On the adequacy of current empirical evaluations of formal models of categorization

Categorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of noncomplementary models with little consensus on the relative adequacy of these accounts. Progress in assessing the relative adequacy of formal categorization models has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus).     

On a unified theory of models and thought experiments in natural sciences

In this paper a unified theory of models and thought experiments is proposed by considering them as fictions, á la Vaihinger. In order to reach this aim, the Hertzian and Botzmannian interpretation of theories as Bilder is reconsidered.     

Reasoning with conditionals: a test of formal models of four theories

The four dominant theories of reasoning from conditionals are translated into formal models: The theory of mental models (Johnson-Laird, P. N., & Byrne, R. M. J. (2002). Conditionals: a theory of meaning, pragmatics, and inference. Psychological Review, 109, 646-678), the suppositional theory (Evans, J. S. B. T., & Over, D. E. (2004). If. Oxford: Oxford University Press), a dual-process variant of the model theory (Verschueren, N., Schaeken, W., & d'Ydewalle, G. (2005). A dual-process specification of causal conditional reasoning. Thinking &Reasoning, 11, 278-293), and the probabilistic theory (Oaksford, M., Chater, N., & Larkin, J. (2000). Probabilities and polarity biases in conditional inference. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 883-899). The first three theories are formalized as multinomial models. The models are applied to the frequencies of patterns of acceptance or rejection across the four basic inferences modus ponens, acceptance of the consequent, denial of the antecedent, and modus tollens. Model fits are assessed for two large data sets, one representing reasoning with abstract, basic conditionals, the other reflecting reasoning with pseudo-realistic causal and non-causal conditionals. The best account of the data was provided by a modified version of the mental-model theory, augmented by directionality, and by the dual-process model.     

Fractional designs: A simulation study of usefulness in the social sciences

Fractional designs can be extremely useful in social science research, especially when a large number of factors is involved. Reluctance for the use of fractional designs seems to be warranted for two reasons: (1) In the social sciences, the amount of measurement error is often large, which may decrease the power, and (2) higher order interactions are assumed to be nonsignificant, which is difficult to guarantee without sufficient research. This simulation study shows the effects of measurement error and assumption violations under various conditions. It is concluded that fractional designs handle measurement error gracefully and that they are as powerful as a full design when equal degrees of freedom are available. Significant interaction effects can cause serious problems, especially in situations with low or intermediate measurement error, and can lead to erroneous conclusions. Only when estimated confounded effects are clearly not significant, the chance of a wrong decision is reasonably small. Therefore, fractional designs are especially warranted for the exclusion of irrelevant factors. However, we note pitfalls in the use of Version 1.0 of the program Trail Run from SPSS, Inc., to implement the procedures.     

Towards computational models of intention detection and intention prediction

Intention recognition is one of the core components of mindreading, an important process in social cognition. Human beings, from age of 18 months, have been shown to be able to extrapolate intentions from observed actions, even when the performer failed at achieving the goal. Existing accounts of intention recognition emphasize the use of an intent (plan) library, which is matched against observed actions for recognition. These therefore cannot account for recognition of failed sequences of actions, nor novel actions. In this paper, we begin to tackle these open questions by examining computational models for components of human intention recognition, which emphasize the ability of humans to detect and identify intentions in a sequence of observed actions, based solely on the rationality of movement (its efficiency). We provide a high-level overview of intention recognition as a whole, and then elaborate on two components of the model, which we believe to be at its core, namely, those of intention detection and intention prediction. By intention detection we mean the ability to discern whether a sequence of actions has any underlying intention at all, or whether it was performed in an arbitrary manner with no goal in mind. By intention prediction we mean the ability to extend an incomplete sequence of actions to its most likely intended goal. We evaluate the model, and these two components, in context of existing literature, and in a number of experiments with more than 140 human subjects. For intention detection, our model was able to attribute high levels of intention to those traces perceived by humans as intentional, and vice versa. For intention prediction as well, our model performed in a way that closely matched that of humans. The work highlights the intimate relationship between the ability to generate plans, and the ability to recognize intentions.     

Indeterminacy in the social sciences

It is maintained that a principle of indeterminacy exists in the social sciences which bears some resemblance to the Heisenberg principle in the realm of physics. In the social sciences, however, the principle is grounded not on physical interference, but on the capacity of human beings to alter their behavior on the basis of changing conceptions of their social condition, and so the contention of writers like Nagel ‘that no distinct principle of explanation is involved’ must be rejected. The paper concludes with a brief consideration of the causes of social indeterminacy, which suggests that the phenomenon is ineradicable.     

Reasoning in the social sciences

In 1981, A. C. Crombie identified six styles of scientific thinking in the European tradition that constitute our ways of reasoning in the natural sciences. In this paper, I try to show that these styles constitute reasoning in the social sciences as well, and that, as a result, the differences between reasoning about the physical world and about human beings are not so different as some interpretevists have supposed.     

Methodological naturalism in the sciences

International Journal for Philosophy of Religion - Creationists have long argued that evolutionary science is committed to a dogmatic metaphysics of naturalism and materialism, which is based on...     

Latent trait models in the study of intelligence

This article examines the potential contribution of latent trait models to the study of intelligence. Nontechnical introductions to both unidimensional and multidimensional latent trait models are given, and possible research applications are considered. Latent trait models are shown to resolve several measurement problems in studies of intellectual change, including ability modification studies and life-span development studies. Furthermore, under certain conditions, latent trait models are found useful for construct validation research, since they can represent an individual differences model of cognitive processing on ability test items. Multidimensional latent trait models are shown to be especially useful as processing models, because they can be used to test alternative multiple component theories of test item processing. Furthermore, multidimensional models can be used to decompose test item difficulty into component contributions and estimate individual differences in processing abilities.     

Improving the quality of careers guidance: Towards an understanding of the development of personal models

The process by which careers officers develop personal models of guidance through the interaction of theoretical knowledge with their experiences of training and practice is examined. Semi-structured interviews were conducted with 11 careers officers. Questions were asked about the training and development of careers officers in the practice of careers guidance and counselling with young people. The responses are considered in relation to theories of careers choice, careers counselling and guidance, models of counselling, and the supervisory role in the development of individual practice. Some recommendations are made for improvements to training and supervision for careers guidance.