A Computational Theory of Learning Causal Relationships

I present a cognitive model of the human ability to acquire causal relationships. I report on experimental evidence demonstrating that human learners acquire accurate causal relationships more rapidly when training examples are consistent with a general theory of causality. This article describes a learning process that uses a general theory of causality as background knowledge. The learning process, which I call theory-driven learning (TDL), hypothesizes causal relationships consistent both with observed data and the general theory of causality. TDL accounts for data on both the rate at which human learners acquire causal relationships, and the types of causal relationships they acquire. Experiments with TDL demonstrate the advantage of TDL for acquiring causal relationships over similarity-based approaches to learning: Fewer examples are required to learn an accurate relationship.     

The integration of knowledge and experimentation strategies in understanding a physical system

This study investigated how people integrate different kinds of knowledge in coming to understand the functions of components of a physical system. Twenty-three undergraduates worked on a hypothesis-testing task in which they constructed test circuits to decide the identity of electrical components hidden inside boxes. The students' problem-solving was driven by qualitative causal models that included ideas about cause and effect, task demands, and functions of circuits and circuit components. There was a hierarchy of four causal models, with higher levels representing increasing understanding and supporting increasingly successful problem solution. These causal models were associated with characteristic experimentation strategies, including strategies for generating evidence, interpreting evidence, and managing memory requirements. Some of these strategies followed directly from the causal model a student held, whereas others appeared to be more general. The level of understanding a student eventually attained about this physical system was a function of both domain knowledge and proficiency in experimentation strategies.     

Consciousness as a logically consistent and prognostic model of reality

The work demonstrates that brain might reflect the external world causal relationships in the form of a logically consistent and prognostic model of reality, which shows up as consciousness. The paper analyses and solves the problem of statistical ambiguity and provides a formal model of causal relationships as probabilistic maximally specific rules. We suppose that brain makes all possible inferences from causal relationships. We prove that the suggested formal model has a property of an unambiguous inference: from consistent premises we infer a consistent conclusion. It enables a set of all inferences to form a consistent model of the perceived world. Causal relationships may create fixed points of cyclic inter-predictable properties. We consider the “natural” classification introduced by John St. Mill and demonstrate that a variety of fixed points of the objects’ attributes forms a “natural” classification of the external world. Then we consider notions of “natural” categories and causal models of categories, introduced by Eleanor Rosch and Bob Rehder and demonstrate that fixed points of causal relationships between objects attributes, which we perceive, formalize these notions. If the “natural” classification describes the objects of the external world, and “natural” concepts the perception of these objects, then the theory of integrated information, introduced by G. Tononi, describes the information processes of the brain for “natural” concepts formation that reflects the “natural” classification. We argue that integrated information provides high accuracy of the objects identification. A computer-based experiment is provided that illustrates fixed points formation for coded digits.     

Hierarchies,Networks, and Causality: The Applied Evolutionary Epistemological Approach

Applied Evolutionary Epistemology is a scientific-philosophical theory that defines evolution as the set of phenomena whereby units evolve at levels of ontological hierarchies by mechanisms and processes. This theory also provides a methodology to study evolution, namely, studying evolution involves identifying the units that evolve, the levels at which they evolve, and the mechanisms and processes whereby they evolve. Identifying units and levels of evolution in turn requires the development of ontological hierarchy theories, and examining mechanisms and processes necessitates theorizing about causality. Together, hierarchy and causality theories explain how biorealities form and diversify with time. This paper analyzes how Applied EE redefines both hierarchy and causality theories in the light of the recent explosion of network approaches to causal reasoning associated with studies on reticulate and macroevolution. Causality theories have often been framed from within a rigid, ladder-like hierarchy theory where the rungs of the ladder represent the different levels, and the elements on the rungs represent the evolving units. Causality then is either defined reductionistically as an upward movement along the strands of a singular hierarchy, or holistically as a downward movement along that same hierarchy. Upward causation theories thereby analyze causal processes in time, i.e. over the course of natural history or phylogenetically, as Darwin and the founders of the Modern Synthesis intended. Downward causation theories analyze causal processes in space, ontogenetically or ecologically, as the current eco-evo-devo schools are evidencing. This work demonstrates how macroevolution and reticulate evolution theories add to the complexity by examining reticulate causal processes in space–time, and the interactional hierarchies that such studies bring forth introduce a new form of causation that is here called reticulate causation. Reticulate causation occurs between units and levels belonging to different as well as to the same ontological hierarchies. This article concludes that beyond recognizing the existence of multiple units, levels, and mechanisms or processes of evolution, also the existence of multiple kinds of evolutionary causation as well as the existence of multiple evolutionary hierarchies needs to be acknowledged. This furthermore implies that evolution is a pluralistic process divisible into different kinds.

     

Mind, Self, and Personality: Dynamic Interactions from Late Childhood to Early Adulthood

This paper aims to integrate research on mind, personality, and self-development using a general model which hypothesizes that mind and personality are organized in 3 levels. The first level includes environment-oriented, domain-specific systems that specialize in the representation of and interaction with particular types of relationships in the environment in both the cognitive and the social realms. The second level comprises self-oriented monitoring and representation processes that build maps of the environment-oriented systems. Self and identity derive from the interaction between these 2 levels, which is subject to the constraints and the system's processing capacity at the successive ages. These constraints may be taken as the third level of the self. Efficiency in overcoming these constraints is determined by the self-oriented processes, generating feelings and self-representations of self-worth. These feelings are idiosyncratic, and function as a personal constant, which is applied on self-evaluation and self-representations. This constant is rescaled and differentiated with development. Thus, we have simultaneously both modular and transmodular constructs in the mind and the self, which change with relative independence of each other under the constraints of the general processing capabilities and the personal constant. A series of studies are presented to support this model, and its implications for life-span theories of development are discussed.     

The priority of just-identified,recursive models

Theories often place constraints on causal relationships, and such constraints are often assessed with causal models. Causal models should be recursive and just identified because cause is recursive and is more likely to be just identified than overidentified. A just-identified, recursive model (JIRM) is specified that satisfies both requirements and that can be used to assess a wide range of causal implications in either a norm-referenced or criterion-referenced manner. P. E. Meehl and N. G. Waller (2002) proposed an innovative method for theory appraisal called the delete one-add one (D1-A1) method, which assesses a relatively narrow range of causal implications, allows nonrecursive models, and is only norm referenced. The JIRM and D1-A1 methods are compared.     

Categorization as causal reasoning⋆

A theory of categorization is presented in which knowledge of causal relationships between category features is represented in terms of asymmetric and probabilistic causal mechanisms. According to causal‐model theory, objects are classified as category members to the extent they are likely to have been generated or produced by those mechanisms. The empirical results confirmed that participants rated exemplars good category members to the extent their features manifested the expectations that causal knowledge induces, such as correlations between feature pairs that are directly connected by causal relationships. These expectations also included sensitivity to higher‐order feature interactions that emerge from the asymmetries inherent in causal relationships. Quantitative fits of causal‐model theory were superior to those obtained with extensions to traditional similarity‐based models that represent causal knowledge either as higher‐order relational features or “prior exemplars” stored in memory.     

HUSSERLIAN INTENTIONALITY AND THE CHINESE CONCEPT OF "MIND"

The phenomenology of Edmund Husserl is, in one sense, a theory of pure consciousness that aims to set forth an absolute, ultimate, rigorous ground for the sciences based on the field of pure consciousness. Husserl believed that, on the basis of this field of pure consciousness, he could secure eternal significance for the spiritual life of man. Intentionality is the key element in this theory of pure consciousness and it plays a crucial part in the realization of Husserl's philosophical goal. By contrast, traditional Chinese philosophy was not concerned to seek an absolute, ultimate ground for the sciences or to derive a set of moral norms and a theory of value for human life from logical and scientific truths. Rather, Chinese philosophy sought to adjust the relationships between man and nature and between man and man in their ordinary, secular existence. It placed no value in the ideas of pure logic, pure science, or pure consciousness. Traditional Chinese philosophers inquired into the experiential, intuitive 'mind' ( xin ^a). This approach to 'mind'was understood by the Chinese to require rigorous logical proof or scientific theory:— anyone can perceive one's 'mind'in daily life and, by analogy, anyone can 'perceive'other 'minds'. If Husserl's intentionality is the transcendental reason of Western philosophy, the 'mind'is the practical reason of Chinese philosophy. What, then, are the essential features of Husserl's 'intentionality'and the Chinese 'mind'? What are their respective theoretical features? Can they be brought together and compared in a philosophically significant fashion?     

A tutorial on hierarchically structured constructs

Many psychological constructs are conceived to be hierarchically structured and thus to operate at various levels of generality. Alternative confirmatory factor analytic (CFA) models can be used to study various aspects of this proposition: (a) The one-factor model focuses on the top of the hierarchy and contains only a general construct, (b) the first-order factor model focuses on the intermediate level of the hierarchy and contains only specific constructs, and both (c) the higher order factor model and (d) the nested-factor model consider the hierarchy in its entirety and contain both general and specific constructs (e.g., bifactor model). This tutorial considers these CFA models in depth, addressing their psychometric properties, interpretation of general and specific constructs, and implications for model-based score reliabilities. The authors illustrate their arguments with normative data obtained for the Wechsler Adult Intelligence Scale and conclude with recommendations on which CFA model is most appropriate for which research and diagnostic purposes.     

A general approach to causal mediation analysis

Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for 3 reasons: the lack of a general definition of causal mediation effects independent of a particular statistical model, the inability to specify the key identification assumption, and the difficulty of extending the framework to nonlinear models. In this article, we propose an alternative approach that overcomes these limitations. Our approach is general because it offers the definition, identification, estimation, and sensitivity analysis of causal mediation effects without reference to any specific statistical model. Further, our approach explicitly links these 4 elements closely together within a single framework. As a result, the proposed framework can accommodate linear and nonlinear relationships, parametric and nonparametric models, continuous and discrete mediators, and various types of outcome variables. The general definition and identification result also allow us to develop sensitivity analysis in the context of commonly used models, which enables applied researchers to formally assess the robustness of their empirical conclusions to violations of the key assumption. We illustrate our approach by applying it to the Job Search Intervention Study. We also offer easy-to-use software that implements all our proposed methods.     

Object categorization: reversals and explanations of the basic-level advantage

People are generally faster and more accurate to name or categorize objects at the basic level (e.g., dog) relative to more general (animal) or specific (collie) levels, an effect replicated in Experiment 1 for categorization of object pictures. To some, this pattern suggests a dual-process mechanism, in which objects first activate basic-level categories directly and later engage more general or specific categories through the spread of activation in a processing hierarchy. This account is, however, challenged by data from Experiment 2 showing that neuropsychological patients with impairments of conceptual knowledge categorize more accurately at superordinate levels than at the basic level--suggesting that knowledge about an object's general nature does not depend on prior basic-level categorization. The authors consider how a parallel distributed processing theory of conceptual knowledge can reconcile the apparent discrepancy. This theory predicts that if healthy individuals are encouraged to make rapid categorization responses, the usual basic > general advantage should also reverse, a prediction tested and confirmed in Experiment 3. Implications for theories of visual object recognition are discussed.     

Information integration based predictions about the conscious states of a spiking neural network

This paper describes how Tononi’s information integration theory of consciousness was used to make detailed predictions about the distribution of phenomenal states in a spiking neural network. This network had approximately 18,000 neurons and 700,000 connections and it used models of emotion and imagination to control the eye movements of a virtual robot and avoid ‘negative’ stimuli. The first stage in the analysis was the development of a formal definition of Tononi’s theory of consciousness. The network was then analysed for information integration and detailed predictions were made about the distribution of consciousness for each time step of recorded activity. This work demonstrates how an artificial system can be analysed for consciousness using a particular theory and in the future this approach could be used to make predictions about the phenomenal states associated with biological systems.     

Higher emotions and cognition

The paper presents models describing higher emotions and cognition. It argues that contents of models at the top of the mental hierarchy are higher meanings, which in a simplified way could be called meanings of life. These models are vague-fuzzy and inaccessible to consciousness. Improving these models and making steps toward even tentatively conscious forms of them relates to the highest aesthetic emotions, emotions of the beautiful. Experimental data confirming these relations between the beautiful and higher meanings are presented.     

Moral Development and Higher States of Consciousness

This paper discusses possible relationships between the cognitive–moral development theory of Lawrence Kohlberg and Charles Alexander's life-span model. Central to Alexander's model is the role which levels of mind and higher states of consciousness play throughout the general periods of development. Parallel to Kohlberg's cosmic perspective Stage 7—which goes beyond the representational logic and reasoning identified by Piaget's highest stage—are the postrepresentational stages of development, described by Vedic psychology and Alexander's model as "higher states of consciousness"—transcendental consciousness, cosmic consciousness, God consciousness, and unity consciousness. Research indicates that EEG brainwave coherence is associated with the development of higher states of consciousness and Kohlberg's Stage 7. Studies on the Transcendental Meditation (TM) and TM-Sidhi programs as effective technologies for promoting moral development are further discussed.     

Waves,streams, states,and self: An outline of an integral psychology 1

Abstract

Although far from unanimous, there seems to be a general consensus that neither mind nor brain can be reduced without remainder to the other. This essay argues that indeed both mind and brain need to be included in a nonreductionistic way in any genuinely integral theory of consciousness. In order to facilitate such integration, this essay presents the results of an extensive cross‐cultural literature search on the “mind” side of the equation, suggesting that the mental phenomena that need to be considered in any integral theory include developmental levels or waves of consciousness, developmental lines or streams of consciousness, states of consciousness, and the self (or self‐system). A “master template” of these various phenomena, culled from over one‐hundred psychological systems East and West, is presented. It is suggested that this master template represents a general summary of the “mind” side of the brain‐mind integration. The essay concludes with reflections on the “hard problem,” or how the mind‐side can be integrated with the brain‐side to generate a more integral theory of consciousness.