A distributed neural network model called SPEC for processing sentences with recursive relative clauses is described. The model is based on separating the tasks of segmenting the input word sequence into clauses, forming the case‐role representations, and keeping track of the recursive embeddings into different modules. The system needs to be trained only with the basic sentence constructs, and it generalizes not only to new instances of familiar relative clause structures but to novel structures as well. SPEC exhibits plausible memory (...) degradation as the depth of the center embeddings increases, its memory is primed by earlier constituents, and its performance is aided by semantic constraints between the constituents. The ability to process structure is largely due to a central executive network that monitors and controls the execution of the entire system. This way, in contrast to earlier subsymbolic systems, parsing is modeled as a controlled high‐level process rather than one based on automatic reflex responses. (shrink)

PDP networks that use nonmonotonic activation functions often produce hidden unit regularities that permit the internal structure of these networks to be interpreted (Berkeley et al., 1995; McCaughan, 1997; Dawson, 1998). In particular, when the responses of hidden units to a set of patterns are graphed using jittered density plots, these plots organize themselves into a set of discrete stripes or bands. In some cases, each band is associated with a local interpretation. On the basis of these observations, Berkeley (2000) (...) has suggested that these bands are both subsymbolic and symbolic in nature, and has used the analysis of one network to support the claim that there are fewer differences between symbols and subsymbols than one might expect. We suggest below that this conclusion is premature. First, in many cases the local interpretation of each band is difficult to relate to the interpretation of a network's response; a more appropriate relationship only emerges when a band associated with one hidden unit is considered in the context of other bands associated with other hidden units (i.e., interpretations of distributed representations are more useful than interpretations of local representations). Second, the content that a band designates to an external observer (i.e., the interpretation assigned to a band by the researcher) can be quite different from the content that a band designates to the output units of the network itself.. We use two different network simulations – including the one described by Berkeley (2000) – to illustrate these points. We conclude that current evidence involving interpretations of nonmonotonic PDP networks actually illustrates the differences between symbolic and subsymbolic processing. (shrink)

More than a decade ago, philosopher John Searle started a long-running controversy with his paper “Minds, Brains, and Programs” (Searle, 1980a), an attack on the ambitious claims of artificial intelligence (AI). With his now famous _Chinese Room_ argument, Searle claimed to show that despite the best efforts of AI researchers, a computer could never recreate such vital properties of human mentality as intentionality, subjectivity, and understanding. The AI research program is based on the underlying assumption that all important aspects of (...) human cognition may in principle be captured in a computational model. This assumption stems from the belief that beyond a certain level, implementational details are irrelevant to cognition. According to this belief, neurons, and biological wetware in general, have no preferred status as the substrate for a mind. As it happens, the best examples of minds we have at present have arisen from a carbon-based substrate, but this is due to constraints of evolution and possibly historical accidents, rather than to an absolute metaphysical necessity. As a result of this belief, many cognitive scientists have chosen to focus not on the biological substrate of the mind, but instead on the abstract causal structure_ _that the mind embodies (at an appropriate level of abstraction). The view that it is abstract causal structure that is essential to mentality has been an implicit assumption of the AI research program since Turing (1950), but was first articulated explicitly, in various forms, by Putnam (1960), Armstrong (1970) and Lewis (1970), and has become known as _functionalism_. From here, it is a very short step to _computationalism_, the view that computational structure is what is important in capturing the essence of mentality. This step follows from a belief that any abstract causal structure can be captured computationally: a belief made plausible by the Church–Turing Thesis, which articulates the power. (shrink)

To deal with reactive sequential decision tasks we present a learning model Clarion which is a hybrid connectionist model consisting of both localist and dis tributed representations based on the two level ap proach proposed in Sun The model learns and utilizes procedural and declarative knowledge tapping into the synergy of the two types of processes It uni es neural reinforcement and symbolic methods to perform on line bottom up learning Experiments in various situations are reported that shed light on (...) the working of the model.. (shrink)

In 1988, Smolensky proposed that connectionist processing systems should be understood as operating at what he termed the `subsymbolic' level. Subsymbolic systems should be understood by comparing them to symbolic systems, in Smolensky's view. Up until recently, there have been real problems with analyzing and interpreting the operation of connectionist systems which have undergone training. However, recently published work on a network trained on a set of logic problems originally studied by Bechtel and Abrahamsen (1991) seems to offer the potential (...) to provide a detailed, empirically based answer to questions about the nature of subsymbols. In this paper, a network analysis procedure and the results obtained using it are discussed. This provides the basis for an insight into the nature of subsymbols, which is surprising. (shrink)

The article criticises the attempt to establish connectionism as an alternative theory of human cognitive architecture through the introduction of thesymbolic/subsymbolic distinction (Smolensky, 1988). The reasons for the introduction of this distinction are discussed and found to be unconvincing. It is shown that thebrittleness problem has been solved for a large class ofsymbolic learning systems, e.g. the class oftop-down induction of decision-trees (TDIDT) learning systems. Also, the process of articulating expert knowledge in rules seems quite practical for many important domains, (...) including common sense knowledge.The article discusses several experimental comparisons betweenTDIDT systems and artificial neural networks using the error backpropagation algorithm (ANNs usingBP). The properties of one of theTDIDT systemsID3 (Quinlan, 1986a) are examined in detail. It is argued that the differences in performance betweenANNs usingBP andTDIDT systems reflect slightly different inductive biases but are not systematic; these differences do not support the view that symbolic and subsymbolic systems are fundamentally incompatible. It is concluded, that thesymbolic/subsymbolic distinction is spurious. It cannot establish connectionism as an alternative cognitive architecture. (shrink)

This paper explores an approach for autonomous generation of symbolic representations from an agent's subsymbolic activities within the agent-environment interaction. The paper describes a psychologically plausible general framework and its various methods for autonomously creating symbolic representations. The symbol generation is accomplished within, and is intrinsic to, a generic and comprehensive cognitive architecture for capturing a wide variety of psychological processes (namely, CLARION). This work points to ways of obtaining more psychologically/cognitively realistic symbolic and subsymbolic representations within the framework of (...) a cognitive architecture, and accentuates the relevance of such an approach to cognitive science and psychology. (shrink)

Language has long been a problem‐case for subsymbolic theories of mind. The reason for this is obvious: Language seems essentially symbolic. However, recent work has developed a potential solution to this problem, arguing that linguistic symbols are public objects which augment a fundamentally subsymbolic mind, rather than components of cognitive symbol‐processing. I shall argue that this strategy cannot work, on the grounds that human language acquisition consists in projecting linguistic structure onto environmental entities, rather than extracting this structure from them.

The constructivist notion that features are purely functional is incompatible with the classical computational metaphor of mind. I suggest that the discontent expressed by Schyns, Goldstone and Thibaut about fixed-features theories of categorization reflects the growing impact of connectionism, and show how their perspective is similar to recent research on implicit learning, consciousness, and development. A hard problem remains, however: How to bridge the gap between subsymbolic and symbolic cognition.

This paper outlines the process of verbal communication of emotion as this occurs through the phases of the referential process, including arousal of an emotion schema; detailed and specific descriptions of images and episodes that are exemplars of emotion schemas; and reflection and reorganization, which may include emotion labels and other types of categorical terms. The concepts of emotion schemas and the referential process are defined in the theoretical framework of multiple code theory which includes subsymbolic sensory, visceral and motoric (...) processes, symbolic images and words. Emotion schemas are defined as clusters of representations of events incorporating similar bodily, sensory and motoric processes activated in relation to different people in different contexts. Through the referential process subsymbolic components of a schema that have been activated in a speaker or writer and that may be connected only partially to words may be evoked in a listener or reader. The concept of the emotion schemas is examined in relation to current work in emotion theory and neuroscience. The unique effects of detailed descriptions of episodes in conveying complex aspects of emotional experience are discussed, as recognized by writers, and as demonstrated in empirical research. Computerized measures of the phases of the referential process are presented, focusing particularly on the central measure, the Weighted Referential Activity Dictionary which identifies points of narrative and imagery. The operation of the function words that dominate the WRAD are examined in relation to the structure of narrative expression underlying the verbal representation of emotion schemas. (shrink)

This introduction to the thematic section of Zygon: Journal of Religion and Science on “Artificial Intelligence and Religion: Recent Advances and Future Directions” outlines the five articles by dividing them into two groups: the three that analyze the impact of recent advances in subsymbolic artificial intelligence (AI) on religion and theology, and the two that explore theological concepts in symbolic AI environments. These five articles are five steps toward a strong, deep, and interdisciplinary dialogue between the research in religion and (...) the research in AI. (shrink)

Theoretical gaps of the cognitive science. First of all the gap-thesis is based on a criticism 1. of the computer-orientated cognitive science (it confuses information with the information carrier), 2. of connectivism (its linguistic borrowing from the neurobiology is not appropriate), 3. of Varelas production model (the elimination of the function of representation results in the loss of the cognitive ability). From the context of meaning and time, then the author sketches a cognitive theoretical approach, in which thinking as a (...) (symbolic and/or subsymbolic) representation of meaning is introduced, which develops in a three-digit relation between world, language and substrate on the basis of isomorphy of time. (shrink)

Berkeley [Minds Machines 10 (2000) 1] described a methodology that showed the subsymbolic nature of an artificial neural network system that had been trained on a logic problem, originally described by Bechtel and Abrahamsen [Connectionism and the mind. Blackwells, Cambridge, MA, 1991]. It was also claimed in the conclusion of this paper that the evidence was suggestive that the network might, in fact, count as a symbolic system. Dawson and Piercey [Minds Machines 11 (2001) 197] took issue with this latter (...) claim. They described some lesioning studies that they argued showed that Berkeley’s (2000) conclusions were premature. In this paper, these lesioning studies are replicated and it is shown that the effects that Dawson and Piercey rely upon for their argument are merely an artifact of a threshold function they chose to employ. When a threshold function much closer to that deployed in the original studies is used, the significant effects disappear. (shrink)

Connectionism and computationalism are currently vying for hegemony in cognitive modeling. At first glance the opposition seems incoherent, because connectionism is itself computational, but the form of computationalism that has been the prime candidate for encoding the "language of thought" has been symbolic computationalism (Dietrich 1990, Fodor 1975, Harnad 1990c; Newell 1980; Pylyshyn 1984), whereas connectionism is nonsymbolic (Fodor & Pylyshyn 1988, or, as some have hopefully dubbed it, "subsymbolic" Smolensky 1988). This paper will examine what is and is not (...) a symbol system. A hybrid nonsymbolic/symbolic system will be sketched in which the meanings of the symbols are grounded bottom-up in the system's capacity to discriminate and identify the objects they refer to. Neural nets are one possible mechanism for learning the invariants in the analog sensory projection on which successful categorization is based. "Categorical perception" (Harnad 1987a), in which similarity space is "warped" in the service of categorization, turns out to be exhibited by both people and nets, and may mediate the constraints exerted by the analog world of objects on the formal world of symbols. (shrink)

According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation (...) and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individual's existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction. Key Words: concept learning; conceptual development; features; perceptual learning; stimulus encoding. (shrink)

The representational dynamics of the brain is a subsymbolic process, and it has to be conceived as an "agent-free" type of dynamical self-organization. However, in generating a coherent internal world-model, the brain decomposes target space in a certain way. In doing so, it defines an "ontology": to have an ontology is to interpret a world. In this paper we argue that the brain, viewed as a representational system aimed at interpreting the world, possesses an ontology too. It decomposes target space (...) in a way that exhibits certain invariances, which in turn are functionally significant. A challenge for empirical research is to determine which are the functional regularities guiding this decomposition process. What are the explicit and implicit assumptions about the structure of reality, which at the same time shape the causal profile of the brain's motor output and the representational deep structure of the conscious mind arising from it (its "phenomenal output")? How do they constrain high-level phenomena like conscious experience, the emergence of a first-person perspective, or social cognition? By reviewing a series of neuroscientific results, we focus on the contribution the motor system makes to this process. As it turns out, the motor system constructs goals, actions, and intending selves as basic constituents of the world it interprets. It does so by assigning a single, unified causal role to them. Empirical evidence now clearly shows how the brain actually codes movements and action goals in terms of multimodal representations of organism-object relations. Under a representationalist analysis, this process can be interpreted as an internal representation of the intentionality relation itself. We try to show how such a more complex form of representational content, once it is in place, can later function as the building block for social cognition and a for more complex, consciously experienced representation of the first-person perspective as well. The motor system may therefore play a decisive role in understanding how the functional ontology of the human brain could be gradually extended into the subjective and social domains. (shrink)

The idea that reasoning is a singular accomplishment of the human species has an ancient pedigree.Yet this idea remains as controversial as it is ancient. Those who would deny reasoning to nonhuman animals typically hold a language-based conception of inference which places it beyond the reach of languageless creatures. Others reject such an anthropocentric conception of reasoning on the basis of similar performance by humans and animals in some reasoning tasks, such as transitive inference. Here, building on the modal similarity (...) theory of Vigo [J Exp Theor Artif Intell, 2008 (in press)], we offer an account in which reasoning depends on a core suite of subsymbolic processes for similarity assessment, discrimination, and categorization. We argue that premise-based inference operates through these subsymbolic processes, even in humans. Given the robust discrimination and categorization abilities of some species of nonhuman animals, we believe that they should also be regarded as capable of simple forms of inference. Finally, we explain how this account of reasoning applies to the kinds of transitive inferences that many nonhuman animals display. (shrink)

Classical cognitive science assumes that intelligently behaving systems must be symbol processors that are implemented in physical systems such as brains or digital computers. By contrast, connectionists suppose that symbol manipulating systems could be approximations of neural networks dynamics. Both classicists and connectionists argue that symbolic computation and subsymbolic dynamics are incompatible, though on different grounds. While classicists say that connectionist architectures and symbol processors are either incompatible or the former are mere implementations of the latter, connectionists reply that neural (...) networks might be incompatible with symbol processors because the latter cannot be implementations of the former. In this contribution, the notions of 'incompatibility' and 'implementation' will be criticized to show that they must be revised in the context of the dynamical system approach to cognitive science. Examples for implementations of symbol processors that are incompatible with respect to contextual topologies will be discussed. (shrink)

The field of cognition and emotion is characterised as the cognitive psychology of evaluative and affective processes. The most important development in this field is the fruitful adoption of cognitive psychology paradigms to study automatic evaluation processes, for example. This has led to a plethora of findings and theories. Two points are emphasised: First, the (often metaphorical) theoretical way of thinking has changed over the decades. Theorising with symbolic models (e.g. semantic networks), which was prevalent in earlier years, has been (...) replaced more recently by subsymbolic models (i.e. PDP models). It is argued that – despite their still metaphorical character – the latter are better suited to capturing characteristics of emotional processes. Second, the field has adopted the methods of experimental cognitive psychology to develop and refine paradigms as “windows to the mind”. (shrink)

Newell proposed that cognitive theories be developed in an effort to satisfy multiple criteria and to avoid theoretical myopia. He provided two overlapping lists of 13 criteria that the human cognitive architecture would have to satisfy in order to be functional. We have distilled these into 12 criteria: flexible behavior, real-time performance, adaptive behavior, vast knowledge base, dynamic behavior, knowledge integration, natural language, learning, development, evolution, and brain realization. There would be greater theoretical progress if we evaluated theories by a (...) broad set of criteria such as these and attended to the weaknesses such evaluations revealed. To illustrate how theories can be evaluated we apply these criteria to both classical connectionism and the ACT-R theory. The strengths of classical connectionism on this test derive from its intense effort in addressing empirical phenomena in such domains as language and cognitive development. Its weaknesses derive from its failure to acknowledge a symbolic level to thought. In contrast, ACT-R includes both symbolic and subsymbolic components. The strengths of the ACT-R theory derive from its tight integration of the symbolic component with the subsymbolic component. Its weaknesses largely derive from its failure, as yet, to adequately engage in intensive analyses of issues related to certain criteria on Newell's list. Key Words: cognitive architecture; connectionism; hybrid systems; language; learning; symbolic systems. (shrink)

This article presents counter evidence against Smolensky's theory that human intuitive/nonconscious congnitive processes can only be accurately explained in terms of subsymbolic computations carried out in artificial neural networks. We presentsymboliclearning models of two well‐studied, complicated cognitive tasks involving nonconscious acquisition of information: learning production rules and artificial finite state grammars. Our results demonstrate that intuitive learning does not imply subsymbolic computation, and that the already well‐established, perceived correlation between “conscious” and “symbolic” on the one hand, and between “nonconscious” and (...) “subsymbolic” on the other, does not exist. (shrink)

In line with Allen Newell's challenge to develop complete cognitive architectures, and motivated by a recent proposal for a unifying subsymbolic computational theory of cognition, we introduce the cognitive control architecture SEMLINCS. SEMLINCS models the development of an embodied cognitive agent that learns discrete production rule-like structures from its own, autonomously gathered, continuous sensorimotor experiences. Moreover, the agent uses the developing knowledge to plan and control environmental interactions in a versatile, goal-directed, and self-motivated manner. Thus, in contrast to several well-known (...) symbolic cognitive architectures, SEMLINCS is not provided with production rules and the involved symbols, but it learns them. In this paper, the actual implementation of SEMLINCS causes learning and self-motivated, autonomous behavioral control of the game figure Mario in a clone of the computer game Super Mario Bros. Our evaluations highlight the successful development of behavioral versatility as well as the learning of suitable production rules and the involved symbols from sensorimotor experiences. Moreover, knowledge- and motivation-dependent individualizations of the agents’ behavioral tendencies are shown. Finally, interaction sequences can be planned on the sensorimotor-grounded production rule level. Current limitations directly point toward the need for several further enhancements, which may be integrated into SEMLINCS in the near future. Overall, SEMLINCS may be viewed as an architecture that allows the functional and computational modeling of embodied cognitive development, whereby the current main focus lies on the development of production rules from sensorimotor experiences. (shrink)

In the book "Conceptual Spaces: the Geometry of Thought" [2000] Peter Gärdenfors proposes a new framework for cognitive science. Complementary to symbolic and subsymbolic [connectionist] descriptions, conceptual spaces are semantic structures — constructed from empirical data — representing the universe of mental states. We argue that Gärdenfors' modeling can be used in consciousness research to describe the phenomenal conscious world, its elements and their intrinsic relations. The conceptual space approach affords the construction of a universal state space of human consciousness, (...) where all possible kinds of human conscious states could be mapped. Starting from this approach, we discuss the inclusion of feelings and emotions in conceptual spaces, and their relation to perceptual and cognitive states. Current debate on integration of affect/emotion and perception/cognition allows three possible descriptive alternatives: emotion resulting from basic cognition; cognition resulting from basic emotion, and both as relatively independent functions integrated by brain mechanisms. Finding a solution for this issue is an important step in any attempt of successful modeling of natural or artificial consciousness. After making a brief review of proposals in this area, we summarize the essentials of a new model of consciousness based on neuro-astroglial interactions. (shrink)

Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed Symbol Systems, (...) have both combinatorial and gradient structure. They are processed through Subsymbolic Optimization–Quantization, in which an optimization process favoring representations that satisfy well-formedness constraints operates in parallel with a distributed quantization process favoring discrete symbolic structures. We apply a particular instantiation of this framework, λ-Diffusion Theory, to phonological production. Simulations of the resulting model suggest that Gradient Symbol Processing offers a way to unify accounts of grammatical competence with both discrete and continuous patterns in language performance. (shrink)

Empirical results suggest that defocusing attention results in primary process or associative thought, conducive to finding unusual connections, while focusing attention results in secondary process or analytic thought, conducive to rule-based operations. Creativity appears to involve both. It is widely believed that it is possible to escape mental fixation by spontaneously and temporarily engaging in a more divergent or associative mode of thought. The resulting insight may be refined in a more analytic mode of thought. The question addressed here is: (...) how does the architecture of memory support these two modes of thought, and what is happening at the neural level when one shifts between them? Recent advances in neuroscience shed light on this. It was demonstrated that activated cell assemblies are composed of multiple ‘neural cliques’, groups of neurons that respond differentially to general or context-specific aspects of a situation. I refer to neural cliques that would not be included in the assembly if one were in an analytic mode, but would be if one were in an associative mode, as ‘neurds’. It is posited that the shift to a more associative mode of thought conducive to insight is accomplished by recruiting neurds that respond to abstract or atypical subsymbolic microfeatures of the problem or situation. Since memory is distributed and content-addressable this fosters remindings and the forging of creative connections to potentially relevant items previously encoded in those neurons. Thus it is proposed that creative thought involves neither randomness, nor search through a space of predefined alternatives, but emerges naturally through the recruitment of neurds. It is suggested this occurs when there is a need to resolve conceptual gaps in ones’ internal model of the world, and resolution involves context-driven actualization of the potentiality afforded by its fine-grained associative structure. (shrink)

. The human brain combines empathy and imagination via the old brain which sets our destiny in the evolutionary scheme of things. This new understanding of cognition is an emergent phenomenon—basically an expressive ordering of reality as part of “a single natural system.” The holographic and subsymbolic paradigms suggest that we live in a contextual universe, one which we create and yet one in which we are required to adapt. The inadequacy of the new brain—specially the left hemisphere's rational view (...) of destiny—is replaced by a view of a new relatedness in reality in which human destiny comes from and depends upon the mutual interchange between the new brain and the old brain for the survival of what is significant to the whole systemic context in which we live. (shrink)

A cognitive architecture aimed at cumulative learning must provide the necessary information and control structures to allow agents to learn incrementally and autonomously from their experience. This involves managing an agent's goals as well as continuously relating sensory information to these in its perception-cognition information processing stack. The more varied the environment of a learning agent is, the more general and flexible must be these mechanisms to handle a wider variety of relevant patterns, tasks, and goal structures. While many researchers (...) agree that information at different levels of abstraction likely differs in its makeup and structure and processing mechanisms, agreement on the particulars of such differences is not generally shared in the research community. A dual processing architecture has been proposed as a model of cognitive processing, and they are often considered as responsible for low- and high-level information, respectively. We posit that cognition is not binary in this way and that knowledge at any level of abstraction involves what we refer to as neurosymbolic information, meaning that data at both high and low levels must contain both symbolic and subsymbolic information. Further, we argue that the main differentiating factor between the processing of high and low levels of data abstraction can be largely attributed to the nature of the involved attention mechanisms. We describe the key arguments behind this view and review relevant evidence from the literature. (shrink)

We present an account of processing capacity in the ACT-R theory. At the symbolic level, the number of chunks in the current goal provides a measure of relational complexity. At the subsymbolic level, limits on spreading activation, measured by the attentional parameter W, provide a theory of processing capacity, which has been applied to performance, learning, and individual differences data.

The author traces the path from split brains to basic beliefs by linking the deautomatized pattern of spiritual masters, as reorted in Rorschach protocols, with subsymbolic, parallel, distriguted processing, The older brain structures constitute humanity's common heritage, while the new brain constitutes particular cultural heritages. Expanding levels of complexity move from the limbic system throuh conitive left‐mind vigilance and right‐mind responsiveness to %Pelie patterns of proclamation and manifestation to the world‐integrating mysticism of limbic input and the world‐fulfilling action of the (...) new brain. Whole brain activity combines emotional meaning and propositional explanation. Analogically seaking, the brain provides clues to understanding God. A ialectical theology parallels the reciprocal integration of brain processes. Whole brain belief originates in the old brain's evolutionary adaptation to our genetic inheritance and in the new brain's conscious intention to fulfill the will of God through our cultural inheritances. (shrink)

ACT is compared with a particular type of connectionist model that cannot handle symbols and use nonbiological operations which do not learn in real time. This focus continues an unfortunate trend of straw man debates in cognitive science. Adaptive Resonance Theory, or ART-neural models of cognition can handle both symbols and subsymbolic representations, and meet the Newell criteria at least as well as connectionist models.

The debate between Classicism and Connectionism can be properly characterized as a debate concerning the appropriate levels of analysis for psychological theorizing. Classicists maintain that the level of analysis defined by the Classical architecture is the level of analysis at which psychological theorizing should reside. This level is called the symbolic level. On the other hand, Connectionists claim that the proper level of analysis for cognitive modeling is at the subsymbolic level which is considered a level lower than the symbolic (...) level. This thesis argues that both Classicism and Connectionism characterized in the above way are single-level approaches. It is argued in this thesis that Classicism is wrong in denying the cognitive relevancy of the lower-level theorizing; and some forms of Connectionism should be rejected because they underestimate the theoretical significance of the higher-level modeling. ;The concept of level is widely employed by philosophers and cognitive scientists, but has not received sufficient analysis. This thesis offers a general theory of levels, and evaluates Classicism and Connectionism on the basis of this general theory of levels. Basically this thesis argues that a cognitive system should be regarded as a system organized into a hierarchy of levels of organization. Different levels of organization may demand different levels of analysis. This thesis argues that each level of analysis enjoys a degree of autonomy, i.e., it is not totally reducible to the lower-level analysis. The traditional dichotomy of the cognitive and the implementational levels is too simplistic and overlooks the complexity of a cognitive system. Not all levels of organization of a system are cognitively relevant. But much empirical evidence has shown that many levels of organization are either strongly relevant or at least weakly relevant to cognitive modeling. It would be theoretically more plausible and fruitful to adopt a multi-level approach which takes many levels of analysis seriously in cognitive modeling. A very general picture of a multi-level approach to the mind is depicted in this thesis. (shrink)

Steels & Belpaeme (S&B) refer to the neural plausibility and evolutionary plausibility of their algorithms. Although this is not central to their goal of effective artificial agents, their algorithms are not neurally or evolutionarily plausible. Their communication games are interesting, and more complex games would lead to more effective agents. However, the algorithms could be improved either by using standard subsymbolic algorithms or by algorithms that are really neurally or evolutionarily plausible.

Implicit processes are thought to be relatively fast, inaccessible, holistic, and imprecise, while explicit processes are slow, accessible and precise (e.g., Reber, 1989, Sun 2002). This dichotomy is closely related to some other well-known dichotomies including symbolic versus subsymbolic processing (Rumelhart et al., 1986), conceptual versus subconceptual processing (Smolensky, 1988), and conscious versus unconscious processing (Jacoby et al., 1994). This dichotomy has been justified by extensive studies of implicit and explicit learning, implicit and explicit memory, and implicit versus explicit metacognition (...) (Reder, 1996). (shrink)

As part of a trend in modern cognitive science, cognitive linguist, George Lakoff, and philosopher, Mark Johnson claim to provide a biologically-based account of subsymbolic meaningful experiences. They argue that human beings understand objects by extrapolating from their sensory motor activities and primary perceptions. Lakoff and Johnson’s writings have generated a good deal of interest among scholars of Early China because they maintain that “our common embodiment allows for common stable truths.” Although there are many grounds on which Lakoff and (...) Johnson’s theories have been criticized, this essay focuses in particular on problems related to their schema of Self as Container. Lakoff and Johnson contend that there are no pure experiences outside of culture, while nevertheless arguing that the experience of being a closed-off container is “direct.” “The concepts OBJECT, SUBSTANCE and CONTAINER emerge directly,” they write. “We experience ourselves as entities, separate from the rest of the world—as containers with an inside and an outside.” By “emerge directly,” they do not mean emerging free of culture, but rather that some experiences within culture, specifically physical experiences, are more directly given than others. My study explores the pitfalls of presuming the “direct” experience of containment makes good sense of texts from Early China (ca. 500–100 B.C.E). (shrink)