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. (shrink)

David Marr provided an influential account of levels of description in classical cognitive science. In this paper we contrast Marr'ent with some alternatives that are suggested by the recent emergence of connectionism. Marr's account is interesting and important both because of the levels of description it distinguishes, and because of the way his presentation reflects some of the most basic, foundational, assumptions of classical AI-style cognitive science. Thus, by focusing on levels of description, one can sharpen foundational (...) differences between classicism and potential non-classical conceptions of mentality that might emerge under the rubric of connectionism. (shrink)

In approaching the question of whether groups of people can have cognitive capacities that are fundamentally different than the cognitive capacities of the individuals within the group, we lay out a Multiple, Interactive Levels of Cognitive Systems (MILCS) framework. The goal of MILCS is to explain the kinds of cognitive processes typically studied by cognitive scientists, such as perception, attention, memory, categorization, decision making, problem solving, and judgment. Rather than focusing on high-level constructs such as modules in an information (...) processing flow diagram or internal representations, MILCS focuses on mechanisms that allow systems to engage in flexible, adaptive behavior. Examples of these mechanisms are network structure-process pairs that: combine data from perception with top-down theories, achieve consistency and consensus through information exchange, selectively attend to information globally assessed as relevant, and develop specialized units from originally homogeneous networks through competition among the units. Two such systems are considered in some detail -- lateral inhibition within a network for selecting from a candidate set the option that is most attractive, and a diffusion process for accumulating evidence to reach a generally rapid and accurate decision. These system descriptions are aptly applied at multiple levels, including within and across people. These systems provide accounts that unify cognitive processes across multiple levels, can be expressed in a common vocabulary provided by network science, are inductively powerful yet appropriately constrained, and are applicable to a large number of superficially diverse cognitive systems. Given that people are typically highly motivated to participate in strong groups, cognitively resourceful people will tend to form groups that effectively employ cognitive systems at higher levels than the individuals. The impressive cognitive capacities of individual people do not eliminate the need to talk about group cognition. Instead, smart people generally form smart groups. (shrink)

A Notional Level of Cognitive Distortions in Depression: Does It Exist? A Voice for Interdisciplinarity in Studying Cognitive Functioning of Individuals with Depressive Disorders This aritcle raises the problem of cognitive depressive distortions observed at the notional level. It relates to recent neuropsychological, psychological, and linguistic studies, taking an interdisciplinary theoretical perspective, and illustrating the advantages of interdisciplinarity in modern psycholinguistic projects. It shows that, generally, the notional level has been neglected in psychopathological and psychological research on depressive functioning. The (...) problem is described with reference to linguistic and psycholinguistic theories linking language with cognition. Particular emphasis is devoted to theories and methods of metaphorical processing analysis which, taking into account the similarities between cognitive functions engaged in understanding metaphors, and those disordered as a result of depression, seem to be an adequate frame to study the problem. The text ends with a proposal of an interdisciplinary research project dedicated to the study of metaphorical conceptualizations of some notions created by people suffering from depression. (shrink)

Marr's levels of analysis—computational, algorithmic, and implementation—have served cognitive science well over the last 30 years. But the recent increase in the popularity of the computational level raises a new challenge: How do we begin to relate models at different levels of analysis? We propose that it is possible to define levels of analysis that lie between the computational and the algorithmic, providing a way to build a bridge between computational- and algorithmic-level models. The key idea is (...) to push the notion of rationality, often used in defining computational-level models, deeper toward the algorithmic level. We offer a simple recipe for reverse-engineering the mind's cognitive strategies by deriving optimal algorithms for a series of increasingly more realistic abstract computational architectures, which we call “resource-rational analysis.”. (shrink)

Some controversies in cognitive science, such as arguments about whether classical or distributed connectionist architectures best model the human cognitive system, reenact long‐standing debates in the philosophy of science. For millennia, philosophers have pondered whether mentality can submit to scientific explanation generally and to physical explanation particularly. Recently, positive answers have gained popularity. The question remains, though, as to the analytical level at which mentality is best explained. Is there a level of analysis that is peculiarly appropriate to the explanation (...) of either consciousness or mental contents? Are human consciousness, cognition, and conduct best understood in terms of talk about neurons and networks or schemas and scripts or intentions and inferences? If our best accounts make no appeal to our hopes or beliefs or desires, how do we square those views with our conception of ourselves as rational beings? Moreover, can models of physical processes explain our mental lives? Does mentality require a special level of rational or cognitive explanation, or is it best understood in terms of overall brain functioning or neuronal or molecular or even quantum activities – or any of a dozen levels of physical explanation in between? Also, regardless of how they compare with explanations cast at physical levels, what is the status of psychological explanations that appeal fundamentally to mental contents? (shrink)

The notion of levels has been widely used in discussions of cognitive science, especially in discussions of the relation of connectionism to symbolic modeling of cognition. I argue that many of the notions of levels employed are problematic for this purpose, and develop an alternative notion grounded in the framework of mechanistic explanation. By considering the source of the analogies underlying both symbolic modeling and connectionist modeling, I argue that neither is likely to provide an adequate analysis (...) of processes at the level at which cognitive theories attempt to function: One is drawn from too low a level, the other from too high a level. If there is a distinctly cognitive level, then we still need to determine what are the basic organizational principles at that level. (shrink)

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build arti cial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the (...) comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the fi nal part of the paper further directions of research will be explored, trying to address current limitations and future challenges. (shrink)

Cognitive science began as a multidisciplinary endeavor to understand how the mind works. Since the beginning, cognitive scientists have been asking questions about the right methodologies and levels of explanation to pursue this goal, and make cognitive science a coherent science of the mind. Key questions include: Is there a privileged level of explanation in cognitive science? How do different levels of explanation fit together, or relate to one another? How should explanations at one level inform or constrain (...) explanations at some other level? Can the different approaches to the mind, brain, and culture be unified? The aim of this issue of topiCS is to provide a platform for discussing different answers to such questions and to facilitate a better understanding between the different strands of thinking about the right levels of explanation in cognitive science. (shrink)

David Marr provided an influential account of levels of description in classical cognitive science. In this paper we contrast Marr'ent with some alternatives that are suggested by the recent emergence of connectionism. Marr's account is interesting and important both because of the levels of description it distinguishes, and because of the way his presentation reflects some of the most basic, foundational, assumptions of classical AI-style cognitive science . Thus, by focusing on levels of description, one can sharpen (...) foundational differences between classicism and potential non-classical conceptions of mentality that might emerge under the rubric of connectionism. (shrink)

We suggest that the hierarchical predictive processing account detailed by Clark can be usefully integrated with narrative psychology by situating personal narratives at the top of an individual's knowledge hierarchy. Narrative representations function as high-level generative models that direct our attention and structure our expectations about unfolding events. Implications for integrating scientific and humanistic views of human experience are discussed.

Over the past century or more, cognitive neuropsychologists have discussed many of the issues raised in this volume. On the basis of this literature, we argue that autism is not a single homogeneous condition, and so can have no single cause. Instead, each of its symptoms has a cause, and the proper study of autism is the separate study of each of these symptoms and its cause. We also offer evidence to support the radical view advanced by Stoljar and Gold (...) in this volume, namely, that in explaining such syndromes the cognitive level of explanation has priority over the neuroscientific. (shrink)

Thirty years after the publication of Marr's seminal book Vision the papers in this topic consider the contemporary status of his influential conception of three distinct levels of analysis for information-processing systems, and in particular the role of the algorithmic and representational level with its cognitive-level concepts. This level has been downplayed or eliminated both by reductionist neuroscience approaches from below that seek to account for behavior from the implementation level and by Bayesian approaches from above that seek to (...) account for behavior in purely computational-level terms. (shrink)

Over the past century or more, cognitive neuropsychologists have discussed many of the issues raised in this volume. On the basis of this literature, we argue that autism is not a single homogeneous condition, and so can have no single cause. Instead, each of its symptoms has a cause, and the proper study of autism is the separate study of each of these symptoms and its cause. We also offer evidence to support the radical view advanced by Stoljar and Gold (...) in this volume, namely, that in explaining such syndromes the cognitive level of explanation has priority over the neuroscientific. (shrink)

This informative and conceptually stimulating target article is very useful. I merely query whether the term “illusory contours” is appropriate for gap filling; “illusory surfaces” seems better – and “fictional surfaces” better still. These seem to be rule based rather than knowledge based, suggesting indeed the importance of distinguishing rules (analogous to syntax in language) from knowledge (equivalent to semantics) for classifying perceptual phenomena.

This paper considers the question of whether the influential distinction between levels of explanation introduced by David Marr can be used as a general framework for contemplating levels of explanation in cognitive sciences. Marr introduced three levels at which we can explain cognitive processes: the computational, algorithmic, and implementational levels. Some argue that Marr’s levels of explanation can only be applied to modular cognitive systems. However, since many psychological processes are non-modular, it seems that Marr’s (...) levels of explanation cannot explain such psychological processes. To show that the latter claim is not convincing, the paper draws upon an influential paradigm from cognitive sciences that is based on the principle of free energy. Based on this paradigm, the paper argues that even non-modular psychological processes can be computationally analyzed and algorithmically implemented. The conclusion of the paper is that, at least under the assumption that the func- tion of the mind is to minimize free energy, Marr’s levels of explanation can be successfully used as a general framework for understanding psychological processes at different levels of description. (shrink)

We consider approaches to explanation within the cognitive sciences that begin with Marr's computational level or Marr's implementational level and argue that each is subject to fundamental limitations which impair their ability to provide adequate explanations of cognitive phenomena. For this reason, it is argued, explanation cannot proceed at either level without tight coupling to the algorithmic and representation level. Even at this level, however, we argue that additional constraints relating to the decomposition of the cognitive system into a set (...) of interacting subfunctions are required. Integrated cognitive architectures that permit abstract specification of the functions of components and that make contact with the neural level provide a powerful bridge for linking the algorithmic and representational level to both the computational level and the implementational level. (shrink)

Philosophers of mind commonly draw a distinction between the personal level – the distinctive realm of conscious experience and reasoned deliberation – and the subpersonal level, the domain of mindless mechanism and brute cause and effect. Moreover, they tend to view cognitive science through the lens of this distinction. Facts about the personal level are given a priori, by introspection, or by common sense; the job of cognitive science is merely to investigate the mechanistic basis of these facts. I argue (...) that this view misrepresents the structure of cognitive-scientific enquiry. Taken at face value, cognitive science makes no commitment to the existence of a distinctive level at which persons or selves appear. Thus, in the age of cognitive science, we should not expect to find the self in an ontologically distinct realm. Instead, we should expect to locate it in cognitive-scientific models themselves. In closing, I indicate likely results of this approach. (shrink)

This article probes the usefulness of selected theories from Cognitive Linguistics in the context of statutory interpretation. The presumption of legal language is a well-established rule of statutory construction in Polish legal practice that comes from the internationally recognised theory by Jerzy Wróblewski. It rests on a controversial assumption that there are different levels of generality in legal language (i.e. the language of statutes) and a single term may be given different meanings depending on the level of generality that (...) is referred to. The authors use the notion of basic level categorisation from the cognitive theory of prototypes to explain this mechanism. Legal concepts, not unlike ordinary concepts, form hierarchical taxonomies. A single term may denote concepts that are situated at different levels in different conceptual taxonomies. In the absence of special circumstances, a legal term should be given the meaning that resides at a higher level of categorisation in the respective legal taxonomy. This way, the presumption of legal language is found to be justified on both linguistic and psychological grounds. (shrink)

This paper focuses on the cognitive/computational and evolutionary levels. It describes three proposals to make cognition computationally tractable, namely: Resource Rationality, the Adaptive Toolbox and Massive Modularity. While each of these proposals appeals to evolutionary considerations to dissolve the intractability of cognition, Rich, Blokpoel, de Haan, and van Rooij argue that, in each case, the intractability challenge is not resolved, but just relocated to the level of evolution.

This paper focuses on the cognitive/computational and evolutionary levels. It describes three proposals to make cognition computationally tractable, namely: Resource Rationality, the Adaptive Toolbox and Massive Modularity. While each of these proposals appeals to evolutionary considerations to dissolve the intractability of cognition, Rich, Blokpoel, de Haan, and van Rooij argue that, in each case, the intractability challenge is not resolved, but just relocated to the level of evolution.

During the last decades, many cognitive architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR [35]) adopt a classical symbolic approach, some (e.g. LEABRA[ 48]) are based on a purely connectionist model, while others (e.g. CLARION [59]) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning (...) are also available [34]. In this paper we propose a reflection on the role that Conceptual Spaces, a framework developed by Peter G¨ardenfors [24] more than fifteen years ago, can play in the current development of the Knowledge Level in Cognitive Systems and Architectures. In particular, we claim that Conceptual Spaces offer a lingua franca that allows to unify and generalize many aspects of the symbolic, sub-symbolic and diagrammatic approaches (by overcoming some of their typical problems) and to integrate them on a common ground. In doing so we extend and detail some of the arguments explored by G¨ardenfors [23] for defending the need of a conceptual, intermediate, representation level between the symbolic and the sub-symbolic one. In particular we focus on the advantages offered by Conceptual Spaces (w.r.t. symbolic and sub-symbolic approaches) in dealing with the problem of compositionality of representations based on typicality traits. Additionally, we argue that Conceptual Spaces could offer a unifying framework for interpreting many kinds of diagrammatic and analogical representations. As a consequence, their adoption could also favor the integration of diagrammatical representation and reasoning in CAs. (shrink)

This Research Topic aimed at deepening our understanding of the levels and explanations that are of interest for cognitive sci- entists, neuroscientists, psychologists, behavioral scientists, and philosophers of science. Indeed, contemporary developments in neuroscience and psy- chology suggest that scientists are likely to deal with a multiplicity of levels, where each of the different levels entails laws of behavior appropriate to that level (Berntson et al., 2012). Also, gathering and modeling data at the different levels of (...) analysis is not suffi- cient: the integration of information across levels of analysis is a crucial issue. Given such state of affairs, a number of interesting questions arise. How can the autonomy of explanatory levels be properly understood in behavioral explanation? Is reductionism a satis- factory strategy? How can high-level and low-level models be constrained in order to be actually explanatory of both behav- ioral and neurological or molecular evidence? What is the kind of relationship between those models? (shrink)

This paper connects information with computation and cognition via concept of agents that appear at variety of levels of organization of physical/chemical/cognitive systems – from elementary particles to atoms, molecules, life-like chemical systems, to cognitive systems starting with living cells, up to organisms and ecologies. In order to obtain this generalized framework, concepts of information, computation and cognition are generalized. In this framework, nature can be seen as informational structure with computational dynamics, where an (info-computational) agent is (...) needed for the potential information of the world to actualize. Starting from the definition of information as the difference in one physical system that makes a difference in another physical system – which combines Bateson and Hewitt’s definitions, the argument is advanced for natural computation as a computational model of the dynamics of the physical world, where information processing is constantly going on, on a variety of levels of organization. This setting helps us to elucidate the relationships between computation, information, agency and cognition, within the common conceptual framework, with special relevance for biology and robotics. (shrink)

I begin by tracing some of the confusions regarding levels and reduction to a failure to distinguish two different principles according to which theories can be viewed as hierarchically arranged — epistemic authority and ontological constitution. I then argue that the notion of levels relevant to the debate between symbolic and connectionist paradigms of mental activity answers to neither of these models, but is rather correlative to the hierarchy of functional decompositions of cognitive tasks characteristic of homuncular functionalism. (...) Finally, I suggest that the incommensurability of the intentional and extensional vocabularies constitutes a strongprima facie reason to conclude that there is little likelihood of filling in the story of Bechtel''s missing level in such a way as to bridge the gap between such homuncular functionalism and his own model of mechanistic explanation. (shrink)

Marr’s celebrated contribution to cognitive science (Marr 1982, chap. 1) was the introduction of (at least) three levels of description/explanation. However, most contemporary research has relegated the distinction between levels to a rather dispensable remark. Ignoring such an important contribution comes at a price, or so we shall argue. In the present paper, first we review Marr’s main points and motivations regarding levels of explanation. Second, we examine two cases in which the distinction between levels has (...) been neglected when considering the structure of mental representations: Cummins et al.’s distinction between structural representation and encodings (Cummins in Journal of Philosophy, 93(12):591–614, 1996; Cummins et al. in Journal of Philosophical Research, 30:405–408, 2001) and Fodor’s account of iconic representation (Fodor 2008). These two cases illustrate the kind of problems in which researchers can find themselves if they overlook distinctions between levels and how easily these problems can be solved when levels are carefully examined. The analysis of these cases allows us to conclude that researchers in the cognitive sciences are well advised to avoid risks of confusion by respecting Marr’s old lesson. (shrink)

In its origins educational neuroscience has started as an endeavor to discuss implications of neuroscience studies for education. However, it is now on its way to become a transdisciplinary field, incorporating findings, theoretical frameworks and methodologies from education, and cognitive and brain sciences. Given the differences and diversity in the originating disciplines, it has been a challenge for educational neuroscience to integrate both theoretical and methodological perspective in education and neuroscience in a coherent way. We present a multi-level framework for (...) educational neuroscience, which argues for integration of multiple levels of analysis, some originating in brain and cognitive sciences, others in education, as a roadmap for the future of educational neuroscience with concrete examples in moral education. (shrink)

The dominant assumptions -- throughout contemporary philosophy, psychology, cognitive science, and artificial intelligence -- about the ontology underlying intentionality, and its core of representationality, is that of encodings -- some sort of informational or correspondence or covariation relationship between the represented and its representation that constitutes that representational relationship. There are many disagreements concerning details and implementations, and even some suggestions about claimed alternative ontologies, such as connectionism (though none that escape what I argue is the fundamental flaw in these (...) dominant approaches). One assumption that seems to be held by all, however, usually without explication or defense, is that there is _one_ singular underlying ontology to representationality. In this paper, I argue that there are in fact quite a number of ontologies that manifest representationality -- levels of representationality -- and that _none_ of them are the standard "manipulations of encoded symbols" ontology, nor any other variation on the informational approach to representation. Collectively, these multiple representational ontologies constitute a framework for cognition, whether natural or artificial. (shrink)

This monograph provides a new account of justified inference as a cognitive process. In contrast to the prevailing tradition in epistemology, the focus is on low-level inferences, i.e., those inferences that we are usually not consciously aware of and that we share with the cat nearby which infers that the bird which she sees picking grains from the dirt, is able to fly. Presumably, such inferences are not generated by explicit logical reasoning, but logical methods can be used to describe (...) and analyze such inferences. Part 1 gives a purely system-theoretic explication of belief and inference. Part 2 adds a reliabilist theory of justification for inference, with a qualitative notion of reliability being employed. Part 3 recalls and extends various systems of deductive and nonmonotonic logic and thereby explains the semantics of absolute and high reliability. In Part 4 it is proven that qualitative neural networks are able to draw justified deductive and nonmonotonic inferences on the basis of distributed representations. This is derived from a soundness/completeness theorem with regard to cognitive semantics of nonmonotonic reasoning. The appendix extends the theory both logically and ontologically, and relates it to A. Goldman's reliability account of justified belief. This text will be of interest to epistemologists and logicians, to all computer scientists who work on nonmonotonic reasoning and neural networks, and to cognitive scientists. (shrink)

Framed by social identity and cognitive moral reasoning theories, this paper assessed Gen Zs' level of principled moral reasoning, discovered during their undergraduate college years, and influences revealed by demographic variations using the Moral Reasoning Inventory. We discovered that Gen Zs' principled moral reasoning was lower than research reporting on Millennials, but not as dramatic a decline as scholars reported when investigating Baby Boomers, Generation Xers, and Millennials' Pscores. We found some differences within our sample based on business discipline (academic (...) major), the most powerful influence on Gen Zs' principled moral reasoning, followed by gender, work experience, and academic intelligence (GPA). Years in an academic program were not seen as influencing Gen Zs' principled moral reasoning. Overall, we conclude that Gen Zs may not exhibit a homogenous ethical identity as measured by their level of principled moral reasoning. Implications of these findings are discussed. (shrink)

Quite a few recent models are rapidly introducing new concepts describing different levels of consciousness. This situation is getting confusing because some theorists formulate their models without making reference to existing views, redundantly adding complexity to an already difficult problem. In this paper, I present and compare nine neurocognitive models to highlight points of convergence and divergence. Two aspects of consciousness seem especially important: perception of self in time and complexity of self-representations. To this I add frequency of self-focus, (...) amount of self-related information, and accuracy of self-knowledge. Overall, I conclude that many novel concepts (e.g., reflective, primary, core, extended, recursive, and minimal consciousness) are useful in helping us distinguish between delicate variations in consciousness and in clarifying theoretical issues that have been intensely debated in the scientific literature—e.g., consciousness in relation to mirror self-recognition and language. Ó 2005 Elsevier Inc. All rights reserved. (shrink)

There is a general conception of levels in philosophy which says that the world is arrayed into a hierarchy of levels and that there are different modes of analysis that correspond to each level of this hierarchy, what can be labelled the ‘Hierarchical Correspondence View of Levels” (or HCL). The trouble is that despite its considerable lineage and general status in philosophy of science and metaphysics the HCL has largely escaped analysis in specific domains of inquiry. The (...) goal of this paper is to take up a recent call to domain-specificity by examining the role of the HCL in cognitive science. I argue that the HCL is, in fact, a conception of levels that has been employed in cognitive science and that cognitive scientists should avoid its use where possible. The argument is that the HCL is problematic when applied to cognitive science specifically because it fails to distinguish two important kinds of shifts used when analysing information processing systems: shifts in grain and shifts in analysis. I conclude by proposing a revised version of the HCL which accommodates the distinction. (shrink)

This paper examines explanations for human artistic behavior in two reductionist research programs, cognitive neuroscience and evolutionary psychology. Despite their different methodological outlooks, both approaches converge on an explanation of art production and appreciation as byproducts of normal perceptual and motivational cognitive skills that evolved in response to problems originally not related to art, such as the discrimination of salient visual stimuli and speech sounds. The explanatory power of this reductionist framework does not obviate the need for higher-level accounts of (...) art from the humanities, such as aesthetics, art history or anthropology of art. (shrink)

A 3rd person Knowledge Level analysis of cognitive architectures -/- Abstract I provide a knowledge level analysis of the main representational and reasoning problems affecting the cognitive architectures for what concerns this issue. In providing this analysis I will show, by considering some of the main cognitive architectures currently available (e.g. SOAR, ACT-R, CLARION), how one of the main problems of such architectures is represented by the fact that their knowledge representation and processing mechanisms are not sufficiently constrained with “structural (...) insights” (Lieto 2021) coming from cognitive science for dealing with commonsense knowledge and reasoning (Lebiere, Oltramari, 2018). As a possible way out to such knowledge processing issues, I present the main assumptions that have led to the development of the Dual PECCS categorization system (Lieto, Radicioni, Rho 2017) and discuss some of the lessons learned and their possible implications in the design of the knowledge modules and knowledge-processing mechanisms of integrated cognitive architectures. (shrink)

A modern classic, this powerful and sophisticated account of embodiment was first published in German in 1928 and now appears in English for the first time. With reference simultaneously to science, social theory, and philosophy, Plessner shows how life can be seen on its own terms to establish its own boundaries. Plessner's account of how the human establishes itself in relation to the nonhuman will invigorate a range of current conversations around the animal, posthumanism, the material turn, and the biology (...) and sociology of cognition. (shrink)

In philosophy, there is an as yet unresolved discussion on whether there are different kinds of kinds and what those kinds are. In particular, there is a distinction between indifferent kinds, which are unaffected by observation and representation, and interactive kinds, which respond to being studied in ways that alter the very kinds under study. This is in essence a discussion on ontologies and, I argue, more precisely about ontological levels. The discussion of kinds of kinds can be resolved (...) by using a semiotic approach to ontological levels, building on the key semiotic concept of representation. There are three, and only three, levels of semiosis: non- or proto-semiotic processes without representation, such as physical or causal processes; semiotic processes with representation, such as the processes of life and cognition; and second-order semiotic processes with representation of representation, such as self-awareness and self-reflexive communication. This leads to the distinction between not two, but three kinds of kinds: indifferent, adaptive and reflexive kinds, of which the last two hitherto have not been clearly distinguished. (shrink)

When do children become aware of themselves as differentiated and unique entity in the world? When and how do they become self-aware? Based on some recent empirical evidence, 5 levels of self-awareness are presented and discussed as they chronologically unfold from the moment of birth to approximately 4-5 years of age. A natural history of children's developing self-awareness is proposed as well as a model of adult self-awareness that is informed by the dynamic of early development. Adult self-awareness is (...) viewed as the dynamic flux between basic levels of consciousness that develop chronologically early in life. (shrink)

Two main theories about metacognition are reviewed, each of which claims to provide a better explanation of this phenomenon, while discrediting the other theory as inappropriate. The paper claims that in order to do justice to the complex phenomenon of metacognition, we must distinguish two levels of this capacity—each having a different structure, a different content and a different function within the cognitive architecture. It will be shown that each of the reviewed theories has been trying to explain only (...) one of the two levels and that, consequently, the conflict between them can be dissolved. The paper characterizes the high-level as a rationalizing level where the subject uses concepts and theories to interpret her own behavior and the low-level as a controlling level where the subject exploits epistemic feelings to adjust her cognitive activities. Finally, the paper explores three kinds of interaction between the levels. (shrink)

For most philosophers of psychology and cognitive science, inquiry into human cognitive activity begins at the level of intrapersonal processes. A central question is whether these processes are sufficiently autonomous from more basic neurophysiological processes to be investigated in their own terms, or whether all explanations must be in neurophysiological terms. Some philosophers have insisted on the relative autonomy of the cognitive level. One currently quite popular view, eliminative materialism, however, holds that the explanations that have been advanced at the (...) intrapersonal cognitive level are misguided, and must be replaced by accounts developed from an understanding of the underlying neurophysiological processes. (shrink)

Quite a few recent models are rapidly introducing new concepts describing different levels of consciousness. This situ- ation is getting confusing because some theorists formulate their models without making reference to existing views, redun- dantly adding complexity to an already difficult problem. In this paper, I present and compare nine neurocognitive models to highlight points of convergence and divergence. Two aspects of consciousness seem especially important: perception of self in time and complexity of self-representations. To this I add frequency (...) of self-focus, amount of self-related informa- tion, and accuracy of self-knowledge. Overall, I conclude that many novel concepts (e.g., reflective, primary, core, extend- ed, recursive, and minimal consciousness) are useful in helping us distinguish between delicate variations in consciousness and in clarifying theoretical issues that have been intensely debated in the scientific literature—e.g., consciousness in rela- tion to mirror self-recognition and language. (shrink)

Intermediate constructs are required as bridges between complex behaviors and realistic models of neural circuitry. For cognitive scientists in general, schemas are the appropriate functional units; brain theorists can work with neural layers as units intermediate between structures subserving schemas and small neural circuits.After an account of different levels of analysis, we describe visuomotor coordination in terms of perceptual schemas and motor schemas. The interest of schemas to cognitive science in general is illustrated with the example of perceptual schemas (...) in high-level vision and motor schemas in the control of dextrous hands.Rana computatrix, the computational frog, is introduced to show how one constructs an evolving set of model families to mediate flexible cooperation between theory and experiment. Rana computatrix may be able to do for the study of the organizational principles of neural circuitry what Aplysia has done for the study of subcellular mechanisms of learning. Approach, avoidance, and detour behavior in frogs and toads are analyzed in terms of interacting schemas. Facilitation and prey recognition are implemented as tectal-pretectal interactions, with the tectum modeled by an array of tectal columns. We show how layered neural computation enters into models of stereopsis and how depth schemas may involve the interaction of accommodation and binocular cues in anurans. (shrink)