Religions in a Secular World

The task of astrotheology is to speculate on the theological, cultural, and ethical implications of space exploration, especially the exploration of astrobiologists into the (1) origin of life; (2) a second genesis of life; and (3) expansion of life beyond earth. When assumptions within the field of astrobiology are examined, we find that the Darwinian model of evolutionary development is imaginatively projected onto extrasolar planets; and this model includes a built-in doctrine of progress. The assumption of progress within evolution permits astrobiologists to look forward to contact with an extraterrestrial civilization that is more intelligent and more advanced than that on earth. Such an extraterrestrial civilization will allegedly have an advanced science that can save earth from its primitive and under-evolved propensity for violence. However, no empirical evidence for a more highly evolved or advanced civilization currently exists, despite these beliefs. The theologian labels the constellation of scientific assumptions here the “ETI myth.” Astrotheology celebrates hard-nosed empirical science and even encourages space exploration; but the mythical assumptions regarding the doctrine of progress within evolution are here given critical analysis.     

Bayesian t tests for accepting and rejecting the null hypothesis

Progress in science often comes from discovering invariances in relationships among variables; these invariances often correspond to null hypotheses. As is commonly known, it is not possible to state evidence for the null hypothesis in conventional significance testing. Here we highlight a Bayes factor alternative to the conventional t test that will allow researchers to express preference for either the null hypothesis or the alternative. The Bayes factor has a natural and straightforward interpretation, is based on reasonable assumptions, and has better properties than other methods of inference that have been advocated in the psychological literature. To facilitate use of the Bayes factor, we provide an easy-to-use, Web-based program that performs the necessary calculations.     

What Are the “True” Statistics of the Environment?

A widespread assumption in the contemporary discussion of probabilistic models of cognition, often attributed to the Bayesian program, is that inference is optimal when the observer's priors match the true priors in the world—the actual “statistics of the environment.” But in fact the idea of a “true” prior plays no role in traditional Bayesian philosophy, which regards probability as a quantification of belief, not an objective characteristic of the world. In this paper I discuss the significance of the traditional Bayesian epistemic view of probability and its mismatch with the more objectivist assumptions about probability that are widely held in contemporary cognitive science. I then introduce a novel mathematical framework, the observer lattice, that aims to clarify this issue while avoiding philosophically tendentious assumptions. The mathematical argument shows that even if we assume that “ground truth” probabilities actually do exist, there is no objective way to tell what they are. Different observers, conditioning on different information, will inevitably have different probability estimates, and there is no general procedure to determine which one is right. The argument sheds light on the use of probabilistic models in cognitive science, and in particular on what exactly it means for the mind to be “tuned” to its environment.     

Analyzing the Rate at Which Languages Lose the Influence of a Common Ancestor

Analyzing the rate at which languages change can clarify whether similarities across languages are solely the result of cognitive biases or might be partially due to descent from a common ancestor. To demonstrate this approach, we use a simple model of language evolution to mathematically determine how long it should take for the distribution over languages to lose the influence of a common ancestor and converge to a form that is determined by constraints on language learning. We show that modeling language learning as Bayesian inference of n binary parameters or the ordering of n constraints results in convergence in a number of generations that is on the order of n log n. We relax some of the simplifying assumptions of this model to explore how different assumptions about language evolution affect predictions about the time to convergence; in general, convergence time increases as the model becomes more realistic. This allows us to characterize the assumptions about language learning (given the models that we consider) that are sufficient for convergence to have taken place on a timescale that is consistent with the origin of human languages. These results clearly identify the consequences of a set of simple models of language evolution and show how analysis of convergence rates provides a tool that can be used to explore questions about the relationship between accounts of language learning and the origins of similarities across languages.     

Hierarchies and levels of reality

We examine some assumptions about the nature of ‘levels of reality’ in the light of examples drawn from physics. Three central assumptions of the standard view of such levels (for instance, Oppenheim and Putnam 1958) are (i) that levels are populated by entities of varying complexity, (ii) that there is a unique hierarchy of levels, ranging from the very small to the very large, and (iii) that the inhabitants of adjacent levels are related by the parthood relation. Using examples from physics, we argue that it is more natural to view the inhabitants of levels as the behaviors of entities, rather than entities themselves. This suggests an account of reduction between levels, according to which one behavior reduces to another if the two are related by an appropriate limit relation. By considering cases where such inter-level reduction fails, we show that the hierarchy of behaviors differs in several respects from the standard hierarchy of entities. In particular, while on the standard view, lower-level entities are ‘micro’ parts of higher-level entities, on our view, a system’s macro-level behavior can be seen as a (‘non-spatial’) part of its micro-level behavior. We argue that this second hierarchy is not really in conflict with the standard view and that it better suits examples of explanation in science.     

Conceptual complexity and the bias/variance tradeoff

In this paper we propose that the conventional dichotomy between exemplar-based and prototype-based models of concept learning is helpfully viewed as an instance of what is known in the statistical learning literature as the bias/variance tradeoff. The bias/variance tradeoff can be thought of as a sliding scale that modulates how closely any learning procedure adheres to its training data. At one end of the scale (high variance), models can entertain very complex hypotheses, allowing them to fit a wide variety of data very closely—but as a result can generalize poorly, a phenomenon called overfitting. At the other end of the scale (high bias), models make relatively simple and inflexible assumptions, and as a result may fit the data poorly, called underfitting. Exemplar and prototype models of category formation are at opposite ends of this scale: prototype models are highly biased, in that they assume a simple, standard conceptual form (the prototype), while exemplar models have very little bias but high variance, allowing them to fit virtually any combination of training data. We investigated human learners’ position on this spectrum by confronting them with category structures at variable levels of intrinsic complexity, ranging from simple prototype-like categories to much more complex multimodal ones. The results show that human learners adopt an intermediate point on the bias/variance continuum, inconsistent with either of the poles occupied by most conventional approaches. We present a simple model that adjusts (regularizes) the complexity of its hypotheses in order to suit the training data, which fits the experimental data better than representative exemplar and prototype models.     

The Philosophical Foundations of Humanistic Psychology: A Reply to McMullen

McMullen (1982) attempted in his critique of humanistic psychology to attack its philosophical foundations, taking these to be phenomenology and a doctrine of self determination. In this paper we argue that McMullen has misrepresented the philosophical position of the humanists, then we examine McMullen's own philosophical assumptions. We show that he has assumed the philosophy of empirical realism which we argue is inadequate as a foundation for science. It is shown that it is the inadequacy of McMullen's assumptions which underlies his failure to comprehend the position of the humanists. We then present a version of theoretical realism and show how, in terms of this, a conception of being can be justified which allows for the emergence of hierarchical order. On the basis of this conception of being we reassess the nature of self-determination and motivation, showing how, in opposition to both the empirical realism of McMullen and the philosophical dualism of conventional humanistic psychology, a naturalistic form of humanistic psychology is justified.     

Knowledge in science and engineering

It is now fashionable to say that science and technology are social constructions. This is true, or rather, a truism. Man is a social animal. Man is a linguistic animal, and language is social. Hence all products of human activities and everything that involves language are social constructions. But an assertion that covers everything becomes empty. The constructionist mantra that science or technology is “not a simple input from nature” attacks a straw man, for no one denies the necessity of enormous human efforts in research, development, and design. To say that these are social activities should not imply that they are indistinguishable from other social activities such as politicking or profiteering. An investigation into their peculiarities will bring to relief their intellectual and technical characteristics. The argument that science and technology are social constructions because they involve many assumptions is again a truism. Whenever we think, whenever we find things intelligible, we invariably have used some concepts and made some assumptions. Philosophers such as Kant have painstakingly analyzed concepts without which intelligibility is impossible. The important questions are not whether scientists and engineers make assumptions but what kind of assumptions they make; not whether they make judgments, but what kind of reasons they offer to support their judgments. Are the assumptions and justifications all social? Or are they mainly technical? Admittedly, the boundaries between the two are not always sharp, but is it impossible to make any differentiation at all?     

OAKESHOTT ON THE CHARACTER OF RELIGIOUS EXPERIENCE: NEED THERE BE A CONFLICT BETWEEN SCIENCE AND RELIGION?

Michael Oakeshott reflected on the character of religious experience in various writings throughout his life. In Experience and Its Modes (1933) he analyzed science as a distinctive “mode,” or account of experience as a whole, identifying those assumptions necessary for science to achieve its coherent account of experience in contrast to other modes of experience whose quests for coherence depend on different assumptions. Religious experience, he thought, was integral to the practical mode. The latter experiences the world as interminable tension between what is and what ought to be. The question, Is there a conflict between science and religion? is, in Oakeshott's approach, the question, Is there a conflict between the scientific mode of experience and the practical mode? Insofar as we tend to treat every question as a practical one, these questions seem to make sense. But Oakeshott's analysis leads to the view that scientific experience and religious experience are categorically different accounts of experience abstracted from the whole of experience. They are voices of experience that may speak to each other, but they are not ordered hierarchically. Nor can either absorb the other without insoluble contradictions.     

Improving the study of brain-behavior relationships by revisiting basic assumptions

Neuroimaging research has been at the forefront of concerns regarding the failure of experimental findings to replicate. In the study of brain-behavior relationships, past failures to find replicable and robust effects have been attributed to methodological shortcomings. Methodological rigor is important, but there are other overlooked possibilities: most published studies share three foundational assumptions, often implicitly, that may be faulty. In this paper, we consider the empirical evidence from human brain imaging and the study of non-human animals that calls each foundational assumption into question. We then consider the opportunities for a robust science of brain-behavior relationships that await if scientists ground their research efforts in revised assumptions supported by current empirical evidence.     

Randomization tests as alternative analysis methods for behavior‐analytic data

Randomization statistics offer alternatives to many of the statistical methods commonly used in behavior analysis and the psychological sciences, more generally. These methods are more flexible than conventional parametric and nonparametric statistical techniques in that they make no assumptions about the underlying distribution of outcome variables, are relatively robust when applied to small‐n data sets, and are generally applicable to between‐groups, within‐subjects, mixed, and single‐case research designs. In the present article, we first will provide a historical overview of randomization methods. Next, we will discuss the properties of randomization statistics that may make them particularly well suited for analysis of behavior‐analytic data. We will introduce readers to the major assumptions that undergird randomization methods, as well as some practical and computational considerations for their application. Finally, we will demonstrate how randomization statistics may be calculated for mixed and single‐case research designs. Throughout, we will direct readers toward resources that they may find useful in developing randomization tests for their own data.     

THEOLOGICAL METHODOLOGY,CLASSICAL THEISM,AND “LIVED TIME” IN ANTJE JACKELÉN'S TIME AND ETERNITY

Antje Jackelén's Time and Eternity successfully employs the method of correlation and a close study of the question of time to enter the dialogue between science and theology. Hermeneutical attention to language is a central element of this dialogue, but we must be aware that much science is untranslatable into ordinary language; it is when we get to the bigger metaphysical assumptions of science that true dialogue begins to happen. Thus, although the method of correlation is a useful way to approach this dialogue, there is not a strict equivalence in this relationship. Theology needs science more than science needs theology. In speaking of time and God we must keep in mind the relational nature of classical Christian theism, even in its most austere forms. We should not read Enlightenment ideas of God back into the classical Christian tradition or neglect the apophatic emphasis in Christian theism, which warned against assuming knowledge of the divine nature. God's relation to time always lies beyond our understanding. Studying the effects of either the Newtonian or Einsteinian concepts of time on our theological concepts should not detract our attention from the “lived time” that characterizes human experience. Consideration of the notion of time in the Madhyamaka Buddhist tradition reminds us that we cannot control the inner reality of time and that for humans time is something to be considered pragmatically.     

The pursuit of the natural

In recent years, it has become common to defend science against charges of bias against the supernatural by explaining that science must remain methodologically natural but does not assume metaphysical naturalism. While such a response is correct, some details about the distinction between methodological naturalism and ontological or metaphysical naturalism have been lacking, as has a clear understanding of the distinction between the methodological restriction of science to natural explanations and naturalistic claims about the scope of those methods. We still require an account of the natural that explains well why science is restricted to giving naturalistic methods, and why the pursuit of natural explanations is not tantamount to the assumption that only natural causes exist. I suggest that the distinguishing characteristics of the natural are not metaphysical at all but broadly epistemological, concerning goals of intersubjectivity and predictability. I argue that by focusing on naturalistic goals we can better explain why the pursuit of natural explanations need not presume any purely natural metaphysics. But I also suggest that the adoption of natural methods is not entirely metaphysically neutral, as it is associated with values that may be more closely associated with some metaphysical views than others.     

Mandated Interventions with Drug-Dependent, Pregnant Women

Increased concern about the influence of pregnant women's substance use on fetal health has prompted a variety of actions, including calls for legal interventions against some pregnant women with chronic substance abuse problems. In this paper I examine the legal and social science arguments used to support and oppose these interventions. Several assumptions about the behaviors of pregnant women that are used to support the arguments are described. The types of social science research that could inform the ongoing debate about interventions with pregnant women are explored.     

PROCESS ECOLOGY: STEPPING STONES TO BIOSEMIOSIS

Many in science are disposed not to take biosemiotics seriously, dismissing it as too anthropomorphic. Furthermore, biosemiotic apologetics are cast in top‐down fashion, thereby adding to widespread skepticism. An effective response might be to approach biosemiotics from the bottom up, but the foundational assumptions that support Enlightenment science make that avenue impossible. Considerations from ecosystem studies reveal, however, that those conventional assumptions, although once possessing great utilitarian value, have come to impede deeper understanding of living systems because they implicitly depict the evolution of the universe backward. Ecological dynamics suggests instead a smaller set of countervailing postulates that allows evolution to play forward and sets the stage for tripartite causalities, signs, and interpreters—the key elements of biosemiosis—to emerge naturally out of the interaction of chance with configurations of autocatalytic processes. Biosemiosis thereby appears as a fully legitimate outgrowth of the new metaphysic and shows promise for becoming the supervenient focus of a deeper perspective on the phenomenon of life.     

A Historical Perspective on the Future of Behavior Science

Like all natural sciences, behavior science has much to offer toward an understanding of the world. The extent to which the promise of behavior science is realized, though, depends upon the extent to which we keep what we know before us. This paper considers fundamental concepts in behavior science, including the concepts of behavior, stimulation, setting conditions, and language. In considering these concepts, we revisit comments from B. F. Skinner and J. R. Kantor and also consider some areas of behavior analytic research and the implications they have for reconsidering long-held assumptions about the analysis of behavior. We hope that, in considering our foundations, the vitality and strength of the discipline might be enhanced, our impact on science improved, and our future secured.     

The Need for a Revolution in the Philosophy of Science

There is a need to bring about a revolution in the philosophy of science, interpreted to be both the academic discipline, and the official view of the aims and methods of science upheld by the scientific community. At present both are dominated by the view that in science theories are chosen on the basis of empirical considerations alone, nothing being permanently accepted as a part of scientific knowledge independently of evidence. Biasing choice of theory in the direction of simplicity, unity or explanatory power does not permanently commit science to the thesis that nature is simple or unified. This current ‘paradigm’ is, I argue, untenable. We need a new paradigm, which acknowledges that science makes a hierarchy of metaphysical assumptions concerning the comprehensibility and knowability of the universe, theories being chosen partly on the basis of compatibility with these assumptions. Eleven arguments are given for favouring this new ‘paradigm’ over the current one. This revised version was published online in August 2006 with corrections to the Cover Date.     

Handling Missing Covariates in Conditional Mixture Models Under Missing at Random Assumptions

Mixture modeling is a popular method that accounts for unobserved population heterogeneity using multiple latent classes that differ in response patterns. Psychologists use conditional mixture models to incorporate covariates into between-class and/or within-class regressions. Although psychologists often have missing covariate data, conditional mixtures are currently fit with a conditional likelihood, treating covariates as fixed and fully observed. Under this exogenous-x approach, missing covariates are handled primarily via listwise deletion. This sacrifices efficiency and does not allow missingness to depend on observed outcomes. Here we describe a modified joint likelihood approach that (a) allows inference about parameters of the exogenous-x conditional mixture even with nonnormal covariates, unlike a conventional multivariate mixture; (b) retains all cases under missing at random assumptions; (c) yields lower bias and higher efficiency than the exogenous-x approach under a variety of conditions with missing covariates; and (d) is straightforward to implement in available commercial software. The proposed approach is illustrated with an empirical analysis predicting membership in latent classes of conduct problems. Recommendations for practice are discussed.     

Internal Deliberation Defending Climate-Harmful Behavior

Most people in countries with the highest climate impact per capita are well aware of the climate crisis and do not deny the science. They worry about climate and have climate engaged attitudes. Still, their greenhouse-gas emissions are often high. How can we understand acting contrary to our knowledge? A simple answer is that we do not want to give up on benefits or compromise our quality of life. However, it is painful to live with discrepancies between knowledge and action. To be able to avoid taking the consequences of our knowledge, we deal with the gap by motivating to ourselves that the action is still acceptable. In this article, we use topical analysis to examine such processes of motivation by looking at the internal deliberation of 399 climate engaged people’s accounts of their reasoning when acting against their own knowledge. We found that these topical processes can be described in at least four different ways which we call rationalization, legitimization, justification and imploration. By focusing on topoi we can make visible how individual forms of reasoning interact with culturally developed values, habits and assumptions in creating enthymemes. We believe that these insights can contribute to understanding the conditions for climate transition communication.

     

Community Psychology Is (Thank God) More Than Science

Thinking about Community Psychology primarily as a science may make it harder, rather than easier, to embrace certain aspects of the field to which we are deeply committed, but usually fall outside the conventional meaning of doing science. While community psychologists use (and expand) the tools of science, this is different than saying that Community Psychology is only, or even primarily, a science. The field is just as much social criticism as it is science. In order to further conversation about these matters, seven thoughts about why (thank God) community psychology is more than a science are offered, the most basic of which is that today the greatest danger to freedom is not in the union of church and state, but in the union of science and state.Based on a paper presented at a Symposium, A. Wandersman (Chair), Science and Community Psychology, Held at the 9th Biennial Meeting of the Society for Community Research and Action, June, 2003, Las Vegas, New Mexico.