“认知科学”与“第二代认知科学”刍议

以计算隐喻为核心假设的传统认知心理学以及联结主义心理学均不能克服离身心智（disembodied mind）的根本缺陷,当代认知心理学正面临着新的范型转换。以具身性和情境性为重要特征的第二代认知科学将日受重视,并促使认知神经科学进入新的发展阶段。作者认为在身心关系上应该坚持生理只是心理的必要条件,而非充分条件的立场,克服生理还原论的危险;应该重新审视基于二元论的生理机制这种说法;心理学传统中的科学主义和人文主义有可能在第二代认知科学强调认知情境性的基础上达成某种融合;第一代认知科学对意识的研究是不成功的,因为对知觉、注意、记忆、思维等心理过程的研究不能代替意识的研究,同时还应避免以意识内容的研究取代心理学研究的倾向。第二代认知科学中的动力系统理论关于变量（因素）之间的偶合（coupling）关系完全不同于变差分析中的变量之间的交互作用关系”,其动力系统模式可能更有助于破解意识的产生（涌现）之谜,并引发心理学研究的方法论的变革新潮。第二代认知科学的兴起将启发人们对身心关系、生理还原论、意识研究在心理学中的地位、人工智能对心智完全模拟的可能性等重大问题重新思考     

Knowledge Representation: Two Kinds Of Emergence

Two different but closely related issues in current cognitive science will be considered in this essay. One is the controversial and extensively discussed question of how connectionist and symbolic representations of knowledge are related to each other. The other concerns the notion of connectionist learning and its relevance for the understanding of the distinction between propositional and nonpropositional knowledge. More specifically, I shall give an overview of a result in Rantala and Vadén (1994) establishing a limiting case correspondence between symbolic and connectionist representations and, on the other hand, study the problem, preliminarily investigated in Rantala (1998), of how propositional knowledge may arise from nonpropositional knowledge. I shall also try to point out that on some more or less plausible assumptions, often made by cognitive scientists, these results may have some significance when we try to comprehend the nature of human knowledge representation. Some of these assumptions are rather hypothethical and debatable for the time being and they will become justified in the future only if there will be more progress in the empirical and theoretical research on the brain and on artificial networks. The assumptions concern, besides some questions of the behavior of neural networks, such things as the relevance of pattern recognition for modelling human cognition, in particular, knowledge acquisition, and the relation between emergence and reduction.     

WHAT IS AN "EXPLANATION" OF BEHAVIOR?

The cognitive "revolution" in psychology introduced a new concept of explanation and somewhat novel methods of gathering and interpreting evidence. These innovations assume that it is essential to explain complex phenomena at several levels, symbolic as well as physiological: complementary, not competitive. As with the other sciences, such complementary makes possible a comprehensive and unified experimental psychology. Contemporary cognitive psychology also introduced complementary of another kind, drawing upon, and drawing together, both the behaviorist and the Gestalt traditions.     

Psychology as a Science of Subject and Comportment,beyond the Mind and Behavior

The turn of qualitative inquiry suggests a more open, plural conception of psychology than just the science of the mind and behavior as it is most commonly defined. Historical, ontological and epistemological binding of this conception of psychology to the positivist method of natural science may have exhausted its possibilities, and after having contributed to its prestige as a science, has now become an obstacle. It is proposed that psychology be reconceived as a science of subject and comportment in the framework of a contextual hermeneutic, social, human behavioral science. Thus, without rejecting quantitative inquiry, psychology recovers territory left aside like introspection and pre-reflective self-awareness, and reconnects with traditions marginalized from the main stream. From this perspective psychology might also recover its credibility as a human science in view of current skepticism.     

Connectionist models and their properties

Much of the progress in the fields constituting cognitive science has been based upon the use of explicit information processing models, almost exclusively patterned after conventional serial computers. An extension of these ideas to massively parallel, connectionist models appears to offer a number of advantages. After a preliminary discussion, this paper introduces a general connectionist model and considers how it might be used in cognitive science. Among the issues addressed are: stability and noise-sensitivity, distributed decision-making, time and sequence problems, and the representation of complex concepts.     

"认知革命"与"第二代认知科学"刍议

以计算隐喻为核心假设的传统认知心理学以及联结主义心理学均不能克服离身心智（disembodied mind）的根本缺陷,当代认知心理学正面临着新的范型转换．以具身性和情境性为重要特征的第二代认知科学将日受重视,并促使认知神经科学进入新的发展阶段。作者认为在身心关系上应该坚持生理只是心理的必要条件,而非充分条件的赢场,克服生理还原论的危险;应该重新审视基于二元论的生理机制这种说法;心理学传统中的科学主义和人文主义有可能在第二代认知科学强调认知情境性的基础上达成某种融合;第一代认知科学对意识的研究是不成功的,因为对知觉、注意、记忆、思维等心理过程的研究不能代替意识的研究,同时还应避免以意识内容的研究取代心理学研究的倾向、第二代认知科学中的动力系统理论关于变量（因素）之间的偶合（coupling）关系完全不同于变差分析巾的变量之间的交互作用关系,其动力系统模式可能更有助于破解意识的产生（涌现）之谜,并引发心理学研究的方法论的变革新潮,第二代认知科学的兴起将启发人们对身心关系、生理还原论、意识研究在心理学中的地位、人工智能对心智完全模拟的可能性等重夫问题重新思考。     

Dynamical approaches to cognitive science

Dynamical ideas are beginning to have a major impact on cognitive science, from foundational debates to daily practice. In this article, I review three contrasting examples of work in this area that address the lexical and grammatical structure of language, Piaget's classic 'A-not-B' error, and active categorical perception in an embodied, situated agent. From these three examples, I then attempt to articulate the major differences between dynamical approaches and more traditional symbolic and connectionist approaches. Although the three models reviewed here vary considerably in their details, they share a focus on the unfolding trajectory of a system's state and the internal and external forces that shape this trajectory, rather than the representational content of its constituent states or the underlying physical mechanisms that instantiate the dynamics. In some work, this dynamical viewpoint is augmented with a situated and embodied perspective on cognition, forming a promising unified theoretical framework for cognitive science broadly construed.     

文化视域下认知心理学范式演进探新

认知科学的发展历程经历了符号范式、联结主范式和具身认知范式的演变。认知心理学范式演变折射出认知心理学研究方式转变和对人的心智探索的深化,同时也反映出关于身心关系实现了从传统的身心二元对立到当下身心一体论的超越;对其文化反思是引领心理学从离身认知到具身认知进程更趋合理化的基础。     

A model of response time effects in symbolic comparison

A cognitive process model is developed that predicts the 3 major symbolic comparison response time effects (distance, end, and semantic congruity) found in the results of the linear syllogistic reasoning task. The model includes a simple connectionist learning component and dual evidence accumulation decision-making components. It assumes that responses can be based either on information concerning the positional difference between the presented stimulus items or on information concerning the endpoint status of each of these items. The model provides an excellent quantitative account of the mean correct response times obtained from 16 participants who performed paired comparisons of 6 ordered symbolic stimuli (3-letter names).     

Pavlov's methodological behaviorism as a pre-Socratic contribution of the melding of the differential and experimental psychology

The differential/experimental distinction that Cronbach specified is important because any adequate account of psychological phenomena requires the recognition of the validity of both approaches, and a meaningful melding of the two. This paper suggests that Pavlov's work in psychology, based on earlier traditions of inquiry that can be traced back to the pre-Socratics, provides a potential way of achieving this melding, although such features as systematic rather than anecdotal methods of observation need to be added. Pavlov's methodological behaviorist approach is contrasted with metaphysical behaviorism (as exemplified explicitly in Watson and Skinner, and implicitly in the computer-metaphorical, information-processing explanations employed by current "cognitive" psychology). A common feature of the metaphysical approach is that individual-differences variables like sex are essentially ignored, or relegated to ideological categories such as the treatment of sex as merely a "social construction." Examples of research both before and after the "cognitive revolution" are presented where experimental and differential methods are melded, and individual differences are treated as phenomena worthy of investigation rather than as nuisance factors that merely add to experimental error.     

Hybrid preference machines based on inspiration from neuroscience

In the past, a variety of computational problems have been tackled with different connectionist network approaches. However, very little research has been done on a framework which connects neuroscience-inspired models with connectionist models and higher level symbolic processing. In this paper, we outline a preference machine framework which focuses on a hybrid integration of various neural and symbolic techniques in order to address how we may process higher level concepts based on concepts from neuroscience. It is a first hybrid framework which allows a link between spiking neural networks, connectionist preference machines and symbolic finite state machines. Furthermore, we present an example experiment on interpreting a neuroscience-inspired network by using preferences which may be connected to connectionist or symbolic interpretations.     

John Dewey and the Role of Scientific Method in Aesthetic Experience

In this paper I examine a controversy ongoingwithin current Deweyan philosophy of educationscholarship regarding the proper role and scopeof science in Dewey's concept of inquiry. Theside I take is nuanced. It is one that issensitive to the importance that Dewey attachesto science as the best method of solvingproblems, while also sensitive to thosestatements in Dewey that counter a wholesalereductivism of inquiry to scientific method. Iutilize Dewey's statements regarding the placeaccorded to inquiry in aesthetic experiences ascharacteristic of his method, as bestconceived.     

Concepts as Semantic Pointers: A Framework and Computational Model

The reconciliation of theories of concepts based on prototypes, exemplars, and theory‐like structures is a longstanding problem in cognitive science. In response to this problem, researchers have recently tended to adopt either hybrid theories that combine various kinds of representational structure, or eliminative theories that replace concepts with a more finely grained taxonomy of mental representations. In this paper, we describe an alternative approach involving a single class of mental representations called “semantic pointers.” Semantic pointers are symbol‐like representations that result from the compression and recursive binding of perceptual, lexical, and motor representations, effectively integrating traditional connectionist and symbolic approaches. We present a computational model using semantic pointers that replicates experimental data from categorization studies involving each prior paradigm. We argue that a framework involving semantic pointers can provide a unified account of conceptual phenomena, and we compare our framework to existing alternatives in accounting for the scope, content, recursive combination, and neural implementation of concepts.     

Computer Modeling and the Fate of Folk Psychology

Although Paul Churchland and Jerry Fodor both subscribe to the so-called theory-theory – the theory that folk psychology (FP) is an empirical theory of behavior – they disagree strongly about FP's fate. Churchland contends that FP is a fundamentally flawed view analogous to folk biology, and he argues that recent advances in computational neuroscience and connectionist AI point toward development of a scientifically respectable replacement theory that will give rise to a new common-sense psychology. Fodor, however, wagers that FP will be largely preserved and vindicated by scientific investigations of behavior. Recent findings by developmental psychologists, I argue, will push both Churchlandians and Fodorians toward the pessimistic view that FP is a misguided theory that will never be displaced, because it is, so to speak, built into our cognitive systems. I explore the possibility of preserving optimism by rejecting the theory-theory and adopting the simulation theory , a competing view developed by Robert Gordon, Alvin Goldman, and Jane Heal. According to simulationists, common-sense interpretation of behavior is accomplished by means of pretense-like operations that deploy the cognitive system's own reasoning capabilities in a disengaged manner. Since on this view no theory-like set of principles would be needed, the simulation theory seems to enjoy a simplicity advantage over the theory-theory. Steven Stich and Shawn Nichols, however, contend that as the cognitive system would require special mechanisms for disengaged operation, the simplicity question cannot be resolved until suitable computational models are developed. I describe a set of models I have constructed to meet this need, and I discuss the contribution such models can make to determining FP's fate.     

Jung's mediatory science as a psychology beyond objectivism

In this paper, the author argues that Jung's non-objectivist--yet scientific--epistemology and his empirical/hermeneatic methods of inquiry situate him within a psychological tradition that, in many respects, began with William James and, today, is finding expression in the work of many non-Jungian cognitive scientists. In an effort to encourage dialogue between Jungians and scholars within related intellectual traditions, the author presents evidence from the corpus of Jung's work that demonstrates that, like William James, Jung intentionally rejected the absolutist claims of objectivism and the opposite position on 'anything goes' relativism, emotivism, or subjectivism. Instead, Jung forged a path that led to the meta-psychological position similar to internal realism (Putnam 1981) or experientialism (Lakoff 1987) and to a theoretical psychology that gave a central place both to unconscious cognitive structure and to imagination. This he labelled a 'mediatory science'. The psychological theories developed within this mediatory science framework represent an early articulation of key constructs that are currently used by a number of cognitive scientists seeking to understand how we make sense of experience.     

Are rules and modules really necessary for explaining language?

Over the past few years Steven Pinker has argued that although some aspects of language may be more associational, and therefore properly modeled in connectionist networks, for the most part human language is still best characterized as a modularized set of rulesymbol systems. In support of his claim, Pinker garners a broad array of clinical, experimental, and observational data from neurology, psychology, and linguistics. Those data, unfortunately, are not compelling because they do not support his position uniquely. In this paper, I show how each of his arguments is compatible with alternative interpretations. I argue, moreover, that in focusing on certain details of connectionist models Pinker and his colleagues actually overlooked both the most serious deficiencies of the connectionist approach and its most significant theoretical contribution. I conclude by sketching briefly some emerging alternatives to connectionism which avoid those deficiencies while retaining its strengths over the rule-symbol systems of linguistic theory.This research was funded in part by University of Idaho Seed Grant 681-Y304.     

Computational modeling/cognitive robotics complements functional modeling/experimental psychology

This position paper explores the possible contributions to the science of psychology from insights obtained by building and experimenting with cognitive robots. First, the functional modeling characteristic of experimental psychology is discussed. Second, the computational modeling required for cognitive robotics is described, and possible experiments with them are illustrated. Next, we argue that cognitive developmental robots, robots that “live” through a development phase where they learn about their environments in several different modes, can provide additional benefits to the science of psychology. Finally, the reciprocal interactions between computational modeling/cognitive robotics and functional modeling/experimental psychology are explored. We conclude that each can contribute significantly to the other.     

Distributed Connectionist Models in Social Psychology

Distributed connectionist models of mental representation (also termed PDP or parallel distributed processing, or ANN or artificial neural networks) constitute a fundamental alternative to the associative or schematic models that have been much more prevalent in social psychology. A connectionist model is made up of a large number of very simple processing units, richly interconnected and able to send signals to each other depending on their momentary activation levels. No individual processing unit represents a meaningful concept; instead, overall patterns of activation hold representational meaning. This article emphasizes the novel properties of connectionist representation that might appeal to theorists and researchers in social psychology, including their context sensitivity and flexibility, ability to represent prototypes and exemplars within a single network, and ability to determine whether a stimulus is familiar even before the stimulus can be identified or categorized.     

Political goals versus scientific truths: a response to Jackson (2003)

Three fundamental issues separate Jackson's (2003) methodological views from mine. One, whereas he believes an absolute moral view can prevail in a democracy, I assume moral pluralism is an inevitable byproduct of an open society. Two, Jackson feels that psychology can identify a correct moral position, whereas I postulate natural science psychology is only capable of revealing the empirical consequences of competing social policies and their moral implications. Three, Jackson espouses a politically active psychology that from my perspective is antithetical to a democratic and scientific ethic. In sum, Jackson's coupling of science with political advocacy will lead to a mistrust of psychology that will deny a democracy the opportunity to base its social policies on reliable psychological information.