The Contingency of Creation and Modern Science

The Bible reveals a free, contingent act of the triune, personal God as the origin of the world. This paper explores the fruitfulness of taking the contingency of creation as the starting-point for our thinking about the natural world. We will see that the contingency of creation implies a conception of the natural order which is in harmony with modern science. It is the foundation for the experimental method and the use of mathematics, provides an understanding of contingent laws of nature, shows that natural order is open to historical evolution, and makes room for chance and novelty.     

Divine Activity and Motive Power in Descartes's Physics

This paper is the first of a two-part reexamination of causation in Descartes's physics. Some scholars – including Gary Hatfield and Daniel Garber – take Descartes to be a `partial' Occasionalist, who thinks that God alone is the cause of all natural motion. Contra this interpretation, I agree with literature that links Descartes to the Thomistic theory of divine concurrence. This paper surveys this literature, and argues that it has failed to provide an interpretation of Descartes's view that both distinguishes his position from that of his later, Occasionalist followers and is consistent with his broader metaphysical commitments. I provide an analysis that tries to address these problems with earlier `Concurentist' readings of Descartes. On my analysis, Occasionalism entails that created substances do not have intrinsic active causal powers. As I read him, Descartes thinks that bodies have active causal powers that are partly grounded in their intrinsic natures. But I argue – pace a recent account by Tad Schmaltz – that Descartes also thinks that God immediately causes all motion in the created world. On the picture that emerges, Descartes's position is both continuous with, and a subtle departure from, the Thomisitic theory of divine concurrence.     

Causal attributions in premenstrual syndrome

Abstract

The relationship between causal attributions and symptoms was investigated with self-report inventories in patients with premenstrual syndrome (n=38) and controls (n=26) during the premenstrual as well as the intermenstrual phase of the cycle. Patients with prospectively confirmed premenstrual exacerbations of physical and psychological symptoms more frequently attributed their complaints during premenstruum to the cycle than was the case during the intermenstrual period. Controls showed no differences in types of explanations for complaints in either phase of the cycle. It appeared that patients used not only the menstrual cycle, but also psychological distress and physical exertion, more often then controls as an explanation for complaints during both phases of the cycle. Implications of the finding that patients used medical as well as non-medical explanations for complaints during the cycle are discussed with regard to treatment strategies for PMS.     

From Blickets to Synapses: Inferring Temporal Causal Networks by Observation

How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. The network uses spike‐time dependent plasticity, long‐term depression, and heterosynaptic competition rules to implement Rescorla–Wagner‐like learning. Transmission delays between neurons allow the network to learn a forward model of the temporal relationships between events. Within this framework, biologically realistic synaptic plasticity rules account for well‐known behavioral data regarding cognitive causal assumptions such as backwards blocking and screening‐off. These models can then be run as emulators for state inference. Furthermore, this mechanism is capable of copying synaptic connectivity patterns between neuronal networks by observing the spontaneous spike activity from the neuronal circuit that is to be copied, and it thereby provides a powerful method for transmission of circuit functionality between brain regions.     

Gravity is not the only ruler for falling events: Young children stop making the gravity error after receiving additional perceptual information about the tubes mechanism

Young children seem to operate under the assumption that objects always fall in a straight vertical line. When asked to search for a ball dropped down an S-shaped opaque tube, they repeatedly search directly below. Hood proposed that children have difficulty in inhibiting their prepotent expectation that objects fall in a straight line (Hood, 1995). We asked whether the inability to inhibit this prepotent response is the only factor determining children’s performance on the tubes task. In one condition the openings to the tubes were covered by chimneys, whereas in another condition the openings were visible. When first tested with the apparatus, children performed better when the openings were visible. Furthermore, children’s performance on equally complex configurations (i.e., with or without chimneys) was modulated by their previous experience. Thus, children’s understanding of the tubes mechanism seems to play an important role in addition to inhibitory control.     

Children's imitation of causal action sequences is influenced by statistical and pedagogical evidence

Children are ubiquitous imitators, but how do they decide which actions to imitate? One possibility is that children rationally combine multiple sources of information about which actions are necessary to cause a particular outcome. For instance, children might learn from contingencies between action sequences and outcomes across repeated demonstrations, and they might also use information about the actor’s knowledge state and pedagogical intentions. We define a Bayesian model that predicts children will decide whether to imitate part or all of an action sequence based on both the pattern of statistical evidence and the demonstrator’s pedagogical stance. To test this prediction, we conducted an experiment in which preschool children watched an experimenter repeatedly perform sequences of varying actions followed by an outcome. Children’s imitation of sequences that produced the outcome increased, in some cases resulting in production of shorter sequences of actions that the children had never seen performed in isolation. A second experiment established that children interpret the same statistical evidence differently when it comes from a knowledgeable teacher versus a naïve demonstrator. In particular, in the pedagogical case children are more likely to “overimitate” by reproducing the entire demonstrated sequence. This behavior is consistent with our model’s predictions, and suggests that children attend to both statistical and pedagogical evidence in deciding which actions to imitate, rather than obligately imitating successful action sequences.     

What mediation analysis can (not) do

The present article is concerned with a common misunderstanding in the interpretation of statistical mediation analyses. These procedures can be sensibly used to examine the degree to which a third variable (Z) accounts for the influence of an independent (X) on a dependent variable (Y) conditional on the assumption that Z actually is a mediator. However, conversely, a significant mediation analysis result does not prove that Z is a mediator. This obvious but often neglected insight is substantiated in a simulation study. Using different causal models for generating Z (genuine mediator, spurious mediator, correlate of the dependent measure, manipulation check) it is shown that significant mediation tests do not allow researchers to identify unique mediators, or to distinguish between alternative causal models. This basic insight, although well understood by experts in statistics, is persistently ignored in the empirical literature and in the reviewing process of even the most selective journals.     

Equilibria of the Rescorla-Wagner model

The Rescorla-Wagner model has been a leading theory of animal causal induction for nearly 30 years, and human causal induction for the past 15 years. Recent theories (especially Psychol. Rev. 104 (1997) 367) have provided alternative explanations of how people draw causal conclusions from covariational data. However, theoretical attempts to compare the Rescorla-Wagner model with more recent models have been hampered by the fact that the Rescorla-Wagner model is an algorithmic theory, while the more recent theories are all computational. This paper provides a detailed derivation of the long-run behavior of the Rescorla-Wagner model under a wide range of parameters and experimental setups, so that the model can be compared with computational theories. It also shows that the model agrees with competing theories on a wider range of cases than had previously been thought. The paper concludes by showing how recently suggested modifications of the Rescorla-Wagner model impact the long-run behavior of the model.     

What Poetry Brings to the Table of Science and Religion

Ever since Plato's famous attack on artists and poets in Book 10 of The Republic, lovers of literature have felt pressed to defend poetry, and indeed from ancient times down to the present, literature and art have had to fight various battles against philosophy, religion, and science. After providing a brief overview of this conflict and then arguing that between poetry and science there are some noteworthy similarities—that is, that some of the basic mental structures with which the scientist studies the “text” of nature (facts, laws, theories) find their counterparts in ways an informed reader studies the poetic text, I develop what I see as the most important differences between poetry, on the one hand, and science, philosophy, and theology, on the other. These differences lie chiefly in two areas: (1) in the stance that each takes toward language itself and (2) in the stance each takes toward that ancient polarity between the one and the many. The aim of my argument is neither to privilege poetry over the other modes of knowing the world nor to grant, particularly to science in its reductive “objectivity,” a higher epistemological status than that accorded to poetry and the arts. Instead, I wish to argue that science, by pushing the boundaries of knowledge about the material world, shows the poet, as well as the theologian, some of the more important work to be done and that poetry, with its emphasis on the particular over the abstract and on the ambiguities and paradoxes of language as inherently metaphorical, serves science and religion by providing a caution against the naive acceptance of language as literal and the consequent enthrallment to the power of absolutes and totalizing abstractions.     

Adjusting for the correspondence bias: Effects of causal uncertainty, cognitive busyness, and causal strength of situational information

This research examined the conditions under which people who have more chronic doubt about their ability to make sense of social behavior (i.e., are causally uncertain; [Weary and Edwards, 1994] and [Weary and Edwards, 1996]) are more likely to adjust their dispositional inferences for a target’s behaviors. Using a cognitive busyness manipulation within the attitude attribution paradigm, we found in Study 1 that higher causal uncertainty predicted increased correction of dispositional inferences, but only when participants had sufficient attentional resources to devote to the task. In Study 2, we found that higher-causal uncertainty predicted greater inferential correction, but only when the additional information provided a more compelling alternative explanation for the observed behavior. Results of this research are discussed in terms of their relevance to the Causal Uncertainty (Weary & Edwards, 1994) and dispositional inference models.