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- Peter R. Krebs, Models of Cognition: Neurological Possibility Does Not Indicate Neurological Plausibility.Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Cognitive functions, like learning to speak, or discovering syntactical structures in language, have been modeled and these models are the basis for many claims about human cognitive capacities. Artificial neural nets (ANNs) have had some successes in the field of Artificial Intelligence, but the results from experiments with simple ANNs may have little value in explaining cognitive functions. The problem seems to be in relating cognitive concepts that belong in the `top-down' approach to models grounded in the `bottom-up' connectionist methodology. Merging the two fundamentally different paradigms within a single model can obfuscate what is really modeled. When the tools (simple artificial neural networks) to solve the problems (explaining aspects of higher cognitive functions) are mismatched, models with little value in terms of explaining functions of the human mind are produced. The ability to learn functions from data-points makes ANNs very attractive analytical tools. These tools can be developed into valuable models, if the data is adequate and a meaningful interpretation of the data is possible. The problem is, that with appropriate data and labels that fit the desired level of description, almost any function can be modeled. It is my argument that small networks offer a universal framework for modeling any conceivable cognitive theory, so that neurological possibility can be demonstrated easily with relatively simple models. However, a model demonstrating the possibility of implementation of a cognitive function using a distributed methodology, does not necessarily add support to any claims or assumptions that the cognitive function in question, is neurologically plausible.Reading list | Discuss | Edit | Categorize |My bibliography
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The employment of a particular class of computer programs known as "connectionist networks" to model mental processes is a widespread approach to research in cognitive science these days. Little has been written, however, on the precise connection that is thought to hold between such programs and actual in vivo cognitive processes such that the former can be said to "model" the latter in a scientific sense. What is more, this relation can be shown to be problematic. In this paper I give a brief overview of the use of connectionist models in cognitive science, and then explore some of the statements connectionists have made about the nature of the "modeling relation" thought to hold between them and cognitive processes. Finally I show that these accounts are inadequate and that more work is necessary if connectionist networks are to be seriously regarded as scientific models of cognitive processes.
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| | Other links: cogprints.org | Scholar | At my library | More options ... High-level perception--”the process of making sense of complex data at an abstract, conceptual level--”is fundamental to human cognition. Through high-level perception, chaotic environmen- tal stimuli are organized into the mental representations that are used throughout cognitive pro- cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis- missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models--”notably BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thought--”and argue that these are flawed pre- cisely because they downplay the role of high-level perception. Further, we argue that perceptu- al processes cannot be separated from other cognitive processes even in principle, and therefore that traditional artificial-intelligence models cannot be defended by supposing the existence of a --œrepresentation module--� that supplies representations ready-made. Finally, we describe a model of high-level perception and analogical thought in which perceptual processing is integrated with analogical mapping, leading to the flexible build-up of representations appropriate to a given context.
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| Discuss | Edit | Categorize | My bibliography | Export citation | Other links: citeseer.ist.psu.edu portal.acm.org u.arizona.edu nbu.bg csa.com informaworld.com | Scholar | At my library | More options ...
| | Other links: citeseer.ist.psu.edu portal.acm.org u.arizona.edu nbu.bg csa.com informaworld.com | Scholar | At my library | More options ... The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that none of these kinds of evidence can be relevant to a choice among competing computational models unless there are in fact type-type correlations between the states postulated by computational models and brain states. Thus, I conclude, research into the computational procedures employed in human cognition must be conducted hand-in-hand with research into the brain processes which realize those procedures.
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| | Other links: springerlink.com cogprints.org | Scholar | At my library | More options ... The article first addresses the importance of cognitive modeling, in terms of its value to cognitive science (as well as other social and behavioral sciences). In particular, it emphasizes the use of cognitive architectures in this undertaking. Based on this approach, the article addresses, in detail, the idea of a multi-level approach that ranges from social to neural levels. In physical sciences, a rigorous set of theories is a hierarchy of descriptions/explanations, in which causal relationships among entities at a high level can be reduced to causal relationships among simpler entities at a more detailed level. We argue that a similar hierarchy makes possible an equally productive approach toward cognitive modeling. The levels of models that we conceive in relation to cognition include, at the highest level, sociological/anthropological models of collective human behavior, behavioral models of individual performance, cognitive models involving detailed mechanisms, representations, and processes, as well as biological/physiological models of neural circuits, brain regions, and other detailed biological processes.
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| Discuss | Edit | Categorize | My bibliography | Export citation | Other links: cogsci.rpi.edu informaworld.com taylorandfrancis.metapress.com ingentaconnect.com dx.doi.org | Scholar | At my library | More options ...
| | Other links: cogsci.rpi.edu informaworld.com taylorandfrancis.metapress.com ingentaconnect.com dx.doi.org | Scholar | At my library | More options ... Reading list
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| | Scholar | At my library | More options ... The prevailing concept in modern cognitive neuroscience is that cognitive functions are performed predominantly at the network level, whereas the role of individual neurons is unlikely to extend beyond forming the simple basic elements of these networks. Within this conceptual framework, individuals of outstanding cognitive abilities appear as a result of a favorable configuration of the microarchitecture of the cognitive-implicated networks, whose final formation in ontogenesis may occur in a relatively random way. Here I suggest an alternative concept, which is based on neurological data and on data from human behavioral genetics. I hypothesize that cognitive functions are performed mainly at the intracellular, probably at the molecular level. Central to this hypothesis is the idea that the neurons forming the networks involved in cognitive processes are complex elements whose functions are not limited to generating electrical potentials and releasing neurotransmitters. According to this hypothesis, individuals of outstanding abilities are so due to a lucky combination of specific genes that determine the intrinsic properties of neurons involved in cognitive functions of the brain.
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| | Other links: springerlink.com dx.doi.org | Scholar | At my library | More options ... Recently, there has been a resurgence of interest in general, comprehensive models of human cognition. Such models aim to explain higher-order cognitive faculties, such as deliberation and planning. Given a computational representation, the validity of these models can be tested in computer simulations such as software agents or embodied robots. The push to implement computational models of this kind has created the field of artificial general intelligence (AGI). Moral decision making is arguably one of the most challenging tasks for computational approaches to higher-order cognition. The need for increasingly autonomous artificial agents to factor moral considerations into their choices and actions has given rise to another new field of inquiry variously known as Machine Morality, Machine Ethics, Roboethics, or Friendly AI. In this study, we discuss how LIDA, an AGI model of human cognition, can be adapted to model both affective and rational features of moral decision making. Using the LIDA model, we will demonstrate how moral decisions can be made in many domains using the same mechanisms that enable general decision making. Comprehensive models of human cognition typically aim for compatibility with recent research in the cognitive and neural sciences. Global workspace theory, proposed by the neuropsychologist Bernard Baars (1988), is a highly regarded model of human cognition that is currently being computationally instantiated in several software implementations. LIDA (Franklin, Baars, Ramamurthy, & Ventura, 2005) is one such computational implementation. LIDA is both a set of computational tools and an underlying model of human cognition, which provides mechanisms that are capable of explaining how an agent’s selection of its next action arises from bottom-up collection of sensory data and top-down processes for making sense of its current situation. We will describe how the LIDA model helps integrate emotions into the human decision-making process, and we will elucidate a process whereby an agent can work through an ethical problem to reach a solution that takes account of ethically relevant factors.
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| | Other links: dx.doi.org | Scholar | At my library | More options ... The standard view of classical cognitive science stated that cognition consists in the manipulation of language-like structures according to formal rules. Since cognition is ‘linguistic’ in itself, according to this view language is just a complex communication system and does not influence cognitive processes in any substantial way. This view has been criticized from several perspectives and a new framework (Embodied Cognition) has emerged that considers cognitive processes as non-symbolic and heavily dependent on the dynamical interactions between the cognitive system and its environment. But notwithstanding the successes of the embodied cognitive science in explaining low-level cognitive behaviors, it is still not clear whether and how it can scale up for explaining high-level cognition. In this paper we argue that this can be done by considering the role of language as a cognitive tool: i.e. how language transforms basic cognitive functions in the high-level functions that are characteristic of human cognition. In order to do that, we review some computational models that substantiate this view with respect to categorization and memory. Since these models are based on a very rudimentary form of non-syntactic ‘language’ we argue that the use of language as a cognitive tool might have been an early discovery in hominid evolution, and might have played a substantial role in the evolution of language itself.
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| | Other links: springerlink.com dx.doi.org | Scholar | At my library | More options ... Connectionism is a style of modeling based upon networks of interconnected simple processing devices. This style of modeling goes by a number of other names too. Connectionist models are also sometimes referred to as 'Parallel Distributed Processing' (or PDP for short) models or networks.1 Connectionist systems are also sometimes referred to as 'neural networks' (abbreviated to NNs) or 'artificial neural networks' (abbreviated to ANNs). Although there may be some rhetorical appeal to this neural nomenclature, it is in fact misleading as connectionist networks are commonly significantly dissimilar to neurological systems. For this reason, I will avoid using this terminology, other than in direct quotations. Instead, I will follow the practice I have adopted above and use 'connectionist' as my primary term for systems of this kind.
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| | Scholar | More options ... Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Simulations of cognitive functions, like learning to speak, or discovering syntactical structures in language, are the basis for many claims about human capacities in language acquisition. This raises the question whether results obtained from experiments that are essentially performed on data structures are equivalent to results from "real" experiments. This paper examines some design methodologies for models of cognitive functions using artificial neural nets. The process of conducting the cognitive simulations is largely a projection of theories, or even unsubstantiated conjectures, onto simulated neural structures and an interpretation of the experimental results in terms of the human brain. The problem with this process is that results from virtual experiments are taken to refer unambiguously to the human brain; and the more the language of human cognitive function is deployed in both theory construction and (virtual) experimental interpretation, the more convincing the impression. Additionally, the complexity of the methodologies, principles, and visualization techniques, in the implementation of the computational model, masks the lack of actual similarities between model and real world phenomena. Some computational models involving artificial neural nets have had some success, even commercially, but there are indications that the results from virtual experiments have little value in explaining cognitive functions. The problem seems to be in relating computational, or mathematical, entities to real world objects, like neurons and brains. I argue that the role of Artificial Intelligence as a contributor to the knowledge base of Cognitive Science is diminished as a consequence.
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| | Other links: cogprints.org | Scholar | More options ... Discussion of Peter R. Krebs, Models of cognition: Neurological possibility does not indicate neurological plausibility
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