Where is imagination in imaginative resistance? We seek to answer this question by connecting two ongoing lines of inquiry in different subfields of philosophy. In philosophy of mind, philosophers have been trying to understand imaginative attitudes’ place in cognitive architecture. In aesthetics, philosophers have been trying to understand the phenomenon of imaginative resistance. By connecting these two lines of inquiry, we hope to find mutual illumination of an attitude (or cluster of attitudes) and a phenomenon that have vexed (...) philosophers. Our strategy is to reorient the imaginative resistance literature from the perspective of cognitive architecture. Whereas existing taxonomies of positions in the imaginative resistance literature have focused on disagreements over the source and scope of the phenomenon, our taxonomy focuses on the psychological components necessary for explaining imaginative resistance. (shrink)

Cognitive architectures - task-general theories of the structure and function of the complete cognitive system - are sometimes argued to be more akin to frameworks or belief systems than scientific theories. The argument stems from the apparent non-falsifiability of existing cognitive architectures. Newell was aware of this criticism and argued that architectures should be viewed not as theories subject to Popperian falsification, but rather as Lakatosian research programs based on cumulative growth. Newell's argument is undermined because he (...) failed to demonstrate that the development of Soar, his own candidate architecture, adhered to Lakatosian principles. This paper presents detailed case studies of the development of two cognitive architectures, Soar and ACT-R, from a Lakatosian perspective. It is demonstrated that both are broadly Lakatosian, but that in both cases there have been theoretical progressions that, according to Lakatosian criteria, are pseudo-scientific. Thus, Newell's defense of Soar as a scientific rather than pseudo-scientific theory is not supported in practice. The ACT series of architectures has fewer pseudo-scientific progressions than Soar, but it too is vulnerable to accusations of pseudo-science. From this analysis, it is argued that successive versions of theories of the human cognitive architecture must explicitly address five questions to maintain scientific credibility. (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)

As we know, a cognitive architecture is a domain-generic computational cognitive model that may be used for a broad analysis of cognition and behavior. Cognitive architectures embody theories of cognition in computer algorithms and programs. Social simulation with multi-agent systems can benefit from incorporating cognitive architectures, as they provide a realistic basis for modeling individual agents (as argued in Sun 2001). In this survey, an example cognitive architecture will be given, and its application (...) to social simulation will be sketched. (shrink)

_Abstract_: Rather than seeking a common architecture for cognitive processing, this paper argues that we should recognize that the brain employs multiple information processing structures. Many of these are manifest in brain areas outside the neocortex such as the hypothalamus, brain stem pattern generators, the basal ganglia, and various nuclei releasing neuromodulators. Rather than employing one mode of information processing, the brain employs multiple modes integrated in a heterarchical network. These in turn affect processing within the neocortex and (...) together with the neocortex regulate vertebrate behavior, including human. Cognitive science can better understand human information processing by attending to the plurality of information-processing architectures employed in the brain. _Keywords_: Basal Ganglia; Cognitive Architectures; Heterarchical Networks; Hypothalamus; Pattern Generators; Neuromodulators _Ripensare l’architettura cognitiva: una rete eterarchica di differenti tipi di elaborazione di informazione_ _Riassunto_: Anziché cercare un’architettura comune ai processi cognitivi, in questo lavoro si sosterrà che dovremmo riconoscere come il cervello impieghi molteplici strutture per processare l’informazione. Molte di loro si trovano nelle aree cerebrali esterne alla neocorteccia, come l’ipotalamo, i generatori di pattern del tronco encefalico, i gangli basali e i vari nuclei che rilasciano i neuromodulatori. Anziché impiegare un’unica modalità per l’elaborazione dell’informazione, il cervello usa molteplici modalità integrate in una rete eterarchica. Queste, a loro volta, influenzano i processi all’interno della neocorteccia e, assieme alla neocorteccia, regolano il comportamento dei vertebrati, compreso quello umano. La scienza cognitiva può meglio comprendere l’elaborazione dell’informazione da parte degli esseri umani concentrandosi sulla pluralità delle architetture impiegate nel cervello per realizzare questa elaborazione. _Parole chiave_: Gangli basali; Architetture cognitive; Reti eterarchiche; Ipotalamo; Generatori di pattern; Neuromodulatori. (shrink)

"Cognitive Architecture" asks how evolving modalities--from bio-politics to "noo-politics"--can be mapped upon the city under contemporary conditions of urbanization and globalization. Noo-politics, most broadly understood as the power exerted over the life of the mind, reconfigures perception, memory and attention, and also implicates potential ways and means by which neurobiological architecture is undergoing reconfiguration. This volume, motivated by theories such as 'cognitive capitalism' and concepts such as 'neural plasticity, ' shows how architecture and urban processes (...) and products commingle to form complex systems that produce novel forms of networks that empower the imagination and constitute the cultural landscape. This volume rethinks the relations between form and forms of communication, calling for a new logic of representation; it examines the manner in which information, with its non-hierarchical and distributed format is contributing both to the sculpting of brain and production of mind. "Cognitive Architecture" brings together renowned specialists in the areas of political and aesthetic philosophy, neuroscience, socio-cultural and architecture theory, visual and spatial theorists and practitioners. (shrink)

This chapter contains sections titled: The Systematicity of Inference The Systematicity of Cognitive Representations The Compositionality of Representations Another Systematicity Argument Can Functional Combinatorialism Explain the Systematic Relations in Thought? Conclusion.

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)

Diagrams are a form of spatial representation that supports reasoning and problem solving. Even when diagrams are external, not to mention when there are no external representations, problem solving often calls for internal representations, that is, representations in cognition, of diagrammatic elements and internal perceptions on them. General cognitive architectures—Soar and ACT-R, to name the most prominent—do not have representations and operations to support diagrammatic reasoning. In this article, we examine some requirements for such internal representations and processes in (...) cognitive architectures. We discuss the degree to which DRS, our earlier proposal for such an internal representation for diagrams, meets these requirements. In DRS, the diagrams are not raw images, but a composition of objects that can be individuated and thus symbolized, while, unlike traditional symbols, the referent of the symbol is an object that retains its perceptual essence, namely, its spatiality. This duality provides a way to resolve what anti-imagists thought was a contradiction in mental imagery: the compositionality of mental images that seemed to be unique to symbol systems, and their support of a perceptual experience of images and some types of perception on them. We briefly review the use of DRS to augment Soar and ACT-R with a diagrammatic representation component. We identify issues for further research. (shrink)

This paper proposes a brain-inspired cognitive architecture that incorporates approximations to the concepts of consciousness, imagination, and emotion. To emulate the empirically established cognitive efficacy of conscious as opposed to non-conscious information processing in the mammalian brain, the architecture adopts a model of information flow from global workspace theory. Cognitive functions such as anticipation and planning are realised through internal simulation of interaction with the environment. Action selection, in both actual and internally simulated interaction with (...) the environment, is mediated by affect. An implementation of the architecture is described which is based on weightless neurons and is used to control a simulated robot. (shrink)

Human vision supports social perception by efficiently detecting agents and extracting rich information about their actions, goals, and intentions. Here, we explore the cognitive architecture of perceived animacy by constructing Bayesian models that integrate domain‐specific hypotheses of social agency with domain‐general cognitive constraints on sensory, memory, and attentional processing. Our model posits that perceived animacy combines a bottom–up, feature‐based, parallel search for goal‐directed movements with a top–down selection process for intent inference. The interaction of these architecturally distinct (...) processes makes perceived animacy fast, flexible, and yet cognitively efficient. In the context of chasing, in which a predator (the “wolf”) pursues a prey (the “sheep”), our model addresses the computational challenge of identifying target agents among varying numbers of distractor objects, despite a quadratic increase in the number of possible interactions as more objects appear in a scene. By comparing modeling results with human psychophysics in several studies, we show that the effectiveness and efficiency of human perceived animacy can be explained by a Bayesian ideal observer model with realistic cognitive constraints. These results provide an understanding of perceived animacy at the algorithmic level—how it is achieved by cognitive mechanisms such as attention and working memory, and how it can be integrated with higher‐level reasoning about social agency. (shrink)

The central contention of The Implicit Mind is that understanding the two faces of spontaneity-its virtues and vices-requires understanding the "implicit mind." In turn, Michael Brownstein maintains that understanding the implicit mind requires the consideration of three sets of questions. First, what are implicit mental states? What kind of cognitive structure do they have? Second, how should we relate to our implicit attitudes? Are we responsible for them? Third, how can we improve the ethics of our implicit minds?

In this paper we compare two theories about the cognitive architecture underlying morality. One theory, proposed by Sripada and Stich (forthcoming), posits an interlocking set of innate mechanisms that internalize moral norms from the surrounding community and generate intrinsic motivation to comply with these norms and to punish violators. The other theory, which we call the M/C model was suggested by the widely discussed and influential work of Elliott Turiel, Larry Nucci and others on the “moral/conventional task”. This (...) theory posits two distinct mental domains, the moral and the conventional, each of which gives rise to a characteristic suite of judgments about rules in that domain and about transgressions of those rules. We give an overview of both theories and of the data each was designed to explain. We go on to consider a growing body of evidence that suggests the M/C model is mistaken. That same evidence, however, is consistent with the Sripada and Stich theory. Thus, we conclude that the M/C model does not pose a serious challenge for the Sripada and Stich theory. (shrink)

The dynamic approach to understanding of the human consciousness, its cognitive activities and cognitive architecture is one of the most promising approaches in the modern epistemology and cognitive science. The conception of embodied mind is under discussion in the light of nonlinear dynamics and of the idea co-evolution of complex systems developed by the Moscow scientific school. The cognitive architecture of the embodied mind is rather complex: data from senses and products of rational thinking, (...) the verbal and the pictorial, logic and intuition, the analytical and synthetic abilities of perception and of thinking, the local and the global, the analogue and the digital, the archaic and the post-modern are intertwined in it. In the process of cognition, co-evolution of embodied mind as an autopoietic system and its surroundings takes place. The perceptual and mental processes are bound up with the structure of human body. Nonlinear and circular connecting links between the subject of cognition and the world constructed by him can be metaphorically called a nonlinear cobweb of cognition. Cognition is an autopoietic activity because it is directed to the search of elements that are missed; it serves to completing integral structures. According to the theory of blow-up regimes in complex systems elaborated by Sergey P.Kudyumov and his followers, the idea of co-evolution is connected with the concept of tempoworlds. To co-evolve means to start to develop in one and the same tempoworld and to use the possibility – in case of a proper intergation into a whole structure – to accelerate the tempo of evolution. The cognitive activities of the human being can be considered as a movement (active walk) in landscapes of co-evolution when he cognizes and changes environment and is changed himself by the very activities. The similar conclusion can be drawn from Francisco Varela’s conception of enactive cognition. (shrink)

This paper explores the difference between Connectionist proposals for cognitive a r c h i t e c t u r e a n d t h e s o r t s o f m o d e l s t hat have traditionally been assum e d i n c o g n i t i v e s c i e n c e . W e c l a i m t h a t t (...) h e m a j o r d i s t i n c t i o n i s t h a t , w h i l e b o t h Connectionist and Classical architectures postulate representational mental states, the latter but not the former are committed to a symbol-level of representation, or to a ‘language of thought’: i.e., to representational states that have combinatorial syntactic and semantic structure. Several arguments for combinatorial structure in mental representations are then reviewed. These include arguments based on the ‘systematicity’ of mental representation: i.e., on the fact that cognitive capacities always exhibit certain symmetries, so that the ability to entertain a given thought implies the ability to entertain thoughts with semantically related contents. We claim that such arguments make a powerful case that mind/brain architecture is not Connectionist at the cognitive level. We then consider the possibility that Connectionism may provide an account of the neural (or ‘abstract neurological’) structures in which Classical cognitive architecture is implemented. We survey a n u m b e r o f t h e s t a n d a r d a r g u m e n t s t h a t h a v e b e e n o f f e r e d i n f a v o r o f Connectionism, and conclude that they are coherent only on this interpretation. (shrink)

Cognitive architectures, like programming languages, make commitments only at the implementation level and have limited explanatory power. Their universality implies that it is hard, if not impossible, to justify them in detail from finite quantities of data. It is more fruitful to focus on particular tasks such as language understanding and propose testable theories at the computational and algorithmic levels.

Cognitive architectures (i.e., theorized blueprints on the structure of the mind) can be used to make predictions about the effect of multiregion brain activity on the systems level. Recent work has connected one high-level cognitive architecture, known as the “Common Model of Cognition,” to task-based functional MRI data with great success. That approach, however, was limited in that it was intrinsically top-down, and could thus only be compared with alternate architectures that the experimenter could contrive. In this (...) paper, we propose a bottom-up method to infer a cognitive architecture directly from brain imaging data itself, overcoming this limitation. Specifically, Granger causality modeling was applied to the same task-based fMRI data to infer a network of causal connections between brain regions based on their functional connectivity. The resulting network shares many connections with those proposed by the Common Model of Cognition but also suggests important additions likely related to the role of episodic memory. This combined top-down and bottom-up modeling approach can be used to help formalize the computational instantiation of cognitive architectures and further refine a comprehensive theory of cognition. (shrink)

Research in computational cognitive modeling investigates the nature of cognition through developing process-based understanding by specifying computational models of mechanisms (including representations) and processes. In this enterprise, a cognitive architecture is a domaingeneric computational cognitive model that may be used for a broad, multiple-level, multipledomain analysis of behavior. It embodies generic descriptions of cognition in computer algorithms and programs. Developing cognitive architectures is a difficult but important task. In this article, discussions of issues and challenges (...) in developing cognitive architectures will be undertaken, and an example cognitive architecture (CLARION) will be described. (shrink)

This volume rethinks the relations between form and forms of communication, calling for a new logic of representation; it examines the manner in which ...

As artificial intelligence (AI) thrives and propagates through modern life, a key question to ask is how to include humans in future AI? Despite human involvement at every stage of the production process from conception and design through to implementation, modern AI is still often criticized for its “black box” characteristics. Sometimes, we do not know what really goes on inside or how and why certain conclusions are met. Future AI will face many dilemmas and ethical issues unforeseen by their (...) creators beyond those commonly discussed (e.g., trolley problems and variants of it) and to which solutions cannot be hard-coded and are often still up for debate. Given the sensitivity of such social and ethical dilemmas and the implications of these for human society at large, when and if our AI make the “wrong” choice we need to understand how they got there in order to make corrections and prevent recurrences. This is particularly true in situations where human livelihoods are at stake (e.g., health, well-being, finance, law) or when major individual or household decisions are taken. Doing so requires opening up the “black box” of AI; especially as they act, interact, and adapt in a human world and how they interact with other AI in this world. In this article, we argue for the application of cognitive architectures for ethical AI. In particular, for their potential contributions to AI transparency, explainability, and accountability. We need to understand how our AI get to the solutions they do, and we should seek to do this on a deeper level in terms of the machine-equivalents of motivations, attitudes, values, and so on. The path to future AI is long and winding but it could arrive faster than we think. In order to harness the positive potential outcomes of AI for humans and society (and avoid the negatives), we need to understand AI more fully in the first place and we expect this will simultaneously contribute towards greater understanding of their human counterparts also. (shrink)

In cognitive science, the concept of dissociation has been central to the functional individuation and decomposition of cognitive systems. Setting aside debates about the legitimacy of inferring the existence of dissociable systems from ‘behavioural’ dissociation data, the main idea behind the dissociation approach is that two cognitive systems are dissociable, and thus viewed as distinct, if each can be damaged, or impaired, without affecting the other system’s functions. In this article, I propose a notion of functional independence (...) that does not require dissociability, and describe an approach to the functional decomposition and modelling of cognitive systems that complements the dissociation approach. I show that highly integrated cognitive and neurocognitive systems can be decomposed into non-dissociable but functionally independent components, and argue that this approach can provide a general account of cognitive specialization in terms of a stable structure–function relationship. 1 Introduction2 Functional Independence without Dissociability3 FI Systems and Cognitive Architecture4 FI Systems and Cognitive Specialization. (shrink)

This article addresses issues in developing cognitive architectures--generic computational models of cognition. Cognitive architectures are believed to be essential in advancing understanding of the mind, and therefore, developing cognitive architectures is an extremely important enterprise in cognitive science. The article proposes a set of essential desiderata for developing cognitive architectures. It then moves on to discuss in detail some of these desiderata and their associated concepts and ideas relevant to developing better cognitive architectures. It (...) argues for the importance of taking into full consideration these desiderata in developing future architectures that are more cognitively and ecologically realistic. A brief and preliminary evaluation of existing cognitive architectures is attempted on the basis of these ideas. (shrink)

Quantum probability (QP) theory provides an alternative account of empirical phenomena in decision making that classical probability (CP) theory cannot explain. Cognitive architectures combine probabilistic mechanisms with symbolic knowledge-based representations (e.g., heuristics) to address effects that motivate QP. They provide simple and natural explanations of these phenomena based on general cognitive processes such as memory retrieval, similarity-based partial matching, and associative learning.

For the last thirty years, cognitive scientists have attempted to describe the cognitive architecture of typically developing human beings, using, among other sources of evidence, the dissociations that result from developmental psychopathologies such as autism spectrum disorders, Williams syndrome, and Down syndrome. Thus, in his recent defense of the massive modularity hypothesis, Steven Pinker insists on the importance of such dissociations to identify the components of the typical cognitive architecture (2005, 4; my emphasis): This kind (...) of faculty psychology has numerous advantages (...). It is supported by the existence of neurological and genetic disorders that target these faculties unevenly, such as a difficulty in recognizing faces (and facelike shapes) but not other objects, or a difficulty in reasoning about minds but not about objects or pictures. Similarly, Simon Baron-Cohen writes (1998, 335; my emphasis; see also Temple, 1997): I suggest that the study of mental retardation would profit from the application of the framework of cognitive neuropsychology (…). In cognitive neuropsychology, one key question running through the investigator’s mind is “Is this process or mechanism intact or impaired in this person?” When cognitive neuropsychology is done well, a patient’s cognitive system is examined with specific reference to a model of the normal cognitive system. And, not infrequently, evidence from the patient’s cognitive deficits leads to a revision of the model of the normal system. However, in recent years, the use of developmental psychopathologies to identify the components of the typical cognitive architecture has come under heavy fire. In a series of influential articles, neuropsychologist Annette Karmiloff-Smith has argued that findings about the pattern of impairments and preserved capacities in people with developmental psychopathologies say nothing about the cognitive architecture of.. (shrink)

The “grand problem” of AI has always been to build artificial agents with human-like intelligence. That is the stuff of science fiction, but it is also the ultimate aspiration of AI. In retrospect, we can understand what a difficult problem this is, so since its inception AI has focused more on small manageable problems, with the hope that progress there will have useful implications for the grand problem. Now there is a resurgence of interest in tackling the grand problem head-on. (...) Perhaps AI has made enough progress on the little problems that we can fruitfully address the big problem. The objective is to build agents of human-level intelligence capable of operating in environments of real-world complexity. I will refer to these as GIAs — “generally intelligent agents”. OSCAR is a cognitive architecture for GIAs, implemented in LISP.1 OSCAR draws heavily on my work in philosophy concerning both epistemology (Pollock 1974, 1986, 1990, 1995, 1998, 2008b, 2008; Pollock and Cruz 1999; Pollock and Oved, 2005) and rational decision making (2005, 2006, 2006a). (shrink)

In this paper we propose a computational framework aimed at extending the problem solving capabilities of cognitive artificial agents through the introduction of a novel, goal-directed, dynamic knowledge generation mechanism obtained via a non monotonic reasoning procedure. In particular, the proposed framework relies on the assumption that certain classes of problems cannot be solved by simply learning or injecting new external knowledge in the declarative memory of a cognitive artificial agent but, on the other hand, require a mechanism (...) for the automatic and creative re-framing, or re-formulation, of the available knowledge. We show how such mechanism can be obtained trough a framework of dynamic knowledge generation that is able to tackle the problem of commonsense concept combination. In addition, we show how such a framework can be employed in the field of cognitive architectures in order to overcome situations like the impasse in SOAR by extending the possible options of its subgoaling procedures. (shrink)

Heuristics are often invoked in the philosophical, psychological, and cognitive science literatures to describe or explain methodological techniques or "shortcut" mental operations that help in inference, decision-making, and problem-solving. Yet there has been surprisingly little philosophical work done on the nature of heuristics and heuristic reasoning, and a close inspection of the way(s) in which "heuristic" is used throughout the literature reveals a vagueness and uncertainty with respect to what heuristics are and their role in cognition. This dissertation seeks (...) to remedy this situation by motivating philosophical inquiry into heuristics and heuristic reasoning, and then advancing a theory of how heuristics operate in cognition. I develop a positive working characterization of heuristics that is coherent and robust enough to account for a broad range of phenomena in reasoning and inference, and makes sense of empirical data in a systematic way. I then illustrate the work this characterization does by considering the sorts of problems that many philosophers believe heuristics solve, namely those resulting from the so-called frame problem. Considering the frame problem motivates the need to gain a better understanding of how heuristics work and the cognitive structures over which they operate. I develop a general theory of cognition which I argue underwrites the heuristic operations that concern this dissertation. I argue that heuristics operate over highly organized systems of knowledge, and I offer a cognitive architecture to accommodate this view. I then provide an account of the systems of knowledge that heuristics are supposed to operate over, in which I suggest that such systems of knowledge are concepts. The upshot, then, is that heuristics operate over concepts. I argue, however, that heuristics do not operate over conceptual content, but over metainformational relations between activated and primed concepts and their contents. Finally, to show that my thesis is empirically adequate, I consider empirical evidence on heuristic reasoning and argue that my account of heuristics explains the data. (shrink)

Agent-based social simulation (with multi-agent systems), which is an important aspect of social computing, can benefit from incorporating cognitive architectures, as they provide a realistic basis for modeling individual agents and therefore their social interactions. A cognitive architecture is a domain-generic computational cognitive model that may be used for a broad multiple-domain analysis of individual behavior. In this article, an example of a cognitive architecture is given, and its applications to social simulation described. Some (...) challenging issues in this regard are outlined. (shrink)

Two long-standing arguments in cognitive science invoke the assumption that holistic inference is computationally infeasible. The first is Fodor’s skeptical argument toward computational modeling of ordinary inductive reasoning. The second advocates modular computational mechanisms of the kind posited by Cosmides, Tooby and Sperber. Based on advances in machine learning related to Bayes nets, as well as investigations into the structure of scientific and ordinary information, I maintain neither argument establishes its architectural conclusion. Similar considerations also undermine Fodor’s decades-long diagnosis (...) of artificial intelligence research as confounded by an inability to circumscribe the amount of information relevant to inferential processes. This diagnosis is particularly inapposite with respect to Bayes nets, since one of their strengths as machine learning systems has been their capacity to reason probabilistically about large data sets whose size overwhelms the capacities of individual human reasoners. A general moral follows from these criticisms: Insights into artificial and human cognitive systems are likely to be cultivated by focusing greater attention on the structure and density of connections among items of information that are available to them. (shrink)

First published in 1998. Routledge is an imprint of Taylor & Francis, an informa company.

Cognitive architectures are theories of cognition that try to capture the essential representations and mechanisms that underlie cognition. Research in cognitive architectures has gradually moved from a focus on the functional capabilities of architectures to the ability to model the details of human behavior, and, more recently, brain activity. Although there are many different architectures, they share many identical or similar mechanisms, permitting possible future convergence. In judging the quality of a particular cognitive model, it is pertinent (...) to not just judge its fit to the experimental data but also its simplicity and ability to make predictions. (shrink)

It has been argued that dual process theories are not consistent with Oaksford and Chater’s probabilistic approach to human reasoning (Oaksford and Chater in Psychol Rev 101:608–631, 1994 , 2007 ; Oaksford et al. 2000 ), which has been characterised as a “single-level probabilistic treatment[s]” (Evans 2007 ). In this paper, it is argued that this characterisation conflates levels of computational explanation. The probabilistic approach is a computational level theory which is consistent with theories of general cognitive architecture (...) that invoke a WM system and an LTM system. That is, it is a single function dual process theory which is consistent with dual process theories like Evans’ ( 2007 ) that use probability logic (Adams 1998 ) as an account of analytic processes. This approach contrasts with dual process theories which propose an analytic system that respects standard binary truth functional logic (Heit and Rotello in J Exp Psychol Learn 36:805–812, 2010 ; Klauer et al. in J Exp Psychol Learn 36:298–323, 2010 ; Rips in Psychol Sci 12:29–134, 2001 , 2002 ; Stanovich in Behav Brain Sci 23:645–726, 2000 , 2011 ). The problems noted for this latter approach by both Evans Psychol Bull 128:978–996, ( 2002 , 2007 ) and Oaksford and Chater (Mind Lang 6:1–38, 1991 , 1998 , 2007 ) due to the defeasibility of everyday reasoning are rehearsed. Oaksford and Chater’s ( 2010 ) dual systems implementation of their probabilistic approach is then outlined and its implications discussed. In particular, the nature of cognitive decoupling operations are discussed and a Panglossian probabilistic position developed that can explain both modal and non-modal responses and correlations with IQ in reasoning tasks. It is concluded that a single function probabilistic approach is as compatible with the evidence supporting a dual systems theory. (shrink)

The aim of this paper is to understand the functional role of mental representations and intentionality in skilled actions from a systems related perspective. Therefore, we will evaluate the function of representation and then discuss the cognitive architecture of skilled actions in more depth. We are going to describe the building blocks and levels of the action system that enable us to control movements such as striking the tennis ball at the right time, or grasping tools in manual (...) action. Based on this theoretical understanding the measurement of mental representations and related research results concerning mental representation in skilled action are presented in an overview. This leads to the question how mental representations develop and change during learning. Finally, to consolidate the functional understanding of mental representation in skilled action and interaction, we provide examples how to use the measurement of mental representation in humans to inform technical systems. (shrink)

In this paper, I reinterpret Kant’s Transcendental Analytic as a description of a cognitive architecture. I describe a computer implementation of this architecture, and show how it has been applied to two unsupervised learning tasks. The resulting program is very data efficient, able to learn from a tiny handful of examples. I show how the program achieves data-efficiency: the constraints described in the Analytic of Principles are reinterpreted as strong prior knowledge, constraining the set of possible solutions.

Sober and Wilson have propose a cluster of arguments for the conclusion that “natural selection is unlikely to have given us purely egoistic motives” and thus that psychological altruism is true. I maintain that none of these arguments is convincing. However, the most powerful of their arguments raises deep issues about what egoists and altruists are claiming and about the assumptions they make concerning the cognitive architecture underlying human motivation.

The view that moral cognition is subserved by a two-tieredarchitecture is defended: Moral reasoning is the result both ofspecialized, informationally encapsulated modules which automaticallyand effortlessly generate intuitions; and of general-purpose,cognitively penetrable mechanisms which enable moral judgment in thelight of the agent's general fund of knowledge. This view is contrastedwith rival architectures of social/moral cognition, such as Cosmidesand Tooby's view that the mind is wholly modular, and it is argued thata two-tiered architecture is more plausible.

This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness. To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST). A general qualitative evaluation criteria (...) for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems. (shrink)

© 2004 by The Johns Hopkins University Press.

Recent work in cognitive neuroscience on the child's Theory of Mind (ToM) has pursued the idea that the ability to metarepresent mental states depends on a domain-specific cognitive subystem implemented in specific neural circuitry: a Theory of Mind Module. We argue that the interaction of several domain-general mechanisms and lower-level domain-specific mechanisms accounts for the flexibility and sophistication of behavior, which has been taken to be evidence for a domain-specific ToM module. This finding is of more general interest (...) since it suggests a parsimonious cognitive architecture can account for apparent domain specificity. We argue for such an architecture in two stages. First, on conceptual grounds, contrasting the case of language with ToM, and second, by showing that recent evidence in the form of fMRI and lesion studies supports the more parsimonious hypothesis. 1. Theory of Mind, Metarepresentation, and Modularity2. Developmental Components of ToM3. The Analogy with Modularity of Language4. Dissociations without Modules5. The Evidence from Neuroscience6. Conclusion. (shrink)

An important application of cognitive architectures is to provide human performance models that capture psychological mechanisms in a form that can be “programmed” to predict task performance of human–machine system designs. Although many aspects of human performance have been successfully modeled in this approach, accounting for multitalker speech task performance is a novel problem. This article presents a model for performance in a two-talker task that incorporates concepts from psychoacoustics, in particular, masking effects and stream formation.

The novel approach presented in this paper accounts for the occurrence of the epistemic gap and defends physicalism against anti-physicalist arguments without relying on so-called phenomenal concepts. Instead of concentrating on conceptual features, the focus is shifted to the special characteristics of experiences themselves. To this extent, the account provided is an alternative to the Phenomenal Concept Strategy. It is argued that certain sensory representations, as accessed by higher cognition, lack constituent structure. Unstructured representations could freely exchange their causal roles (...) within a given system which entails their functional unanalysability. These features together with the encapsulated nature of low level complex processes giving rise to unstructured sensory representations readily explain those peculiarities of phenomenal consciousness which are usually taken to pose a serious problem for contemporary physicalism. I conclude that if those concepts which are related to the phenomenal character of conscious experience are special in any way, their characteristics are derivative of and can be accounted for in terms of the cognitive and representational features introduced in the present paper. (shrink)