The enactive computational basis of cognition and the explanatory cognitive basis for computing

The computational theory of cognition, or computationalism, holds that cognition is a form of computation. Two issues related to this view are comprised by the goal of this paper: A) Computing systems are traditionally seen as representational systems, but functional and enactive approaches support non-representational theories; B) Recently, a sociocultural theory against computationalism was proposed with the aim of ontologically reducing computing to cognition. We defend, however, that cognition and computation are in action, thus cognition is just a form of computing and that cognition is the explanatory basis for computation. We state that: 1. Representational theories of computing recurring to intentional content run into metaphysical problems. 2. Functional non-representational theories do not incur this metaphysical problem when describing computing in terms of the abstract machine. 3. Functional theories are consistent with enactive in describing computing machines not in a strictly functional way, but especially in terms of their organization. 4. Enactive cognition is consistent with the computationalism in describing Turing machines as functionally and organizationally closed systems. 5. The cognitive explanatory basis for computing improves the computational theory of cognition. When developed in the human linguistic domain, computer science is seen as a product of human socionatural normative practices, however, cognition is just an explanatory, not ontological, basis for computing. The paper concludes by supporting that computation is in action, that cognition is just one form of computing in the world and the explanatory basis for computation.     

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.     

A computational unification of cognitive behavior and emotion

Existing models that integrate emotion and cognition generally do not fully specify why cognition needs emotion and conversely why emotion needs cognition. In this paper, we present a unified computational model that combines an abstract cognitive theory of behavior control (PEACTIDM) and a detailed theory of emotion (based on an appraisal theory), integrated in a theory of cognitive architecture (Soar). The theory of cognitive control specifies a set of required computational functions and their abstract inputs and outputs, while the appraisal theory specifies in more detail the nature of these inputs and outputs and an ontology for their representation. We argue that there is a surprising functional symbiosis between these two independently motivated theories that leads to a deeper theoretical integration than has been previously obtained in other computational treatments of cognition and emotion. We use an implemented model in Soar to test the feasibility of the resulting integrated theory, and explore its implications and predictive power in several task domains.     

A vehicular theory of corporeal qualia (a gift to computationalists)

I have argued elsewhere that non-sentential representations that are the close kin of scale models can be, and often are, realized by computational processes. I will attempt here to weaken any resistance to this claim that happens to issue from those who favor an across-the-board computational theory of cognitive activity. I will argue that embracing the idea that certain computers harbor nonsentential models gives proponents of the computational theory of cognition the means to resolve the conspicuous disconnect between the sentential character of the data structures they posit and the nonsentential qualitative character of our perceptual experiences of corporeal (i.e., spatial, kinematic, and dynamic) properties. Along the way, I will question the viability of some externalist remedies for this disconnect, and I will explain why the computational theory put forward here falls quite clearly beyond the useful bounds of the Chinese-Room argument.     

Thinking Without Global Generalisations: A Cognitive Defence of Moral Particularism1

In their article entitled “Ethical Particularism and Patterns”, Frank Jackson, Philip Pettit, and Michael Smith (JPS henceforth) argue that moral particularism is a cognitively implausible theory since it appears to entail the view that one might have a skill that is not grounded in an ability to recognise and represent natural patterns in the world. This charge echoes the complaints of computational theorists of cognition against their embodied cognition counterparts, namely that, theories of cognition that eschew talk of mental representation are implausible qua theories of cognition. In both debates, the cognitive role of generalisation is central to the discussion; however, contrary to the received wisdom, I want to suggest that the dispute is not between generalisation or mental representation on the one hand and no generalisation or mental representation on the other, but rather between what I will call global and local generalisation. Using the dialogue between JPS and Dancy (our paradigm particularist) to frame this discussion, I show that by replacing Dancy's connectionist model for particularist reasoning with a case‐based one, we not only vindicate his response to JPS, but we also gain insight into how it is the global/local distinction rather than the generalisation/no generalisation distinction that divides the two views.     

Simplicity: a unifying principle in cognitive science?

Much of perception, learning and high-level cognition involves finding patterns in data. But there are always infinitely many patterns compatible with any finite amount of data. How does the cognitive system choose 'sensible' patterns? A long tradition in epistemology, philosophy of science, and mathematical and computational theories of learning argues that patterns 'should' be chosen according to how simply they explain the data. This article reviews research exploring the idea that simplicity drives a wide range of cognitive processes. We outline mathematical theory, computational results and empirical data that underpin this viewpoint.     

Self-directedness: A Process Approach to Cognition

Standard approaches to cognition emphasise structures (representations and rules) much more than processes, in part because this appears to be necessary to capture the normative features of cognition. However the resultant models are inflexible and face the problem of computational intractability. I argue that the ability of real world cognition to cope with complexity results from deep and subtle coupling between cognitive and non-cognitive processes. In order to capture this, theories of cognition must shift from a structural rule-defined conception of cognition to a thoroughgoing embedded process approach.     

统计认知理论及应用研究

长期以来大家认为人类认知尽管可以看成是非确定的推理计算过程,但它的知识表达、模型结构、及计算方法和概率统计理论在本质上是不同的,因此认知科学和概率统计方法存在巨大的鸿沟,过去两者基本上独立发展。近年来随着Bayesian概率统计模型研究的一系列突破性工作和认知过程本质的不断被发现和挖掘,两者的相关性和互补性逐渐突显出来。许多研究者认为认知是近似遵循概率统计推理原则的,一些研究工作显示两者的结合有可能对人工智能发展产生深远的影响。本文对当前统计认知理论及应用研究的现状进行系统的梳理,并结合自身的研究对它今后的发展提出自己的看法。     

The logic of social exchange: has natural selection shaped how humans reason? Studies with the Wason selection task

In order to successfully engage in social exchange--cooperation between two or more individuals for mutual benefit--humans must be able to solve a number of complex computational problems, and do so with special efficiency. Following Marr (1982), Cosmides (1985) and Cosmides and Tooby (1989) used evolutionary principles to develop a computational theory of these adaptive problems. Specific hypotheses concerning the structure of the algorithms that govern how humans reason about social exchange were derived from this computational theory. This article presents a series of experiments designed to test these hypotheses, using the Wason selection task, a test of logical reasoning. Part I reports experiments testing social exchange theory against the availability theories of reasoning; Part II reports experiments testing it against Cheng and Holyoak's (1985) permission schema theory. The experimental design included eight critical tests designed to choose between social exchange theory and these other two families of theories; the results of all eight tests support social exchange theory. The hypothesis that the human mind includes cognitive processes specialized for reasoning about social exchange predicts the content effects found in these experiments, and parsimoniously explains those that have already been reported in the literature. The implications of this line of research for a modular view of human reasoning are discussed, as well as the utility of evolutionary biology in the development of computational theories.     

A connectionist theory of phenomenal experience

When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many have been doing recently, there are two fundamentally distinct approaches available. Consciousness is to be explained either in terms of the nature of the representational vehicles the brain deploys or in terms of the computational processes defined over these vehicles. We call versions of these two approaches vehicle and process theories of consciousness, respectively. However, although there may be space for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because of the influence exerted, on the one hand, by a large body of research that purports to show that the explicit representation of information in the brain and conscious experience are dissociable, and on the other, by the classical computational theory of mind--the theory that takes human cognition to be a species of symbol manipulation. Two recent developments in cognitive science combine to suggest that a reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the experimental methodologies used in the dissociation studies--so critical, in fact, that it is no longer reasonable to assume that the dissociability of conscious experience and explicit representation has been adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in cognitive science as it once was. It now has a lively competitor in the form of connectionism; and connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory of consciousness. In this target article we develop and defend this connectionist vehicle theory of consciousness. It takes the form of the following simple empirical hypothesis: phenomenal experience consists of the explicit representation of information in neurally realized parallel distributed processing (PDP) networks. This hypothesis leads us to reassess some common wisdom about consciousness, but, we argue, in fruitful and ultimately plausible ways.     

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。     

Models of habituation in infancy

Research on infant cognition using habituation methods has sparked considerable controversy in recent years. At the core of the debates is the issue of whether infants have early (and possibly innate) conceptual understandings. This article reviews a range of computational models of habituation that might provide insights into such discussions. The models are assessed against key behavioral and neural features of habituation: temporal unfolding, exponential decrease, familiarity-to-novelty shift, habituation to repeated testing, discriminability of habitual items, selective inhibition and cortical-subcortical interactions. The review suggests that current models fail to offer comprehensive explanations of the behavioral phenomena.     

Swimming and Speaking Spanish

The dominant view of the status of knowledge of language is that it is theoretical or what Gilbert Ryle called knowledge-that. Defenders of this thesis may differ among themselves over the precise nature of the knowledge which underlies language, as for example, Michael Dummett and Noam Chomsky differ over the issue of unconscious knowledge; however, they all agree that acquisition, understanding and use of language require that the speaker have access to a theory of language. In this paper, I argue that this view is mistaken. Knowledge of language is properly seen as practical knowledge, knowledge-how. My target is Michael Dummett’s treatment of theory of meaning in The Seas of Language. If my argument goes through, underlying assumptions about the nature of cognition as computational must be adjusted to allow for other forms of knowledge, which are arguably more basic, and which underlie knowledge-that.     

Children's Cognition as the Foundation of Spirituality

The theory offered in this article articulates the position that the cognitive skills manifested by children are those which are necessary preconditions for spirituality. I argue that psychological theories that posit a hierarchical stage theory of cognition and affect are grounded on premises, which devalue these skills. Theories of children's spirituality have foundered in their ability to appreciate that children's cognition is organized through structures that are quintessential to spirituality, due to their misplaced reliance on these theories. I offer a critique of traditional psychological approaches and an alternative model of children's cognition, which reveals that the cognitive capacities of children are essential to the phenomenology of spirituality.     

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.     

Neuroimaging as a New Tool in the Toolbox of Psychological Science

ABSTRACT— During the past quarter century, advances in imaging technology have helped transform scientific fields. As important as the data made available by these new technologies have been, equally important have been the guides provided by existing theories and the converging evidence provided by other methodologies. The field of psychological science is no exception. Neuroimaging is an important new tool in the toolbox of psychological science, but it is most productive when its use is guided by psychological theories and complemented by converging methodologies including (but not limited to) lesion, electrophysiological, computational, and behavioral studies. Based on this approach, the articles in this special issue specify neural mechanisms involved in perception, attention, categorization, memory, recognition, attitudes, social cognition, language, motor coordination, emotional regulation, executive function, decision making, and depression. Understanding the contributions of individual and functionally connected brain regions to these processes benefits psychological theory by suggesting functional representations and processes, constraining these processes, producing means of falsifying hypotheses, and generating new hypotheses. From this work, a view is emerging in which psychological processes represent emergent properties of a widely distributed set of component processes.     

Autopoietic theory,enactivism, and their incommensurable marks of the cognitive

This paper examines a fundamental philosophical difference between two radical postcognitivist theories that are usually assumed to offer (more or less) the same view of cognition; namely the autopoietic theory (AT) and the enactive approach. The ways these two theories understand cognition, it is argued, are not compatible nor incompatible but rather incommensurable. The reason, so it is argued, is that while enactivism, following the traditional stance held by most of the cognitive theories, understands cognitive systems as constituting a (sort of) natural kind, the autopoietic theory understands them as constituting only a conventional kind. Additionally, the paper shows that AT’s conventionalist stance about cognition, far from being an undesirable or useless position, offers some methodological virtues that might be timely and welcome in the agitated and revolutionary climate of current cognitive science.

     

彝族、白族和纳西族大学生的基本颜色词分类

运用自然分类和多维标度法,研究了彝族、白族和纳西族237名大学生对11种基本颜色词的分类。结果表明,三个民族的大学生对11种基本颜色词的分类有一致之处,但也有文化差异。主要表现在：⑴基本颜色词的分类数目不同;⑵基本颜色词的类别不同;⑶基本颜色词语义空间的维度不完全相同;⑷每一类别中包含的颜色词及颜色词的数目不同。这些差异体现了文化和语言对颜色认知的影响     

Criterial problems in creative cognition research

In creative cognition research, the Romantic view about creative cognition is traditionally rejected in favor of the modern view. The modern view about creative cognition maintains that creativity is neither mysterious nor unintelligible and that it is indeed susceptible to analysis. The paradigmatic objects of analysis in creative cognition research have been creative output and the creative process. The degree of creativity of an output is assessed in accordance with certain criterial definitions. The degree of creativity of a cognitive process is assessed in accordance with certain models of creative cognition, psychometric test measures, and neuroimaging studies that are grounded in certain criteria for assessment. The reliance on criterial definitions and criteria for assessment in analyzing either the creative output or the creative process suggests that creative cognition researchers remain under the sway of the classical, bundles-of-criteria theory of meaning. In this paper, I will critically evaluate the criterial problems that confront both criterial definitions and criteria for assessment before proposing an alternative theory of meaning.     

Two Models of Moral Judgment

This paper compares two theories and their two corresponding computational models of human moral judgment. In order to better address psychological realism and generality of theories of moral judgment, more detailed and more psychologically nuanced models are needed. In particular, a motivationally based theory of moral judgment (and its corresponding computational model) is developed in this paper that provides a more accurate account of human moral judgment than an existing emotion‐reason conflict theory. Simulations based on the theory capture and explain a range of relevant human data. They account not only for the original data that were used to support the emotion – reason conflict theory, but also for a wider range of data and phenomena.