。Fletcher和Henson提出工作记忆额叶分区整合理论。该文通过对上述模型或理论的评价,总结了工作记忆脑成像研究中存在的问题,展望了该研究领域的发展趋势。     

Fronto-parietal mirror neuron system modeling: Visuospatial transformations support imitation learning independently of imitator perspective

Although the human mirror neuron system (MNS) is critical for action observation and imitation, most MNS investigations overlook the visuospatial transformation processes that allow individuals to interpret and imitate actions observed from differing perspectives. This problem is not trivial since accurately reaching for and grasping an object requires a visuospatial transformation mechanism capable of precisely remapping fine motor skills where the observer’s and imitator’s arms and hands may have quite different orientations and sizes. Accordingly, here we describe a novel neural model to investigate the dynamics between the fronto-parietal MNS and visuospatial processes during observation and imitation of a reaching and grasping action. Our model encompasses i) the inferior frontal gyrus (IFG) and inferior parietal lobule (IPL), regions that are postulated to produce neural drive and sensory predictions, respectively; ii) the middle temporal (MT) and middle superior temporal (MST) regions that are postulated to process visual motion of a particular action; and iii) the superior parietal lobule (SPL) and intra-parietal sulcus (IPS) that are hypothesized to encode the visuospatial transformations enabling action observation/imitation based on different visuospatial viewpoints. The results reveal that when a demonstrator executes an action, an imitator can reproduce it with similar kinematics, independently of differences in anthropometry, distance, and viewpoint. As with prior empirical findings, similar model synaptic activity was observed during both action observation and execution along with the existence of both view-independent and view-dependent neural populations in the frontal MNS. Importantly, this work generates testable behavioral and neurophysiological predictions. Namely, the model predicts that i) during observation/imitation the response time increases linearly as the rotation angle of the observed action increases but remain similar when performing both clockwise and counterclockwise rotation and ii) IPL embeds essentially view-independent neurons while SPL/IPS includes both view-independent and view-dependent neurons. Overall, this work suggests that MT/MST visuomotion processes combined with the SPL/IPS allow the MNS to observe and imitate actions independently of demonstrator-imitator spatial relationships.     

On scaling models applied to data from several groups

Statistical methods are presented to facilitate a more complete analysis of results obtained when a scaling model is applied to data from two or more groups. These methods can be used to (a) compare the corresponding estimated latent distributions obtained using the scaling model applied to the different groups, (b) compare the corresponding estimated item reliabilities (or item response error rates) for the different groups, and (c) test whether the scaling model applied to the several groups can be replaced by a more parsimonious scaling model that includes various homogeneity constraints (i.e., constraints that describe which parameters in the model are the same for the several groups). Various kinds of scaling models are considered here in the multiple-group context.Support for this research was provided in part by the National Science Foundation, to Clogg by Grant No. SES-7823759 and to Goodman by Grant No. SES-8303838. Clogg and Goodman were Fellows at the Center for Advanced Study in the Behavioral Sciences when part of the research was done, with financial support provided in part by National Science Foundation grant BNS-8011494 to the Center. The authors are indebted to Mark P. Becker and James W. Shockey for helpful comments.     

Bayes modal estimation in item response models

This article describes a Bayesian framework for estimation in item response models, with two-stage prior distributions on both item and examinee populations. Strategies for point and interval estimation are discussed, and a general procedure based on the EM algorithm is presented. Details are given for implementation under one-, two-, and three-parameter binary logistic IRT models. Novel features include minimally restrictive assumptions about examinee distributions and the exploitation of dependence among item parameters in a population of interest. Improved estimation in a moderately small sample is demonstrated with simulated data.This research was supported by a grant from the Spencer Foundation, Chicago, IL. Comments and suggestions on earlier drafts by Charles Lewis, Frederic Lord, Rosenbaum, James Ramsey, Hiroshi Watanabe, the editor, and two anonymous referees are gratefully acknowledged.     

Extended similarity trees

Proximity data can be represented by an extended tree, which generalizes traditional trees by including marked segments that correspond to overlapping clusters. An extended tree is a graphical representation of the distinctive features model. A computer program (EXTREE) that constructs extended trees is described and applied to several sets of conceptual and perceptual proximity data.This research was supported in part by a National Science Foundation Pre-doctoral Fellowship to the first author.A magnetic tape containing both the EXTREE program described in the article and ADDTREE/P program for fitting additive trees can also be obtained from the above address. Requests for the program should be accompanied by a check for $25 made out to Teachers College, to cover the costs of the tape and postage.     

Sequential sampling designs for the two-parameter item response theory model

In optimal design research, designs are optimized with respect to some statistical criterion under a certain model for the data. The ideas from optimal design research have spread into various fields of research, and recently have been adopted in test theory and applied to item response theory (IRT) models. In this paper a generalized variance criterion is used for sequential sampling in the two-parameter IRT model. Some general principles are offered to enable a researcher to select the best sampling design for the efficient estimation of item parameters.     

The finite model property for BCI and related systems

We prove the finite model property (fmp) for BCI and BCI with additive conjunction, which answers some open questions in Meyer and Ono [11]. We also obtain similar results for some restricted versions of these systems in the style of the Lambek calculus [10, 3]. The key tool is the method of barriers which was earlier introduced by the author to prove fmp for the product-free Lambek calculus [2] and the commutative product-free Lambek calculus [4].Presented by H. Ono     

An alternative two stage least squares (2SLS) estimator for latent variable equations

The Maximum-likelihood estimator dominates the estimation of general structural equation models. Noniterative, equation-by-equation estimators for factor analysis have received some attention, but little has been done on such estimators for latent variable equations. I propose an alternative 2SLS estimator of the parameters in LISREL type models and contrast it with the existing ones. The new 2SLS estimator allows observed and latent variables to originate from nonnormal distributions, is consistent, has a known asymptotic covariance matrix, and is estimable with standard statistical software. Diagnostics for evaluating instrumental variables are described. An empirical example illustrates the estimator. I gratefully acknowledge support for this research from the Sociology Program of the National Science Foundation (SES-9121564) and the Center for Advanced Study in the Behavioral Sciences, Stanford, California. This paper was presented at the Interdisciplinary Consortium for Statistical Applications at Indiana University at Bloomington (March 2, 1994) and at the RMD Conference on Causal Modeling at Purdue University, West Lafayette, Indiana (March 3-5, 1994).     

Polytomous IRT models and monotone likelihood ratio of the total score

In a broad class of item response theory (IRT) models for dichotomous items the unweighted total score has monotone likelihood ratio (MLR) in the latent trait. In this study, it is shown that for polytomous items MLR holds for the partial credit model and a trivial generalization of this model. MLR does not necessarily hold if the slopes of the item step response functions vary over items, item steps, or both. MLR holds neither for Samejima's graded response model, nor for nonparametric versions of these three polytomous models. These results are surprising in the context of Grayson's and Huynh's results on MLR for nonparametric dichotomous IRT models, and suggest that establishing stochastic ordering properties for nonparametric polytomous IRT models will be much harder.Hemker's research was supported by the Netherlands Research Council, Grant 575-67-034. Junker's research was supported in part by the National Institutes of Health, Grant CA54852, and by the National Science Foundation, Grant DMS-94.04438.