Predicting environmental learning from spatial abilities: An indirect route

Relationships among spatial abilities, as assessed by a battery of psychometric tests and experimental tasks, and environmental learning, as assessed by a series of macrospatial tasks, were examined in two studies using confirmatory factor analysis with directional paths. The initial study indicated the utility of a five-factor model, one (general spatial ability) derived from psychometric tests, two (spatial-sequential memory and spatial perspective-taking latency) from experimental tasks, and two (topological knowledge and Euclidean direction knowledge) from measures of environmental learning. The best fitting path model further indicated that the spatial-sequential memory factor mediated the relationship between general spatial ability and topological knowledge, and that perspective-taking latency mediated the relationship between general spatial ability and Euclidean direction knowledge. The second study confirmed the five-factor path model using a different participant sample and environmental setting. The only failure to replicate involved the path between perspective-taking latency in the lab and Euclidean direction knowledge in the environment. Results indicate that the relationship between basic spatial abilities and environmental learning is significantly mediated by cognitive processes that can be assessed using laboratory tasks.     

Analyzing human random time generation behavior: A methodology and a computer program

In the present article, it is argued that in addition to the traditional random generation tasks discussed by Towse and Neil (1998), random time interval generation tasks should be considered as useful alternatives, because they allow a better empirical control of the executive task component in dualtask situations. First, a framework for discussing randomness over time is presented. Then, the article goes on to present three methods for the analysis of such tasks. A first method is based on the correlation between the intervals produced. The second method calculates the approximate entropy, and the third method converts the time sequences into binary sequences and estimates the statistical properties of the sequence on the basis of these binary data. A principal components analysis on 19 different measures based on 1,381 sequences produced in a number of single-task and dual-task experiments shows that the proposed measures form two general clusters, one related to output probability, perseveration, and alternation, and one related to sequential commonalities. The article also briefly describes a computer program that implements these methodologies.     

Using Excel to estimate parameters from observed data: An example from source memory data

We present an application, using Excel, that can solve best-fitting parameters for multinomial models. Multinomial modeling has become increasingly popular and can be used in a variety of domains, such as memory, perception, and other domains in which processes are assumed to be dissociable. We offer an application that can be used for a variety of psychological models and can be used on both PC and Macintosh platforms. We illustrate the use of our program by analyzing data from a source memory experiment.     

An interactive multiobjective programming approach to combinatorial data analysis

Combinatorial optimization problems in the social and behavioral sciences are frequently associated with a variety of alternative objective criteria. Multiobjective programming is an operations research methodology that enables the quantitative analyst to investigate tradeoffs among relevant objective criteria. In this paper, we describe an interactive procedure for multiobjective asymmetric unidimensional seriation problems. This procedure uses a dynamic-programming algorithm to partially generate the efficient set of sequences for small to medium-sized problems, and a multioperation heuristic to estimate the efficient set for larger problems. The interactive multiobjective procedure is applied to an empirical data set from the psychometric literature. We conclude with a discussion of other potential areas of application in combinatorial data analysis.Stephanie Stahl is a freelance writer and editor. She can be reached via e-mail at s-stahl@worldnet.att.net.     

Resampling approach to statistical inference: Bootstrapping from event-related potentials data

We propose the use of the bootstrap resampling technique as a tool to assess the within-subject reliability of experimental modulation effects on event-related potentials (ERPs). The assessment of the within-subject reliability is relevant in all those cases when the subject score is obtained by some estimation procedure, such as averaging. In these cases, possible deviations from the assumptions on which the estimation procedure relies may lead to severely biased results and, consequently, to incorrect functional inferences. In this study, we applied bootstrap analysis to data from an experiment aimed at investigating the relationship between ERPs and memory processes. ERPs were recorded from two groups of subjects engaged in a recognition memory task. During the study phase, subjects in Group A were required to make an orthographic judgment on 160 visually presented words, whereas subjects in Group B were only required to pay attention to the words. During the test phase all subjects were presented with the 160 previously studied words along with 160 new words and were required to decide whether the current word was “old” or “new.” To assess the effect of word imagery value, half of the words had a high imagery value and half a low imagery value. Analyses of variance performed on ERPs showed that an imagery-induced modulation of the old/new effect was evident only for subjects who were not engaged in the orthographic task during the study phase. This result supports the hypothesis that this modulation is due to some aspect of the recognition memory process and not to the stimulus encoding operations that occur during the recognition memory task. However, bootstrap analysis on the same data showed that the old/new effect on ERPs was not reliable for all the subjects. This result suggests that only a cautious inference can be made from these data.     

Analyzing sequential categorical data: Individual variation in markov chains

Markov chains are probabilistic models for sequences of categorical events, with applications throughout scientific psychology. This paper provides a method for anlayzing data consisting of event sequences and covariate observations. It is assumed that each sequence is a Markov process characterized by a distinct transition probability matrix. The objective is to use the covariate data to explain differences between individuals in the transition probability matrices characterizing their sequential data. The elements of the transition probability matrices are written as functions of a vector of latent variables, with variation in the latent variables explained through a multivariate regression on the covariates. The regression is estimated using the EM algorithm, and requires the numerical calculation of a multivariate integral. An example using simulated cognitive developmental data is presented, which shows that the estimation of individual variation in the parameters of a probability model may have substantial theoretical importance, even when individual differences are not the focus of the investigator's concerns.Research contributing to this article was supported by B.R.S. Subgrant 5-35345 from the University of Virginia. I thank the DADA Group, Bill Fabricius, Don Hartmann, William Griffin, Jack McArdle, Ivo Molenaar, Ronald Schoenberg, Simon Tavaré, and several anonymous reviewers for their discussion of these points.     

Analyzing sequential relations in observational data: A practical guide

Many observational systems in basic and applied research produce a record of sequences of events over time. Within such observational systems, important information may be found in the frequency of the transitions between events that does not emerge in the typical researcher's focus on absolute event frequency. Indeed, if many behaviors are controlled by closely adjacent preceding events, then substantial prediction and control can be obtained through knowledge and manipulation of causal event transitions. A method for analyzing and testing sequential dependencies between events is proposed as part of an integrated package of computer-based data entry, storage, and analysis procedures. The mathematical portion of these techniques is based on the statistic, kappa,which is applicable to determining whether particular transitions among events differ from chance and whether particular transitions differ significantly across groups of subjects. Low-cost hardware and software to implement the proposed procedures are described.This work has been supported by Grant 1 RO1 HD19245-01A1 from the National Institute of Child Health and Human Development to the two senior authors and by grants of equipment from Commodore Computers, Inc., Koala Corporation, and NEC Telephones, Inc.     

An incomplete data approach to the analysis of covariance structures

In this paper, linear structural equation models with latent variables are considered. It is shown how many common models arise from incomplete observation of a relatively simple system. Subclasses of models with conditional independence interpretations are also discussed. Using an incomplete data point of view, the relationships between the incomplete and complete data likelihoods, assuming normality, are highlighted. For computing maximum likelihood estimates, the EM algorithm and alternatives are surveyed. For the alternative algorithms, simplified expressions for computing function values and derivatives are given. Likelihood ratio tests based on complete and incomplete data are related, and an example on using their relationship to improve the fit of a model is given.This research forms part of the author's doctoral thesis and was supported by a Commonwealth Postgraduate Research Award. The author also wishes to acknowledge the support of CSIRO during the preparation of this paper and the referees' comments which led to substantial improvements.     

An entropy approach to the scaling of ordinal categorical data

This paper studies the problem of scaling ordinal categorical data observed over two or more sets of categories measuring a single characteristic. Scaling is obtained by solving a constrained entropy model which finds the most probable values of the scales given the data. A Kullback-Leibler statistic is generated which operationalizes a measure for the strength of consistency among the sets of categories. A variety of data of two and three sets of categories are analyzed using the entropy approach.This research was partially supported by the Air Force Office of Scientific Research under grant AFOSR 83-0234. The support by the Air Force through grant AFOSR-83-0234 is gratefully acknowledged. The comments of the editor and referees have been most helpful in improving the paper, and in bringing several additional references to our attention.     

An attentional-adaptation account of spatial negative priming: Evidence from event-related potentials

Negative priming (NP) refers to a slower response to a target stimulus if it has been previously ignored. To examine theoretical accounts of spatial NP, we recorded behavioral measures and event-related potentials (ERPs) in a target localization task. A target and distractor briefly appeared, and the participant pressed a key corresponding to the target’s location. The probability of the distractor appearing in each of four locations varied, whereas the target appeared with equal probabilities in all locations. We found that response times (RTs) were fastest when the prime distractor appeared in its most probable (frequent) location and when the prime target appeared in the location that never contained a distractor. Moreover, NP effects varied as a function of location: They were smallest when targets followed distractors in the frequent distractor location—a finding not predicted by episodic-retrieval or suppression accounts of NP. The ERP results showed that the P2, an ERP component associated with attentional orientation, was smaller in prime displays when the distractor appeared in its frequent location. Moreover, no differences were apparent between negative-prime and control trials in the N2, which is associated with suppression processes, nor in the P3, which is associated with episodic retrieval processes. These results indicate that the spatial NP effect is caused by both short- and long-term adaptation in preferences based on the history of inspecting unsuccessful locations. This article is dedicated to the memory of Edward E. Smith, and we indicate how this study was inspired by his research career.     

Sequential decision making: An aggregate scheduling methodology

Recent studies of sequential decision making performance have utilized optimal dynamic programming solutions as the criteria for evaluating the quality of human decisions. The difficulties incumbent in increasing the dimensionality of dynamic programming formulations effectively prohibit their extension beyond the case of a single decision variable. In the following article an alternative optimization formulation for a two-decision-variable case is presented, followed by the development of a corresponding one-decision-variable problem. In addition, related methodological problems limiting the comparability of results among previous studies are presented. It is suggested that the proposed problem formulation and its accompanying interpretation for the decision maker largely eliminate these previous methodological problems. Furthermore, the problem formulation permits quantification of the relative weights that should optimally be placed on future decision periods in the multiple-stage decision problem.     

The transition from school to work: An organisational approach

The thinking which has been done on the development of the self-concept as a factor in careers guidance is now being supplemented by thinking about the contexts within which young people develop these self-concepts. Adults who engage in guidance and counselling need to understand what they do against the background not only of the class origins of their students but also of the organisational settings which are relevant to their work. The different assumptions lying behind the organisations of schools and work-places are of critical importance. If those providing careers guidance do not understand some of these differences, the advice they give may be more confusing than supportive: rather than helping, it may handicap young people who are trying to be creative and responsible for themselves. Drawing on research done by the Grubb Institute, this paper outlines some key points about the organisation of schools and firms which can be taken into account by teachers, employers or careers officers seeking to advise young people in transition. These are: the different kinds of groups found in work and in school, especially in terms of size, stability and behaviour; the related assumptions about authority and leadership found in each; and the effect of these upon each individual's relationship with the organisation to which he belongs. Some suggestions are made about courses of action implied by the findings discussed in the paper, including implications for the role of the careers teacher.     

Analyzing multitrait-mulitmethod data with multilevel confirmatory factor analysis: An application to the validation of the State-Trait Cheerfulness Inventory

This article gives a didactic introduction to the analysis of multitrait-multimethod data with models of multilevel confirmatory factor analysis. In particular, the principles of the multilevel CT-C(M-1) model for interchangeable and structural different methods are explained in detail, and the first application of this model to more than two structurally different methods is presented. The model is illustrated by an application to the analysis of the convergent and discriminant validity of the trait subscales of the State-Trait Cheerfulness Inventory (STCI-T, Ruch, Köhler, & van Thriel, 1996). The results show that the STCI is a reliable and valid questionnaire for assessing the temperamental basis of sense of humor.     

The phenomenology of spatial integration: data and models

A briefly presented visual stimulus followed by darkness seems to persist beyond its physical offset. We are concerned here with the relation between two characteristics of this visible persistence: first, its phenomenological resemblance to the stimulus that spawned it and second, its usefulness as a basis for integrating visual stimuli that are separated in time. We describe two experiments using a task in which two halves of a visual stimulus were presented successively and observers reported how complete the stimulus appeared to be. Stimuli appeared less complete with increases in both the duration of the interval intervening between presentation of the two halves and the duration of the initially presented stimulus half. This data pattern is similar to that obtained in tasks in which spatial integration of two temporally disparate stimuli is necessary for correct responding. On the basis of this similarity, we argue that phenomenological appearance and ability to integrate stimuli over time are two facets of the same perceptual events. We describe a formal model to account for these and other data.     

Analyzing behavior implied by EWA learning: An emphasis on distinguishing reinforcement from belief learning

An important issue in the field of learning is to what extent one can distinguish between behavior resulting from either belief or reinforcement learning. Previous research suggests that it is difficult or even impossible to distinguish belief from reinforcement learning: belief and reinforcement models often fit the empirical data equally well. However, previous research has been confined to specific games in specific settings. In the present study we derive predictions for behavior in games using the EWA learning model (e.g., Camerer & Ho, 1999), a model that includes belief learning and a specific type of reinforcement learning as special cases. We conclude that belief and reinforcement learning can be distinguished, even in 2×2 games. Maximum differentiation in behavior resulting from either belief or reinforcement learning is obtained in games with pure Nash equilibria with negative payoffs and at least one other strategy combination with only positive payoffs. Our results help researchers to identify games in which belief and reinforcement learning can be discerned easily.     

An SEM approach to continuous time modeling of panel data: relating authoritarianism and anomia

Panel studies, in which the same subjects are repeatedly observed at multiple time points, are among the most popular longitudinal designs in psychology. Meanwhile, there exists a wide range of different methods to analyze such data, with autoregressive and cross-lagged models being 2 of the most well known representatives. Unfortunately, in these models time is only considered implicitly, making it difficult to account for unequally spaced measurement occasions or to compare parameter estimates across studies that are based on different time intervals. Stochastic differential equations offer a solution to this problem by relating the discrete time model to its underlying model in continuous time. It is the goal of the present article to introduce this approach to a broader psychological audience. A step-by-step review of the relationship between discrete and continuous time modeling is provided, and we demonstrate how continuous time parameters can be obtained via structural equation modeling. An empirical example on the relationship between authoritarianism and anomia is used to illustrate the approach.