CONTENT Introduction .................................................................................. 9 1. The unexpected: “Epistemologically Different Worlds” .......... 15 1.1 Introduction ........................................................................ 15 1.2 Definitions .......................................................................... 16 1.3 Propositions for its .............................................................. 18 1.4 Propositions for Its and being ............................................ 24 1.5 The hyperverse ................................................................... 30 2. A general view on cognitive neuroscience................................ 37 3. Optimism for localization and the “mind reading”................... 58 3.1 Bechtel’s optimism ............................................................. 58 3.2 Gallant’s laboratory work................................................... 67 3.3 Other optimistic works ....................................................... 75 4. Skepticism in cognitive neuroscience....................................... 81 4.1 Hardcastle’s skepticism (...) ...................................................... 81 4.2 Uttal’s skepticism ............................................................... 88 5. Blood-oxygen-level-dependent (BOLD) of fMRI and local field potentials (LFPs)............................................................. 107 6. The localization problem (segmentation vs. integration) ......... 126 7. The binding problem ................................................................ 138 7.1 Introduction ....................................................................... 138 7.2 The “Feature-Integration Theory” (FIT)............................. 146 7.3 The synchrony or temporal coding theory (temporal binding). 153 7.3.1 Oscillations – a general framework ....................... 153 7.3.2 More details about frequency bands, activated neural areas and cognitive functions ........................ 160 8 7.3.3 Gamma range in visual cognition ............................. 168 7.3.4 Communication among neural areas through synchronized oscillations.......................................... 177 7.3.5 The main critics for temporal coding hypothesis... 187 8. Perception and object recognition ........................................... 194 8.1 Perception and object recognition.................................... 194 8.2 A few words about other notions in cognitive neuroscience .................................................................... 210 9. Space and the mind................................................................... 218 10. Crossmodal interactions ......................................................... 244 11. Holism in cognitive neuroscience........................................... 254 11.1 The parts-whole relationship ........................................... 254 11.2 Raichle’s default network............................................... 269 11.3 Conscious and unconscious mental states...................... 278 12. Fingelkurts’ approach or the status of cognitive neuroscience........................................................................... 293 Conclusion: The status of cognitive neuroscience: “No ontology landscape”..................................... 317. (shrink)

To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness (...) in cognitive systems. We identify three mechanisms that enhance robustness in biological and engineered systems: system control, redundancy, and adaptability. After surveying the psychological literature for evidence of these mechanisms, we provide simulations illustrating how each contributes to robust cognition in a different psychological domain: psychomotor vigilance, semantic memory, and strategy selection. These simulations highlight features of a mathematical approach for quantifying robustness, and they provide concrete examples of mechanisms for robust cognition. (shrink)

This special issue is dedicated to one of the oldest and most controversial philosophical topics, the mind–body problem. Paradoxically, since Descartes until the present days, nobody has proposed a viable solution to this problem. In the last decades, through the unification of neuroscience and psychology, a new science, cognitive neuroscience, was created to deal with this problem. Using EEG, fMRI, and other apparatus, scientists try to grasp the “correlations” between any mental state and some neural patterns of activation. Articles and (...) presentations on the “brain imaging” results (mainly using fMRI) have invaded journals and conferences. There are many optimists (with some quite remarkable results) and few skeptics regarding these results in cognitive neuroscience. Anyway, there are exciting times ahead for people working in this interdisciplinary field. (shrink)

Enabling constraints are bottom up causes which create the possibility of the existence of a system. Disabling constraints reduce the degrees of freedom and narrow the choices of the system which are structural, functional, meaningful relations that assign executive roles to the component parts. In this paper, we discuss causality as enabling and disabling constraints in order to critique the absurdity of transitivity in causal relations. If downward causation is viewed as causation by constraints, we argue that it will not (...) face the absurdity of self-causation which is incurred because of the transitivity principle. In the process of self-organization, organizationally new structures are created from the lower level subcomponents of the system. Such self-organizing systems can be broadly considered as sui-generis and transcend the transitivity principle by causally influencing its subcomponents. These higher level systemic constraints that causally influence the components in a self-organizing system without violating any physical laws via causal constraints are termed as Downward Causation. (shrink)

Network neuroscience provides a systems approach to the study of the brain and enables the examination of interactions measured at different temporal and spatial scales. We review current methods to quantify the structure of brain networks and compare that structure across different clinical cohorts, cognitive states, and subjects. We further introduce the emerging mathematical concept of multilayer networks and describe the advantages of this approach to model changing brain dynamics over time. We conclude by offering several concrete examples of how (...) multilayer network approaches to neuroimaging data provide novel insights into brain structure and evolving function. (shrink)

We present a novel spontaneous collapse model where size is no longer the property of a physical system which determines its rate of collapse. Instead, we argue that the rate of spontaneous localization should depend on a system’s quantum Integrated Information, a novel physical property which describes a system’s capacity to act like a quantum observer. We introduce quantum Integrated Information, present our QII collapse model and briefly explain how it may be experimentally tested against quantum theory.

In this paper, I examined whether evidence from the neuroscience of morality supports the standard models of phronesis, i.e., Jubilee and Aretai Centre Models. The standard models explain phronesis as a multifaceted construct based on interaction and coordination among functional components. I reviewed recent neuroscience studies focusing on brain networks associated with morality and their connectivity to examine the validity of the models. Simultaneously, I discussed whether the evidence helps the models address challenges, particularly those from the phronesis eliminativism. Neuroscientific (...) evidence supported the importance of brain networks, i.e., the default mode, salience, and central executive functioning networks, in moral functioning in general. The findings favorably supported the multifaceted and integrative nature of phronesis proposed by the standard models. Finally, I considered how the two models could explain the mechanisms of phronesis more integratively based on neuroscientific findings. At the end of this paper, with the evidence, I proposed several practical ideas to promote the cultivation of phronesis, e.g., the consideration of coordination among components for moral functioning and the use of moral exemplars. (shrink)

There is no controversy in psychology or brain sciences that brains create mind and consciousness. Doubts and opinions to the contrary are quite frequently expressed in non-scientific publications. In particular the idea that conscious mind is received, rather than created by the brain, is quite often used against “materialistic” understanding of consciousness. I summarize here arguments against such position, show that neuroscience gives coherent view of mind and consciousness, and that this view is intrinsically non-materialistic.

How can we explain the intentional nature of an expert’s actions, performed without immediate and conscious control, relying instead on automatic cognitive processes? How can we account for the differences and similarities with a novice’s performance of the same actions? Can a naturalist explanation of intentional expert action be in line with a philosophical concept of intentional action? Answering these and related questions in a positive sense, this dissertation develops a three-step argument. Part I considers different methods of explanations in (...) cognitive neuroscience (Bennett & Hacker’s philosophical, conceptual analysis; Marr’s three levels of explanation; Neural Correlates of Consciousness research; mechanistic explanation), defending ‘mechanistic explanation’ as a method that provides the necessary tools for integrating interdisciplinary insights into human action. Furthermore, a dynamic, explanatory mechanism allows the assessment of the impact of learning and development on expert action in a valuable way that other methods don’t. Part II continues by scrutinizing several cognitive neuroscientific theories of learning and development (neuroconstructivism; dual-processing theories; simulation theory; extended mind/cognition hypothesis), arguing for the complex interactions between different types of processing and different action representations involved in expert action performances. Moreover, according to our discussion of a particular ‘simulation theory’ these interactions can be influenced in several ways with the use of language, allowing an agent to configure a specific action representation for performance at a later stage. The results of Parts I and II are then applied in Part III to a parallel discussion of philosophical analyses of intentional action (discussing i.a. Frankfurt, Bratman, Pacherie and Ricoeur) and cognitive neuroscientific insights in it. Both approaches are found to converge in emphasizing the importance for an expert to develop stable patterns of actions that comply maximally not only with his intentions, but also with his motor expertise and with situational conditions. Consequently, his actions – automatic, or not – rely on this ‘sculpted space of actions’. (shrink)