The case for regularity in mechanistic causal explanation

How regular do mechanisms need to be, in order to count as mechanisms? This paper addresses two arguments for dropping the requirement of regularity from the definition of a mechanism, one motivated by examples from the sciences and the other motivated by metaphysical considerations regarding causation. I defend a broadened regularity requirement on mechanisms that takes the form of a taxonomy of kinds of regularity that mechanisms may exhibit. This taxonomy allows precise explication of the degree and location of regular operation within a mechanism, and highlights the role that various kinds of regularity play in scientific explanation. I defend this regularity requirement in terms of regularity??s role in individuating mechanisms against a background of other causal processes, and by prioritizing mechanisms?? ability to serve as a model of scientific explanation, rather than as a metaphysical account of causation. It is because mechanisms are regular, in the expanded sense described here, that they are capable of supporting the kinds of generalizations that figure prominently in scientific explanations.     

医学伪科学产生的宗教学析因

我国以气功、人体科学名义出现的医学伪科学是一种宗教现象或准宗教,其组织形式为膜拜教团,教主与信徒之间是欺骗与被欺骗的关系。医学伪科学的产生有一定的文化学背景,对文化的过高评价造成中国传统文化的超科学假象。虽非真正的宗教,医学伪科学却可以填补宗教的空缺发挥替代作用,这是医学伪科学得以出现和发展的重要原因。     

Explaining features of fine-grained phenomena using abstract analyses of phenomena and mechanisms: two examples from chronobiology

Explanations of biological phenomena such as cell division, protein synthesis or circadian rhythms commonly take the form of models of the responsible mechanisms. Recently philosophers of science have attempted to analyze this practice, presenting mechanisms as organized collections of parts performing operations that together produce the phenomenon. But in some cases what researchers seek to explain is not a general phenomenon, but a specific feature of a more fine-grained phenomenon. In some of these cases, it is not the model of the mechanism that performs the explanatory work. I consider a case in which the investigator offered an abstract representation of a fine-grained phenomenon to show why in had the feature in question. I consider a second case in which a researcher abstracted from the mechanism to identify a design principle that explains why the functioning mechanism exhibits a specific feature.

     

A Type of Non-Causal Explanation1

There is a type of explanation frequently employed in the sciences, particularly the quantitative ones, that I intend to focus on. It seeks to understand some phenomenon or regularity that occurs in a type of system S by deriving an equation describing that phenomenon or regularity from a set of equations that govern S. I want to propose a way of understanding such explanations.     

Functions and Mechanisms in Structural-Modelling Explanations

One way social scientists explain phenomena is by building structural models. These models are explanatory insofar as they manage to perform a recursive decomposition on an initial multivariate probability distribution, which can be interpreted as a mechanism. Explanations in social sciences share important aspects that have been highlighted in the mechanisms literature. Notably, spelling out the functioning the mechanism gives it explanatory power. Thus social scientists should choose the variables to include in the model on the basis of their function in the mechanism. This paper examines the notion of ‘function’ within structural modelling. We argue that ‘functions’ ought to be understood as the theoretical underpinnings of the causes, namely as the role that causes play in the functioning of the mechanism.     

Life: Definition,Origin, and Evolution

Out of all the natural phenomena surrounding us, the phenomena Life, as a matter of course, is that which is the most mysterious and least understood of all. In many aspects, it seems as though it would be possible to investigate, study, and explain Life, like so many of the other subjects that fall within the category of the natural sciences: time, space, mass, movement, and so on. However, in its essence, Life is organized and managed differently than all other natural phenomena, operating, as it were, according to its own laws. Life certainly has certain clearly recognizable characteristics, such as: metabolism, homeostasis, continuity, and more. However, up until the present time, no definition for Life has been provided, which unifies it as an entity, and differentiates it unmistakably from other natural phenomenon. The current article offers a definition of the true nature of the phenomenon of Life, which in turn helps to explain the big questions of life—evolution, the mind–body problem, consciousness, and more.     

Models of Machines and Models of Phenomena

Experimental engineering models have been used both to model general phenomena, such as the onset of turbulence in fluid flow, and to predict the performance of machines of particular size and configuration in particular contexts. Various sorts of knowledge are involved in the method—logical consistency, general scientific principles, laws of specific sciences, and experience. I critically examine three different accounts of the foundations of the method of experimental engineering models (scale models), and examine how theory, practice, and experience are involved in employing the method to obtain practical results. Models of machines and mechanisms can be (and generally are) involved in establishing criteria for similar phenomena, which provide guidance in using events to model other events. Conversely, models of phenomena such as events that model other events can be (and generally are) involved in experimentation on models of machines. I conclude that often it is not more detailed models or the more precise equations they engender that leads to better understanding, but rather an insightful use of knowledge at hand to determine which similarity principles are appropriate in allowing us to infer what we do not know from what we are able to observe.     

The altered rationale for the choice of a standard animal in experimental psychology: Henry H. Donaldson, Adolf Meyer, and "the" albino rat

The mid 20th-century dominance of albino rats in nonhuman experimental psychology research often presumed that the animal embodied fundamental psychological processes that could generalize to a wide range of vertebrates. The author describes the conceptual basis for the original choice of white rats by the 2 individuals most responsible for establishing rats as a prominent animal model in the life sciences at the turn of the century: Henry H. Donaldson and Adolf Meyer. The author stresses the comparative rationale that justified their choice and argues that they sought generality through attention to diversity and species differences. Their approach contrasts sharply with the later view of the rat as a generic animal model that could represent similarities shared by all vertebrates. It is suggested that the change resulted from an emphasis on standardization produced by the growing industrialization of the life sciences in America.     

Why Social Science is Biological Science

The social sciences need to take seriously their status as divisions of biology. As such they need to recognize the central role of Darwinian processes in all the phenomena they seek to explain. An argument for this claim is formulated in terms of a small number of relatively precise premises that focus on the nature of the kinds and taxonomies of all the social sciences. The analytical taxonomies of all the social sciences are shown to require a Darwinian approach to human affairs, though not a nativist or genetically driven theory by any means. Non-genetic Darwinian processes have the fundamental role on all human affairs. I expound a general account of how Darwinian processes operate in human affairs by selecting for strategies and sets of strategies individuals and groups employ. I conclude by showing how a great deal of social science can be organized in accordance with Tinbergen’s approach to biological inquiry, an approach required by the fact that the social sciences are all divisions of biology, and in particular the studies of one particular biological species.     

The effects of different intensity levels of background noise on dichotic listening to consonant-vowel syllables

It has previously been shown that the right ear advantage in dichotic listening to consonant-vowel syllables is affected if a background noise is presented at the same time as the dichotic stimuli. What is not known is, however, if there is also an effect of varying the intensity level of the background noise. We therefore presented conversation or traffic noise background noise simultaneously with the dichotic syllable stimuli to healthy adult subjects. The intensity of the background noises varied between 50-65 dB in steps of 5 dB. The results showed that the right ear correct reports decreased, while left ear correct reports increased as a consequence of increasing background noise intensity. The effects were also stronger for the right ear, and for the traffic background noise condition, particularly at the two highest intensity levels. The results are discussed in terms of alertness and attentional mechanisms.     

Noise exclusion in spatial attention

Precue validity affects the performance of perceptual tasks. These spatial attention effects have been variously attributed to facilitation of processing, capacity allocation, or noise reduction. We used a new attention-plus-external (stimulus)-noise paradigm and model to identify the mechanisms of attention in cue-validity paradigms. A new phenomenon is reported: a large effect of location cue validity in an orientation identification task that specifically occurs when the stimulus is embedded in external (environmental or stimulus) noise. This result identifies the mechanism of the effect as external-noise exclusion, distinguished from stimulus enhancement that manifests itself only in noiseless stimulus environments.     

Ability, breadth, and parsimony in computational models of higher-order cognition

Computational models will play an important role in our understanding of human higher‐order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher‐order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do; (b) the breadth of situations in which it can do so; and (c) the parsimony of the mechanisms it posits. This article argues that fits of models to quantitative experimental data, although valuable for other reasons, do not address these criteria. Further, using analogies with other sciences, the history of cognitive science, and examples from modern‐day research programs, this article identifies five activities that have been demonstrated to play an important role in our understanding of human higher‐order cognition. These include modeling within a cognitive architecture, conducting artificial intelligence research, measuring and expanding a model's ability, finding mappings between the structure of different domains, and attempting to explain multiple phenomena within a single model.     

Manipulation is key: on why non-mechanistic explanations in the cognitive sciences also describe relations of manipulation and control

A popular view presents explanations in the cognitive sciences as causal or mechanistic and argues that an important feature of such explanations is that they allow us to manipulate and control the explanandum phenomena. Nonetheless, whether there can be explanations in the cognitive sciences that are neither causal nor mechanistic is still under debate. Another prominent view suggests that both causal and non-causal relations of counterfactual dependence can be explanatory, but this view is open to the criticism that it is not clear how to distinguish explanatory from non-explanatory relations. In this paper, I draw from both views and suggest that, in the cognitive sciences, relations of counterfactual dependence that allow manipulation and control can be explanatory even when they are neither causal nor mechanistic. Furthermore, the ability to allow manipulation can determine whether non-causal counterfactual dependence relations are explanatory. I present a preliminary framework for manipulation relations that includes some non-causal relations and use two examples from the cognitive sciences to show how this framework distinguishes between explanatory and non-explanatory, non-causal relations. The proposed framework suggests that, in the cognitive sciences, causal and non-causal relations have the same criterion for explanatory value, namely, whether or not they allow manipulation and control.     

Collective memory, group minds, and the extended mind thesis

While memory is conceptualized predominantly as an individual capacity in the cognitive and biological sciences, the social sciences have most commonly construed memory as a collective phenomenon. Collective memory has been put to diverse uses, ranging from accounts of nationalism in history and political science to views of ritualization and commemoration in anthropology and sociology. These appeals to collective memory share the idea that memory “goes beyond the individual” but often run together quite different claims in spelling out that idea. This paper reviews a sampling of recent work on collective memory in the light of emerging externalist views within the cognitive sciences, and through some reflection on broader traditions of thought in the biological and social sciences that have appealed to the idea that groups have minds. The paper concludes with some thoughts about the relationship between these kinds of cognitive metaphors in the social sciences and our notion of agency.     

Boundary Work,Associative Argumentation and Switching in the Advocacy of Agricultural Biotechnology

Some scientists argue that we face a worldwide reproductive crisis known as ‘endocrine disruption’, a term used to encompass the effects of hormonally active chemicals in our environment. Everyday chemicals such as plastics, pesticides and flame retardants can mimic hormones and thereby disrupt the reproductive, neurological and immunological development of humans and other animals. This surprising discovery is causing consternation in scientific, policy, academic and corporate arenas as they attempt to define, assess and control for this understudied phenomenon. How can the social sciences participate in understanding and solving the problem generated by endocrine disruption? First, there is an early history of endocrine disruption; the development of insect hormone mimics as pesticides, that has yet to be brought to bear in contemporary discussions of endocrine disruption. Second a social science analysis of the mishaps resulting from the 1970s' development of insect hormone mimics as pesticides offers a new way of thinking scientifically about mimesis. Employing the social science insight that mimesis is productive of social change sheds light on how insect hormone mimicking pesticides produced a number of scientific surprises. By analyzing the outcome of using mimesis in the laboratory, this paper participates in the discussion of endocrine disruption by arguing for a re-evaluation of the predominance of laboratory based sciences when dealing with epigenetic phenomena.     

Multisensory integration, perception and ecological validity

Studies of multimodal integration have relied to a large extent on conflict situations, in which two sensory modalities receive incongruent data concerning one aspect of the source. Exposure to such situations produces immediate crossmodal biases as well as longer lasting aftereffects, revealing recalibrations of data-to-percept matches. In the natural environment, such phenomena might be adaptive, by reducing the perturbing effects of factors like noise or growth-induced changes in receptor organs, and by enriching the percept. However, experimental results generalize to real life only when they reflect automatic perceptual processes, and not response strategies adopted to satisfy the particular demands of laboratory tasks. Here, we focus on this issue and review ways of addressing it that have been developed recently.     

Believe It Or Not: Religious and Other Paranormal Beliefs in the United States

Paranormal beliefs are often divided between those that are central to traditional Christian doctrine, such as the belief in heaven and hell, and those that are commonly associated with the supernatural or occult, such as the belief in ESP and psychic healing. This study employs data from a recent nationwide random sample general population survey to catalog the social correlates of paranormal beliefs and to examine the relationships between religious and other paranormal beliefs. The results indicate that standard social background factors do a poor job of accounting for who believes in paranormal phenomena and that the importance of specific background factors changes dramatically from phenomenon to phenomenon. The results also show that the correlations between belief in religious phenomena and other paranormal phenomena are largely insignificant. These findings call into question many prevailing theories about paranormal beliefs.     

Modeling the Developmental Patterning of Finiteness Marking in English, Dutch, German, and Spanish Using MOSAIC

In this study, we apply MOSAIC (model of syntax acquisition in children) to the simulation of the developmental patterning of children's optional infinitive (OI) errors in 4 languages: English, Dutch, German, and Spanish. MOSAIC, which has already simulated this phenomenon in Dutch and English, now implements a learning mechanism that better reflects the theoretical assumptions underlying it, as well as a chunking mechanism that results in frequent phrases being treated as 1 unit. Using 1, identical model that learns from child-directed speech, we obtain a close quantitative fit to the data from all 4 languages despite there being considerable cross-linguistic and developmental variation in the OI phenomenon. MOSAIC successfully simulates the difference between Spanish (a pro-drop language in which OI errors are virtually absent) and obligatory subject languages that do display the OI phenomenon. It also highlights differences in the OI phenomenon across German and Dutch, 2 closely related languages whose grammar is virtually identical with respect to the relation between finiteness and verb placement. Taken together, these results suggest that (a) cross-linguistic differences in the rates at which children produce OIs are graded, quantitative differences that closely reflect the statistical properties of the input they are exposed to and (b) theories of syntax acquisition need to consider more closely the role of input characteristics as determinants of quantitative differences in the cross-linguistic patterning of phenomena in language acquisition.     

Theoretical and computational analysis of skill learning, repetition priming, and procedural memory

This article analyzes the relationship between skill learning and repetition priming, 2 implicit memory phenomena. A number of reports have suggested that skill learning and repetition priming can be dissociated from each other and are therefore based on different mechanisms. The authors present a theoretical analysis showing that previous results cannot be regarded as evidence of a processing dissociation between skill learning and repetition priming. The authors also present a single-mechanism computational model that simulates a specific experimental task and exhibits both skill learning and repetition priming, as well as a number of apparent dissociations between these measures. These theoretical and computational analyses provide complementary evidence that skill learning and repetition priming are aspects of a single underlying mechanism that has the characteristics of procedural memory.