The third edition of a work that defines the field of cognitive neuroscience, with extensive new material including new chapters and new contributors.

Different sciences approach the brain-behaviour system at various levels, but often apparently share terminology. “Function” is used both ontogenetically and phylogenetically. Within the ontogeny it has various meanings; the one adopted by Arbib et al. is that of mainstream cognitive psychology. This usage is relatively imprecise, and the psychologists who are sceptical about the ability of cognitive psychology to predict behavioural outcomes may have the same reservations about Arbib et al.'s cognitive neuroscience.

Les relations humaines peuvent-elles transformer nos vies? En croisant le discours de Bergson avec celui des neurosciences cognitives sur la notion de plasticité, ce livre ouvre l'horizon d'une philosophie des relations interhumaines. Il s'agit d'une enquête originale, stimulante et importante, qui ouvre des pistes pour de futures recherches. Y est étudiée a manière dont la plasticité ouvre sur les relations vitales entre les humains, conduisant à des transformations sociales, et à l'espérance que celles-ci doivent continuer à susciter.

This article reviews concepts of, as well as neurocognitive and genetic studies on, empathy. Whereas cognitive empathy can be equated with affective theory of mind, that is, with mentalizing the emotions of others, affective empathy is about sharing emotions with others. The neural circuits underlying different forms of empathy do overlap but also involve rather specific brain areas for cognitive (ventromedial prefrontal cortex) and affective (anterior insula, midcingulate cortex, and possibly inferior frontal gyrus) empathy. Furthermore, behavioral and imaging genetic studies (...) provide evidence for a genetic basis for empathy, indicating a possible role for oxytocin and dopamine as well as for a genetic risk variant for schizophrenia near the gene ZNF804A. (shrink)

We outline a framework of multilevel neurocognitive mechanisms that incorporates representation and computation. We argue that paradigmatic explanations in cognitive neuroscience fit this framework and thus that cognitive neuroscience constitutes a revolutionary break from traditional cognitive science. Whereas traditional cognitive scientific explanations were supposed to be distinct and autonomous from mechanistic explanations, neurocognitive explanations aim to be mechanistic through and through. Neurocognitive explanations aim to integrate computational and representational functions and structures across multiple levels of organization in order to explain (...) cognition. To a large extent, practicing cognitive neuroscientists have already accepted this shift, but philosophical theory has not fully acknowledged and appreciated its significance. As a result, the explanatory framework underlying cognitive neuroscience has remained largely implicit. We explicate this framework and demonstrate its contrast with previous approaches. (shrink)

This volume provides an up to date and comprehensive overview of the philosophy and neuroscience movement, which applies the methods of neuroscience to traditional philosophical problems and uses philosophical methods to illuminate issues in neuroscience. At the heart of the movement is the conviction that basic questions about human cognition, many of which have been studied for millennia, can be answered only by a philosophically sophisticated grasp of neuroscience's insights into the processing of information by the human brain. Essays in (...) this volume are clustered around five major themes: data and theory in neuroscience; neural representation and computation; visuomotor transformations; color vision; and consciousness. (shrink)

This carefully designed, multi-authored textbook covers a broad range of theoretical issues in cognitive science, psychology, and neuroscience. With accessible language, a uniform structure, and many pedagogical features, Mind, Cognition, and Neuroscience: A Philosophical Introduction is the best high-level overview of this area for an interdisciplinary readership of students. Written specifically for this volume by experts in their fields who are also experienced teachers, the book’s thirty chapters are organized into the following parts: I. Background Knowledge, II. Classical Debates, III. (...) Consciousness, IV. Crossing Boundaries. -/- Each chapter starts with relevant key words and definitions and a chapter overview, then presents historical coverage of the topic, explains and analyzes contemporary debates, and ends with a sketch of cutting edge research. A list of suggested readings and helpful discussion topics conclude each chapter. This uniform, student-friendly design makes it possible to teach a cohort of both philosophy and interdisciplinary students without assuming prior understanding of philosophical concepts, cognitive science, or neuroscience. -/- Key Features: -/- Synthesizes the now decades-long explosion of scientifically informed philosophical research in the study of mind. Expands on the offerings of other textbooks by including chapters on language, concepts and non-conceptual content, and animal cognition. Offers the same structure in each chapter, moving the reader through an overview, historical coverage, contemporary debates, and finally cutting-edge research. Packed with pedagogical features, like defined Key Terms, Suggested Readings, and Discussion Questions for each chapter, as well as a General Glossary. Provides readers with clear, chapter-long introductions to Cognitive Neuroscience, Molecular and Cellular Cognition, Experimental Methods in Cognitive Neuroscience, Philosophy of Mind, Philosophy of Science, Metaphysical Issues, and Epistemic Issues. (shrink)

The Cognitive Neuroscience of Vision begins by introducing the reader to the anatomy of the eye and visual cortex and then proceeds to discuss image and...

The traditional approach to explanation in cognitive neuroscience is realist about psychological constructs, and treats them as explanatory. On the “standard framework,” cognitive neuroscientists explain behavior as the result of the instantiation of psychological functions in brain activity. This strategy is questioned by results suggesting the distribution of function in the brain, the multifunctionality of individual parts of the brain, and the overlap in neural realization of purportedly distinct psychological constructs. One response to this in the field has been to (...) employ the tools of databasing and machine learning to attempt to find and quantify specific correlations between psychological kinds such as ‘memory’ or ‘attention’ (or sub-kinds thereof) and patterns of activity in the brain. I assess the status and prospects of these projects. I argue that current proponents of the project are vague about their aims, vis-à-vis the standard framework, sometimes suggesting substantiation of the framework, sometimes suggesting retaining the framework but revising the ontology of mental constructs, and sometimes suggesting abandonment of the framework. I argue that extant results from within the projects fail to substantiate the standard framework, and propose an alternative. On my view, psychological constructs should not be viewed as explanantia, but instead as heuristic concepts that help us uncover ways that behaviors can vary and the ways that the brain implements those distinctions. I then discuss the normative upshot of these views for databasing and brain mapping projects. (shrink)

"The Cognitive Neurosciences III is a magnificent accomplishment. It covers topics trom ions to consciousness, from reflexes to social psychology. ...

Working memory has been one of the most intensively studied systems in cognitive psychology. The Cognitive Neuroscience of Working Memory brings together world class researchers from around the world to summarise our current knowledge of this field, and directions for future research.

The problem of animal consciousness has profound implications on our concept of nature and of our place in the natural world. In philosophy of mind and cognitive neuroscience the problem of animal consciousness raises two main questions (Velmans, 2007): the distribution question (“are there conscious animals beside humans?”) and the phenomenological question (“what is it like to be a non-human animal?”). In order to answer these questions, many approaches take into account similarities and dissimilarities in animal and human behavior, e.g. (...) the use of language or tools and mirror self-recognition (Allen and Bekoff, 2007), however behavioral arguments don’t seem to be conclusive (Baars, 2005). Cognitive neuroscience is providing comparative data on structural and functional similarities, respectively called “homologies” and “analogies”. Many experimental results suggest that the thalamocortical system is essential for consciousness (Edelman and Tononi, 2000; Tononi, 2008). The argument from homology states that the general structure of thalamocortical system remained the same in the last 100-200 million years, for it is neuroanatomically similar in all the present and past mammals and it didn’t change much during phylogeny (Allen and Bekoff, 2007). The argument from analogy states that the key functional processes correlated with consciousness in humans are still present in all other mammals and many other animals (Baars, 2005). These processes are information integration through effective cortical connectivity (Massimini et al., 2005; Rosanova et al., 2012) and elaboration of information at a global level (Dehaene and Changeux, 2011). On this basis, the Cambridge Declaration on Consciousness states that all mammals, birds, and many other animals (such as octopuses) possess the neurological substrates of consciousness (Low et al., 2012). Conscious experience is private (Chalmers, 1995; Nagel, 1974) therefore the answer to the phenomenological question may be impossible. Nevertheless, cognitive neuroscience may provide an answer to the distribution question, showing that conscious experience is not limited to humans since it is a major biological adaptation going back millions of years. (shrink)

This book investigates the philosophical, empirical, and theoretical bases on which a cognitive neuroscience of consciousness can be founded.

The broad issue in this paper is the relationship between cognitive psychology and neuroscience. That issue arises particularly sharply for cognitive neurospsychology, some of whose practitioners claim a methodological autonomy for their discipline. They hold that behavioural data from neuropsychological impairments are sufficient to justify assumptions about the underlying modular structure of human cognitive architecture, as well as to make inferences about its various components. But this claim to methodological autonomy can be challenged on both philosophical and empirical grounds. A (...) priori considerations about (cognitive) multiple realisability challenge the thesis on philosophical grounds, and neuroscientific findings from developmental disorders substantiate that challenge empirically. The conclusion is that behavioural evidence alone is inadequate for scientific progress since appearances of modularity can be thoroughly deceptive, obscuring both the dynamic processes of neural development and the endstate network architecture of real cognitive systems. (shrink)

Following the levels of intentionality and semiosis distinguished by the Semiotic Hierarchy, and the distinction between original agency and enhanced agency, we propose a model of an agency hierarchy, consisting of six layers. Consistent with the phenomenological orientation of cognitive semiotics, a central claim is that agency and subjectivity are complementary aspects of intentionality. Hence, there is no agency without at least the minimal sense/feeling of agency. This perspective rules out all artefacts as genuine agents, as well as simple organisms, (...) since it is highly unlikely that e.g. bacteria have any first-person perspective. Using this model, we review and assess recent proposals on the nature of agency from cognitive science, and neuroscience, and draw conclusions on how to incorporate aspects of them within a synthetic cognitive-semiotic framework. (shrink)

This book, a member of the Series in Affective Science, is a unique interdisciplinary sequence of articles on the cognitive neuroscience of emotion by some of ...

This is a work in the epistemology of functional neuroimaging (fNI) and it applies the error-statistical (ES) philosophy to inferential problems in fNI to formulate and address these problems. This gives us a clear, accurate, and more complete understanding of what we can learn from fNI and how we can learn it. I review the works in the epistemology of fNI which I group into two categories; the first category consists of discussions of the theoretical significance of fNI findings and (...) the second category discusses methodological difficulties of fNI. Both types of works have shortcomings; the first category has been too theory-centered in its approach and the second category has implicitly or explicitly adopted the assumption that methodological difficulties of fNI cannot be satisfactorily addressed. In this dissertation, I address these shortcomings and show how and what kind of experimental knowledge fNI can reliably produce which would be theoretically significant. I take fMRI as a representative fNI procedure and discuss the history of its development. Two independent trajectories of research in physics and physiology eventually converge to give rise to fMRI. Thus, fMRI findings are laden in the theories of physics and physiology and I propose how this creates a kind of useful theory-ladenness which allows for the representation of and intervention in the constructs of cognitive neuroscience. Duhemian challenges and problems of underdetermination are often raised to argue that fNI is of little, if any, epistemic value for psychology. I show how the ES notions of severe tests and error probabilities can be applied in epistemological analyses of fMRI. The result is that hemodynamic hypotheses can be severely tested in fMRI experiments and I demonstrate how these hypotheses are theoretically significant and fuel the growth of experimental knowledge in cognitive neuroscience. Throughout this dissertation, I put the emphasis on the experimental knowledge we obtain from fNI and argue that this is the fruitful approach that enables us to see how fNI can contribute to psychology. In doing so, I offer an error-statistical epistemology of fNI, which hopefully will be a significant contribution to the philosophy of psychology. (shrink)

How do cognitive neuroscientists explain phenomena like memory or language processing? This book examines the different kinds of experiments and manipulative research strategies involved in understanding and eventually explaining such phenomena. Against this background, it evaluates contemporary accounts of scientific explanation, specifically the mechanistic and interventionist accounts, and finds them to be crucially incomplete. Besides, mechanisms and interventions cannot actually be combined in the way usually done in the literature. This book offers solutions to both these problems based on insights (...) from experimental practice. It defends a new reading of the interventionist account, highlights the importance of non-interventionist studies for scientific inquiry, and supplies a taxonomy of experiments that makes it easy to see how the gaps in contemporary accounts of scientific explanation can be filled. The book concludes that a truly empirically adequate philosophy of science must take into account a much wider range of experimental research than has been done to date. With the taxonomy provided, this book serves a stepping-stone leading into a new era of philosophy of science—for cognitive neuroscience and beyond. (shrink)

As an emerging discipline, neuroeconomics faces considerable methodological and practical challenges. In this paper, I suggest that these challenges can be understood by exploring the similarities and dissimilarities between the emergence of neuroeconomics and the emergence of cognitive and computational neuroscience two decades ago. From these parallels, I suggest the major challenge facing theory formation in the neural and behavioural sciences is that of being under-constrained by data, making a detailed understanding of physical implementation necessary for theory construction in neuroeconomics. (...) Rather than following a top-down strategy, neuroeconomists should be pragmatic in the use of available data from animal models, information regarding neural pathways and projections, computational models of neural function, functional imaging and behavioural data. By providing convergent evidence across multiple levels of organization, neuroeconomics will have its most promising prospects of success. (shrink)

This chapter provides an overview of the basic research strategies and analytic techniques deployed in computational cognitive neuroscience. On the one hand, “top-down” strategies are used to infer, from formal characterizations of behavior and cognition, the computational properties of underlying neural mechanisms. On the other hand, “bottom-up” research strategies are used to identify neural mechanisms and to reconstruct their computational capacities. Both of these strategies rely on experimental techniques familiar from other branches of neuroscience, including functional magnetic resonance imaging, single-cell (...) recording, and electroencephalography. What sets computational cognitive neuroscience apart, however, is the explanatory role of analytic techniques from disciplines as varied as computer science, statistics, machine learning, and mathematical physics. These techniques serve to describe neural mechanisms computationally, but also to drive the process of scientific discovery by influencing which kinds of mechanisms are most likely to be identified. For this reason, understanding the nature and unique appeal of computational cognitive neuroscience requires not just an understanding of the basic research strategies that are involved, but also of the formal methods and tools that are being deployed, including those of probability theory, dynamical systems theory, and graph theory. (shrink)

Music offers a unique opportunity to better understand the organization of the human brain. Like language, music exists in all human societies. Like language, music is a complex, rule-governed activity that seems specific to humans, and associated with a specific brain architecture. Yet unlike most other high-level functions of the human brain - and unlike language - music is a skill at which only a minority of people become proficient. The study of music as a major brain function has for (...) some time been relatively neglected. Just recently, however, we have witnessed an explosion in research activities on music perception and performance and their correlates in the human brain. This volume brings together an outstanding collection of international authorities - from the fields of music, neuroscience, psychology, and neurology - to describe the amazing advances being made in understanding the complex relationship between music and the brain. Aimed at psychologists and neuroscientists, this is a book that will lay the foundations for a cognitive neuroscience of music. (shrink)

The fourth of four lectures at the Collège de France in 2000 on the subject of conceptual mappings and conceptual structure.

_Abstract_: Fueled by the rapid development of neuroscientific tools and techniques, some scholars consider the shift from traditional cognitive psychology toward cognitive neuroscience to be a _revolution_ (most notably Boone and Piccinini). However, the term “revolution” in philosophy of science can easily be construed as involving a paradigm shift in the sense of Kuhn’s _The Structure of Scientific Revolutions_. Is a Kuhnian account sound in the case at hand? To answer this question, we consider heuristic indicators of two features of (...) paradigm shifts: the incommensurability of ontologies; and a gap between scientific communities. Based on our evidence, we argue that no revolution has occurred (at least, not yet). _Keywords_: Cognitive Neuroscience; Cognitive Psychology; Philosophy of Science; Thomas Kuhn; Scientometrics _La “rivoluzione delle neuroscienze cognitive” non è una rivoluzione (in senso kuhniano). Evidenze scientometriche _ _Riassunto_:_ _Complice il rapido sviluppo di strumenti e tecniche in neuroscienze, alcuni studiosi (in particolare Boone e Piccinini) intendono il passaggio dalla psicologia cognitiva classica alla neuroscienza cognitiva nei termini di una _rivoluzione_. Tuttavia, il termine ‘rivoluzione’ in filosofia della scienza è strettamente associato alla nozione di successione di paradigmi esposta da Kuhn ne _La struttura delle rivoluzioni scientifiche_. Obiettivo di questo lavoro è capire se effettivamente la concezione kuhniana offra una corretta descrizione di questa dinamica storica. In particolare, prenderemo in esame due indicatori euristici delle rivoluzioni kuhniane: l’incommensurabilità ontologica trai due paradigmi e la diversa composizione demografica delle comunità scientifiche. Sulla base delle evidenze scientometriche che prenderemo in esame, affermeremo che non è avvenuta nessuna rivoluzione (almeno per ora). _Parole chiave_: Neuroscienze cognitive; Psicologia cognitiva; Filosofia della scienza; Thomas Kuhn; Scientometria. (shrink)

Many empirically minded philosophers have used neuroscientific data to argue against the multiple realization of cognitive functions in existing biological organisms. I argue that neuroscientists themselves have proposed a biologically based concept of multiple realization as an alternative to interpreting empirical findings in terms of one‐to‐one structure‐function mappings. I introduce this concept and its associated research framework and also how some of the main neuroscience‐based arguments against multiple realization go wrong. *Received October 2009; revised December 2009. †To contact the author, (...) please write to: Department of Philosophy, 260 English‐Philosophy Building, University of Iowa, Iowa City, IA 52242; e‐mail: carrie‐[email protected]. (shrink)

The greatest contemporary challenge in the arena of cognitive neuroscience concerns the relation between consciousness and the brain. Over recent years the focus of work in this area has switched from the analysis of diverse spatial regions of the brain to that of the timing of neural events. It appears that two conditions are necessary in order for neural events to become correlated with conscious experience. First, the firing of assemblies of neurones must achieve a degree of coherence, and, second, (...) reflexive (i.e. top-down, or reentrant) neural pathways must be activated. It does not, of course, follow that such neural activity causes consciousness; it may be, for example, that the neural activity formats the brain to interact with consciousness. The latter possibility is suggested by analysis of mystical texts suggesting that coherence and reflexivity constitute the conditions for the influx of “spirit.” Kabbalistic sources, for example, describe a hierarchy of “brains” in the human and divine realms through which the principles of coherence and reflexivity operate. Whilst the ontological assumptions of such a scheme place it beyond the realm of psychology, parallels with the picture deriving from the contemporary cognitive neuroscience of consciousness are striking. (shrink)

We discuss the development of cognitive neuroscience in terms of the tension between the greater sophistication in cognitive concepts and methods of the cognitive sciences and the increasing power of more standard biological approaches to understanding brain structure and function. There have been major technological developments in brain imaging and advances in simulation, but there have also been shifts in emphasis, with topics such as thinking, consciousness, and social cognition becoming fashionable within the brain sciences. The discipline has great promise (...) in terms of applications to mental health and education, provided it does not abandon the cognitive perspective and succumb to reductionism. (shrink)

We situate the debate on intentionality within the rise of cognitive neuroscience and argue that cognitive neuroscience can explain intentionality. We discuss the explanatory significance of ascribing intentionality to representations. At first, we focus on views that attempt to render such ascriptions naturalistic by construing them in a deflationary or merely pragmatic way. We then contrast these views with staunchly realist views that attempt to naturalize intentionality by developing theories of content for representations in terms of information and biological function. (...) We echo several other philosophers by arguing that these theories over-generalize unless they are constrained by a theory of the functional role of representational vehicles. This leads to a discussion of the functional roles of representations, and how representations might be realized in the brain. We argue that there’s work to be done to identify a distinctively mental kind of representation. We close by sketching a way forward for the project of naturalizing intentionality. This will not be achieved simply by ascribing the content of mental states to generic neural representations, but by identifying specific neural representations that explain the puzzling intentional properties of mental states. (shrink)

The perception and cognition of art, a venture done effortlessly by all members of our species, is a complicated affair in which visual perception, brain structures, sensory reasoning, and aesthetic evaluation are made in less time that it takes to read this sentence. Only recently, through perceptual/brain studies, have we begun to understand the many neurological sub-routines involved in visual perception. The discoveries made in cognitive neuroscience laboratories have helped us better understand the perception of everyday visual phenomena, including the (...) perception of art. In addition, cognitive neuroscientists have provided techniques and instruments which are valuable in the study of the psychology of art. In this paper I give an overview of the past research which has advanced our understanding of how people process visual art. In addition the results of a preliminary study of an accomplished artist as he drew a portrait while in an MRI machine are presented. (shrink)

This chapter discusses neurocognitive work relevant to moral psychology and the proposition that innate factors make important contributions to moral judgment. It reviews various sources of evidence for an innate moral faculty, before presenting brain-imaging data in support of the same conclusion. It is argued that our moral thought is the product of an interaction between some ‘gut-reaction’ moral emotions and our capacity for abstract reflection.

Cognitive and Social Neuroscience of Aging is an introduction to how aging affects the brain, intended for audiences with some knowledge of psychology, aging, or neuroscience. The book includes figures illustrating brain regions so that extensive familiarity with neuroanatomy is not a pre-requisite. The depth of coverage also makes this book appropriate for those with considerable knowledge about aging. This book adopts an integrative perspective, including topics such as memory, cognition, cognitive training, emotion, and social processes. Topics include consideration of (...) individual differences and the impact of disorders on brain function with age. Although many declines occur with age, cognitive neuroscience research reveals plasticity and adaptation in the brain as a function of normal aging. This book is written with this perspective in mind, emphasizing the ways in which neuroscience methods have enriched and changed thinking about aging. (shrink)

Dynamical systems theory (DST) is a branch of mathematics that assesses abstract or physical systems that change over time. It has a quantitative part (mathematical equations) and a related qualitative part (plotting equations in a state space). Nonlinear dynamical systems theory applies the same tools in research involving phenomena such as chaos and hysteresis. These approaches have provided different ways of investigating and understanding cognitive systems in cognitive science and neuroscience. The ‘dynamical hypothesis’ claims that cognition is and can be (...) understood as dynamical systems. Common consequences for such an approach include rejecting understanding cognition as information processing in nature, including eschewing explanatory roles for computation or representation. Contemporary applications of DST include mouse‐tracking studies in cognitive science and nonrepresentational perspectives on motor control in neuroscience. Such work has philosophical implications concerning the boundaries of cognition, explanation, and representations. DST offers powerful methodology and theories that raise many topics of philosophical significance. (shrink)

Cognitive neuroscience increasingly relies on complex data analysis methods. Researchers in this field come from highly diverse scientific backgrounds, such as psychology, engineering, and medicine. This poses challenges with respect to acquisition of appropriate scientific computing and data analysis skills, as well as communication among researchers with different knowledge and skills sets. Are researchers in cognitive neuroscience adequately equipped to address these challenges? Here, we present evidence from an online survey of methods skills. Respondents (n= 307) mainly comprised students and (...) post-doctoral researchers working in the cognitive neurosciences. Multiple choice questions addressed a variety of basic and fundamental aspects of neuroimaging data analysis, such as signal analysis, linear algebra, and statistics. We analyzed performance with respect to the following factors: undergraduate degree (grouped into Psychology, Methods, and Biology), current researcher status (undergraduate student, PhD student, and post-doctoral researcher), gender, and self-rated expertise levels. Overall accuracy was 72%. Not surprisingly, the Methods group performed best (87%), followed by Biology (73%) and Psychology (66%). Accuracy increased from undergraduate (59%) to PhD (74%) level, but not from PhD to post-doctoral (74%) level. The difference in performance for the Methods vs. non-methods (Psychology/Biology) groups was especially striking for questions related to signal analysis and linear algebra, two areas particularly relevant to neuroimaging research. Self-rated methods expertise was not strongly predictive of performance. The majority of respondents (93%) indicated they would like to receive at least some additional training on the topics covered in this survey. In conclusion, methods skills among junior researchers in cognitive neuroscience can be improved, researchers are aware of this, and there is strong demand for more skills-oriented training opportunities. We hope that this survey will provide an empirical basis for the development of bespoke skills-oriented training programs in cognitive neuroscience institutions. We will provide practical suggestions on how to achieve this. (shrink)