Karl Schweizer

The Speed-Accuracy Transition Due to Task Complexity.

Abstract The transition of speed into accuracy was investigated by studying different degrees of transformation. A task requiring different numbers of simple exchanges indicating different degrees of complexity was designed for this purpose. The correlations between accuracy, represented by the numbers of correct responses, and ability scores indicated that accuracy is associated with cognitive ability to a greater extent in complex tasks, as compared to simple tasks. Mental speed, represented by the reaction time observed for the most simple task demand, correlated with accuracy in the more complex demands. A reduction of the correlations between accuracy and cognitive abilities was observed due to the removal of mental speed. The percentage of reduction in common variance varied between 30% and 40% for the highest degree of complexity.

Cognitive Bias of Optimism and its Influence on Psychological Well-Being

An investigation of the cognitive bias observed in optimism and the influence of optimism on psychological well-being was conducted. The cognitive bias was assumed to be related to the repressive style of information processing, represented by the trait scale of the State-Trait Anxiety Inventory and the Social Desirability scale. Optimism and well-being were measured by the Life Orientation Test and the Social Optimism scale as well as by the Life Satisfaction and Depression scales, respectively. These scales were applied to a sample of 200 individuals. Trait Anxiety and Social Desirability led to a high multiple correlation with the Life Orientation Test (R = .54) and a moderate multiple correlation with the Social Optimism scale (R = .30). Higher correlations were obtained for the expectation of a positive than of a negative future. Both the Life Orientation Test and the Social Optimism scale contributed to the high multiple correlation with psychological well-being (R= .52).

An Overview of Research into the Cognitive Basis of Intelligence

Abstract. This paper provides an overview of the research into the cognitive basis of intelligence. This research explains cognitive abilities in terms of cognitive units or properties of such units. Furthermore, this research is characterized by the application of so-called elementary cognitive tasks. The various approaches of this research originate from the concepts of cognitive psychology: Mental (and perceptual) speed, attention, working memory, memory access, and learning. All the approaches led to measures which correlate with measures of intelligence. The enormous importance of the cognitive basis is highlighted by the observation that predictors taken from the various approaches explain approximately 50% of the variance of intelligence. At the latent level the rate of explanation seems to surmount the 70% barrier. Furthermore, the problems and perspectives of the approaches are addressed.

A revision of Cattell's Investment Theory: Cognitive properties influencing learning

Abstract A revision of Cattells Investment Theory is presented. The revision assumes that learning mediates the influence of fluid on crystallized intelligence. Furthermore, it is assumed that there is a cognitive basis for the influence of fluid intelligence on learning. Processing speed and capacity are assumed as the cognitive basis. In a sample of 104 participants evidence for the cognitive basis of fluid intelligence and learning was found and, consequently, for a relationship between fluid intelligence and learning. In the subsample of younger participants, even the expected influences of fluid intelligence on learning and of learning on crystallized intelligence could be demonstrated. In the other subsample, an attempt of such a demonstration failed. The observed difference between the subsamples supported Cattells expectation that professional specialization impairs the observability of the relationship between fluid and crystallized intelligence.

Investigating Experimental Effects Within the Framework of Structural Equation Modeling: An Example With Effects on Both Error Scores and Reaction Times

Structural equation modeling provides the framework for investigating experimental effects on the basis of variances and covariances in repeated measurements. A special type of confirmatory factor analysis as part of this framework enables the appropriate representation of the experimental effect and the separation of experimental and nonexperimental parts of variance. The constraint of the matrix of loadings is essential for the representation of the effect. Appropriate constraints of loadings are achievable with the aid of the polynomial function. The representation can even bear on several response modes. The usefulness of this method is demonstrated in data obtained by an experimental task with 3 treatment levels with respect to reaction times and error scores. A model with latent variables representing constancy and increase in reaction times and one latent variable representing increase in error scores serves best in these data. Both reaction times and error scores show experimental effects.

The assessment of components of optimism by POSO-E

Abstract This paper presents the POSO-E which is a self-report questionnaire for the assessment of three components of optimism: personal, social and self-efficacy optimism. Three studies which were conducted for extending the existing set of scales and for obtaining validity information are described. In the first study the development of the scale for measuring self-efficacy optimism, which was added to the other scales, is described. In the second study correlations between the optimism scales on the one hand and the scales of the Freiburger-Personality-Inventory [Fahrenberg, J., Hampel, R., & Selg, H. (1994). Das Freiburger Personlichkeitsinventar FPI.6. Aufl. Gottingen: Hogrefe.], the State-Trait-Anxiety-Inventory [Laux, L., Glanzmann, P., Schaffner, P., & Spielberger, C. D. (1981). State-Trait-Angstinventar (STAI). Weinheim: Beltz] and the Beck-Depression-Inventory [Hautzinger, M., Bailer, M., Worall, H., & Keller, F., (1994). Beck-Depressions-Inventar (BDI). Bern: Huber] were obtained. The third study was designed to investigate the relationships between the optimism scales and the NEO questionnaire [Borkenau, P., & Ostendorf, F., (1993). NEO-Funf-Faktoren Inventar (NEO-FFI) nach Costa und McCrea, Handanweisung. Gottingen: Hogrefe]. High degrees of internal consistency were observed for the scales of personal, social and self-efficacy optimism. The correlations between the optimism scales and the personality scales corresponded to expectation.

Does impulsivity influence performance in reasoning

Abstract The influence of impulsivity on reasoning was investigated in a sample of 108 high school and university students. Impulsivity was represented by the PRF Impulsivity Scale [Jackson, D. N. (1974). Manual for the Personality Research Form (2nd ed.). Goshen: Research Psychologists Press.], the MMPI Impulsivity Scale [Gough, H. G. (1957). California psychological inventory manual. Palo Alto: Consulting Psychologists Press.] and the FPI Impulsiveness Scale [Fahrenberg, J., Hampel, R., & Selg, H. (1994). Das Freiburger Personlichkeitsinventar FPI. 6. Aufl. Gottingen: Hogrefe.]. Reasoning was measured by the Figural Reasoning Scale ( Horn, W., 1983 ) and the Numeric/Alphabetical Reasoning Scale [Horn, W. (1983). Leistungs-Pruf-System. Gottingen: Hogrefe.]. Furthermore, measures of neuroticism, extraversion, achievement orientation and surgency were applied. Substantial negative correlations of reasoning with impulsivity and neuroticism were observed. Structural equation modeling was applied to predict reasoning by means of an impulsivity composite. A path coefficient of −0.33 was observed. This result suggests the interpretation that a high degree of impulsivity impairs performance in completing reasoning tasks.

The Fixed-Links Model in Combination With the Polynomial Function as a Tool for Investigating Choice Reaction Time Data

A model with fixed relations between manifest and latent variables is presented for investigating choice reaction time data. The numbers for fixation originate from the polynomial function. Two options are considered: the component-based (1 latent variable for each component of the polynomial function) and composite-based options (1 latent variable for the weighted sum of components). The choice reaction time procedure yields reaction times for different numbers of stimuli that need to be monitored. The application of the model to such data enables the identification of the components of the polynomial function, which describe the effects of the different numbers of potential stimuli on reaction time. The investigation of the data stemming from a meta-analytic study revealed that the model with constant and quadratic components was most appropriate. Both the component-based and composite-based options led to this result. The composite-based option proved to be the more robust alternative.

Fixed-links models for investigating experimental effects combined with processing strategies in repeated measures designs: a cognitive task as example.

Fixed-links models enable the investigation of experimental effects in combination with processing strategies in the framework of repeated measures designs. The consideration of different processing strategies is reasonable whenever the task requirements can be met in different ways. Effects due to specific processing strategies become obvious in comparing the models representing these processing strategies and also by investigating the variances of the corresponding latent variables. The usefulness of such models is demonstrated by the reaction time data of a cognitive task including three treatment levels. Two different processing strategies were considered: analytic and holistic. The results indicated that the participants applied both processing strategies. The analytic strategy was primarily applied for performing with respect to the second and third treatment levels and the holistic strategy with respect to the first treatment level.

Scaling Variances of Latent Variables by Standardizing Loadings: Applications to Working Memory and the Position Effect

The standardization of loadings gives a metric to the corresponding latent variable and thus scales the variance of this latent variable. By assigning an appropriately estimated weight to all the loadings on the same latent variable it can be achieved that the average squared loading is 1 as the result of standardization. As a consequence, there is comparability of the variances of the latent variables of a confirmatory factor model. A precondition of comparability is that the latent variables must have loadings of the same manifest variables and that the variances are estimated with respect to the same covariance matrix. The usefulness of this standardization method is demonstrated by applying it for the evaluation of the sources of performance in a working memory task and for the evaluation of the impact of the position effect on performance in completing a reasoning measure. In these examples the scaled variances of the latent variables provided useful information.