Face perception plays a central role in social communication and is, arguably, one of the most sophisticated visual perceptual skills in humans. The organization of neural systems for face perception has stimulated intense debate. This article presents an updated model of distributed human neural systems for face perception. It opens up with a discussion of the Core System for visual analysis of faces with an emphasis on the distinction between perception of invariant features for identity recognition (...) and changeable features for recognition of facial gestures such as expression and eye gaze. The study analyses the roles of systems for the representation of emotion, for person knowledge, and for action understanding in face recognition and perception of expression and gaze. It presents systems that are of particular relevance for social communication and that are illustrative of how distributed systems are engaged by face perception. It concludes with a discussion of modularity and distributed processing in neural representation. (shrink)

Face perception, perhaps the most highly developed visual skill in humans, is mediated by a distributed neural system in humans that is comprised of multiple, bilateral regions. We propose a model for the organization of this system that emphasizes a distinction between the representation of invariant and changeable aspects of faces. The representation of invariant aspects of faces underlies the recognition of individuals, whereas the representation of changeable aspects of faces, such as eye gaze, expression, and lip movement, underlies (...) the perception of information that facilitates social communication. The model is also hierarchical insofar as it is divided into a core system and an extended system. The core system is comprised of occipitotemporal regions in extrastriate visual cortex that mediate the visual analysis of faces. In the core system, the representation of invariant aspects is mediated more by the face-responsive region in the fusiform gyrus, whereas the representation of changeable aspects is mediated more by the face-responsive region in the superior temporal sulcus. The extended system is comprised of regions from neural systems for other cognitive functions that can be recruited to act in concert with the regions in the core system to extract meaning from faces. (shrink)

The human brain is the first computer to which all others are compared. Yet we know painfully little about how a brain accomplishes its peculiar computations. In particular, consciousness is at once familiar and mysterious, and needs to be understood both for science and for medicine. Boldly, but gently this book introduces a reader to the neural circuitry that achieves consciousness. This amazing interconnection enables consciousness to flow like a stream, intimately relevant to the outside world; and for this (...) to happen, fundamental cues emerge from mental images to bring forth associated recalls. Alas cues can be inconsistent, causing memory failure; fortunately a subliminal cue editor encourages remembering forgotten items. Furthermore, cues generally address several memories, forcing the brain to make a selection. This necessitates another special circuit whose purpose is subliminal editing. The simplified explanations provided in this book make it clear that neurons do far more than ordinary devices, since a single neuron is capable of remarkably dense combinational and sequential logic. Beginning with their interesting and unexpected logical behavior, the reader will genuinely enjoy Dr. Burger's synthesis of a system for biological consciousness, a system that may someday result in credible artificial consciousness. (shrink)

Identifying the physiological processes in the central nervous system that underlie our conscious experiences has been at the forefront of cognitive neuroscience. While the principles of classical physics were long found to be unaccommodating for a causally effective consciousness, the inherent indeterminism of quantum physics, together with its characteristic dichotomy between quantum states and quantum observables, provides a fertile ground for the physical modeling of consciousness. Here, we utilize the Schrödinger equation, together with the Planck-Einstein relation between energy and frequency, (...) in order to determine the appropriate quantum dynamical timescale of conscious processes. Furthermore, with the help of a simple two-qubit toy model we illustrate the importance of non-zero interaction Hamiltonian for the generation of quantum entanglement and manifestation of observable correlations between different measurement outcomes. Employing a quantitative measure of entanglement based on Schmidt decomposition, we show that quantum evolution governed only by internal Hamiltonians for the individual quantum subsystems preserves quantum coherence of separable initial quantum states, but eliminates the possibility of any interaction and quantum entanglement. The presence of non-zero interaction Hamiltonian, however, allows for decoherence of the individual quantum subsystems along with their mutual interaction and quantum entanglement. The presented results show that quantum coherence of individual subsystems cannot be used for cognitive binding because it is a physical mechanism that leads to separability and non-interaction. In contrast, quantum interactions with their associated decoherence of individual subsystems are instrumental for dynamical changes in the quantum entanglement of the composite quantum state vector and manifested correlations of different observable outcomes. Thus, fast decoherence timescales could assist cognitive binding through quantum entanglement across extensive neural networks in the brain cortex. (shrink)

Two strategies used to uncover neural systems for episodic-like memory in animals are discussed: (i) an attribute of episodic memory (what? when? where?) is examined in order to reveal the neuronal interactions supporting that component of memory; and (ii) the connections of a structure thought to be central to episodic memory in humans are studied at a level of detail not feasible in humans. By focusing on spatial memory (where?) and the hippocampus, it has proved possible to bring (...) the strategies together. A review of lesion, disconnection and immediate early-gene studies in animals reveals the importance of interactions between the hippocampus and specific nuclei in the diencephalon (most notably the anterior thalamic nuclei) for spatial memory. Other parts of this extended hippocampal system include the mammillary bodies and the posterior cingulate (retrosplenial) cortex. Furthermore, by combining lesion and immediate early-gene studies it is possible to show how the loss of one component structure or tract can influence the remaining regions in this group of structures. The validity of this convergent approach is supported by new findings showing that the same set of regions is implicated in anterograde amnesia in humans. (shrink)

Two hypotheses concerning nonlinear elements in complex systems are contrasted: that neurons, intrinsically unstable, are stabilized through embedding in networks and populations; and, conversely, that cortical neurons are intrinsically stable, but are destabilized through embedding in cortical populations and corticostriatal feedback systems. Tests are made by piecewise linearization of nonlinear dynamics at nonequilibriumoperating points, followed by linear stability analysis.

Much recent research has sought to uncover the neural basis of moral judgment. However, it has remained unclear whether "moral judgments" are sufficiently homogenous to be studied scientifically as a unified category. We tested this assumption by using fMRI to examine the neural correlates of moral judgments within three moral areas: (physical) harm, dishonesty, and (sexual) disgust. We found that the judgment ofmoral wrongness was subserved by distinct neural systems for each of the different moral areas (...) and that these differences were much more robust than differences in wrongness judgments within a moral area. Dishonest, disgusting, and harmful moral transgression recruited networks of brain regions associated with mentalizing, affective processing, and action understanding, respectively. Dorsal medial pFC was the only region activated by all scenarios judged to be morally wrong in comparison with neutral scenarios. However, this region was also activated by dishonest and harmful scenarios judged not to be morally wrong, suggestive of a domain-general role that is neither peculiar to nor predictive of moral decisions. These results suggest that moral judgment is not a wholly unified faculty in the human brain, but rather, instantiated in dissociable neural systems that are engaged differentially depending on the type of transgression being judged. (shrink)

Arguably the most influential models of human decision-making today are based on the assumption that two separable systems – intuition and deliberation – underlie the judgments that people make. Our recent work is among the first to present neural evidence contrary to the predictions of these dual-systems accounts. We measured brain activations using functional magnetic resonance imaging (fMRI) while participants were specifically instructed to either intuitively or deliberately judge the authenticity of emotional facial expressions. Results from three (...) different analyses revealed both common brain networks of activation across decision mode and differential activations as a function of strategy adherence. We take our results to contradict popular dual-systems accounts that propose a clear-cut dichotomy of the processing systems, and to support rather a unified model. According to this, intuitive and deliberate judgment processes rely on the same rules, though only the former are thought to be characterized by non-conscious processing. (shrink)

The “reward” systems described by Rolls are systems for drive-reinforced associations of contact and distant stimuli and not for emotional behaviours. The neural systems delineated may be associated with distinct categories of “affect,” namely “hedonic feelings,” “moods,” and “emotions.” Awareness of these affects requires external perceptual as well as internal feedback. Levels of feedback in evolution and development suggest sensory qualia may not require language.

Modularity in the human brain remains a controversial issue, with disagreement over the nature of the modules that exist, and why, when, and how they emerge. It is a natural assumption that modularity offers some form of computational advantage, and hence evolution by natural selection has translated those advantages into the kind of modular neural structures familiar to cognitive scientists. However, simulations of the evolution of simplified neural systems have shown that, in many cases, it is actually (...) non-modular architectures that are most efficient. In this paper, the relevant issues are discussed and a series of simulations are presented that reveal crucial dependencies on the details of the learning algorithms and tasks that are being modelled, and the importance of taking into account known physical brain constraints, such as the degree of neural connectivity. A pattern is established which provides one explanation of why modularity should emerge reliably across a range of neural processing tasks. (shrink)

If connectionism is to be an adequate theory of mind, we must have a theory of representation for neural networks that allows for individual differences in weighting and architecture while preserving sameness, or at least similarity, of content. In this paper we propose a procedure for measuring sameness of content of neural representations. We argue that the correct way to compare neural representations is through analysis of the distances between neural activations, and we present a method (...) for doing so. We then use the technique to demonstrate empirically that different artificial neural networks trained by backpropagation on the same categorization task, even with different representational encodings of the input patterns and different numbers of hidden units, reach states in which representations at the hidden units are similar. We discuss how this work provides a rebuttal to Fodor and Lepore's critique of Paul Churchland's state space semantics. (shrink)

Functional decomposition is an important goal in the life sciences, and is central to mechanistic explanation and explanatory reduction. A growing literature in philosophy of science, however, has challenged decomposition-based notions of explanation. ‘Holists’ posit that complex systems exhibit context-sensitivity, dynamic interaction, and network dependence, and that these properties undermine decomposition. They then infer from the failure of decomposition to the failure of mechanistic explanation and reduction. I argue that complexity, so construed, is only incompatible with one notion of (...) decomposition, which I call ‘atomism’, and not with decomposition writ large. Atomism posits that function ascriptions must be made to parts with minimal reference to the surrounding system. Complexity does indeed falsify atomism, but I contend that there is a weaker, ‘contextualist’ notion of decomposition that is fully compatible with the properties that holists cite. Contextualism suggests that the function of parts can shift with external context, and that interactions with other parts might help determine their context-appropriate functions. This still admits of functional decomposition within a given context. I will give examples based on the notion of oscillatory multiplexing in systems neuroscience. If contextualism is feasible, then holist inferences are faulty—one cannot infer from the presence of complexity to the failure of decomposition, mechanism, and reductionism. (shrink)

We propose category theory, the mathematical theory of structure, as a vehicle for defining ontologies in an unambiguous language with analytical and constructive features. Specifically, we apply categorical logic and model theory, based upon viewing an ontology as a sub-category of a category of theories expressed in a formal logic. In addition to providing mathematical rigor, this approach has several advantages. It allows the incremental analysis of ontologies by basing them in an interconnected hierarchy of theories, with an operation on (...) the hierarchy that expresses the formation of complex theories from simple theories that express first principles. Another operation forms abstractions expressing the shared concepts in an array of theories. The use of categorical model theory makes possible the incremental analysis of possible worlds, or instances, for the theories, and the mapping of instances of a theory to instances of its more abstract parts. We describe the theoretical approach by applying it to the semantics of neural networks. (shrink)

…behind the diversity of discoveries [in molecular biology] moved a unity, a constant direction of change, the development of the concept of biological specificity.In his landmark book Evolution by Gene Duplication, geneticist Susumu Ohno pointed out that whereas most evolutionary developments represent adjustments to past challenges and mutations, two unique systems had evolved to deal with future challenges: the immune response and neural memory functions. He named these two evolutionary modes after the Titan brothers of Greek mythology: “standard” (...) evolutionary developments were named for rearward-looking Epimetheus, and “anticipatory” evolutionary mechanisms for foresighted Prometheus (Ohno 1976; Ohno et.. (shrink)