PREDICTIVE PROCESSING AND THE SEMIOLOGICAL PRINCIPLE: COMMENTARY TO DUFFLEY

Math anxiety is a clinical pathology impairing cognitive processing in math-related contexts. Originally thought to affect only inexperienced, low-achieving students, recent investigations show how math anxiety is vastly diffused even among high-performing learners. This review of data-informed studies outlines math anxiety as a complex system that: (i) cripples well-being, self-confidence and information processing on both conscious and subconscious levels, (ii) can be transmitted by social interactions, like a pathogen, and worsened by distorted perceptions, (iii) affects roughly 20$\%$ of students in 63 out of 64 worldwide educational systems but correlates weakly with academic performance and (iv) poses a concrete threat to students’ well-being, computational literacy and career prospects in science. These patterns underline the crucial need to go beyond performance for estimating math anxiety. Recent advances in network psychometrics and cognitive network science provide ideal frameworks for detecting, interpreting and intervening upon such clinical condition. Merging education research, psychology and data science, the approaches reviewed here reconstruct psychological constructs as complex systems, represented either as multivariate correlation models (e.g. graph exploratory analysis) or as cognitive networks of semantic/emotional associations (e.g. free association networks or forma mentis networks). Not only can these interconnected networks detect otherwise hidden levels of math anxiety but—more crucially—they can unveil the specific layout of interacting factors, for example, key sources and targets, behind math anxiety in a given cohort. As discussed here, these network approaches open concrete ways for unveiling students’ perceptions, emotions and mental well-being, and can enable future powerful data-informed interventions untangling math anxiety.

The ascendance of emotion theory, recent advances in cognitive science and neuroscience, and increasingly important findings from developmental psychology and learning make possible an integrative account of the nature and etiology of anxiety and its disorders. This model specifies an integrated set of triple vulnerabilities: a generalized biological (heritable) vulnerability, a generalized psychological vulnerability based on early experiences in developing a sense of control over salient events, and a more specific psychological vulnerability in which one learns to focus anxiety on specific objects or situations. The author recounts the development of anxiety and related disorders based on these triple vulnerabilities and discusses implications for the classification of emotional disorders.

The theme of the preceding chapter, namely, the embodied nature of cognitive processes, is continued. Intuition is referred to as innateness in the epigenetics-inspired sense that innate beliefs/understandings/instincts are crucially dependent on interactions between the developing individual and its environment. As for disciplinarity, epistemology is explored in order to establish the nature of knowledge and knowledge boundaries. Through this process of coming to know our computational machinery, the loop is closed with the concept of innate modular computational elements. These modules have their form in response to the regularity of reality. If the external environment constitutes resources for cognition, and there exist innate cognitive modules primed to develop intuitive understandings of the way the world behaves, it is suggested that educators seek to design learning activities and environments in order to develop and surface these otherwise tacit intuitions. Thusly informed by recent advances in the cognitive sciences, Disciplinary Intuitions are defined as innate computational modules of the mind which are in the process of being exercised and developed as the learner interacts with his or her external environment.

Reviewed by: Cognitive Science, Literature, and the Arts: A Guide for Humanists Claiborne Rice Hogan, Patrick Colm . 2003. Cognitive Science, Literature, and the Arts: A Guide for Humanists. London and New York: Routledge. $85.00 hc. $19.95 sc. 244 pp. The cognitive revolution that has affected so many areas of contemporary thought is currently capturing literary studies as part of its stronghold. In addition to the many popular cognitive scientists who have extended the discipline's reach into the literary mind, like Steven Pinker or George Lakoff, we have begun to see an impact on current literature and also literary criticism. Patrick Colm Hogan's aptly named book, Cognitive Science, Literature, and the Arts: A Guide for Humanists, is a solid introduction to the growing range of artistic forms and functions amenable to explanation in the cognitive sciences. For many years now, Hogan has been a prolific scholar of literature who is fully conversant with what we have come to call "theory." His many books and articles rewardingly engage highly theorized sub-topics within literary studies, such as influence, social and cultural identity, professionalism, law, and colonialism, from a serious philosophical, psychoanalytic, and, now, cognitive scientific perspective. Like all of Hogan's work, the current volume is significantly informed by his easy familiarity with the literature, art, and philosophy of many distinct non-Western traditions. The book argues for the cognitive study of the arts because of the benefits such study offers to both the humanities and the cognitive sciences. Specifically, the issues familiarly addressed by humanists (Hogan's term for [End Page 242] humanities scholars) are rarely treated effectively by neurobiologists or psychologists who see in certain instances of art fodder for their own pet theories. Only practicing humanists comprehend the complexity and range of humanities questions and, as such, can be expected to see how what we are beginning to learn about functions of the mind and body can best be applied to humanistic concerns. Such complexity and range in turn challenges cognitive scientists to refine their models and methods. The book's opening chapter, "My Favorite Things," introduces the procedural model that a cognitivist approach to art suggests by analyzing the form of John Coltrane's famous version of the tune "My Favorite Things." Though much less is known specifically about how humans process music than, say, vision, music is a good place to start because it avoids some of the issues of "meaning" that language processing entails. In the course of introducing readers to certain basic concerns of cognitive science, such as short- and long- term memory structures, segmentation, and structuration, he also offers explanations of basic art appreciation issues like boredom, improvisation, and listener variability. Chapter two, "Is It Cognitive Science Yet?", moves on to a lengthier discussion of elements of cognitive science theory that will play a role in all of the analyses to follow. He introduces an overall framework of cognitive science approaches that helps guide the reader through the labyrinth of technical terminology. Briefly, three stages of analysis pertain to any cognitive science approach to a problem: conceiving of the problem in terms of information processing, specifying the exact steps (an algorithmic sequence) that will lead from input to output, and identifying the cognitive architecture that will be necessary for dealing with the problem. Cognitive architecture, in its turn, is understood as having three separate components: structures, processes, and contents. Structure refers to "the general organizational principles of the mind" (30), such as the distinction between long-term and short-term (working) memory. Content usually refers to representations or symbols that have specific locations in structures. Finally, processes are structurally constrained operations that are performed on contents. For example, our mental storehouse of words, the lexicon, is a substructure of our long-term memory system that provides structural links between individual words; the activation of one word primes, or prepares, many other words for access. The meanings of each word, in this view, are representational contents, and formulating a sentence for pronunciation would be a process that at some point involves accessing the lexicon to retrieve the phonemic profile for the appropriate words. Because of the complexities of mental contents and...

Cognitive science and science dynamics. Philosophical and epistemological issues for information science

A range of digital psychological interventions have demonstrated a positive impact on trauma-related problems in controlled trials, but there is room for further improvements in their form, reach and impact. Most to date have been adaptions of established face-to-face treatments. In this paper, we highlight a complementary emerging route to their development, which draws on advances in cognitive science theory and research and applies them to clinical contexts. Three examples are given regarding laboratory research with potential applications to digital interventions for trauma-related mental health problems: a digital game to reduce intrusive memories of trauma, novel cognitive techniques for worry, and digitally supported mental imagery to enhance motivation for functional behavior change. Much of this research is still at an early stage, meriting a balance of optimism and caution. However, even if only a few digital applications of cognitive science constitute substantial improvements to complement current treatments, their potential for large-scale use at low unit cost may provide significant benefits across populations.

Back to table of contents Previous article Next article Book ForumFull AccessCognitive Neuroscience of Aging: Linking Cognitive and Cerebral AgingDAN G. BLAZER, M.D., PH.D., DAN G. BLAZERSearch for more papers by this author, M.D., PH.D., Durham, N.C.Published Online:1 Mar 2006https://doi.org/10.1176/appi.ajp.163.3.560AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail Psychiatry today is firmly grounded in the neurosciences, such as the genetics, molecular biology, anatomy, and physiology of the brain. It is less aware of (and less grounded in) the significant advances in the cognitive sciences, such as attention, perception, and memory. Nevertheless, psychiatry has applied the cognitive sciences through its psychotherapeutic approaches, especially the cognitive/behavioral therapies. Yet the neurosciences and the cognitive sciences are now evolving into the “transdisciplinary” discipline of cognitive neuroscience. And the construct of transdisciplinary is critical to understanding this evolution. Rather than neuroscientists and cognitive scientists simply working in parallel or even working together on the same project (what we typically think of as interdisciplinary), a new “transdiscplinary” breed of scientists is emerging. This new breed of scientists tends to focus upon the extremities of the life cycle—childhood and late life—primarily because both cognitive and neuroanatomical/neurophysiological changes occur at a faster rate during these stages than during the majority of adulthood (and therefore can be effectively studied over a briefer interval). This discipline potentially could become a key basic science of geriatric psychiatry.In this edited volume, which seeks to become a handbook of the new transdisciplinary field of the cognitive neuroscience of aging, a group of international investigators present the basics of this emerging discipline. In my view, they easily accomplish their goal. The book is divided into four main sections. The first section reviews the main neuroimaging methods, documenting, for example, age-related decline in both post- and presynaptic dopamine markers. This decline is associated with age-related cognitive decline (it is not just acetylcholine). The second section focuses upon basic cognitive processes. With aging, frontal and hippocampal structures that are central to higher-order cognition show decreases in volume, yet these decreases do not map directly onto decreases in basic functions such as attention and memory (the brain, even in later life, exhibits significant plasticity).The third section focuses upon clinical and applied issues. One area that has received much attention in recent years is the degree to which cognitive exercises (e.g., learning a novel task such as playing chess) might delay or even prevent the onset of cognitive decline in later life. Though few seriously believe that such exercises can absolutely prevent the dementing disorders, such exercises could be protective. Evidence is emerging that such exercises can be minimally effective in later life but are much more effective earlier in life. Nevertheless, recent and more refined studies suggest that certain types of deficits are more prominent in later life (production versus processing deficits) and that interventions may be targeted toward those deficits. In the final section, a group of investigators propose a series of models that can be applied to the cognitive neuroscience of aging. These models emphasize that the brain is an open system and that lifespan cognitive development is a dynamic, cumulative process that shapes the neurocognitive representation of ongoing interactions with the environment and sociocultural contexts through experience. In other words, neural mechanisms not only feed up to cognition and behavior, cognition and behavior feed down to basic neurobiology.Psychiatrists will perhaps be disappointed because foundational psychological constructs to our field are not explored, such as emotion, motivation, and temperament. We must remember, however, that the cognitive neuroscience of aging has developed to explore the association between the cognition and the neuroscience. The cognitive sciences, although neglected in our studies, are critical to the psychological well-being of our patients.Reprints are not available; however, Book Forum reviews can be downloaded at http://ajp.psychiatryonline.org.Edited by Roberto Cabeza, Lars Nyberg, and Denise Park. New York, Oxford University Press, 2004, 408 pp, $69.50 FiguresReferencesCited byDetailsCited ByRole of age and neuroinflammation in the mechanism of cognitive deficits in sickle cell disease22 September 2020 | Experimental Biology and Medicine, Vol. 246, No. 1 Volume 163Issue 3 March 2006Pages 560-561 Metrics PDF download History Published online 1 March 2006 Published in print 1 March 2006

The present paper briefly describes recent advances in cognitive science on the embodied nature of human cognition with the aim to better situating contemporary work on embodied metaphor in language and thought. We do this by talking about key experimental findings in five areas main areas of research in cognitive science: perception, concepts, mental imagery, memory, and language processing (Gibbs 2006a) We also describe some psycholinguistic studies on embodied metaphor understanding, and offer some details on one series of experiments in regard to people's embodied understanding of the DIFFICULTIES ARE WEIGHTS primary metaphor. Our conclusion draws connections between the research on embodied cognition and contemporary linguistic and psychological work on embodied metaphor.

The general question of our paper concerns the relationship between Bayesian models of cognition and predictive processing, and whether predictive processing can provide explanatory insight over and above Bayesian models. Bayesian models have been gaining influence in neuroscience and the cognitive sciences since they are able to predict human behavior with high accuracy. Models based on a Bayesian optimal observer are fitted on behavioral data. A good fit is hence interpreted as human subjects “behaving” in a Bayes’ optimal fashion. However, these models are performance-oriented and do not specify which processes could give rise to the observed behavior. Here, David Marr’s (Marr 1982) levels of analysis can help understand the relationship between performance- and process-oriented models or explanations. Bayesian models are situated at the computational level since they specify what the system (in this case the brain) does and why it does it in this manner. Although Bayesian models can constrain the search space for hypotheses at the algorithmic level, they do not provide a precise solution about how a system realizes the observed behavior. Here predictive processing can shed more light on the underlying principles. Predictive processing provides a unifying functional theory of cognition and can thus i) provide an answer at the algorithmic level by answering how the brain realizes cognition, ii) can aid in the interpretation of neurophysiological findings at the implementational level.

Notions of embodiment, situatedness, and dynamics are increasingly being debated in cognitive sci ence. However, these debates are often carried out in the absence of concrete examples. In order to build intuition, this paper explores a model agent to illustrate how the perspective and tools of dynam ical systems theory can be applied to the analysis of situated, embodied agents capable of minimally cognitive behavior. Specifically, we study a model agent whose “nervous system” was evolved using a genetic algorithm to catch circular objects and to avoid diamond-shaped ones. After characterizing the performance, behavioral strategy and psychophysics of the best-evolved agent, its dynamics are analyzed in some detail at three different levels: (1) the entire coupled brain/body/environment sys tem; (2) the interaction between agent and environment that generates the observed coupled dynam ics; (3) the underlying neuronal properties responsible for the agent dynamics. This analysis offers both explanatory insight and testable predictions. The paper concludes with discussions of the overall picture that emerges from this analysis, the challenges this picture poses to traditional notions of rep resentation, and the utility of a research methodology involving the analysis of simpler idealized mod els of complete brain/body/environment systems.

In cognitive science, there is a tacit norm that phenomena such as cultural variation or synaesthesia are worthy examples of cognitive diversity that contribute to a better understanding of cognition, but that other forms of cognitive diversity (e.g., autism, attentiondeficithyperactivitydisorder/ADHD, and dyslexia) are primarily interesting only as examples of deficit, dysfunction, or impairment. This status quo is dehumanizing and holds back much-needed research. In contrast, the neurodiversity paradigm argues that such experiences are not necessarily deficits but rather are natural reflections of biodiversity. Here, we propose that neurodiversity is an important topic for future research in cognitive science. We discuss why cognitive science has thus far failed to engage with neurodiversity, why this gap presents both ethical and scientific challenges for the field, and, crucially, why cognitive science will produce better theories of human cognition if the field engages with neurodiversity in the same way that it values other forms of cognitive diversity. Doing so will not only empower marginalized researchers but will also present an opportunity for cognitive science to benefit from the unique contributions of neurodivergent researchers and communities.

Meaning has traditionally been regarded as a problem for philosophers and psychologists. Advances in cognitive science since the early 1960s, however, broadened discussions of meaning, or more technically, the semantics of perceptions, representations, and/or actions, into biology and computer science. Here, we review the notion of “meaning” as it applies to living systems, and argue that the question of how living systems create meaning unifies the biological and cognitive sciences across both organizational and temporal scales.

Since the mid-2000s, cognitive science approaches have been used in biblical studies. Cognitive science came into existence in the 1950s as a reaction to the psychological behaviorism of the time, and it studies human thought processes, such as memory, learning, decision making, and social cognition, including the role of emotions. Cognitive science is multidisciplinary in nature, with contributions from linguistics, philosophy, neuroscience, experimental psychology, ethology, computer modeling, and other disciplines. It has influenced a variety of academic fields, ranging from anthropology and education to musicology and medicine. Its appearance and impact is often referred to as the “cognitive turn.” Cognitive approaches to religion examine cross-culturally recurrent religious thought and behavior from the perspective of cognitive science. Cognitive approaches to religion, in turn, offer important insights and theories for the cognitive scientific study of biblical texts and traditions. Three research areas can be identified that especially influenced the emergence of cognitive science approaches to biblical studies: (1) A considerable part of cognitive research on religion can be linked to the program called cognitive science of religion (CSR), which started in the 1990s. In the beginning, CSR focused mainly on god concepts and rituals, with a strong emphasis on the evolved cognitive structure of the human mind. New research areas of CSR include the role of religion in social cooperation, experimental studies in ethnography, discussions and applications of the theory of cultural evolution, quantitative studies of large-scale historical processes, and computational models of religious thought and behavior. While some scholars have argued that religion is a by-product of cognitive mechanisms (such as traits underlying social cooperation) that evolved previously for other purposes (the so-called by-product hypothesis), others see religion as an evolutionary adaptation. (2) The neuroscientific study of religious experience started in the 1980s and gained momentum with the rapid development of new neuroimaging technologies in the 1990s. Work on religious experience in biblical studies draws on insights from this area. (3) Finally, the field of cognitive linguistics also started in the 1980s and examines the role of the human body in shaping human thought and language. Biblical scholars have drawn especially on cognitive poetics and conceptual metaphor theory. In this article we will first discuss overviews of cognitive science approaches in biblical studies. Second, major contributions to the cognitive science of religion and conceptual metaphor theory will be presented, with an emphasis on publications that have exerted an influence on biblical scholars. This part will also include cognitive studies of ancient religions outside biblical studies. Third, cognitive science approaches to various research questions and topics in biblical studies will be discussed, including Jewish and early Christian history, memory and transmission, ritual, miracle and magic, ethics and morality, religious experience, theological and Christological concepts, applications of conceptual metaphor theory, the use of computational modeling, and other exegetical topics.

TICS 2001

In the fourth industrial revolution (Industry 4.0), Artificial Intelligence (AI) had been a cognitive science that produces actual value needed for relevant data with processing capabilities and algorithms. Manufacturing in the Internet-of-Thing (IoT) era will be more efficient, better quality, easier to manage, and more transparent through integration of physical and cyber technologies in Industry 4.0-based smart factories. Factory automation relies heavily on sensors and AI to make the system intelligent. Sensor technology advancements and developments linked to Industry 4.0 serve as the backbone for the inclusive expansion of industry and the economic success of any country. It is imperative that manufacturing organizations and supply chains have access to the latest low-cost sensor technology for collecting data and putting it to good use. Standard sensor types include position sensors, flow, temperature, flow rate, pressure, and force. A wide range of fields, including motorsport, health care, manufacturing, the armed forces, and agriculture all make use of them on a day-to-day basis. Increasing efficiency through automation is the goal of Industry 4.0. The purpose of this paper is to provide a brief overview and viewpoint on the most recent advancements in AI and the associated problems.Attempts to define the main ideas and tools behind this new era of manufacturing in the early years of the so-called fourth industrial revolution (Industry 4.0) always ended up referring to the concept of smart machines that could communicate with each other and with the environment. When it comes to the new industry 4.0, it’s the defined cyber physical systems connected by the IoT that get all the attention. Nonetheless, several tools and applications will benefit the new industrial environment, complementing the actual formation of a smart, embedded system capable of performing autonomous tasks. And the majority of these revolutionary ideas are based on the same background theory as artificial intelligence, in which the analysis and filtration of massive amounts of incoming data from various types of sensors aids in the interpretation and recommendation of the best course of action. As a result, artificial intelligence science is well suited to the challenges that arise during the fourth industrial revolution’s consolidation. The purpose of this paper is to provide a brief overview and viewpoint on the most recent advancements in AI and the associated problems.KeywordsIndustry 4.0Smart sensorArtificial Intelligence (AI)Machine Learning (ML)Digital metrology