_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)

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)

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)

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)

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.

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)

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.

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)

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.

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)

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)

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)

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)

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)

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)

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

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?

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)

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.

In this response to Edouard Machery's comments on On Folk Epistemology, I address his methodological challenges, the first of which concerns cross-cultural and interpersonal instability of case judgments. Whereas I agree with Machery about the importance of not going too quickly from folk epistemological judgments to epistemological theory, I argue against skepticism about such judgments. Machery's second challenge concerns my bias account of patterns of knowledge ascriptions. Whereas I agree with Machery that bias accounts should not be overgeneralized, I argue (...) that the evidence he cites is consistent with the restricted applications of the book's bias accounts. (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)

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.

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)

To take an empirical approach toward belief, I suggest thinking of belief as a putative kind within the domain of cognitive science. Adopting realist, naturalist, and non-reductionist account of kinds according to which kinds are clusters of causal properties, I argue that a plausible place to begin an inquiry on belief is the architecture of human reasoning. I offer the Sound Board Account of Human Reasoning (S-BAR) (in contrast to Dual Process Theory), according to which there is one (...) reasoning system, which can operate consciously or unconsciously, automatically or controlled, concretely or abstractly, and inductively or deductively. I then argue that, on this architecture, belief will have two properties: inferential promiscuity and settling state. In the final section, I argue that wishful thinkings are beliefs, acceptance and belief are distinct, and argue for a disambiguation of implicit measures. (shrink)

According to Carruthers ants and bees have minds. This claim is to be understood realistically. We do not interpret the overt behaviour of ants and bees by ascribing to them beliefs and desires in an instrumental manner. They rather possess minds in the relevant cognitive sense. In this paper, I propose to pave the way for a reductio against such a polemic view. In particular, I shall argue that if ants and bees have minds, by the same token, plants (...) do have minds too. In my view, the problem has to do with Carruthers' underlying technical concept of cognitive architecture; a concept which, as I shall argue, can be called into question both on empirical and conceptual grounds. Según Carruthers, las hormigas y las abejas tienen mente. Esta afirmación debe entenderse de modo realista. No es que interpretemos la conducta abierta de hormigas y abejas en términos de creencias y deseos atribuidos instrumentalmente. Se trata más bien de que tienen tales estados mentales en el sentido cognitivo relevante. En este trabajo, me propongo llevar esta polémica concepción a una reductio. En particular, argumentaré que si las hormigas y las abejas tienen mente, por la misma razón las plantas también la tendrían. A mi modo de ver, el problema tiene que ver con el concepto técnico de arquitectura cognitiva de Carruthers ; un concepto que puede ser cuestionado sobre bases empíricas y conceptuales, según argüiré. (shrink)

Several attempts have been made previously to provide a biological grounding for cognitive architectures by relating their components to the computations of specific brain circuits. Often, the architecture's action selection system is identified with the basal ganglia. However, this identification overlooks one of the most important features of the basal ganglia—the existence of a direct and an indirect pathway that compete against each other. This characteristic has important consequences in decision-making tasks, which are brought to light by Parkinson's (...) disease as well as genetic differences in dopamine receptors. This paper shows that a standard model of action selection in a cognitive architecture cannot replicate any of these findings, details an alternative solution that reconciles action selection in the architecture with the physiology of the basal ganglia, and extends the domain of application of cognitive architectures. The implication of this solution for other architectures and existing models are discussed. (shrink)

In recent years, we have experienced rapid development of advanced technology, machine learning, and artificial intelligence (AI), intended to interact with and augment the abilities of humans in practically every area of life. With the rapid growth of new capabilities, such as those enabled by generative AI (e.g., ChatGPT), AI is increasingly at the center of human communication and collaboration, resulting in a growing recognition of the need to understand how humans and AI can integrate their inputs in collaborative teams. (...) However, there are many unanswered questions regarding how human–AI collective intelligence will emerge and what the barriers might be. Truly integrated collaboration between humans and intelligent agents may result in a different way of working that looks nothing like what we know now, and it is important to keep the essential goal of human societal well-being and prosperity a priority. In this special issue, we begin to scope out the underpinnings of a socio-cognitive architecture for Collective HUman-MAchine INtelligence (COHUMAIN), which is the study of the capability of an integrated human and machine (i.e., intelligent technology) system to achieve goals in a wide range of environments. This topic consists of nine papers including a description of the conceptual foundation for a socio-cognitive architecture for COHUMAIN, empirical tests of some aspects of this architecture, research on proposed representations of intelligent agents that can jointly interact with humans, empirical tests of human–human and human–machine interactions, and philosophical and ethical issues to consider as we develop these systems. (shrink)

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)

The representational nature of human cognition and thought in general has been a source of controversies. This is particularly so in the context of studies of unconscious cognition, in which representations tend to be ontologically and structurally segregated with regard to their conscious status. However, it appears evolutionarily and developmentally unwarranted to posit such segregations, as,otherwise, artifact structures and ontologies must be concocted to explain them from the viewpoint of the human cognitive architecture. Here, from a by-and-large Classical (...) cognitivist viewpoint, I show why this segregation is wrong, and elaborate on the need to postulate an ontological and structural continuity between unconscious and conscious representations. Specifically, I hypothesize that this continuity is to be found in the symbolic-based interplay between the syntax and the semantics of thought, and I propose a model of human information processing characterized by the integration of syntactic and semantic representations. (shrink)

It has been suggested that the enterprise of developing mechanistic theories of the human cognitive architecture is flawed because the theories produced are not directly falsifiable. Newell attempted to sidestep this criticism by arguing for a Lakatosian model of scientific progress in which cognitive architectures should be understood as theories that develop over time. However, Newell's own candidate cognitive architecture adhered only loosely to Lakatosian principles. This paper reconsiders the role of falsification and the potential (...) utility of Lakatosian principles in the development of cognitive architectures. It is argued that a lack of direct falsifiability need not undermine the scientific development of a cognitive architecture if broadly Lakatosian principles are adopted. Moreover, it is demonstrated that the Lakatosian concepts of positive and negative heuristics for theory development and of general heuristic power offer methods for guiding the development of an architecture and for evaluating the contribution and potential of an architecture's research program. (shrink)

Do accounts of scientific theory formation and revision have implications for theories of everyday cognition? We maintain that failing to distinguish between importantly different types of theories of scientific inference has led to fundamental misunderstandings of the relationship between science and everyday cognition. In this article, we focus on one influential manifestation of this phenomenon which is found in Fodor's well-known critique of theories of cognitive architecture. We argue that in developing his critique, Fodor confounds a variety of (...) distinct claims about the holistic nature of scientific inference. Having done so, we outline more promising relations that hold between theories of scientific inference and ordinary cognition. (shrink)

This article describes the Adaptive Control of Thought–Rational (ACT–R) cognitive architecture (Anderson et al., 2004; Anderson & Lebiere, 1998) and its detailed application to the learning of algebraic symbol manipulation. The theory is applied to modeling the data from a study by Qin, Anderson, Silk, Stenger, & Carter (2004) in which children learn to solve linear equations and perfect their skills over a 6‐day period. Functional MRI data show that: (a) a motor region tracks the output of equation (...) solutions, (b) a prefrontal region tracks the retrieval of declarative information, (c) a parietal region tracks the transformation of mental representations of the equation, (d) an anterior cingulate region tracks the setting of goal information to control the information flow, and (e) a caudate region tracks the firing of productions in the ACT–R model. The article concludes with an architectural comparison of the competence children display in this task and the competence that monkeys have shown in tasks that require manipulations of sequences of elements. (shrink)

This paper presents a blueprint for an ecological cognitive architecture. Ecological psychology, I contend, must be complemented with a story about the role of the CNS in perception, action, and cognition. To arrive at such a story while staying true to the tenets of ecological psychology, it will be necessary to flesh out the central metaphor according to which the animal perceives its environment by ‘resonating’ to information in energy patterns: what is needed is a theory of resonance. (...) I offer here the two main elements of such a theory: a framework and a methodology. (shrink)

The paper addresses a central problem in evolutionary biology and cognitive science; evolution of a neural based learning phenotype from a structured genotype. It describes morphogenesis of a neural network-based cognitive system, starting from a single genotype having a modulon control structure. It further shows how such a system, denoted as GALA architecture, growing its own recurrent axon connections, can further develop into various structures capable of learning in different learning modes, such as advice learning, reinforcement learning, (...) and emotion learning. The paper particularly considers the emotion learning systems and their motivational structure. A simulation experiment is provided to illustrate the theoretical issues discussed. (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)

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)

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)

© 2004 by The Johns Hopkins University Press.

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)

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.