Towards a model theory of diagrams

A logical system is studied whose well-formed representations consist of diagrams rather than formulas. The system, due to Shin [2, 3], is shown to be complete by an argument concerning maximally consistent sets of diagrams. The argument is complicated by the lack of a straight forward counterpart of atomic formulas for diagrams, and by the lack of a counterpart of negation for most diagrams.The authors are grateful to Jon Barwise and an anonymous referee for valuable comments and suggestions.     

An analysis of reinforcement sensitivity theory and the five-factor model

Reinforcement Sensitivity Theory (RST) and the Five-Factor Model (FFM) are two prominent personality accounts that have emerged from different backgrounds. Although the two accounts are applied to similar research topics, there is limited empirical work examining the correspondence between them. The current study explored the relationship between RST-based personality traits and the FFM domains and facets in an undergraduate sample (n = 668). Regression analyses indicated that Sensitivity to Punishment (SP) was positively associated with Neuroticism and Agreeableness, and negatively associated with Extraversion, Openness, and Conscientiousness. In contrast, Sensitivity to Reward (SR) was positively associated with Extraversion and Neuroticism, and negatively associated with Agreeableness and Conscientiousness. Exploratory analyses at the facet level specified the relationship between SP, SR, and each domain. A factor analysis was also conducted to explore the higher-order factor structure of RST and the FFM domains. Three factors emerged, which we labelled SP, Stability-Impulsivity, and Sensation Seeking. Taken together, these findings suggest that there is substantial overlap between these two accounts of personality. Copyright © 2007 John Wiley & Sons, Ltd.     

An autocorrelation theory of visual form detection: A computer experiment and a computer model

A theoretical model is described for the effect of the organizational properties of dotted visual forms on visual form detection. The model assumes that a human observer functions more as an organization detector than as a feature detector. It is supported by a wide variety of experimental studies of human perception.     

An outline of a theory of action systems

It is argued that the traditional psychological construal of the distinction between central ("open loop") and peripheral ("closed loop") neural processes is no longer tenable. A review of the relevant psychobiological research on the control of movement shows that both the central-peripheral dichotomy and the distinction between sensory and motor systems derived from that dichotomy are incompatible with what is known about the processes underlying action. Based on experimental and comparative data, a new theory of action is proposed. The basic concept of this new theory is the action system: Unlike motor systems, action systems involve sensory as well as motor processes; action systems are not organized into response hierarchies, but rather in coalitional structures of adjustable movements and postures. The phylogeny of action systems is discussed, and the eight most important action systems are distinguished in terms of their evolved distinctive functions. The difficulties of developing a taxonomy of actions for functionally specific types of behavior made up of that can be controlled is resolved by showing how kinds of action are differentiated by the kinds of affordances their components help to realize.     

Analysis of a two-alternative force-choice signal detection theory model

A basic problem in psychophysics is recovering the mean internal response and noise amplitude from sensory discrimination data. Since these components cannot be estimated independently, several indirect methods were suggested to resolve this issue. Here we analyze the two-alternative force-choice method (2AFC), using a signal detection theory approach, and show analytically that the 2AFC data are not always suitable for a reliable estimation of the mean internal responses and noise amplitudes. Specifically, we show that there is a subspace of internal parameters that are highly sensitive to sampling errors (singularities), which results in a large range of estimated parameters with a finite number of experimental trials. Four types of singular models were identified, including the models where the noise amplitude is independent of the stimulus intensity, a situation often encountered in visual contrast discrimination. Finally, we consider two ways to avoid singularities: (1) inserting external noise to the stimuli, and (2) using one-interval forced-choice scaling methods (such as the Thurstonian scaling method for successive intervals).     

The mapping model: a cognitive theory of quantitative estimation

How do people make quantitative estimations, such as estimating a car's selling price? Traditionally, linear-regression-type models have been used to answer this question. These models assume that people weight and integrate all information available to estimate a criterion. The authors propose an alternative cognitive theory for quantitative estimation. The mapping model, inspired by the work of N. R. Brown and R. S. Siegler (1993) on metrics and mappings, offers a heuristic approach to decision making. The authors test this model against established alternative models of estimation, namely, linear regression, an exemplar model, and a simple estimation heuristic. With 4 experimental studies the authors compare the models under different environmental conditions. The mapping model proves to be a valid model to predict people's estimates.     

Expanding stress generation theory: test of a transdiagnostic model

Originally formulated to understand the recurrence of depressive disorders, the stress generation hypothesis has recently been applied in research on anxiety and externalizing disorders. Results from these investigations, in combination with findings of extensive comorbidity between depression and other mental disorders, suggest the need for an expansion of stress generation models to include the stress generating effects of transdiagnostic pathology as well as those of specific syndromes. Employing latent variable modeling techniques to parse the general and specific elements of commonly co-occurring Axis I syndromes, the current study examined the associations of transdiagnostic internalizing and externalizing dimensions with stressful life events over time. Analyses revealed that, after adjusting for the covariation between the dimensions, internalizing was a significant predictor of interpersonal dependent stress, whereas externalizing was a significant predictor of noninterpersonal dependent stress. Neither latent dimension was associated with the occurrence of independent, or fateful, stressful life events. At the syndrome level, once variance due to the internalizing factor was partialed out, unipolar depression contributed incrementally to the generation of interpersonal dependent stress. In contrast, the presence of panic disorder produced a "stress inhibition" effect, predicting reduced exposure to interpersonal dependent stress. Additionally, dysthymia was associated with an excess of noninterpersonal dependent stress. The latent variable modeling framework used here is discussed in terms of its potential as an integrative model for stress generation research.     

Naive probability: a mental model theory of extensional reasoning

This article outlines a theory of naive probability. According to the theory, individuals who are unfamiliar with the probability calculus can infer the probabilities of events in an extensional way: They construct mental models of what is true in the various possibilities. Each model represents an equiprobable alternative unless individuals have beliefs to the contrary, in which case some models will have higher probabilities than others. The probability of an event depends on the proportion of models in which it occurs. The theory predicts several phenomena of reasoning about absolute probabilities, including typical biases. It correctly predicts certain cognitive illusions in inferences about relative probabilities. It accommodates reasoning based on numerical premises, and it explains how naive reasoners can infer posterior probabilities without relying on Bayes's theorem. Finally, it dispels some common misconceptions of probabilistic reasoning.     

A model theory of induction

Theories of induction in psychology and artificial intelligence assume that the process leads from observation and knowledge to the formulation of linguistic conjectures. This paper proposes instead that the process yields mental models of phenomena. It uses this hypothesis to distinguish between deduction, induction, and creative forms of thought. It shows how models could underlie inductions about specific matters. In the domain of linguistic conjectures, there are many possible inductive generalizations of a conjecture. In the domain of models, however, generalization calls for only a single operation: the addition of information to a model. If the information to be added is inconsistent with the model, then it eliminates the model as false: this operation suffices for all generalizations in a Boolean domain. Otherwise, the information that is added may have effects equivalent (a) to the replacement of an existential quantifier by a universal quantifier, or (b) to the promotion of an existential quantifier from inside to outside the scope of a universal quantifier. The latter operation is novel, and does not seem to have been used in any linguistic theory of induction. Finally, the paper describes a set of constraints on human induction, and outlines the evidence in favor of a model theory of induction.     

Paraconsistent logic and model theory

The object of this paper is to show how one is able to construct a paraconsistent theory of models that reflects much of the classical one. In other words the aim is to demonstrate that there is a very smooth and natural transition from the model theory of classical logic to that of certain categories of paraconsistent logic. To this end we take an extension of da Costa'sC ₁ ⁼ (obtained by adding the axiom A A) and prove for it results which correspond to many major classical model theories, taken from Shoenfield [5]. In particular we prove counterparts of the theorems of o-Tarski and Chang-o-Suszko, Craig-Robinson and the Beth definability theorem.     

Modal logic and model theory

We propose a first order modal logic, theQS4E-logic, obtained by adding to the well-known first order modal logicQS4 arigidity axiom schemas:A → □A, whereA denotes a basic formula. In this logic, thepossibility entails the possibility of extending a given classical first order model. This allows us to express some important concepts of classical model theory, such as existential completeness and the state of being infinitely generic, that are not expressibile in classical first order logic. Since they can be expressed in -logic, we are also induced to compare the expressive powers ofQS4E and . Some questions concerning the power of rigidity axiom are also examined.