PALEY'S iPOD: THE COGNITIVE BASIS OF THE DESIGN ARGUMENT WITHIN NATURAL THEOLOGY

The argument from design stands as one of the most intuitively compelling arguments for the existence of a divine Creator. Yet, for many scientists and philosophers, Hume's critique and Darwin's theory of natural selection have definitely undermined the idea that we can draw any analogy from design in artifacts to design in nature. Here, we examine empirical studies from developmental and experimental psychology to investigate the cognitive basis of the design argument. From this it becomes clear that humans spontaneously discern purpose in nature. When constructed theologically and philosophically correctly, the design argument is not presented as conclusive evidence for God's existence but rather as an abductive, probabilistic argument. We examine the cognitive basis of probabilistic judgments in relationship to natural theology. Placing emphasis on how people assess improbable events, we clarify the intuitive appeal of Paley's watch analogy. We conclude that the reason why some scientists find the design argument compelling and others do not lies not in any intrinsic differences in assessing design in nature but rather in the prior probability they place on complexity being produced by chance events or by a Creator. This difference provides atheists and theists with a rational basis for disagreement.     

PROBabilities from EXemplars (PROBEX): a “lazy” algorithm for probabilistic inference from generic knowledge

PROBEX (PROBabilities from EXemplars), a model of probabilistic inference and probability judgment based on generic knowledge is presented. Its properties are that: (a) it provides an exemplar model satisfying bounded rationality; (b) it is a “lazy” algorithm that presumes no pre‐computed abstractions; (c) it implements a hybrid‐representation, similarity‐graded probability. We investigate the ecological rationality of PROBEX and find that it compares favorably with Take‐The‐Best and multiple regression (Gigerenzer, Todd, & the ABC Research Group, 1999). PROBEX is fitted to the point estimates, decisions, and probability assessments by human participants. The best fit is obtained for a version that weights frequency heavily and retrieves only two exemplars. It is proposed that PROBEX implements speed and frugality in a psychologically plausible way.     

Postselection Inference in Structural Equation Modeling

Abstract

Most statistical inference methods were established under the assumption that the fitted model is known in advance. In practice, however, researchers often obtain their final model by some data-driven selection process. The selection process makes the finally fitted model random, and it also influences the sampling distribution of the estimator. Therefore, implementing naive inference methods may result in wrong conclusions—which is probably a prime source of the reproducibility crisis in psychological science. The present study accommodates three valid state-of-the-art postselection inference methods for structural equation modeling (SEM) from the statistical literature: data splitting (DS), postselection inference (PoSI), and the polyhedral (PH) method. A simulation is conducted to compare the three methods with the commonly used naive procedure under selection events made by L₁-penalized SEM. The results show that the naive method often yields incorrect inference, and that the valid methods control the coverage rate in most cases with their own pros and cons. Real world data examples show the practical use of the valid inference methods.     

Feature centrality and property induction

A feature is central to a concept to the extent that other features depend on it. Four studies tested the hypothesis that people will project a feature from a base concept to a target concept to the extent that they believe the feature is central to the two concepts. This centrality hypothesis implies that feature projection is guided by a principle that aims to maximize the structural commonality between base and target concepts. Participants were told that a category has two or three novel features. One feature was the most central in that more properties depended on it. The extent to which the target shared the feature’s dependencies was manipulated by varying the similarity of category pairs. Participants’ ratings of the likelihood that each feature would hold in the target category support the centrality hypothesis with both natural kind and artifact categories and with both well-specified and vague dependency structures.     

Model Selection of Nested and Non-Nested Item Response Models Using Vuong Tests

Abstract

In this paper, we apply Vuong’s general approach of model selection to the comparison of nested and non-nested unidimensional and multidimensional item response theory (IRT) models. Vuong’s approach of model selection is useful because it allows for formal statistical tests of both nested and non-nested models. However, only the test of non-nested models has been applied in the context of IRT models to date. After summarizing the statistical theory underlying the tests, we investigate the performance of all three distinct Vuong tests in the context of IRT models using simulation studies and real data. In the non-nested case we observed that the tests can reliably distinguish between the graded response model and the generalized partial credit model. In the nested case, we observed that the tests typically perform as well as or sometimes better than the traditional likelihood ratio test. Based on these results, we argue that Vuong’s approach provides a useful set of tools for researchers and practitioners to effectively compare competing nested and non-nested IRT models.     

Show me the meaning! The contextual relevance of images influences the recall and understanding of multimedia RSVP paragraphs

ABSTRACT

This study examined whether images facilitate contextual grouping as effectively as words. College students (N?=?102) read RSVP paragraphs with topics located at the beginning, middle, or end of the paragraph. Paragraph topic was presented as a topic-related image, topic-unrelated image, or written explicitly in text. Topic identification was equally accurate for topic-related images and topic sentences, and less accurate for unrelated images. Paragraphs from the text condition were recalled more accurately than the related and unrelated image conditions. Further, recognition of unrelated images was a positive predictor of the number of logical errors committed during recall. Therefore, related images did not improve memory and comprehension as much as text, and paragraphs with unrelated images negatively affected understanding of the meaning of the paragraph, but not recall. These findings have implications for multimedia information sources such as textbooks or articles that use images to clarify the meaning of the material.