The extended cognition thesis: Its significance for the philosophy of (cognitive) science

While the extended cognition (EC) thesis has gained more followers in cognitive science and in the philosophy of mind and knowledge, our main goal is to discuss a different area of significance of the EC thesis: its relation to philosophy of science. In this introduction, we outline two major areas: (I) The role of the thesis for issues in the philosophy of cognitive science, such as: How do notions of EC figure in theories or research programs in cognitive science? Which versions of the EC thesis appear, and with which arguments to support them? (II) The potentials and limits of the EC thesis for topics in general philosophy of science, such as: Can naturalism perhaps be further advanced by means of the more recent EC thesis? Can we understand “big science” or laboratory research better by invoking some version of EC? And can the EC thesis help in overcoming the notorious cognitive/social divide in science studies?     

Criteria for the design and evaluation of cognitive architectures

Cognitive architectures are unified theories of cognition that take the form of computational formalisms. They support computational models that collectively account for large numbers of empirical regularities using small numbers of computational mechanisms. Empirical coverage and parsimony are the most prominent criteria by which architectures are designed and evaluated, but they are not the only ones. This paper considers three additional criteria that have been comparatively undertheorized. (a) Successful architectures possess subjective and intersubjective meaning, making cognition comprehensible to individual cognitive scientists and organizing groups of like-minded cognitive scientists into genuine communities. (b) Successful architectures provide idioms that structure the design and interpretation of computational models. (c) Successful architectures are strange: They make provocative, often disturbing, and ultimately compelling claims about human information processing that demand evaluation.     

Intractability and the use of heuristics in psychological explanations

Many cognitive scientists, having discovered that some computational-level characterization f of a cognitive capacity ${\phi}$ is intractable, invoke heuristics as algorithmic-level explanations of how cognizers compute f. We argue that such explanations are actually dysfunctional, and rebut five possible objections. We then propose computational-level theory revision as a principled and workable alternative.     

Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT-R

Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a "model fixed, data variable" paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood-based, "data fixed, model variable" paradigm in which models are treated as stochastic processes in experiments with participant-to-participant variation that can be applied to a broad range of mechanistic cognitive architectures. This article discusses the implementation and implications of this view in model validation, with a concrete focus on a simple class of ACT-R models of cognition. This article is not intended as a recipe for broad application of these preliminary, proof-of-concept methods, but as a framework for communication between statisticians searching for interesting problems in the cognitive modeling sphere, and cognitive modelers interested in generalizing from deterministic to stochastic model validation, in the face of random variation in human experimental data.     

生成认知：理论基础与实践走向

生成认知是具身认知思潮中的一个新取向。它主张认知是通过身体活动“生成的” (enacted)。认知的起点不是一个怎样精确表征世界的信息加工问题, 而是行动者在情境中怎样利用知觉来指导自己的行动。认知不是通过精确的心理表征“恢复”世界, 而是通过知觉引导的行动“生成”或“造就”一个自己的世界。认知是具身的行动, 认知结构形成于经常和反复出现的感觉运动模式, 与身体构造和身体活动具有深刻连续性。梅洛·庞蒂的身体现象学对生成认知具有深刻影响。同时, 詹姆斯、杜威等人的实用主义哲学对于实践行动的强调也深刻影响了生成认知。这种认知观强调了“行动”对心智的意义, 引发许多争议, 也促进了心理学研究范式的转变。     

Points of View

An adequate evaluation of argumentation requires identification of the object to which the argumentation pertains: the point of view. What are the distinguishing features of this object? In the pragma-dialectical argumentation theory, the object of argumentation is referred to by means of the notion ‘standpoint’. In other theories concerned with argumentation, reasoning, convincing or persuading, notions are used such as ‘thesis’, ‘conclusion’, ‘opinion’ and ‘attitude’. This paper is a survey of the characterisations of the object of argumentation given in the various theories. It discusses the pragma-dialectical argumentation theory, socio-psychological research on persuasion, cognitive research on reasoning, argumentative discourse analysis, two variants of informal logic, advocacy and debate, and the theory of communicative action. Next, it explores some relations between the notions used in these theories. Finally, it outlines some starting points for further research into the problems of identification.     

风险决策心理因素的理论综述

风险决策理论对于人们是否作出冒险行为的选择提出了两种比较典型的解释:一种解释把风险决策归因于人们共有的基本过程,即“较冷”的心理和认知过程。这些理论认为风险选择是由人类基本的心理和感觉机制引起的。而另一种解释则把风险决策归因于“较热”的情感和动机过程。这些理论认为,情境和人格因素会增强风险决策的动机并导致风险决策个体差异的存在。本文对这两种解释的不同理论作了较为系统的论述。     

Constraining bridges between levels of analysis: A computational justification for locally Bayesian learning

Different levels of analysis provide different insights into behavior: computational-level analyses determine the problem an organism must solve and algorithmic-level analyses determine the mechanisms that drive behavior. However, many attempts to model behavior are pitched at a single level of analysis. Research into human and animal learning provides a prime example, with some researchers using computational-level models to understand the sensitivity organisms display to environmental statistics but other researchers using algorithmic-level models to understand organisms’ trial order effects, including effects of primacy and recency. Recently, attempts have been made to bridge these two levels of analysis. Locally Bayesian Learning (LBL) creates a bridge by taking a view inspired by evolutionary psychology: Our minds are composed of modules that are each individually Bayesian but communicate with restricted messages. A different inspiration comes from computer science and statistics: Our brains are implementing the algorithms developed for approximating complex probability distributions. We show that these different inspirations for how to bridge levels of analysis are not necessarily in conflict by developing a computational justification for LBL. We demonstrate that a scheme that maximizes computational fidelity while using a restricted factorized representation produces the trial order effects that motivated the development of LBL. This scheme uses the same modular motivation as LBL, passing messages about the attended cues between modules, but does not use the rapid shifts of attention considered key for the LBL approximation. This work illustrates a new way of tying together psychological and computational constraints.     

Bridging between basic theory and clinical practice

This paper articulates and discusses the parts played by different processes and representations in the overall conduct of applied clinical science. It distinguishes two sorts of representation, theories in the science base and bridging representations needed to map from real world behaviour to basic theory and from theory back to the real world. It is then argued that macro-theories of the "normal" human mental architecture could help synthesise basic theoretical accounts of diverse psychopathologies, without recourse to special purpose clinical cognitive theories of particular psychopathologies or even specific symptoms. Using the Interacting Cognitive Subsystems model [Affect, Cognition and Change: Re-modelling Depressive Thought, Lawrence Erlbaum Associates, Hove, 1993], some specific macro-theoretic variables are identified. Concrete illustrations are given of how the essence of quite complex basic theory can be translated into a simpler representational format to help clinicians conceptualise a psychopathological state and pinpoint relevant variables that might be changed by therapeutic interventions. Some suggestions are also offered about how the inevitable problem of complexity in multiple component theories might be directly confronted.     

Why we view the brain as a computer

The view that the brain is a sort of computer has functioned as a theoretical guideline both in cognitive science and, more recently, in neuroscience. But since we can view every physical system as a computer, it has been less than clear what this view amounts to. By considering in some detail a seminal study in computational neuroscience, I first suggest that neuroscientists invoke the computational outlook to explain regularities that are formulated in terms of the information content of electrical signals. I then indicate why computational theories have explanatory force with respect to these regularities:in a nutshell, they underscore correspondence relations between formal/mathematical properties of the electrical signals and formal/mathematical properties of the represented objects. I finally link my proposal to the philosophical thesis that content plays an essential role in computational taxonomy.     

有关具身认知思潮的理论心理学思考

具身认知强调了心智或认知对身体及其感觉运动系统的依赖性。与传统认知心理学的符号加工模式不同的是, 它认为身体的构造和状态、身体的物理属性及其大脑与身体的特殊感觉—运动通道对认知具有塑造作用。认知既是具身的, 也是嵌入的, 大脑嵌入身体、身体嵌入环境, 构成了一体的认知系统。最初, 具身认知仅仅是一种反对笛卡尔身心二元论的哲学思潮, 经由认知科学哲学和理论心理学而逐渐进入了实验领域。具身认知研究充分利用了神经科学的方法和技术、数据和结论, 但是它并不主张把心理还原为生理或物理, 同时, 具身认知的神经科学取向仅仅给传统心理研究提供一个认识心智的新视角, 其目标并非取消行为水平的心理研究, 因此不会损害心理学的独立地位。     

Knowledge and man

Collective Intelligence (CI) can be formalized as a specific1 computational process through the use of a molecular model of computations and mathematical logic, in terms of interacting information_molecules, which are chaotically or quasi-chaotically displacing and running natural-based inference processes in their own environment. The formal definition of Collective Intelligence as a property of a social structure of beings of any nature is surprisingly short and abstract (which is astonishing) from definitions of Life. The formal definition of Collective Intelligence proposed by the author in the last few years seems to be valid for the whole spectrum of beings, in human social structures to ants in colonies, and even for bacterial colonies. It has recently been found that the CI definition also has an engineering value. The theory of CI can also be used to better understand Evolution because it allows us to locate and relate Life and Intelligence in Evolution. Moreover, this approach presents Evolution as something more complex than can be concluded from Darwinism. Probably the most surprising fact is that a simple extrapolation of the definition of Collective Intelligence brings us to the conclusion that most probably the first elementary Collective Intelligence emerged on Earth in the "chemical soup of primeval molecules," much before Life emerged. Collective Intelligence can be defined with fewer and weaker conditions than Life requires. Perhaps the emergence of that early elementary Collective Intelligence provided the basic momentum to build Life as we now know it. Thus Evolution caused Intelligence to create Life. Our hypothesis is consistent with biochemistry theories that "primeval biochemical molecules" started to interact, "firing" the Collective Intelligence of their "elementary chemical social structure" for survival. This successful action boosted further growth of complexity in that "elementary social structure," which finally resulted in the emergence of "well-defined Life." Furthermore, it provided a self-propagating cycle of growth of individual and collective Intelligence and individual and collective Life. The Collective Intelligence of ants, wolves, humans, and so forth today is only a higher level of Collective Intelligence development. Thus the present Evolution is a computational process of unidentified complexity where Life, Intelligence, and perhaps other as yet undiscovered components play temporary roles. In this paper we provide formalization and a proposed partial proof for this hypothesis.     

Representation facilitates reasoning: what natural frequencies are and what they are not

A good representation can be crucial for finding the solution to a problem. Gigerenzer and Hoffrage (Psychol. Rev. 102 (1995) 684; Psychol. Rev. 106 (1999) 425) have shown that representations in terms of natural frequencies, rather than conditional probabilities, facilitate the computation of a cause's probability (or frequency) given an effect--a problem that is usually referred to as Bayesian reasoning. They also have shown that normalized frequencies--which are not natural frequencies--do not lead to computational facilitation, and consequently, do not enhance people's performance. Here, we correct two misconceptions propagated in recent work (Cognition 77 (2000) 197; Cognition 78 (2001) 247; Psychol. Rev. 106 (1999) 62; Organ. Behav. Hum. Decision Process. 82 (2000) 217): normalized frequencies have been mistaken for natural frequencies and, as a consequence, "nested sets" and the "subset principle" have been proposed as new explanations. These new terms, however, are nothing more than vague labels for the basic properties of natural frequencies.     

Adequate formalization

This article identifies problems with regard to providing criteria that regulate the matching of logical formulae and natural language. We then take on to solve these problems by defining a necessary and sufficient criterion of adequate formalization. On the basis of this criterion we argue that logic should not be seen as an ars iudicandi capable of evaluating the validity or invalidity of informal arguments, but as an ars explicandi that renders transparent the formal structure of informal reasoning.     

Communication and Cognition: The Social Beyond Language,Interaction and Culture

Cognition theories describe the social with terms like language, interaction or culture, whose theoretical status has also been discussed in modern sociology. These concepts are not well-positioned to understand the emergence and autonomy of social orders. Sociological theory of self-referential systems can be useful to reconstruct the bottom-up process which contributes to the emergence of the social as communication as well as to describe the way in which society exerts downward causation upon cognitive phenomena. The core of this theory is the systemic category of meaning as a shared horizon for psychic and social systems.     

ARE EVOLUTIONARY/COGNITIVE THEORIES OF RELIGION RELEVANT FOR PHILOSOPHY OF RELIGION?

Biological theories of religious belief are sometimes understood to undermine the very beliefs they are describing, proposing an alternative explanation for the causes of belief different from that given by religious believers themselves. This article surveys three categories of biological theorizing derived from evolutionary biology, cognitive science of religion, and neuroscience. Although each field raises important issues and in some cases potential challenges to the legitimacy of religious belief, in most cases the significance of these theories for the holding of religious beliefs is not very great.     

A hierarchical competing systems model of the emergence and early development of executive function

The hierarchical competing systems model (HCSM) provides a framework for understanding the emergence and early development of executive function – the cognitive processes underlying the conscious control of behavior – in the context of search for hidden objects. According to this model, behavior is determined by the joint influence of a developmentally invariant habit system and a conscious representational system that becomes increasingly influential as children develop. This article describes a computational formalization of the HCSM, reviews behavioral and computational research consistent with the model, and suggests directions for future research on the development of executive function.