Topics in Cognitive Science, Volume 14, Issue 1, Page 143-162, January 2022.

People undergo many idiosyncratic experiences throughout their lives that may contribute to individual differences in the size and structure of their knowledge representations. Ultimately, these can have important implications for individuals' cognitive performance. We review evidence that suggests a relationship between individual experiences, the size and structure of semantic representations, as well as individual and age differences in cognitive performance. We conclude that the extent to which experience-dependent changes in semantic representations contribute to individual differences in cognitive (...) aging remains unclear. To help fill this gap, we outline an empirical agenda that utilizes network analysis and involves the concurrent assessment of large-scale semantic networks and cognitive performance in younger and older adults. We present preliminary data to establish the feasibility and limitations of such empirical, network-analytical approaches. (shrink)

Topics in Cognitive Science, Volume 14, Issue 1, Page 78-92, January 2022.

Behavioural Network Science provides a comprehensive introduction to network science for social and behavioral researchers and students. It is a self-contained guide to the fundamentals of network science, beginning with principles of representing and making networks, network metrics, and network evolution. It then delves into specific applications of network science to behavioral research including language evolution, learning, memory, aging, creativity, conspiracies, group problem-solving, opinion polarization, and social conflict. Within each application, (...) theoretical aspects surrounding a core problem are discussed, providing readers with an intuitive understanding of how network science can be employed to quantify and generate behavioural phenomena. Accompanying online R code allows readers to replicate simulations and generate figures. This book is an essential reference for graduate students and researchers in cognitive sciences, behavioural economics, and health and social policy seeking a nuanced understanding of the intersection between network science and behavioural research. (shrink)

Topics in Cognitive Science, Volume 14, Issue 1, Page 189-208, January 2022.

Some properties of cognitive networks are discussed in the article in the context of the modern achievements of the network science. It is the study in network structures and their surprising properties that gives a new impetus to the development of the theory of complex systems. The analysis of cognitive processes from the point of view of the network structures that arise in them not only fits with such concepts already existing in cognitive (...) science and epistemology, as cognitive niches, cognitive maps, cognitive coherence, etc.), but also brings some new aspects to the understanding of interactivity, intersubjectivity, synergy in cognition and creative activities, empathy. (shrink)

The increasing frequency and scope of financial crises has made global financial stability one of the major concerns of economic policy and decision makers. Under this highly complex environment, supervision of the financial system has to be thought of as a systemic task, focusing not only on the strength of the institutions but also on the interdependent relations among them, unraveling the structure and dynamic of the system as a whole. In recent years, network science has emerged as (...) a leading tool for the investigation of complex systems. Here we review several applications of network science in finance and economics, and discuss existing challenges and future directions which will substantiate network science as a key tool for financial academics, practitioners, and policy and decision makers. (shrink)

Contemporary psycholinguistic models place significant emphasis on the cognitive processes involved in the acquisition, recognition, and production of language but neglect many issues related to the representation of language-related information in the mental lexicon. In contrast, a central tenet of network science is that the structure of a network influences the processes that operate in that system, making process and representation inextricably connected. Here, we consider how the structure found across phonological networks of several languages from (...) different language families may influence language processing as we age and experience diseases that affect cognition during the typical and atypical acquisition of new words, during typical perception and production of speech in adults, and during language change over time. We conclude that the network science approach may not only provide insights into specific language processes but also provide a way to connect the work from these domains, which are becoming increasingly balkanized. (shrink)

Smaldino suggests that patterns that give rise to group-level cultural traits can also increase individual-level cultural diversity. I distinguish social roles and related social network structures and discuss ways in which each might maintain diversity. I suggest that cognitive analogs of “cohesion,” a property of networks that helps maintenance of diversity, might mediate the effects of social roles on diversity.

Network neuroscience represents the brain as a collection of regions and inter-regional connections. Given its ability to formalize systems-level models, network neuroscience has generated unique explanations of neural function and behavior. The mechanistic status of these explanations and how they can contribute to and fit within the field of neuroscience as a whole has received careful treatment from philosophers. However, these philosophical contributions have not yet reached many neuroscientists. Here we complement formal philosophical efforts by providing an applied (...) perspective from and for neuroscientists. We discuss the mechanistic status of the explanations offered by network neuroscience and how they contribute to, enhance, and interdigitate with other types of explanations in neuroscience. In doing so, we rely on philosophical work concerning the role of causality, scale, and mechanisms in scientific explanations. In particular, we make the distinction between an explanation and the evidence supporting that explanation, and we argue for a scale-free nature of mechanistic explanations. In the course of these discussions, we hope to provide a useful applied framework in which network neuroscience explanations can be exercised across scales and combined with other fields of neuroscience to gain deeper insights into the brain and behavior. (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)

_Abstract_: This paper aims to propose a relational approach to the study of cognition that can offer a perspective on the cognitive behaviours of plants – sessile organisms without a nervous system – when considered in the reciprocal interrogation of philosophy and the cognitive and ecological sciences. When leveraging the inspiring, clarifying, and occasionally heuristic potential of different epistemic tools, plant cognition can be understood as the result of processes constantly shaped by multiple co-constructive relationships between organisms and (...) their ecological niches. Organisms and niches are conceivable as dense multi-functional systems of resources and information interchange. The concepts of network and ramification are fruitful keys to frame forms of dynamic relationships between elements. In their alternatively iconical, metaphorical, and conceptual-modelling potentialities, networks and ramifications have been used to identify different types of relationships and transmitted information. The explanatory and heuristic scope of these two concepts needs to be further investigated when linked to cognitive aspects, especially with regard the concept of ramification. Looking at the plant world, whilst much has been written about networks, relatively little has been said explicitly about ramifications (branching capacity, branching characteristics and habits) and their relationship with aspects related to plant behaviour and cognition. _Keywords_: Plant Cognition; Ramifications; Networks; Ecology; Cognitive Science _Reti e ramificazioni: prospettive relazionali nello studio della cognizione vegetale _ _Riassunto_: Questo lavoro intende proporre un approccio relazionale allo studio della cognizione che possa offrire una prospettiva sui comportamenti cognitivi delle piante – organismi sessili privi di sistema nervoso – considerati dalla prospettiva filosofica e delle scienze cognitive ed ecologiche. Dato il potenziale ispiratore, chiarificatore e a volte euristico dei diversi strumenti epistemici, la cognizione delle piante può essere vista come risultato di processi costantemente plasmati da molteplici relazioni co-costruttive tra gli organismi e le loro nicchie ecologiche. Organismi e nicchie sono infatti concepibili come un denso sistema multi-funzionale di scambio di risorse e informazioni. I concetti di rete e ramificazione sono utili chiavi di lettura per inquadrare forme di relazione dinamica tra elementi. Nelle loro potenzialità iconiche, metaforiche e concettuali, le reti e le ramificazioni sono state usate per identificare diversi tipi di relazioni e informazioni trasmesse. Lo scopo esplicativo ed euristico di questi due concetti necessita di essere ulteriormente indagato, quando legato agli aspetti cognitivi, specialmente per quanto riguarda il concetto di ramificazione. Guardando il mondo vegetale, mentre molto è stato scritto sulle reti relativamente poco si è detto esplicitamente sulle ramificazioni (capacità di ramificazione, caratteristiche e abitudini di ramificazione) e sulle loro relazioni con aspetti legati al comportamento e alla cognizione. _Parole chiave_: Cognizione vegetale; Ramificazioni; Reti; Ecologia; Scienza cognitiva. (shrink)

The interpretation of functional imaging experiments is complicated by the pluripotency of brain regions. As there is a many-to-one mapping between cognitive functions and their neural substrates, region-based analyses of imaging data provide only weak support for cognitive theories. Price and Friston argue that we need a ‘cognitive ontology’ that abstractly categorizes the function of regions. I argue that abstract characterizations are unlikely to be cognitively interesting. I argue instead that we should attribute functions to regions in (...) a context-sensitive manner. I review recent meta-analyses that approach fMRI data in this light and argue that they have revisionary potential. (shrink)

The distribution of cultural variants in a population is shaped by both neutral evolutionary dynamics and by selection pressures. The temporal dynamics of social network connectivity, that is, the order in which individuals in a population interact with each other, has been largely unexplored. In this paper, we investigate how, in a fully connected social network, connectivity dynamics, alone and in interaction with different cognitive biases, affect the evolution of cultural variants. Using agent‐based computer simulations, we manipulate (...) population connectivity dynamics (early, mid, and late full‐population connectivity); content bias, or a preference for high‐quality variants; coordination bias, or whether agents tend to use self‐produced variants (egocentric bias), or to switch to variants observed in others (allocentric bias); and memory size, or the number of items that agents can store in their memory. We show that connectivity dynamics affect the time‐course of variant spread, with lower connectivity slowing down convergence of the population onto a single cultural variant. We also show that, compared to a neutral evolutionary model, content bias accelerates convergence and amplifies the effects of connectivity dynamics, while larger memory size and coordination bias, especially egocentric bias, slow down convergence. Furthermore, connectivity dynamics affect the frequency of high‐quality variants (adaptiveness), with late connectivity populations showing bursts of rapid change in adaptiveness followed by periods of relatively slower change, and early connectivity populations following a single‐peak evolutionary dynamic. We evaluate our simulations against existing data collected from previous experiments and show how our model reproduces the empirical patterns of convergence. (shrink)

For five individuals, a social network was constructed from a series of his or her dreams. Three important network measures were calculated for each network: transitivity, assortativity, and giant component proportion. These were monotonically related; over the five networks as transitivity increased, assortativity increased and giant component proportion decreased. The relations indicate that characters appear in dreams systematically. Systematicity likely arises from the dreamer's memory of people and their relations, which is from the dreamer's cognitive social (...) network. But the dream social network is not a copy of the cognitive social network. Waking life social networks tend to have positive assortativity; that is, people tend to be connected to others with similar connectivity. Instead, in our sample of dream social networks assortativity is more often negative or near 0, as in online social networks. We show that if characters appear via a random walk, negative assortativity can result, particularly if the random walk is biased as suggested by remote associations. (shrink)

Autocatalytic networks have been used to model the emergence of self‐organizing structure capable of sustaining life and undergoing biological evolution. Here, we model the emergence of cognitive structure capable of undergoing cultural evolution. Mental representations (MRs) of knowledge and experiences play the role of catalytic molecules, and interactions among them (e.g., the forging of new associations) play the role of reactions and result in representational redescription. The approach tags MRs with their source, that is, whether they were acquired through (...) social learning, individual learning (of pre‐existing information), or creative thought (resulting in the generation of new information). This makes it possible to model how cognitive structure emerges and to trace lineages of cumulative culture step by step. We develop a formal representation of the cultural transition from Oldowan to Acheulean tool technology using Reflexively Autocatalytic and Food set generated (RAF) networks. Unlike more primitive Oldowan stone tools, the Acheulean hand axe required not only the capacity to envision and bring into being something that did not yet exist, but hierarchically structured thought and action, and the generation of new MRs: the concepts EDGING, THINNING, SHAPING, and a meta‐concept, HAND AXE. We show how this constituted a key transition toward the emergence of semantic networks that were self‐organizing, self‐sustaining, and autocatalytic, and we discuss how such networks replicated through social interaction. The model provides a promising approach to unraveling one of the greatest anthropological mysteries: that of why development of the Acheulean hand axe was followed by over a million years of cultural stasis. (shrink)

Cognitive science continues to make a compelling case for having a coherent, unique, and fundamental subject of inquiry: What is the nature of minds, where do they come from, and how do they work? Central to this inquiry is the notion of agents that have goals, one of which is their own persistence, who use dynamically constructed knowledge to act in the world to achieve those goals. An agentive perspective explains why a special class of systems have a (...) cluster of co‐occurring capacities that enable them to exhibit adaptive behavior in a complex environment: perception, attention, memory, representation, planning, and communication. As an intellectual endeavor, cognitive science may not have achieved a hard core of uncontested assumptions that Lakatos (1978) identifies as emblematic of a successful research program, but there are alternative conceptions according to which cognitive science has been successful. First, challenges of the early, core tenet of “Mind as Computation” have helped put cognitive science on a stronger foundation—one that incorporates relations between minds and their environments. Second, even if a full cross‐disciplinary theoretic consensus is elusive, cognitive science can inspire distant, deep, and transformative connections between pairs of fields. To be intellectually vital, cognitive science need not resemble a traditional discipline with its associated insularity and unchallenged assumptions. Instead, there is strength and resilience in the diverse perspectives and methods that cognitive science assembles together. This interdisciplinary enterprise is fragile and perhaps inherently unstable, as the looming absorption of cognitive science into psychology shows. Still, for many researchers, the excitement and benefits of triangulating on the nature of minds by integrating diverse cases cannot be secured by a stable discipline with an uncontested core of assumptions. (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)

Since Darwin, the idea of psychological continuity between humans and other animals has dominated theory and research in investigating the minds of other species. Indeed, the field of comparative psychology was founded on two assumptions. First, it was assumed that introspection could provide humans with reliable knowledge about the causal connection between specific mental states and specific behaviors. Second, it was assumed that in those cases in which other species exhibited behaviors similar to our own, similar psychological causes were at (...) work. In this paper. we show how this argument by analogy is flowed with respect to the case of second-order mental states. As a test case, we focus on the question of how other species conceive of visual attention, and in particular whether chimpanzees interpret seeing as a mentalistic event involving internal states of perception, attention, and belief. We conclude that chimpanzees do not reason about seeing in this manner, and indeed, there is considerable reason to suppose that they do not harbor representations of mental states in general. We propose a reinterpretation model in which the majority of the rich social behaviors that humans and other primates share in common emerged long before the human lineage evolved the psychological means of interpreting those behaviors in mentalistic terms. Although humans, chimpanzees, and most other species may be said to possess mental states, humans alone may have evolved a cognitive specialization for reasoning about such states. (shrink)

Scientific collaboration can only be understood along the epistemic and cognitive grounding of scientific disciplines. New scientific discoveries in astrophysics led to a major restructuring of the elite network of astrophysics. To study the interplay of the epistemic grounding and the social network structure of a discipline, a mixed-methods approach is necessary. It combines scientometrics, quantitative network analysis and visualization tools with a qualitative network analysis approach. The centre of the international collaboration network of (...) astrophysics is demarcated by identifying the 225 most productive astrophysicists. For the years 1998–1999 and 2001–2006 four co-authorship networks are constructed comprehending each a two year period. A visualization of the longitudinal network data gives first hints on the structural development of the network. The network of 2005–2006 is analyzed in depth. Based on cohesion analysis tools for network analysis, two main cores and three smaller ones are identified. Scientists in each core and additionally in structurally interesting positions are identified and 17 qualitative expert interviews are conducted with them. The visualization of the network of 2005–2006 is used in the interviews as a stimulus for the interviewees. An analysis of the three most often used keywords of the 225 astrophysicists is included and combined with the other data. The triangulation of these approaches shows that major epistemic changes in astrophysics, e.g. the discovery of the accelerating expansion of the universe, together with technical and organizational innovations, leads to a restructuring of the network of the discipline. The importance of a combination of qualitative and quantitative network analysis tools for the understanding of the interplay of cognitive and social structure in the sociology of science is substantiated. (shrink)

The Emergic Cognitive Model (ECM) is a unified computational model of visual filling-in based on the Emergic Network architecture. The Emergic Network was designed to help realize systems undergoing continuous change. In this thesis, eight different filling-in phenomena are demonstrated under a regime of continuous eye movement (and under static eye conditions as well). -/- ECM indirectly demonstrates the power of unification inherent with Emergic Networks when cognition is decomposed according to finer-grained functions supporting change. These can (...) interact to raise additional emergent behaviours via cognitive re-use, hence the Emergic prefix throughout. Nevertheless, the model is robust and parameter free. Differential re-use occurs in the nature of model interaction with a particular testing paradigm. -/- ECM has a novel decomposition due to the requirements of handling motion and of supporting unified modelling via finer functional grains. The breadth of phenomenal behaviour covered is largely to lend credence to our novel decomposition. -/- The Emergic Network architecture is a hybrid between classical connectionism and classical computationalism that facilitates the construction of unified cognitive models. It helps cutting up of functionalism into finer-grains distributed over space (by harnessing massive recurrence) and over time (by harnessing continuous change), yet simplifies by using standard computer code to focus on the interaction of information flows. Thus while the structure of the network looks neurocentric, the dynamics are best understood in flowcentric terms. Surprisingly, dynamic system analysis (as usually understood) is not involved. An Emergic Network is engineered much like straightforward software or hardware systems that deal with continuously varying inputs. Ultimately, this thesis addresses the problem of reduction and induction over complex systems, and the Emergic Network architecture is merely a tool to assist in this epistemic endeavour. -/- ECM is strictly a sensory model and apart from perception, yet it is informed by phenomenology. It addresses the attribution problem of how much of a phenomenon is best explained at a sensory level of analysis, rather than at a perceptual one. As the causal information flows are stable under eye movement, we hypothesize that they are the locus of consciousness, howsoever it is ultimately realized. (shrink)

Topics in Cognitive Science, Volume 14, Issue 1, Page 54-77, January 2022.

Mindware: An Introduction to the Philosophy of Cognitive Science invites readers to join in up-to-the-minute conceptual discussions of the fundamental issues, problems, and opportunities in cognitive science. Written by one of the most renowned scholars in the field, this vivid and engaging introductory text relates the story of the search for a cognitive scientific understanding of mind. This search is presented as a no-holds-barred journey from early work in artificial intelligence, through connectionist (artificial neural (...) class='Hi'>network) counter-visions, and on to neuroscience, artificial life, dynamics, and robotics. The journey ends with some wide-ranging and provocative speculation about the complex coadaptive dance between mind, culture, and technology. Each chapter opens with a brief sketch of a major research tradition or perspective, followed by short yet substantial critical discussions dealing with key topics and problems. Ranging across both standard philosophical territory and the landscape of cutting-edge cognitive science, Clark highlights challenging issues in an effort to engage readers in active debate. Topics covered include mental causation; machine intelligence; the nature and status of folk psychology; the hardware/software distinction; emergence; relations between life and mind; the nature of perception, cognition, and action; and the continuity (or otherwise) of high-level human intelligence with other forms of adaptive response. Numerous illustrations, text boxes, and extensive suggestions for further reading enhance the text's utility. Helpful appendices provide background information on dualism, behaviorism, identity theory, consciousness, and more. An exceptional text for introductory and more advanced courses in cognitive science and the philosophy of mind, Mindware is also essential reading for anyone interested in these fascinating and ever-changing fields. (shrink)

An introductory text on cognitive science from an interdisciplinary perspective. Containing chapters on philosophy, psychology, cognition, neuroscience, the network and evolutionary approaches. Covers theories and models of mind looking at all major information processing categories: perception, attention, language, emotions, social, and artificial intelligence.

The book is an epistemological monograph written from a multidisciplinary perspective. It provides a complex and realistic picture of cognition and rationality, as endowments aimed at making sense and reacting smartly to one's environment, be it epistemic, social or simply ecological. The first part of the book analyzes scientific modeling as products of the biological necessity to cope with the environment and be able to draw as many inferences as possible about it. Moreover, it develops an epistemological framework which will (...) be exploited in both the second part of the book, focusing on social cognition and cognitive niche construction, and the third part, dealing with the apparent irrationality of magical and religious belief. The book also discusses how both social networking and online religion influence cognition, rationality and irrational belief. (shrink)

In the late summer of 1998, the authors, a cognitive scientist and a logician, started talking about the relevance of modern mathematical logic to the study of human reasoning, and we have been talking ever since. This book is an interim report of that conversation. It argues that results such as those on the Wason selection task, purportedly showing the irrelevance of formal logic to actual human reasoning, have been widely misinterpreted, mainly because the picture of logic current in (...) psychology and cognitive science is completely mistaken. We aim to give the reader a more accurate picture of mathematical logic and, in doing so, hope to show that logic, properly conceived, is still a very helpful tool in cognitive science. The main thrust of the book is therefore constructive. We give a number of examples in which logical theorizing helps in understanding and modeling observed behavior in reasoning tasks, deviations of that behavior in a psychiatric disorder (autism), and even the roots of that behavior in the evolution of the brain. (shrink)

An integrated representation of large‐scale space, or cognitive map, colled PLAN, is presented that attempts to address a broader spectrum of issues than has been previously attempted in a single model. Rather than examining way‐finding as a process separate from the rest of cognition, one or the fundamental goals of this work is to examine how the wayfinding process is integrated into general cognition. One result of this approach is that the model is “heads‐up,” or scene‐based, because it takes (...) advantage of the properties of the human visual system and, particularly, the visual system's split into two pathways. The emphasis on the human location or “where” system is new to cognitive mapping and is port of an attempt to synthesize prototype theory, associative networks and location together in a connectionist system. Not all of PLAN is new, however. Many of its parts have analogues in one or another preexisting theory. What makes PLAN unique is integrating the various components into a coherent whole, and the capacity of this resulting system to speak to a wide range of constraints. Our approach emphasizes adaptiveness; thus, our focus on such issues as ease of use and efficiency of learning. The result is a model that has a stronger relationship both to the environment, and to the ways that humans interact with it, compared with previous models. The resulting model is examined in some detail and compared to other systems. (shrink)

Lindquist et al. provide a convincing case against what they call the locationist account of emotion. Their quantitative approach elegantly illustrates the shortcomings of this still-entrenched viewpoint. Here, I discuss how a network perspective will advance our understanding of structure-function mappings in general, and the relationship between emotion and cognition in the brain.

David Marr provided a useful framework for theorizing about cognition within classical, AI-style cognitive science, in terms of three levels of description: the levels of (i) cognitive function, (ii) algorithm and (iii) physical implementation. We generalize this framework: (i) cognitive state transitions, (ii) mathematical/functional design and (iii) physical implementation or realization. Specifying the middle, design level to be the theory of dynamical systems yields a nonclassical, alternative framework that suits (but is not committed to) connectionism. We (...) consider how a brain's (or a network's) being a dynamical system might be the key both to its realizing various essential features of cognition — productivity, systematicity, structure-sensitive processing, syntax — and also to a non-classical solution of (frame-type) problems plaguing classical cognitive science. (shrink)

�� This work describes a cognitively realistic ap- proach to social simulation. It begins with a model created by Gilbert [4] for capturing the growth of academic science. Gilbert’s model, which was equation-based, is replaced here by an agent-based (neural network) model, with the (neural net- work based) cognitive architecture CLARION providing greater cognitive realism. Using this agent model, results comparable to previous simulations and to human data are obtained. It is found that while different (...) class='Hi'>cognitive settings may affect the aggregate number of scientific articles produced by the model, they do not generally lead to different distributions of number of articles per author. It is argued that using more cognitively realistic models in simulations may lead to novel insights. (shrink)

Purpose: This, the second part of a two-part paper, describes how the concerns of enactive cognitive science have been realized in actual research: methodological issues, proposed explanatory mechanisms and models, some of the potential as both a theoretical and applied science, and several of the major open research questions. Findings: Despite some skepticism about "mechanisms" in constructivist literature, enactive cognitive science attempts to develop cognitive formalisms and models. Such techniques as feedback loops, self-organization, autocatalytic (...) networks, and dynamical systems modeling are used to develop alternatives to cognitivist models. A number of technical similarities are starting to emerge in the different models being proposed. Research Implications: The need to resolve the interplay between autonomy and coupling with the environment suggests the need for further technical research. And the reintroduction of first-person concerns into cognitive science raises some questions of method, particularly with regard to the relationship between first-person experience, neuroscience, and methods of description, analysis, and explanation. Results to date suggest that insights from enactive cognitive science could lead to innovations in the design of artifacts. (shrink)

In recent years, the family of algorithms collected under the term ``deep learning'' has revolutionized artificial intelligence, enabling machines to reach human-like performances in many complex cognitive tasks. Although deep learning models are grounded in the connectionist paradigm, their recent advances were basically developed with engineering goals in mind. Despite of their applied focus, deep learning models eventually seem fruitful for cognitive purposes. This can be thought as a kind of biological exaptation, where a physiological structure becomes applicable (...) for a function different from that for which it was selected. In this paper, it will be argued that it is time for cognitive science to seriously come to terms with deep learning, and we try to spell out the reasons why this is the case. First, the path of the evolution of deep learning from the connectionist project is traced, demonstrating the remarkable continuity, and the differences as well. Then, it will be considered how deep learning models can be useful for many cognitive topics, especially those where it has achieved performance comparable to humans, from perception to language. It will be maintained that deep learning poses questions that cognitive sciences should try to answer. One of such questions is the reasons why deep convolutional models that are disembodied, inactive, unaware of context, and static, are by far the closest to the patterns of activation in the brain visual system. (shrink)

It is sometimes suggested that the history of computation in cognitive science is one in which the formal apparatus of Turing-equivalent computation, or effective computability, was exported from mathematical logic to ever wider areas of cognitive science and its environs. This paper, however, indicates some respects in which this suggestion is inaccurate. Computability theory has not been focused exclusively on Turing-equivalent computation. Many essential features of Turing-equivalent computation are not captured in definitions of computation as symbol (...) manipulation. Turing-equivalent computation did not play the role in McCulloch and Pitts’s early cybernetic work that is sometimes attributed to it. Finally, various segments of the neuroscientific community invoke a notion of computation that differs from the Turing-equivalent notion.Keywords: Circular causality; Computation; Cortical maps; Neural networks; Symbol manipulation; Turing-equivalent computation. (shrink)

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate–argument structure; (iv) logical operators; (v) (...) inferential promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures – that is, LoTs – only prove more flexible and well-supported over time. (shrink)

The study of human intelligence was once dominated by symbolic approaches, but over the last 30 years an alternative approach has arisen. Symbols and processes that operate on them are often seen today as approximate characterizations of the emergent consequences of sub- or nonsymbolic processes, and a wide range of constructs in cognitive science can be understood as emergents. These include representational constructs (units, structures, rules), architectural constructs (central executive, declarative memory), and developmental processes and outcomes (stages, sensitive (...) periods, neurocognitive modules, developmental disorders). The greatest achievements of human cognition may be largely emergent phenomena. It remains a challenge for the future to learn more about how these greatest achievements arise and to emulate them in artificial systems. (shrink)

This volume, derived from the Royal Institute of Philosophy 1992 conference, brings together some of the leading figures in the burgeoning field of cognitive science to explore current and potential advances in the philosophical understanding of mind and cognition. Drawing on work in psychology, computer science and artificial intelligence, linguistics and philosophy, the papers tackle such issues as concept acquisition, blindsight, rationality and related questions as well as contributing to the lively debates about connectionism and neural networks. (...) The collection as a whole reflects the theoretical and methodological dynamism of this interdisciplinary field. (shrink)

Two widely accepted assumptions within cognitive science are that (1) the goal is to understand the mechanisms responsible for cognitive performances and (2) computational modeling is a major tool for understanding these mechanisms. The particular approaches to computational modeling adopted in cognitive science, moreover, have significantly affected the way in which cognitive mechanisms are understood. Unable to employ some of the more common methods for conducting research on mechanisms, cognitive scientists’ guiding ideas about (...) mechanism have developed in conjunction with their styles of modeling. In particular, mental operations often are conceptualized as comparable to the processes employed in classical symbolic AI or neural network models. These models, in turn, have been interpreted by some as themselves intelligent systems since they employ the same type of operations as does the mind. For this paper, what is significant about these approaches to modeling is that they are constructed specifically to account for behavior and are evaluated by how well they do so—not by independent evidence that they describe actual operations in mental mechanisms. (shrink)