Biomedical subjects

Han L J van der Maas

Publications and source records attributed to Han L J van der Maas.

9 recordsLinked to original sources

PubMed2006

The effects of time pressure on chess skill: an investigation into fast and slow processes underlying expert performance.

The ability to play chess is generally assumed to depend on two types of processes: slow processes such as search, and fast processes such as pattern recognition. It has been argued that an increase in time pressure during a game selectively hinders the ability to engage in slow processes. Here we study the effect of time pressure on expert chess performance in order to test the hypothesis that compared to weak players, strong players depend relatively heavily on fast processes. In the first study we examine the performance of players of various strengths at an online chess server, for games played under different time controls. In a second study we examine the effect of time controls on performance in world championship matches. Both studies consistently show that skill differences between players become less predictive of the game outcome as the time controls are tightened. This result indicates that slow processes are at least as important for strong players as they are for weak players. Our findings pose a challenge for current theorizing in the field of expertise and chess.

Cognition↗

PubMed2006

Re-thinking stages of cognitive development: an appraisal of connectionist models of the balance scale task.

The present paper re-appraises connectionist attempts to explain how human cognitive development appears to progress through a series of sequential stages. Models of performance on the Piagetian balance scale task are the focus of attention. Limitations of these models are discussed and replications and extensions to the work are provided via the Cascade-Correlation algorithm. An application of multi-group latent class analysis for examining performance of the networks is described and these results reveal fundamental functional characteristics of the networks. Evidence is provided that strongly suggests that the networks are unable to acquire a mastery of torque and, although they do recover certain rules of operation that humans do, they also show a propensity to acquire rules never previously seen.

Cognition↗

PubMed2006

A dynamical model of general intelligence: the positive manifold of intelligence by mutualism.

Scores on cognitive tasks used in intelligence tests correlate positively with each other, that is, they display a positive manifold of correlations. The positive manifold is often explained by positing a dominant latent variable, the g factor, associated with a single quantitative cognitive or biological process or capacity. In this article, a new explanation of the positive manifold based on a dynamical model is proposed, in which reciprocal causation or mutualism plays a central role. It is shown that the positive manifold emerges purely by positive beneficial interactions between cognitive processes during development. A single underlying g factor plays no role in the model. The model offers explanations of important findings in intelligence research, such as the hierarchical factor structure of intelligence, the low predictability of intelligence from early childhood performance, the integration/differentiation effect, the increase in heritability of g, and the Jensen effect, and is consistent with current explanations of the Flynn effect.

Humans↗

PubMed2005

Inferring the structure of latent class models using a genetic algorithm.

Present optimization techniques in latent class analysis apply the expectation maximization algorithm or the Newton-Raphson algorithm for optimizing the parameter values of a prespecified model. These techniques can be used to find maximum likelihood estimates of the parameters, given the specified structure of the model, which is defined by the number of classes and, possibly, fixation and equality constraints. The model structure is usually chosen on theoretical grounds. A large variety of structurally different latent class models can be compared using goodness-of-fit indices of the chi-square family, Akaike's information criterion, the Bayesian information criterion, and various other statistics. However, finding the optimal structure for a given goodness-of-fit index often requires a lengthy search in which all kinds of model structures are tested. Moreover, solutions may depend on the choice of initial values for the parameters. This article presents a new method by which one can simultaneously infer the model structure from the data and optimize the parameter values. The method consists of a genetic algorithm in which any goodness-of-fit index can be used as a fitness criterion. In a number of test cases in which data sets from the literature were used, it is shown that this method provides models that fit equally well as or better than the models suggested in the original articles.

Algorithms↗

PubMed2005

A psychometric analysis of chess expertise.

This study introduces the Amsterdam Chess Test (ACT). The ACT measures chess playing proficiency through 5 tasks: a choose-a-move task (comprising two parallel tests), a motivation questionnaire, a predict-a-move task, a verbal knowledge questionnaire, and a recall task. The validity of these tasks was established using external criteria based on the Elo chess rating system. Results from a representative sample of active chess players showed that the ACT is a very reliable test for chess expertise and that ACT has high predictive validity. Several hypotheses about the relationships between chess expertise, chess knowledge, motivation, and memory were tested. Incorporating response latencies in test scores is shown to lead to an increase in criterion validity, particularly for easy items.

Achievement↗

PubMed2003

What response times tell of children's behavior on the balance scale task.

Analysis of accuracy of responses to balance scale problems gives a global idea of the cognitive processes that underlie problem-solving behavior on this task. We show that response times (RTs) provide additional detailed information about the kind and duration of these processes. We derive predictions about the RTs from Siegler's (1981) model for the balance scale task, including the counterintuitive prediction that young adults are slower than children in solving particular balance scale problems. The predictions were tested in a study in which 191 6- to 22-year-old participants were presented with a computerized balance scale task. RTs were analyzed with regression models. In addition to qualitative differences between items, we also modeled quantitative differences between items in the regression models. Analyses supported the predictions and provided additional knowledge on the rules. Rule II was reformulated as a rule that always involves the encoding, but not always the correct application of the distance cue. RTs provided evidence for the use of a buggy-rule and not an addition-rule. Finally, a relation between rule inconsistency and increased RT was found.

Adolescent↗

PubMed2002

The development of children's rule use on the balance scale task.

Cognitive development can be characterized by a sequence of increasingly complex rules or strategies for solving problems. Our work focuses on the development of children's proportional reasoning, assessed by the balance scale task using Siegler's (1976, 1981) rule assessment methodology. We studied whether children use rules, whether children of different ages use qualitatively different rules, and whether rules are used consistently. Nonverbal balance scale problems were administered to 805 participants between 5 and 19 years of age. Latent class analyses indicate that children use rules, that children of different ages use different rules, and that both consistent and inconsistent use of rules occurs. A model for the development of reasoning about the balance scale task is proposed. The model is a restricted form of the overlapping waves model (Siegler, 1996) and predicts both discontinuous and gradual transitions between rules.

Adolescent↗

PubMed2002

A framework for ML estimation of parameters of (mixtures of) common reaction time distributions given optional truncation or censoring.

We present a framework for distributional reaction time (RT) analysis, based on maximum likelihood (ML) estimation. Given certain information relating to chosen distribution functions, one can estimate the parameters of these distributions and of finite mixtures of these distributions. In addition, left and/or right censoring or truncation may be imposed. Censoring and truncation are useful methods by which to accommodate outlying observations, which are a pervasive problem in RT research. We consider five RT distributions: the Weibull, the ex-Gaussian, the gamma, the log-normal, and the Wald. We employ quasi-Newton optimization to obtain ML estimates. Multicase distributional analyses can be carried out, which enable one to conduct detailed (across or within subjects) comparisons of RT data by means of loglikelihood difference tests. Parameters may be freely estimated, estimated subject to boundary constraints, constrained to be equal (within or over cases), or fixed. To demonstrate the feasibility of ML estimation and to illustrate some of the possibilities offered by the present approach, we present three small simulation studies. In addition, we present three illustrative analyses of real data.

Humans↗

PubMed2002

Stochastic catastrophe analysis of switches in the perception of apparent motion.

Dynamical phenomena such as bistability and hysteresis have been found in a number of studies on perception of apparent motion. We show that new developments in stochastic catastrophe theory make it possible to test models of these phenomena empirically. Catastrophe theory explains discontinuous changes in responses caused by continuous changes in experimental parameters. We propose catastrophe models for two experimental paradigms on perception of apparent motion and present experiments that support these models. We test these models by using an algorithm for fitting stochastic catastrophe models. We derive from catastrophe theory the prediction that a dynamical phenomenon called divergence is necessary when hysteresis is found. This new prediction is supported by the data.

Humans↗