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An emotional version of Sapir-Whorf hypothesis suggests that differences in language emotionalities influence differences among cultures no less than conceptual differences. Conceptual contents of languages and cultures to significant extent are determined by words and their semantic differences; these could be borrowed among languages and exchanged among cultures. Emotional differences, as suggested in the paper, are related to grammar and mostly cannot be borrowed. The paper considers conceptual and emotional mechanisms of language along with their role in the mind and cultural evolution. Language evolution from primordial undifferentiated animal cries is discussed: while conceptual contents increase, emotional reduced. Neural mechanisms of these processes are suggested as well as their mathematical models: the knowledge instinct, the dual model connecting language and cognition, neural modeling fields. Mathematical results are related to cognitive science, linguistics, and psychology. Experimental evidence and theoretical arguments are discussed. Dynamics of the hierarchy-heterarchy of human minds and cultures is formulated using mean-field approach and approximate equations are obtained. The knowledge instinct operating in the mind heterarchy leads to mechanisms of differentiation and synthesis determining ontological development and cultural evolution. These mathematical models identify three types of cultures: ''conceptual'' pragmatic cultures in which emotionality of language is reduced and differentiation overtakes synthesis resulting in fast evolution at the price of uncertainty of values, self doubts, and internal crises; ''traditional-emotional'' cultures where differentiation lags behind synthesis, resulting in cultural stability at the price of stagnation; and ''multi-cultural'' societies combining fast cultural evolution and stability. Unsolved problems and future theoretical and experimental directions are discussed.

This paper is an attempt at understanding the issue of consciousness through investigating its functional role, especially in learning, and through devising hybrid neural network models that (in a qualitative manner) approximate characteristics of human consciousness. In doing so, the paper examines explicit and implicit learning in a variety of psychological experiments and delineates the conscious/unconscious distinction in terms of the two types of learning and their respective products. The distinctions are captured in a two-level action-based model CLARION. Some fundamental theoretical issues are also clarified with the help of the model. Comparisons with existing models of consciousness are made to accentuate the present approach.

Patients with dementia with Lewy bodies (DLB) frequently experience visual hallucination (VH), which has been aptly described as people seeing things that are not there. The distinctive character of VH in DLB necessitates a new theory of visual cognition. We have conducted a series of studies with the aim to understand the mechanism of this dysfunction of the cognitive system. We have proposed that if we view the disease from the internal mechanism of neurocognitive processes, and if also take into consideration recent experimental data on conduction abnormality, at least some of the symptoms can be understood within the framework of network (or disconnection) syndromes. This paper describes the problem from a computational aspect and tries to determine whether conduction disturbances in a computational model can in fact produce a "computational" hallucination under appropriate assumptions.

In this paper, we propose a limbic-based artificial emotional neural network (LiAENN) for a pattern recognition problem. LiAENN is a novel computational neural model of the emotional brain that models emotional situations such as anxiety and confidence in the learning process, the short paths, the forgetting processes, and inhibitory mechanisms of the emotional brain. In the model, the learning weights are adjusted by the proposed anxious confident decayed brain emotional learning rules (ACDBEL). In engineering applications, LiAENN is utilized in facial detection, and emotion recognition. According to the comparative results on ORL and Yale datasets, LiAENN shows a higher accuracy than other applied emotional networks such as brain emotional learning (BEL) and emotional back propagation (EmBP) based networks.

In this paper I offer an analysis of social meaning in terms of coding intentional relations between agents and objects. This analysis is amenable to embodied interpretations of intentionality and Theory of mind and may prove of help for the task of simulating the attribution of intentionality with neural networks. Work with non-human primates suggests that evolution first dealt with the problem domain of social meaning by selecting specific adaptations for coding behaviour as intentional in the sense of Brentano, i.e., as directed to or being about objects (for example, gaze following behaviours). Work with autism suggests that alterations in these basic mechanisms of intentional attribution may explain some of the most prevalent problems with social meaning in this condition. Brentanian mechanisms for encoding intentional relations between agents and objects are more basic and evolutionarily more primitive than representational theories of mind. However, some of the key components of representational theories of mind may be already present in the intentional abilities of primates, suggesting that the decisive evolutionary step may have consisted of a re-organization of existing abilities rather than the creation of new skills. Neural network models and models of the evolution and development of primitive mechanisms for intentional encoding may benefit from mutual cross-fertilization.

In this paper, I discuss some of the contributions that modeling emotions in autonomous robots can make towards understanding human emotions-'as sited in the brain' and as used in our interactions with the environment-and emotions in general. Such contributions are linked, on the one hand, to the potential use of such robotic models as tools and 'virtual laboratories' to test and explore systematically theories and models of human emotions, and on the other hand to a modeling approach that fosters conceptual clarification and operationalization of the relevant aspects of theoretical notions and models. As illustrated by an overview of recent advances in the field, this area is still in its infancy. However, the work carried out already shows that we share many conceptual problems and interests with other disciplines in the affective sciences and that sound progress necessitates multidisciplinary efforts.

The primary visual cortex (V1) is a fascinating part of the brain. The reason for the fascination is because V1 is tied to the visual world through its relatively strong driving from the retina through the lateral geniculate nucleus (LGN), but it is also part of the cerebral cortex. The function of the cerebral cortex is a major challenge to science. It is one of the most important neural networks in Nature. Naturally scientists have been drawn to study V1 as a way of comprehending the greater problem of cortical function. But the visual particularities of V1 are also fascinating for understanding one important stage in visual perception. There has been intensive study of V1 in many laboratories around the world for decades. This paper is a review of recent work, by Ringach, Hawken, and Shapley, about a new view of V1 as a nonlinear dynamical system designed to find local stimulus features in the visual scene. Also, Ringach, Hawken, Sceniak, and Shapley (and others whose work is discussed below) have studied how V1 is influenced by spatial context. First we will consider our recent results about the dynamics of V1 responses to oriented visual stimuli. Then we will consider the evidence for the sensitivity to two-dimensional features in V1. This will lead to a discussion of the role of feedback in the cerebral cortex, particularly in V1.

In the temporal difference model of primate dopamine neurons, their phasic activity reports a prediction error for future reward. This model is supported by a wealth of experimental data. However, in certain circumstances, the activity of the dopamine cells seems anomalous under the model, as they respond in particular ways to stimuli that are not obviously related to predictions of reward. In this paper, we address two important sets of anomalies, those having to do with generalization and novelty. Generalization responses are treated as the natural consequence of partial information; novelty responses are treated by the suggestion that dopamine cells multiplex information about reward bonuses, including exploration bonuses and shaping bonuses. We interpret this additional role for dopamine in terms of the mechanistic attentional and psychomotor effects of dopamine, having the computational role of guiding exploration.

Pattern completion in a neural network model of the thalamus and a biologically plausible model of synaptic plasticity are the key concepts used in this paper for analyzing some cognitive disorders that involve hallucinations of several kinds: visual hallucinations in the Charles Bonnet syndrome and psychedelic drugs consumption, somatic hallucination in phantom limbs, cognitive hallucinations in schizophrenia and even in multiple personality disorders. All these types of hallucinations are proposed to be the result of a pattern completion dynamics performed in thalamic deafferented areas. Effective treatments of some of these disorders involve peripheral stimulation jointly with a central inhibition so that the neural circuits generating the disorders are depressed according to the proposed model of synaptic plasticity.

The paper traces theoretical work concerning the understanding and simulation of brain functions from early studies of artificial neural nets to present considerations of human consciousness. The emphasis is on work carried out since about 1963 at my laboratory in collaboration with my students. The discussion centers on sensory, especially visual, information processing, some of the cerebral mechanisms involved, and current approaches to an understanding of conscious perception. The sketchpad model, in which the ubiquitous feedback pathways in the brain play a dominant role, is described, together with a discussion of the meaning and applicability of scientific reductionism to the problem of consciousness.