KANT ON RELIGION AND SCIENCE: INDEPENDENCE OR INTEGRATION?

Abstract. Immanuel Kant's theoretical knowledge and practical knowledge tempt conclusion that natural science and religion are two independent discourses of a dualistic system. To be sure, knowledge is anchored in two kinds of causality. Theoretical knowledge is governed by physical causality. Practical knowledge is concerned with the human capacity to initiate a sequence of events that nature could not accomplish on its own—although in conformity with, not independent of, natural causality. Furthermore, the two realms presuppose a common totality of order not of humanity's creation. Without these presuppositions, we could not experience the world as we do, and it would never occur to us to engage in a scientific investigation of the natural world. Hence, we should first exhaust our attempts at explanation on the basis of physical causality before turning to the aid of teleology. The anomalous becomes an occasion to seek a physical law not yet known whereas the miraculous hinders search for a natural law. However, higher than knowledge of “what is” is our capacity to discern “what should be.” This is an inclusive moral capacity that establishes what it means to be human and unites all moral agents in an invisible kingdom of ends that constitutes a moral culture in the physical world uniting religion and science.     

The how and why of disagreement among perceivers: An exploration of person models

Impression formation has been conceptualized as a process whereby perceivers form person models of others, describing what the person is like and why (Park, DeKay, & Kraus, 1994). If they exist, person models could help explain why there appears to be little consensus in personality judgments; perceivers forming different models of a person would also view them differently on trait and affect ratings. Two experiments were conducted to test assumptions of the person-model paradigm using naturalistic targets. The structure of person models was examined, along with their content. Results demonstrated that three distinct person models emerge for each target and that the majority of perceivers can easily identify with one of the three models. We found that person models were valid across different groups of perceivers, and that the models differed in terms of masculinity-femininity and affect. Implications for the person model formulation process are discussed.     

Semiparametric Thurstonian Models for Recurrent Choices: A Bayesian Analysis

We develop semiparametric Bayesian Thurstonian models for analyzing repeated choice decisions involving multinomial, multivariate binary or multivariate ordinal data. Our modeling framework has multiple components that together yield considerable flexibility in modeling preference utilities, cross-sectional heterogeneity and parameter-driven dynamics. Each component of our model is specified semiparametrically using Dirichlet process (DP) priors. The utility (latent variable) component of our model allows the alternative-specific utility errors to semiparametrically deviate from a normal distribution. This generates a robust alternative to popular Thurstonian specifications that are based on underlying normally distributed latent variables. Our second component focuses on flexibly modeling cross-sectional heterogeneity. The semiparametric specification allows the heterogeneity distribution to mimic either a finite mixture distribution or a continuous distribution such as the normal, whichever is supported by the data. Thus, special features such as multimodality can be readily incorporated without the need to overtly search for the best heterogeneity specification across a series of models. Finally, we allow for parameter-driven dynamics using a semiparametric state-space approach. This specification adds to the literature on robust Kalman filters. The resulting framework is very general and integrates divergent strands of the literatures on flexible choice models, Bayesian nonparametrics and robust time series specifications. Given this generality, we show how several existing Thurstonian models can be obtained as special forms of our model. We describe Markov chain Monte Carlo methods for the inference of model parameters, report results from two simulation studies and apply the model to consumer choice data from a frequently purchased product category. The results from our simulations and application highlight the benefits of using our semiparametric approach.     

Investigations of HPA function and the enduring consequences of stressors in adolescence in animal models

Developmental differences in hypothalamic–pituitary–adrenal (HPA) axis responsiveness to stressors and ongoing development of glucocorticoid-sensitive brain regions in adolescence suggest that similar to the neonatal period of ontogeny, adolescence may also be a sensitive period for programming effects of stressors on the central nervous system. Although research on this period of life is scarce compared to early life and adulthood, the available research indicates that effects of stress exposure during adolescence differ from, and may be longer-lasting than, effects of the same stress exposure in adulthood. Research progress in animal models in this field is reviewed including HPA function and the enduring effects of stress exposures in adolescence on sensitivity to drugs of abuse, learning and memory, and emotional behaviour in adulthood. The effects of adolescent stress depend on a number of factors, including the age, gender, the duration of stress exposure, the type of stressor, and the time between stress exposure and testing.     

Learning the form of causal relationships using hierarchical bayesian models

People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.     

Young children’s acquisition of knowledge about the Earth: A longitudinal study

This longitudinal study examined the acquisition of early knowledge of astronomy to determine whether children’s knowledge at any point in time is consistent with a naive “mental model.” Children were first assessed by means of open questions and drawing tasks at 2 and 3 years of age (N = 143). The knowledge was reassessed over the course of the following 3 years. The results showed that although a few indications of naive mental models were found, in most cases young children’s knowledge was fragmented and accurate knowledge was often expressed alongside inaccurate/synthetic ideas. Furthermore, it was shown that children need to know scientific facts before they start taking the global perspective when describing the world and, when faced with ambiguous open questions, children often experience difficulties that can induce them to change the types of answers they provide.     

Robust versus optimal strategies for two-alternative forced choice tasks

It has been proposed that animals and humans might choose a speed-accuracy tradeoff that maximizes reward rate. For this utility function the simple drift-diffusion model of two-alternative forced-choice tasks predicts a parameter-free optimal performance curve that relates normalized decision times to error rates under varying task conditions. However, behavioral data indicate that only ≈30% of subjects achieve optimality, and here we investigate the possibility that, in allowing for uncertainties, subjects might exercise robust strategies instead of optimal ones. We consider two strategies in which robustness is achieved by relinquishing performance: maximin and robust-satisficing. The former supposes maximization of guaranteed performance under a presumed level of uncertainty; the latter assumes that subjects require a critical performance level and maximize the level of uncertainty under which it can be guaranteed. These strategies respectively yield performance curves parameterized by a presumed uncertainty level and required performance. Maximin performance curves for uncertainties in response-to-stimulus interval match data for the lower-scoring 70% of subjects well, and are more likely to explain it than robust-satisficing or alternative optimal performance curves that emphasize accuracy. For uncertainties in signal-to-noise ratio, neither maximin nor robust-satisficing performance curves adequately describe the data. We discuss implications for decisions under uncertainties, and suggest further behavioral assays.     

Using diffusion models to understand clinical disorders

Sequential sampling models provide an alternative to traditional analyses of reaction times and accuracy in two-choice tasks. These models are reviewed with particular focus on the diffusion model (Ratcliff, 1978) and how its application can aid research on clinical disorders. The advantages of a diffusion model analysis over traditional comparisons are shown through simulations and a simple lexical decision experiment. Application of the diffusion model to a clinically relevant topic is demonstrated through an analysis of data from nonclinical participants with high- and low-trait anxiety in a recognition memory task. The model showed that after committing an error, participants with high-trait anxiety responded more cautiously by increasing their boundary separation, whereas participants with low-trait anxiety did not. The article concludes with suggestions for ways to improve and broaden the application of these models to studies of clinical disorders.     

Extraversion and psychopathology: A multilevel hierarchical review

We examine relations between extraversion and psychopathology, using a four-level hierarchical structure in which the general domain is divided into two aspects (Communal Extraversion, Agentic Extraversion), four consensual facets (Sociability, Liveliness, Venturesomeness, Dominance), and six NEO facets (Gregariousness, Warmth, Positive Emotions, Activity, Excitement-Seeking, Assertiveness). Our review indicates that extraversion’s negative correlations with depression primarily reflect the aspect of Communal Extraversion, the consensual facet of Liveliness, and NEO Positive Emotions. Its negative associations with social dysfunction largely are due to the aspect of Communal Extraversion, the consensual facet of Sociability, and NEO Warmth, Gregariousness, and Positive Emotions. Finally, externalizing and mania are positively related to the aspect of Agentic Extraversion, the consensual facets of Venturesomeness and Dominance, and NEO Excitement-Seeking.