In the context of psychiatric diagnosis, operationists claim that mental disorders are nothing more than the satisfying of objective diagnostic criteria, whereas realists claim that mental disorders are latent entities that are detected by applying those criteria. The implications of this distinction are substantial in actual clinical situations, such as in the co-occurrence of disorders that may interfere with one another's detection, or when patients falsify their symptoms. Realist and operationist conceptions of diagnosis may lead to different clinical decisions in (...) these situations, affecting treatment efficacy and ultimate patient outcomes. (shrink)

A pressing need for interrater reliability in the diagnosis of mental disorders emerged during the mid-twentieth century, prompted in part by the development of diverse new treatments. The Diagnostic and Statistical Manual of Mental Disorders (DSM), third edition answered this need by introducing operationalized diagnostic criteria that were field-tested for interrater reliability. Unfortunately, the focus on reliability came at a time when the scientific understanding of mental disorders was embryonic and could not yield valid disease definitions. Based on accreting problems (...) with the current DSM-fourth edition (DSM-IV) classification, it is apparent that validity will not be achieved simply by refining criteria for existing disorders or by the addition of new disorders. Yet DSM-IV diagnostic criteria dominate thinking about mental disorders in clinical practice, research, treatment development, and law. As a result, the modernDSMsystem, intended to create a shared language, also creates epistemic blinders that impede progress toward valid diagnoses. Insights that are beginning to emerge from psychology, neuroscience, and genetics suggest possible strategies for moving forward. (shrink)

A pluralistic view of psychiatric classification is defended, according to which psychiatric categories take a variety of structural forms. An ordered taxonomy of these forms—non-kinds, practical kinds, fuzzy kinds, discrete kinds, and natural kinds—is presented and exemplified. It is argued that psychiatric categories cannot all be understood as pragmatically grounded, and at least some reflect naturally occurring discontinuities without thereby representing natural kinds. Even if essentialist accounts of mental disorders are generally mistaken, they are not implied whenever a psychiatric category (...) that is not pragmatically grounded is posited. (shrink)

The paper argues for a pragmatic account of genetic explanation. This is to say that when a disease or other trait is termed genetic, the reasons for singling out genes as causes over other, also necessary, genetic and nongenetic conditions are not wholly theoretical but include pragmatic dimensions. Whether the explanation is the presence of a trait in an individual or differences in a trait among individuals, genetic explanations are context-dependent in three ways: they are relative to a causal background (...) of genetic and nongenetic factors; they are relative to a population; and they are relative to the present state of knowledge. Criteria like causal priority, nonstandardness, and causal efficacy that purport to distinguish objectively between genetic causes and nongenetic conditions either incorporate pragmatic elements or fail for other reasons. When the pragmatic dimensions of genetic explanations are recognized, we come to understand the current phenomenon of geneticization to be a reflection of increased technological capacities to manipulate genes in the laboratory, and potentially the clinic, rather than theoretical progress in understanding how diseases and other traits arise. This calls into question the value of the search for theoretical definitions of designations like genetic disease or genetic susceptibility as directives for action. (shrink)

One of the most notable aspects of Fodor's work is that it articulates features not only of speculative cognitive architectures but also of current research in ...

Is it possible to measure psychological attributes like intelligence, personality and attitudes and if so, how does that work? What does the term 'measurement' mean in a psychological context? This fascinating and timely book discusses these questions and investigates the possible answers that can be given response. Denny Borsboom provides an in-depth treatment of the philosophical foundations of widely used measurement models in psychology. The theoretical status of classical test theory, latent variable theory and positioned in terms of the underlying (...) philosophy of science. Special attention is devoted to the central concept of test validity and future directions to improve the theory and practice of psychological measurement are outlined. (shrink)

It is common to defend the Homeostatic Property Cluster ( HPC ) view as a third way between conventionalism and essentialism about natural kinds ( Boyd , 1989, 1991, 1997, 1999; Griffiths , 1997, 1999; Keil , 2003; Kornblith , 1993; Wilson , 1999, 2005; Wilson , Barker , & Brigandt , forthcoming ). According to the HPC view, property clusters are not merely conventionally clustered together; the co-occurrence of properties in the cluster is sustained by a similarity generating ( (...) or homeostatic ) mechanism . I argue that conventional elements are involved partly but ineliminably in deciding which mechanisms define kinds , for deciding when two mechanisms are mechanisms of the same type, and for deciding where one particular mechanism ends and another begins. This intrusion of conventional perspective into the idea of a mechanism raises doubts as to whether the HPC view is sufficiently free of conventional elements to serve as an objective arbiter in scientific disputes about what the kinds of the special sciences should be. (shrink)

In _Complexity and Postmodernism_, Paul Cilliers explores the idea of complexity in the light of contemporary perspectives from philosophy and science. Cilliers offers us a unique approach to understanding complexity and computational theory by integrating postmodern theory into his discussion. _Complexity and Postmodernism_ is an exciting and an original book that should be read by anyone interested in gaining a fresh understanding of complexity, postmodernism and connectionism.

The author shows how simple models based on mutual interactions between children and their environments explain not only smooth evolutions but also sudden jumps, temporal regressions, cycles and chaotic change in cognitive and language development. A central concept of the system is non-linearity - small causes can have big effects and variables may be sensitive to threshold effects.

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the (...) brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components – not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored. (shrink)

Stuart Kauffman here presents a brilliant new paradigm for evolutionary biology, one that extends the basic concepts of Darwinian evolution to accommodate recent findings and perspectives from the fields of biology, physics, chemistry and mathematics. The book drives to the heart of the exciting debate on the origins of life and maintenance of order in complex biological systems. It focuses on the concept of self-organization: the spontaneous emergence of order widely observed throughout nature. Kauffman here argues that self-organization plays an (...) important role in the emergence of life itself and may play as fundamental a role in shaping life's subsequent evolution as does the Darwinian process of natural selection. Yet until now no systematic effort has been made to incorporate the concept of self-organization into evolutionary theory. The construction requirements which permit complex systems to adapt remain poorly understood, as is the extent to which selection itself can yield systems able to adapt more successfully. This book explores these themes. It shows how complex systems, contrary to expectations, can spontaneously exhibit stunning degrees of order, and how this order, in turn, is essential for understanding the emergence and development of life on Earth. Topics include the new biotechnology of applied molecular evolution, with its important implications for developing new drugs and vaccines; the balance between order and chaos observed in many naturally occurring systems; new insights concerning the predictive power of statistical mechanics in biology; and other major issues. Indeed, the approaches investigated here may prove to be the new center around which biologicalscience itself will evolve. The work is written for all those interested in the cutting edge of research in the life sciences. (shrink)

It is often assumed that similar domain-specific behavioural impairments found in cases of adult brain damage and developmental disorders correspond to similar underlying causes, and can serve as convergent evidence for the modular structure of the normal adult cognitive system. We argue that this correspondence is contingent on an unsupported assumption that atypical development can produce selective deficits while the rest of the system develops normally (Residual Normality), and that this assumption tends to bias data collection in the field. Based (...) on a review of connectionist models of acquired and developmental disorders in the domains of reading and past tense, as well as on new simulations, we explore the computational viability of Residual Normality and the potential role of development in producing behavioural deficits. Simulations demonstrate that damage to a developmental model can produce very different effects depending on whether it occurs prior to or following the training process. Because developmental disorders typically involve damage prior to learning, we conclude that the developmental process is a key component of the explanation of endstate impairments in such disorders. Further simulations demonstrate that in simple connectionist learning systems, the assumption of Residual Normality is undermined by processes of compensation or alteration elsewhere in the system. We outline the precise computational conditions required for Residual Normality to hold in development, and suggest that in many cases it is an unlikely hypothesis. We conclude that in developmental disorders, inferences from behavioural deficits to underlying structure crucially depend on developmental conditions, and that the process of ontogenetic development cannot be ignored in constructing models of developmental disorders. Key Words: Acquired and developmental disorders; connectionist models; modularity; past tense; reading. (shrink)

Comment on an article by Peter Zachar An account of emotion must include categories and dimensions. Categories because humans categorize reality, and a person's categorization of their own state influences aspects of that state. Dimensions because humans are always in some state of Core Affect, which varies by degree along dimensions of valence and activation . In Psychological Construction, Core Affect and a host of other "components" are separate on-going processes, always in some pattern. Occasionally the pattern resembles a prototype (...) of a category of emotion sufficiently to count as an instance of that category. 2012 APA, all rights reserved). (shrink)

In this book, J. L. Mackie makes a careful study of several philosophical issues involved in his account of causation. Mackie follows Hume's distinction between causation as a concept and causation as it is ‘in the objects’ and attempts to provide an account of both aspects. Mackie examines the treatment of causation by philosophers such as Hume, Kant, Mill, Russell, Ducasse, Kneale, Hart and Honore, and von Wright. Mackie's own account involves an analysis of causal statements in terms of counterfactual (...) conditionals though these are judged to be incapable of giving a complete account of causation. Mackie argues that regularity theory too can only offer an incomplete picture of the nature of causation. In the course of his analysis, Mackie critically examines the account of causation offered by Kant, as well as the contemporary Kantian approaches offered by philosophers such as Bennett and Strawson. Also addressed are issues such as the direction of causation, the relation of statistical laws and functional laws, the role of causal statements in legal contexts, and the understanding of causes both as ‘facts’ and ‘events’. Throughout the discussion of these topics, Mackie develops his own complex account of the nature of causation, finally bringing his analysis to bear in regard to the topic of teleology and the question of whether final causes can be justifiably reduced to efficient causes. (shrink)

Most causal discovery algorithms in the literature exploit an assumption usually referred to as the Causal Faithfulness or Stability Condition. In this paper, we highlight two components of the condition used in constraint-based algorithms, which we call “Adjacency-Faithfulness” and “Orientation- Faithfulness.” We point out that assuming Adjacency-Faithfulness is true, it is possible to test the validity of Orientation- Faithfulness. Motivated by this observation, we explore the consequence of making only the Adjacency-Faithfulness assumption. We show that the familiar PC algorithm has (...) to be modified to be correct under the weaker, Adjacency-Faithfulness assumption. The modified algorithm, called Conservative PC (CPC), checks whether Orientation- Faithfulness holds in the orientation phase, and if not, avoids drawing certain causal conclusions the PC algorithm would draw. Howtion: ever, if the stronger, standard causal Faith-. (shrink)

Sections include: experiments and generalised causal inference; statistical conclusion validity and internal validity; construct validity and external validity; quasi-experimental designs that either lack a control group or lack pretest observations on the outcome; quasi-experimental designs that use both control groups and pretests; quasi-experiments: interrupted time-series designs; regresssion discontinuity designs; randomised experiments: rationale, designs, and conditions conducive to doing them; practical problems 1: ethics, participation recruitment and random assignment; practical problems 2: treatment implementation and attrition; generalised causal inference: a grounded theory; (...) generalised causal inference: methods for single studies; generalised causal inference: methods for multiple studies; a critical assessment of our assumptions. (shrink)

Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.

This book traces how such a seemingly immutable idea as measurement proved so malleable when it collided with the subject matter of psychology. It locates philosophical and social influences reshaping the concept and, at the core of this reshaping, identifies a fundamental problem: the issue of whether psychological attributes really are quantitative. It argues that the idea of measurement now endorsed within psychology actually subverts attempts to establish a genuinely quantitative science and it urges a new direction. It relates views (...) on measurement by thinkers such as Holder, Russell, Campbell and Nagel to earlier views, like those of Euclid and Oresme. Within the history of psychology, it considers contributions by Fechner, Cattell, Thorndike, Stevens and Suppes, among others. It also contains a non-technical exposition of conjoint measurement theory and recent foundational work by leading measurement theorist R. Duncan Luce. (shrink)

In this path-breaking work, Paul Thagard draws on history and philosophy of science, cognitive psychology, and the field of artificial intelligence to develop a ...

This book, the product of a lifetime of research by one of the founders of developmental psychology, takes on the powerful assumptions behind these questions- ...

We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the procedure is point-wise consistent assuming (a) the causal relations can be represented by a directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption; (b) unrecorded variables are not caused by recorded (...) variables; and (c) dependencies are linear. We compare the procedure with standard approaches over a variety of simulated structures and sample sizes, and illustrate its practical value with brief studies of social science data sets. Finally, we consider generalizations for non-linear systems. Keywords: latent variable models, causality, graphical models.. (shrink)