Cognitive Distinctions as a Language for Cognitive Science: Comparing Methods of Description in a Model of Referential Communication.

According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive systems may well be dynamical systems, and only sustained empirical research in cognitive science will determine the extent to which that is true.

Decision science have begun to enter the lexicon of risk professionals as the concepts from Prospect Theory become popular in media outlets who increasingly warn about the risk of human biases. Decision-making under uncertainty, popularized by Dan Kahneman, Amos Tversky, Paul Slovic, Herbert Simon and other economists, is more than an examination of human biases. Prospect Theory is a reexamination of the theory of choice and the causes of violations of utility theory that has blossomed into a broad and diverse body of research in behavioral and cognitive science. This paper is an outline for a proposed draft of a cognitive risk framework that will be developed to incorporate behavioral and cognitive science into an enterprise risk framework for cybersecurity and enterprise risk governance. Herbert Simon coined the term “Bounded Rationality” in his seminal book of the same name. “Broadly stated, the task is to replace the global rationality of economic man with the kind of rational behavior that is compatible with the access to information and the computational capacities that are actually possessed by organisms, including man, in the kinds of environments in which such organisms exist” (Simon 1955a: 99). Before the development of modern of PCs and even more powerful machine learning algorithms, Simon foresaw the opportunity at the intersection of human decision-making and technology. Since Simon, other economists and researchers have broaden insights from a multidisciplinary offering of academic studies into applied behavioral science. Notwithstanding these advances, only a few scientists have developed decision science solutions at scale at the enterprise level. Machine learning and other forms of artificial intelligence will require new rules of engagement and governance controls to ensure that bias and ethical use standards have been put in place. Data, the newest commodity in all digital strategies, must be better organized and structured in organizations to allow for efficacious information workflows needed to power organizations to higher performance. And lastly, the role of humans working with and alongside machines as decision-support tools are in the early stage of deployment. The research for the book, Cognitive Risks, will examine the last frontier in risk management – the role of human actors in a business environment that is transitioning to digital products and services. A new level of awareness is needed in a digital environment that differs from the physical world. We know this because of the advent of misinformation that now permeates the Internet. Nation states and Dark Web criminals have weaponized trust in the Internet through misinformation campaigns in social media sites by using behavioral science, or more specifically, cognitive hacks to change our behavior when surfing the web. These attacks are low cost and very effective because most observers are not aware of cognitive risks. There are many variations of “cognitive hacks” and “cognitive risks” which will be explained in detail in the book. Dimitry Kiselev, director general of Russia’s state-controlled Rossiyua Segodnya media conglomerate, “Objectivity is a myth which is proposed and imposed on us.” Today, thanks to the Internet and social media, the manipulation of our perception of the world is taking place on previously unimaginable scales of time, space and intentionality. Cognitive hacks and cognitive risks are part of a new lexicon of risks we must learn. Cognitive risks are commonly referred to as heuristic behavior. Heuristics is any approach to problem solving or self-discovery that employs a practical method that is not guaranteed to be optimal, perfect, or rational, but is nevertheless sufficient for reaching an immediate, short-term goal or approximation. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. Heuristics can be mental shortcuts that ease the cognitive load of making a decision. Large swaths of the economy have already misjudged the potential, and the threats, of digital transformation. The questions explored in this paper and the subsequent book, Cognitive Risks, that will follow is why? Why do some leaders see opportunity when others only see problems? Why has the retail industry been blindsided by firms like Amazon, Google, Apple, and so many others? The research for the book will also include an exhaustive review of how applied behavioral science can be used to enhance organizational performance, risk management and cybersecurity in all organizations. Few, if any studies to date, have combined a multidisciplinary approach to enterprise risk management and organizational performance. This will be the first study that builds on a 2020 study of advancements in enterprise risk and board governance to provide a comprehensive analysis of methods and processes to apply behavioral science to address a range of risks facing organizations as they transition to a digital economy.

Summary form only given. A fundamental challenge for almost all scientific disciplines is to explain how natural intelligence is generated by physiological organs and what the logical model of the brain is beyond its neural architectures. According to cognitive informatics and abstract intelligence, the exploration of the brain is a complicated recursive problem where contemporary denotational mathematics is needed to efficiently deal with it. Cognitive psychology and medical science were used to explain that the brain works in a certain way based on empirical observations on related activities in usually overlapped brain areas. However, the lack of precise models and rigorous causality in brain studies has dissatisfied the formal expectations of researchers in computational intelligence and mathematics, because a computer, the logical counterpart of the brain, might not be explained in such a vague and empirical approach without the support of formal models and rigorous means. In order to fonnally explain the architectures and functions of the brain, as well as their intricate relations and interactions, systematic models of t he brain are s ought for revealing the principles and mechanisms of the brain at the neural, physiological, cognitive, and logical (abstract) levels. Cognitive and brain informatics investigate into the brain via not only inductive syntheses through these four cognitive levels from the bottom up in order to form theories based on empirical observations, but also deductive analyses from the top down in order to explain various functional and behavioral instances according to the abstract intelligence theory. This keynote lecture presents systematic models of the brain from the facets of cognitive informatics, abstract intelligence, brain Informatics, neuroinformatics, and cognitive psychology. A logical model of the brain is introduced that maps the cognitive functions of the brain onto its neural and physiological architectures. This work leads to a coherent abstract intelligence theory based on both denotational mathematical models and cognitive psychology observations, which rigorously explains the underpinning principles and mechanisms of the brain. On the basis of the abstract intelligence theories and the logical models of the brain, a comprehensive set of cognitive behaviors as identified in the Layered Reference Model of the Brain (LRMB) such as perception, inference and learning can be rigorously explained and simulated.The logical model of the brain and the abstract intelligence theory of natural intelligence will enable the development of cognitive computers that perceive, think and learn. The functional and theoretical difference between cognitive computers and classic computers are that the latter are data processors based on Boolean algebra and its logical counterparts; while the former are knowledge processors based on contemporary denotational mathematics. A wide range of applications of cognitive computers have been developing in ICIC and my laboratory such as, inter alia, cognitive robots, cognitive learning engines, cognitive Internet, cognitive agents, cognitive search engines, cognitive translators, cognitive control systems, and cognitive automobiles.

About 30 years ago, the Dynamical Hypothesis instigated a variety of insights and transformations in cognitive science. One of them was the simple observation that, quite unlike trial-based tasks in a laboratory, natural ecologically valid behaviors almost never have context-free starting points. Instead, they produce lengthy time series data that can be recorded with dense-sampling measures, such as heartrate, eye movements, EEG, etc. That emphasis on studying the temporal dynamics of extended behaviors may have been the trigger that led to a rethinking of what a "representation" is, and then of what a "cognitive agent" is. This most recent and perhaps most revolutionary transformation is the idea that a cognitive agent need not be a singular physiological organism. Perhaps a group of organisms, such as several people working on a joint task, can temporarily function as one cognitive agent - at least while they're working adaptively and successfully.

During the summer of 2008 in Washington, DC, the Cognitive Science Society celebratedthe 30th anniversary of its seminal 1979 conference in San Diego. The 2008 conferenceorganizers—Bradley Love, Ken McRae, and Vladimir Sloutsky—commissioned a sympo-sium to celebrate the occasion. In discussing possibilities, we agreed that the symposiumshould not simply address the Society’s origins and subsequent history, but that it shouldfocus on contributions from the disciplines and theoretical perspectives central to CognitiveScience, along with their future directions.We originally settled on five disciplines and five theoretical perspectives, and then weinvited 10 active established researchers to address them at the conference. To accommo-date these 10 speakers, two symposia were presented, one on disciplines and one onperspectives. Each speaker was asked to address: (a) What was your discipline⁄perspectivelike at the time of the 1979 conference? (b) How has the discipline⁄perspective changedover the past 30 years to what it is today? (c) How do you foresee the discipline⁄perspectivechanging in the next 30 years?Because of time constraints, we could not include all disciplines and perspectives centralto Cognitive Science. Fortunately, however, we were able to remedy this limitation byasking additional researchers to contribute articles here. The resulting collection of articlescovers disciplines and perspectives that have been central to Cognitive Science for the past30 years and that are likely to be central for the next 30 years and beyond. Specifically, thedisciplines (and the authors addressing them) include the following:Psychology (Dedre Gentner)Artificial Intelligence (Kenneth D. Forbus)Philosophy (William Bechtel)Linguistics (Elissa L. Newport)

This paper presents a cognitive (belief-desire-intention based) agent that can self-explain its behaviour based on its goals and emotions. We implement a cognitive agent, embodied by a nao-robot or virtual avatar thereof, to play a quiz with its user. During the interaction the agent intelligently selects questions to optimally educate the user. We show how the simulation of emotions can be used to generate end-user explanations of the agent's behaviour. With this we provide a first proof of concept showing the value of using simulated emotions in addition to goals for generating agent behaviour explanations.

The 1998 article by van Gelder proposed a Dynamical Hypothesis (DH) in cognitive science consisting of Nature (cognitive agents are dynamical systems) and Knowledge (cognitive agents should be understood dynamically) hypotheses in contrast to the Computational Hypothesis (CH) that cognitive agents are computers. My commentary focuses on the contributions of Paxton and Necaise etal. in interpersonal motor coordination and radicalization across social media. I do not think that either contribution supports the Nature hypothesis but does conform with the Knowledge hypothesis. I conclude by describing cognitive agents as living systems (or nonliving systems that mimic aspects of living systems) that can be alternately viewed to support the DH or CH or both at the same time.

The article is devoted to the description of the differences in the conceptualization of space observed in informants, large language models and computer vision models capable of generating a text describing what they “saw”. We use the concept of a cognitive agent and substantiate the distinction between “natural vs artificial cognitive agent”: the first is understood as a person, the second is an AI model capable of making decisions and performing tasks adequately in a given situation. The aim of the study is to compare the ways of understanding the location of an object in space in natural cognitive agents and artificial cognitive agents of two types: large language models and models created for Image to Text task. The main methods are the method of linguistic experiment and the method of semantic description based on the theory of topological semantics by L. Talmi. As an incentive material, six paintings from the collection of the State Hermitage Museum were used, divided into three groups: portraits, monofigure paintings on mythological or religious themes, and multifigure compositions. The participants of the experiments were: 63 informants (Mean age = 19.1, 48 females, 15 males), 5 LLMs, 6 Image to Text models based on computer vision technology and capable of generating descriptions of recognized images in English. Using the typology of configurational topological schemes and “figure – background” type schemes, we compared the ways of understanding space that the models rely on. As a result, we have formulated a number of conclusions, the most important of which is that natural cognitive agents differ from artificial cognitive agents in its ability to integrate the process of conceptualization of an object in space into other cognitive processes: entity recognition and categorization, attention mechanisms, awareness of cause-and-effect relationships. Artificial cognitive agents are only learning such integrativity and mutual coordination, for example, when generative models conceptualize those objects in which they are not sure, since these are products of hallucination, as objects with fuzzy boundaries, and Image to Text models combine into a single heterogeneous human object and the most striking original detail of its environment, because they “believe” that this is the most important thing for description tasks.

[1] In Music and Probability, David Temperley presents a meaningful analysis of the cognitive resources implied in music perception, providing a sound and coherent series of models based on a probabilistic perspective.[2] Cognitive sciences aim at understanding how our minds-and perhaps even artificial ones-are able to combine information about the external world with internal mental representations, in order to perform certain actions and achieve specific goals. Cognition gives us a more or less realistic, accurate perspective of the existence and behavior of the outside world. By processing this information we are able to plan our actions, solve problems, and obtain desired results (Von Eckardt 1993).[3] Originally, cognitive sciences employed a symbolic approach. According to this paradigm, minds are viewed as symbolic processors, and syntactic rules-rules that correlate with the form of information only, not its contents-were enough in principle to represent knowledge and the way humans think and solve problems (Gardner 1985). Since computers were particularly well suited to perform syntactic analysis, it seemed conceivable that one could turn them into cognitive agents (Turing 1950).[4] Due to its formal structure, music was one of the early targets in cognitive research. Several researchers and experimental musicians were convinced that music could be analyzed mathematically as some sort of code, and could therefore be artificially generated (Meyer 1967). Avant-garde artists dreamed of ensembles of humans and computers improvising in jam sessions. Those interested in cognitive processes and music believed we could understand those processes better by assimilating them into the way computers and humans processed syntax (Jackendoff 1983).[5] When this paradigm proved to be too limiting (Dreyfus 1992, Varela 1991), emphasis was directed towards statistical and probabilistic approaches, the same as in Music and Probability. The older models, however, were black box approaches to mathematical generation of music. There was no attempt to give any psychological reality to models, despite the fact that this has been one of the main aims of the cognitive sciences since their inception (Johnson-Laird 1983).[6] Much of this earlier research mirrored cognitive science's understanding of neural networks at the time (Rumelhart 1989). Neural networks were trained to recognize certain patterns and structures, and they did astonishing things that symbolic AI (Artificial Intelligence) could not. For example, they could recognize faces and speech, and they could play backgammon better than humans. Yet cognitive scientists could not explain how these networks worked. They found it very difficult to extract useful information, which would help them to better understand how humans perceive or generate music. An early appreciation of such difficulties within a probabilistic study of music can be found in Cohen (1962).[7] Early statistical and probabilistic approaches only showed that, when humans recognize a musical passage, some sort of statistical process occurred in their neurons. Although the process was thought to be equivalent in some sense to that of neural networks, it was impossible to develop an algorithm that could predict what the cognitive processes would be like. This is a general problem that most probabilistic and neural networks models share, as stated in Clark (1989).[8] Music and Probability is different from earlier probabilistic studies of music cognition. Temperley clearly understands mathematics, music, and cognitive sciences, and he successfully and convincingly combines them in his book. He leads the reader into a deeper interaction with cognitive processes. The reader learns how the brain uses the mathematical nature of music to perceive and create music.Music and Probability? Main Contents[9] This book serves as an introductory and systematic course on the probabilistic analysis of music, and on how to use that approach in music theory as well as in the cognitive sciences. …

Communicative interactions across different species have so far received relatively little attention from cognitive or behavioral scientists. Most research in this area views the process of communication as the adaptive interaction of manipulative signalers and information-assessing receivers. This paper discusses some shortcomings of the information/influence model of communication, particularly in the empirical study of interspecific communicative interactions. It then presents an alternative theoretical model, based on recent contributions in psycholinguistics and semiotics. The semiotic alignment model views communication as a dynamic process of joint semiosis resulting in the alignment of the interactants’ own-worlds (Umwelten). It is argued that this model can improve our understanding of communicative interactions between heterospecifics and provide the basis for future work in the empirical study of interspecific communication.

A longstanding tradition in the social sciences contrasts instrumental rationality and social norms as alternative ways of explaining action. Rational choice theory defines action as the outcome of a practical inference that takes preferences and beliefs as premises. An explanation in terms of norms depicts a socialized actor whose behavior is not outcome oriented, since when acting in accordance with a norm one does not engage in a rational calculation nor does one pay very much attention to the consequences. Attempts at bridging the gap have either tried to establish that social norms are rational, in the sense of being efficient means to achieve individual or social welfare, or that it is rational to conform to norms, thus reducing compliance to utility maximization. The first reductionist strategy makes a typical post hoc, ergo propter hoc fallacy, since the mere presence of a social norm does not justify inferring that it is there to accomplish some social function. Besides, it does not account for the fact that many social norms are inefficient, as in the case of discriminatory norms against women and blacks, or are so rigid as to prevent the fine-tuning that would be necessary to successfully accommodate new cases. Even if a norm is a means to achieve a social end, such as cooperation, retribution, or fairness, usually it is not the sole means. Many social norms are underdetermined with respect to the collective objectives they may serve, nor can they be ordered according to a criterion of greater or lesser efficiency in meeting these goals. Such an ordering would be feasible only if it were possible to show that one norm among others is the best means to attain a given social objective. Often, though, the objectives themselves are defined by means of some norm. Consider as an example norms of revenge; until not long ago, a Sicilian man who "dishonored" another man's daughter or sister had to make amends for the wrong by marrying the woman or pay for his rashness with his own life. The objective was to restore the family's lost

1. IntroductionThis paper articulates and explores a novel form of Mental Fictionalism: Fictionalism about the neural representations posited by cognitive science. Cognitive science appears to be committed to neural representations. These representations are claimed to be the springs of our thought and action: they drive our behaviour, determine our thoughts, memories, and inferences. However, despite the central role of neural representations in cognitive science, it is hard to explain what is meant by 'representation' in a way that does not incur problematic commitments. The representations in question clearly cannot be conventional representations that gain their representational content and status through our social conventions; for we are rarely aware that such representations exist, and no adequate social conventions regarding them appear to be in play. The standard reply is that neural representations are representations of a different sort: original or natural representations. This class of representations gain their representational status independently of, and in some sense prior to, our social conventions. But what is a natural representation? Attempts to answer this question-naturalising representation-have been on-going since the 1970s. Unfortunately, this project to date has been largely unsuccessful. Many contemporary theorists are sceptical that an adequate naturalistic theory of representation will ever emerge.For this reason, some theorists have been drawn to Eliminativism about talk of neural representations in cognitive science.1 If cognitive science could stop appealing to neural representations, then there would be no need to give an account of representation, and therefore no need to give a naturalistic account. However, thoroughgoing Eliminativism about neural-representation talk is a hard road to follow. Although some cognitive phenomena can be explained in nonrepresentational terms, other aspects of cognition appear stubbornly resistant to nonrepresentational explanation. Attributing neural representations seems to be the best way to explain many of our cognitive abilities.2Cognitive science appears to face a dilemma: either it uses neuralrepresentation talk and is lumbered with the task of naturalising representation, or a radical and undesirable revision to the practice of cognitive science is required.Neural Representation Fictionalism (NRF) offers a neat way out. NRF opens up a third option: allowing us to use neural-representation talk in cognitive science without the cost of naturalising representation. NRF promises to rid us of one of the biggest problems facing representation talk in cognitive science without the pain required by Eliminativism. NRF purports to deliver the benefits of both Realism and Eliminativism with the costs of neither. The only downside of NRF is that it would require us to reinterpret neural-representation talk in cognitive science in a fictionalist way. At least on the face of it, it is not obvious that this is not a price worth paying. NRF seems worth exploring.Fictionalism about a given discourse is the view that claims C in that discourse involve genuine statements of fact-they aim to describe the world-but, in contrast to Realism, those claims C do not aim at truth. Instead, they serve some other purpose. A fictionalist might remain agnostic about the truth value of her claims C (as van Fraassen [1980] does), or she may declare that the claims C are literally false in spite of their cognitive value (as do Nolan, Restall, and West [2005]).Fictionalism of many stripes has become popular in recent years. Forms of Fictionalism have been developed for mathematical discourse, moral discourse, modal discourse, and negative existential talk. In each case, the motivation bears a striking resemblance to the problem facing cognitive science above. We have a practice-mathematical talk, moral talk, modal talk, or negative existential talk-that appears to commit us to the existence of troublesome entities-numbers, moral facts, possible worlds, nonexistent objects. …

This chapter discusses guidelines and models of Mind from Cognitive Sciences in order to generate an integrated architecture for an artificial mind that allows various behavior aspects to be simulated in a coherent and harmonious way, showing believability and computational processing viability. Motivations are considered the quantitative, driving forces of the action selection mechanism that guides behavior. The proposed architecture is based on a multi-agent structure, where reactive agents represent motivations (Motivation Agents) or actions (Execution Agents), and cognitive agents (Cognition Agents) embody knowledge-based attention, goal-oriented perception and decision-making processes. Motivation Agents compete for priority, and only winners can activate their corresponding Cognition Agents, thus filtering knowledge processing. Active Cognition Agents negotiate with each other to trigger a specific Execution Agent, which then may change internal and external states, displaying the corresponding animation. If no motivation satisfaction occurs, frustration is expressed by a discharge procedure. Motivations intensities are then accordingly decreased.

This chapter discusses guidelines and models of Mind from Cognitive Sciences in order to generate an integrated architecture for an artificial mind that allows various behavior aspects to be simulated in a coherent and harmonious way, showing believability and computational processing viability. Motivations are considered the quantitative, driving forces of the action selection mechanism that guides behavior. The proposed architecture is based on a multi-agent structure, where reactive agents represent motivations (Motivation Agents) or actions (Execution Agents), and cognitive agents (Cognition Agents) embody knowledge-based attention, goal-oriented perception and decision-making processes. Motivation Agents compete for priority, and only winners can activate their corresponding Cognition Agents, thus filtering knowledge processing. Active Cognition Agents negotiate with each other to trigger a specific Execution Agent, which then may change internal and external states, displaying the corresponding animation. If no motivation satisfaction occurs, frustration is expressed by a discharge procedure. Motivations intensities are then accordingly decreased. Request access from your librarian to read this chapter's full text.

This chapter discusses guidelines and models of Mind from Cognitive Sciences in order to generate an integrated architecture for an artificial mind that allows various behavior aspects to be simulated in a coherent and harmonious way, showing believability and computational processing viability. Motivations are considered the quantitative, driving forces of the action selection mechanism that guides behavior. The proposed architecture is based on a multi-agent structure, where reactive agents represent motivations (Motivation Agents) or actions (Execution Agents), and cognitive agents (Cognition Agents) embody knowledge-based attention, goal-oriented perception and decision-making processes. Motivation Agents compete for priority, and only winners can activate their corresponding Cognition Agents, thus filtering knowledge processing. Active Cognition Agents negotiate with each other to trigger a specific Execution Agent, which then may change internal and external states, displaying the corresponding animation. If no motivation satisfaction occurs, frustration is expressed by a discharge procedure. Motivations intensities are then accordingly decreased.